From d26c6d3a724564db3a9c812f0c29515d4f0c1e0b Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Tue, 29 Sep 2015 09:07:37 -0700
Subject: [PATCH 001/493] Create README.md

---
 README.md | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)
 create mode 100644 README.md

diff --git a/README.md b/README.md
new file mode 100644
index 0000000..f50527e
--- /dev/null
+++ b/README.md
@@ -0,0 +1,16 @@
+# R-software
+R software for selective inference
+Authors: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid
+Maintainer: Rob Tibshirani <tibs@stanford.edu>
+
+New tools for inference after selection, for use with forward stepwise regression, least angle regression, the lasso, and the many means problem.
+
+Code is in the directory selectiveInference/R.
+* funs.common.R: Basic functions used by many other functions, such as standardization.
+* funs.fixed.R: Inference for LASSO at a fixed, deterministic value of lambda.
+* funs.fs.R: Inference for forward stepwise.
+* funs.groupfs.R: Inference for forward stepwise with groups of variables, e.g. factors.
+* funs.inf.R: Common functions for inference with fixed, fs, lar, and manymeans (but not group).
+* funs.lar.R: Inference for least angle regression.
+* funs.max.R: Some numerical approximations. Deprecated?
+

From 48add166ef5cd3fa1331776d03b4dee09bdeda79 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Tue, 29 Sep 2015 09:10:19 -0700
Subject: [PATCH 002/493] Editing readme

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index f50527e..2d38cdf 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 # R-software
-R software for selective inference
-Authors: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid
-Maintainer: Rob Tibshirani <tibs@stanford.edu>
+R software for selective inference  
+Authors: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid  
+Maintainer: Rob Tibshirani <tibs@stanford.edu>  
 
 New tools for inference after selection, for use with forward stepwise regression, least angle regression, the lasso, and the many means problem.
 

From c8ab08afcdc978df30840bb0993c7572205d5b7e Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Tue, 29 Sep 2015 09:13:54 -0700
Subject: [PATCH 003/493] Fixed test.groupfs.R

---
 tests/test.groupfs.R | 28 +---------------------------
 1 file changed, 1 insertion(+), 27 deletions(-)

diff --git a/tests/test.groupfs.R b/tests/test.groupfs.R
index 167dd0a..b590228 100644
--- a/tests/test.groupfs.R
+++ b/tests/test.groupfs.R
@@ -92,7 +92,7 @@ cnames[fit$action]#[1:length(fsnames)]
     print("empty")
 }
 
-<<<<<<< HEAD:forLater/josh/tests/test.groupfs.R
+
 =======
 set.seed(1)
 n = 100
@@ -133,29 +133,3 @@ print(colMeans(pvalmk))
 
 print(mean(pvalm))
 print(mean(pvalmk))
-<<<<<<< HEAD
->>>>>>> 5c372de287b5ff9455ddce7dd513fb868d09e7e6:tests/test.groupfs.R
-=======
-
-
-
- set.seed(1)
- n <- 40
- p <- 20
- index <- sort(rep(1:(p/2), 2))
- steps <- 10
- sparsity <- 5
- snr <- 3
- sigma=3
- 
-     y <- rnorm(n)*sigma
-     x <- matrix(rnorm(n*p), nrow=n)
- 
-   
-       beta <- rep(0, p)
-       beta[which(index %in% 1:sparsity)] <- snr
-       y <- y + x %*% beta
-   
- fit <- groupfs(x, y, index=index, maxsteps = steps)
- foo=groupfsInf(fit)
->>>>>>> bc141572a79c89a69ca48574049b3006fa4b38ca

From fdb0814926d132437378e1cae4056a02afad9b2e Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Tue, 29 Sep 2015 10:21:05 -0700
Subject: [PATCH 004/493] More test.groupfs fixes

---
 forLater/josh/cv.R            | 103 ----------------------------------
 forLater/josh/tests/robsNotes |  37 ------------
 tests/test.groupfs.R          |  17 ++----
 3 files changed, 4 insertions(+), 153 deletions(-)
 delete mode 100644 forLater/josh/cv.R
 delete mode 100644 forLater/josh/tests/robsNotes

diff --git a/forLater/josh/cv.R b/forLater/josh/cv.R
deleted file mode 100644
index 2787990..0000000
--- a/forLater/josh/cv.R
+++ /dev/null
@@ -1,103 +0,0 @@
-# ------------------------------------------------
-# Cross-validation, preliminary
-
-
-cv_make_folds <- function(x, nfolds = 10) {
-    #inds <- sample(1:nrow(x), replace=FALSE)
-    inds <- 1:nrow(x)
-    foldsize <- floor(nrow(x)/nfolds)
-    lapply(1:nfolds, function(f) return(inds[1:foldsize+(f-1)*foldsize]))
-}
-
-cv_hat_matrix <- function(x, folds, active.sets) {
-    nfolds <- length(folds)
-    lapply(1:nfolds, function(f) {
-        fold <- folds[[f]]
-        active <- active.sets[[f]]
-        x_tr <- x[ -fold, active]
-        x_te <- x[fold, active]
-        hatm <- matrix(0, nrow=length(fold), ncol=nrow(x))
-        hatm[, -fold] <- x_te %*% ginv(x_tr)
-        return(hatm)
-    })
-}
-
-product_cv_hat <- function(folds, inds, finds, ginds, hat_matrices) {
-    nfolds <- length(folds)
-    terms <- lapply(inds, function(h) {
-        t(hat_matrices[[h]][, finds]) %*% hat_matrices[[h]][, ginds]
-    })
-    return(Reduce('+', terms))
-}
-
-cv_RSSquad <- function(x, folds, active.sets) {
-    hat_matrices <- cv_hat_matrix(x, folds, active.sets)
-    nfolds <- length(folds)
-    rows <- lapply(1:nfolds, function(f) {
-        do.call(cbind, lapply(1:nfolds, function(g) {
-            ginds <- folds[[g]]
-            finds <- folds[[f]]                
-            if (f == g) {
-                return(product_cv_hat(folds, setdiff(1:nfolds, f), finds, ginds, hat_matrices))
-            } else {
-                return(
-                    product_cv_hat(folds, setdiff(1:nfolds, c(f,g)), finds, ginds, hat_matrices) - hat_matrices[[f]][, ginds] - t(hat_matrices[[g]][, finds]))
-            }
-        }))
-    })
-    Q <- do.call(rbind, rows)
-    return(Q)
-}
-
-cv_fs <- function(x, y, steps, nfolds = 10) {
-    
-    n <- nrow(x)
-    if (steps >= n*(1-1/nfolds)) stop("Too many steps")
-    
-    folds <- cv_make_folds(x, nfolds)
-    nfolds <- length(folds)
-    projections <- list()
-    active.sets <- list()
-    cv_perm <- sample(1:n)
-    Y <- y[cv_perm] - mean(y)
-    X <- x[cv_perm, ]
-    
-    for (f in 1:nfolds) {
-        fold <- folds[[f]]
-        fit <- groupfs(X[-fold,], Y[-fold], steps=steps)
-        path.projs <- fit$projections
-        path.projs <- lapply(path.projs, function(step.projs) {
-            lapply(step.projs, function(proj) {
-                expanded.proj <- matrix(0, n, n)
-                expanded.proj[-fold, -fold] <- proj
-                return(expanded.proj)
-            })
-        })
-        projections[[f]] <- path.projs
-        active.sets[[f]] <- fit$variable
-    }
-    projections <- do.call(c, projections)
-
-    RSSquads <- list()
-    for (s in 1:steps) {
-        initial.active <- lapply(active.sets, function(a) a[1:s])
-        RSSquads[[s]] <- cv_RSSquad(X, folds, initial.active)
-    }
-
-    RSSs <- lapply(RSSquads, function(Q) t(Y) %*% Q %*% Y)
-    sstar <- which.min(RSSs)
-    quadstar <- RSSquads[sstar][[1]]
-
-    RSSquads <- lapply(RSSquads, function(quad) quad - quadstar)
-    RSSquads[[sstar]] <- NULL # remove the all zeroes case
-
-    fit <- groupfs(X, Y, steps=sstar)
-    fit$projections <- flatten(fit$projections)
-    fit$foldprojections <- flatten(projections)
-    fit$rssprojections <- flatten(RSSquads)
-
-    fit$cvperm <- cv_perm
-
-    invisible(fit)
-}
-
diff --git a/forLater/josh/tests/robsNotes b/forLater/josh/tests/robsNotes
deleted file mode 100644
index 8d7c3be..0000000
--- a/forLater/josh/tests/robsNotes
+++ /dev/null
@@ -1,37 +0,0 @@
-notes
-
-see bottom of test.groupfs.R
-
-1. with n=100,  p=120,14 groups, groupfs(x, y,index)  returned
- 
-
-  Error in checkargs.groupfs(x, index, maxsteps) : 
-  maxsteps is too large. If the largest groups are included the model will be saturated/overdetermined
-
-should have a sensible default so this doesn;t happen
-
-
-2. a=groupfs(x,y,index,maxsteps=4)
-groupfsInf(a)   gives
-
-
-Using sigma value:  
-Step 1 - computing p-value for group 14 
-Step 2 - computing p-value for group 12 
-Step 3 - computing p-value for group 1 
-Step 4 - computing p-value for group 13 
-Warning messages:
-1: In groupfsInf(a) :
-  p > n/2, and sigmahat = 0.434 used as an estimate of sigma; you may want to use the estimateSigma function
-2: In groupfsInf(a) :
-  P-value NaNs of the form 0/0 converted to 0. This typically occurs for numerical reasons in the presence of a large signal-to-noise ratio.
-> aa
-
-Standard deviation of noise (specified or estimated) sigma = 0.434
-Error in lapply(pvals$support, size) : object 'pvals' not found
-
-
-
-3.  groupfs  has a sigma arg listed in the Rd file, but it's  not in the function??
-
-you need to do a R CMD check to  pick up this stuff
diff --git a/tests/test.groupfs.R b/tests/test.groupfs.R
index b590228..629bec1 100644
--- a/tests/test.groupfs.R
+++ b/tests/test.groupfs.R
@@ -1,10 +1,4 @@
-library(intervals)
-source("../selectiveInference/R/funs.common.R")
-source("../selectiveInference/R/funs.groupfs.R")
-source("../selectiveInference/R/funs.quadratic.R")
-source("../selectiveInference/R/funs.fs.R")
-source("../selectiveInference/R/funs.lar.R")
-#library(selectiveInference)
+library(selectiveInference)
 #library(lars)
 
 set.seed(1)
@@ -65,8 +59,8 @@ for (j in 1:ncol(state.x77)) {
     states[,j] <- var
 }
 states <- cbind(states, state.division)
-x <- factor_design(states)$x
-X <- scale_groups(x, index)$x
+x <- factorDesign(states)$x
+X <- scaleGroups(x, index)$x
 
 p <- ncol(x)
 y <- rnorm(n)
@@ -92,13 +86,10 @@ cnames[fit$action]#[1:length(fsnames)]
     print("empty")
 }
 
-
-=======
-set.seed(1)
 n = 100
 p = 120
 maxsteps = 9
-niter = 50
+niter = 500
 # 10 groups of size 10, 10 groups of size 2
 index = sort(c(c(1, 1), rep(2:11, 10), rep(12:20, 2)))
 pvalm = pvalmk = matrix(NA, nrow=niter, ncol=maxsteps)

From 5119f8613f5d6bd175355e334fba07f82658888e Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Tue, 29 Sep 2015 22:49:39 -0700
Subject: [PATCH 005/493] Unfinished, but modifying cv to work with refactored
 groupfs functions

---
 forLater/josh/selectiveInference/R/cv.R | 105 ++++++++++++++++++++++++
 forLater/josh/tests/test.cv.R           |   9 ++
 2 files changed, 114 insertions(+)
 create mode 100644 forLater/josh/selectiveInference/R/cv.R
 create mode 100644 forLater/josh/tests/test.cv.R

diff --git a/forLater/josh/selectiveInference/R/cv.R b/forLater/josh/selectiveInference/R/cv.R
new file mode 100644
index 0000000..cdc5092
--- /dev/null
+++ b/forLater/josh/selectiveInference/R/cv.R
@@ -0,0 +1,105 @@
+# ------------------------------------------------
+# Cross-validation, preliminary
+
+cvMakeFolds <- function(x, nfolds = 10) {
+    #inds <- sample(1:nrow(x), replace=FALSE)
+    inds <- 1:nrow(x)
+    foldsize <- floor(nrow(x)/nfolds)
+    lapply(1:nfolds, function(f) return(inds[1:foldsize+(f-1)*foldsize]))
+}
+
+cvHatMatrix <- function(x, folds, active.sets) {
+    nfolds <- length(folds)
+    lapply(1:nfolds, function(f) {
+        fold <- folds[[f]]
+        active <- active.sets[[f]]
+        x_tr <- x[ -fold, active]
+        x_te <- x[fold, active]
+        hatm <- matrix(0, nrow=length(fold), ncol=nrow(x))
+        hatm[, -fold] <- x_te %*% ginv(x_tr)
+        return(hatm)
+    })
+}
+
+cvProductHat <- function(folds, inds, finds, ginds, hat_matrices) {
+    nfolds <- length(folds)
+    terms <- lapply(inds, function(h) {
+        t(hat_matrices[[h]][, finds]) %*% hat_matrices[[h]][, ginds]
+    })
+    return(Reduce('+', terms))
+}
+
+cvRSSquad <- function(x, folds, active.sets) {
+    hat_matrices <- cvHatMatrix(x, folds, active.sets)
+    nfolds <- length(folds)
+    rows <- lapply(1:nfolds, function(f) {
+        do.call(cbind, lapply(1:nfolds, function(g) {
+            ginds <- folds[[g]]
+            finds <- folds[[f]]
+            if (f == g) {
+                return(cvProductHat(folds, setdiff(1:nfolds, f), finds, ginds, hat_matrices))
+            } else {
+                return(
+                    cvProductHat(folds, setdiff(1:nfolds, c(f,g)), finds, ginds, hat_matrices) - hat_matrices[[f]][, ginds] - t(hat_matrices[[g]][, finds]))
+            }
+        }))
+    })
+    Q <- do.call(rbind, rows)
+    return(Q)
+}
+
+cvfs <- function(x, y, index = 1:ncol(x), maxsteps, nfolds = 10) {
+
+    n <- nrow(x)
+    if (maxsteps >= n*(1-1/nfolds)) {
+        maxsteps <- floor(n*(1-1/nfolds))
+        warning(paste("maxsteps too large for training fold size, set to", maxsteps))
+    }
+
+    folds <- cvMakeFolds(x, nfolds)
+    nfolds <- length(folds)
+    projections <- list(1:nfolds)
+    active.sets <- list(1:nfolds)
+    cv_perm <- sample(1:n)
+    Y <- y[cv_perm] - mean(y)
+    X <- x[cv_perm, ]
+
+    # Flatten list or something?
+    for (f in 1:nfolds) {
+        fold <- folds[[f]]
+        fit <- groupfs(X[-fold,], Y[-fold], index, maxsteps=maxsteps)
+        projections[[f]] <- lapply(fit$projections, function(step.projs) {
+            lapply(step.projs, function(proj) {
+                # Reduce from n by n matrix to svdu_thresh
+                expanded.proj <- matrix(0, n, ncol(proj))
+                expanded.proj[-fold, ] <- proj
+                return(expanded.proj)
+            })
+        })
+        active.sets[[f]] <- fit$action
+    }
+    projections <- do.call(c, projections)
+
+    RSSquads <- list()
+    for (s in 1:maxsteps) {
+        initial.active <- lapply(active.sets, function(a) a[1:s])
+        RSSquads[[s]] <- cvRSSquad(X, folds, initial.active)
+    }
+
+    RSSs <- lapply(RSSquads, function(Q) t(Y) %*% Q %*% Y)
+    sstar <- which.min(RSSs)
+    quadstar <- RSSquads[sstar][[1]]
+
+    RSSquads <- lapply(RSSquads, function(quad) quad - quadstar)
+    RSSquads[[sstar]] <- NULL # remove the all zeroes case
+
+    fit <- groupfs(X, Y, index, maxsteps=sstar)
+    fit$projections <- flatten(fit$projections)
+    fit$foldprojections <- flatten(projections)
+    fit$rssprojections <- flatten(RSSquads)
+
+    fit$cvperm <- cv_perm
+
+    invisible(fit)
+}
+
diff --git a/forLater/josh/tests/test.cv.R b/forLater/josh/tests/test.cv.R
new file mode 100644
index 0000000..22743ec
--- /dev/null
+++ b/forLater/josh/tests/test.cv.R
@@ -0,0 +1,9 @@
+library(selectiveInference)
+
+source("../selectiveInference/R/cv.R")
+
+n = 100
+p = 20
+x <- matrix(rnorm(n*p), nrow=n)
+y <- rnorm(n)
+fit <- cvfs(x, y, maxsteps = 5)

From d03455fc2e12d5b5e70cc38fe789a968198c3d2a Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 30 Sep 2015 18:03:13 -0700
Subject: [PATCH 006/493] Some documentation edits, significant progress on CV

---
 forLater/josh/selectiveInference/R/cv.R | 35 ++++++++-----
 selectiveInference/DESCRIPTION          | 15 +++---
 selectiveInference/R/funs.groupfs.R     | 67 ++++++++++++++-----------
 selectiveInference/R/funs.quadratic.R   |  3 +-
 selectiveInference/man/factorDesign.Rd  |  3 +-
 selectiveInference/man/groupfs.Rd       |  7 ++-
 selectiveInference/man/groupfsInf.Rd    | 27 ++--------
 selectiveInference/man/scaleGroups.Rd   | 11 ++--
 8 files changed, 83 insertions(+), 85 deletions(-)

diff --git a/forLater/josh/selectiveInference/R/cv.R b/forLater/josh/selectiveInference/R/cv.R
index cdc5092..adbc011 100644
--- a/forLater/josh/selectiveInference/R/cv.R
+++ b/forLater/josh/selectiveInference/R/cv.R
@@ -8,6 +8,9 @@ cvMakeFolds <- function(x, nfolds = 10) {
     lapply(1:nfolds, function(f) return(inds[1:foldsize+(f-1)*foldsize]))
 }
 
+############################################
+# Can this be optimized using svdu_thresh? #
+############################################
 cvHatMatrix <- function(x, folds, active.sets) {
     nfolds <- length(folds)
     lapply(1:nfolds, function(f) {
@@ -16,7 +19,10 @@ cvHatMatrix <- function(x, folds, active.sets) {
         x_tr <- x[ -fold, active]
         x_te <- x[fold, active]
         hatm <- matrix(0, nrow=length(fold), ncol=nrow(x))
-        hatm[, -fold] <- x_te %*% ginv(x_tr)
+        svdtr <- svd(x_tr)
+        inds <- svdtr$d > .Machine$double.eps * svdtr$d[1]
+        xtrinv <- svdtr$v[, inds, drop = FALSE] %*% ((1/svdtr$d[inds]) * t(svdtr$u[, inds, drop = FALSE]))
+        hatm[, -fold] <- x_te %*% xtrinv
         return(hatm)
     })
 }
@@ -59,7 +65,9 @@ cvfs <- function(x, y, index = 1:ncol(x), maxsteps, nfolds = 10) {
     folds <- cvMakeFolds(x, nfolds)
     nfolds <- length(folds)
     projections <- list(1:nfolds)
+    maxprojs <- list(1:nfolds)
     active.sets <- list(1:nfolds)
+    cvobj <- list(1:nfolds)
     cv_perm <- sample(1:n)
     Y <- y[cv_perm] - mean(y)
     X <- x[cv_perm, ]
@@ -68,17 +76,19 @@ cvfs <- function(x, y, index = 1:ncol(x), maxsteps, nfolds = 10) {
     for (f in 1:nfolds) {
         fold <- folds[[f]]
         fit <- groupfs(X[-fold,], Y[-fold], index, maxsteps=maxsteps)
-        projections[[f]] <- lapply(fit$projections, function(step.projs) {
-            lapply(step.projs, function(proj) {
-                # Reduce from n by n matrix to svdu_thresh
-                expanded.proj <- matrix(0, n, ncol(proj))
-                expanded.proj[-fold, ] <- proj
-                return(expanded.proj)
-            })
-        })
+        fit$fold <- fold
+        ## projections[[f]] <- lapply(fit$projections, function(step.projs) {
+        ##     lapply(step.projs, function(proj) {
+        ##         # Reduce from n by n matrix to svdu_thresh
+        ##         expanded.proj <- matrix(0, n, ncol(proj))
+        ##         expanded.proj[-fold, ] <- proj
+        ##         return(expanded.proj)
+        ##     })
+        ## })
         active.sets[[f]] <- fit$action
+        cvobj[[f]] <- fit
     }
-    projections <- do.call(c, projections)
+    #projections <- do.call(c, projections)
 
     RSSquads <- list()
     for (s in 1:maxsteps) {
@@ -94,9 +104,8 @@ cvfs <- function(x, y, index = 1:ncol(x), maxsteps, nfolds = 10) {
     RSSquads[[sstar]] <- NULL # remove the all zeroes case
 
     fit <- groupfs(X, Y, index, maxsteps=sstar)
-    fit$projections <- flatten(fit$projections)
-    fit$foldprojections <- flatten(projections)
-    fit$rssprojections <- flatten(RSSquads)
+    fit$cvobj <- cvobj
+    fit$rssprojections <- RSSquads
 
     fit$cvperm <- cv_perm
 
diff --git a/selectiveInference/DESCRIPTION b/selectiveInference/DESCRIPTION
index df66eb5..0f352b6 100644
--- a/selectiveInference/DESCRIPTION
+++ b/selectiveInference/DESCRIPTION
@@ -3,12 +3,15 @@ Type: Package
 Title: Tools for Selective Inference
 Version: 1.1.1
 Date: 2015-09-01
-Author: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, 
-  Joshua Loftus, Stephen Reid
+Author: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor,
+    Joshua Loftus, Stephen Reid
 Maintainer: Rob Tibshirani <tibs@stanford.edu>
-Depends: glmnet, intervals
-Suggests: Rmpfr
+Depends:
+    glmnet,
+    intervals
+Suggests:
+    Rmpfr
 Description: New tools for inference after selection, for use
-  with forward stepwise regression, least angle regression, the 
-  lasso, and the many means problem.
+    with forward stepwise regression, least angle regression, the
+    lasso, and the many means problem.
 License: GPL-2
diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 3434bd6..8fae045 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -1,12 +1,12 @@
 #' Select a model with forward stepwise.
 #'
-#' This function implements forward selection of linear models almost identically to \code{\link{stepAIC}} with \code{direction = "forward"}. The reason this is a separate function from \code{\link{fs}} is that groups of variables (e.g. dummies encoding levels of a categorical variable) must be handled differently in the selective inference framework.
+#' This function implements forward selection of linear models almost identically to \code{\link[stats]{step}} with \code{direction = "forward"}. The reason this is a separate function from \code{\link{fs}} is that groups of variables (e.g. dummies encoding levels of a categorical variable) must be handled differently in the selective inference framework.
 #'
 #' @param x Matrix of predictors (n by p).
 #' @param y Vector of outcomes (length n).
 #' @param index Group membership indicator of length p.
 #' @param maxsteps Maximum number of steps for forward stepwise.
-#' @param sigma Estimate of error standard deviation for use in AIC criterion. This determines the relative scale between RSS and the degrees of freedom penalty. Default is NULL corresponding to unknown sigma. See \code{\link{extractAIC}} for details.
+#' @param sigma Estimate of error standard deviation for use in AIC criterion. This determines the relative scale between RSS and the degrees of freedom penalty. Default is NULL corresponding to unknown sigma. See \code{\link[stats]{extractAIC}} for details.
 #' @param k Multiplier of model size penalty, the default is \code{k = 2} for AIC. Use \code{k = log(n)} for BIC, or \code{k = log(p)} for RIC.
 #' @param intercept Should an intercept be included in the model? Default is TRUE.
 #' @param normalize Should the design matrix be normalized? Default is TRUE.
@@ -196,11 +196,10 @@ add1.groupfs <- function(x, y, index, labels, inactive, k, sigma = NULL) {
 
 #' Compute selective p-values for a model fitted by \code{groupfs}.
 #'
-#' Computes p-values for each group of variables in a model fitted by \code{\link{groupfs}}. These p-values adjust for selection by truncating the usual \code{chi^2} statistics to the regions implied by the model selection event. Details are provided in a forthcoming work.
+#' Computes p-values for each group of variables in a model fitted by \code{\link{groupfs}}. These p-values adjust for selection by truncating the usual \eqn{\chi^2} statistics to the regions implied by the model selection event. Details are provided in a forthcoming work.
 #'
 #' @param obj Object returned by \code{\link{groupfs}} function
 #' @param sigma Estimate of error standard deviation. If NULL (default), this is estimated using the mean squared residual of the full least squares fit when n >= 2p, and the mean squared residual of the selected model when n < 2p. In the latter case, the user should use \code{\link{estimateSigma}} function for a more accurate estimate.
-#' @param projs Additional projections to define model selection event. For use with cross-validation. Default is NULL and it is not recommended to change this.
 #' @param verbose Print out progress along the way? Default is FALSE.
 #' @return An object of class "groupfsInf" containing selective p-values for the fitted model \code{obj}. The default printing behavior should supply adequate information.
 #'
@@ -212,18 +211,16 @@ add1.groupfs <- function(x, y, index, labels, inactive, k, sigma = NULL) {
 #'   \item{df}{Rank of group of variables when it was added to the model.}
 #'   \item{support}{List of intervals defining the truncation region of the truncated chi.}
 #' }
-groupfsInf <- function(obj, sigma = NULL, projs = NULL, verbose = FALSE) {
+groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
 
   n <- nrow(obj$x)
   p <- ncol(obj$x)
-  active <- obj$action
   maxsteps <- attr(obj, "maxsteps")
   k <- attr(obj, "k")
   index <- obj$index
   x <- obj$x
   y <- obj$y
-  Eindex <- which(index %in% active)
-  Ep <- length(Eindex)
+  Ep <- sum(index %in% obj$action)
 
   nanconv <- FALSE
   pvals <- numeric(maxsteps)
@@ -250,40 +247,43 @@ groupfsInf <- function(obj, sigma = NULL, projs = NULL, verbose = FALSE) {
 
   # Compute p-value for each active group
   for (j in 1:maxsteps) {
-    i <- active[j]
-    if (verbose) cat(paste0("Step ", j, "/", maxsteps, ": computing P-value for group ", i, "\n"))    
+    i <- obj$action[j]
+    if (verbose) cat(paste0("Step ", j, "/", attr(obj, "maxsteps"), ": computing P-value for group ", i, "\n"))    
     # Form projection onto active set minus i
     # and project x_i orthogonally
-    x_i <- x[,which(index == i), drop = FALSE]
-    if (length(active) > 1) {
-        minus_i <- setdiff(active, i)
-        x_minus_i <- svdu_thresh(x[,which(index %in% minus_i), drop = FALSE])
+    x_i <- obj$x[,which(obj$index == i), drop = FALSE]
+    if (length(obj$action) > 1) {
+        minus_i <- setdiff(obj$action, i)
+        x_minus_i <- svdu_thresh(obj$x[,which(obj$index %in% minus_i), drop = FALSE])
         x_i <- x_i - x_minus_i %*% t(x_minus_i) %*% x_i
     }
 
     # Project y onto what remains of x_i
     Ugtilde <- svdu_thresh(x_i)
-    R <- t(Ugtilde) %*% y
+    R <- t(Ugtilde) %*% obj$y
     TC <- sqrt(sum(R^2))
     eta <- Ugtilde %*% R / TC
+    Z <- obj$y - eta * TC    
     df <- ncol(Ugtilde)
-    dfs[j] <- df
-    TCs[j] <- TC
-
-    # For each step...
-    L <- interval_groupfs(obj, TC, R, eta, Ugtilde)
-
-    # Any additional projections, e.g. from cross-validation?
-    if (!is.null(projs)) L <- c(L, projs)
-
+      
+    intervallist <- truncationRegion(obj, TC, R, eta, Z)
+    if (!is.null(fit$cvobj)) {
+        intervallist <- c(intervallist, do.call(c,
+        lapply(fit$cvobj, function(cvf) {
+            truncationRegion(cvf, TC, R[-cvf$fold], eta[-cvf$fold], Z[-cvf$fold])
+        })))
+    }
+    
     # Compute intersection:
-    Lunion <- do.call(interval_union, L)
-    Lunion <- interval_union(Lunion, Intervals(c(-Inf,0)))
-    E <- interval_complement(Lunion, check_valid = FALSE)
+    region <- do.call(interval_union, intervallist)
+    region <- interval_union(region, Intervals(c(-Inf,0)))
+    E <- interval_complement(region, check_valid = FALSE)
     supports[[j]] <- E
 
     # E is now potentially a union of intervals
-    if (length(E) == 0) stop("Trivial intersection")
+    if (length(E) == 0) {
+        stop(paste("Empty TC support at step", j))
+    }
 
     # Sum truncated cdf over each part of E
     denom <- do.call(sum, lapply(1:nrow(E), function(v) {
@@ -323,7 +323,7 @@ groupfsInf <- function(obj, sigma = NULL, projs = NULL, verbose = FALSE) {
   }
   if (nanconv) warning("P-value NaNs of the form 0/0 converted to 0. This typically occurs for numerical reasons in the presence of a large signal-to-noise ratio.")
   names(pvals) <- obj$action
-  out <- list(vars = active, pv=pvals, sigma=sigma, TC=TCs, df = dfs, support=supports)
+  out <- list(vars = obj$action, pv=pvals, sigma=sigma, TC=TCs, df = dfs, support=supports)
   class(out) <- "groupfsInf"
   if (!is.null(attr(obj, "varnames"))) {
       attr(out, "varnames") <- attr(obj, "varnames")
@@ -356,11 +356,18 @@ num_int_chi <- function(a, b, df, nsamp = 10000) {
 
 #' Center and scale design matrix by groups
 #'
+#' For internal use by \code{\link{groupfs}}.
+#' 
 #' @param x Design matrix.
 #' @param index Group membership indicator of length p.
 #' @param center Center groups, default is TRUE.
 #' @param scale Scale groups by Frobenius norm, default is TRUE.
-#' @return Scaled design matrix
+#' @return
+#' \describe{
+#'   \item{x}{Optionally centered/scaled design matrix.}
+#'   \item{xm}{Means of groups in original design matrix.}
+#'   \item{xs}{Frobenius norms of groups in original design matrix.}
+#' }
 scaleGroups <- function(x, index, center = TRUE, scale = TRUE) {
   keys <- unique(index)
   xm <- rep(0, ncol(x))
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 1aff7ec..e8b1408 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -1,7 +1,6 @@
 
-interval_groupfs <- function(obj, TC, R, eta, Ugtilde, tol = 1e-15) {
+truncationRegion <- function(obj, TC, R, eta, Z, tol = 1e-15) {
 
-  Z <- obj$y - eta * TC
   n <- nrow(obj$x)
 
   L <- lapply(1:length(obj$action), function(s) {
diff --git a/selectiveInference/man/factorDesign.Rd b/selectiveInference/man/factorDesign.Rd
index d5d2576..3a42e2e 100644
--- a/selectiveInference/man/factorDesign.Rd
+++ b/selectiveInference/man/factorDesign.Rd
@@ -1,5 +1,4 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
-% Please edit documentation in R/funs.groupfs.R
+% Generated by roxygen2 (4.0.1.99): do not edit by hand
 \name{factorDesign}
 \alias{factorDesign}
 \title{Expand a data frame with factors to form a design matrix with the full binary encoding of each factor.}
diff --git a/selectiveInference/man/groupfs.Rd b/selectiveInference/man/groupfs.Rd
index 4567c6f..6f719a9 100644
--- a/selectiveInference/man/groupfs.Rd
+++ b/selectiveInference/man/groupfs.Rd
@@ -1,5 +1,4 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
-% Please edit documentation in R/funs.groupfs.R
+% Generated by roxygen2 (4.0.1.99): do not edit by hand
 \name{groupfs}
 \alias{groupfs}
 \title{Select a model with forward stepwise.}
@@ -16,7 +15,7 @@ groupfs(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE,
 
 \item{maxsteps}{Maximum number of steps for forward stepwise.}
 
-\item{sigma}{Estimate of error standard deviation for use in AIC criterion. This determines the relative scale between RSS and the degrees of freedom penalty. Default is NULL corresponding to unknown sigma. See \code{\link{extractAIC}} for details.}
+\item{sigma}{Estimate of error standard deviation for use in AIC criterion. This determines the relative scale between RSS and the degrees of freedom penalty. Default is NULL corresponding to unknown sigma. See \code{\link[stats]{extractAIC}} for details.}
 
 \item{k}{Multiplier of model size penalty, the default is \code{k = 2} for AIC. Use \code{k = log(n)} for BIC, or \code{k = log(p)} for RIC.}
 
@@ -30,7 +29,7 @@ groupfs(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE,
 An object of class "groupfs" containing information about the sequence of models in the forward stepwise algorithm. Call the function \code{\link{groupfsInf}} on this object to compute selective p-values.
 }
 \description{
-This function implements forward selection of linear models almost identically to \code{\link{stepAIC}} with \code{direction = "forward"}. The reason this is a separate function from \code{\link{fs}} is that groups of variables (e.g. dummies encoding levels of a categorical variable) must be handled differently in the selective inference framework.
+This function implements forward selection of linear models almost identically to \code{\link[stats]{step}} with \code{direction = "forward"}. The reason this is a separate function from \code{\link{fs}} is that groups of variables (e.g. dummies encoding levels of a categorical variable) must be handled differently in the selective inference framework.
 }
 \examples{
 x = matrix(rnorm(20*40), nrow=20)
diff --git a/selectiveInference/man/groupfsInf.Rd b/selectiveInference/man/groupfsInf.Rd
index 43cf25b..4926896 100644
--- a/selectiveInference/man/groupfsInf.Rd
+++ b/selectiveInference/man/groupfsInf.Rd
@@ -1,18 +1,15 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
-% Please edit documentation in R/funs.groupfs.R
+% Generated by roxygen2 (4.0.1.99): do not edit by hand
 \name{groupfsInf}
 \alias{groupfsInf}
 \title{Compute selective p-values for a model fitted by \code{groupfs}.}
 \usage{
-groupfsInf(obj, sigma = NULL, projs = NULL, verbose = FALSE)
+groupfsInf(obj, sigma = NULL, verbose = FALSE)
 }
 \arguments{
 \item{obj}{Object returned by \code{\link{groupfs}} function}
 
 \item{sigma}{Estimate of error standard deviation. If NULL (default), this is estimated using the mean squared residual of the full least squares fit when n >= 2p, and the mean squared residual of the selected model when n < 2p. In the latter case, the user should use \code{\link{estimateSigma}} function for a more accurate estimate.}
 
-\item{projs}{Additional projections to define model selection event. For use with cross-validation. Default is NULL and it is not recommended to change this.}
-
 \item{verbose}{Print out progress along the way? Default is FALSE.}
 }
 \value{
@@ -28,24 +25,6 @@ An object of class "groupfsInf" containing selective p-values for the fitted mod
 }
 }
 \description{
-Computes p-values for each group of variables in a model fitted by \code{\link{groupfs}}. These p-values adjust for selection by truncating the usual \code{chi^2} statistics to the regions implied by the model selection event. Details are provided in a forthcoming work.
-}
-\examples{
-#NOT RUN
-#set.seed(1)
-#n <- 40
-#p <- 20
-#index <- sort(rep(1:(p/2), 2))
-#steps <- 10
-#sparsity <- 5
-#snr <- 3
-#   x <- matrix(rnorm(n*p), nrow=n)
-#      beta <- rep(0, p)
-#      beta[which(index %in% 1:sparsity)] <- snr
-#      y <-  x %*% beta+rnorm(n)
-
-#fit <- groupfs(x, y, index=1:p, maxsteps = steps)
-
-#out<- groupfsInf(fit)
+Computes p-values for each group of variables in a model fitted by \code{\link{groupfs}}. These p-values adjust for selection by truncating the usual \eqn{\chi^2} statistics to the regions implied by the model selection event. Details are provided in a forthcoming work.
 }
 
diff --git a/selectiveInference/man/scaleGroups.Rd b/selectiveInference/man/scaleGroups.Rd
index 6e41ef6..bfb899c 100644
--- a/selectiveInference/man/scaleGroups.Rd
+++ b/selectiveInference/man/scaleGroups.Rd
@@ -1,5 +1,4 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
-% Please edit documentation in R/funs.groupfs.R
+% Generated by roxygen2 (4.0.1.99): do not edit by hand
 \name{scaleGroups}
 \alias{scaleGroups}
 \title{Center and scale design matrix by groups}
@@ -16,9 +15,13 @@ scaleGroups(x, index, center = TRUE, scale = TRUE)
 \item{scale}{Scale groups by Frobenius norm, default is TRUE.}
 }
 \value{
-Scaled design matrix
+\describe{
+  \item{x}{Optionally centered/scaled design matrix.}
+  \item{xm}{Means of groups in original design matrix.}
+  \item{xs}{Frobenius norms of groups in original design matrix.}
+}
 }
 \description{
-Center and scale design matrix by groups
+For internal use by \code{\link{groupfs}}.
 }
 

From c9a08a6f14dc96027f3788f1794943adb9a24c9b Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 30 Sep 2015 18:42:04 -0700
Subject: [PATCH 007/493] Simulation to test cv

---
 forLater/josh/sim.cv.R              | 53 +++++++++++++++++++++++++++++
 selectiveInference/R/funs.groupfs.R | 16 +++++----
 2 files changed, 62 insertions(+), 7 deletions(-)
 create mode 100644 forLater/josh/sim.cv.R

diff --git a/forLater/josh/sim.cv.R b/forLater/josh/sim.cv.R
new file mode 100644
index 0000000..28d9beb
--- /dev/null
+++ b/forLater/josh/sim.cv.R
@@ -0,0 +1,53 @@
+library(intervals)
+source("selectiveInference/R/cv.R")
+source("../../selectiveInference/R/funs.groupfs.R")
+source("../../selectiveInference/R/funs.quadratic.R")
+source("../../selectiveInference/R/funs.common.R")
+
+set.seed(1)
+niters <- 500
+n <- 50
+p <- 100
+maxsteps <- 10
+sparsity <- 5
+snr <- 1
+nfolds <- 5
+
+instance <- function(n, p, sparsity, snr, maxsteps, nfolds) {
+
+    x <- matrix(rnorm(n*p), nrow=n)
+    y <- rnorm(n)
+
+    if (sparsity > 0) {
+      beta <- rep(0, p)
+      beta[1:sparsity] <- snr * sample(c(-1,1), sparsity, replace=T)
+      y <- y + x %*% beta
+    }
+
+    fit <- cvfs(x, y, maxsteps=maxsteps, nfolds=nfolds)
+    pvals <- groupfsInf(fit, verbose=T)
+    ## pvals_naive <- interval.groupfs(fit, x, y, index = 1:ncol(x))
+    ## fit$projections <- c(fit$projections, fit$rssprojections)
+    ## pvals_reduced <- interval.groupfs(fit, x, y, index = 1:ncol(x))
+    ## fit$projections <- c(fit$projections, fit$foldprojections)
+    return(list(variable = fit$action, pvals = pvals$pv))
+                #pvals_naive = pvals_naive, pvals_reduced = pvals_reduced))
+}
+
+time <- system.time({
+          output <- replicate(niters, instance(n, p, sparsity, snr, maxsteps, nfolds))
+})
+
+#pvals_reduced <- do.call(c, list(output[4,]))
+#pvals_naive <- do.call(c, list(output[3,]))
+pvals <- do.call(c, list(output[2,]))
+vars <- do.call(c, list(output[1,]))
+
+save(pvals, vars, file = paste0(
+                      "results_cv_n", n,
+                      "_p", p,
+                      "_sparsity", sparsity,
+                      "_snr", snr,
+                      ".RData"))
+
+print(time)
diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 8fae045..cddf907 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -248,7 +248,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
   # Compute p-value for each active group
   for (j in 1:maxsteps) {
     i <- obj$action[j]
-    if (verbose) cat(paste0("Step ", j, "/", attr(obj, "maxsteps"), ": computing P-value for group ", i, "\n"))    
+    if (verbose) cat(paste0("Step ", j, "/", attr(obj, "maxsteps"), ": computing P-value for group ", i, "\n"))
     # Form projection onto active set minus i
     # and project x_i orthogonally
     x_i <- obj$x[,which(obj$index == i), drop = FALSE]
@@ -263,9 +263,11 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
     R <- t(Ugtilde) %*% obj$y
     TC <- sqrt(sum(R^2))
     eta <- Ugtilde %*% R / TC
-    Z <- obj$y - eta * TC    
+    Z <- obj$y - eta * TC
     df <- ncol(Ugtilde)
-      
+    TCs[j] <- TC
+    dfs[j] <- df
+
     intervallist <- truncationRegion(obj, TC, R, eta, Z)
     if (!is.null(fit$cvobj)) {
         intervallist <- c(intervallist, do.call(c,
@@ -273,7 +275,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
             truncationRegion(cvf, TC, R[-cvf$fold], eta[-cvf$fold], Z[-cvf$fold])
         })))
     }
-    
+
     # Compute intersection:
     region <- do.call(interval_union, intervallist)
     region <- interval_union(region, Intervals(c(-Inf,0)))
@@ -357,7 +359,7 @@ num_int_chi <- function(a, b, df, nsamp = 10000) {
 #' Center and scale design matrix by groups
 #'
 #' For internal use by \code{\link{groupfs}}.
-#' 
+#'
 #' @param x Design matrix.
 #' @param index Group membership indicator of length p.
 #' @param center Center groups, default is TRUE.
@@ -393,8 +395,8 @@ scaleGroups <- function(x, index, center = TRUE, scale = TRUE) {
 }
 
 #' Expand a data frame with factors to form a design matrix with the full binary encoding of each factor.
-#' 
-#' When using \code{\link{groupfs}} with factor variables call this function first to create a design matrix. 
+#'
+#' When using \code{\link{groupfs}} with factor variables call this function first to create a design matrix.
 #'
 #' @param df Data frame containing some columns which are \code{factors}.
 #' @return List containing

From 35c3a02281b81dc36b3f57e29db4d5c9868bd774 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 30 Sep 2015 18:45:31 -0700
Subject: [PATCH 008/493] Typo fix, change fit to obj

---
 selectiveInference/R/funs.groupfs.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index cddf907..0a4eb6e 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -269,9 +269,9 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
     dfs[j] <- df
 
     intervallist <- truncationRegion(obj, TC, R, eta, Z)
-    if (!is.null(fit$cvobj)) {
+    if (!is.null(obj$cvobj)) {
         intervallist <- c(intervallist, do.call(c,
-        lapply(fit$cvobj, function(cvf) {
+        lapply(obj$cvobj, function(cvf) {
             truncationRegion(cvf, TC, R[-cvf$fold], eta[-cvf$fold], Z[-cvf$fold])
         })))
     }

From f3043a9b05faab39c33f66d4dc7e4f9f2bc35363 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 30 Sep 2015 21:12:41 -0700
Subject: [PATCH 009/493] Included cvquad interval

---
 forLater/josh/selectiveInference/R/cv.R |  2 +-
 forLater/josh/tests/test.cv.R           | 22 +++++--
 selectiveInference/R/funs.groupfs.R     | 40 ++++++++-----
 selectiveInference/R/funs.quadratic.R   | 76 +++++++++++++------------
 4 files changed, 82 insertions(+), 58 deletions(-)

diff --git a/forLater/josh/selectiveInference/R/cv.R b/forLater/josh/selectiveInference/R/cv.R
index adbc011..0a4661b 100644
--- a/forLater/josh/selectiveInference/R/cv.R
+++ b/forLater/josh/selectiveInference/R/cv.R
@@ -105,7 +105,7 @@ cvfs <- function(x, y, index = 1:ncol(x), maxsteps, nfolds = 10) {
 
     fit <- groupfs(X, Y, index, maxsteps=sstar)
     fit$cvobj <- cvobj
-    fit$rssprojections <- RSSquads
+    fit$cvquad <- RSSquads
 
     fit$cvperm <- cv_perm
 
diff --git a/forLater/josh/tests/test.cv.R b/forLater/josh/tests/test.cv.R
index 22743ec..28b3f44 100644
--- a/forLater/josh/tests/test.cv.R
+++ b/forLater/josh/tests/test.cv.R
@@ -1,9 +1,21 @@
-library(selectiveInference)
-
+library(intervals)
 source("../selectiveInference/R/cv.R")
+source("../../../selectiveInference/R/funs.groupfs.R")
+source("../../../selectiveInference/R/funs.quadratic.R")
+source("../../../selectiveInference/R/funs.common.R")
 
-n = 100
-p = 20
+set.seed(1)
+n <- 50
+p <- 100
+maxsteps <- 10
+sparsity <- 5
+snr <- 1
+nfolds <- 5
 x <- matrix(rnorm(n*p), nrow=n)
 y <- rnorm(n)
-fit <- cvfs(x, y, maxsteps = 5)
+beta <- rep(0, p)
+beta[1:sparsity] <- snr * sample(c(-1,1), sparsity, replace=T)
+y <- y + x %*% beta
+fit <- cvfs(x, y, maxsteps=maxsteps, nfolds=nfolds)
+pvals <- groupfsInf(fit, verbose=T)
+
diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 0a4eb6e..516d834 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -223,9 +223,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
   Ep <- sum(index %in% obj$action)
 
   nanconv <- FALSE
-  pvals <- numeric(maxsteps)
-  dfs <- numeric(maxsteps)
-  TCs <- numeric(maxsteps)
+  pvals = dfs = TCs = numeric(maxsteps)
   supports <- list()
 
   if (!is.null(sigma)) {
@@ -274,6 +272,14 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
         lapply(obj$cvobj, function(cvf) {
             truncationRegion(cvf, TC, R[-cvf$fold], eta[-cvf$fold], Z[-cvf$fold])
         })))
+        intervallist <- c(intervallist, 
+        lapply(obj$cvquad, function(cvquad) {
+            etacvquad <- t(eta) %*% cvquad
+            A <- etacvquad %*% eta
+            B <- 2 * etacvquad %*% Z
+            C <- t(Z) %*% cvquad %*% Z
+            quadratic_roots(A, B, C, tol = 1e-15)
+        }))
     }
 
     # Compute intersection:
@@ -283,6 +289,21 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
     supports[[j]] <- E
 
     # E is now potentially a union of intervals
+    pvals[j] <- TC_surv(TC, sigma, df, E)
+  }
+  if (nanconv) warning("P-value NaNs of the form 0/0 converted to 0. This typically occurs for numerical reasons in the presence of a large signal-to-noise ratio.")
+  names(pvals) <- obj$action
+  out <- list(vars = obj$action, pv=pvals, sigma=sigma, TC=TCs, df = dfs, support=supports)
+  class(out) <- "groupfsInf"
+  if (!is.null(attr(obj, "varnames"))) {
+      attr(out, "varnames") <- attr(obj, "varnames")
+  }
+  invisible(out)
+}
+
+# -----------------------------------------------------------
+
+TC_surv <- function(TC, sigma, df, E) {
     if (length(E) == 0) {
         stop(paste("Empty TC support at step", j))
     }
@@ -321,20 +342,9 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
     # Force p-value to lie in the [0,1] interval
     # in case of numerical issues
     value <- max(0, min(1, value))
-    pvals[j] <- value
-  }
-  if (nanconv) warning("P-value NaNs of the form 0/0 converted to 0. This typically occurs for numerical reasons in the presence of a large signal-to-noise ratio.")
-  names(pvals) <- obj$action
-  out <- list(vars = obj$action, pv=pvals, sigma=sigma, TC=TCs, df = dfs, support=supports)
-  class(out) <- "groupfsInf"
-  if (!is.null(attr(obj, "varnames"))) {
-      attr(out, "varnames") <- attr(obj, "varnames")
-  }
-  invisible(out)
+    value
 }
 
-# -----------------------------------------------------------
-
 tchi_interval <- function(lower, upper, sigma, df) {
   a <- (lower/sigma)^2
   b <- (upper/sigma)^2
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index e8b1408..09dee4a 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -47,74 +47,76 @@ truncationRegion <- function(obj, TC, R, eta, Z, tol = 1e-15) {
           B <- 2 * as.numeric(t(Ugeta) %*% UgZ - t(Uheta) %*% UhZ)
           C <- sum(UgZ^2) - sum(UhZ^2) - pendiff
       }
+      
+      quadratic_roots(A, B, C, tol)
+    })
+    }
+    # LL is a list of intervals
+  })
+  # L is now a list of lists of intervals
+  return(unlist(L, recursive = FALSE, use.names = FALSE))
+}
 
-      disc <- B^2 - 4*A*C
-      b2a <- -B/(2*A)
+quadratic_roots <- function(A, B, C, tol) {
+    disc <- B^2 - 4*A*C
+    b2a <- -B/(2*A)
 
-      if (disc > tol) {
-        # Real roots
+    if (disc > tol) {
+        # Real roots        
         pm <- sqrt(disc)/(2*A)
         endpoints <- sort(c(b2a - pm, b2a + pm))
 
-      } else {
-
+    } else {
         # No real roots
         if (A > -tol) {
           # Quadratic form always positive
-          return(Intervals(c(-Inf,0)))
+            return(Intervals(c(-Inf,0)))
         } else {
           # Quadratic form always negative
-          stop(paste("Empty TC support is infeasible", s, "-", l))
+            stop(paste("Empty TC support is infeasible", s, "-", l))
         }
-      }
+    }
 
-      if (A > tol) {
+    if (A > tol) {
         # Parabola opens upward
         if (min(endpoints) > 0) {
           # Both roots positive, union of intervals
-          return(Intervals(rbind(c(-Inf,0), endpoints)))
+            return(Intervals(rbind(c(-Inf,0), endpoints)))
         } else {
           # At least one negative root
-          return(Intervals(c(-Inf, max(0, endpoints[2]))))
+            return(Intervals(c(-Inf, max(0, endpoints[2]))))
         }
-      } else {
+    } else {
         if (A < -tol) {
           # Parabola opens downward
-          if (endpoints[2] < 0) {
+            if (endpoints[2] < 0) {
             # Positive quadratic form only when t negative
-            stop(paste("Negative TC support is infeasible", s, "-", l))
-          } else {
-            # Part which is positive
-            if (endpoints[1] > 0) {
-                return(Intervals(rbind(c(-Inf, endpoints[1]), c(endpoints[2], Inf))))
+                stop(paste("Negative TC support is infeasible", s, "-", l))
             } else {
-                return(Intervals(c(endpoints[2], Inf)))
+            # Part which is positive
+                if (endpoints[1] > 0) {
+                    return(Intervals(rbind(c(-Inf, endpoints[1]), c(endpoints[2], Inf))))
+                } else {
+                    return(Intervals(c(endpoints[2], Inf)))
+                }
             }
-          }
         } else {
           # a is too close to 0, quadratic is actually linear
-          if (abs(B) > tol) {
-            if (B > 0) {
-              return(Intervals(c(-Inf, max(0, -C/B))))
+            if (abs(B) > tol) {
+                if (B > 0) {
+                    return(Intervals(c(-Inf, max(0, -C/B))))
+                } else {
+                    if (-C/B < 0) stop("Error: infeasible linear equation")
+                    return(Intervals(rbind(c(-Inf, 0), c(-C/B, Inf))))
+                }
             } else {
-              if (-C/B < 0) stop("Error: infeasible linear equation")
-              return(Intervals(rbind(c(-Inf, 0), c(-C/B, Inf))))
+                warning("Ill-conditioned quadratic")
+                return(Intervals(c(-Inf,0)))
             }
-          } else {
-            warning("Ill-conditioned quadratic")
-            return(Intervals(c(-Inf,0)))
-          }
         }
-      }
-    })
     }
-    # LL is a list of intervals
-  })
-  # L is now a list of lists of intervals
-  return(unlist(L, recursive = FALSE, use.names = FALSE))
 }
 
-
 roots_to_checkpoints <- function(roots) {
     checkpoints <- unique(sort(c(0, roots)))
     return(c(0, (checkpoints + c(checkpoints[-1], 2 + checkpoints[length(checkpoints)]))/2))

From 786ea4d1ae9b97e60b0457cef8dcb2de992e4ee4 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 30 Sep 2015 21:23:30 -0700
Subject: [PATCH 010/493] Changing simulation to known sigma

---
 forLater/josh/sim.cv.R | 9 +--------
 1 file changed, 1 insertion(+), 8 deletions(-)

diff --git a/forLater/josh/sim.cv.R b/forLater/josh/sim.cv.R
index 28d9beb..b8d27cb 100644
--- a/forLater/josh/sim.cv.R
+++ b/forLater/josh/sim.cv.R
@@ -25,21 +25,14 @@ instance <- function(n, p, sparsity, snr, maxsteps, nfolds) {
     }
 
     fit <- cvfs(x, y, maxsteps=maxsteps, nfolds=nfolds)
-    pvals <- groupfsInf(fit, verbose=T)
-    ## pvals_naive <- interval.groupfs(fit, x, y, index = 1:ncol(x))
-    ## fit$projections <- c(fit$projections, fit$rssprojections)
-    ## pvals_reduced <- interval.groupfs(fit, x, y, index = 1:ncol(x))
-    ## fit$projections <- c(fit$projections, fit$foldprojections)
+    pvals <- groupfsInf(fit, sigma = 1, verbose=T)
     return(list(variable = fit$action, pvals = pvals$pv))
-                #pvals_naive = pvals_naive, pvals_reduced = pvals_reduced))
 }
 
 time <- system.time({
           output <- replicate(niters, instance(n, p, sparsity, snr, maxsteps, nfolds))
 })
 
-#pvals_reduced <- do.call(c, list(output[4,]))
-#pvals_naive <- do.call(c, list(output[3,]))
 pvals <- do.call(c, list(output[2,]))
 vars <- do.call(c, list(output[1,]))
 

From ba4be55e03d37dab6e2ed92f041535c72969218f Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Fri, 2 Oct 2015 11:31:29 -0700
Subject: [PATCH 011/493] Added links

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 2d38cdf..7b6b8ff 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,9 @@
 # R-software
-R software for selective inference  
+R software for [selective inference](http://cran.r-project.org/web/packages/selectiveInference/).
 Authors: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid  
 Maintainer: Rob Tibshirani <tibs@stanford.edu>  
 
-New tools for inference after selection, for use with forward stepwise regression, least angle regression, the lasso, and the many means problem.
+New tools for inference after selection, for use with forward stepwise regression, least angle regression, the lasso, and the many means problem. The package is available on [CRAN](http://cran.r-project.org/web/packages/selectiveInference/). See [this paper](http://www.pnas.org/content/112/25/7629.full) for a high level introduction to selective inference.
 
 Code is in the directory selectiveInference/R.
 * funs.common.R: Basic functions used by many other functions, such as standardization.

From 6641e09b4d15d9f94bcd031b97cbab517a6ea18d Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Fri, 2 Oct 2015 11:45:32 -0700
Subject: [PATCH 012/493] Fixing whitespace problems in markdown

---
 README.md | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 7b6b8ff..1a50e5b 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 # R-software
-R software for [selective inference](http://cran.r-project.org/web/packages/selectiveInference/).
+R software for [selective inference](http://cran.r-project.org/web/packages/selectiveInference/).  
 Authors: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid  
-Maintainer: Rob Tibshirani <tibs@stanford.edu>  
+Maintainer: Rob Tibshirani <tibs@stanford.edu>
 
 New tools for inference after selection, for use with forward stepwise regression, least angle regression, the lasso, and the many means problem. The package is available on [CRAN](http://cran.r-project.org/web/packages/selectiveInference/). See [this paper](http://www.pnas.org/content/112/25/7629.full) for a high level introduction to selective inference.
 
@@ -13,4 +13,3 @@ Code is in the directory selectiveInference/R.
 * funs.inf.R: Common functions for inference with fixed, fs, lar, and manymeans (but not group).
 * funs.lar.R: Inference for least angle regression.
 * funs.max.R: Some numerical approximations. Deprecated?
-

From a874ffa57230cd5e2e6b52a586815fff7bcb20dd Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Fri, 2 Oct 2015 15:52:59 -0700
Subject: [PATCH 013/493] groupfs should now compute AIC exactly as base R

---
 selectiveInference/R/funs.groupfs.R | 70 ++++++++++++++---------------
 tests/test.groupfs.R                | 10 +++--
 2 files changed, 40 insertions(+), 40 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 516d834..8c69aa0 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -9,6 +9,7 @@
 #' @param sigma Estimate of error standard deviation for use in AIC criterion. This determines the relative scale between RSS and the degrees of freedom penalty. Default is NULL corresponding to unknown sigma. See \code{\link[stats]{extractAIC}} for details.
 #' @param k Multiplier of model size penalty, the default is \code{k = 2} for AIC. Use \code{k = log(n)} for BIC, or \code{k = log(p)} for RIC.
 #' @param intercept Should an intercept be included in the model? Default is TRUE.
+#' @param center Should the columns of the design matrix be centered? Default is TRUE.
 #' @param normalize Should the design matrix be normalized? Default is TRUE.
 #' @param verbose Print out progress along the way? Default is FALSE.
 #' @return An object of class "groupfs" containing information about the sequence of models in the forward stepwise algorithm. Call the function \code{\link{groupfsInf}} on this object to compute selective p-values.
@@ -19,7 +20,7 @@
 #' fit = groupfs(x, y, index, maxsteps = 5)
 #' pvals = groupfsInf(fit)
 #' @seealso \code{\link{groupfsInf}}
-groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE, normalize = TRUE, verbose = FALSE) {
+groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE, center = TRUE, normalize = TRUE, verbose = FALSE) {
 
   if (missing(index)) stop("Missing argument: index.")
   p <- ncol(x)
@@ -50,7 +51,7 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
   x.update <- x
 
   # Center and scale design matrix
-  xscaled <- scaleGroups(x.update, index, scale = normalize)
+  xscaled <- scaleGroups(x.update, index, center, normalize)
   xm <- xscaled$xm
   xs <- xscaled$xs
   x.update <- xscaled$x
@@ -61,9 +62,9 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
   terms = projections = maxprojs = aicpens = maxpens = cumprojs = vector("list", maxsteps)
 
   # Store other information from each step
-  path.info <- data.frame(imax=integer(maxsteps), L=numeric(maxsteps), df=integer(maxsteps), RSS=numeric(maxsteps), RSSdrop=numeric(maxsteps), chisq=numeric(maxsteps))
+  path.info <- data.frame(imax=integer(maxsteps), df=integer(maxsteps), AIC=numeric(maxsteps), RSS=numeric(maxsteps), RSSdrop=numeric(maxsteps), chisq=numeric(maxsteps))
 
-  modelrank <- 1
+  modelrank <- as.numeric(intercept)
 
   # Begin main loop
   for (step in 1:maxsteps) {
@@ -76,6 +77,8 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
     active <- union(active, imax)
     inactive.inds <- which(!index %in% active)
 
+    # Compute AIC
+    added$AIC <- n*log(exp(k*modelrank/n) * added$maxterm/n) + n*(log(2*pi) + 1)
     # Rank of group
     modelrank <- modelrank + added$df
 
@@ -86,11 +89,6 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
 
     # Regress added group out of y and inactive x
     P.imax <- added$maxproj %*% t(added$maxproj)
-    if (is.null(sigma)) {
-        P.imax <- P.imax / exp(k*added$df/n)
-    } else {
-        P.imax <- P.imax * sigma^2
-    }
     P.imax <- diag(rep(1, n)) - P.imax
     y.update <- P.imax %*% y.update
     x.update[, inactive.inds] <- P.imax %*% x.update[, inactive.inds]
@@ -103,6 +101,7 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
     if (step > 1) cumprojs[[step]] <- P.imax %*% cumprojs[[step-1]]
     terms[[step]] <- added$terms
 
+
     # Compute RSS for unadjusted chisq p-values
     added$RSS <- sum(y.update^2)
     scale.chisq <- 1
@@ -112,14 +111,14 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
     y.last <- y.update
 
     # Projections are stored separately
-    step.info <- data.frame(added[-c(4:(length(added)-3))])
+    step.info <- data.frame(added[-c(3:(length(added)-4))])
     path.info[step, ] <- step.info
 
     if (verbose) print(step.info)
   }
 
   # Create output object
-  value <- list(action=path.info$imax, L=path.info$L, projections = projections, maxprojs = maxprojs, aicpens = aicpens, maxpens = maxpens, cumprojs = cumprojs, log = path.info, index = index, y = y.begin, x = x.begin, bx = xm, sx = xs, sigma = sigma, intercept = intercept, terms = terms)
+  value <- list(action=path.info$imax, L=path.info$L, projections = projections, maxprojs = maxprojs, aicpens = aicpens, maxpens = maxpens, cumprojs = cumprojs, log = path.info, index = index, y = y.begin, x = x.begin, bx = xm, sx = xs, sigma = sigma, intercept = intercept, call = match.call(), terms = terms)
   class(value) <- "groupfs"
   attr(value, "labels") <- labels
   attr(value, "index") <- index
@@ -147,15 +146,14 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
 #' @param k Multiplier of model size penalty, use \code{k = 2} for AIC, \code{k = log(n)} for BIC, or \code{k = log(p)} for RIC.
 #' @param sigma Estimate of error standard deviation for use in AIC criterion. This determines the relative scale between RSS and the degrees of freedom penalty. See \code{\link{extractAIC}} for details.
 #' @return Index \code{imax} of added group, value \code{L} of maximized negative AIC, lists of projection matrices defining quadratic model selection event.
-add1.groupfs <- function(x, y, index, labels, inactive, k, sigma = NULL) {
+add1.groupfs <- function(xr, yr, index, labels, inactive, k, sigma = NULL) {
 
   # Use characters to avoid issues where
   # list() populates NULL lists in the positions
   # of the active variables
   ### Question for later: does this slow down lapply?
   keys = as.character(inactive)
-  n2y <- sum(y^2)
-  n <- ncol(x)
+  n <- nrow(xr)
 
   # Compute sums of squares to determine which group is added
   # penalized by rank of group if k > 0
@@ -163,33 +161,31 @@ add1.groupfs <- function(x, y, index, labels, inactive, k, sigma = NULL) {
   names(projections) = names(terms) = names(aicpens) = keys
   for (key in keys) {
       inds <- which(index == key)
-      xi <- x[,inds]
+      xi <- xr[,inds]
       ui <- svdu_thresh(xi)
       dfi <- ncol(ui)
       projections[[key]] <- ui
-      dfi <- ncol(ui)
-      uy <- t(ui) %*% y
+      uy <- t(ui) %*% yr
       if (is.null(sigma)) {
-          aicpens[[key]] <- exp(k*dfi/n)
-          terms[[key]] <- (sum(uy^2)  - sum(y^2)) * aicpens[[key]]
+          aicpens[[key]] <- exp(k*(dfi+1)/n)
+          terms[[key]] <- (sum(yr^2) - sum(uy^2)) * aicpens[[key]]
       } else {
-          aicpens[[key]] <- sigma^2 * k * dfi/n
-          terms[[key]] <- (sum(uy^2) - sum(y^2)) - aicpens[[key]]
+          aicpens[[key]] <- 2 * sigma^2 * k * dfi/n
+          terms[[key]] <- (sum(yr^2) - sum(uy^2)) - aicpens[[key]]
       }
   }
 
   # Maximizer = group to be added
-  terms.maxind <- which.max(terms)
-  imax <- inactive[terms.maxind]
-  keyind <- which(keys == imax)
-  maxproj <- projections[[keyind]]
-  maxpen <- aicpens[[keyind]]
-  projections[[keyind]] <- NULL
-  aicpens[[keyind]] <- NULL
-
-  L <- terms[[terms.maxind]]
-
-  return(list(imax=imax, L=L, df = ncol(maxproj), projections = projections, maxproj = maxproj, aicpens = aicpens, maxpen = maxpen, terms = terms))
+  terms.optind <- which.min(terms)
+  imax <- inactive[terms.optind]
+  optkey <- which(keys == imax)
+  maxproj <- projections[[optkey]]
+  maxpen <- aicpens[[optkey]]
+  maxterm <- terms[[optkey]]
+  projections[[optkey]] <- NULL
+  aicpens[[optkey]] <- NULL
+
+  return(list(imax=imax, df = ncol(maxproj), projections = projections, maxproj = maxproj, aicpens = aicpens, maxpen = maxpen, maxterm = maxterm, terms = terms))
 }
 
 # -----------------------------------------------------------
@@ -272,7 +268,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
         lapply(obj$cvobj, function(cvf) {
             truncationRegion(cvf, TC, R[-cvf$fold], eta[-cvf$fold], Z[-cvf$fold])
         })))
-        intervallist <- c(intervallist, 
+        intervallist <- c(intervallist,
         lapply(obj$cvquad, function(cvquad) {
             etacvquad <- t(eta) %*% cvquad
             A <- etacvquad %*% eta
@@ -373,14 +369,14 @@ num_int_chi <- function(a, b, df, nsamp = 10000) {
 #' @param x Design matrix.
 #' @param index Group membership indicator of length p.
 #' @param center Center groups, default is TRUE.
-#' @param scale Scale groups by Frobenius norm, default is TRUE.
+#' @param normalize Scale groups by Frobenius norm, default is TRUE.
 #' @return
 #' \describe{
 #'   \item{x}{Optionally centered/scaled design matrix.}
 #'   \item{xm}{Means of groups in original design matrix.}
 #'   \item{xs}{Frobenius norms of groups in original design matrix.}
 #' }
-scaleGroups <- function(x, index, center = TRUE, scale = TRUE) {
+scaleGroups <- function(x, index, center = TRUE, normalize = TRUE) {
   keys <- unique(index)
   xm <- rep(0, ncol(x))
   xs <- rep(1, ncol(x))
@@ -396,7 +392,7 @@ scaleGroups <- function(x, index, center = TRUE, scale = TRUE) {
     xsj <- sqrt(normsq)
     xs[inds] <- xsj
     if (xsj > 0) {
-        if (scale) x[, inds] <- x[, inds] / xsj
+        if (normalize) x[, inds] <- x[, inds] / xsj
     } else {
         stop(paste("Design matrix contains identically zero group of variables:", j))
     }
@@ -469,7 +465,7 @@ print.groupfs <- function(x, ...) {
     action <- x$action
     vnames <- attr(x, "varnames")
     if (length(vnames) > 0) action <- vnames[action]
-    tab = data.frame(Group = action, Rank = x$log$df, RSS = round(x$log$RSS, 3))
+    tab = data.frame(Group = action, Rank = x$log$df, RSS = round(x$log$RSS, 3), AIC = round(x$log$AIC, 3))
     rownames(tab) = 1:nsteps
     print(tab)
     cat("\nUse groupfsInf() to compute P-values\n")
diff --git a/tests/test.groupfs.R b/tests/test.groupfs.R
index 629bec1..c0e70e3 100644
--- a/tests/test.groupfs.R
+++ b/tests/test.groupfs.R
@@ -1,5 +1,9 @@
-library(selectiveInference)
+#library(selectiveInference)
 #library(lars)
+library(intervals)
+source("../selectiveInference/R/funs.groupfs.R")
+source("../selectiveInference/R/funs.quadratic.R")
+source("../selectiveInference/R/funs.common.R")
 
 set.seed(1)
 n <- 40
@@ -72,7 +76,7 @@ y <- y + x %*% beta
 y <- y-mean(y)
 df <- data.frame(y = y, states)
 fsfit <- step(lm(y ~ 0, df), direction="forward", scope = formula(lm(y~., df)), steps = maxsteps, k = 2)
-fit <- groupfs(x, y, index, maxsteps, k = 2, normalize = T)
+fit <- groupfs(x, y, index, maxsteps, k = 2, intercept = F, center = F, normalize = T)
 # names(fsfit$coefficients)[-1]
 if (length(fsfit$coefficients) > 0) {
     fsnames <- cnames[which(!is.na(charmatch(cnames,names(fsfit$coefficients)[-1])))][order(unlist(lapply(cnames, function(cn) {
@@ -99,7 +103,7 @@ for (iter in 1:niter) {
     y = rnorm(n)
     fit = groupfs(x, y, index, maxsteps)
     pvals = groupfsInf(fit)
-    pvalm[iter, ] = pvals$pv    
+    pvalm[iter, ] = pvals$pv
     fitk = groupfs(x, y, index, maxsteps, sigma = 1)
     pvalsk = groupfsInf(fitk)
     pvalmk[iter, ] = pvalsk$pv

From 918b74d40ceaf581a79915fa91d739fa76353c5c Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Fri, 2 Oct 2015 15:54:03 -0700
Subject: [PATCH 014/493] Updating documentation

---
 selectiveInference/man/groupfs.Rd     | 4 +++-
 selectiveInference/man/scaleGroups.Rd | 4 ++--
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/selectiveInference/man/groupfs.Rd b/selectiveInference/man/groupfs.Rd
index 6f719a9..db2e512 100644
--- a/selectiveInference/man/groupfs.Rd
+++ b/selectiveInference/man/groupfs.Rd
@@ -4,7 +4,7 @@
 \title{Select a model with forward stepwise.}
 \usage{
 groupfs(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE,
-  normalize = TRUE, verbose = FALSE)
+  center = TRUE, normalize = TRUE, verbose = FALSE)
 }
 \arguments{
 \item{x}{Matrix of predictors (n by p).}
@@ -21,6 +21,8 @@ groupfs(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE,
 
 \item{intercept}{Should an intercept be included in the model? Default is TRUE.}
 
+\item{center}{Should the columns of the design matrix be centered? Default is TRUE.}
+
 \item{normalize}{Should the design matrix be normalized? Default is TRUE.}
 
 \item{verbose}{Print out progress along the way? Default is FALSE.}
diff --git a/selectiveInference/man/scaleGroups.Rd b/selectiveInference/man/scaleGroups.Rd
index bfb899c..1e7e1c4 100644
--- a/selectiveInference/man/scaleGroups.Rd
+++ b/selectiveInference/man/scaleGroups.Rd
@@ -3,7 +3,7 @@
 \alias{scaleGroups}
 \title{Center and scale design matrix by groups}
 \usage{
-scaleGroups(x, index, center = TRUE, scale = TRUE)
+scaleGroups(x, index, center = TRUE, normalize = TRUE)
 }
 \arguments{
 \item{x}{Design matrix.}
@@ -12,7 +12,7 @@ scaleGroups(x, index, center = TRUE, scale = TRUE)
 
 \item{center}{Center groups, default is TRUE.}
 
-\item{scale}{Scale groups by Frobenius norm, default is TRUE.}
+\item{normalize}{Scale groups by Frobenius norm, default is TRUE.}
 }
 \value{
 \describe{

From b28900b16fd6e511e51126112ff9ddae1b1fa74e Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Fri, 2 Oct 2015 16:10:51 -0700
Subject: [PATCH 015/493] Fixing warnings from CMD check

---
 selectiveInference/R/funs.groupfs.R   |  9 ++++++---
 selectiveInference/R/funs.quadratic.R |  8 ++++----
 tests/test.groupfs.R                  | 10 +++++-----
 3 files changed, 15 insertions(+), 12 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 8c69aa0..b44b600 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -138,8 +138,8 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
 #'
 #' For internal use by \code{\link{groupfs}}.
 #'
-#' @param x Design matrix.
-#' @param y Response vector.
+#' @param xr Design matrix at current step.
+#' @param yr Response vector residual at current step.
 #' @param index Group membership indicator of length p.
 #' @param labels The unique elements of \code{index}.
 #' @param inactive Labels of inactive groups.
@@ -282,6 +282,9 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
     region <- do.call(interval_union, intervallist)
     region <- interval_union(region, Intervals(c(-Inf,0)))
     E <- interval_complement(region, check_valid = FALSE)
+    if (length(E) == 0) {
+        stop(paste("Empty TC support at step", j))
+    }
     supports[[j]] <- E
 
     # E is now potentially a union of intervals
@@ -301,7 +304,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
 
 TC_surv <- function(TC, sigma, df, E) {
     if (length(E) == 0) {
-        stop(paste("Empty TC support at step", j))
+        stop("Empty TC support")
     }
 
     # Sum truncated cdf over each part of E
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 09dee4a..2a33cf4 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -47,7 +47,7 @@ truncationRegion <- function(obj, TC, R, eta, Z, tol = 1e-15) {
           B <- 2 * as.numeric(t(Ugeta) %*% UgZ - t(Uheta) %*% UhZ)
           C <- sum(UgZ^2) - sum(UhZ^2) - pendiff
       }
-      
+
       quadratic_roots(A, B, C, tol)
     })
     }
@@ -62,7 +62,7 @@ quadratic_roots <- function(A, B, C, tol) {
     b2a <- -B/(2*A)
 
     if (disc > tol) {
-        # Real roots        
+        # Real roots
         pm <- sqrt(disc)/(2*A)
         endpoints <- sort(c(b2a - pm, b2a + pm))
 
@@ -73,7 +73,7 @@ quadratic_roots <- function(A, B, C, tol) {
             return(Intervals(c(-Inf,0)))
         } else {
           # Quadratic form always negative
-            stop(paste("Empty TC support is infeasible", s, "-", l))
+            stop("Empty TC support is infeasible")
         }
     }
 
@@ -91,7 +91,7 @@ quadratic_roots <- function(A, B, C, tol) {
           # Parabola opens downward
             if (endpoints[2] < 0) {
             # Positive quadratic form only when t negative
-                stop(paste("Negative TC support is infeasible", s, "-", l))
+                stop("Negative TC support is infeasible")
             } else {
             # Part which is positive
                 if (endpoints[1] > 0) {
diff --git a/tests/test.groupfs.R b/tests/test.groupfs.R
index c0e70e3..db4feff 100644
--- a/tests/test.groupfs.R
+++ b/tests/test.groupfs.R
@@ -1,9 +1,9 @@
-#library(selectiveInference)
+library(selectiveInference)
 #library(lars)
-library(intervals)
-source("../selectiveInference/R/funs.groupfs.R")
-source("../selectiveInference/R/funs.quadratic.R")
-source("../selectiveInference/R/funs.common.R")
+#library(intervals)
+#source("../selectiveInference/R/funs.groupfs.R")
+#source("../selectiveInference/R/funs.quadratic.R")
+#source("../selectiveInference/R/funs.common.R")
 
 set.seed(1)
 n <- 40

From b55101f429ddf5d10126756e5f984dbe0ec5e3d3 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Sun, 4 Oct 2015 22:04:57 -0700
Subject: [PATCH 016/493] Comprehensive test comparing step to groupfs with
 categorical designs

---
 tests/test.categorical.R | 49 ++++++++++++++++++++++++++++++++++++++++
 tests/test.groupfs.R     | 11 +++++----
 2 files changed, 55 insertions(+), 5 deletions(-)
 create mode 100644 tests/test.categorical.R

diff --git a/tests/test.categorical.R b/tests/test.categorical.R
new file mode 100644
index 0000000..a999a1b
--- /dev/null
+++ b/tests/test.categorical.R
@@ -0,0 +1,49 @@
+#library(selectiveInference)
+#library(lars)
+library(intervals)
+source("../selectiveInference/R/funs.groupfs.R")
+source("../selectiveInference/R/funs.quadratic.R")
+source("../selectiveInference/R/funs.common.R")
+
+set.seed(1)
+n <- 100
+G <- 10
+maxsteps <- 10
+snr <- 1
+niter <- 200
+
+print("Comparing step with groupfs on random categorical designs")
+aicdiffs <- numeric(niter)
+mismatchcount <- 0
+for (iter in 1:niter) {
+    rles <- 2 + rpois(G, 2)
+    df <- data.frame(do.call(cbind, lapply(rles, function(g) {
+        sample(LETTERS[1:g], n, replace = TRUE, prob = runif(g))
+    })), stringsAsFactors = TRUE)
+    if (any(apply(df, 2, function(col) length(unique(col))) == 1)) next
+    fd <- factorDesign(df)
+    if (any(duplicated(fd$x, MARGIN = 2))) next
+    y <- rnorm(n)
+    x1 <- fd$x[, fd$index == 1]
+    y <- y + x1 %*% c(snr, rep(0, ncol(x1) - 2), -snr)
+    y <- y - mean(y)
+    df$y <- y
+    capture.output(fsfit <- step(lm(y ~ 0, df), direction="forward", scope = formula(lm(y~.-1, df)), steps = maxsteps, trace = 1000), file = "/dev/null")
+    fit <- groupfs(fd$x, df$y, fd$index, maxsteps = 10, intercept = F, center = F, normalize = F)
+    fsnames <- names(fsfit$coefficients)
+    if (length(fsnames) > 0) {
+        fsnames <- unique(substr(fsnames, 1, nchar(fsnames) - 1))
+        k <- length(fsnames)
+        fitnames <- attr(fit, "varnames")[fit$action][1:k]
+        aicdiffs[iter] <- AIC(fsfit) - fit$log$AIC[k]
+        if (any(fsnames != fitnames)) {
+            print(paste("Mismatch at iteration", iter))
+            print(fsnames)
+            print(fitnames)
+            mismatchcount <- mismatchcount + 1
+        }
+    }
+}
+print(paste("Total mismatches:", mismatchcount, "out of", niter))
+print("Summary of differences in AIC")
+summary(aicdiffs)
diff --git a/tests/test.groupfs.R b/tests/test.groupfs.R
index db4feff..38cc85d 100644
--- a/tests/test.groupfs.R
+++ b/tests/test.groupfs.R
@@ -1,9 +1,9 @@
-library(selectiveInference)
+#library(selectiveInference)
 #library(lars)
-#library(intervals)
-#source("../selectiveInference/R/funs.groupfs.R")
-#source("../selectiveInference/R/funs.quadratic.R")
-#source("../selectiveInference/R/funs.common.R")
+library(intervals)
+source("../selectiveInference/R/funs.groupfs.R")
+source("../selectiveInference/R/funs.quadratic.R")
+source("../selectiveInference/R/funs.common.R")
 
 set.seed(1)
 n <- 40
@@ -128,3 +128,4 @@ print(colMeans(pvalmk))
 
 print(mean(pvalm))
 print(mean(pvalmk))
+

From 0a3e0881b002f38f3d0b29cd22332f343569380f Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Sun, 4 Oct 2015 23:08:40 -0700
Subject: [PATCH 017/493] Started coding early stopping if AIC increases

---
 selectiveInference/R/funs.groupfs.R    | 15 +++++++++++----
 selectiveInference/man/factorDesign.Rd |  3 ++-
 selectiveInference/man/groupfs.Rd      |  7 +++++--
 selectiveInference/man/groupfsInf.Rd   |  3 ++-
 selectiveInference/man/scaleGroups.Rd  |  3 ++-
 5 files changed, 22 insertions(+), 9 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index b44b600..17d2834 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -11,6 +11,7 @@
 #' @param intercept Should an intercept be included in the model? Default is TRUE.
 #' @param center Should the columns of the design matrix be centered? Default is TRUE.
 #' @param normalize Should the design matrix be normalized? Default is TRUE.
+#' @param aicstop Early stopping if AIC increases, default is FALSE.
 #' @param verbose Print out progress along the way? Default is FALSE.
 #' @return An object of class "groupfs" containing information about the sequence of models in the forward stepwise algorithm. Call the function \code{\link{groupfsInf}} on this object to compute selective p-values.
 #' @examples
@@ -20,7 +21,7 @@
 #' fit = groupfs(x, y, index, maxsteps = 5)
 #' pvals = groupfsInf(fit)
 #' @seealso \code{\link{groupfsInf}}
-groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE, center = TRUE, normalize = TRUE, verbose = FALSE) {
+groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE, center = TRUE, normalize = TRUE, aicstop = FALSE, verbose = FALSE) {
 
   if (missing(index)) stop("Missing argument: index.")
   p <- ncol(x)
@@ -42,7 +43,6 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
       warning(paste("If the largest groups are included the model will be saturated/overdetermined. To prevent this maxsteps has been changed to", maxsteps))
   }
 
-
   # Initialize copies of data for loop
   by <- mean(y)
   y.update <- y
@@ -65,6 +65,7 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
   path.info <- data.frame(imax=integer(maxsteps), df=integer(maxsteps), AIC=numeric(maxsteps), RSS=numeric(maxsteps), RSSdrop=numeric(maxsteps), chisq=numeric(maxsteps))
 
   modelrank <- as.numeric(intercept)
+  aic.last <- n*(log(2*pi) + log(mean(y.update^2)) + 1 + k * (is.null(sigma) + intercept))
 
   # Begin main loop
   for (step in 1:maxsteps) {
@@ -101,7 +102,6 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
     if (step > 1) cumprojs[[step]] <- P.imax %*% cumprojs[[step-1]]
     terms[[step]] <- added$terms
 
-
     # Compute RSS for unadjusted chisq p-values
     added$RSS <- sum(y.update^2)
     scale.chisq <- 1
@@ -109,11 +109,18 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
     added$RSSdrop <- sum((y.last - y.update)^2)
     added$chisq <- pchisq(added$RSSdrop/scale.chisq, lower.tail=FALSE, df = added$df)
     y.last <- y.update
-
+    
     # Projections are stored separately
     step.info <- data.frame(added[-c(3:(length(added)-4))])
     path.info[step, ] <- step.info
 
+    if (aic.last < added$AIC) {
+        # TODO tomorrow
+        # Modify the object somehow
+        # and groupfsInf
+        break
+    }
+    aic.last <- added$AIC
     if (verbose) print(step.info)
   }
 
diff --git a/selectiveInference/man/factorDesign.Rd b/selectiveInference/man/factorDesign.Rd
index 3a42e2e..d5d2576 100644
--- a/selectiveInference/man/factorDesign.Rd
+++ b/selectiveInference/man/factorDesign.Rd
@@ -1,4 +1,5 @@
-% Generated by roxygen2 (4.0.1.99): do not edit by hand
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/funs.groupfs.R
 \name{factorDesign}
 \alias{factorDesign}
 \title{Expand a data frame with factors to form a design matrix with the full binary encoding of each factor.}
diff --git a/selectiveInference/man/groupfs.Rd b/selectiveInference/man/groupfs.Rd
index db2e512..c279c9e 100644
--- a/selectiveInference/man/groupfs.Rd
+++ b/selectiveInference/man/groupfs.Rd
@@ -1,10 +1,11 @@
-% Generated by roxygen2 (4.0.1.99): do not edit by hand
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/funs.groupfs.R
 \name{groupfs}
 \alias{groupfs}
 \title{Select a model with forward stepwise.}
 \usage{
 groupfs(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE,
-  center = TRUE, normalize = TRUE, verbose = FALSE)
+  center = TRUE, normalize = TRUE, aicstop = FALSE, verbose = FALSE)
 }
 \arguments{
 \item{x}{Matrix of predictors (n by p).}
@@ -25,6 +26,8 @@ groupfs(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE,
 
 \item{normalize}{Should the design matrix be normalized? Default is TRUE.}
 
+\item{aicstop}{Early stopping if AIC increases, default is FALSE.}
+
 \item{verbose}{Print out progress along the way? Default is FALSE.}
 }
 \value{
diff --git a/selectiveInference/man/groupfsInf.Rd b/selectiveInference/man/groupfsInf.Rd
index 4926896..f5ee970 100644
--- a/selectiveInference/man/groupfsInf.Rd
+++ b/selectiveInference/man/groupfsInf.Rd
@@ -1,4 +1,5 @@
-% Generated by roxygen2 (4.0.1.99): do not edit by hand
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/funs.groupfs.R
 \name{groupfsInf}
 \alias{groupfsInf}
 \title{Compute selective p-values for a model fitted by \code{groupfs}.}
diff --git a/selectiveInference/man/scaleGroups.Rd b/selectiveInference/man/scaleGroups.Rd
index 1e7e1c4..e5a93fa 100644
--- a/selectiveInference/man/scaleGroups.Rd
+++ b/selectiveInference/man/scaleGroups.Rd
@@ -1,4 +1,5 @@
-% Generated by roxygen2 (4.0.1.99): do not edit by hand
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/funs.groupfs.R
 \name{scaleGroups}
 \alias{scaleGroups}
 \title{Center and scale design matrix by groups}

From 680a56ddf9057461bd882207798a125f81ac728d Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Mon, 5 Oct 2015 13:22:20 -0700
Subject: [PATCH 018/493] Partially complete with early stopping and checking
 known sigma vs step

---
 selectiveInference/R/funs.groupfs.R | 63 ++++++++++++++++++++---------
 tests/test.categorical.R            |  8 ++--
 tests/test.groupfs.R                |  2 +-
 3 files changed, 48 insertions(+), 25 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 17d2834..6abf324 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -11,17 +11,17 @@
 #' @param intercept Should an intercept be included in the model? Default is TRUE.
 #' @param center Should the columns of the design matrix be centered? Default is TRUE.
 #' @param normalize Should the design matrix be normalized? Default is TRUE.
-#' @param aicstop Early stopping if AIC increases, default is FALSE.
+#' @param aicstop Early stopping if AIC increases. Default is 0 corresponding to no early stopping. Positive integer values specify the number of times the AIC is allowed to increase in a row, e.g. with \code{aicstop = 2} the algorithm will stop if the AIC criterion increases for 2 steps in a row. The default of \code{\link[stats]{step}} corresponds to \code{aicstop = 1}.
 #' @param verbose Print out progress along the way? Default is FALSE.
 #' @return An object of class "groupfs" containing information about the sequence of models in the forward stepwise algorithm. Call the function \code{\link{groupfsInf}} on this object to compute selective p-values.
 #' @examples
 #' x = matrix(rnorm(20*40), nrow=20)
 #' index = sort(rep(1:20, 2))
-#' y = rnorm(20) + 2 * (x[,1] - x[,2]) - (x[,3] - x[,4])
+#' y = rnorm(20) + 2 * x[,1] - x[,4]
 #' fit = groupfs(x, y, index, maxsteps = 5)
 #' pvals = groupfsInf(fit)
-#' @seealso \code{\link{groupfsInf}}
-groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE, center = TRUE, normalize = TRUE, aicstop = FALSE, verbose = FALSE) {
+#' @seealso \code{\link{groupfsInf}}, \code{\link{factorDesign}}.
+groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE, center = TRUE, normalize = TRUE, aicstop = 0, verbose = FALSE) {
 
   if (missing(index)) stop("Missing argument: index.")
   p <- ncol(x)
@@ -65,7 +65,11 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
   path.info <- data.frame(imax=integer(maxsteps), df=integer(maxsteps), AIC=numeric(maxsteps), RSS=numeric(maxsteps), RSSdrop=numeric(maxsteps), chisq=numeric(maxsteps))
 
   modelrank <- as.numeric(intercept)
-  aic.last <- n*(log(2*pi) + log(mean(y.update^2)) + 1 + k * (is.null(sigma) + intercept))
+  if (is.null(sigma)) {
+      aic.begin <- aic.last <- n*(log(2*pi) + log(mean(y.update^2)) + 1 + k * (is.null(sigma) + intercept))
+  } else {
+      aic.begin <- aic.last <- sum(y.update^2)/sigma^2 - n + k * intercept
+  }
 
   # Begin main loop
   for (step in 1:maxsteps) {
@@ -78,8 +82,6 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
     active <- union(active, imax)
     inactive.inds <- which(!index %in% active)
 
-    # Compute AIC
-    added$AIC <- n*log(exp(k*modelrank/n) * added$maxterm/n) + n*(log(2*pi) + 1)
     # Rank of group
     modelrank <- modelrank + added$df
 
@@ -94,6 +96,13 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
     y.update <- P.imax %*% y.update
     x.update[, inactive.inds] <- P.imax %*% x.update[, inactive.inds]
 
+    # Compute AIC
+    if (is.null(sigma)) {
+        added$AIC <- n*log(exp(k*modelrank/n) * added$maxterm/n) + n*(log(2*pi) + 1)
+    } else {
+        added$AIC <- sum(y.update^2)/sigma^2 - n + k * modelrank
+    }
+
     projections[[step]] <- added$projections
     maxprojs[[step]] <- added$maxproj
     aicpens[[step]] <- added$aicpens
@@ -109,36 +118,47 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
     added$RSSdrop <- sum((y.last - y.update)^2)
     added$chisq <- pchisq(added$RSSdrop/scale.chisq, lower.tail=FALSE, df = added$df)
     y.last <- y.update
-    
+
     # Projections are stored separately
     step.info <- data.frame(added[-c(3:(length(added)-4))])
     path.info[step, ] <- step.info
 
-    if (aic.last < added$AIC) {
-        # TODO tomorrow
-        # Modify the object somehow
-        # and groupfsInf
-        break
+    if (aicstop > 0 && step >= aicstop && aic.last < added$AIC) {
+        ########## Incomplete ##########
+        ## cut off the last aicstop variables as well?
+        if (all(diff(c(aic.begin, path.info$AIC)[(step+1-aicstop):(step+1)]) > 0)) {
+            path.info <- path.info[1:step, ]
+            projections[(step+1):maxsteps] <- NULL
+            maxprojs[(step+1):maxsteps] <- NULL
+            aicpens[(step+1):maxsteps] <- NULL
+            maxpens[(step+1):maxsteps] <- NULL
+            cumprojs[(step+1):maxsteps] <- NULL
+            terms[(step+1):maxsteps] <- NULL
+            maxsteps <- step
+            # add additional projections
+            break
+        }
     }
     aic.last <- added$AIC
     if (verbose) print(step.info)
   }
 
   # Create output object
-  value <- list(action=path.info$imax, L=path.info$L, projections = projections, maxprojs = maxprojs, aicpens = aicpens, maxpens = maxpens, cumprojs = cumprojs, log = path.info, index = index, y = y.begin, x = x.begin, bx = xm, sx = xs, sigma = sigma, intercept = intercept, call = match.call(), terms = terms)
+  value <- list(action=path.info$imax, L=path.info$L, AIC=path.info$AIC, projections = projections, maxprojs = maxprojs, aicpens = aicpens, maxpens = maxpens, cumprojs = cumprojs, log = path.info, index = index, y = y.begin, x = x.begin, bx = xm, sx = xs, sigma = sigma, intercept = intercept, call = match.call(), terms = terms)
   class(value) <- "groupfs"
   attr(value, "labels") <- labels
   attr(value, "index") <- index
   attr(value, "maxsteps") <- maxsteps
   attr(value, "sigma") <- sigma
   attr(value, "k") <- k
+  attr(value, "aicstop") <- aicstop
   if (is.null(attr(x, "varnames"))) {
     attr(value, "varnames") <- colnames(x)
   } else {
     attr(value, "varnames") <- attr(x, "varnames")
   }
 
-  invisible(value)
+  return(value)
 }
 
 #' Add one group to the model in \code{groupfs}.
@@ -177,8 +197,8 @@ add1.groupfs <- function(xr, yr, index, labels, inactive, k, sigma = NULL) {
           aicpens[[key]] <- exp(k*(dfi+1)/n)
           terms[[key]] <- (sum(yr^2) - sum(uy^2)) * aicpens[[key]]
       } else {
-          aicpens[[key]] <- 2 * sigma^2 * k * dfi/n
-          terms[[key]] <- (sum(yr^2) - sum(uy^2)) - aicpens[[key]]
+          aicpens[[key]] <- k * dfi/n
+          terms[[key]] <- (sum(yr^2) - sum(uy^2))/sigma^2 - aicpens[[key]]
       }
   }
 
@@ -203,8 +223,10 @@ add1.groupfs <- function(xr, yr, index, labels, inactive, k, sigma = NULL) {
 #'
 #' @param obj Object returned by \code{\link{groupfs}} function
 #' @param sigma Estimate of error standard deviation. If NULL (default), this is estimated using the mean squared residual of the full least squares fit when n >= 2p, and the mean squared residual of the selected model when n < 2p. In the latter case, the user should use \code{\link{estimateSigma}} function for a more accurate estimate.
+#' @param type Type of conditional p-values to compute. With "all" (default), p-values are computed conditional on the final model with all variables up to \code{maxsteps}; with "aic" the number of steps is chosen after which the AIC criterion increases \code{ntimes} in a row, and then the same type of analysis as in "all" is carried out for the active variables at that number of steps.
+#' @param ntimes Number of steps for which AIC criterion has to increase before minimizing point is declared.
 #' @param verbose Print out progress along the way? Default is FALSE.
-#' @return An object of class "groupfsInf" containing selective p-values for the fitted model \code{obj}. The default printing behavior should supply adequate information.
+#' @return An object of class "groupfsInf" containing selective p-values for the fitted model \code{obj}. For comparison with \code{\link{fsInf}}, note that the option \code{type = "active"} is not available.
 #'
 #' \describe{
 #'   \item{vars}{Labels of the active groups in the order they were included.}
@@ -214,8 +236,9 @@ add1.groupfs <- function(xr, yr, index, labels, inactive, k, sigma = NULL) {
 #'   \item{df}{Rank of group of variables when it was added to the model.}
 #'   \item{support}{List of intervals defining the truncation region of the truncated chi.}
 #' }
-groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
+groupfsInf <- function(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2, verbose = FALSE) {
 
+  type <- match.arg(type)
   n <- nrow(obj$x)
   p <- ncol(obj$x)
   maxsteps <- attr(obj, "maxsteps")
@@ -304,7 +327,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
   if (!is.null(attr(obj, "varnames"))) {
       attr(out, "varnames") <- attr(obj, "varnames")
   }
-  invisible(out)
+  return(out)
 }
 
 # -----------------------------------------------------------
diff --git a/tests/test.categorical.R b/tests/test.categorical.R
index a999a1b..4edf069 100644
--- a/tests/test.categorical.R
+++ b/tests/test.categorical.R
@@ -10,7 +10,7 @@ n <- 100
 G <- 10
 maxsteps <- 10
 snr <- 1
-niter <- 200
+niter <- 100
 
 print("Comparing step with groupfs on random categorical designs")
 aicdiffs <- numeric(niter)
@@ -29,14 +29,14 @@ for (iter in 1:niter) {
     y <- y - mean(y)
     df$y <- y
     capture.output(fsfit <- step(lm(y ~ 0, df), direction="forward", scope = formula(lm(y~.-1, df)), steps = maxsteps, trace = 1000), file = "/dev/null")
-    fit <- groupfs(fd$x, df$y, fd$index, maxsteps = 10, intercept = F, center = F, normalize = F)
+    fit <- groupfs(fd$x, df$y, fd$index, maxsteps = 10, intercept = F, center = F, normalize = F, aicstop = 1)
     fsnames <- names(fsfit$coefficients)
     if (length(fsnames) > 0) {
         fsnames <- unique(substr(fsnames, 1, nchar(fsnames) - 1))
         k <- length(fsnames)
-        fitnames <- attr(fit, "varnames")[fit$action][1:k]
+        fitnames <- attr(fit, "varnames")[fit$action][1:(length(fit$action)-attr(fit, "aicstop"))]
         aicdiffs[iter] <- AIC(fsfit) - fit$log$AIC[k]
-        if (any(fsnames != fitnames)) {
+        if (length(fitnames) !=k || any(fsnames != fitnames)) {
             print(paste("Mismatch at iteration", iter))
             print(fsnames)
             print(fitnames)
diff --git a/tests/test.groupfs.R b/tests/test.groupfs.R
index 38cc85d..d35800e 100644
--- a/tests/test.groupfs.R
+++ b/tests/test.groupfs.R
@@ -14,7 +14,7 @@ sparsity <- 5
 snr <- 3
 
 system.time({
-for (iter in 1:10) {
+for (iter in 1:100) {
     y <- rnorm(n)
     x <- matrix(rnorm(n*p), nrow=n)
     beta <- rep(0, p)

From e9f061810d74c818d5a56cb7d6356365b10a9588 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Mon, 5 Oct 2015 16:05:43 -0700
Subject: [PATCH 019/493] groupfs matches step exactly for both known and
 unknown sigma

---
 selectiveInference/R/funs.groupfs.R | 21 ++++++++---
 tests/test.categorical.R            | 55 +++++++++++++++++++----------
 2 files changed, 52 insertions(+), 24 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 6abf324..44f4523 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -58,6 +58,7 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
 
   x.begin <- x.update
   y.begin <- y.update
+  stopped <- FALSE
   # Store all projections computed along the path
   terms = projections = maxprojs = aicpens = maxpens = cumprojs = vector("list", maxsteps)
 
@@ -70,6 +71,7 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
   } else {
       aic.begin <- aic.last <- sum(y.update^2)/sigma^2 - n + k * intercept
   }
+  if (verbose) print(paste0("Start:  AIC=", round(aic.begin, 3)), quote = FALSE)
 
   # Begin main loop
   for (step in 1:maxsteps) {
@@ -98,9 +100,14 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
 
     # Compute AIC
     if (is.null(sigma)) {
-        added$AIC <- n*log(exp(k*modelrank/n) * added$maxterm/n) + n*(log(2*pi) + 1)
+        added$AIC <- n * log(added$maxterm/n) - k * added$df + n + n*log(2*pi) + k * modelrank
     } else {
         added$AIC <- sum(y.update^2)/sigma^2 - n + k * modelrank
+        if (verbose) {
+            aics <- matrix(round(unlist(added$terms) - n + k * (modelrank - added$df), 2), ncol = 1)
+            rownames(aics) <- names(added$terms)
+            write.table(aics, col.names = F, quote = F)
+        }
     }
 
     projections[[step]] <- added$projections
@@ -123,6 +130,8 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
     step.info <- data.frame(added[-c(3:(length(added)-4))])
     path.info[step, ] <- step.info
 
+    if (verbose) print(round(step.info, 3))
+
     if (aicstop > 0 && step >= aicstop && aic.last < added$AIC) {
         ########## Incomplete ##########
         ## cut off the last aicstop variables as well?
@@ -135,16 +144,17 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
             cumprojs[(step+1):maxsteps] <- NULL
             terms[(step+1):maxsteps] <- NULL
             maxsteps <- step
+            stopped <- TRUE
             # add additional projections
             break
         }
     }
     aic.last <- added$AIC
-    if (verbose) print(step.info)
   }
 
   # Create output object
   value <- list(action=path.info$imax, L=path.info$L, AIC=path.info$AIC, projections = projections, maxprojs = maxprojs, aicpens = aicpens, maxpens = maxpens, cumprojs = cumprojs, log = path.info, index = index, y = y.begin, x = x.begin, bx = xm, sx = xs, sigma = sigma, intercept = intercept, call = match.call(), terms = terms)
+
   class(value) <- "groupfs"
   attr(value, "labels") <- labels
   attr(value, "index") <- index
@@ -152,6 +162,7 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
   attr(value, "sigma") <- sigma
   attr(value, "k") <- k
   attr(value, "aicstop") <- aicstop
+  if (aicstop > 0) attr(value, "stopped") <- stopped
   if (is.null(attr(x, "varnames"))) {
     attr(value, "varnames") <- colnames(x)
   } else {
@@ -194,11 +205,11 @@ add1.groupfs <- function(xr, yr, index, labels, inactive, k, sigma = NULL) {
       projections[[key]] <- ui
       uy <- t(ui) %*% yr
       if (is.null(sigma)) {
-          aicpens[[key]] <- exp(k*(dfi+1)/n)
+          aicpens[[key]] <- exp(k*dfi/n)
           terms[[key]] <- (sum(yr^2) - sum(uy^2)) * aicpens[[key]]
       } else {
-          aicpens[[key]] <- k * dfi/n
-          terms[[key]] <- (sum(yr^2) - sum(uy^2))/sigma^2 - aicpens[[key]]
+          aicpens[[key]] <- k * dfi
+          terms[[key]] <- (sum(yr^2) - sum(uy^2))/sigma^2 + aicpens[[key]]
       }
   }
 
diff --git a/tests/test.categorical.R b/tests/test.categorical.R
index 4edf069..2b5f4bd 100644
--- a/tests/test.categorical.R
+++ b/tests/test.categorical.R
@@ -12,9 +12,31 @@ maxsteps <- 10
 snr <- 1
 niter <- 100
 
+check.mismatch <- function(fsfit, fit) {
+    fsnames <- names(fsfit$coefficients)
+    if (length(fsnames) > 0) {
+        fsnames <- unique(substr(fsnames, 1, nchar(fsnames) - 1))
+        k <- length(fsnames)
+        fitnames <- attr(fit, "varnames")[fit$action][1:(length(fit$action)-attr(fit, "aicstop"))]
+        if (is.null(fit$sigma)) {
+            aicdiff <- AIC(fsfit) - fit$log$AIC[k]
+        } else {
+            aicdiff <- extractAIC(fsfit, scale = fit$sigma)[2] - fit$log$AIC[k]
+        }
+        if (length(fitnames) !=k || any(fsnames != fitnames)) {
+            print(paste("Mismatch at iteration", iter, ifelse(is.null(fit$sigma), "unknown", "known")))
+            print(fsnames)
+            print(fitnames)
+            return(list(count = 1, aicdiff = aicdiff))
+        }
+        return(list(count = 0, aicdiff = aicdiff))
+    }
+    return(list(count = 0, aicdiff = 0))
+}
+
 print("Comparing step with groupfs on random categorical designs")
-aicdiffs <- numeric(niter)
-mismatchcount <- 0
+umismatchcount <- kmismatchcount <- 0
+uaicdiffs <- kaicdiffs <- numeric(niter)
 for (iter in 1:niter) {
     rles <- 2 + rpois(G, 2)
     df <- data.frame(do.call(cbind, lapply(rles, function(g) {
@@ -28,22 +50,17 @@ for (iter in 1:niter) {
     y <- y + x1 %*% c(snr, rep(0, ncol(x1) - 2), -snr)
     y <- y - mean(y)
     df$y <- y
-    capture.output(fsfit <- step(lm(y ~ 0, df), direction="forward", scope = formula(lm(y~.-1, df)), steps = maxsteps, trace = 1000), file = "/dev/null")
+    capture.output(fsfit <- step(lm(y ~ 0, df), direction="forward", scope = formula(lm(y~.-1, df)), steps = maxsteps), file = "/dev/null")
     fit <- groupfs(fd$x, df$y, fd$index, maxsteps = 10, intercept = F, center = F, normalize = F, aicstop = 1)
-    fsnames <- names(fsfit$coefficients)
-    if (length(fsnames) > 0) {
-        fsnames <- unique(substr(fsnames, 1, nchar(fsnames) - 1))
-        k <- length(fsnames)
-        fitnames <- attr(fit, "varnames")[fit$action][1:(length(fit$action)-attr(fit, "aicstop"))]
-        aicdiffs[iter] <- AIC(fsfit) - fit$log$AIC[k]
-        if (length(fitnames) !=k || any(fsnames != fitnames)) {
-            print(paste("Mismatch at iteration", iter))
-            print(fsnames)
-            print(fitnames)
-            mismatchcount <- mismatchcount + 1
-        }
-    }
+    mismatches <- check.mismatch(fsfit, fit)
+    umismatchcount <- umismatchcount + mismatches$count
+    uaicdiffs[iter] <- mismatches$aicdiff
+    capture.output(fsfit <- step(lm(y ~ 0, df), scale = 1, direction="forward", scope = formula(lm(y~.-1, df)), steps = maxsteps), file = "/dev/null")
+    fit <- groupfs(fd$x, df$y, fd$index, maxsteps = 10, sigma = 1, intercept = F, center = F, normalize = F, aicstop = 1)
+    mismatches <- check.mismatch(fsfit, fit)
+    kmismatchcount <- kmismatchcount + mismatches$count
+    kaicdiffs[iter] <- mismatches$aicdiff
 }
-print(paste("Total mismatches:", mismatchcount, "out of", niter))
-print("Summary of differences in AIC")
-summary(aicdiffs)
+print(paste("Mismatches:", umismatchcount, "for unknown sigma and", kmismatchcount, "for known"))
+summary(uaicdiffs)
+summary(kaicdiffs)

From a18943602aae11572b63af64d8ff87417bdb8124 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Mon, 5 Oct 2015 16:08:06 -0700
Subject: [PATCH 020/493] Removed print statement used for debugging

---
 selectiveInference/R/funs.groupfs.R | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 44f4523..e9b03d7 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -103,11 +103,6 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
         added$AIC <- n * log(added$maxterm/n) - k * added$df + n + n*log(2*pi) + k * modelrank
     } else {
         added$AIC <- sum(y.update^2)/sigma^2 - n + k * modelrank
-        if (verbose) {
-            aics <- matrix(round(unlist(added$terms) - n + k * (modelrank - added$df), 2), ncol = 1)
-            rownames(aics) <- names(added$terms)
-            write.table(aics, col.names = F, quote = F)
-        }
     }
 
     projections[[step]] <- added$projections

From 23e1599591a93dc9100ee5e661490f7dcbfad1ca Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Mon, 5 Oct 2015 18:06:49 -0700
Subject: [PATCH 021/493] Currently broken, but almost done implementing
 AICstop

---
 selectiveInference/R/funs.groupfs.R | 65 +++++++++++++++++++++++++----
 1 file changed, 56 insertions(+), 9 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index e9b03d7..a3f0b7c 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -131,6 +131,40 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
         ########## Incomplete ##########
         ## cut off the last aicstop variables as well?
         if (all(diff(c(aic.begin, path.info$AIC)[(step+1-aicstop):(step+1)]) > 0)) {
+            stopquads <- ulist <- zlist <- penlist <- vector("list", aicstop)
+            for (s in seq(aicstop)) {
+                etas <- cumprojs[[step - aicstop + s]] %*% eta
+                Zs <- cumprojs[[step - aicstop + s]] %*% Z
+                maxprojs[[(step-aicstop):step]][[s]]
+                maxpens[[(step-aicstop):step]][[s]]
+            }
+
+      Uheta <- t(Uh) %*% etas
+      Ugeta <- t(Ug) %*% etas
+      UhZ <- t(Uh) %*% Zs
+      UgZ <- t(Ug) %*% Zs
+      etasZs <- t(etas) %*% Zs
+      peng <- obj$maxpens[[s]]
+      penh <- obj$aicpens[[s]][[l]]
+      pendiff <- peng-penh
+      if (is.null(obj$sigma)) {
+          A <- sum(Ugeta^2) * peng - sum(Uheta^2) * penh - sum(etas^2) * pendiff
+          B <- 2 * as.numeric(t(Ugeta) %*% UgZ * peng - t(Uheta) %*% UhZ * penh - etasZs * pendiff)
+          C <- sum(UgZ^2) * peng - sum(UhZ^2) * penh - sum(Zs^2) * pendiff
+      } else {
+          # Check this
+          A <- sum(Ugeta^2) - sum(Uheta^2)
+          B <- 2 * as.numeric(t(Ugeta) %*% UgZ - t(Uheta) %*% UhZ)
+          C <- sum(UgZ^2) - sum(UhZ^2) - pendiff
+      }
+
+
+            if (is.null(sigma)) {
+                added$AIC <- n * log(added$maxterm/n) - k * added$df + n + n*log(2*pi) + k * modelrank
+            } else {
+                added$AIC <- sum(y.update^2)/sigma^2 - n + k * modelrank
+            }
+
             path.info <- path.info[1:step, ]
             projections[(step+1):maxsteps] <- NULL
             maxprojs[(step+1):maxsteps] <- NULL
@@ -157,13 +191,15 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
   attr(value, "sigma") <- sigma
   attr(value, "k") <- k
   attr(value, "aicstop") <- aicstop
-  if (aicstop > 0) attr(value, "stopped") <- stopped
+  attr(value, "stopped") <- stopped
+  if (stopped) {
+      value$stopquads <- stopquads
+  }
   if (is.null(attr(x, "varnames"))) {
     attr(value, "varnames") <- colnames(x)
   } else {
     attr(value, "varnames") <- attr(x, "varnames")
   }
-
   return(value)
 }
 
@@ -300,18 +336,29 @@ groupfsInf <- function(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2, ve
 
     intervallist <- truncationRegion(obj, TC, R, eta, Z)
     if (!is.null(obj$cvobj)) {
+        if (attr(obj, "stopped")) stop("Cross-validation and early stopping cannot be used simultaneously")
         intervallist <- c(intervallist, do.call(c,
         lapply(obj$cvobj, function(cvf) {
             truncationRegion(cvf, TC, R[-cvf$fold], eta[-cvf$fold], Z[-cvf$fold])
         })))
         intervallist <- c(intervallist,
-        lapply(obj$cvquad, function(cvquad) {
-            etacvquad <- t(eta) %*% cvquad
-            A <- etacvquad %*% eta
-            B <- 2 * etacvquad %*% Z
-            C <- t(Z) %*% cvquad %*% Z
-            quadratic_roots(A, B, C, tol = 1e-15)
-        }))
+                          lapply(obj$cvquad, function(cvquad) {
+                              etacvquad <- t(eta) %*% cvquad
+                              A <- etacvquad %*% eta
+                              B <- 2 * etacvquad %*% Z
+                              C <- t(Z) %*% cvquad %*% Z
+                              quadratic_roots(A, B, C, tol = 1e-15)
+                          }))
+    }
+    if (attr(obj, "stopped")) {
+        intervallist <- c(intervallist,
+                          lapply(obj$stopquads, function(squad) {
+                              etasquad <- t(eta) %*% squad
+                              A <- etasquad %*% eta
+                              B <- 2 * etasquad %*% Z
+                              C <- t(Z) %*% squad %*% Z
+                              quadratic_roots(A, B, C, tol = 1e-15)
+                          }))
     }
 
     # Compute intersection:

From d2d9db43c4516842c2b13d3cbf25c75d089862bf Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Mon, 5 Oct 2015 22:12:45 -0700
Subject: [PATCH 022/493] Still broken, closer to finishing AICstop

---
 selectiveInference/R/funs.groupfs.R   | 65 +++++++++++++--------------
 selectiveInference/R/funs.quadratic.R | 41 ++++++++---------
 2 files changed, 53 insertions(+), 53 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index a3f0b7c..686d248 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -131,33 +131,6 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
         ########## Incomplete ##########
         ## cut off the last aicstop variables as well?
         if (all(diff(c(aic.begin, path.info$AIC)[(step+1-aicstop):(step+1)]) > 0)) {
-            stopquads <- ulist <- zlist <- penlist <- vector("list", aicstop)
-            for (s in seq(aicstop)) {
-                etas <- cumprojs[[step - aicstop + s]] %*% eta
-                Zs <- cumprojs[[step - aicstop + s]] %*% Z
-                maxprojs[[(step-aicstop):step]][[s]]
-                maxpens[[(step-aicstop):step]][[s]]
-            }
-
-      Uheta <- t(Uh) %*% etas
-      Ugeta <- t(Ug) %*% etas
-      UhZ <- t(Uh) %*% Zs
-      UgZ <- t(Ug) %*% Zs
-      etasZs <- t(etas) %*% Zs
-      peng <- obj$maxpens[[s]]
-      penh <- obj$aicpens[[s]][[l]]
-      pendiff <- peng-penh
-      if (is.null(obj$sigma)) {
-          A <- sum(Ugeta^2) * peng - sum(Uheta^2) * penh - sum(etas^2) * pendiff
-          B <- 2 * as.numeric(t(Ugeta) %*% UgZ * peng - t(Uheta) %*% UhZ * penh - etasZs * pendiff)
-          C <- sum(UgZ^2) * peng - sum(UhZ^2) * penh - sum(Zs^2) * pendiff
-      } else {
-          # Check this
-          A <- sum(Ugeta^2) - sum(Uheta^2)
-          B <- 2 * as.numeric(t(Ugeta) %*% UgZ - t(Uheta) %*% UhZ)
-          C <- sum(UgZ^2) - sum(UhZ^2) - pendiff
-      }
-
 
             if (is.null(sigma)) {
                 added$AIC <- n * log(added$maxterm/n) - k * added$df + n + n*log(2*pi) + k * modelrank
@@ -351,13 +324,39 @@ groupfsInf <- function(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2, ve
                           }))
     }
     if (attr(obj, "stopped")) {
+
+            ulist <- etalist <- zlist <- penlist <- vector("list", aicstop)
+            for (s in seq(aicstop)) {
+                stepind <- maxsteps - aicstop + s
+                if (stepind > 1) {
+                    etalist[[s]] <- cumprojs[[stepind]] %*% eta
+                    zlist[[s]] <- cumprojs[[stepind]] %*% Z
+                } else {
+                    etalist[[s]] <- eta
+                    zlist[[s]] <- Z
+                }
+                ulist[[s]] <- obj$maxprojs[[stepind]]
+                penlist[[s]] <- obj$maxpens[[stepind]]
+            }
+
         intervallist <- c(intervallist,
-                          lapply(obj$stopquads, function(squad) {
-                              etasquad <- t(eta) %*% squad
-                              A <- etasquad %*% eta
-                              B <- 2 * etasquad %*% Z
-                              C <- t(Z) %*% squad %*% Z
-                              quadratic_roots(A, B, C, tol = 1e-15)
+                          lapply(0:(aicstop-1), function(s) {
+                               # ^ fix this ^
+                              # check indexing direction
+                              Ug <- ulist[[s]]
+                              Uh <- ulist[[s+1]]
+                              peng <- penlist[[s]]
+                              penh <- penlist[[s+1]]
+                              etag <- etalist[[s]]
+                              etah <- etalist[[s+1]]
+                              Zg <- zlist[[s]]
+                              Zh <- zlist[[s+1]]
+                              
+                              coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh)
+                              quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol)
+
+                              # additional constant term
+                              # pendiff * (sum(rs^2) - sum(r_(s-1)^2)) ?
                           }))
     }
 
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 2a33cf4..db73cc8 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -6,8 +6,7 @@ truncationRegion <- function(obj, TC, R, eta, Z, tol = 1e-15) {
   L <- lapply(1:length(obj$action), function(s) {
 
     Ug <- obj$maxprojs[[s]]
-    dfg <- ncol(Ug)
-
+    peng <- obj$maxpens[[s]]
     if (s > 1) {
         etas <- obj$cumprojs[[s-1]] %*% eta
         Zs <- obj$cumprojs[[s-1]] %*% Z
@@ -19,25 +18,34 @@ truncationRegion <- function(obj, TC, R, eta, Z, tol = 1e-15) {
     num.projs <- length(obj$projections[[s]])
     if (num.projs == 0) {
         return(list(Intervals(c(-Inf,0))))
+        
     } else {
       lapply(1:num.projs, function(l) {
 
-      Uh <- obj$projections[[s]][[l]]
-      dfh <- ncol(Uh)
-      # The quadratic form corresponding to
-      # (t*U + Z)^T %*% Q %*% (t*U + Z) \geq 0
-      # we find the roots in t, if there are any
-      # and return the interval of potential t
+          Uh <- obj$projections[[s]][[l]]
+          penh <- obj$aicpens[[s]][[l]]      
+          # The quadratic form corresponding to
+          # (t*U + Z)^T %*% Q %*% (t*U + Z) \geq 0
+          # we find the roots in t, if there are any
+          # and return the interval of potential t
+          coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etas, etas, Zs, Zs)
+          quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol)
+      })
+    }
+    # LL is a list of intervals
+  })
+  # L is now a list of lists of intervals
+  return(unlist(L, recursive = FALSE, use.names = FALSE))
+}
 
+quadratic_coefficients <- function(sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh) {
       Uheta <- t(Uh) %*% etas
       Ugeta <- t(Ug) %*% etas
       UhZ <- t(Uh) %*% Zs
       UgZ <- t(Ug) %*% Zs
       etasZs <- t(etas) %*% Zs
-      peng <- obj$maxpens[[s]]
-      penh <- obj$aicpens[[s]][[l]]
       pendiff <- peng-penh
-      if (is.null(obj$sigma)) {
+      if (is.null(sigma)) {
           A <- sum(Ugeta^2) * peng - sum(Uheta^2) * penh - sum(etas^2) * pendiff
           B <- 2 * as.numeric(t(Ugeta) %*% UgZ * peng - t(Uheta) %*% UhZ * penh - etasZs * pendiff)
           C <- sum(UgZ^2) * peng - sum(UhZ^2) * penh - sum(Zs^2) * pendiff
@@ -45,16 +53,9 @@ truncationRegion <- function(obj, TC, R, eta, Z, tol = 1e-15) {
           # Check this
           A <- sum(Ugeta^2) - sum(Uheta^2)
           B <- 2 * as.numeric(t(Ugeta) %*% UgZ - t(Uheta) %*% UhZ)
-          C <- sum(UgZ^2) - sum(UhZ^2) - pendiff
+          C <- sum(UgZ^2) - sum(UhZ^2) - sigma^2 * pendiff
       }
-
-      quadratic_roots(A, B, C, tol)
-    })
-    }
-    # LL is a list of intervals
-  })
-  # L is now a list of lists of intervals
-  return(unlist(L, recursive = FALSE, use.names = FALSE))
+      return(list(A = A, B = B, C= C))
 }
 
 quadratic_roots <- function(A, B, C, tol) {

From 78f1cb84218ee23d8e1fa03198b0309e1d970ce1 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 7 Oct 2015 14:12:01 -0700
Subject: [PATCH 023/493] AICstop may be finished. Needs more testing

---
 selectiveInference/R/funs.groupfs.R   | 73 +++++++++++++--------------
 selectiveInference/R/funs.quadratic.R | 36 ++++++-------
 2 files changed, 55 insertions(+), 54 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 686d248..b6b95b6 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -165,9 +165,6 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
   attr(value, "k") <- k
   attr(value, "aicstop") <- aicstop
   attr(value, "stopped") <- stopped
-  if (stopped) {
-      value$stopquads <- stopquads
-  }
   if (is.null(attr(x, "varnames"))) {
     attr(value, "varnames") <- colnames(x)
   } else {
@@ -212,8 +209,8 @@ add1.groupfs <- function(xr, yr, index, labels, inactive, k, sigma = NULL) {
           aicpens[[key]] <- exp(k*dfi/n)
           terms[[key]] <- (sum(yr^2) - sum(uy^2)) * aicpens[[key]]
       } else {
-          aicpens[[key]] <- k * dfi
-          terms[[key]] <- (sum(yr^2) - sum(uy^2))/sigma^2 + aicpens[[key]]
+          aicpens[[key]] <- sigma^2 * k * dfi
+          terms[[key]] <- (sum(yr^2) - sum(uy^2)) + aicpens[[key]]
       }
   }
 
@@ -253,6 +250,10 @@ add1.groupfs <- function(xr, yr, index, labels, inactive, k, sigma = NULL) {
 #' }
 groupfsInf <- function(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2, verbose = FALSE) {
 
+  if (!is.null(obj$cvobj) && attr(obj, "stopped")) {
+      stop("Cross-validation and early stopping cannot be used simultaneously")
+  }
+
   type <- match.arg(type)
   n <- nrow(obj$x)
   p <- ncol(obj$x)
@@ -309,7 +310,6 @@ groupfsInf <- function(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2, ve
 
     intervallist <- truncationRegion(obj, TC, R, eta, Z)
     if (!is.null(obj$cvobj)) {
-        if (attr(obj, "stopped")) stop("Cross-validation and early stopping cannot be used simultaneously")
         intervallist <- c(intervallist, do.call(c,
         lapply(obj$cvobj, function(cvf) {
             truncationRegion(cvf, TC, R[-cvf$fold], eta[-cvf$fold], Z[-cvf$fold])
@@ -324,40 +324,39 @@ groupfsInf <- function(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2, ve
                           }))
     }
     if (attr(obj, "stopped")) {
-
-            ulist <- etalist <- zlist <- penlist <- vector("list", aicstop)
-            for (s in seq(aicstop)) {
-                stepind <- maxsteps - aicstop + s
-                if (stepind > 1) {
-                    etalist[[s]] <- cumprojs[[stepind]] %*% eta
-                    zlist[[s]] <- cumprojs[[stepind]] %*% Z
-                } else {
-                    etalist[[s]] <- eta
-                    zlist[[s]] <- Z
-                }
-                ulist[[s]] <- obj$maxprojs[[stepind]]
-                penlist[[s]] <- obj$maxpens[[stepind]]
+        aicstop <- attr(obj, "aicstop")
+
+        ulist <- etalist <- zlist <- penlist <- vector("list", aicstop+1)
+        for (s in seq(aicstop+1)) {
+            stepind <- maxsteps - (aicstop+1) + s
+#            print(c(s, stepind))
+            if (stepind > 1) {
+                etalist[[s]] <- obj$cumprojs[[stepind-1]] %*% eta
+                zlist[[s]] <- obj$cumprojs[[stepind-1]] %*% Z
+            } else {
+                etalist[[s]] <- eta
+                zlist[[s]] <- Z
             }
+            ulist[[s]] <- obj$maxprojs[[stepind]]
+            penlist[[s]] <- obj$maxpens[[stepind]]
+        }
 
         intervallist <- c(intervallist,
-                          lapply(0:(aicstop-1), function(s) {
-                               # ^ fix this ^
-                              # check indexing direction
-                              Ug <- ulist[[s]]
-                              Uh <- ulist[[s+1]]
-                              peng <- penlist[[s]]
-                              penh <- penlist[[s+1]]
-                              etag <- etalist[[s]]
-                              etah <- etalist[[s+1]]
-                              Zg <- zlist[[s]]
-                              Zh <- zlist[[s+1]]
-                              
-                              coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh)
-                              quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol)
-
-                              # additional constant term
-                              # pendiff * (sum(rs^2) - sum(r_(s-1)^2)) ?
-                          }))
+                          do.call(c, lapply(1:aicstop, function(s) {
+                              lapply((s+1):(aicstop+1), function(sp) {
+                                  Ug <- ulist[[s]]
+                                  Uh <- ulist[[sp]]
+                                  peng <- penlist[[s]]
+                                  penh <- prod(unlist(penlist[s:sp]))
+                                  etag <- etalist[[s]]
+                                  etah <- etalist[[sp]]
+                                  Zg <- zlist[[s]]
+                                  Zh <- zlist[[sp]]
+
+                                  coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh)
+                                  quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol = 1e-15)
+                              })
+                          })))
     }
 
     # Compute intersection:
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index db73cc8..53372be 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -39,23 +39,25 @@ truncationRegion <- function(obj, TC, R, eta, Z, tol = 1e-15) {
 }
 
 quadratic_coefficients <- function(sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh) {
-      Uheta <- t(Uh) %*% etas
-      Ugeta <- t(Ug) %*% etas
-      UhZ <- t(Uh) %*% Zs
-      UgZ <- t(Ug) %*% Zs
-      etasZs <- t(etas) %*% Zs
-      pendiff <- peng-penh
-      if (is.null(sigma)) {
-          A <- sum(Ugeta^2) * peng - sum(Uheta^2) * penh - sum(etas^2) * pendiff
-          B <- 2 * as.numeric(t(Ugeta) %*% UgZ * peng - t(Uheta) %*% UhZ * penh - etasZs * pendiff)
-          C <- sum(UgZ^2) * peng - sum(UhZ^2) * penh - sum(Zs^2) * pendiff
-      } else {
+    # g indexes minimizer, h the comparison
+    Uheta <- t(Uh) %*% etah
+    Ugeta <- t(Ug) %*% etag
+    UhZ <- t(Uh) %*% Zh
+    UgZ <- t(Ug) %*% Zg
+    etaZh <- t(etah) %*% Zh
+    etaZg <- t(etag) %*% Zg
+    if (is.null(sigma)) {
+        # Check the signs, make it consistent
+        A <- penh * (sum(etah^2) - sum(Uheta^2)) - peng * (sum(etag)^2 - sum(Ugeta^2))
+        B <- 2 * penh * (etaZh - t(Uheta) %*% UhZ) - 2 * peng * (etaZg - t(Ugeta) %*% UgZ)
+        C <- penh * (sum(Zh^2) - sum(UhZ^2)) - peng * (sum(Zg^2) - sum(UgZ^2))
+    } else {
           # Check this
-          A <- sum(Ugeta^2) - sum(Uheta^2)
-          B <- 2 * as.numeric(t(Ugeta) %*% UgZ - t(Uheta) %*% UhZ)
-          C <- sum(UgZ^2) - sum(UhZ^2) - sigma^2 * pendiff
-      }
-      return(list(A = A, B = B, C= C))
+        A <- (sum(etah^2) - sum(Uheta^2)) - (sum(etag)^2 - sum(Ugeta^2))
+        B <- 2 * (etaZh - t(Uheta) %*% UhZ) - 2 * (etaZg - t(Ugeta) %*% UgZ)
+        C <- (sum(Zh^2) - sum(UhZ^2) + penh) - (sum(Zg^2) - sum(UgZ^2) + peng) 
+    }
+    return(list(A = A, B = B, C= C))
 }
 
 quadratic_roots <- function(A, B, C, tol) {
@@ -107,7 +109,7 @@ quadratic_roots <- function(A, B, C, tol) {
                 if (B > 0) {
                     return(Intervals(c(-Inf, max(0, -C/B))))
                 } else {
-                    if (-C/B < 0) stop("Error: infeasible linear equation")
+                    if (-C/B < 0) stop("Infeasible linear equation")
                     return(Intervals(rbind(c(-Inf, 0), c(-C/B, Inf))))
                 }
             } else {

From c2b4cf0853d96e0efe0374c0abb24e4a771f1387 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 7 Oct 2015 17:56:54 -0700
Subject: [PATCH 024/493] Fixed some critical typos, more testing of AICstop

---
 forLater/josh/funs.sims.R             | 46 +++++++++++++++++++++++++++
 forLater/josh/sim.aicstop.R           | 45 ++++++++++++++++++++++++++
 selectiveInference/R/funs.groupfs.R   |  6 ++--
 selectiveInference/R/funs.quadratic.R | 11 +++----
 4 files changed, 99 insertions(+), 9 deletions(-)
 create mode 100644 forLater/josh/funs.sims.R
 create mode 100644 forLater/josh/sim.aicstop.R

diff --git a/forLater/josh/funs.sims.R b/forLater/josh/funs.sims.R
new file mode 100644
index 0000000..7f36d45
--- /dev/null
+++ b/forLater/josh/funs.sims.R
@@ -0,0 +1,46 @@
+# Functions for simulation/testing
+
+randomGroupSizes <- function(G, lambda = 2) return(2 + rpois(G, lambda))
+
+randomGroups <- function(G, lambda = 2) {
+    rles <- randomGroupSizes(G, lambda)
+    return(rep(1:G, rles))
+}
+
+randomIndexFixedP <- function(p, G) sort(c(sample(1:G), sample(1:G, size = p-G, replace=T)))
+
+randomFactorDesign <- function(n, G, lambda = 2) {
+    if (n < (1+lambda)*G) stop("Larger n required to avoid duplicate columns")
+    rles <- randomGroupSizes(G, lambda)
+    print(rles)
+    df <- data.frame(do.call(cbind, lapply(rles, function(g) {
+        sample(LETTERS[1:g], n, replace = TRUE, prob = runif(g))
+    })), stringsAsFactors = TRUE)
+    if (any(apply(df, 2, function(col) length(unique(col))) == 1)) return(randomFactorDesign(n, G, lambda))
+    fd <- factorDesign(df)
+    if (any(duplicated(fd$x, MARGIN = 2))) return(randomFactorDesign(n, G, lambda))
+    return(list(df=df, fd=fd))
+}
+
+randomFactorsFixedP <- function(p, G) {
+#    index <-
+}
+
+randomGaussianFixedP <- function(n, p, G = p, sparsity = 0, snr = 0, sigma = 1, rho = 0) {
+    index <- 1:p
+    if (G < p) index <- randomIndexFixedP(p, G)
+    x <- matrix(rnorm(n*p), nrow=n)
+    if (rho != 0) {
+        z <- matrix(rep(t(rnorm(n)), p), nrow = n)
+        x <- sqrt(1-rho)*x + sqrt(rho)*z
+    }
+    beta <- rep(0, p)
+    if (sparsity > 0 && snr > 0) {
+        for (j in 1:sparsity) {
+            inds <- which(index == j)
+            beta[inds] <- snr/sqrt(length(inds))
+        }
+    }
+    y <- x %*% beta + sigma * rnorm(n)
+    return(list(x=x, y=y, beta = beta, index=index, sigma = sigma))
+}
diff --git a/forLater/josh/sim.aicstop.R b/forLater/josh/sim.aicstop.R
new file mode 100644
index 0000000..33037d2
--- /dev/null
+++ b/forLater/josh/sim.aicstop.R
@@ -0,0 +1,45 @@
+library(intervals)
+source("selectiveInference/R/cv.R")
+source("../../selectiveInference/R/funs.groupfs.R")
+source("../../selectiveInference/R/funs.quadratic.R")
+source("../../selectiveInference/R/funs.common.R")
+
+set.seed(1)
+niters <- 500
+n <- 100
+p <- 100
+G <- 50
+maxsteps <- 15
+sparsity <- 5
+snr <- 1
+rho <- .1
+aicstop <- 1
+
+instance <- function(n, p, G, sparsity, snr, rho, maxsteps, aicstop) {
+
+    simd <- randomGaussianFixedP(n, p, G, sparsity, snr, rho)
+    x <- simd$x
+    y <- simd$y
+    index <- simd$index
+
+    fit <- groupfs(x, y, index, maxsteps, k = log(n), aicstop = aicstop)
+    pvals <- groupfsInf(fit, verbose=T)
+    return(list(variable = fit$action, pvals = pvals$pv, stopped = attr(fit, "stopped")))
+}
+
+time <- system.time({
+          output <- replicate(niters, instance(n, p, G, sparsity, snr, rho, maxsteps, aicstop))
+})
+
+stopped <- do.call(c, list(output[3,]))
+pvals <- do.call(c, list(output[2,]))
+vars <- do.call(c, list(output[1,]))
+
+save(pvals, vars, stopped, file = paste0(
+                      "results_aic", aicstop, "_n", n,
+                      "_p", p,
+                      "_sparsity", sparsity,
+                      "_snr", snr,
+                      ".RData"))
+
+print(time)
diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index b6b95b6..ae5e972 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -304,9 +304,9 @@ groupfsInf <- function(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2, ve
     TC <- sqrt(sum(R^2))
     eta <- Ugtilde %*% R / TC
     Z <- obj$y - eta * TC
-    df <- ncol(Ugtilde)
+    dfi <- ncol(Ugtilde)
     TCs[j] <- TC
-    dfs[j] <- df
+    dfs[j] <- dfi
 
     intervallist <- truncationRegion(obj, TC, R, eta, Z)
     if (!is.null(obj$cvobj)) {
@@ -369,7 +369,7 @@ groupfsInf <- function(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2, ve
     supports[[j]] <- E
 
     # E is now potentially a union of intervals
-    pvals[j] <- TC_surv(TC, sigma, df, E)
+    pvals[j] <- TC_surv(TC, sigma, dfi, E)
   }
   if (nanconv) warning("P-value NaNs of the form 0/0 converted to 0. This typically occurs for numerical reasons in the presence of a large signal-to-noise ratio.")
   names(pvals) <- obj$action
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 53372be..2a15ce2 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -18,12 +18,11 @@ truncationRegion <- function(obj, TC, R, eta, Z, tol = 1e-15) {
     num.projs <- length(obj$projections[[s]])
     if (num.projs == 0) {
         return(list(Intervals(c(-Inf,0))))
-        
+
     } else {
       lapply(1:num.projs, function(l) {
-
           Uh <- obj$projections[[s]][[l]]
-          penh <- obj$aicpens[[s]][[l]]      
+          penh <- obj$aicpens[[s]][[l]]
           # The quadratic form corresponding to
           # (t*U + Z)^T %*% Q %*% (t*U + Z) \geq 0
           # we find the roots in t, if there are any
@@ -48,14 +47,14 @@ quadratic_coefficients <- function(sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh
     etaZg <- t(etag) %*% Zg
     if (is.null(sigma)) {
         # Check the signs, make it consistent
-        A <- penh * (sum(etah^2) - sum(Uheta^2)) - peng * (sum(etag)^2 - sum(Ugeta^2))
+        A <- penh * (sum(etah^2) - sum(Uheta^2)) - peng * (sum(etag^2) - sum(Ugeta^2))
         B <- 2 * penh * (etaZh - t(Uheta) %*% UhZ) - 2 * peng * (etaZg - t(Ugeta) %*% UgZ)
         C <- penh * (sum(Zh^2) - sum(UhZ^2)) - peng * (sum(Zg^2) - sum(UgZ^2))
     } else {
           # Check this
-        A <- (sum(etah^2) - sum(Uheta^2)) - (sum(etag)^2 - sum(Ugeta^2))
+        A <- (sum(etah^2) - sum(Uheta^2)) - (sum(etag^2) - sum(Ugeta^2))
         B <- 2 * (etaZh - t(Uheta) %*% UhZ) - 2 * (etaZg - t(Ugeta) %*% UgZ)
-        C <- (sum(Zh^2) - sum(UhZ^2) + penh) - (sum(Zg^2) - sum(UgZ^2) + peng) 
+        C <- (sum(Zh^2) - sum(UhZ^2) + penh) - (sum(Zg^2) - sum(UgZ^2) + peng)
     }
     return(list(A = A, B = B, C= C))
 }

From 0c4cfc0ae29ceb4637f0c55258f44f8a640cd319 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 7 Oct 2015 17:59:48 -0700
Subject: [PATCH 025/493] sourcing sim functions file

---
 forLater/josh/sim.aicstop.R | 1 +
 1 file changed, 1 insertion(+)

diff --git a/forLater/josh/sim.aicstop.R b/forLater/josh/sim.aicstop.R
index 33037d2..f2bfa66 100644
--- a/forLater/josh/sim.aicstop.R
+++ b/forLater/josh/sim.aicstop.R
@@ -1,4 +1,5 @@
 library(intervals)
+source("funs.sims.R")
 source("selectiveInference/R/cv.R")
 source("../../selectiveInference/R/funs.groupfs.R")
 source("../../selectiveInference/R/funs.quadratic.R")

From a730e91fbd69c0c3c84891170bee28b31bb19fb6 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Sun, 11 Oct 2015 09:03:09 -0700
Subject: [PATCH 026/493] rob fixed lucas bug

---
 selectiveInference/R/funs.fixed.R            |  4 ++++
 selectiveInference/man/selectiveInference.Rd | 17 +++++++++++++++++
 tests/test.fixed.R                           |  6 ++++++
 3 files changed, 27 insertions(+)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index f3d99ea..d376090 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -37,6 +37,10 @@ fixedLassoInf <- function(x, y, beta, lambda, intercept=TRUE, sigma=NULL, alpha=
                   "(to within specified tolerances)"))
 
   vars = which(abs(beta) > tol.beta / sqrt(colSums(x^2)))
+  if(length(vars)==0){
+      cat("Empty model",fill=T)
+      return()
+  }
   if (any(sign(g[vars]) != sign(beta[vars])))
     warning(paste("Solution beta does not satisfy the KKT conditions",
                   "(to within specified tolerances). You might try rerunning",
diff --git a/selectiveInference/man/selectiveInference.Rd b/selectiveInference/man/selectiveInference.Rd
index e08f69a..0b1ca4c 100644
--- a/selectiveInference/man/selectiveInference.Rd
+++ b/selectiveInference/man/selectiveInference.Rd
@@ -139,6 +139,23 @@ arXiv:1405.3340.
 #mu = c(rep(signal, floor (n/5)), rep(0, n-floor(n/5))) # 20% of elements get the signal; rest 0
 #y = mu + rnorm (n, 0, 1)
 #mmObj = manyMeans(y, bh.q=0.1)
+#
+#
+# Forward stepwise with grouped variables
+#set.seed(1)
+#n <- 40
+#p <- 20
+#index <- sort(rep(1:(p/2), 2))
+#steps <- 10
+#sparsity <- 5
+#snr <- 3
+#   x <- matrix(rnorm(n*p), nrow=n)
+#      beta <- rep(0, p)
+#      beta[which(index %in% 1:sparsity)] <- snr
+#      y <-  x %*% beta+rnorm(n)
+#fit <- groupfs(x, y, index=1:p, maxsteps = steps)
+#out<- groupfsInf(fit)
+
 
 
 ##estimation of sigma for use in fsInf or larInf or fixedLassoInf
diff --git a/tests/test.fixed.R b/tests/test.fixed.R
index e214fe9..624fb65 100644
--- a/tests/test.fixed.R
+++ b/tests/test.fixed.R
@@ -281,3 +281,9 @@ gfit = glmnet(X,y,standardize=F)
 coef = coef(gfit, s=lam/n, exact=T)[-1]
 sint = fixedLassoInf(X,y,coef,lam,sigma=sigma,alpha=alpha,type="partial")
 # Error in v %*% diag(d) : non-conformable arguments
+
+## lucas again
+
+load("params_for_Rob.rdata") #variables: X, y, coef, lam, sigma, alpha
+
+sint = fixedLassoInf(X,y,coef,lam,sigma=sigma,alpha=alpha,type="partial")

From 9c19d7bbc7dd9e4cf34b71cb79a6690576a8c50b Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Mon, 12 Oct 2015 13:06:55 -0700
Subject: [PATCH 027/493] groupfs computes and stores coefficients

---
 selectiveInference/R/funs.groupfs.R | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index ae5e972..447afe9 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -154,8 +154,13 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
     aic.last <- added$AIC
   }
 
+  # Is there a better way of doing this?
+  # Use some projections already computed?
+  beta <- coef(lm(y.begin ~ x.begin[,index %in% path.info$imax]-1))
+  names(beta) <- index[index %in% path.info$imax]
+  
   # Create output object
-  value <- list(action=path.info$imax, L=path.info$L, AIC=path.info$AIC, projections = projections, maxprojs = maxprojs, aicpens = aicpens, maxpens = maxpens, cumprojs = cumprojs, log = path.info, index = index, y = y.begin, x = x.begin, bx = xm, sx = xs, sigma = sigma, intercept = intercept, call = match.call(), terms = terms)
+  value <- list(action=path.info$imax, L=path.info$L, AIC=path.info$AIC, projections = projections, maxprojs = maxprojs, aicpens = aicpens, maxpens = maxpens, cumprojs = cumprojs, log = path.info, index = index, y = y.begin, x = x.begin, coefficients = beta, bx = xm, sx = xs, sigma = sigma, intercept = intercept, call = match.call(), terms = terms)
 
   class(value) <- "groupfs"
   attr(value, "labels") <- labels
@@ -301,6 +306,7 @@ groupfsInf <- function(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2, ve
     # Project y onto what remains of x_i
     Ugtilde <- svdu_thresh(x_i)
     R <- t(Ugtilde) %*% obj$y
+    print(R)
     TC <- sqrt(sum(R^2))
     eta <- Ugtilde %*% R / TC
     Z <- obj$y - eta * TC

From 5e1bbee3da78a2cfdcb0580c506dbb8cbfe0776a Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Mon, 12 Oct 2015 14:56:22 -0700
Subject: [PATCH 028/493] Added coef and predict functions for groupfs

---
 selectiveInference/R/funs.groupfs.R       | 34 ++++++++++++++++++++---
 selectiveInference/man/factorDesign.Rd    |  3 +-
 selectiveInference/man/groupfs.Rd         | 11 ++++----
 selectiveInference/man/groupfsInf.Rd      | 12 +++++---
 selectiveInference/man/predict.groupfs.Rd | 23 +++++++++++++++
 selectiveInference/man/scaleGroups.Rd     |  3 +-
 6 files changed, 68 insertions(+), 18 deletions(-)
 create mode 100644 selectiveInference/man/predict.groupfs.Rd

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 447afe9..4e19bcb 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -158,13 +158,14 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
   # Use some projections already computed?
   beta <- coef(lm(y.begin ~ x.begin[,index %in% path.info$imax]-1))
   names(beta) <- index[index %in% path.info$imax]
-  
+
   # Create output object
-  value <- list(action=path.info$imax, L=path.info$L, AIC=path.info$AIC, projections = projections, maxprojs = maxprojs, aicpens = aicpens, maxpens = maxpens, cumprojs = cumprojs, log = path.info, index = index, y = y.begin, x = x.begin, coefficients = beta, bx = xm, sx = xs, sigma = sigma, intercept = intercept, call = match.call(), terms = terms)
+  value <- list(action = path.info$imax, L = path.info$L, AIC = path.info$AIC, projections = projections, maxprojs = maxprojs, aicpens = aicpens, maxpens = maxpens, cumprojs = cumprojs, log = path.info, index = index, y = y.begin, x = x.begin, coefficients = beta, bx = xm, by = by, sx = xs, sigma = sigma, intercept = intercept, call = match.call(), terms = terms)
 
   class(value) <- "groupfs"
+  attr(value, "center") <- center
+  attr(value, "normalize") <- normalize
   attr(value, "labels") <- labels
-  attr(value, "index") <- index
   attr(value, "maxsteps") <- maxsteps
   attr(value, "sigma") <- sigma
   attr(value, "k") <- k
@@ -306,7 +307,6 @@ groupfsInf <- function(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2, ve
     # Project y onto what remains of x_i
     Ugtilde <- svdu_thresh(x_i)
     R <- t(Ugtilde) %*% obj$y
-    print(R)
     TC <- sqrt(sum(R^2))
     eta <- Ugtilde %*% R / TC
     Z <- obj$y - eta * TC
@@ -562,6 +562,32 @@ print.groupfs <- function(x, ...) {
     invisible()
 }
 
+
+coef.groupfs <- function(object, ...) {
+    return(object$coefficients)
+}
+
+#' @name predict.groupfs
+#' @aliases predict.groupfs
+#' @aliases coef.groupfs
+#'
+#' @title Prediction and coefficient functions for \code{\link{groupfs}}.
+#'
+#' Make predictions or extract coefficients from a groupfs forward stepwise object.
+#'
+#' @param object Object returned by a call to \code{\link{groupfs}}.
+#' @param newx Matrix of x values at which the predictions are desired. If NULL, the x values from groupfs fitting are used.
+#' @return A vector of predictions or a vector of coefficients.
+predict.groupfs <- function(object, newx, ...) {
+    beta <- coef.groupfs(object)
+    if (missing(newx)) {
+        newx = object$x
+    } else {
+        newx <- scaleGroups(newx, object$index, attr(object, "center"), attr(object, "normalize"))
+    }
+    return(newx[, index %in% object$action] %*% beta + ifelse(object$intercept, object$by, 0))
+}
+
 print.groupfsInf <- function(x, ...) {
     cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n", x$sigma))
     action <- x$vars
diff --git a/selectiveInference/man/factorDesign.Rd b/selectiveInference/man/factorDesign.Rd
index d5d2576..3a42e2e 100644
--- a/selectiveInference/man/factorDesign.Rd
+++ b/selectiveInference/man/factorDesign.Rd
@@ -1,5 +1,4 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
-% Please edit documentation in R/funs.groupfs.R
+% Generated by roxygen2 (4.0.1.99): do not edit by hand
 \name{factorDesign}
 \alias{factorDesign}
 \title{Expand a data frame with factors to form a design matrix with the full binary encoding of each factor.}
diff --git a/selectiveInference/man/groupfs.Rd b/selectiveInference/man/groupfs.Rd
index c279c9e..78cd32f 100644
--- a/selectiveInference/man/groupfs.Rd
+++ b/selectiveInference/man/groupfs.Rd
@@ -1,11 +1,10 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
-% Please edit documentation in R/funs.groupfs.R
+% Generated by roxygen2 (4.0.1.99): do not edit by hand
 \name{groupfs}
 \alias{groupfs}
 \title{Select a model with forward stepwise.}
 \usage{
 groupfs(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE,
-  center = TRUE, normalize = TRUE, aicstop = FALSE, verbose = FALSE)
+  center = TRUE, normalize = TRUE, aicstop = 0, verbose = FALSE)
 }
 \arguments{
 \item{x}{Matrix of predictors (n by p).}
@@ -26,7 +25,7 @@ groupfs(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE,
 
 \item{normalize}{Should the design matrix be normalized? Default is TRUE.}
 
-\item{aicstop}{Early stopping if AIC increases, default is FALSE.}
+\item{aicstop}{Early stopping if AIC increases. Default is 0 corresponding to no early stopping. Positive integer values specify the number of times the AIC is allowed to increase in a row, e.g. with \code{aicstop = 2} the algorithm will stop if the AIC criterion increases for 2 steps in a row. The default of \code{\link[stats]{step}} corresponds to \code{aicstop = 1}.}
 
 \item{verbose}{Print out progress along the way? Default is FALSE.}
 }
@@ -39,11 +38,11 @@ This function implements forward selection of linear models almost identically t
 \examples{
 x = matrix(rnorm(20*40), nrow=20)
 index = sort(rep(1:20, 2))
-y = rnorm(20) + 2 * (x[,1] - x[,2]) - (x[,3] - x[,4])
+y = rnorm(20) + 2 * x[,1] - x[,4]
 fit = groupfs(x, y, index, maxsteps = 5)
 pvals = groupfsInf(fit)
 }
 \seealso{
-\code{\link{groupfsInf}}
+\code{\link{groupfsInf}}, \code{\link{factorDesign}}.
 }
 
diff --git a/selectiveInference/man/groupfsInf.Rd b/selectiveInference/man/groupfsInf.Rd
index f5ee970..7c7190f 100644
--- a/selectiveInference/man/groupfsInf.Rd
+++ b/selectiveInference/man/groupfsInf.Rd
@@ -1,20 +1,24 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
-% Please edit documentation in R/funs.groupfs.R
+% Generated by roxygen2 (4.0.1.99): do not edit by hand
 \name{groupfsInf}
 \alias{groupfsInf}
 \title{Compute selective p-values for a model fitted by \code{groupfs}.}
 \usage{
-groupfsInf(obj, sigma = NULL, verbose = FALSE)
+groupfsInf(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2,
+  verbose = FALSE)
 }
 \arguments{
 \item{obj}{Object returned by \code{\link{groupfs}} function}
 
 \item{sigma}{Estimate of error standard deviation. If NULL (default), this is estimated using the mean squared residual of the full least squares fit when n >= 2p, and the mean squared residual of the selected model when n < 2p. In the latter case, the user should use \code{\link{estimateSigma}} function for a more accurate estimate.}
 
+\item{type}{Type of conditional p-values to compute. With "all" (default), p-values are computed conditional on the final model with all variables up to \code{maxsteps}; with "aic" the number of steps is chosen after which the AIC criterion increases \code{ntimes} in a row, and then the same type of analysis as in "all" is carried out for the active variables at that number of steps.}
+
+\item{ntimes}{Number of steps for which AIC criterion has to increase before minimizing point is declared.}
+
 \item{verbose}{Print out progress along the way? Default is FALSE.}
 }
 \value{
-An object of class "groupfsInf" containing selective p-values for the fitted model \code{obj}. The default printing behavior should supply adequate information.
+An object of class "groupfsInf" containing selective p-values for the fitted model \code{obj}. For comparison with \code{\link{fsInf}}, note that the option \code{type = "active"} is not available.
 
 \describe{
   \item{vars}{Labels of the active groups in the order they were included.}
diff --git a/selectiveInference/man/predict.groupfs.Rd b/selectiveInference/man/predict.groupfs.Rd
new file mode 100644
index 0000000..4a4c149
--- /dev/null
+++ b/selectiveInference/man/predict.groupfs.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2 (4.0.1.99): do not edit by hand
+\name{predict.groupfs}
+\alias{predict.groupfs}
+\title{Prediction and coefficient functions for \code{\link{groupfs}}.
+
+Make predictions or extract coefficients from a groupfs forward stepwise object.}
+\usage{
+\method{predict}{groupfs}(object, newx, ...)
+}
+\arguments{
+\item{object}{Object returned by a call to \code{\link{groupfs}}.}
+
+\item{newx}{Matrix of x values at which the predictions are desired. If NULL, the x values from groupfs fitting are used.}
+}
+\value{
+A vector of predictions or a vector of coefficients.
+}
+\description{
+Prediction and coefficient functions for \code{\link{groupfs}}.
+
+Make predictions or extract coefficients from a groupfs forward stepwise object.
+}
+
diff --git a/selectiveInference/man/scaleGroups.Rd b/selectiveInference/man/scaleGroups.Rd
index e5a93fa..1e7e1c4 100644
--- a/selectiveInference/man/scaleGroups.Rd
+++ b/selectiveInference/man/scaleGroups.Rd
@@ -1,5 +1,4 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
-% Please edit documentation in R/funs.groupfs.R
+% Generated by roxygen2 (4.0.1.99): do not edit by hand
 \name{scaleGroups}
 \alias{scaleGroups}
 \title{Center and scale design matrix by groups}

From ee616483eb0fe8ff232eb6bf794bf8c1d8674219 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Mon, 12 Oct 2015 18:07:44 -0700
Subject: [PATCH 029/493] Fixing an aicstop bug

---
 selectiveInference/R/funs.groupfs.R | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 4e19bcb..8dbef78 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -127,9 +127,7 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
 
     if (verbose) print(round(step.info, 3))
 
-    if (aicstop > 0 && step >= aicstop && aic.last < added$AIC) {
-        ########## Incomplete ##########
-        ## cut off the last aicstop variables as well?
+    if (aicstop > 0 && step < maxstep && step >= aicstop && aic.last < added$AIC) {
         if (all(diff(c(aic.begin, path.info$AIC)[(step+1-aicstop):(step+1)]) > 0)) {
 
             if (is.null(sigma)) {

From 0dbf85a32babc0244b8812e847448baffeee717f Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Mon, 12 Oct 2015 18:08:20 -0700
Subject: [PATCH 030/493] Typo

---
 selectiveInference/R/funs.groupfs.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 8dbef78..4097808 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -127,7 +127,7 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
 
     if (verbose) print(round(step.info, 3))
 
-    if (aicstop > 0 && step < maxstep && step >= aicstop && aic.last < added$AIC) {
+    if (aicstop > 0 && step < maxsteps && step >= aicstop && aic.last < added$AIC) {
         if (all(diff(c(aic.begin, path.info$AIC)[(step+1-aicstop):(step+1)]) > 0)) {
 
             if (is.null(sigma)) {

From d6f5d87fad907e34bb2100fe7e01c195662db1eb Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Mon, 12 Oct 2015 21:12:59 -0700
Subject: [PATCH 031/493] Created .gitignore

---
 .gitignore | 9 +++++++++
 1 file changed, 9 insertions(+)
 create mode 100644 .gitignore

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..863d843
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,9 @@
+**DS_Store**
+**Rcheck**
+**tar.gz
+**Rapp.history
+**.pdf
+**.RData
+**.o
+**.so
+forLater/josh/**
\ No newline at end of file

From fa8636291c7f06473789e5afa5d1e6b05cb8989a Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Mon, 12 Oct 2015 21:30:51 -0700
Subject: [PATCH 032/493] Changed SNR in simulation function

---
 forLater/josh/funs.sims.R   | 2 +-
 forLater/josh/sim.aicstop.R | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/forLater/josh/funs.sims.R b/forLater/josh/funs.sims.R
index 7f36d45..e51dd2f 100644
--- a/forLater/josh/funs.sims.R
+++ b/forLater/josh/funs.sims.R
@@ -38,7 +38,7 @@ randomGaussianFixedP <- function(n, p, G = p, sparsity = 0, snr = 0, sigma = 1,
     if (sparsity > 0 && snr > 0) {
         for (j in 1:sparsity) {
             inds <- which(index == j)
-            beta[inds] <- snr/sqrt(length(inds))
+            beta[inds] <- snr/sqrt(n*length(inds))
         }
     }
     y <- x %*% beta + sigma * rnorm(n)
diff --git a/forLater/josh/sim.aicstop.R b/forLater/josh/sim.aicstop.R
index f2bfa66..014d18d 100644
--- a/forLater/josh/sim.aicstop.R
+++ b/forLater/josh/sim.aicstop.R
@@ -6,7 +6,7 @@ source("../../selectiveInference/R/funs.quadratic.R")
 source("../../selectiveInference/R/funs.common.R")
 
 set.seed(1)
-niters <- 500
+niters <- 200
 n <- 100
 p <- 100
 G <- 50

From 7ebb7e021da06a415061c5c0d62317364255ac5c Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Mon, 12 Oct 2015 21:37:54 -0700
Subject: [PATCH 033/493] Modifying simulations

---
 forLater/josh/funs.sims.R   |  2 +-
 forLater/josh/sim.aicstop.R | 10 +++++-----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/forLater/josh/funs.sims.R b/forLater/josh/funs.sims.R
index e51dd2f..3fdd7dd 100644
--- a/forLater/josh/funs.sims.R
+++ b/forLater/josh/funs.sims.R
@@ -38,7 +38,7 @@ randomGaussianFixedP <- function(n, p, G = p, sparsity = 0, snr = 0, sigma = 1,
     if (sparsity > 0 && snr > 0) {
         for (j in 1:sparsity) {
             inds <- which(index == j)
-            beta[inds] <- snr/sqrt(n*length(inds))
+            beta[inds] <- sample(c(-1,1), length(inds), replace=T) * snr/sqrt(n*length(inds))
         }
     }
     y <- x %*% beta + sigma * rnorm(n)
diff --git a/forLater/josh/sim.aicstop.R b/forLater/josh/sim.aicstop.R
index 014d18d..7248573 100644
--- a/forLater/josh/sim.aicstop.R
+++ b/forLater/josh/sim.aicstop.R
@@ -6,10 +6,10 @@ source("../../selectiveInference/R/funs.quadratic.R")
 source("../../selectiveInference/R/funs.common.R")
 
 set.seed(1)
-niters <- 200
+niters <- 100
 n <- 100
-p <- 100
-G <- 50
+p <- 200
+G <- 100
 maxsteps <- 15
 sparsity <- 5
 snr <- 1
@@ -24,7 +24,7 @@ instance <- function(n, p, G, sparsity, snr, rho, maxsteps, aicstop) {
     index <- simd$index
 
     fit <- groupfs(x, y, index, maxsteps, k = log(n), aicstop = aicstop)
-    pvals <- groupfsInf(fit, verbose=T)
+    pvals <- groupfsInf(fit, sigma = 1, verbose=T)
     return(list(variable = fit$action, pvals = pvals$pv, stopped = attr(fit, "stopped")))
 }
 
@@ -41,6 +41,6 @@ save(pvals, vars, stopped, file = paste0(
                       "_p", p,
                       "_sparsity", sparsity,
                       "_snr", snr,
-                      ".RData"))
+                      "_known.RData"))
 
 print(time)

From 844235e56fa30e130593e59caefc0ceca2528577 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Mon, 12 Oct 2015 21:49:10 -0700
Subject: [PATCH 034/493] More changing simulations

---
 forLater/josh/funs.sims.R   | 2 +-
 forLater/josh/sim.aicstop.R | 7 ++++---
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/forLater/josh/funs.sims.R b/forLater/josh/funs.sims.R
index 3fdd7dd..f07fa55 100644
--- a/forLater/josh/funs.sims.R
+++ b/forLater/josh/funs.sims.R
@@ -38,7 +38,7 @@ randomGaussianFixedP <- function(n, p, G = p, sparsity = 0, snr = 0, sigma = 1,
     if (sparsity > 0 && snr > 0) {
         for (j in 1:sparsity) {
             inds <- which(index == j)
-            beta[inds] <- sample(c(-1,1), length(inds), replace=T) * snr/sqrt(n*length(inds))
+            beta[inds] <- sqrt(2*log(G)) * sample(c(-1,1), length(inds), replace=T) * snr/sqrt(n*length(inds))
         }
     }
     y <- x %*% beta + sigma * rnorm(n)
diff --git a/forLater/josh/sim.aicstop.R b/forLater/josh/sim.aicstop.R
index 7248573..466f9b4 100644
--- a/forLater/josh/sim.aicstop.R
+++ b/forLater/josh/sim.aicstop.R
@@ -8,17 +8,18 @@ source("../../selectiveInference/R/funs.common.R")
 set.seed(1)
 niters <- 100
 n <- 100
-p <- 200
-G <- 100
+p <- 100
+G <- 50
 maxsteps <- 15
 sparsity <- 5
 snr <- 1
+sigma <- 1
 rho <- .1
 aicstop <- 1
 
 instance <- function(n, p, G, sparsity, snr, rho, maxsteps, aicstop) {
 
-    simd <- randomGaussianFixedP(n, p, G, sparsity, snr, rho)
+    simd <- randomGaussianFixedP(n, p, G, sparsity, snr, sigma, rho)
     x <- simd$x
     y <- simd$y
     index <- simd$index

From 978d53ee98e7109bafbb673079f32549ee18c783 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 4 Nov 2015 22:04:24 -0800
Subject: [PATCH 035/493] Started including F test code for groupfsInf

---
 selectiveInference/R/funs.groupfs.R   | 174 ++++++++++++++++++--------
 selectiveInference/R/funs.quadratic.R |  91 ++++++++++----
 2 files changed, 183 insertions(+), 82 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 4097808..148e35a 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -239,8 +239,6 @@ add1.groupfs <- function(xr, yr, index, labels, inactive, k, sigma = NULL) {
 #'
 #' @param obj Object returned by \code{\link{groupfs}} function
 #' @param sigma Estimate of error standard deviation. If NULL (default), this is estimated using the mean squared residual of the full least squares fit when n >= 2p, and the mean squared residual of the selected model when n < 2p. In the latter case, the user should use \code{\link{estimateSigma}} function for a more accurate estimate.
-#' @param type Type of conditional p-values to compute. With "all" (default), p-values are computed conditional on the final model with all variables up to \code{maxsteps}; with "aic" the number of steps is chosen after which the AIC criterion increases \code{ntimes} in a row, and then the same type of analysis as in "all" is carried out for the active variables at that number of steps.
-#' @param ntimes Number of steps for which AIC criterion has to increase before minimizing point is declared.
 #' @param verbose Print out progress along the way? Default is FALSE.
 #' @return An object of class "groupfsInf" containing selective p-values for the fitted model \code{obj}. For comparison with \code{\link{fsInf}}, note that the option \code{type = "active"} is not available.
 #'
@@ -252,13 +250,14 @@ add1.groupfs <- function(xr, yr, index, labels, inactive, k, sigma = NULL) {
 #'   \item{df}{Rank of group of variables when it was added to the model.}
 #'   \item{support}{List of intervals defining the truncation region of the truncated chi.}
 #' }
-groupfsInf <- function(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2, verbose = FALSE) {
+groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
 
   if (!is.null(obj$cvobj) && attr(obj, "stopped")) {
-      stop("Cross-validation and early stopping cannot be used simultaneously")
+      stop("Cross-validation and early stopping cannot be used simultaneously.")
+      # This shouldn't happen in the first place!
+      # (it wouldn't anyway unless someone tries to trick it)
   }
 
-  type <- match.arg(type)
   n <- nrow(obj$x)
   p <- ncol(obj$x)
   maxsteps <- attr(obj, "maxsteps")
@@ -268,8 +267,7 @@ groupfsInf <- function(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2, ve
   y <- obj$y
   Ep <- sum(index %in% obj$action)
 
-  nanconv <- FALSE
-  pvals = dfs = TCs = numeric(maxsteps)
+  pvals = dfs = Tstats = numeric(maxsteps)
   supports <- list()
 
   if (!is.null(sigma)) {
@@ -278,6 +276,7 @@ groupfsInf <- function(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2, ve
       }
   } else {
       if (is.null(obj$sigma)) {
+          type <- "TF"
           if (n >= 2*p) {
               sigma <- sqrt(sum(lsfit(obj$x, obj$y, intercept = obj$intercept)$res^2)/(n-p-obj$intercept))
           } else {
@@ -285,6 +284,7 @@ groupfsInf <- function(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2, ve
               warning(paste(sprintf("p > n/2, and sigmahat = %0.3f used as an estimate of sigma;",sigma), "you may want to use the estimateSigma function"))
           }
       } else {
+          type <- "TC"
           sigma <- obj$sigma
       }
   }
@@ -293,47 +293,92 @@ groupfsInf <- function(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2, ve
   for (j in 1:maxsteps) {
     i <- obj$action[j]
     if (verbose) cat(paste0("Step ", j, "/", attr(obj, "maxsteps"), ": computing P-value for group ", i, "\n"))
-    # Form projection onto active set minus i
-    # and project x_i orthogonally
-    x_i <- obj$x[,which(obj$index == i), drop = FALSE]
-    if (length(obj$action) > 1) {
-        minus_i <- setdiff(obj$action, i)
-        x_minus_i <- svdu_thresh(obj$x[,which(obj$index %in% minus_i), drop = FALSE])
-        x_i <- x_i - x_minus_i %*% t(x_minus_i) %*% x_i
+
+    if (!is.null(obj$sigma)) {
+        # Form projection onto active set minus i
+        # and project x_i orthogonally
+        x_i <- obj$x[,which(obj$index == i), drop = FALSE]
+        if (length(obj$action) > 1) {
+            minus_i <- setdiff(obj$action, i)
+            x_minus_i <- svdu_thresh(obj$x[,which(obj$index %in% minus_i), drop = FALSE])
+            x_i <- x_i - x_minus_i %*% t(x_minus_i) %*% x_i
+        }
+
+        # Project y onto what remains of x_i
+        Ugtilde <- svdu_thresh(x_i)
+        R <- t(Ugtilde) %*% obj$y
+        TC <- sqrt(sum(R^2))
+        eta <- Ugtilde %*% R / TC
+        Z <- obj$y - eta * TC
+        dfi <- ncol(Ugtilde)
+        Tstats[j] <- TC
+        dfs[j] <- dfi
+
+        ydecomp <- list(R=R, Z=Z, eta=eta)
+
+    } else {
+
+        Pfull <- svdu_thresh(obj$x[,which(obj$index %in% obj$action), drop = FALSE])
+        if (length(obj$action) > 1) {
+            minus_i <- setdiff(obj$action, i)
+            Psub <- svdu_thresh(obj$x[,which(obj$index %in% minus_i), drop = FALSE])
+            Z <- Psub %*% y
+            C <- (ncol(Pfull) - ncol(Psub))/(n - ncol(Pfull))
+        } else {
+            Z <- 0
+            C <- ncol(Pfull)/(n-ncol(Pfull))
+        }
+        R1 <- y - z        
+        R2 <- y - Pfull %*% y
+        R <- sqrt(sum(R1^2))
+        U <- R1/r
+        delta <- R1-R2
+        Vdelta <- delta/sqrt(sum(delta^2))
+        V2 <- R2/sqrt(sum(R2^2))
+        ydecomp <- list(R=R, Z=Z, Vd=Vdelta, V2=V2)
+        
     }
+    
+    intervallist <- truncationRegion(obj, ydecomp, type)
 
-    # Project y onto what remains of x_i
-    Ugtilde <- svdu_thresh(x_i)
-    R <- t(Ugtilde) %*% obj$y
-    TC <- sqrt(sum(R^2))
-    eta <- Ugtilde %*% R / TC
-    Z <- obj$y - eta * TC
-    dfi <- ncol(Ugtilde)
-    TCs[j] <- TC
-    dfs[j] <- dfi
-
-    intervallist <- truncationRegion(obj, TC, R, eta, Z)
+    # Additional constraints from cross-validation?
     if (!is.null(obj$cvobj)) {
         intervallist <- c(intervallist, do.call(c,
-        lapply(obj$cvobj, function(cvf) {
-            truncationRegion(cvf, TC, R[-cvf$fold], eta[-cvf$fold], Z[-cvf$fold])
-        })))
+                          lapply(obj$cvobj, function(cvf) {
+                              if (type == "TC") {
+                                  ydecomp <- list(R=R[-cvf$fold], eta=eta[-cvf$fold], Z=Z[-cvf$fold])
+                              } else {
+ ####################################                                 
+           # THINK ABOUT THIS AGAIN
+     # when you're not falling asleep
+ ####################################                                  
+                                  ydecomp <- list(R=R[-cvf$fold], Z=Z[-cvf$fold], Vd=Vdelta[-cvf$fold], V2=V2[-cvf$fold])
+                              }
+                              truncationRegion(cvf, ydecomp, type)
+                          })))
         intervallist <- c(intervallist,
                           lapply(obj$cvquad, function(cvquad) {
-                              etacvquad <- t(eta) %*% cvquad
-                              A <- etacvquad %*% eta
-                              B <- 2 * etacvquad %*% Z
-                              C <- t(Z) %*% cvquad %*% Z
-                              quadratic_roots(A, B, C, tol = 1e-15)
+                              if (type == "TC") {
+                                  etacvquad <- t(eta) %*% cvquad
+                                  A <- etacvquad %*% eta
+                                  B <- 2 * etacvquad %*% Z
+                                  C <- t(Z) %*% cvquad %*% Z
+                                  quadratic_roots(A, B, C, tol = 1e-15)
+                              } else {
+ ####################################
+                # DO THIS NEXT
+ ####################################                                  
+                              }
                           }))
     }
+
+    # Additional constraints from AIC stopping?
     if (attr(obj, "stopped")) {
         aicstop <- attr(obj, "aicstop")
-
+        
         ulist <- etalist <- zlist <- penlist <- vector("list", aicstop+1)
         for (s in seq(aicstop+1)) {
             stepind <- maxsteps - (aicstop+1) + s
-#            print(c(s, stepind))
             if (stepind > 1) {
                 etalist[[s]] <- obj$cumprojs[[stepind-1]] %*% eta
                 zlist[[s]] <- obj$cumprojs[[stepind-1]] %*% Z
@@ -348,22 +393,27 @@ groupfsInf <- function(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2, ve
         intervallist <- c(intervallist,
                           do.call(c, lapply(1:aicstop, function(s) {
                               lapply((s+1):(aicstop+1), function(sp) {
-                                  Ug <- ulist[[s]]
-                                  Uh <- ulist[[sp]]
-                                  peng <- penlist[[s]]
-                                  penh <- prod(unlist(penlist[s:sp]))
-                                  etag <- etalist[[s]]
-                                  etah <- etalist[[sp]]
-                                  Zg <- zlist[[s]]
-                                  Zh <- zlist[[sp]]
-
-                                  coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh)
-                                  quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol = 1e-15)
+                                  if (type == "TC") {
+                                      Ug <- ulist[[s]]
+                                      Uh <- ulist[[sp]]
+                                      peng <- penlist[[s]]
+                                      penh <- prod(unlist(penlist[s:sp]))
+                                      etag <- etalist[[s]]
+                                      etah <- etalist[[sp]]
+                                      Zg <- zlist[[s]]
+                                      Zh <- zlist[[sp]]
+                                      coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh)
+                                      quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol = 1e-15)
+                                  } else {
+###################################
+              # DO THIS TOO
+###################################                                      
+                                  }
                               })
                           })))
     }
 
-    # Compute intersection:
+        # Compute intersection:
     region <- do.call(interval_union, intervallist)
     region <- interval_union(region, Intervals(c(-Inf,0)))
     E <- interval_complement(region, check_valid = FALSE)
@@ -371,13 +421,31 @@ groupfsInf <- function(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2, ve
         stop(paste("Empty TC support at step", j))
     }
     supports[[j]] <- E
-
+        
     # E is now potentially a union of intervals
-    pvals[j] <- TC_surv(TC, sigma, dfi, E)
+    if (type == "TC") {
+        pvals[j] <- TC_surv(TC, sigma, dfi, E)
+    } else {
+        pvals[j] <- TF_surv(TF, df1, df2, E)
+    }
+    
   }
-  if (nanconv) warning("P-value NaNs of the form 0/0 converted to 0. This typically occurs for numerical reasons in the presence of a large signal-to-noise ratio.")
+
+  if (any(is.nan(pvals))) {
+      nanp <- which(is.nan(pvals))
+      pvals[nanp] <- 0
+      warning(paste0("P-value NaNs of the form 0/0 converted to 0 for group(s) ", paste(obj$action[nanp], collapse=","), ". This typically occurs for numerical reasons in the presence of a large signal-to-noise ratio."))
+  }
+  
   names(pvals) <- obj$action
-  out <- list(vars = obj$action, pv=pvals, sigma=sigma, TC=TCs, df = dfs, support=supports)
+  out <- list(vars = obj$action, pv=pvals, sigma=sigma)
+  if (type == "TC") {
+      out$TC <- Tstats
+  } else {
+      out$TF <- Tstats
+  }
+  out$df <- dfs
+  out$support <- supports
   class(out) <- "groupfsInf"
   if (!is.null(attr(obj, "varnames"))) {
       attr(out, "varnames") <- attr(obj, "varnames")
@@ -419,10 +487,6 @@ TC_surv <- function(TC, sigma, df, E) {
 
     # Survival function
     value <- numer/denom
-    if (is.nan(value)) {
-      value <- 0
-      nanconv <- TRUE
-    }
     # Force p-value to lie in the [0,1] interval
     # in case of numerical issues
     value <- max(0, min(1, value))
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 2a15ce2..335bb78 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -1,8 +1,15 @@
 
-truncationRegion <- function(obj, TC, R, eta, Z, tol = 1e-15) {
+truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
 
   n <- nrow(obj$x)
-
+  R <- ydecomp$R
+  Z <- ydecomp$Z
+  if (type == "TC") {
+      eta <- ydecomp$eta
+  } else {
+      Vd <- ydecomp$Vd
+      V2 <- ydecomp$V2
+  }
   L <- lapply(1:length(obj$action), function(s) {
 
     Ug <- obj$maxprojs[[s]]
@@ -27,8 +34,13 @@ truncationRegion <- function(obj, TC, R, eta, Z, tol = 1e-15) {
           # (t*U + Z)^T %*% Q %*% (t*U + Z) \geq 0
           # we find the roots in t, if there are any
           # and return the interval of potential t
-          coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etas, etas, Zs, Zs)
-          quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol)
+          if (type == "TC") {
+              coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etas, etas, Zs, Zs)
+              quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol)
+          } else {
+              coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h)
+              TF_roots(R, C, coeffs)
+          }
       })
     }
     # LL is a list of intervals
@@ -119,32 +131,44 @@ quadratic_roots <- function(A, B, C, tol) {
     }
 }
 
-roots_to_checkpoints <- function(roots) {
-    checkpoints <- unique(sort(c(0, roots)))
-    return(c(0, (checkpoints + c(checkpoints[-1], 2 + checkpoints[length(checkpoints)]))/2))
-}
 
-roots_to_partition <- function(roots) {
-    checkpoints <- unique(sort(c(0, roots)))
-    return(list(endpoints = c(checkpoints, Inf), midpoints = (checkpoints + c(checkpoints[-1], 2 + checkpoints[length(checkpoints)]))/2))
-}
+# Efficiently compute coefficients of one-dimensional TF slice function
+TF_coefficients <- function(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h) {
 
-# Tchi_roots <- function(Q, a, b, )
+    UhZ <- t(Uh) %*% Zh
+    UgZ <- t(Ug) %*% Zg
+    UhVd <- t(Uh) %*% Vdh
+    UgVd <- t(Ug) %*% Vdg
+    UhV2 <- t(Uh) %*% V2h
+    UgV2 <- t(Ug) %*% V2g
+    VdZh <- t(Vdh) %*% Zh    
+    VdZg <- t(Vdg) %*% Zg
+    V2Zh <- t(V2h) %*% Zh
+    V2Zg <- t(V2g) %*% Zg
+    
+    x0 <- peng * (sum(Zg^2) - sum(UgZ^2)) - penh * (sum(Zh^2) - sum(UhZ^2))
+    x1 <- 2*R*(peng * (VdZg - t(UgZ) %*% UgVd) - penh * (VdZh - t(UhZ) %*% UhVd))
+    x2 <- 2*R*(peng * (V2Zg - t(UgZ) %*% UgV2) - penh * (V2Zh - t(UhZ) %*% UhV2))
+    x12 <- 2*R*(peng * (t(Vdg) %*% V2g - t(UgVd) %*% UgV2) - penh * (t(Vdh) %*% V2h - t(UhVd) %*% UhV2))
+    x11 <- R^2*(peng * (sum(Vdg^2) - sum(UgVd^2)) - penh*(sum(Vdh^2) - sum(UhVd^2)))
+    x22 <- R^2*(peng * (sum(V2g^2) - sum(UgV2^2)) - penh*(sum(V2h^2) - sum(UhV2^2)))
+
+    return(list(x11=x11, x22=x22, x12=x12, x1=x1, x2=x2, x0=x0))
+}
 
-TF_roots <- function(Q, a, b, Vdelta, V2, z, C, r, tol = 1e-14) {
+# Numerically solve for roots of TF slice using
+# hybrid polyroot/uniroot approach
+TF_roots <- function(R, C, coeffs, tol = 1e-14) {
 
-    # z = y - R1
-    VdeltaQ <- t(Vdelta) %*% Q
-    V2Q <- t(V2) %*% Q
-    x11 <- VdeltaQ %*% Vdelta
-    x12 <- 2 * VdeltaQ %*% V2
-    x22 <- V2Q %*% V2
-    x1 <- 2 * VdeltaQ %*% z + t(a) %*% Vdelta
-    x2 <- 2 * V2Q %*% z + t(a) %*% V2
-    x0 <- t(z) %*% Q %*% z + t(a) %*% z + b
+    x11 <- coeffs$x11
+    x22 <- coeffs$x22
+    x12 <- coeffs$x12
+    x1 <- coeffs$x1
+    x2 <- coeffs$x2
+    x0 <- coeffs$x0
 
-    g1 <- function(t) r*sqrt(C*t/(1+C*t))
-    g2 <- function(t) r/sqrt(1+C*t)
+    g1 <- function(t) R*sqrt(C*t/(1+C*t))
+    g2 <- function(t) R/sqrt(1+C*t)
     I <- function(t) x11*g1(t)^2 + x12*g1(t)*g2(t) + x22*g2(t)^2 + x1*g1(t) + x2*g2(t) + x0
 
     z4 <- r*complex(real = -x11 + x22, imaginary = -x12)/2
@@ -152,7 +176,7 @@ TF_roots <- function(Q, a, b, Vdelta, V2, z, C, r, tol = 1e-14) {
     z2 <- complex(real = r*x11+r*x22+2*x0/r)
     z1 <- Conj(z3)
     z0 <- Conj(z4)
-    zcoefs <- r*c(z0, z1, z2, z3, z4)/2
+    zcoefs <- R*c(z0, z1, z2, z3, z4)/2
     croots <- polyroot(zcoefs)
     thetas <- Arg(croots)
     modinds <- Mod(croots) <= 1 + tol & Mod(croots) >= 1 - tol
@@ -176,7 +200,10 @@ TF_roots <- function(Q, a, b, Vdelta, V2, z, C, r, tol = 1e-14) {
         }))
         partition <- roots_to_partition(roots)
         positive <- which(I(partition$midpoints) > 0)
-
+########################################        
+        # Store the complement!
+        # interval_intersect uses complement anyway
+########################################
         intervals <- matrix(NA, ncol=2)
         for (i in 1:length(positive)) {
             ind <- positive[i]
@@ -192,3 +219,13 @@ TF_roots <- function(Q, a, b, Vdelta, V2, z, C, r, tol = 1e-14) {
 
     return(list(intervals = Intervals(c(0,Inf)), I=I))
 }
+
+# Helper functions for TF roots
+roots_to_checkpoints <- function(roots) {
+    checkpoints <- unique(sort(c(0, roots)))
+    return(c(0, (checkpoints + c(checkpoints[-1], 2 + checkpoints[length(checkpoints)]))/2))
+}
+roots_to_partition <- function(roots) {
+    checkpoints <- unique(sort(c(0, roots)))
+    return(list(endpoints = c(checkpoints, Inf), midpoints = (checkpoints + c(checkpoints[-1], 2 + checkpoints[length(checkpoints)]))/2))
+}

From 6b21cadbfabdf1e7afb47be492ca27b104ade4f0 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Thu, 5 Nov 2015 18:24:53 -0800
Subject: [PATCH 036/493] Almost finished porting F code into groupfsInf

---
 selectiveInference/R/funs.groupfs.R   | 104 ++++++++++++++++++--------
 selectiveInference/R/funs.quadratic.R |  35 ++++-----
 2 files changed, 92 insertions(+), 47 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 148e35a..80e1529 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -267,7 +267,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
   y <- obj$y
   Ep <- sum(index %in% obj$action)
 
-  pvals = dfs = Tstats = numeric(maxsteps)
+  pvals = dfs = dfs2 = Tstats = numeric(maxsteps)
   supports <- list()
 
   if (!is.null(sigma)) {
@@ -322,23 +322,34 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
         if (length(obj$action) > 1) {
             minus_i <- setdiff(obj$action, i)
             Psub <- svdu_thresh(obj$x[,which(obj$index %in% minus_i), drop = FALSE])
-            Z <- Psub %*% y
-            C <- (ncol(Pfull) - ncol(Psub))/(n - ncol(Pfull))
+            Z <- Psub %*% t(Psub) %*% y
+            df2 <- ncol(Pfull) - ncol(Psub)
+            C <- df2/(n - ncol(Pfull))
+
         } else {
             Z <- 0
-            C <- ncol(Pfull)/(n-ncol(Pfull))
+            df2 <- ncol(Pfull)
+            C <- df2/(n-ncol(Pfull))
         }
-        R1 <- y - z        
-        R2 <- y - Pfull %*% y
+        R1 <- y - Z
+        R2 <- y - Pfull %*% t(Pfull) %*% y
         R <- sqrt(sum(R1^2))
-        U <- R1/r
+        R2sq <- sum(R2^2)
+        U <- R1/R
+
+        # cv-folds redefine Vdelta/V2?
+
         delta <- R1-R2
         Vdelta <- delta/sqrt(sum(delta^2))
         V2 <- R2/sqrt(sum(R2^2))
-        ydecomp <- list(R=R, Z=Z, Vd=Vdelta, V2=V2)
-        
+        TF <- (R^2-R2sq)/(C*R2sq)
+        Tstats[j] <- TF
+        dfs[j] <- ncol(Psub)
+
+        ydecomp <- list(R=R, Z=Z, Vd=Vdelta, V2=V2, C=C)
+
     }
-    
+
     intervallist <- truncationRegion(obj, ydecomp, type)
 
     # Additional constraints from cross-validation?
@@ -348,11 +359,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
                               if (type == "TC") {
                                   ydecomp <- list(R=R[-cvf$fold], eta=eta[-cvf$fold], Z=Z[-cvf$fold])
                               } else {
- ####################################                                 
-           # THINK ABOUT THIS AGAIN
-     # when you're not falling asleep
- ####################################                                  
-                                  ydecomp <- list(R=R[-cvf$fold], Z=Z[-cvf$fold], Vd=Vdelta[-cvf$fold], V2=V2[-cvf$fold])
+                                  ydecomp <- list(R=R, Z=Z[-cvf$fold], Vd=Vdelta[-cvf$fold], V2=V2[-cvf$fold], C=C) # C correct?
                               }
                               truncationRegion(cvf, ydecomp, type)
                           })))
@@ -365,25 +372,52 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
                                   C <- t(Z) %*% cvquad %*% Z
                                   quadratic_roots(A, B, C, tol = 1e-15)
                               } else {
- ####################################
-                # DO THIS NEXT
- ####################################                                  
+
+                                  zcvquad <- t(Z) %*% cvquad
+                                  vdcvquad <- t(Vdelta) %*% cvquad
+                                  v2cvquad <- t(V2) %*% cvquad
+                                  # (r*(vd*g1 + v2*g2) + z)^T cvquad (r*(vd*g1 + v2*g2) + z)
+                                  x0 <- zcvquad %*% Z
+                                  x1 <- 2*R*zcvquad %*% Vd
+                                  x2 <- 2*R*zcvquad %*% V2
+                                  x12 <- 2*R*vdcvquad %*% V2
+                                  x11 <- R^2*vdcvquad %*% Vd
+                                  x22 <- R^2*v2cvquad %*% V2
+                                  TF_roots(R, C, coeffs = list(x0=x0, x1=x1, x2=x2, x12=x12, x11=x11, x22=x22))
                               }
                           }))
     }
 
     # Additional constraints from AIC stopping?
     if (attr(obj, "stopped")) {
+
         aicstop <- attr(obj, "aicstop")
-        
-        ulist <- etalist <- zlist <- penlist <- vector("list", aicstop+1)
+        ulist <- penlist <- zlist <- vector("list", aicstop+1)
+
+        if (type == "TC") {
+            etalist <- vector("list", aicstop+1)
+        } else {
+            vdlist <- v2list <- vector("list", aicstop+1)
+        }
+
         for (s in seq(aicstop+1)) {
             stepind <- maxsteps - (aicstop+1) + s
             if (stepind > 1) {
-                etalist[[s]] <- obj$cumprojs[[stepind-1]] %*% eta
-                zlist[[s]] <- obj$cumprojs[[stepind-1]] %*% Z
+                cproj <- obj$cumprojs[[stepind-1]]
+                if (type == "TC") {
+                    etalist[[s]] <- cproj %*% eta
+                } else {
+                    v2list[[s]] <- cproj %*% V2
+                    vdlist[[s]] <- cproj %*% Vdelta
+                }
+                zlist[[s]] <- cproj %*% Z
             } else {
-                etalist[[s]] <- eta
+                if (type == "TC") {
+                    etalist[[s]] <- eta
+                } else {
+                    v2list[[s]] <- V2
+                    vdlist[[s]] <- Vdelta
+                }
                 zlist[[s]] <- Z
             }
             ulist[[s]] <- obj$maxprojs[[stepind]]
@@ -405,9 +439,19 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
                                       coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh)
                                       quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol = 1e-15)
                                   } else {
-###################################
-              # DO THIS TOO
-###################################                                      
+
+                                      Ug <- ulist[[s]]
+                                      Uh <- ulist[[sp]]
+                                      peng <- penlist[[s]]
+                                      penh <- prod(unlist(penlist[s:sp]))
+                                      Vdg <- vdlist[[s]]
+                                      Vdh <- vdlist[[sp]]
+                                      V2g <- vdlist[[s]]
+                                      V2h <- vdlist[[sp]]
+                                      Zg <- zlist[[s]]
+                                      Zh <- zlist[[sp]]
+                                      coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h)
+                                      TF_roots(R, C, coeffs)
                                   }
                               })
                           })))
@@ -418,17 +462,17 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
     region <- interval_union(region, Intervals(c(-Inf,0)))
     E <- interval_complement(region, check_valid = FALSE)
     if (length(E) == 0) {
-        stop(paste("Empty TC support at step", j))
+        stop(paste("Empty support at step", j))
     }
     supports[[j]] <- E
-        
+
     # E is now potentially a union of intervals
     if (type == "TC") {
         pvals[j] <- TC_surv(TC, sigma, dfi, E)
     } else {
         pvals[j] <- TF_surv(TF, df1, df2, E)
     }
-    
+
   }
 
   if (any(is.nan(pvals))) {
@@ -436,7 +480,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
       pvals[nanp] <- 0
       warning(paste0("P-value NaNs of the form 0/0 converted to 0 for group(s) ", paste(obj$action[nanp], collapse=","), ". This typically occurs for numerical reasons in the presence of a large signal-to-noise ratio."))
   }
-  
+
   names(pvals) <- obj$action
   out <- list(vars = obj$action, pv=pvals, sigma=sigma)
   if (type == "TC") {
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 335bb78..59b2b7e 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -3,23 +3,26 @@ truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
 
   n <- nrow(obj$x)
   R <- ydecomp$R
-  Z <- ydecomp$Z
+  Z = Zs = ydecomp$Z
   if (type == "TC") {
-      eta <- ydecomp$eta
+      eta = etas = ydecomp$eta
   } else {
-      Vd <- ydecomp$Vd
-      V2 <- ydecomp$V2
+      Vd = Vds = ydecomp$Vd
+      V2 = V2s = ydecomp$V2
+      C = ydecomp$C
   }
   L <- lapply(1:length(obj$action), function(s) {
 
     Ug <- obj$maxprojs[[s]]
     peng <- obj$maxpens[[s]]
     if (s > 1) {
-        etas <- obj$cumprojs[[s-1]] %*% eta
-        Zs <- obj$cumprojs[[s-1]] %*% Z
-    } else {
-        etas <- eta
-        Zs <- Z
+        if (type == "TC") {
+            etas <- obj$cumprojs[[s-1]] %*% eta
+            Zs <- obj$cumprojs[[s-1]] %*% Z
+        } else {
+            Vds <- obj$cumprojs[[s-1]] %*% Vd
+            V2s <- obj$cumprojs[[s-1]] %*% V2
+        }
     }
 
     num.projs <- length(obj$projections[[s]])
@@ -38,7 +41,7 @@ truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
               coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etas, etas, Zs, Zs)
               quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol)
           } else {
-              coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h)
+              coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zs, Zs, Vds, Vds, V2s, V2s)
               TF_roots(R, C, coeffs)
           }
       })
@@ -58,17 +61,15 @@ quadratic_coefficients <- function(sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh
     etaZh <- t(etah) %*% Zh
     etaZg <- t(etag) %*% Zg
     if (is.null(sigma)) {
-        # Check the signs, make it consistent
         A <- penh * (sum(etah^2) - sum(Uheta^2)) - peng * (sum(etag^2) - sum(Ugeta^2))
         B <- 2 * penh * (etaZh - t(Uheta) %*% UhZ) - 2 * peng * (etaZg - t(Ugeta) %*% UgZ)
         C <- penh * (sum(Zh^2) - sum(UhZ^2)) - peng * (sum(Zg^2) - sum(UgZ^2))
     } else {
-          # Check this
         A <- (sum(etah^2) - sum(Uheta^2)) - (sum(etag^2) - sum(Ugeta^2))
         B <- 2 * (etaZh - t(Uheta) %*% UhZ) - 2 * (etaZg - t(Ugeta) %*% UgZ)
         C <- (sum(Zh^2) - sum(UhZ^2) + penh) - (sum(Zg^2) - sum(UgZ^2) + peng)
     }
-    return(list(A = A, B = B, C= C))
+    return(list(A = A, B = B, C = C))
 }
 
 quadratic_roots <- function(A, B, C, tol) {
@@ -141,11 +142,11 @@ TF_coefficients <- function(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h) {
     UgVd <- t(Ug) %*% Vdg
     UhV2 <- t(Uh) %*% V2h
     UgV2 <- t(Ug) %*% V2g
-    VdZh <- t(Vdh) %*% Zh    
+    VdZh <- t(Vdh) %*% Zh
     VdZg <- t(Vdg) %*% Zg
     V2Zh <- t(V2h) %*% Zh
     V2Zg <- t(V2g) %*% Zg
-    
+
     x0 <- peng * (sum(Zg^2) - sum(UgZ^2)) - penh * (sum(Zh^2) - sum(UhZ^2))
     x1 <- 2*R*(peng * (VdZg - t(UgZ) %*% UgVd) - penh * (VdZh - t(UhZ) %*% UhVd))
     x2 <- 2*R*(peng * (V2Zg - t(UgZ) %*% UgV2) - penh * (V2Zh - t(UhZ) %*% UhV2))
@@ -200,10 +201,10 @@ TF_roots <- function(R, C, coeffs, tol = 1e-14) {
         }))
         partition <- roots_to_partition(roots)
         positive <- which(I(partition$midpoints) > 0)
-########################################        
+########################################
         # Store the complement!
         # interval_intersect uses complement anyway
-########################################
+
         intervals <- matrix(NA, ncol=2)
         for (i in 1:length(positive)) {
             ind <- positive[i]

From d791988742f1d9c9c802c0ee8e9b033d0fcfe2ce Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Fri, 6 Nov 2015 15:38:38 -0800
Subject: [PATCH 037/493] Now debugging F test code...

---
 selectiveInference/R/funs.groupfs.R   | 14 ++--
 selectiveInference/R/funs.quadratic.R | 93 ++++++++++++++++-----------
 2 files changed, 63 insertions(+), 44 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 80e1529..a0fe91a 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -314,7 +314,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
         Tstats[j] <- TC
         dfs[j] <- dfi
 
-        ydecomp <- list(R=R, Z=Z, eta=eta)
+        ydecomp <- list(Z=Z, eta=eta)
 
     } else {
 
@@ -335,10 +335,6 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
         R2 <- y - Pfull %*% t(Pfull) %*% y
         R <- sqrt(sum(R1^2))
         R2sq <- sum(R2^2)
-        U <- R1/R
-
-        # cv-folds redefine Vdelta/V2?
-
         delta <- R1-R2
         Vdelta <- delta/sqrt(sum(delta^2))
         V2 <- R2/sqrt(sum(R2^2))
@@ -352,6 +348,9 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
 
     intervallist <- truncationRegion(obj, ydecomp, type)
 
+    region <- do.call(interval_union, intervallist)
+    # DELETE THIS ###########
+
     # Additional constraints from cross-validation?
     if (!is.null(obj$cvobj)) {
         intervallist <- c(intervallist, do.call(c,
@@ -461,8 +460,9 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
     region <- do.call(interval_union, intervallist)
     region <- interval_union(region, Intervals(c(-Inf,0)))
     E <- interval_complement(region, check_valid = FALSE)
+    print(E)
     if (length(E) == 0) {
-        stop(paste("Empty support at step", j))
+#        stop(paste("Empty support at step", j))
     }
     supports[[j]] <- E
 
@@ -470,7 +470,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
     if (type == "TC") {
         pvals[j] <- TC_surv(TC, sigma, dfi, E)
     } else {
-        pvals[j] <- TF_surv(TF, df1, df2, E)
+#        pvals[j] <- TF_surv(TF, df1, df2, E)
     }
 
   }
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 59b2b7e..6d9667a 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -2,7 +2,6 @@
 truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
 
   n <- nrow(obj$x)
-  R <- ydecomp$R
   Z = Zs = ydecomp$Z
   if (type == "TC") {
       eta = etas = ydecomp$eta
@@ -10,6 +9,7 @@ truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
       Vd = Vds = ydecomp$Vd
       V2 = V2s = ydecomp$V2
       C = ydecomp$C
+      R = ydecomp$R
   }
   L <- lapply(1:length(obj$action), function(s) {
 
@@ -28,7 +28,7 @@ truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
     num.projs <- length(obj$projections[[s]])
     if (num.projs == 0) {
         return(list(Intervals(c(-Inf,0))))
-
+        print("no projections")
     } else {
       lapply(1:num.projs, function(l) {
           Uh <- obj$projections[[s]][[l]]
@@ -136,30 +136,35 @@ quadratic_roots <- function(A, B, C, tol) {
 # Efficiently compute coefficients of one-dimensional TF slice function
 TF_coefficients <- function(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h) {
 
-    UhZ <- t(Uh) %*% Zh
-    UgZ <- t(Ug) %*% Zg
-    UhVd <- t(Uh) %*% Vdh
-    UgVd <- t(Ug) %*% Vdg
-    UhV2 <- t(Uh) %*% V2h
-    UgV2 <- t(Ug) %*% V2g
-    VdZh <- t(Vdh) %*% Zh
-    VdZg <- t(Vdg) %*% Zg
-    V2Zh <- t(V2h) %*% Zh
-    V2Zg <- t(V2g) %*% Zg
-
-    x0 <- peng * (sum(Zg^2) - sum(UgZ^2)) - penh * (sum(Zh^2) - sum(UhZ^2))
-    x1 <- 2*R*(peng * (VdZg - t(UgZ) %*% UgVd) - penh * (VdZh - t(UhZ) %*% UhVd))
-    x2 <- 2*R*(peng * (V2Zg - t(UgZ) %*% UgV2) - penh * (V2Zh - t(UhZ) %*% UhV2))
-    x12 <- 2*R*(peng * (t(Vdg) %*% V2g - t(UgVd) %*% UgV2) - penh * (t(Vdh) %*% V2h - t(UhVd) %*% UhV2))
-    x11 <- R^2*(peng * (sum(Vdg^2) - sum(UgVd^2)) - penh*(sum(Vdh^2) - sum(UhVd^2)))
-    x22 <- R^2*(peng * (sum(V2g^2) - sum(UgV2^2)) - penh*(sum(V2h^2) - sum(UhV2^2)))
+    UhZ <- t(Uh) %*% Zh #check
+    UgZ <- t(Ug) %*% Zg #check
+    UhVd <- t(Uh) %*% Vdh #check
+    UgVd <- t(Ug) %*% Vdg #check
+    UhV2 <- t(Uh) %*% V2h #check
+    UgV2 <- t(Ug) %*% V2g #check
+    VdZh <- sum(Vdh*Zh) #check
+    VdZg <- sum(Vdg*Zg) #check
+    V2Zh <- sum(V2h*Zh) #check
+    V2Zg <- sum(V2g*Zg) #check
+
+    x0 <- peng * (sum(Zg^2) - sum(UgZ^2)) - penh * (sum(Zh^2) - sum(UhZ^2)) #check
+
+    x1 <- 2*R*(peng * (VdZg - sum(UgZ*UgVd)) - penh * (VdZh - sum(UhZ*UhVd))) #check
+
+    x2 <- 2*R*(peng * (V2Zg - sum(UgZ*UgV2)) - penh * (V2Zh - sum(UhZ*UhV2))) #check
+
+    x12 <- 2*R^2*(peng * (sum(Vdg*V2g) - sum(UgVd*UgV2)) - penh * (sum(Vdh*V2h) - sum(UhVd*UhV2))) #fixed R -> R^2
+
+    x11 <- R^2*(peng * (sum(Vdg^2) - sum(UgVd^2)) - penh * (sum(Vdh^2) - sum(UhVd^2))) #check
+    x22 <- R^2*(peng * (sum(V2g^2) - sum(UgV2^2)) - penh * (sum(V2h^2) - sum(UhV2^2))) #check
+#    if (x22 == 0) print(c(sum(UgV2^2), sum(UhV2^2)))
 
     return(list(x11=x11, x22=x22, x12=x12, x1=x1, x2=x2, x0=x0))
 }
 
 # Numerically solve for roots of TF slice using
 # hybrid polyroot/uniroot approach
-TF_roots <- function(R, C, coeffs, tol = 1e-14) {
+TF_roots <- function(R, C, coeffs, tol = 1e-14, tol2 = 1e-8) {
 
     x11 <- coeffs$x11
     x22 <- coeffs$x22
@@ -168,13 +173,24 @@ TF_roots <- function(R, C, coeffs, tol = 1e-14) {
     x2 <- coeffs$x2
     x0 <- coeffs$x0
 
+    # Handle some special cases
+    if ((x11 == 0) && (max(abs(c(x22, x12, x1, x2))) < tol2)) {
+#        print("Special case 1")
+        return(Intervals(c(-Inf, 0)))
+    }
+
+    if ((x22 == 0) && (max(abs(c(x2, x12))) < tol2)) {
+        x2 <- 0
+        x12 <- 0
+    }
+
     g1 <- function(t) R*sqrt(C*t/(1+C*t))
     g2 <- function(t) R/sqrt(1+C*t)
     I <- function(t) x11*g1(t)^2 + x12*g1(t)*g2(t) + x22*g2(t)^2 + x1*g1(t) + x2*g2(t) + x0
 
-    z4 <- r*complex(real = -x11 + x22, imaginary = -x12)/2
+    z4 <- R*complex(real = -x11 + x22, imaginary = -x12)/2
     z3 <- complex(real = x2, imaginary = -x1)
-    z2 <- complex(real = r*x11+r*x22+2*x0/r)
+    z2 <- complex(real = R*x11+R*x22+2*x0/R)
     z1 <- Conj(z3)
     z0 <- Conj(z4)
     zcoefs <- R*c(z0, z1, z2, z3, z4)/2
@@ -185,8 +201,9 @@ TF_roots <- function(R, C, coeffs, tol = 1e-14) {
     roots <- unique(thetas[modinds * angleinds])
     troots <- tan(roots)^2/C
 
-    if (length(roots) == 0) {
-        return(list(intervals = Intervals(c(0,Inf)), I=I))
+    if (length(troots) == 0) {
+        #return(list(intervals = Intervals(c(0,Inf)), I=I))
+        return(Intervals(c(-Inf,0)))
     }
 
     checkpoints <- roots_to_checkpoints(troots)
@@ -197,36 +214,38 @@ TF_roots <- function(R, C, coeffs, tol = 1e-14) {
     if (length(changeinds) > 0) {
 
         roots <- unlist(lapply(changeinds, function(ind) {
-            uniroot(I, lower = checkpoints[ind-1], upper = checkpoints[ind])$root
+            uniroot(I, lower = checkpoints[ind-1], upper = checkpoints[ind], tol = tol2)$root
         }))
+
+#        if (x2 == 0) print(roots)
+
         partition <- roots_to_partition(roots)
-        positive <- which(I(partition$midpoints) > 0)
-########################################
-        # Store the complement!
-        # interval_intersect uses complement anyway
+        negative <- which(I(partition$midpoints) < 0)
 
         intervals <- matrix(NA, ncol=2)
-        for (i in 1:length(positive)) {
-            ind <- positive[i]
-            if ((i > 1) && (ind == positive[i-1] + 1)) {
+        for (i in 1:length(negative)) {
+            ind <- negative[i]
+            if ((i > 1) && (ind == negative[i-1] + 1)) {
+                # There was not a sign change at end of previous interval
                 intervals[nrow(intervals), 2] <- partition$endpoints[ind+1]
             } else {
                 intervals <- rbind(intervals, c(partition$endpoints[ind], partition$endpoints[ind+1]))
             }
         }
 
-        return(list(intervals = Intervals(intervals[-1,]), I=I))
+        #return(list(intervals = Intervals(intervals[-1,]), I=I))
+        return(Intervals(intervals[-1,]))
     }
-
-    return(list(intervals = Intervals(c(0,Inf)), I=I))
+    #return(list(intervals = Intervals(c(0,Inf)), I=I))
+    return(Intervals(c(-Inf,0)))
 }
 
 # Helper functions for TF roots
 roots_to_checkpoints <- function(roots) {
     checkpoints <- unique(sort(c(0, roots)))
-    return(c(0, (checkpoints + c(checkpoints[-1], 2 + checkpoints[length(checkpoints)]))/2))
+    return(c(0, (checkpoints + c(checkpoints[-1], 200 + checkpoints[length(checkpoints)]))/2))
 }
 roots_to_partition <- function(roots) {
     checkpoints <- unique(sort(c(0, roots)))
-    return(list(endpoints = c(checkpoints, Inf), midpoints = (checkpoints + c(checkpoints[-1], 2 + checkpoints[length(checkpoints)]))/2))
+    return(list(endpoints = c(checkpoints, Inf), midpoints = (checkpoints + c(checkpoints[-1], 200 + checkpoints[length(checkpoints)]))/2))
 }

From 7564fcb1c934ec7a5f7b6bb0f819eafaea25b6db Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Fri, 6 Nov 2015 15:41:18 -0800
Subject: [PATCH 038/493] Not quite finished with F test

---
 selectiveInference/R/funs.groupfs.R | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index a0fe91a..55a3d6b 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -348,8 +348,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
 
     intervallist <- truncationRegion(obj, ydecomp, type)
 
-    region <- do.call(interval_union, intervallist)
-    # DELETE THIS ###########
+    #region <- do.call(interval_union, intervallist)
 
     # Additional constraints from cross-validation?
     if (!is.null(obj$cvobj)) {
@@ -462,7 +461,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
     E <- interval_complement(region, check_valid = FALSE)
     print(E)
     if (length(E) == 0) {
-#        stop(paste("Empty support at step", j))
+        stop(paste("Empty support at step", j))
     }
     supports[[j]] <- E
 
@@ -470,6 +469,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
     if (type == "TC") {
         pvals[j] <- TC_surv(TC, sigma, dfi, E)
     } else {
+        # write TF_surv function first
 #        pvals[j] <- TF_surv(TF, df1, df2, E)
     }
 

From 80feb547218e9c4fe5eefe4f4bcac778b6f4577b Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Fri, 6 Nov 2015 17:33:08 -0800
Subject: [PATCH 039/493] Searching for a bug...

---
 selectiveInference/R/funs.quadratic.R | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 6d9667a..41f2704 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -28,9 +28,9 @@ truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
     num.projs <- length(obj$projections[[s]])
     if (num.projs == 0) {
         return(list(Intervals(c(-Inf,0))))
-        print("no projections")
     } else {
       lapply(1:num.projs, function(l) {
+
           Uh <- obj$projections[[s]][[l]]
           penh <- obj$aicpens[[s]][[l]]
           # The quadratic form corresponding to
@@ -76,6 +76,7 @@ quadratic_roots <- function(A, B, C, tol) {
     disc <- B^2 - 4*A*C
     b2a <- -B/(2*A)
 
+
     if (disc > tol) {
         # Real roots
         pm <- sqrt(disc)/(2*A)
@@ -207,6 +208,7 @@ TF_roots <- function(R, C, coeffs, tol = 1e-14, tol2 = 1e-8) {
     }
 
     checkpoints <- roots_to_checkpoints(troots)
+
     signs <- sign(I(checkpoints))
     diffs <- c(0, diff(signs))
     changeinds <- which(diffs != 0)
@@ -217,8 +219,6 @@ TF_roots <- function(R, C, coeffs, tol = 1e-14, tol2 = 1e-8) {
             uniroot(I, lower = checkpoints[ind-1], upper = checkpoints[ind], tol = tol2)$root
         }))
 
-#        if (x2 == 0) print(roots)
-
         partition <- roots_to_partition(roots)
         negative <- which(I(partition$midpoints) < 0)
 
@@ -236,6 +236,7 @@ TF_roots <- function(R, C, coeffs, tol = 1e-14, tol2 = 1e-8) {
         #return(list(intervals = Intervals(intervals[-1,]), I=I))
         return(Intervals(intervals[-1,]))
     }
+
     #return(list(intervals = Intervals(c(0,Inf)), I=I))
     return(Intervals(c(-Inf,0)))
 }

From c9a40a907a5ff8cbf506d2c5d7d1a30fde0fdd40 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Sat, 7 Nov 2015 15:48:47 -0800
Subject: [PATCH 040/493] Fixed a sign change, added more TF functions

---
 selectiveInference/R/funs.groupfs.R   | 90 ++++++++++++++++++++++-----
 selectiveInference/R/funs.quadratic.R | 70 ++++++++++-----------
 2 files changed, 110 insertions(+), 50 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 55a3d6b..6432d1e 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -322,17 +322,16 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
         if (length(obj$action) > 1) {
             minus_i <- setdiff(obj$action, i)
             Psub <- svdu_thresh(obj$x[,which(obj$index %in% minus_i), drop = FALSE])
-            Z <- Psub %*% t(Psub) %*% y
-            df2 <- ncol(Pfull) - ncol(Psub)
-            C <- df2/(n - ncol(Pfull))
-
+            Z <- Psub %*% t(Psub) %*% obj$y
+            df1 <- ncol(Pfull) - ncol(Psub)
         } else {
             Z <- 0
-            df2 <- ncol(Pfull)
-            C <- df2/(n-ncol(Pfull))
+            df1 <- ncol(Pfull)
         }
-        R1 <- y - Z
-        R2 <- y - Pfull %*% t(Pfull) %*% y
+        df2 <- n - ncol(Pfull)
+        C <- df1/df2
+        R1 <- obj$y - Z
+        R2 <- obj$y - Pfull %*% t(Pfull) %*% obj$y
         R <- sqrt(sum(R1^2))
         R2sq <- sum(R2^2)
         delta <- R1-R2
@@ -340,7 +339,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
         V2 <- R2/sqrt(sum(R2^2))
         TF <- (R^2-R2sq)/(C*R2sq)
         Tstats[j] <- TF
-        dfs[j] <- ncol(Psub)
+        dfs[j] <- df1
 
         ydecomp <- list(R=R, Z=Z, Vd=Vdelta, V2=V2, C=C)
 
@@ -348,8 +347,6 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
 
     intervallist <- truncationRegion(obj, ydecomp, type)
 
-    #region <- do.call(interval_union, intervallist)
-
     # Additional constraints from cross-validation?
     if (!is.null(obj$cvobj)) {
         intervallist <- c(intervallist, do.call(c,
@@ -459,7 +456,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
     region <- do.call(interval_union, intervallist)
     region <- interval_union(region, Intervals(c(-Inf,0)))
     E <- interval_complement(region, check_valid = FALSE)
-    print(E)
+
     if (length(E) == 0) {
         stop(paste("Empty support at step", j))
     }
@@ -470,7 +467,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
         pvals[j] <- TC_surv(TC, sigma, dfi, E)
     } else {
         # write TF_surv function first
-#        pvals[j] <- TF_surv(TF, df1, df2, E)
+        pvals[j] <- TF_surv(TF, df1, df2, E)
     }
 
   }
@@ -557,6 +554,63 @@ num_int_chi <- function(a, b, df, nsamp = 10000) {
   return((b-a)*mean(integrand))
 }
 
+TF_surv <- function(TF, df1, df2, E) {
+    if (length(E) == 0) {
+        stop("Empty TF support")
+    }
+
+    # Sum truncated cdf over each part of E
+    denom <- do.call(sum, lapply(1:nrow(E), function(v) {
+      TF_interval(E[v,1], E[v,2], df1, df2)
+    }))
+
+    # Sum truncated cdf from observed value to max of
+    # truncation region
+    numer <- do.call(sum, lapply(1:nrow(E), function(v) {
+      lower <- E[v,1]
+      upper <- E[v,2]
+      if (upper > TF) {
+        # Observed value is left of this interval's right endpoint
+        if (lower < TF) {
+          # Observed value is in this interval
+          return(TF_interval(TF, upper, df1, df2))
+        } else {
+          # Observed value is not in this interval
+          return(TF_interval(lower, upper, df1, df2))
+        }
+      } else {
+        # Observed value is right of this entire interval
+        return(0)
+      }
+    }))
+
+    # Survival function
+    value <- numer/denom
+    # Force p-value to lie in the [0,1] interval
+    # in case of numerical issues
+    value <- max(0, min(1, value))
+    value
+}
+
+TF_interval <- function(lower, upper, df1, df2) {
+  a <- lower
+  b <- upper
+  if (b == Inf) {
+      integral <- pf(a, df1, df2, lower.tail = FALSE)
+  } else {
+      integral <- pf(b, df1, df2) - pf(a, df1, df2)
+  }
+  if ((integral < .Machine$double.eps) && (b < Inf)) {
+      integral <- num_int_F(a, b, df1, df2)
+  }
+  return(integral)
+}
+
+num_int_F <- function(a, b, df1, df2, nsamp = 10000) {
+  grid <- seq(from=a, to=b, length.out=nsamp)
+  integrand <- df(grid, df1, df2)
+  return((b-a)*mean(integrand))
+}
 
 #' Center and scale design matrix by groups
 #'
@@ -697,13 +751,21 @@ predict.groupfs <- function(object, newx, ...) {
 print.groupfsInf <- function(x, ...) {
     cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n", x$sigma))
     action <- x$vars
+    isTC <- "TC" %in% names(x)
+    if (isTC) {
+        Tstat <- x$TC
+    } else {
+        Tstat <- x$TF
+    }
     vnames <- attr(x, "varnames")
     if (length(vnames) > 0) action <- vnames[action]
-    tab = data.frame(Group = action, Pvalue = round(x$pv, 3), Tchi = round(x$TC, 3),
+    tab = data.frame(Group = action, Pvalue = round(x$pv, 3),
+        TC = round(Tstat, 3),
         df = x$df, Size = round(unlist(lapply(lapply(x$support, size), sum)), 3),
         Ints = unlist(lapply(x$support, nrow)), Min =round(unlist(lapply(x$support, min)), 3),
         Max = round(unlist(lapply(x$support, max)), 3))
     rownames(tab) = 1:length(x$vars)
+    if (!isTC) names(tab)[3] <- "TF"
     print(tab)
     cat("\nInts is the number of intervals in the truncated chi selection region and Size is the sum of their lengths. Min and Max are the lowest and highest endpoints of the truncation region. No confidence intervals are reported by groupfsInf.\n")
     invisible()
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 41f2704..d0660a1 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -41,6 +41,7 @@ truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
               coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etas, etas, Zs, Zs)
               quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol)
           } else {
+       #       Q <- peng * (diag(rep(1,n)) - Ug %*% t(Ug)) - penh * (diag(rep(1,n)) - Uh %*% t(Uh))
               coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zs, Zs, Vds, Vds, V2s, V2s)
               TF_roots(R, C, coeffs)
           }
@@ -134,6 +135,16 @@ quadratic_roots <- function(A, B, C, tol) {
 }
 
 
+# Helper functions for TF roots
+roots_to_checkpoints <- function(roots) {
+    checkpoints <- unique(sort(c(0, roots)))
+    return(c(0, (checkpoints + c(checkpoints[-1], 200 + checkpoints[length(checkpoints)]))/2))
+}
+roots_to_partition <- function(roots) {
+    checkpoints <- unique(sort(c(0, roots)))
+    return(list(endpoints = c(checkpoints, Inf), midpoints = (checkpoints + c(checkpoints[-1], 200 + checkpoints[length(checkpoints)]))/2))
+}
+
 # Efficiently compute coefficients of one-dimensional TF slice function
 TF_coefficients <- function(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h) {
 
@@ -148,17 +159,12 @@ TF_coefficients <- function(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h) {
     V2Zh <- sum(V2h*Zh) #check
     V2Zg <- sum(V2g*Zg) #check
 
-    x0 <- peng * (sum(Zg^2) - sum(UgZ^2)) - penh * (sum(Zh^2) - sum(UhZ^2)) #check
-
-    x1 <- 2*R*(peng * (VdZg - sum(UgZ*UgVd)) - penh * (VdZh - sum(UhZ*UhVd))) #check
-
-    x2 <- 2*R*(peng * (V2Zg - sum(UgZ*UgV2)) - penh * (V2Zh - sum(UhZ*UhV2))) #check
-
-    x12 <- 2*R^2*(peng * (sum(Vdg*V2g) - sum(UgVd*UgV2)) - penh * (sum(Vdh*V2h) - sum(UhVd*UhV2))) #fixed R -> R^2
-
-    x11 <- R^2*(peng * (sum(Vdg^2) - sum(UgVd^2)) - penh * (sum(Vdh^2) - sum(UhVd^2))) #check
-    x22 <- R^2*(peng * (sum(V2g^2) - sum(UgV2^2)) - penh * (sum(V2h^2) - sum(UhV2^2))) #check
-#    if (x22 == 0) print(c(sum(UgV2^2), sum(UhV2^2)))
+    x0 <- penh * (sum(Zh^2) - sum(UhZ^2)) - peng * (sum(Zg^2) - sum(UgZ^2))
+    x1 <- 2*R*(penh * (VdZh - sum(UhZ*UhVd)) - peng * (VdZg - sum(UgZ*UgVd)))
+    x2 <- 2*R*(penh * (V2Zh - sum(UhZ*UhV2)) - peng * (V2Zg - sum(UgZ*UgV2)))
+    x12 <- 2*R^2*(penh * (sum(Vdh*V2h) - sum(UhVd*UhV2)) - peng * (sum(Vdg*V2g) - sum(UgVd*UgV2)))
+    x11 <- R^2*(penh * (sum(Vdh^2) - sum(UhVd^2)) - peng * (sum(Vdg^2) - sum(UgVd^2))) #check
+    x22 <- R^2*(penh * (sum(V2h^2) - sum(UhV2^2)) - peng * (sum(V2g^2) - sum(UgV2^2)))
 
     return(list(x11=x11, x22=x22, x12=x12, x1=x1, x2=x2, x0=x0))
 }
@@ -174,32 +180,33 @@ TF_roots <- function(R, C, coeffs, tol = 1e-14, tol2 = 1e-8) {
     x2 <- coeffs$x2
     x0 <- coeffs$x0
 
-    # Handle some special cases
-    if ((x11 == 0) && (max(abs(c(x22, x12, x1, x2))) < tol2)) {
-#        print("Special case 1")
-        return(Intervals(c(-Inf, 0)))
-    }
+##     # Handle some special cases
+##     if ((x11 == 0) && (max(abs(c(x22, x12, x1, x2))) < tol2)) {
+## #        print("Special case 1")
+##         return(Intervals(c(-Inf, 0)))
+##     }
 
-    if ((x22 == 0) && (max(abs(c(x2, x12))) < tol2)) {
-        x2 <- 0
-        x12 <- 0
-    }
+##     if ((x22 == 0) && (max(abs(c(x2, x12))) < tol2)) {
+##         x2 <- 0
+##         x12 <- 0
+##     }
 
-    g1 <- function(t) R*sqrt(C*t/(1+C*t))
-    g2 <- function(t) R/sqrt(1+C*t)
+    g1 <- function(t) sqrt(C*t/(1+C*t))
+    g2 <- function(t) 1/sqrt(1+C*t)
     I <- function(t) x11*g1(t)^2 + x12*g1(t)*g2(t) + x22*g2(t)^2 + x1*g1(t) + x2*g2(t) + x0
 
-    z4 <- R*complex(real = -x11 + x22, imaginary = -x12)/2
-    z3 <- complex(real = x2, imaginary = -x1)
-    z2 <- complex(real = R*x11+R*x22+2*x0/R)
+    z4 <- complex(real = -x11 + x22, imaginary = -x12)/4
+    z3 <- complex(real = x2, imaginary = -x1)/2
+    z2 <- complex(real = x11/2+x22/2+x0)
     z1 <- Conj(z3)
     z0 <- Conj(z4)
-    zcoefs <- R*c(z0, z1, z2, z3, z4)/2
+
+    zcoefs <- c(z0, z1, z2, z3, z4)
     croots <- polyroot(zcoefs)
     thetas <- Arg(croots)
     modinds <- Mod(croots) <= 1 + tol & Mod(croots) >= 1 - tol
     angleinds <- thetas >=0 & thetas <= pi/2
-    roots <- unique(thetas[modinds * angleinds])
+    roots <- unique(thetas[which(modinds & angleinds)])
     troots <- tan(roots)^2/C
 
     if (length(troots) == 0) {
@@ -241,12 +248,3 @@ TF_roots <- function(R, C, coeffs, tol = 1e-14, tol2 = 1e-8) {
     return(Intervals(c(-Inf,0)))
 }
 
-# Helper functions for TF roots
-roots_to_checkpoints <- function(roots) {
-    checkpoints <- unique(sort(c(0, roots)))
-    return(c(0, (checkpoints + c(checkpoints[-1], 200 + checkpoints[length(checkpoints)]))/2))
-}
-roots_to_partition <- function(roots) {
-    checkpoints <- unique(sort(c(0, roots)))
-    return(list(endpoints = c(checkpoints, Inf), midpoints = (checkpoints + c(checkpoints[-1], 200 + checkpoints[length(checkpoints)]))/2))
-}

From ee08db5a77bd040904a79edb5a104f5decc80119 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Sat, 7 Nov 2015 16:07:51 -0800
Subject: [PATCH 041/493] Still getting empty support for F, not sure why

---
 selectiveInference/R/funs.groupfs.R   | 37 +++++++++++++++------------
 selectiveInference/R/funs.quadratic.R |  2 +-
 tests/test.groupfs.R                  |  8 +++---
 3 files changed, 25 insertions(+), 22 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 6432d1e..d2c539c 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -277,12 +277,12 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
   } else {
       if (is.null(obj$sigma)) {
           type <- "TF"
-          if (n >= 2*p) {
-              sigma <- sqrt(sum(lsfit(obj$x, obj$y, intercept = obj$intercept)$res^2)/(n-p-obj$intercept))
-          } else {
-              sigma = sqrt(obj$log$RSS[length(obj$log$RSS)]/(n-Ep-obj$intercept))
-              warning(paste(sprintf("p > n/2, and sigmahat = %0.3f used as an estimate of sigma;",sigma), "you may want to use the estimateSigma function"))
-          }
+          ## if (n >= 2*p) {
+          ##     sigma <- sqrt(sum(lsfit(obj$x, obj$y, intercept = obj$intercept)$res^2)/(n-p-obj$intercept))
+          ## } else {
+          ##     sigma = sqrt(obj$log$RSS[length(obj$log$RSS)]/(n-Ep-obj$intercept))
+          ##     warning(paste(sprintf("p > n/2, and sigmahat = %0.3f used as an estimate of sigma;",sigma), "you may want to use the estimateSigma function"))
+          ## }
       } else {
           type <- "TC"
           sigma <- obj$sigma
@@ -294,7 +294,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
     i <- obj$action[j]
     if (verbose) cat(paste0("Step ", j, "/", attr(obj, "maxsteps"), ": computing P-value for group ", i, "\n"))
 
-    if (!is.null(obj$sigma)) {
+    if (type == "TC") {
         # Form projection onto active set minus i
         # and project x_i orthogonally
         x_i <- obj$x[,which(obj$index == i), drop = FALSE]
@@ -332,12 +332,13 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
         C <- df1/df2
         R1 <- obj$y - Z
         R2 <- obj$y - Pfull %*% t(Pfull) %*% obj$y
-        R <- sqrt(sum(R1^2))
+        R1sq <- sum(R1^2)
         R2sq <- sum(R2^2)
+        R <- sqrt(R1sq)
         delta <- R1-R2
-        Vdelta <- delta/sqrt(sum(delta^2))
-        V2 <- R2/sqrt(sum(R2^2))
-        TF <- (R^2-R2sq)/(C*R2sq)
+        Vdelta <- delta/sqrt(delta^2)
+        V2 <- R2/sqrt(R2sq)
+        TF <- (R1sq-R2sq)/(C*R2sq)
         Tstats[j] <- TF
         dfs[j] <- df1
 
@@ -479,9 +480,10 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
   }
 
   names(pvals) <- obj$action
-  out <- list(vars = obj$action, pv=pvals, sigma=sigma)
+  out <- list(vars = obj$action, pv=pvals)
   if (type == "TC") {
       out$TC <- Tstats
+      out$sigma <- sigma
   } else {
       out$TF <- Tstats
   }
@@ -749,14 +751,15 @@ predict.groupfs <- function(object, newx, ...) {
 }
 
 print.groupfsInf <- function(x, ...) {
-    cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n", x$sigma))
-    action <- x$vars
-    isTC <- "TC" %in% names(x)
-    if (isTC) {
+    if (!is.null(x$sigma)) {
+        isTF <- FALSE
         Tstat <- x$TC
+        cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n", x$sigma))
     } else {
+        isTF <- TRUE
         Tstat <- x$TF
     }
+    action <- x$vars
     vnames <- attr(x, "varnames")
     if (length(vnames) > 0) action <- vnames[action]
     tab = data.frame(Group = action, Pvalue = round(x$pv, 3),
@@ -765,7 +768,7 @@ print.groupfsInf <- function(x, ...) {
         Ints = unlist(lapply(x$support, nrow)), Min =round(unlist(lapply(x$support, min)), 3),
         Max = round(unlist(lapply(x$support, max)), 3))
     rownames(tab) = 1:length(x$vars)
-    if (!isTC) names(tab)[3] <- "TF"
+    if (isTF) names(tab)[3] <- "TF"
     print(tab)
     cat("\nInts is the number of intervals in the truncated chi selection region and Size is the sum of their lengths. Min and Max are the lowest and highest endpoints of the truncation region. No confidence intervals are reported by groupfsInf.\n")
     invisible()
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index d0660a1..88cab99 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -41,7 +41,7 @@ truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
               coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etas, etas, Zs, Zs)
               quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol)
           } else {
-       #       Q <- peng * (diag(rep(1,n)) - Ug %*% t(Ug)) - penh * (diag(rep(1,n)) - Uh %*% t(Uh))
+              #Q <- peng * (diag(rep(1,n)) - Ug %*% t(Ug)) - penh * (diag(rep(1,n)) - Uh %*% t(Uh))
               coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zs, Zs, Vds, Vds, V2s, V2s)
               TF_roots(R, C, coeffs)
           }
diff --git a/tests/test.groupfs.R b/tests/test.groupfs.R
index d35800e..0895712 100644
--- a/tests/test.groupfs.R
+++ b/tests/test.groupfs.R
@@ -9,9 +9,9 @@ set.seed(1)
 n <- 40
 p <- 80
 index <- sort(rep(1:(p/2), 2))
-maxsteps <- 10
-sparsity <- 5
-snr <- 3
+maxsteps <- 8
+sparsity <- 4
+snr <- 2
 
 system.time({
 for (iter in 1:100) {
@@ -21,7 +21,7 @@ for (iter in 1:100) {
     beta[which(index %in% 1:sparsity)] <- snr
     y <- y + x %*% beta
     fit <- groupfs(x, y, index, maxsteps = maxsteps)
-    pvals <- groupfsInf(fit)
+    pvals <- groupfsInf(fit, verbose = T)
 }
 })
 

From 504baa1be25ad01773b3295c1b9d4797f2504bee Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Sun, 8 Nov 2015 19:32:16 -0800
Subject: [PATCH 042/493] Fixed another error, but still no dice on the F test

---
 selectiveInference/R/funs.quadratic.R | 84 +++++++++++++++++++--------
 1 file changed, 61 insertions(+), 23 deletions(-)

diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 88cab99..bffc0b8 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -2,27 +2,35 @@
 truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
 
   n <- nrow(obj$x)
-  Z = Zs = ydecomp$Z
+  Z <- ydecomp$Z
   if (type == "TC") {
-      eta = etas = ydecomp$eta
+      eta <- ydecomp$eta
   } else {
-      Vd = Vds = ydecomp$Vd
-      V2 = V2s = ydecomp$V2
-      C = ydecomp$C
-      R = ydecomp$R
+      Vd <- ydecomp$Vd
+      V2 <- ydecomp$V2
+      C <- ydecomp$C
+      R <- ydecomp$R
   }
   L <- lapply(1:length(obj$action), function(s) {
 
     Ug <- obj$maxprojs[[s]]
     peng <- obj$maxpens[[s]]
     if (s > 1) {
+        Zs <- obj$cumprojs[[s-1]] %*% Z # This was a fix!
         if (type == "TC") {
             etas <- obj$cumprojs[[s-1]] %*% eta
-            Zs <- obj$cumprojs[[s-1]] %*% Z
         } else {
             Vds <- obj$cumprojs[[s-1]] %*% Vd
             V2s <- obj$cumprojs[[s-1]] %*% V2
         }
+    } else {
+        Zs <- Z
+        if (type == "TC") {
+            etas <- eta
+        } else {
+            Vds <- Vd
+            V2s <- V2
+        }
     }
 
     num.projs <- length(obj$projections[[s]])
@@ -41,9 +49,27 @@ truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
               coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etas, etas, Zs, Zs)
               quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol)
           } else {
-              #Q <- peng * (diag(rep(1,n)) - Ug %*% t(Ug)) - penh * (diag(rep(1,n)) - Uh %*% t(Uh))
+              
+              # Debugging
+              ## Q <- peng * (diag(rep(1,n)) - Ug %*% t(Ug)) - penh * (diag(rep(1,n)) - Uh %*% t(Uh))
+              ## g1 <- function(t) sqrt(C*t/(1+C*t))
+              ## g2 <- function(t) 1/sqrt(1+C*t)
+              ## Y <- function(t) {
+              ##     Zs + R * (Vds*g1(t) + V2s*g2(t))
+              ## }
+              
               coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zs, Zs, Vds, Vds, V2s, V2s)
-              TF_roots(R, C, coeffs)
+              roots <- TF_roots(R, C, coeffs)
+
+              if (is.null(roots)) print(c(s,l))
+              ## print(do.call(rbind, lapply(roots, function(r) {
+              ##     c(r,
+              ##       t(Y(r-0.000001)) %*% Q %*% Y(r-0.000001),
+              ##       t(Y(r)) %*% Q %*% Y(r),
+              ##       t(Y(r+0.000001)) %*% Q %*% Y(r+0.000001))
+              ## })))
+              
+              return(roots)
           }
       })
     }
@@ -148,22 +174,22 @@ roots_to_partition <- function(roots) {
 # Efficiently compute coefficients of one-dimensional TF slice function
 TF_coefficients <- function(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h) {
 
-    UhZ <- t(Uh) %*% Zh #check
-    UgZ <- t(Ug) %*% Zg #check
-    UhVd <- t(Uh) %*% Vdh #check
-    UgVd <- t(Ug) %*% Vdg #check
-    UhV2 <- t(Uh) %*% V2h #check
-    UgV2 <- t(Ug) %*% V2g #check
-    VdZh <- sum(Vdh*Zh) #check
-    VdZg <- sum(Vdg*Zg) #check
-    V2Zh <- sum(V2h*Zh) #check
-    V2Zg <- sum(V2g*Zg) #check
+    UhZ <- t(Uh) %*% Zh 
+    UgZ <- t(Ug) %*% Zg 
+    UhVd <- t(Uh) %*% Vdh 
+    UgVd <- t(Ug) %*% Vdg 
+    UhV2 <- t(Uh) %*% V2h 
+    UgV2 <- t(Ug) %*% V2g 
+    VdZh <- sum(Vdh*Zh) 
+    VdZg <- sum(Vdg*Zg) 
+    V2Zh <- sum(V2h*Zh) 
+    V2Zg <- sum(V2g*Zg) 
 
     x0 <- penh * (sum(Zh^2) - sum(UhZ^2)) - peng * (sum(Zg^2) - sum(UgZ^2))
     x1 <- 2*R*(penh * (VdZh - sum(UhZ*UhVd)) - peng * (VdZg - sum(UgZ*UgVd)))
     x2 <- 2*R*(penh * (V2Zh - sum(UhZ*UhV2)) - peng * (V2Zg - sum(UgZ*UgV2)))
     x12 <- 2*R^2*(penh * (sum(Vdh*V2h) - sum(UhVd*UhV2)) - peng * (sum(Vdg*V2g) - sum(UgVd*UgV2)))
-    x11 <- R^2*(penh * (sum(Vdh^2) - sum(UhVd^2)) - peng * (sum(Vdg^2) - sum(UgVd^2))) #check
+    x11 <- R^2*(penh * (sum(Vdh^2) - sum(UhVd^2)) - peng * (sum(Vdg^2) - sum(UgVd^2))) 
     x22 <- R^2*(penh * (sum(V2h^2) - sum(UhV2^2)) - peng * (sum(V2g^2) - sum(UgV2^2)))
 
     return(list(x11=x11, x22=x22, x12=x12, x1=x1, x2=x2, x0=x0))
@@ -210,7 +236,15 @@ TF_roots <- function(R, C, coeffs, tol = 1e-14, tol2 = 1e-8) {
     troots <- tan(roots)^2/C
 
     if (length(troots) == 0) {
-        #return(list(intervals = Intervals(c(0,Inf)), I=I))
+        # Something bad happens here
+        # sometimes there is a root and it's not caught
+        # by polyroot or something
+        # do an additional sign change check?
+        #print(c(I(0), I(100), I(10000), unlist(coeffs)), digits=1)        
+        if (I(0) < 0) {
+            return(NULL)
+            return(Intervals(c(0,Inf)))
+        }
         return(Intervals(c(-Inf,0)))
     }
 
@@ -240,11 +274,15 @@ TF_roots <- function(R, C, coeffs, tol = 1e-14, tol2 = 1e-8) {
             }
         }
 
-        #return(list(intervals = Intervals(intervals[-1,]), I=I))
         return(Intervals(intervals[-1,]))
     }
 
-    #return(list(intervals = Intervals(c(0,Inf)), I=I))
+    # Something bad happening, see above
+    #print(c(I(0), I(100), I(10000), unlist(coeffs)), digits=1)
+    if (I(0) < 0) {
+        return(NULL)
+        return(Intervals(c(0,Inf)))
+    }
     return(Intervals(c(-Inf,0)))
 }
 

From 3410957e65bd1c92427a2aefa2a1aa92edd2b006 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Sun, 8 Nov 2015 20:23:35 -0800
Subject: [PATCH 043/493] Found another error, still not done

---
 selectiveInference/R/funs.groupfs.R   |  2 +-
 selectiveInference/R/funs.quadratic.R | 18 +++++++++---------
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index d2c539c..4fd4642 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -336,7 +336,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
         R2sq <- sum(R2^2)
         R <- sqrt(R1sq)
         delta <- R1-R2
-        Vdelta <- delta/sqrt(delta^2)
+        Vdelta <- delta/sqrt(sum(delta^2))
         V2 <- R2/sqrt(R2sq)
         TF <- (R1sq-R2sq)/(C*R2sq)
         Tstats[j] <- TF
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index bffc0b8..401a652 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -50,18 +50,18 @@ truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
               quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol)
           } else {
               
-              # Debugging
+             # Debugging
               ## Q <- peng * (diag(rep(1,n)) - Ug %*% t(Ug)) - penh * (diag(rep(1,n)) - Uh %*% t(Uh))
-              ## g1 <- function(t) sqrt(C*t/(1+C*t))
-              ## g2 <- function(t) 1/sqrt(1+C*t)
-              ## Y <- function(t) {
-              ##     Zs + R * (Vds*g1(t) + V2s*g2(t))
-              ## }
+              ##  g1 <- function(t) sqrt(C*t/(1+C*t))
+              ##  g2 <- function(t) 1/sqrt(1+C*t)
+              ##  Y <- function(t) {
+              ##      Zs + R * (Vds*g1(t) + V2s*g2(t))
+              ##  }
               
               coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zs, Zs, Vds, Vds, V2s, V2s)
               roots <- TF_roots(R, C, coeffs)
 
-              if (is.null(roots)) print(c(s,l))
+              ## if (is.null(roots)) print(c(s,l))
               ## print(do.call(rbind, lapply(roots, function(r) {
               ##     c(r,
               ##       t(Y(r-0.000001)) %*% Q %*% Y(r-0.000001),
@@ -242,7 +242,7 @@ TF_roots <- function(R, C, coeffs, tol = 1e-14, tol2 = 1e-8) {
         # do an additional sign change check?
         #print(c(I(0), I(100), I(10000), unlist(coeffs)), digits=1)        
         if (I(0) < 0) {
-            return(NULL)
+#            return(NULL)
             return(Intervals(c(0,Inf)))
         }
         return(Intervals(c(-Inf,0)))
@@ -280,7 +280,7 @@ TF_roots <- function(R, C, coeffs, tol = 1e-14, tol2 = 1e-8) {
     # Something bad happening, see above
     #print(c(I(0), I(100), I(10000), unlist(coeffs)), digits=1)
     if (I(0) < 0) {
-        return(NULL)
+#        return(NULL)
         return(Intervals(c(0,Inf)))
     }
     return(Intervals(c(-Inf,0)))

From 0ec3b9b841456858719b8ecc336796db3e045961 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Sun, 8 Nov 2015 20:48:34 -0800
Subject: [PATCH 044/493] polyroot precision was too low for modinds tol,
 increased tol and found more roots

---
 selectiveInference/R/funs.quadratic.R | 35 +++++++++++----------------
 1 file changed, 14 insertions(+), 21 deletions(-)

diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 401a652..77f4468 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -61,7 +61,7 @@ truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
               coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zs, Zs, Vds, Vds, V2s, V2s)
               roots <- TF_roots(R, C, coeffs)
 
-              ## if (is.null(roots)) print(c(s,l))
+              if (is.null(roots)) print(c(s,l))
               ## print(do.call(rbind, lapply(roots, function(r) {
               ##     c(r,
               ##       t(Y(r-0.000001)) %*% Q %*% Y(r-0.000001),
@@ -230,26 +230,23 @@ TF_roots <- function(R, C, coeffs, tol = 1e-14, tol2 = 1e-8) {
     zcoefs <- c(z0, z1, z2, z3, z4)
     croots <- polyroot(zcoefs)
     thetas <- Arg(croots)
-    modinds <- Mod(croots) <= 1 + tol & Mod(croots) >= 1 - tol
+    # Can't specify polyroot precision :(
+    modinds <- Mod(croots) <= 1 + tol2 & Mod(croots) >= 1 - tol2
     angleinds <- thetas >=0 & thetas <= pi/2
     roots <- unique(thetas[which(modinds & angleinds)])
     troots <- tan(roots)^2/C
 
     if (length(troots) == 0) {
-        # Something bad happens here
-        # sometimes there is a root and it's not caught
-        # by polyroot or something
-        # do an additional sign change check?
-        #print(c(I(0), I(100), I(10000), unlist(coeffs)), digits=1)        
-        if (I(0) < 0) {
-#            return(NULL)
-            return(Intervals(c(0,Inf)))
-        }
-        return(Intervals(c(-Inf,0)))
+        # Polyroot didn't catch any roots
+        # ad-hoc check:
+        checkpoints <- c(0, tol, tol2,
+                         seq(from = sqrt(tol2), to = 1, length.out = 50),
+                         seq(from = 1.2, to=50, length.out = 20),
+                         100, 1000, 10000)
+    } else {
+        checkpoints <- roots_to_checkpoints(troots)
     }
-
-    checkpoints <- roots_to_checkpoints(troots)
-
+    
     signs <- sign(I(checkpoints))
     diffs <- c(0, diff(signs))
     changeinds <- which(diffs != 0)
@@ -277,12 +274,8 @@ TF_roots <- function(R, C, coeffs, tol = 1e-14, tol2 = 1e-8) {
         return(Intervals(intervals[-1,]))
     }
 
-    # Something bad happening, see above
-    #print(c(I(0), I(100), I(10000), unlist(coeffs)), digits=1)
-    if (I(0) < 0) {
-#        return(NULL)
-        return(Intervals(c(0,Inf)))
-    }
+    # Apparently no roots, always positive
+    if (I(0) < 0) stop("Infeasible constraint!")
     return(Intervals(c(-Inf,0)))
 }
 

From b75bb54466b1b719a383de09851972d56b62c229 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Sun, 8 Nov 2015 21:10:14 -0800
Subject: [PATCH 045/493] Fixed typo in aicstop code for F test

---
 forLater/josh/sim.aicstop.R           | 2 +-
 selectiveInference/R/funs.groupfs.R   | 7 ++++---
 selectiveInference/R/funs.quadratic.R | 4 ++--
 3 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/forLater/josh/sim.aicstop.R b/forLater/josh/sim.aicstop.R
index 466f9b4..2c3e517 100644
--- a/forLater/josh/sim.aicstop.R
+++ b/forLater/josh/sim.aicstop.R
@@ -25,7 +25,7 @@ instance <- function(n, p, G, sparsity, snr, rho, maxsteps, aicstop) {
     index <- simd$index
 
     fit <- groupfs(x, y, index, maxsteps, k = log(n), aicstop = aicstop)
-    pvals <- groupfsInf(fit, sigma = 1, verbose=T)
+    pvals <- groupfsInf(fit, verbose=T)
     return(list(variable = fit$action, pvals = pvals$pv, stopped = attr(fit, "stopped")))
 }
 
diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 4fd4642..d12af1e 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -271,6 +271,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
   supports <- list()
 
   if (!is.null(sigma)) {
+      type <- "TC"
       if (!is.null(obj$sigma)) {
           cat(paste("Using specified value", sigma, "for sigma in place of the value", obj$sigma, "used by groupfs()\n"))
       }
@@ -347,7 +348,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
     }
 
     intervallist <- truncationRegion(obj, ydecomp, type)
-
+    
     # Additional constraints from cross-validation?
     if (!is.null(obj$cvobj)) {
         intervallist <- c(intervallist, do.call(c,
@@ -442,8 +443,8 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
                                       penh <- prod(unlist(penlist[s:sp]))
                                       Vdg <- vdlist[[s]]
                                       Vdh <- vdlist[[sp]]
-                                      V2g <- vdlist[[s]]
-                                      V2h <- vdlist[[sp]]
+                                      V2g <- v2list[[s]]
+                                      V2h <- v2list[[sp]]
                                       Zg <- zlist[[s]]
                                       Zh <- zlist[[sp]]
                                       coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h)
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 77f4468..8d7a19f 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -197,7 +197,7 @@ TF_coefficients <- function(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h) {
 
 # Numerically solve for roots of TF slice using
 # hybrid polyroot/uniroot approach
-TF_roots <- function(R, C, coeffs, tol = 1e-14, tol2 = 1e-8) {
+TF_roots <- function(R, C, coeffs, tol = 1e-8, tol2 = 1e-6) {
 
     x11 <- coeffs$x11
     x22 <- coeffs$x22
@@ -254,7 +254,7 @@ TF_roots <- function(R, C, coeffs, tol = 1e-14, tol2 = 1e-8) {
     if (length(changeinds) > 0) {
 
         roots <- unlist(lapply(changeinds, function(ind) {
-            uniroot(I, lower = checkpoints[ind-1], upper = checkpoints[ind], tol = tol2)$root
+            uniroot(I, lower = checkpoints[ind-1], upper = checkpoints[ind], tol = tol)$root
         }))
 
         partition <- roots_to_partition(roots)

From 17b8b6530a01779773e86ee59f51aacdd4ff1f67 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Mon, 9 Nov 2015 20:43:23 -0800
Subject: [PATCH 046/493] Last commit before a big change in AICstop

---
 forLater/josh/sim.aicstop.R           |  6 +--
 selectiveInference/R/funs.groupfs.R   | 74 ++++++++++++++-------------
 selectiveInference/R/funs.quadratic.R | 34 ++++++------
 3 files changed, 58 insertions(+), 56 deletions(-)

diff --git a/forLater/josh/sim.aicstop.R b/forLater/josh/sim.aicstop.R
index 2c3e517..3d07ce4 100644
--- a/forLater/josh/sim.aicstop.R
+++ b/forLater/josh/sim.aicstop.R
@@ -6,7 +6,7 @@ source("../../selectiveInference/R/funs.quadratic.R")
 source("../../selectiveInference/R/funs.common.R")
 
 set.seed(1)
-niters <- 100
+niters <- 400
 n <- 100
 p <- 100
 G <- 50
@@ -14,7 +14,7 @@ maxsteps <- 15
 sparsity <- 5
 snr <- 1
 sigma <- 1
-rho <- .1
+rho <- 0
 aicstop <- 1
 
 instance <- function(n, p, G, sparsity, snr, rho, maxsteps, aicstop) {
@@ -42,6 +42,6 @@ save(pvals, vars, stopped, file = paste0(
                       "_p", p,
                       "_sparsity", sparsity,
                       "_snr", snr,
-                      "_known.RData"))
+                      "_F_rho0.RData"))
 
 print(time)
diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index d12af1e..2a5471e 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -67,7 +67,7 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
 
   modelrank <- as.numeric(intercept)
   if (is.null(sigma)) {
-      aic.begin <- aic.last <- n*(log(2*pi) + log(mean(y.update^2)) + 1 + k * (is.null(sigma) + intercept))
+      aic.begin <- aic.last <- n*(log(2*pi) + log(mean(y.update^2)) + 1 + k) * (is.null(sigma) + intercept) # This was a fix
   } else {
       aic.begin <- aic.last <- sum(y.update^2)/sigma^2 - n + k * intercept
   }
@@ -145,7 +145,6 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
             terms[(step+1):maxsteps] <- NULL
             maxsteps <- step
             stopped <- TRUE
-            # add additional projections
             break
         }
     }
@@ -293,7 +292,12 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
   # Compute p-value for each active group
   for (j in 1:maxsteps) {
     i <- obj$action[j]
-    if (verbose) cat(paste0("Step ", j, "/", attr(obj, "maxsteps"), ": computing P-value for group ", i, "\n"))
+    if (verbose) {
+        string <- paste0("Step ", j, "/", attr(obj, "maxsteps"), ": computing P-value for group ", i)
+        if (!is.null(obj$cvobj)) string <- paste0(string, ", including constraints from cross-validation")
+        if (attr(obj, "stopped")) string <- paste0(string, ", including constraints from AICstop")
+        cat(paste(string, "\n"))
+    }
 
     if (type == "TC") {
         # Form projection onto active set minus i
@@ -348,7 +352,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
     }
 
     intervallist <- truncationRegion(obj, ydecomp, type)
-    
+
     # Additional constraints from cross-validation?
     if (!is.null(obj$cvobj)) {
         intervallist <- c(intervallist, do.call(c,
@@ -422,39 +426,39 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
         }
 
         intervallist <- c(intervallist,
-                          do.call(c, lapply(1:aicstop, function(s) {
-                              lapply((s+1):(aicstop+1), function(sp) {
-                                  if (type == "TC") {
-                                      Ug <- ulist[[s]]
-                                      Uh <- ulist[[sp]]
-                                      peng <- penlist[[s]]
-                                      penh <- prod(unlist(penlist[s:sp]))
-                                      etag <- etalist[[s]]
-                                      etah <- etalist[[sp]]
-                                      Zg <- zlist[[s]]
-                                      Zh <- zlist[[sp]]
-                                      coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh)
-                                      quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol = 1e-15)
-                                  } else {
-
-                                      Ug <- ulist[[s]]
-                                      Uh <- ulist[[sp]]
-                                      peng <- penlist[[s]]
-                                      penh <- prod(unlist(penlist[s:sp]))
-                                      Vdg <- vdlist[[s]]
-                                      Vdh <- vdlist[[sp]]
-                                      V2g <- v2list[[s]]
-                                      V2h <- v2list[[sp]]
-                                      Zg <- zlist[[s]]
-                                      Zh <- zlist[[sp]]
-                                      coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h)
-                                      TF_roots(R, C, coeffs)
-                                  }
-                              })
-                          })))
+                          lapply(1:aicstop, function(s) {
+                              sp <- s+1
+                              if (type == "TC") {
+                                  Ug <- ulist[[s]]
+                                  Uh <- ulist[[sp]]
+                                  # Check this: known sigma has *additive* pen terms
+                                  peng <- penlist[[s]]
+                                  penh <- prod(unlist(penlist[s:sp]))
+                                  ####################################
+                                  etag <- etalist[[s]]
+                                  etah <- etalist[[sp]]
+                                  Zg <- zlist[[s]]
+                                  Zh <- zlist[[sp]]
+                                  coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh)
+                                  quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol = 1e-15)
+                              } else {
+                                  Ug <- ulist[[s]]
+                                  Uh <- ulist[[sp]]
+                                  peng <- 1 #penlist[[s]]
+                                  penh <- penlist[[sp]]
+                                  Vdg <- vdlist[[s]]
+                                  Vdh <- vdlist[[sp]]
+                                  V2g <- v2list[[s]]
+                                  V2h <- v2list[[sp]]
+                                  Zg <- zlist[[s]]
+                                  Zh <- zlist[[sp]]
+                                  coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h)
+                                  TF_roots(R, C, coeffs)
+                              }
+                          }))
     }
 
-        # Compute intersection:
+    # Compute intersection:
     region <- do.call(interval_union, intervallist)
     region <- interval_union(region, Intervals(c(-Inf,0)))
     E <- interval_complement(region, check_valid = FALSE)
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 8d7a19f..5b59fcb 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -16,7 +16,7 @@ truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
     Ug <- obj$maxprojs[[s]]
     peng <- obj$maxpens[[s]]
     if (s > 1) {
-        Zs <- obj$cumprojs[[s-1]] %*% Z # This was a fix!
+        Zs <- obj$cumprojs[[s-1]] %*% Z
         if (type == "TC") {
             etas <- obj$cumprojs[[s-1]] %*% eta
         } else {
@@ -49,7 +49,7 @@ truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
               coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etas, etas, Zs, Zs)
               quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol)
           } else {
-              
+
              # Debugging
               ## Q <- peng * (diag(rep(1,n)) - Ug %*% t(Ug)) - penh * (diag(rep(1,n)) - Uh %*% t(Uh))
               ##  g1 <- function(t) sqrt(C*t/(1+C*t))
@@ -57,7 +57,7 @@ truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
               ##  Y <- function(t) {
               ##      Zs + R * (Vds*g1(t) + V2s*g2(t))
               ##  }
-              
+
               coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zs, Zs, Vds, Vds, V2s, V2s)
               roots <- TF_roots(R, C, coeffs)
 
@@ -68,7 +68,7 @@ truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
               ##       t(Y(r)) %*% Q %*% Y(r),
               ##       t(Y(r+0.000001)) %*% Q %*% Y(r+0.000001))
               ## })))
-              
+
               return(roots)
           }
       })
@@ -103,7 +103,6 @@ quadratic_roots <- function(A, B, C, tol) {
     disc <- B^2 - 4*A*C
     b2a <- -B/(2*A)
 
-
     if (disc > tol) {
         # Real roots
         pm <- sqrt(disc)/(2*A)
@@ -160,7 +159,6 @@ quadratic_roots <- function(A, B, C, tol) {
     }
 }
 
-
 # Helper functions for TF roots
 roots_to_checkpoints <- function(roots) {
     checkpoints <- unique(sort(c(0, roots)))
@@ -174,22 +172,22 @@ roots_to_partition <- function(roots) {
 # Efficiently compute coefficients of one-dimensional TF slice function
 TF_coefficients <- function(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h) {
 
-    UhZ <- t(Uh) %*% Zh 
-    UgZ <- t(Ug) %*% Zg 
-    UhVd <- t(Uh) %*% Vdh 
-    UgVd <- t(Ug) %*% Vdg 
-    UhV2 <- t(Uh) %*% V2h 
-    UgV2 <- t(Ug) %*% V2g 
-    VdZh <- sum(Vdh*Zh) 
-    VdZg <- sum(Vdg*Zg) 
-    V2Zh <- sum(V2h*Zh) 
-    V2Zg <- sum(V2g*Zg) 
+    UhZ <- t(Uh) %*% Zh
+    UgZ <- t(Ug) %*% Zg
+    UhVd <- t(Uh) %*% Vdh
+    UgVd <- t(Ug) %*% Vdg
+    UhV2 <- t(Uh) %*% V2h
+    UgV2 <- t(Ug) %*% V2g
+    VdZh <- sum(Vdh*Zh)
+    VdZg <- sum(Vdg*Zg)
+    V2Zh <- sum(V2h*Zh)
+    V2Zg <- sum(V2g*Zg)
 
     x0 <- penh * (sum(Zh^2) - sum(UhZ^2)) - peng * (sum(Zg^2) - sum(UgZ^2))
     x1 <- 2*R*(penh * (VdZh - sum(UhZ*UhVd)) - peng * (VdZg - sum(UgZ*UgVd)))
     x2 <- 2*R*(penh * (V2Zh - sum(UhZ*UhV2)) - peng * (V2Zg - sum(UgZ*UgV2)))
     x12 <- 2*R^2*(penh * (sum(Vdh*V2h) - sum(UhVd*UhV2)) - peng * (sum(Vdg*V2g) - sum(UgVd*UgV2)))
-    x11 <- R^2*(penh * (sum(Vdh^2) - sum(UhVd^2)) - peng * (sum(Vdg^2) - sum(UgVd^2))) 
+    x11 <- R^2*(penh * (sum(Vdh^2) - sum(UhVd^2)) - peng * (sum(Vdg^2) - sum(UgVd^2)))
     x22 <- R^2*(penh * (sum(V2h^2) - sum(UhV2^2)) - peng * (sum(V2g^2) - sum(UgV2^2)))
 
     return(list(x11=x11, x22=x22, x12=x12, x1=x1, x2=x2, x0=x0))
@@ -246,7 +244,7 @@ TF_roots <- function(R, C, coeffs, tol = 1e-8, tol2 = 1e-6) {
     } else {
         checkpoints <- roots_to_checkpoints(troots)
     }
-    
+
     signs <- sign(I(checkpoints))
     diffs <- c(0, diff(signs))
     changeinds <- which(diffs != 0)

From 524720c861bce8aece3ace0483780e4d6236be5d Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Mon, 9 Nov 2015 21:24:49 -0800
Subject: [PATCH 047/493] Added constraints for all AIC comparisons

---
 forLater/josh/sim.aicstop.R         |   8 +-
 selectiveInference/R/funs.groupfs.R | 119 +++++++++++++++-------------
 2 files changed, 68 insertions(+), 59 deletions(-)

diff --git a/forLater/josh/sim.aicstop.R b/forLater/josh/sim.aicstop.R
index 3d07ce4..9f16945 100644
--- a/forLater/josh/sim.aicstop.R
+++ b/forLater/josh/sim.aicstop.R
@@ -6,15 +6,15 @@ source("../../selectiveInference/R/funs.quadratic.R")
 source("../../selectiveInference/R/funs.common.R")
 
 set.seed(1)
-niters <- 400
+niters <- 200
 n <- 100
 p <- 100
 G <- 50
 maxsteps <- 15
 sparsity <- 5
-snr <- 1
+snr <- 2
 sigma <- 1
-rho <- 0
+rho <- 0.1
 aicstop <- 1
 
 instance <- function(n, p, G, sparsity, snr, rho, maxsteps, aicstop) {
@@ -42,6 +42,6 @@ save(pvals, vars, stopped, file = paste0(
                       "_p", p,
                       "_sparsity", sparsity,
                       "_snr", snr,
-                      "_F_rho0.RData"))
+                      "_F_rhopt1.RData"))
 
 print(time)
diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 2a5471e..7cb8396 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -67,7 +67,7 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
 
   modelrank <- as.numeric(intercept)
   if (is.null(sigma)) {
-      aic.begin <- aic.last <- n*(log(2*pi) + log(mean(y.update^2)) + 1 + k) * (is.null(sigma) + intercept) # This was a fix
+      aic.begin <- aic.last <- n*(log(2*pi) + log(mean(y.update^2)) + 1 + k) * (is.null(sigma) + intercept) 
   } else {
       aic.begin <- aic.last <- sum(y.update^2)/sigma^2 - n + k * intercept
   }
@@ -289,6 +289,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
       }
   }
 
+
   # Compute p-value for each active group
   for (j in 1:maxsteps) {
     i <- obj$action[j]
@@ -389,73 +390,81 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
                           }))
     }
 
-    # Additional constraints from AIC stopping?
+    # Additional constraints from AIC stopping
     if (attr(obj, "stopped")) {
-
+        aicintervals <- vector("list", maxsteps-1)
         aicstop <- attr(obj, "aicstop")
-        ulist <- penlist <- zlist <- vector("list", aicstop+1)
-
+        AICs <- obj$AIC
+        
+        # No stopping at 0 steps?
+        ## if (is.null(sigma)) {
+        ##     aic.begin <- aic.last <- n*(log(2*pi) + log(mean(obj$y^2)) + 1 + k) * (1 + obj$intercept) 
+        ## } else {
+        ##     aic.begin <- aic.last <- sum(obj$y^2)/sigma^2 - n + k * obj$intercept
+        ## }
+        ## AICs <- c(aic.begin, obj$AIC)
+
+        ulist <- obj$maxprojs
+        penlist <- obj$maxpens
+        zlist <- vector("list", aicstop+1)
+        zlist[[1]] <- Z
         if (type == "TC") {
             etalist <- vector("list", aicstop+1)
+            etalist[[1]] <- eta
         } else {
             vdlist <- v2list <- vector("list", aicstop+1)
+            vdlist[[1]] <- Vdelta
+            v2list[[1]] <- V2
         }
-
-        for (s in seq(aicstop+1)) {
-            stepind <- maxsteps - (aicstop+1) + s
-            if (stepind > 1) {
-                cproj <- obj$cumprojs[[stepind-1]]
-                if (type == "TC") {
-                    etalist[[s]] <- cproj %*% eta
-                } else {
-                    v2list[[s]] <- cproj %*% V2
-                    vdlist[[s]] <- cproj %*% Vdelta
-                }
-                zlist[[s]] <- cproj %*% Z
+        for (step in 2:maxsteps) {
+            cproj <- obj$cumprojs[[step-1]]
+            zlist[[step]] <- cproj %*% Z            
+            if (type == "TC") {
+                etalist[[step]] <- cproj %*% eta
             } else {
-                if (type == "TC") {
-                    etalist[[s]] <- eta
-                } else {
-                    v2list[[s]] <- V2
-                    vdlist[[s]] <- Vdelta
-                }
-                zlist[[s]] <- Z
+                vdlist[[step]] <- cproj %*% Vdelta
+                v2list[[step]] <- cproj %*% V2
             }
-            ulist[[s]] <- obj$maxprojs[[stepind]]
-            penlist[[s]] <- obj$maxpens[[stepind]]
         }
 
-        intervallist <- c(intervallist,
-                          lapply(1:aicstop, function(s) {
-                              sp <- s+1
-                              if (type == "TC") {
-                                  Ug <- ulist[[s]]
-                                  Uh <- ulist[[sp]]
+        for (step in 1:(maxsteps-1)) {
+            # Compare AIC at s-1 to AIC at s
+            # sp indexes step with larger AIC
+            if (AICs[step] >= AICs[step+1]) {
+                sp <- step
+                s <- step+1
+            } else {
+                sp <- step+1
+                s <- step
+            }
+
+            if (type == "TC") {
+                Ug <- ulist[[s]]
+                Uh <- ulist[[sp]]
                                   # Check this: known sigma has *additive* pen terms
-                                  peng <- penlist[[s]]
-                                  penh <- prod(unlist(penlist[s:sp]))
+                peng <- 1
+                penh <- penlist[sp]
                                   ####################################
-                                  etag <- etalist[[s]]
-                                  etah <- etalist[[sp]]
-                                  Zg <- zlist[[s]]
-                                  Zh <- zlist[[sp]]
-                                  coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh)
-                                  quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol = 1e-15)
-                              } else {
-                                  Ug <- ulist[[s]]
-                                  Uh <- ulist[[sp]]
-                                  peng <- 1 #penlist[[s]]
-                                  penh <- penlist[[sp]]
-                                  Vdg <- vdlist[[s]]
-                                  Vdh <- vdlist[[sp]]
-                                  V2g <- v2list[[s]]
-                                  V2h <- v2list[[sp]]
-                                  Zg <- zlist[[s]]
-                                  Zh <- zlist[[sp]]
-                                  coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h)
-                                  TF_roots(R, C, coeffs)
-                              }
-                          }))
+                coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh)
+                intstep <- quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol = 1e-15)
+            } else {
+                Ug <- ulist[[s]]
+                Uh <- ulist[[sp]]
+                peng <- 1 #penlist[[s]]
+                penh <- penlist[[sp]]
+                Vdg <- vdlist[[s]]
+                Vdh <- vdlist[[sp]]
+                V2g <- v2list[[s]]
+                V2h <- v2list[[sp]]
+                Zg <- zlist[[s]]
+                Zh <- zlist[[sp]]
+                coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h)
+                intstep <- TF_roots(R, C, coeffs)
+            }
+
+            aicintervals[[step]] <- intstep
+        }
+        intervallist <- c(intervallist, aicintervals)
     }
 
     # Compute intersection:

From 4601bf85d7f88e4a16013e4174ca152dce1a09e6 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Tue, 10 Nov 2015 12:05:49 -0800
Subject: [PATCH 048/493] Scoping problem with sigma in AIC simulation

---
 forLater/josh/sim.aicstop.R         |  6 ++--
 selectiveInference/R/funs.groupfs.R | 56 ++++++++++++++---------------
 2 files changed, 30 insertions(+), 32 deletions(-)

diff --git a/forLater/josh/sim.aicstop.R b/forLater/josh/sim.aicstop.R
index 9f16945..67d7720 100644
--- a/forLater/josh/sim.aicstop.R
+++ b/forLater/josh/sim.aicstop.R
@@ -13,18 +13,16 @@ G <- 50
 maxsteps <- 15
 sparsity <- 5
 snr <- 2
-sigma <- 1
 rho <- 0.1
 aicstop <- 1
 
 instance <- function(n, p, G, sparsity, snr, rho, maxsteps, aicstop) {
-
-    simd <- randomGaussianFixedP(n, p, G, sparsity, snr, sigma, rho)
+    simd <- randomGaussianFixedP(n, p, G, sparsity, snr, sigma = 1, rho)
     x <- simd$x
     y <- simd$y
     index <- simd$index
 
-    fit <- groupfs(x, y, index, maxsteps, k = log(n), aicstop = aicstop)
+    fit <- groupfs(x, y, index, maxsteps, k = log(n), aicstop = aicstop, verbose=T)
     pvals <- groupfsInf(fit, verbose=T)
     return(list(variable = fit$action, pvals = pvals$pv, stopped = attr(fit, "stopped")))
 }
diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 7cb8396..1fecb69 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -67,9 +67,10 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
 
   modelrank <- as.numeric(intercept)
   if (is.null(sigma)) {
-      aic.begin <- aic.last <- n*(log(2*pi) + log(mean(y.update^2)) + 1 + k) * (is.null(sigma) + intercept) 
+      modelrank <- modelrank + 1
+      aic.begin <- aic.last <- n*(log(2*pi) + log(mean(y.update^2)) + 1) + k * modelrank # fixed... again
   } else {
-      aic.begin <- aic.last <- sum(y.update^2)/sigma^2 - n + k * intercept
+      aic.begin <- aic.last <- sum(y.update^2)/sigma^2 - n + k * modelrank
   }
   if (verbose) print(paste0("Start:  AIC=", round(aic.begin, 3)), quote = FALSE)
 
@@ -392,43 +393,42 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
 
     # Additional constraints from AIC stopping
     if (attr(obj, "stopped")) {
-        aicintervals <- vector("list", maxsteps-1)
+        aicintervals <- vector("list", maxsteps)
         aicstop <- attr(obj, "aicstop")
-        AICs <- obj$AIC
-        
-        # No stopping at 0 steps?
-        ## if (is.null(sigma)) {
-        ##     aic.begin <- aic.last <- n*(log(2*pi) + log(mean(obj$y^2)) + 1 + k) * (1 + obj$intercept) 
-        ## } else {
-        ##     aic.begin <- aic.last <- sum(obj$y^2)/sigma^2 - n + k * obj$intercept
-        ## }
-        ## AICs <- c(aic.begin, obj$AIC)
-
-        ulist <- obj$maxprojs
-        penlist <- obj$maxpens
-        zlist <- vector("list", aicstop+1)
-        zlist[[1]] <- Z
         if (type == "TC") {
-            etalist <- vector("list", aicstop+1)
-            etalist[[1]] <- eta
+            pen0 <- k * obj$intercept
+            aic.begin <- aic.last <- sum(obj$y^2)/sigma^2 - n + k * obj$intercept
         } else {
-            vdlist <- v2list <- vector("list", aicstop+1)
-            vdlist[[1]] <- Vdelta
-            v2list[[1]] <- V2
+            pen0 <- exp(k * (1+obj$intercept)/n)
+            aic.begin <- aic.last <- n*(log(2*pi) + log(mean(obj$y^2)) + 1) + k * (1 + obj$intercept)
+        }
+        AICs <- c(aic.begin, obj$AIC)
+
+        ulist <- c(list(matrix(0, n, 1)), obj$maxprojs)
+        penlist <- c(pen0, obj$maxpens)
+        zlist <- vector("list", maxsteps+1)
+        zlist[[1]] <- zlist[[2]] <- Z
+        if (type == "TC") {
+            etalist <- vector("list", maxsteps+1)
+            etalist[[1]] <- etalist[[2]] <- eta
+        } else {
+            vdlist <- v2list <- vector("list", maxsteps+1)
+            vdlist[[1]] <- vdlist[[2]] <- Vdelta
+            v2list[[1]] <- v2list[[2]] <- V2
         }
         for (step in 2:maxsteps) {
             cproj <- obj$cumprojs[[step-1]]
-            zlist[[step]] <- cproj %*% Z            
+            zlist[[step+1]] <- cproj %*% Z
             if (type == "TC") {
-                etalist[[step]] <- cproj %*% eta
+                etalist[[step+1]] <- cproj %*% eta
             } else {
-                vdlist[[step]] <- cproj %*% Vdelta
-                v2list[[step]] <- cproj %*% V2
+                vdlist[[step+1]] <- cproj %*% Vdelta
+                v2list[[step+1]] <- cproj %*% V2
             }
         }
 
-        for (step in 1:(maxsteps-1)) {
-            # Compare AIC at s-1 to AIC at s
+        for (step in 1:maxsteps) {
+            # Compare AIC at s+1 to AIC at s
             # sp indexes step with larger AIC
             if (AICs[step] >= AICs[step+1]) {
                 sp <- step

From 202acbcef75a3bd271113a4c0639d4376fc9fdd1 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Tue, 10 Nov 2015 15:47:52 -0800
Subject: [PATCH 049/493] Still debugging F with AICstop

---
 forLater/josh/funs.sims.R             |  2 +-
 forLater/josh/sim.aicstop.R           | 11 +++++------
 selectiveInference/R/funs.groupfs.R   | 18 +++++++++++-------
 selectiveInference/R/funs.quadratic.R |  2 ++
 4 files changed, 19 insertions(+), 14 deletions(-)

diff --git a/forLater/josh/funs.sims.R b/forLater/josh/funs.sims.R
index f07fa55..53af73f 100644
--- a/forLater/josh/funs.sims.R
+++ b/forLater/josh/funs.sims.R
@@ -38,7 +38,7 @@ randomGaussianFixedP <- function(n, p, G = p, sparsity = 0, snr = 0, sigma = 1,
     if (sparsity > 0 && snr > 0) {
         for (j in 1:sparsity) {
             inds <- which(index == j)
-            beta[inds] <- sqrt(2*log(G)) * sample(c(-1,1), length(inds), replace=T) * snr/sqrt(n*length(inds))
+            beta[inds] <- snr * sqrt(2*log(G)/(n*length(inds))) * sample(c(-1,1), length(inds), replace=T)
         }
     }
     y <- x %*% beta + sigma * rnorm(n)
diff --git a/forLater/josh/sim.aicstop.R b/forLater/josh/sim.aicstop.R
index 67d7720..0fd2d26 100644
--- a/forLater/josh/sim.aicstop.R
+++ b/forLater/josh/sim.aicstop.R
@@ -10,10 +10,10 @@ niters <- 200
 n <- 100
 p <- 100
 G <- 50
-maxsteps <- 15
-sparsity <- 5
+maxsteps <- 10
+sparsity <- 3
 snr <- 2
-rho <- 0.1
+rho <- 0
 aicstop <- 1
 
 instance <- function(n, p, G, sparsity, snr, rho, maxsteps, aicstop) {
@@ -21,8 +21,7 @@ instance <- function(n, p, G, sparsity, snr, rho, maxsteps, aicstop) {
     x <- simd$x
     y <- simd$y
     index <- simd$index
-
-    fit <- groupfs(x, y, index, maxsteps, k = log(n), aicstop = aicstop, verbose=T)
+    fit <- groupfs(x, y, index, maxsteps, k = log(n), aicstop = aicstop)
     pvals <- groupfsInf(fit, verbose=T)
     return(list(variable = fit$action, pvals = pvals$pv, stopped = attr(fit, "stopped")))
 }
@@ -40,6 +39,6 @@ save(pvals, vars, stopped, file = paste0(
                       "_p", p,
                       "_sparsity", sparsity,
                       "_snr", snr,
-                      "_F_rhopt1.RData"))
+                      "_F_rho0.RData"))
 
 print(time)
diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 1fecb69..521ed57 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -278,6 +278,10 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
   } else {
       if (is.null(obj$sigma)) {
           type <- "TF"
+          Pf <- svdu_thresh(obj$x[,which(obj$index %in% obj$action), drop = FALSE])
+          dffull <- ncol(Pf)
+          df2 <- n - dffull - obj$intercept - 1
+          Pfull <- Pf %*% t(Pf)
           ## if (n >= 2*p) {
           ##     sigma <- sqrt(sum(lsfit(obj$x, obj$y, intercept = obj$intercept)$res^2)/(n-p-obj$intercept))
           ## } else {
@@ -290,7 +294,6 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
       }
   }
 
-
   # Compute p-value for each active group
   for (j in 1:maxsteps) {
     i <- obj$action[j]
@@ -325,20 +328,19 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
 
     } else {
 
-        Pfull <- svdu_thresh(obj$x[,which(obj$index %in% obj$action), drop = FALSE])
         if (length(obj$action) > 1) {
             minus_i <- setdiff(obj$action, i)
             Psub <- svdu_thresh(obj$x[,which(obj$index %in% minus_i), drop = FALSE])
             Z <- Psub %*% t(Psub) %*% obj$y
-            df1 <- ncol(Pfull) - ncol(Psub)
+            df1 <- dffull - ncol(Psub)
         } else {
             Z <- 0
-            df1 <- ncol(Pfull)
+            df1 <- dffull + obj$intercept + 1
         }
-        df2 <- n - ncol(Pfull)
+
         C <- df1/df2
         R1 <- obj$y - Z
-        R2 <- obj$y - Pfull %*% t(Pfull) %*% obj$y
+        R2 <- obj$y - Pfull %*% obj$y
         R1sq <- sum(R1^2)
         R2sq <- sum(R2^2)
         R <- sqrt(R1sq)
@@ -500,6 +502,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
       out$sigma <- sigma
   } else {
       out$TF <- Tstats
+      out$df2 <- df2
   }
   out$df <- dfs
   out$support <- supports
@@ -604,7 +607,7 @@ TF_surv <- function(TF, df1, df2, E) {
     value <- numer/denom
     # Force p-value to lie in the [0,1] interval
     # in case of numerical issues
-    value <- max(0, min(1, value))
+    #value <- max(0, min(1, value))
     value
 }
 
@@ -623,6 +626,7 @@ TF_interval <- function(lower, upper, df1, df2) {
 }
 
 num_int_F <- function(a, b, df1, df2, nsamp = 10000) {
+    print("numerical!")
   grid <- seq(from=a, to=b, length.out=nsamp)
   integrand <- df(grid, df1, df2)
   return((b-a)*mean(integrand))
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 5b59fcb..0e8ebcf 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -100,6 +100,8 @@ quadratic_coefficients <- function(sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh
 }
 
 quadratic_roots <- function(A, B, C, tol) {
+
+    print("CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC")
     disc <- B^2 - 4*A*C
     b2a <- -B/(2*A)
 

From 60fd5efdfc8a6f6bc34d540a021c1b68e32a5d83 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Thu, 12 Nov 2015 16:13:02 -0800
Subject: [PATCH 050/493] cproj index was off, still no cigar

---
 forLater/josh/sim.aicstop.R         | 4 ++--
 selectiveInference/R/funs.groupfs.R | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/forLater/josh/sim.aicstop.R b/forLater/josh/sim.aicstop.R
index 0fd2d26..68934ac 100644
--- a/forLater/josh/sim.aicstop.R
+++ b/forLater/josh/sim.aicstop.R
@@ -13,7 +13,7 @@ G <- 50
 maxsteps <- 10
 sparsity <- 3
 snr <- 2
-rho <- 0
+rho <- 0.1
 aicstop <- 1
 
 instance <- function(n, p, G, sparsity, snr, rho, maxsteps, aicstop) {
@@ -39,6 +39,6 @@ save(pvals, vars, stopped, file = paste0(
                       "_p", p,
                       "_sparsity", sparsity,
                       "_snr", snr,
-                      "_F_rho0.RData"))
+                      "_F_rhopt1.RData"))
 
 print(time)
diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 521ed57..c81645b 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -419,7 +419,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
             v2list[[1]] <- v2list[[2]] <- V2
         }
         for (step in 2:maxsteps) {
-            cproj <- obj$cumprojs[[step-1]]
+            cproj <- obj$cumprojs[[step]]
             zlist[[step+1]] <- cproj %*% Z
             if (type == "TC") {
                 etalist[[step+1]] <- cproj %*% eta

From 550baf4e3fe2eb43b1bbed5337ee855b2907b782 Mon Sep 17 00:00:00 2001
From: Ryan Tibshirani <ryantibs@gmail.com>
Date: Mon, 23 Nov 2015 21:47:11 -0500
Subject: [PATCH 051/493] Fixed very silly bug in fixedLassoInf, that pops up
 when you don't standardize X manually.

---
 selectiveInference/R/funs.fixed.R |  2 +-
 tests/test.fixed.R                | 34 ++++++++++++++++++++++++++++---
 2 files changed, 32 insertions(+), 4 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index d376090..b7f20fd 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -103,7 +103,7 @@ fixedLassoInf <- function(x, y, beta, lambda, intercept=TRUE, sigma=NULL, alpha=
     sign[j] = sign(sum(vj*y))
     vj = sign[j] * vj
     a = poly.pval(y,G,u,vj,sigma,bits)
-    pv[j] = a$pv * mj   # Unstandardize (mult by norm of vj)
+    pv[j] = a$pv 
     vlo[j] = a$vlo * mj # Unstandardize (mult by norm of vj)
     vup[j] = a$vup * mj # Unstandardize (mult by norm of vj)
     vmat[j,] = vj * mj  # Unstandardize (mult by norm of vj)
diff --git a/tests/test.fixed.R b/tests/test.fixed.R
index 624fb65..553fe35 100644
--- a/tests/test.fixed.R
+++ b/tests/test.fixed.R
@@ -1,10 +1,38 @@
-#library(selectiveInference)
-library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
+library(selectiveInference)
+#library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
 
 #options(error=dump.frames)
 #attach("/Users/tibs/dropbox/PAPERS/lasso/lasso3/.RData")
 
-c
+#####
+n=50
+p=10
+sigma=.7
+beta=c(3,2,0,0,rep(0,p-4))
+set.seed(43)
+nsim = 100
+pvals <- matrix(NA, nrow=nsim, ncol=p)
+x = matrix(rnorm(n*p),n,p)
+x = scale(x,T,T)/sqrt(n-1)
+mu = x%*%beta
+for (i in 1:nsim) {
+y=mu+sigma*rnorm(n)
+#y=y-mean(y)
+# first run  glmnet
+gfit=glmnet(x,y,intercept=F,standardize=F,thresh=1e-8)
+lambda = 1
+#extract coef for a given lambda; Note the 1/n factor!
+beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
+# compute fixed lambda p-values and selection intervals
+aa = fixedLassoInf(x,y,beta,lambda,intercept=F,sigma=sigma)
+pvals[i, which(beta != 0)] <- aa$pv
+}
+nulls = which(!is.na(pvals[,1]) & !is.na(pvals[,2]))
+np = pvals[nulls,-(1:2)]
+mean(np[!is.na(np)] < 0.1)
+
+#####
+
 a=lar(x,y)
 aa=larInf(a)
 

From 96113963e891ead82a131fc42c344488fb7a1f39 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 25 Nov 2015 12:10:38 -0800
Subject: [PATCH 052/493] Trying to fix aicstop

---
 forLater/josh/sim.aicstop.R         |  3 +-
 selectiveInference/R/funs.groupfs.R | 43 ++++++++++++++++++++---------
 2 files changed, 32 insertions(+), 14 deletions(-)

diff --git a/forLater/josh/sim.aicstop.R b/forLater/josh/sim.aicstop.R
index 68934ac..e55ce92 100644
--- a/forLater/josh/sim.aicstop.R
+++ b/forLater/josh/sim.aicstop.R
@@ -20,8 +20,9 @@ instance <- function(n, p, G, sparsity, snr, rho, maxsteps, aicstop) {
     simd <- randomGaussianFixedP(n, p, G, sparsity, snr, sigma = 1, rho)
     x <- simd$x
     y <- simd$y
+    y <- y - mean(y)
     index <- simd$index
-    fit <- groupfs(x, y, index, maxsteps, k = log(n), aicstop = aicstop)
+    fit <- groupfs(x, y, index, maxsteps, intercept = F, k = log(n), aicstop = aicstop, verbose = T)
     pvals <- groupfsInf(fit, verbose=T)
     return(list(variable = fit$action, pvals = pvals$pv, stopped = attr(fit, "stopped")))
 }
diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index c81645b..b26f985 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -68,7 +68,7 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
   modelrank <- as.numeric(intercept)
   if (is.null(sigma)) {
       modelrank <- modelrank + 1
-      aic.begin <- aic.last <- n*(log(2*pi) + log(mean(y.update^2)) + 1) + k * modelrank # fixed... again
+      aic.begin <- aic.last <- n*(log(2*pi) + log(mean(y.update^2))) + k * (n + modelrank)
   } else {
       aic.begin <- aic.last <- sum(y.update^2)/sigma^2 - n + k * modelrank
   }
@@ -101,7 +101,7 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
 
     # Compute AIC
     if (is.null(sigma)) {
-        added$AIC <- n * log(added$maxterm/n) - k * added$df + n + n*log(2*pi) + k * modelrank
+        added$AIC <- n * log(added$maxterm/n) - k * added$df + n*log(2*pi) + k * (n + modelrank)
     } else {
         added$AIC <- sum(y.update^2)/sigma^2 - n + k * modelrank
     }
@@ -238,8 +238,8 @@ add1.groupfs <- function(xr, yr, index, labels, inactive, k, sigma = NULL) {
 #' Computes p-values for each group of variables in a model fitted by \code{\link{groupfs}}. These p-values adjust for selection by truncating the usual \eqn{\chi^2} statistics to the regions implied by the model selection event. Details are provided in a forthcoming work.
 #'
 #' @param obj Object returned by \code{\link{groupfs}} function
-#' @param sigma Estimate of error standard deviation. If NULL (default), this is estimated using the mean squared residual of the full least squares fit when n >= 2p, and the mean squared residual of the selected model when n < 2p. In the latter case, the user should use \code{\link{estimateSigma}} function for a more accurate estimate.
-#' @param verbose Print out progress along the way? Default is FALSE.
+#' @param sigma Estimate of error standard deviation. If NULL (default), p-values will be computed from a selective F test.
+#' @param verbose Print out progress along the way? Default is TRUE.
 #' @return An object of class "groupfsInf" containing selective p-values for the fitted model \code{obj}. For comparison with \code{\link{fsInf}}, note that the option \code{type = "active"} is not available.
 #'
 #' \describe{
@@ -250,7 +250,7 @@ add1.groupfs <- function(xr, yr, index, labels, inactive, k, sigma = NULL) {
 #'   \item{df}{Rank of group of variables when it was added to the model.}
 #'   \item{support}{List of intervals defining the truncation region of the truncated chi.}
 #' }
-groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
+groupfsInf <- function(obj, sigma = NULL, verbose = TRUE) {
 
   if (!is.null(obj$cvobj) && attr(obj, "stopped")) {
       stop("Cross-validation and early stopping cannot be used simultaneously.")
@@ -402,7 +402,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
             aic.begin <- aic.last <- sum(obj$y^2)/sigma^2 - n + k * obj$intercept
         } else {
             pen0 <- exp(k * (1+obj$intercept)/n)
-            aic.begin <- aic.last <- n*(log(2*pi) + log(mean(obj$y^2)) + 1) + k * (1 + obj$intercept)
+            aic.begin <- n*(log(2*pi) + log(mean(obj$y^2))) + k * (1 + n + obj$intercept)
         }
         AICs <- c(aic.begin, obj$AIC)
 
@@ -418,7 +418,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
             vdlist[[1]] <- vdlist[[2]] <- Vdelta
             v2list[[1]] <- v2list[[2]] <- V2
         }
-        for (step in 2:maxsteps) {
+        for (step in 1:maxsteps) {
             cproj <- obj$cumprojs[[step]]
             zlist[[step+1]] <- cproj %*% Z
             if (type == "TC") {
@@ -435,31 +435,48 @@ groupfsInf <- function(obj, sigma = NULL, verbose = FALSE) {
             if (AICs[step] >= AICs[step+1]) {
                 sp <- step
                 s <- step+1
+                peng <- 1
+                penh <- penlist[[s]]
             } else {
                 sp <- step+1
                 s <- step
+                peng <- penlist[[sp]]
+                penh <- 1
             }
 
             if (type == "TC") {
                 Ug <- ulist[[s]]
                 Uh <- ulist[[sp]]
-                                  # Check this: known sigma has *additive* pen terms
-                peng <- 1
-                penh <- penlist[sp]
-                                  ####################################
+                    # Fix this: known sigma has *additive* pen terms
+                    ####################################
                 coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh)
                 intstep <- quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol = 1e-15)
             } else {
+                # g indexes the lower AIC
                 Ug <- ulist[[s]]
                 Uh <- ulist[[sp]]
-                peng <- 1 #penlist[[s]]
-                penh <- penlist[[sp]]
                 Vdg <- vdlist[[s]]
                 Vdh <- vdlist[[sp]]
                 V2g <- v2list[[s]]
                 V2h <- v2list[[sp]]
                 Zg <- zlist[[s]]
                 Zh <- zlist[[sp]]
+
+                g1 <- function(t) sqrt(C*t/(1+C*t))
+                g2 <- function(t) 1/sqrt(1+C*t)
+                Yg <- Zg + R*(g1(TF)*Vdg + g2(TF)*V2g)
+                UYg <- t(Ug) %*% Yg
+                sse <- (sum(Yg^2) - sum(UYg^2))
+                AICg <- n*log(sse/n) + n*log(2*pi) + k * (1 + n + 5)
+                Yh <- Zh + R*(g1(TF)*Vdh + g2(TF)*V2h)
+                UYh <- t(Uh) %*% Yh
+                sse <- (sum(Yh^2) - sum(UYh^2))
+                AICh <- n*log(sse/n) + n*log(2*pi) + k * (1 + n + 6)
+                # This does reproduce the correct AIC values
+                # AIC(g) < AIC(h)
+                # <-> log(sseG) + (k/n) * ncol(Ug) < log(sseH)
+
+                # Infeasible constraint if penh = 1, why?
                 coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h)
                 intstep <- TF_roots(R, C, coeffs)
             }

From 787b6bb5e5eaf28233186b2ea93b934aa1ec4ed4 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 25 Nov 2015 12:12:31 -0800
Subject: [PATCH 053/493] Simulation for fixed maxsteps

---
 forLater/josh/sim.groupfs.R | 42 +++++++++++++++++++++++++++++++++++++
 1 file changed, 42 insertions(+)
 create mode 100644 forLater/josh/sim.groupfs.R

diff --git a/forLater/josh/sim.groupfs.R b/forLater/josh/sim.groupfs.R
new file mode 100644
index 0000000..b0e891f
--- /dev/null
+++ b/forLater/josh/sim.groupfs.R
@@ -0,0 +1,42 @@
+library(intervals)
+source("funs.sims.R")
+source("selectiveInference/R/cv.R")
+source("../../selectiveInference/R/funs.groupfs.R")
+source("../../selectiveInference/R/funs.quadratic.R")
+source("../../selectiveInference/R/funs.common.R")
+
+set.seed(1)
+niters <- 200
+n <- 100
+p <- 400
+G <- 200
+maxsteps <- 5
+sparsity <- 3
+snr <- 2
+rho <- .1
+
+instance <- function(n, p, G, sparsity, snr, rho, maxsteps) {
+    simd <- randomGaussianFixedP(n, p, G, sparsity, snr, sigma = 1, rho)
+    x <- simd$x
+    y <- simd$y
+    index <- simd$index
+    fit <- groupfs(x, y, index, maxsteps, k = log(n))
+    pvals <- groupfsInf(fit, verbose=T)
+    return(list(variable = fit$action, pvals = pvals$pv))
+}
+
+time <- system.time({
+          output <- replicate(niters, instance(n, p, G, sparsity, snr, rho, maxsteps))
+})
+
+pvals <- do.call(c, list(output[2,]))
+vars <- do.call(c, list(output[1,]))
+
+save(pvals, vars, file = paste0(
+                      "results_n", n,
+                      "_p", p,
+                      "_sparsity", sparsity,
+                      "_snr", snr,
+                      "_F.RData"))
+
+print(time)

From 99a3fc1f52ee0f0010243885417f160e1e5205b4 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 25 Nov 2015 12:29:56 -0800
Subject: [PATCH 054/493] Setting up simulations for some final tests

---
 forLater/josh/sim.aicstop.R           | 32 +++++++++++++++++----------
 forLater/josh/sim.groupfs.R           | 31 +++++++++++++++++---------
 selectiveInference/R/funs.quadratic.R |  2 +-
 3 files changed, 41 insertions(+), 24 deletions(-)

diff --git a/forLater/josh/sim.aicstop.R b/forLater/josh/sim.aicstop.R
index e55ce92..22f6665 100644
--- a/forLater/josh/sim.aicstop.R
+++ b/forLater/josh/sim.aicstop.R
@@ -1,28 +1,32 @@
 library(intervals)
 source("funs.sims.R")
-source("selectiveInference/R/cv.R")
+#source("selectiveInference/R/cv.R")
 source("../../selectiveInference/R/funs.groupfs.R")
 source("../../selectiveInference/R/funs.quadratic.R")
 source("../../selectiveInference/R/funs.common.R")
 
 set.seed(1)
-niters <- 200
-n <- 100
-p <- 100
-G <- 50
+known <- FALSE
+niters <- 300
+n <- 50
+p <- 150
+G <- 75
 maxsteps <- 10
-sparsity <- 3
+sparsity <- 4
 snr <- 2
-rho <- 0.1
+rho <- 0
 aicstop <- 1
 
 instance <- function(n, p, G, sparsity, snr, rho, maxsteps, aicstop) {
     simd <- randomGaussianFixedP(n, p, G, sparsity, snr, sigma = 1, rho)
     x <- simd$x
     y <- simd$y
-    y <- y - mean(y)
     index <- simd$index
-    fit <- groupfs(x, y, index, maxsteps, intercept = F, k = log(n), aicstop = aicstop, verbose = T)
+    if (known) {
+        fit <- groupfs(x, y, index, maxsteps, sigma = 1, k = 2*log(G), aicstop = aicstop, verbose = T)
+    } else {
+        fit <- groupfs(x, y, index, maxsteps, k = 2*log(G), aicstop = aicstop, verbose = T)
+    }
     pvals <- groupfsInf(fit, verbose=T)
     return(list(variable = fit$action, pvals = pvals$pv, stopped = attr(fit, "stopped")))
 }
@@ -36,10 +40,14 @@ pvals <- do.call(c, list(output[2,]))
 vars <- do.call(c, list(output[1,]))
 
 save(pvals, vars, stopped, file = paste0(
-                      "results_aic", aicstop, "_n", n,
+                      "results_aic_n", n,
                       "_p", p,
+                      "_g", G,
+                      "_rho", gsub(pattern = ".", replacement = "", x = rho, fixed = T),
+                      "_maxsteps", maxsteps,
                       "_sparsity", sparsity,
-                      "_snr", snr,
-                      "_F_rhopt1.RData"))
+                      "_snr", round(snr),
+                      "_known", known,
+                      ".RData"))
 
 print(time)
diff --git a/forLater/josh/sim.groupfs.R b/forLater/josh/sim.groupfs.R
index b0e891f..9e60631 100644
--- a/forLater/josh/sim.groupfs.R
+++ b/forLater/josh/sim.groupfs.R
@@ -1,26 +1,31 @@
 library(intervals)
 source("funs.sims.R")
-source("selectiveInference/R/cv.R")
+#source("selectiveInference/R/cv.R")
 source("../../selectiveInference/R/funs.groupfs.R")
 source("../../selectiveInference/R/funs.quadratic.R")
 source("../../selectiveInference/R/funs.common.R")
 
 set.seed(1)
-niters <- 200
-n <- 100
-p <- 400
-G <- 200
-maxsteps <- 5
-sparsity <- 3
+known <- TRUE
+niters <- 300
+n <- 50
+p <- 150
+G <- 75
+maxsteps <- 8
+sparsity <- 4
 snr <- 2
-rho <- .1
+rho <- 0
 
 instance <- function(n, p, G, sparsity, snr, rho, maxsteps) {
     simd <- randomGaussianFixedP(n, p, G, sparsity, snr, sigma = 1, rho)
     x <- simd$x
     y <- simd$y
     index <- simd$index
-    fit <- groupfs(x, y, index, maxsteps, k = log(n))
+    if (known) {
+        fit <- groupfs(x, y, index, maxsteps, sigma = 1, k = log(n))
+    } else {
+        fit <- groupfs(x, y, index, maxsteps, k = log(n))
+    }
     pvals <- groupfsInf(fit, verbose=T)
     return(list(variable = fit$action, pvals = pvals$pv))
 }
@@ -35,8 +40,12 @@ vars <- do.call(c, list(output[1,]))
 save(pvals, vars, file = paste0(
                       "results_n", n,
                       "_p", p,
+                      "_g", G,
+                      "_rho", gsub(pattern = ".", replacement = "", x = rho, fixed = T),
+                      "_maxsteps", maxsteps,
                       "_sparsity", sparsity,
-                      "_snr", snr,
-                      "_F.RData"))
+                      "_snr", round(snr),
+                      "_known", known,
+                      ".RData"))
 
 print(time)
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 0e8ebcf..f813412 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -101,7 +101,7 @@ quadratic_coefficients <- function(sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh
 
 quadratic_roots <- function(A, B, C, tol) {
 
-    print("CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC")
+#    print("CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC")
     disc <- B^2 - 4*A*C
     b2a <- -B/(2*A)
 

From 6a9eca8ac36f716f338055d70ebd278da24002c8 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 25 Nov 2015 15:10:49 -0800
Subject: [PATCH 055/493] Still fixing aicstop

---
 selectiveInference/R/funs.groupfs.R | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index b26f985..84055aa 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -418,14 +418,14 @@ groupfsInf <- function(obj, sigma = NULL, verbose = TRUE) {
             vdlist[[1]] <- vdlist[[2]] <- Vdelta
             v2list[[1]] <- v2list[[2]] <- V2
         }
-        for (step in 1:maxsteps) {
+        for (step in 1:(maxsteps-1)) {
             cproj <- obj$cumprojs[[step]]
-            zlist[[step+1]] <- cproj %*% Z
+            zlist[[step+2]] <- cproj %*% Z
             if (type == "TC") {
-                etalist[[step+1]] <- cproj %*% eta
+                etalist[[step+2]] <- cproj %*% eta
             } else {
-                vdlist[[step+1]] <- cproj %*% Vdelta
-                v2list[[step+1]] <- cproj %*% V2
+                vdlist[[step+2]] <- cproj %*% Vdelta
+                v2list[[step+2]] <- cproj %*% V2
             }
         }
 
@@ -440,8 +440,8 @@ groupfsInf <- function(obj, sigma = NULL, verbose = TRUE) {
             } else {
                 sp <- step+1
                 s <- step
-                peng <- penlist[[sp]]
-                penh <- 1
+                peng <- 1
+                penh <- penlist[[sp]]
             }
 
             if (type == "TC") {
@@ -467,11 +467,11 @@ groupfsInf <- function(obj, sigma = NULL, verbose = TRUE) {
                 Yg <- Zg + R*(g1(TF)*Vdg + g2(TF)*V2g)
                 UYg <- t(Ug) %*% Yg
                 sse <- (sum(Yg^2) - sum(UYg^2))
-                AICg <- n*log(sse/n) + n*log(2*pi) + k * (1 + n + 5)
+                AICg <- n*log(sse/n) + n*log(2*pi) + k * (2 + n + 6)
                 Yh <- Zh + R*(g1(TF)*Vdh + g2(TF)*V2h)
                 UYh <- t(Uh) %*% Yh
                 sse <- (sum(Yh^2) - sum(UYh^2))
-                AICh <- n*log(sse/n) + n*log(2*pi) + k * (1 + n + 6)
+                AICh <- n*log(sse/n) + n*log(2*pi) + k * (2 + n + 7)
                 # This does reproduce the correct AIC values
                 # AIC(g) < AIC(h)
                 # <-> log(sseG) + (k/n) * ncol(Ug) < log(sseH)

From 47420c564fe8e7ef1345542684ba8f4631174231 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 25 Nov 2015 15:23:03 -0800
Subject: [PATCH 056/493] Fixed error related to models with only one variable

---
 forLater/josh/sim.aicstop.R         | 2 +-
 selectiveInference/R/funs.groupfs.R | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/forLater/josh/sim.aicstop.R b/forLater/josh/sim.aicstop.R
index 22f6665..f2aa653 100644
--- a/forLater/josh/sim.aicstop.R
+++ b/forLater/josh/sim.aicstop.R
@@ -7,7 +7,7 @@ source("../../selectiveInference/R/funs.common.R")
 
 set.seed(1)
 known <- FALSE
-niters <- 300
+niters <- 200
 n <- 50
 p <- 150
 G <- 75
diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 84055aa..866d367 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -334,7 +334,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = TRUE) {
             Z <- Psub %*% t(Psub) %*% obj$y
             df1 <- dffull - ncol(Psub)
         } else {
-            Z <- 0
+            Z <- rep(0, n)
             df1 <- dffull + obj$intercept + 1
         }
 

From b610d03f7f7780ca431402aa3cef4c8080cdfeac Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 25 Nov 2015 15:26:16 -0800
Subject: [PATCH 057/493] Fixed another error related to models with only one
 variable

---
 selectiveInference/R/funs.groupfs.R | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 866d367..96ceacb 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -418,14 +418,16 @@ groupfsInf <- function(obj, sigma = NULL, verbose = TRUE) {
             vdlist[[1]] <- vdlist[[2]] <- Vdelta
             v2list[[1]] <- v2list[[2]] <- V2
         }
-        for (step in 1:(maxsteps-1)) {
-            cproj <- obj$cumprojs[[step]]
-            zlist[[step+2]] <- cproj %*% Z
-            if (type == "TC") {
-                etalist[[step+2]] <- cproj %*% eta
-            } else {
-                vdlist[[step+2]] <- cproj %*% Vdelta
-                v2list[[step+2]] <- cproj %*% V2
+        if (maxsteps > 1) {
+            for (step in 1:(maxsteps-1)) {
+                cproj <- obj$cumprojs[[step]]
+                zlist[[step+2]] <- cproj %*% Z
+                if (type == "TC") {
+                    etalist[[step+2]] <- cproj %*% eta
+                } else {
+                    vdlist[[step+2]] <- cproj %*% Vdelta
+                    v2list[[step+2]] <- cproj %*% V2
+                }
             }
         }
 

From fe44115408ae190d7665aeb27493abae758dc714 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 25 Nov 2015 16:12:55 -0800
Subject: [PATCH 058/493] Working on AICstop for known sigma

---
 selectiveInference/R/funs.groupfs.R | 69 +++++++++++------------------
 1 file changed, 27 insertions(+), 42 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 96ceacb..8fc6bb7 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -433,53 +433,38 @@ groupfsInf <- function(obj, sigma = NULL, verbose = TRUE) {
 
         for (step in 1:maxsteps) {
             # Compare AIC at s+1 to AIC at s
-            # sp indexes step with larger AIC
-            if (AICs[step] >= AICs[step+1]) {
-                sp <- step
-                s <- step+1
-                peng <- 1
-                penh <- penlist[[s]]
-            } else {
-                sp <- step+1
-                s <- step
-                peng <- 1
-                penh <- penlist[[sp]]
-            }
-
+            # roots() functions assume g indexes smaller AIC
+            # this is step+1 until the last step
+            peng <- penlist[[step+1]]
+            Ug <- ulist[[step+1]]
+            Uh <- ulist[[step]]
+            Zg <- zlist[[step+1]]
+            Zh <- zlist[[step]]
+            
             if (type == "TC") {
-                Ug <- ulist[[s]]
-                Uh <- ulist[[sp]]
-                    # Fix this: known sigma has *additive* pen terms
-                    ####################################
+                penh <- 0
+                etag <- etalist[[s]]
+                etah <- etalist[[sp]]
                 coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh)
+
+                if (AICs[step] < AICs[step+1]) {
+                    coeffs <- lapply(coeffs, function(coeff) -coeff)
+                }
+                
                 intstep <- quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol = 1e-15)
+                
             } else {
-                # g indexes the lower AIC
-                Ug <- ulist[[s]]
-                Uh <- ulist[[sp]]
-                Vdg <- vdlist[[s]]
-                Vdh <- vdlist[[sp]]
-                V2g <- v2list[[s]]
-                V2h <- v2list[[sp]]
-                Zg <- zlist[[s]]
-                Zh <- zlist[[sp]]
-
-                g1 <- function(t) sqrt(C*t/(1+C*t))
-                g2 <- function(t) 1/sqrt(1+C*t)
-                Yg <- Zg + R*(g1(TF)*Vdg + g2(TF)*V2g)
-                UYg <- t(Ug) %*% Yg
-                sse <- (sum(Yg^2) - sum(UYg^2))
-                AICg <- n*log(sse/n) + n*log(2*pi) + k * (2 + n + 6)
-                Yh <- Zh + R*(g1(TF)*Vdh + g2(TF)*V2h)
-                UYh <- t(Uh) %*% Yh
-                sse <- (sum(Yh^2) - sum(UYh^2))
-                AICh <- n*log(sse/n) + n*log(2*pi) + k * (2 + n + 7)
-                # This does reproduce the correct AIC values
-                # AIC(g) < AIC(h)
-                # <-> log(sseG) + (k/n) * ncol(Ug) < log(sseH)
-
-                # Infeasible constraint if penh = 1, why?
+                penh <- 1
+                Vdg <- vdlist[[step+1]]
+                Vdh <- vdlist[[step]]
+                V2g <- v2list[[step+1]]
+                V2h <- v2list[[step]]
                 coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h)
+
+                if (AICs[step] < AICs[step+1]) {
+                    coeffs <- lapply(coeffs, function(coeff) -coeff)
+                }
+                
                 intstep <- TF_roots(R, C, coeffs)
             }
 

From 8ce41f1d25de5211bedef4093756ed27c306a20c Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 25 Nov 2015 16:16:24 -0800
Subject: [PATCH 059/493] Still fixing known sigma AICstop

---
 selectiveInference/R/funs.groupfs.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 8fc6bb7..004ba64 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -443,8 +443,8 @@ groupfsInf <- function(obj, sigma = NULL, verbose = TRUE) {
             
             if (type == "TC") {
                 penh <- 0
-                etag <- etalist[[s]]
-                etah <- etalist[[sp]]
+                etag <- etalist[[step+1]]
+                etah <- etalist[[step]]
                 coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh)
 
                 if (AICs[step] < AICs[step+1]) {

From 5ed581f4d41e7195154f57d09d039f618daf0fd5 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 25 Nov 2015 16:40:51 -0800
Subject: [PATCH 060/493] Documentation update

---
 selectiveInference/man/factorDesign.Rd    |  3 ++-
 selectiveInference/man/groupfs.Rd         |  3 ++-
 selectiveInference/man/groupfsInf.Rd      | 14 +++++---------
 selectiveInference/man/predict.groupfs.Rd |  3 ++-
 selectiveInference/man/scaleGroups.Rd     |  3 ++-
 5 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/selectiveInference/man/factorDesign.Rd b/selectiveInference/man/factorDesign.Rd
index 3a42e2e..d5d2576 100644
--- a/selectiveInference/man/factorDesign.Rd
+++ b/selectiveInference/man/factorDesign.Rd
@@ -1,4 +1,5 @@
-% Generated by roxygen2 (4.0.1.99): do not edit by hand
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/funs.groupfs.R
 \name{factorDesign}
 \alias{factorDesign}
 \title{Expand a data frame with factors to form a design matrix with the full binary encoding of each factor.}
diff --git a/selectiveInference/man/groupfs.Rd b/selectiveInference/man/groupfs.Rd
index 78cd32f..1ea9dcd 100644
--- a/selectiveInference/man/groupfs.Rd
+++ b/selectiveInference/man/groupfs.Rd
@@ -1,4 +1,5 @@
-% Generated by roxygen2 (4.0.1.99): do not edit by hand
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/funs.groupfs.R
 \name{groupfs}
 \alias{groupfs}
 \title{Select a model with forward stepwise.}
diff --git a/selectiveInference/man/groupfsInf.Rd b/selectiveInference/man/groupfsInf.Rd
index 7c7190f..42fcbe3 100644
--- a/selectiveInference/man/groupfsInf.Rd
+++ b/selectiveInference/man/groupfsInf.Rd
@@ -1,21 +1,17 @@
-% Generated by roxygen2 (4.0.1.99): do not edit by hand
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/funs.groupfs.R
 \name{groupfsInf}
 \alias{groupfsInf}
 \title{Compute selective p-values for a model fitted by \code{groupfs}.}
 \usage{
-groupfsInf(obj, sigma = NULL, type = c("all", "aic"), ntimes = 2,
-  verbose = FALSE)
+groupfsInf(obj, sigma = NULL, verbose = TRUE)
 }
 \arguments{
 \item{obj}{Object returned by \code{\link{groupfs}} function}
 
-\item{sigma}{Estimate of error standard deviation. If NULL (default), this is estimated using the mean squared residual of the full least squares fit when n >= 2p, and the mean squared residual of the selected model when n < 2p. In the latter case, the user should use \code{\link{estimateSigma}} function for a more accurate estimate.}
+\item{sigma}{Estimate of error standard deviation. If NULL (default), p-values will be computed from a selective F test.}
 
-\item{type}{Type of conditional p-values to compute. With "all" (default), p-values are computed conditional on the final model with all variables up to \code{maxsteps}; with "aic" the number of steps is chosen after which the AIC criterion increases \code{ntimes} in a row, and then the same type of analysis as in "all" is carried out for the active variables at that number of steps.}
-
-\item{ntimes}{Number of steps for which AIC criterion has to increase before minimizing point is declared.}
-
-\item{verbose}{Print out progress along the way? Default is FALSE.}
+\item{verbose}{Print out progress along the way? Default is TRUE.}
 }
 \value{
 An object of class "groupfsInf" containing selective p-values for the fitted model \code{obj}. For comparison with \code{\link{fsInf}}, note that the option \code{type = "active"} is not available.
diff --git a/selectiveInference/man/predict.groupfs.Rd b/selectiveInference/man/predict.groupfs.Rd
index 4a4c149..14af211 100644
--- a/selectiveInference/man/predict.groupfs.Rd
+++ b/selectiveInference/man/predict.groupfs.Rd
@@ -1,4 +1,5 @@
-% Generated by roxygen2 (4.0.1.99): do not edit by hand
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/funs.groupfs.R
 \name{predict.groupfs}
 \alias{predict.groupfs}
 \title{Prediction and coefficient functions for \code{\link{groupfs}}.
diff --git a/selectiveInference/man/scaleGroups.Rd b/selectiveInference/man/scaleGroups.Rd
index 1e7e1c4..e5a93fa 100644
--- a/selectiveInference/man/scaleGroups.Rd
+++ b/selectiveInference/man/scaleGroups.Rd
@@ -1,4 +1,5 @@
-% Generated by roxygen2 (4.0.1.99): do not edit by hand
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/funs.groupfs.R
 \name{scaleGroups}
 \alias{scaleGroups}
 \title{Center and scale design matrix by groups}

From f2c86a847ff712e7bc8a863a3d702d595f6be63b Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 25 Nov 2015 17:15:03 -0800
Subject: [PATCH 061/493] More documenting

---
 selectiveInference/R/funs.groupfs.R   | 26 ++++++++--------------
 selectiveInference/R/funs.quadratic.R | 32 ---------------------------
 selectiveInference/man/groupfs.Rd     |  8 +++----
 selectiveInference/man/groupfsInf.Rd  |  8 +++----
 4 files changed, 17 insertions(+), 57 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 004ba64..00ee26a 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -4,11 +4,11 @@
 #'
 #' @param x Matrix of predictors (n by p).
 #' @param y Vector of outcomes (length n).
-#' @param index Group membership indicator of length p.
+#' @param index Group membership indicator of length p. Check that \code{sort(unique(index)) = 1:G} where \code{G} is the number of distinct groups.
 #' @param maxsteps Maximum number of steps for forward stepwise.
-#' @param sigma Estimate of error standard deviation for use in AIC criterion. This determines the relative scale between RSS and the degrees of freedom penalty. Default is NULL corresponding to unknown sigma. See \code{\link[stats]{extractAIC}} for details.
-#' @param k Multiplier of model size penalty, the default is \code{k = 2} for AIC. Use \code{k = log(n)} for BIC, or \code{k = log(p)} for RIC.
-#' @param intercept Should an intercept be included in the model? Default is TRUE.
+#' @param sigma Estimate of error standard deviation for use in AIC criterion. This determines the relative scale between RSS and the degrees of freedom penalty. Default is NULL corresponding to unknown sigma. When NULL, \code{link{groupfsInf}} performs truncated F inference instead of truncated \eqn{\chi}. See \code{\link[stats]{extractAIC}} for details on the AIC criterion.
+#' @param k Multiplier of model size penalty, the default is \code{k = 2} for AIC. Use \code{k = log(n)} for BIC, or \code{k = 2log(p)} for RIC (best for high dimensions, when \eqn{p > n}). If \eqn{G < p} then RIC may be too restrictive and it would be better to use \code{log(G) < k < 2log(p)}.
+#' @param intercept Should an intercept be included in the model? Default is TRUE. Does not count as a step.
 #' @param center Should the columns of the design matrix be centered? Default is TRUE.
 #' @param normalize Should the design matrix be normalized? Default is TRUE.
 #' @param aicstop Early stopping if AIC increases. Default is 0 corresponding to no early stopping. Positive integer values specify the number of times the AIC is allowed to increase in a row, e.g. with \code{aicstop = 2} the algorithm will stop if the AIC criterion increases for 2 steps in a row. The default of \code{\link[stats]{step}} corresponds to \code{aicstop = 1}.
@@ -235,10 +235,10 @@ add1.groupfs <- function(xr, yr, index, labels, inactive, k, sigma = NULL) {
 
 #' Compute selective p-values for a model fitted by \code{groupfs}.
 #'
-#' Computes p-values for each group of variables in a model fitted by \code{\link{groupfs}}. These p-values adjust for selection by truncating the usual \eqn{\chi^2} statistics to the regions implied by the model selection event. Details are provided in a forthcoming work.
+#' Computes p-values for each group of variables in a model fitted by \code{\link{groupfs}}. These p-values adjust for selection by truncating the usual \eqn{\chi^2} statistics to the regions implied by the model selection event. If the \code{sigma} to \code{\link{groupfs}} was NULL then groupfsInf uses truncated \eqn{F} statistics instead of truncated \eqn{\chi}. The \code{sigma} argument to groupfsInf allows users to override and use \eqn{\chi}, but this is not recommended unless \eqn{\sigma} can be estimated well (i.e. \eqn{n > p}).
 #'
 #' @param obj Object returned by \code{\link{groupfs}} function
-#' @param sigma Estimate of error standard deviation. If NULL (default), p-values will be computed from a selective F test.
+#' @param sigma Estimate of error standard deviation. Default is NULL and in this case groupfsInf uses the value of sigma specified to \code{\link{groupfs}}.
 #' @param verbose Print out progress along the way? Default is TRUE.
 #' @return An object of class "groupfsInf" containing selective p-values for the fitted model \code{obj}. For comparison with \code{\link{fsInf}}, note that the option \code{type = "active"} is not available.
 #'
@@ -246,9 +246,9 @@ add1.groupfs <- function(xr, yr, index, labels, inactive, k, sigma = NULL) {
 #'   \item{vars}{Labels of the active groups in the order they were included.}
 #'   \item{pv}{Selective p-values computed from appropriate truncated distributions.}
 #'   \item{sigma}{Estimate of error variance used in computing p-values.}
-#'   \item{TC}{Observed value of truncated chi.}
+#'   \item{TC or TF}{Observed value of truncated \eqn{\chi} or \eqn{F}.}
 #'   \item{df}{Rank of group of variables when it was added to the model.}
-#'   \item{support}{List of intervals defining the truncation region of the truncated chi.}
+#'   \item{support}{List of intervals defining the truncation region of the corresponding statistic.}
 #' }
 groupfsInf <- function(obj, sigma = NULL, verbose = TRUE) {
 
@@ -282,12 +282,6 @@ groupfsInf <- function(obj, sigma = NULL, verbose = TRUE) {
           dffull <- ncol(Pf)
           df2 <- n - dffull - obj$intercept - 1
           Pfull <- Pf %*% t(Pf)
-          ## if (n >= 2*p) {
-          ##     sigma <- sqrt(sum(lsfit(obj$x, obj$y, intercept = obj$intercept)$res^2)/(n-p-obj$intercept))
-          ## } else {
-          ##     sigma = sqrt(obj$log$RSS[length(obj$log$RSS)]/(n-Ep-obj$intercept))
-          ##     warning(paste(sprintf("p > n/2, and sigmahat = %0.3f used as an estimate of sigma;",sigma), "you may want to use the estimateSigma function"))
-          ## }
       } else {
           type <- "TC"
           sigma <- obj$sigma
@@ -381,7 +375,6 @@ groupfsInf <- function(obj, sigma = NULL, verbose = TRUE) {
                                   zcvquad <- t(Z) %*% cvquad
                                   vdcvquad <- t(Vdelta) %*% cvquad
                                   v2cvquad <- t(V2) %*% cvquad
-                                  # (r*(vd*g1 + v2*g2) + z)^T cvquad (r*(vd*g1 + v2*g2) + z)
                                   x0 <- zcvquad %*% Z
                                   x1 <- 2*R*zcvquad %*% Vd
                                   x2 <- 2*R*zcvquad %*% V2
@@ -630,7 +623,6 @@ TF_interval <- function(lower, upper, df1, df2) {
 }
 
 num_int_F <- function(a, b, df1, df2, nsamp = 10000) {
-    print("numerical!")
   grid <- seq(from=a, to=b, length.out=nsamp)
   integrand <- df(grid, df1, df2)
   return((b-a)*mean(integrand))
@@ -767,7 +759,7 @@ predict.groupfs <- function(object, newx, ...) {
     if (missing(newx)) {
         newx = object$x
     } else {
-        newx <- scaleGroups(newx, object$index, attr(object, "center"), attr(object, "normalize"))
+        newx <- scaleGroups(newx, object$index, attr(object, "center"), attr(object, "normalize"))$x
     }
     return(newx[, index %in% object$action] %*% beta + ifelse(object$intercept, object$by, 0))
 }
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index f813412..003adc6 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -49,26 +49,8 @@ truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
               coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etas, etas, Zs, Zs)
               quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol)
           } else {
-
-             # Debugging
-              ## Q <- peng * (diag(rep(1,n)) - Ug %*% t(Ug)) - penh * (diag(rep(1,n)) - Uh %*% t(Uh))
-              ##  g1 <- function(t) sqrt(C*t/(1+C*t))
-              ##  g2 <- function(t) 1/sqrt(1+C*t)
-              ##  Y <- function(t) {
-              ##      Zs + R * (Vds*g1(t) + V2s*g2(t))
-              ##  }
-
               coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zs, Zs, Vds, Vds, V2s, V2s)
               roots <- TF_roots(R, C, coeffs)
-
-              if (is.null(roots)) print(c(s,l))
-              ## print(do.call(rbind, lapply(roots, function(r) {
-              ##     c(r,
-              ##       t(Y(r-0.000001)) %*% Q %*% Y(r-0.000001),
-              ##       t(Y(r)) %*% Q %*% Y(r),
-              ##       t(Y(r+0.000001)) %*% Q %*% Y(r+0.000001))
-              ## })))
-
               return(roots)
           }
       })
@@ -100,8 +82,6 @@ quadratic_coefficients <- function(sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh
 }
 
 quadratic_roots <- function(A, B, C, tol) {
-
-#    print("CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC")
     disc <- B^2 - 4*A*C
     b2a <- -B/(2*A)
 
@@ -206,17 +186,6 @@ TF_roots <- function(R, C, coeffs, tol = 1e-8, tol2 = 1e-6) {
     x2 <- coeffs$x2
     x0 <- coeffs$x0
 
-##     # Handle some special cases
-##     if ((x11 == 0) && (max(abs(c(x22, x12, x1, x2))) < tol2)) {
-## #        print("Special case 1")
-##         return(Intervals(c(-Inf, 0)))
-##     }
-
-##     if ((x22 == 0) && (max(abs(c(x2, x12))) < tol2)) {
-##         x2 <- 0
-##         x12 <- 0
-##     }
-
     g1 <- function(t) sqrt(C*t/(1+C*t))
     g2 <- function(t) 1/sqrt(1+C*t)
     I <- function(t) x11*g1(t)^2 + x12*g1(t)*g2(t) + x22*g2(t)^2 + x1*g1(t) + x2*g2(t) + x0
@@ -278,4 +247,3 @@ TF_roots <- function(R, C, coeffs, tol = 1e-8, tol2 = 1e-6) {
     if (I(0) < 0) stop("Infeasible constraint!")
     return(Intervals(c(-Inf,0)))
 }
-
diff --git a/selectiveInference/man/groupfs.Rd b/selectiveInference/man/groupfs.Rd
index 1ea9dcd..a57c6dc 100644
--- a/selectiveInference/man/groupfs.Rd
+++ b/selectiveInference/man/groupfs.Rd
@@ -12,15 +12,15 @@ groupfs(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE,
 
 \item{y}{Vector of outcomes (length n).}
 
-\item{index}{Group membership indicator of length p.}
+\item{index}{Group membership indicator of length p. Check that \code{sort(unique(index)) = 1:G} where \code{G} is the number of distinct groups.}
 
 \item{maxsteps}{Maximum number of steps for forward stepwise.}
 
-\item{sigma}{Estimate of error standard deviation for use in AIC criterion. This determines the relative scale between RSS and the degrees of freedom penalty. Default is NULL corresponding to unknown sigma. See \code{\link[stats]{extractAIC}} for details.}
+\item{sigma}{Estimate of error standard deviation for use in AIC criterion. This determines the relative scale between RSS and the degrees of freedom penalty. Default is NULL corresponding to unknown sigma. When NULL, \code{link{groupfsInf}} performs truncated F inference instead of truncated \eqn{\chi}. See \code{\link[stats]{extractAIC}} for details on the AIC criterion.}
 
-\item{k}{Multiplier of model size penalty, the default is \code{k = 2} for AIC. Use \code{k = log(n)} for BIC, or \code{k = log(p)} for RIC.}
+\item{k}{Multiplier of model size penalty, the default is \code{k = 2} for AIC. Use \code{k = log(n)} for BIC, or \code{k = 2log(p)} for RIC (best for high dimensions, when \eqn{p > n}). If \eqn{G < p} then RIC may be too restrictive and it would be better to use \code{log(G) < k < 2log(p)}.}
 
-\item{intercept}{Should an intercept be included in the model? Default is TRUE.}
+\item{intercept}{Should an intercept be included in the model? Default is TRUE. Does not count as a step.}
 
 \item{center}{Should the columns of the design matrix be centered? Default is TRUE.}
 
diff --git a/selectiveInference/man/groupfsInf.Rd b/selectiveInference/man/groupfsInf.Rd
index 42fcbe3..74b9a5e 100644
--- a/selectiveInference/man/groupfsInf.Rd
+++ b/selectiveInference/man/groupfsInf.Rd
@@ -9,7 +9,7 @@ groupfsInf(obj, sigma = NULL, verbose = TRUE)
 \arguments{
 \item{obj}{Object returned by \code{\link{groupfs}} function}
 
-\item{sigma}{Estimate of error standard deviation. If NULL (default), p-values will be computed from a selective F test.}
+\item{sigma}{Estimate of error standard deviation. Default is NULL and in this case groupfsInf uses the value of sigma specified to \code{\link{groupfs}}.}
 
 \item{verbose}{Print out progress along the way? Default is TRUE.}
 }
@@ -20,12 +20,12 @@ An object of class "groupfsInf" containing selective p-values for the fitted mod
   \item{vars}{Labels of the active groups in the order they were included.}
   \item{pv}{Selective p-values computed from appropriate truncated distributions.}
   \item{sigma}{Estimate of error variance used in computing p-values.}
-  \item{TC}{Observed value of truncated chi.}
+  \item{TC or TF}{Observed value of truncated \eqn{\chi} or \eqn{F}.}
   \item{df}{Rank of group of variables when it was added to the model.}
-  \item{support}{List of intervals defining the truncation region of the truncated chi.}
+  \item{support}{List of intervals defining the truncation region of the corresponding statistic.}
 }
 }
 \description{
-Computes p-values for each group of variables in a model fitted by \code{\link{groupfs}}. These p-values adjust for selection by truncating the usual \eqn{\chi^2} statistics to the regions implied by the model selection event. Details are provided in a forthcoming work.
+Computes p-values for each group of variables in a model fitted by \code{\link{groupfs}}. These p-values adjust for selection by truncating the usual \eqn{\chi^2} statistics to the regions implied by the model selection event. If the \code{sigma} to \code{\link{groupfs}} was NULL then groupfsInf uses truncated \eqn{F} statistics instead of truncated \eqn{\chi}. The \code{sigma} argument to groupfsInf allows users to override and use \eqn{\chi}, but this is not recommended unless \eqn{\sigma} can be estimated well (i.e. \eqn{n > p}).
 }
 

From a8a5d758749fd61bcf5f945a18c65f727d08f3ab Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 25 Nov 2015 17:51:34 -0800
Subject: [PATCH 062/493] Documentation fix

---
 selectiveInference/R/funs.groupfs.R       | 8 ++++----
 selectiveInference/man/predict.groupfs.Rd | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 00ee26a..20ef265 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -376,10 +376,10 @@ groupfsInf <- function(obj, sigma = NULL, verbose = TRUE) {
                                   vdcvquad <- t(Vdelta) %*% cvquad
                                   v2cvquad <- t(V2) %*% cvquad
                                   x0 <- zcvquad %*% Z
-                                  x1 <- 2*R*zcvquad %*% Vd
+                                  x1 <- 2*R*zcvquad %*% Vdelta
                                   x2 <- 2*R*zcvquad %*% V2
                                   x12 <- 2*R*vdcvquad %*% V2
-                                  x11 <- R^2*vdcvquad %*% Vd
+                                  x11 <- R^2*vdcvquad %*% Vdelta
                                   x22 <- R^2*v2cvquad %*% V2
                                   TF_roots(R, C, coeffs = list(x0=x0, x1=x1, x2=x2, x12=x12, x11=x11, x22=x22))
                               }
@@ -754,14 +754,14 @@ coef.groupfs <- function(object, ...) {
 #' @param object Object returned by a call to \code{\link{groupfs}}.
 #' @param newx Matrix of x values at which the predictions are desired. If NULL, the x values from groupfs fitting are used.
 #' @return A vector of predictions or a vector of coefficients.
-predict.groupfs <- function(object, newx, ...) {
+predict.groupfs <- function(object, newx) {
     beta <- coef.groupfs(object)
     if (missing(newx)) {
         newx = object$x
     } else {
         newx <- scaleGroups(newx, object$index, attr(object, "center"), attr(object, "normalize"))$x
     }
-    return(newx[, index %in% object$action] %*% beta + ifelse(object$intercept, object$by, 0))
+    return(newx[, object$index %in% object$action] %*% beta + ifelse(object$intercept, object$by, 0))
 }
 
 print.groupfsInf <- function(x, ...) {
diff --git a/selectiveInference/man/predict.groupfs.Rd b/selectiveInference/man/predict.groupfs.Rd
index 14af211..4a382c7 100644
--- a/selectiveInference/man/predict.groupfs.Rd
+++ b/selectiveInference/man/predict.groupfs.Rd
@@ -6,7 +6,7 @@
 
 Make predictions or extract coefficients from a groupfs forward stepwise object.}
 \usage{
-\method{predict}{groupfs}(object, newx, ...)
+\method{predict}{groupfs}(object, newx)
 }
 \arguments{
 \item{object}{Object returned by a call to \code{\link{groupfs}}.}

From b47acd5ca04cbadb0950472a2f96b919df410a25 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Wed, 25 Nov 2015 18:04:32 -0800
Subject: [PATCH 063/493] rob one change

---
 selectiveInference/DESCRIPTION    |  2 +-
 selectiveInference/R/funs.fixed.R | 20 ++++++++++----------
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/selectiveInference/DESCRIPTION b/selectiveInference/DESCRIPTION
index 0f352b6..ce95116 100644
--- a/selectiveInference/DESCRIPTION
+++ b/selectiveInference/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: selectiveInference
 Type: Package
 Title: Tools for Selective Inference
-Version: 1.1.1
+Version: 1.1.2
 Date: 2015-09-01
 Author: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor,
     Joshua Loftus, Stephen Reid
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index b7f20fd..ac00545 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -184,15 +184,15 @@ print.fixedLassoInf <- function(x, tailarea=TRUE, ...) {
   invisible()
 }
 
-estimateLambda <- function(x, sigma, nsamp=1000){
-  checkargs.xy(x,rep(0,nrow(x)))
-  if(nsamp < 10) stop("More Monte Carlo samples required for estimation")
-  if (length(sigma)!=1) stop("sigma should be a number > 0")
-  if (sigma<=0) stop("sigma should be a number > 0")
+#estimateLambda <- function(x, sigma, nsamp=1000){
+#  checkargs.xy(x,rep(0,nrow(x)))
+#  if(nsamp < 10) stop("More Monte Carlo samples required for estimation")
+#  if (length(sigma)!=1) stop("sigma should be a number > 0")
+ # if (sigma<=0) stop("sigma should be a number > 0")
                                       
-  n = nrow(x)
-  eps = sigma*matrix(rnorm(nsamp*n),n,nsamp)
-  lambda = 2*mean(apply(t(x)%*%eps,2,max))
-  return(lambda)
-}
+ # n = nrow(x)
+ # eps = sigma*matrix(rnorm(nsamp*n),n,nsamp)
+ # lambda = 2*mean(apply(t(x)%*%eps,2,max))
+ # return(lambda)
+#}
     

From c353058303b97d29d14c23f1bb95efa2f256d617 Mon Sep 17 00:00:00 2001
From: Ryan Tibshirani <ryantibs@gmail.com>
Date: Thu, 26 Nov 2015 00:55:56 -0500
Subject: [PATCH 064/493] Just added Sen's example ... we still seem to get the
 proper coverage here even if the way the code is being used here is not
 exactly what we'd expect.

---
 tests/test.fixed.R | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)

diff --git a/tests/test.fixed.R b/tests/test.fixed.R
index 553fe35..79aa709 100644
--- a/tests/test.fixed.R
+++ b/tests/test.fixed.R
@@ -31,6 +31,30 @@ nulls = which(!is.na(pvals[,1]) & !is.na(pvals[,2]))
 np = pvals[nulls,-(1:2)]
 mean(np[!is.na(np)] < 0.1)
 
+#####
+library(selectiveInference)
+library(MASS)
+library(scalreg)
+
+S <- diag(10)
+n <- 100
+p <- 10
+pval <- matrix(1, nrow = 100, ncol = p)
+for(i in 1:100){
+    cat(i)
+  X <- mvrnorm(n = n, mu = rep(0, p), Sigma = S)
+  Y <- X[, 1] + X[, 2] + rnorm(n)
+  sig.L <- scalreg(X, Y)$hsigma
+
+  lam <- cv.glmnet(X, Y, standardize = FALSE, intercept = FALSE)$lambda.min
+  bl <- glmnet(X, Y, lambda = lam, standardize = FALSE, intercept = FALSE)$beta[, 1]
+  idx <- which(bl != 0)
+  pval[i, idx] <- fixedLassoInf(X, Y, beta = bl, lambda = lam * n, intercept = FALSE, sigma = sig.L, alpha = 0.05)$pv
+}
+
+p <- pval[, -(1:2)]
+mean(p[p < 1] < 0.05)
+
 #####
 
 a=lar(x,y)

From e71a29df480b7a936a7f52fd4ea70d847bd394a2 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Thu, 26 Nov 2015 07:57:01 -0800
Subject: [PATCH 065/493] rob test.fixed.R

---
 tests/test.fixed.R | 29 +++++++++++++++++++++++++++--
 1 file changed, 27 insertions(+), 2 deletions(-)

diff --git a/tests/test.fixed.R b/tests/test.fixed.R
index 553fe35..e1a581c 100644
--- a/tests/test.fixed.R
+++ b/tests/test.fixed.R
@@ -1,5 +1,5 @@
-library(selectiveInference)
-#library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
+#library(selectiveInference)
+library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
 
 #options(error=dump.frames)
 #attach("/Users/tibs/dropbox/PAPERS/lasso/lasso3/.RData")
@@ -315,3 +315,28 @@ sint = fixedLassoInf(X,y,coef,lam,sigma=sigma,alpha=alpha,type="partial")
 load("params_for_Rob.rdata") #variables: X, y, coef, lam, sigma, alpha
 
 sint = fixedLassoInf(X,y,coef,lam,sigma=sigma,alpha=alpha,type="partial")
+
+
+#### bug from Sen at UW
+
+library(MASS)
+library(scalreg)
+
+S <- diag(10)
+n <- 100
+p <- 10
+pval <- matrix(1, nrow = 100, ncol = p)
+for(i in 1:100){
+    cat(i)
+  X <- mvrnorm(n = n, mu = rep(0, p), Sigma = S)
+  Y <- X[, 1] + X[, 2] + rnorm(n)
+  sig.L <- scalreg(X, Y)$hsigma
+
+  lam <- cv.glmnet(X, Y, standardize = FALSE, intercept = FALSE)$lambda.min
+  bl <- glmnet(X, Y, lambda = lam, standardize = FALSE, intercept = FALSE)$beta[, 1]
+  idx <- which(bl != 0)
+  pval[i, idx] <- fixedLassoInf(X, Y, beta = bl, lambda = lam * n, intercept = FALSE, sigma = sig.L, alpha = 0.05)$pv
+}
+
+p <- pval[, -(1:2)]
+mean(p[p < 1] < 0.05)

From 7993fb39593f26372f96d7a1afe8daa523afcdaf Mon Sep 17 00:00:00 2001
From: Ryan Tibshirani <ryantibs@gmail.com>
Date: Thu, 26 Nov 2015 11:28:10 -0500
Subject: [PATCH 066/493] Got rid of estimateLambda

---
 selectiveInference/NAMESPACE | 1 -
 1 file changed, 1 deletion(-)

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index c6e2d09..b306d85 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -8,7 +8,6 @@ export(lar,fs,
        fixedLassoInf,print.fixedLassoInf,
        forwardStop,
        estimateSigma,
-       estimateLambda,
        manyMeans,print.manyMeans,
        groupfs,groupfsInf,
        scaleGroups,factorDesign

From edb1277ffc33e50713f1fb92d7f5031adcf3bb0a Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Thu, 26 Nov 2015 12:09:59 -0800
Subject: [PATCH 067/493] rob

---
 selectiveInference/man/estimateSigma.Rd      | 25 +++++++------
 selectiveInference/man/factorDesign.Rd       |  2 +-
 selectiveInference/man/fixedLassoInf.Rd      | 37 ++++++++++----------
 selectiveInference/man/forwardStop.Rd        | 28 +++++++--------
 selectiveInference/man/fs.Rd                 | 34 +++++++++---------
 selectiveInference/man/fsInf.Rd              | 30 ++++++++--------
 selectiveInference/man/lar.Rd                | 24 ++++++-------
 selectiveInference/man/larInf.Rd             | 31 ++++++++--------
 selectiveInference/man/manyMeans.Rd          | 14 ++++----
 selectiveInference/man/plot.fs.Rd            | 25 +++++++------
 selectiveInference/man/plot.lar.Rd           | 25 +++++++------
 selectiveInference/man/predict.fs.Rd         | 26 +++++++-------
 selectiveInference/man/predict.lar.Rd        | 25 +++++++------
 selectiveInference/man/selectiveInference.Rd | 33 ++++++++---------
 tests/test.fixed.R                           |  4 +--
 15 files changed, 182 insertions(+), 181 deletions(-)

diff --git a/selectiveInference/man/estimateSigma.Rd b/selectiveInference/man/estimateSigma.Rd
index 100f944..9397a08 100644
--- a/selectiveInference/man/estimateSigma.Rd
+++ b/selectiveInference/man/estimateSigma.Rd
@@ -42,20 +42,19 @@ A study of error variance estimation in lasso regression. arXiv:1311.5274.
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
-#NOT RUN
-#set.seed(43)
-#n=50
-#p=10
-#sigma=.7
-#x=matrix(rnorm(n*p),n,p)
-#x=scale(x,T,F)
-#beta=c(3,2,0,0,rep(0,p-4))
-#y=x%*%beta+sigma*rnorm(n)
-#y=y-mean(y)
+set.seed(43)
+n=50
+p=10
+sigma=.7
+x=matrix(rnorm(n*p),n,p)
+x=scale(x,TRUE,FALSE)
+beta=c(3,2,0,0,rep(0,p-4))
+y=x\%*\%beta+sigma*rnorm(n)
+y=y-mean(y)
 
-#out=estimateSigma(x,y)
-#a=fs(x,y)
-#out2=fsInf(a,sigma=out$sigmahat)
+out=estimateSigma(x,y)
+fit=fs(x,y)
+out2=fsInf(fit,sigma=out$sigmahat)
 
 }
 
diff --git a/selectiveInference/man/factorDesign.Rd b/selectiveInference/man/factorDesign.Rd
index d5d2576..8e061db 100644
--- a/selectiveInference/man/factorDesign.Rd
+++ b/selectiveInference/man/factorDesign.Rd
@@ -23,7 +23,7 @@ When using \code{\link{groupfs}} with factor variables call this function first
 \dontrun{
 fd = factorDesign(warpbreaks)
 y = rnorm(nrow(fd$x))
-fit = groupfs(fd$x, y, fd$index, maxsteps=2, intercept=F)
+fit = groupfs(fd$x, y, fd$index, maxsteps=2, intercept=FALSE)
 pvals = groupfsInf(fit)
 }
 }
diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index b250073..609136e 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -115,27 +115,28 @@ Exact post-selection inference, with application to the lasso. arXiv:1311.6238.
 
 \examples{
 
-#NOT RUN
-#set.seed(43)
-#n=50
-#p=10
-#sigma=.7
-#x=matrix(rnorm(n*p),n,p)
-#x=scale(x,T,T)/sqrt(n-1)
-#beta=c(3,2,0,0,rep(0,p-4))
-#y=x%*%beta+sigma*rnorm(n)
-#y=y-mean(y)
+#generate data
+set.seed(43)
+n=50
+p=10
+sigma=.7
+x=matrix(rnorm(n*p),n,p)
+x=scale(x,TRUE,TRUE)/sqrt(n-1)
+beta=c(3,2,0,0,rep(0,p-4))
+y=x\%*\%beta+sigma*rnorm(n)
+y=y-mean(y)
 # first run  glmnet
-#gfit=glmnet(x,y,standardize=F)
-#lambda = .1
+gfit=glmnet(x,y,standardize=FALSE)
+lambda = .1
 #extract coef for a given lambda; Note the 1/n factor!
-#beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
+beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
 
 # compute fixed lambda p-values and selection intervals
-#aa=fixedLassoInf(x,y,beta,lambda,sigma=sigma)
+out=fixedLassoInf(x,y,beta,lambda,sigma=sigma)
 #
+#NOT RUN
 # as above, but use lar function instead to get initial lasso fit (should get same result)
-# fit=lar(x,y,normalize=F)
+# fit=lar(x,y,normalize=FALSE)
 # beta=coef(fit,s=lambda,mode="lambda")
 # fixedLassoInf(x,y,beta,lambda,sigma=sigma)
 
@@ -144,15 +145,15 @@ Exact post-selection inference, with application to the lasso. arXiv:1311.6238.
 # states data example: predict life expectancy
 #x=state.x77[,-4]
 #y=state.x77[,4]
-#x=scale(x,T,T)
+#x=scale(x,TRUE,TRUE)
 #n=nrow(x)
 #  estimate sigma from cross-validated lasso fit
 #  cvf=cv.glmnet(x,y)
-# sigmahat=estimateSigma(x,y,stand=F)$sigmahat
+# sigmahat=estimateSigma(x,y,stand=FALSE)$sigmahat
 #
 # fit lasso
 # lambda=n*cvf$lambda.min
-# gfit=glmnet(x,y,standardize=F)
+# gfit=glmnet(x,y,standardize=FALSE)
 # bhat=coef(gfit, s=lambda/n, exact=TRUE)[-1]
 #
 # compute p-values and confidence intervals
diff --git a/selectiveInference/man/forwardStop.Rd b/selectiveInference/man/forwardStop.Rd
index 5ca36df..7863afe 100644
--- a/selectiveInference/man/forwardStop.Rd
+++ b/selectiveInference/man/forwardStop.Rd
@@ -34,20 +34,20 @@ To appear in Journal of the Royal Statistical Society: Series B.
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
-#NOT RUN
-#set.seed(433)
-#n=50
-#p=10
-#sigma=.7
-#x=matrix(rnorm(n*p),n,p)
-
-#x=scale(x,T,T)/sqrt(n-1)
-#beta=c(4,2,0,0,rep(0,p-4))
-#y=x%*%beta+sigma*rnorm(n)
-#y=y-mean(y)
+set.seed(433)
+n=50
+p=10
+sigma=.7
+x=matrix(rnorm(n*p),n,p)
+x=scale(x,TRUE,TRUE)/sqrt(n-1)
+beta=c(4,2,0,0,rep(0,p-4))
+y=x\%*\%beta+sigma*rnorm(n)
+y=y-mean(y)
 #first run forward stepwise
-# fsfit=fs(x,y)
+ fsfit=fs(x,y)
 #
-# aa=fsInf(fsfit)
-# forwardStop(aa, alpha=.10)
+# run inference function 
+ out=fsInf(fsfit)
+# estimate optimal stopping point
+ forwardStop(out$pv, alpha=.10)
 }
diff --git a/selectiveInference/man/fs.Rd b/selectiveInference/man/fs.Rd
index 434edc4..102aabd 100644
--- a/selectiveInference/man/fs.Rd
+++ b/selectiveInference/man/fs.Rd
@@ -68,33 +68,35 @@ to enter along the path}
 }
 
 \examples{
-#NOT RUN
-#set.seed(43)
-#n=50
-#p=10
-#sigma=.7
-#x=matrix(rnorm(n*p),n,p)
-#x=scale(x,T,F)
-#beta=c(3,2,0,0,rep(0,p-4))
-#y=x%*%beta+sigma*rnorm(n)
-#y=y-mean(y)
 
-#fsfit=fs(x,y)
-#
-#out=fsInf(x,y)
+set.seed(43)
+n=50
+p=10
+sigma=.7
+x=matrix(rnorm(n*p),n,p)
+x=scale(x,TRUE,FALSE)
+beta=c(3,2,0,0,rep(0,p-4))
+y=x\%*\%beta+sigma*rnorm(n)
+y=y-mean(y)
+# run forward stepwise
+fsfit=fs(x,y)
+# call inference function
+out=fsInf(fsfit)
 #
+
+#NOT RUN
 # states data example: predict life expectancy
 #x=state.x77[,-4]
 #y=state.x77[,4]
-#x=scale(x,T,T)
+#x=scale(x,TRUE,TRUE)
 #n=nrow(x)
 #
 #  estimate sigma from cross-validated lasso fit
 #cvf=cv.glmnet(x,y)
-#sigmahat=estimateSigma(x,y,stand=F)$sigmahat
+#sigmahat=estimateSigma(x,y,stand=FALSE)$sigmahat
 #
 # fit forwardStepwise
-# fsfit=fs(x,y,normalize=F)
+# fsfit=fs(x,y,normalize=FALSE)
 #
 # compute p-values and confidence intervals
 #  fsInf(fsfit,sigma=sigmahat)
diff --git a/selectiveInference/man/fsInf.Rd b/selectiveInference/man/fsInf.Rd
index f8afc93..66fc758 100644
--- a/selectiveInference/man/fsInf.Rd
+++ b/selectiveInference/man/fsInf.Rd
@@ -109,27 +109,27 @@ model selection. arXiv:1405.3920.
 \seealso{\code{\link{fs}}}
 
 \examples{
-#NOT RUN
-#set.seed(433)
+set.seed(433)
 
-#n=50
-#p=10
-#sigma=.7
-#x=matrix(rnorm(n*p),n,p)
+n=50
+p=10
+sigma=.7
+x=matrix(rnorm(n*p),n,p)
 
-#x=scale(x,T,T)/sqrt(n-1)
-#beta=c(4,2,0,0,rep(0,p-4))
-#y=x%*%beta+sigma*rnorm(n)
-#y=y-mean(y)
+x=scale(x,TRUE,TRUE)/sqrt(n-1)
+beta=c(4,2,0,0,rep(0,p-4))
+y=x\%*\%beta+sigma*rnorm(n)
+y=y-mean(y)
 #first run forward stepwise
-# fsfit=fs(x,y)
+ fsfit=fs(x,y)
 #
 # forward stepwise inference for each successive entry of a predictor; 
 #
 #  sigma estimated from mean squared residual
 #  
-# aa=fsInf(fsfit)
+ aa=fsInf(fsfit)
 
+#NOT RUN
 # forward stepwise inference for fixed model of size 4, testing deletion of each predictor; 
 # known value of sigma used
 # aa2=fsInf(fsfit,sigma=sigma,type="all",k=4)
@@ -141,14 +141,14 @@ model selection. arXiv:1405.3920.
 # states data example: predict life expectancy
 #x=state.x77[,-4]
 #y=state.x77[,4]
-#x=scale(x,T,T)
+#x=scale(x,TRUE,TRUE)
 #n=nrow(x)
 #  estimate sigma from cross-validated lasso fit
 #cvf=cv.glmnet(x,y)
-#sigmahat=estimateSigma(x,y,stand=F)$sigmahat
+#sigmahat=estimateSigma(x,y,stand=FALSE)$sigmahat
 #
 # run forward stepwise
-#fsfit=fs(x,y,normalize=F)
+#fsfit=fs(x,y,normalize=FALSE)
 #
 # compute p-values and confidence intervals
 # fsInf(fsfit,sigma=sigmahat)
diff --git a/selectiveInference/man/lar.Rd b/selectiveInference/man/lar.Rd
index 5bafbf9..70e53c5 100644
--- a/selectiveInference/man/lar.Rd
+++ b/selectiveInference/man/lar.Rd
@@ -76,17 +76,17 @@ Jerome Friedman (2002, 2009). Elements of Statistical Learning.
 }
 
 \examples{
-#NOT RUN
-#set.seed(33)
-#n=20
-#p=10
-#sigma=1
-#x=matrix(rnorm(n*p),n,p)
+
+set.seed(33)
+n=20
+p=10
+sigma=1
+x=matrix(rnorm(n*p),n,p)
 #generate data
-#beta=c(3,3,rep(0,p-2))
-#y=x%*%beta+sigma*rnorm(n)
-#y=y-mean(y)
-#run lar
-#larfit=lar(x,y,verbose=TRUE)
-#plot(larfit)                                      
+beta=c(3,3,rep(0,p-2))
+y=x\%*\%beta+sigma*rnorm(n)
+y=y-mean(y)
+#run lar and plot results
+larfit=lar(x,y,verbose=TRUE)
+plot(larfit)                                      
 }
diff --git a/selectiveInference/man/larInf.Rd b/selectiveInference/man/larInf.Rd
index 86de5d2..7947ec5 100644
--- a/selectiveInference/man/larInf.Rd
+++ b/selectiveInference/man/larInf.Rd
@@ -114,34 +114,35 @@ Exact post-selection inference for sequential regression procedures. arXiv:1401.
 
 \examples{
 
-#NOT RUN
-#set.seed(43)
-#n=50
-#p=10
-#sigma=.7
-#x=matrix(rnorm(n*p),n,p)
-#x=scale(x,T,T)/sqrt(n-1)
-#beta=c(3,2,0,0,rep(0,p-4))
-#y=x%*%beta+sigma*rnorm(n)
-#y=y-mean(y)
+#generate data
+set.seed(43)
+n=50
+p=10
+sigma=.7
+x=matrix(rnorm(n*p),n,p)
+x=scale(x,TRUE,TRUE)/sqrt(n-1)
+beta=c(3,2,0,0,rep(0,p-4))
+y=x\%*\%beta+sigma*rnorm(n)
+y=y-mean(y)
 #first run lar
-# larfit=lar(x,y)
+ larfit=lar(x,y)
 #
 #lar inference for each successive entry of a predictor; sigma estimated
 #  from mean squared residual from least squares fit
-# aa=larInf(larfit)
+ out=larInf(larfit)
 #
+#NOT RUN
 # states data example: predict life expectancy
 #x=state.x77[,-4]
 #y=state.x77[,4]
-#x=scale(x,T,T)
+#x=scale(x,TRUE,TRUE)
 #n=nrow(x)
 #  estimate sigma from cross-validated lasso fit
 #cvf=cv.glmnet(x,y)
-#sigmahat=estimateSigma(x,y,stand=F)$sigmahat
+#sigmahat=estimateSigma(x,y,stand=FALSE)$sigmahat
 #
 # run LAR
-#larfit=lar(x,y,normalize=F)
+#larfit=lar(x,y,normalize=FALSE)
 #
 # compute p-values and confidence intervals
 # larInf(larfit,sigma=sigmahat)
diff --git a/selectiveInference/man/manyMeans.Rd b/selectiveInference/man/manyMeans.Rd
index db2d992..62a1400 100644
--- a/selectiveInference/man/manyMeans.Rd
+++ b/selectiveInference/man/manyMeans.Rd
@@ -52,11 +52,11 @@ arXiv:1405.3340.
 
 \examples{
 
-#NOT RUN
-#set.seed(12345)
-#n = 100 # sample size
-#signal = 3 # signal size
-#mu = c(rep(signal, floor (n/5)), rep(0, n-floor(n/5))) # 20% of elements get the signal; rest 0
-#y = mu + rnorm (n, 0, 1)
-#mmObj = manyMeans(y, bh.q=0.1)
+
+set.seed(12345)
+n = 100 # sample size
+signal = 3 # signal size
+mu = c(rep(signal, floor (n/5)), rep(0, n-floor(n/5))) # 20% of elements get the signal; rest 0
+y = mu + rnorm (n, 0, 1)
+out  = manyMeans(y, bh.q=0.1)
 }
diff --git a/selectiveInference/man/plot.fs.Rd b/selectiveInference/man/plot.fs.Rd
index 3c27ad1..e90da5d 100644
--- a/selectiveInference/man/plot.fs.Rd
+++ b/selectiveInference/man/plot.fs.Rd
@@ -28,18 +28,17 @@ Default is TRUE}
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
-#NOT RUN
-#set.seed(33)
-#n=200
-#p=20
-#sigma=1
-#x=matrix(rnorm(n*p),n,p)
-#x=scale(x,T,T)/sqrt(n-1)
+set.seed(33)
+n=200
+p=20
+sigma=1
+x=matrix(rnorm(n*p),n,p)
+x=scale(x,TRUE,TRUE)/sqrt(n-1)
 #generate y
-#beta=c(3,-2,rep(0,p-2))
-#beta=c(rep(3,10),rep(0,p-10))
-#y=x%*%beta+sigma*rnorm(n)
-#y=y-mean(y)
-#fsfit=fs(x,y)
-#plot(fsfit)
+beta=c(3,-2,rep(0,p-2))
+beta=c(rep(3,10),rep(0,p-10))
+y=x\%*\%beta+sigma*rnorm(n)
+y=y-mean(y)
+fsfit=fs(x,y)
+plot(fsfit)
 }
diff --git a/selectiveInference/man/plot.lar.Rd b/selectiveInference/man/plot.lar.Rd
index ebf6c0b..668ce1c 100644
--- a/selectiveInference/man/plot.lar.Rd
+++ b/selectiveInference/man/plot.lar.Rd
@@ -32,18 +32,17 @@ Default is TRUE}
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
-#NOT RUN
-#set.seed(33)
-#n=200
-#p=20
-#sigma=1
-#x=matrix(rnorm(n*p),n,p)
-#x=scale(x,T,T)/sqrt(n-1)
+set.seed(33)
+n=200
+p=20
+sigma=1
+x=matrix(rnorm(n*p),n,p)
+x=scale(x,TRUE,TRUE)/sqrt(n-1)
 #generate y
-#beta=c(3,-2,rep(0,p-2))
-#beta=c(rep(3,10),rep(0,p-10))
-#y=x%*%beta+sigma*rnorm(n)
-#y=y-mean(y)
-#larfit=lar(x,y)
-#plot(larfit)
+beta=c(3,-2,rep(0,p-2))
+beta=c(rep(3,10),rep(0,p-10))
+y=x\%*\%beta+sigma*rnorm(n)
+y=y-mean(y)
+larfit=lar(x,y)
+plot(larfit)
 }
diff --git a/selectiveInference/man/predict.fs.Rd b/selectiveInference/man/predict.fs.Rd
index 32f1a44..0562f6b 100644
--- a/selectiveInference/man/predict.fs.Rd
+++ b/selectiveInference/man/predict.fs.Rd
@@ -35,18 +35,18 @@ Either a vector/matrix of predictions, or a vector/matrix of coefficients.
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
-#NOT RUN
-#set.seed(33)
-#n=200
-#p=20
-#sigma=1
-#x=matrix(rnorm(n*p),n,p)
-#x=scale(x,T,T)/sqrt(n-1)
+set.seed(33)
+n=200
+p=20
+sigma=1
+x=matrix(rnorm(n*p),n,p)
+x=scale(x,TRUE,TRUE)/sqrt(n-1)
 #generate y
-#beta=c(3,-2,rep(0,p-2))
-#beta=c(rep(3,10),rep(0,p-10))
-#y=x%*%beta+sigma*rnorm(n)
-#y=y-mean(y)
-#obj=fs(x,y)
-#fit=predict.fs(obj,x)
+beta=c(3,-2,rep(0,p-2))
+beta=c(rep(3,10),rep(0,p-10))
+y=x\%*\%beta+sigma*rnorm(n)
+y=y-mean(y)
+# run forward stepwise and predict functions
+obj=fs(x,y)
+fit=predict.fs(obj,x)
 }
diff --git a/selectiveInference/man/predict.lar.Rd b/selectiveInference/man/predict.lar.Rd
index 340cd52..a9eebb5 100644
--- a/selectiveInference/man/predict.lar.Rd
+++ b/selectiveInference/man/predict.lar.Rd
@@ -38,18 +38,17 @@ Either a vector/matrix of predictions, or a vector/matrix of coefficients.
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
-#NOT RUN
-#set.seed(33)
-#n=200
-#p=20
-#sigma=1
-#x=matrix(rnorm(n*p),n,p)
-#x=scale(x,T,T)/sqrt(n-1)
+set.seed(33)
+n=200
+p=20
+sigma=1
+x=matrix(rnorm(n*p),n,p)
+x=scale(x,TRUE,TRUE)/sqrt(n-1)
 #generate y
-#beta=c(3,-2,rep(0,p-2))
-#beta=c(rep(3,10),rep(0,p-10))
-#y=x%*%beta+sigma*rnorm(n)
-#y=y-mean(y)
-#larfit=lar(x,y)
-#fit=predict.lar(larfit,x,type="fit")
+beta=c(3,-2,rep(0,p-2))
+beta=c(rep(3,10),rep(0,p-10))
+y=x\%*\%beta+sigma*rnorm(n)
+y=y-mean(y)
+larfit=lar(x,y)
+fit=predict.lar(larfit,x,type="fit")
 }
diff --git a/selectiveInference/man/selectiveInference.Rd b/selectiveInference/man/selectiveInference.Rd
index 0b1ca4c..1043f01 100644
--- a/selectiveInference/man/selectiveInference.Rd
+++ b/selectiveInference/man/selectiveInference.Rd
@@ -70,28 +70,29 @@ arXiv:1405.3340.
 }
 
 \examples{
-#NOT RUN
 # forward stepwise:
 #
 #generate some data
-#set.seed(43)
-#n=50
-#p=10
-#sigma=.7
-#x=matrix(rnorm(n*p),n,p)
-#x=scale(x,T,T)/sqrt(n-1)
-#beta=c(4,2,0,0,rep(0,p-4))
-#y=x%*%beta+sigma*rnorm(n)
-#y=y-mean(y)
+set.seed(43)
+n=50
+p=10
+sigma=.7
+x=matrix(rnorm(n*p),n,p)
+x=scale(x,TRUE,TRUE)/sqrt(n-1)
+beta=c(4,2,0,0,rep(0,p-4))
+y=x\%*\%beta+sigma*rnorm(n)
+y=y-mean(y)
 #
 #first run forward stepwise
-# fsfit=fs(x,y)
+ fsfit=fs(x,y)
 #
 # forward stepwise inference for each successive entry of a predictor; 
 #   sigma estimated from mean squared residual
 #   
-# aa=fsInf(fsfit)
+out=fsInf(fsfit)
+
 ##
+#NOT RUN
 #  lasso with fixed lambda
 #
 #set.seed(43)
@@ -99,13 +100,13 @@ arXiv:1405.3340.
 #p=10
 #sigma=.7
 #x=matrix(rnorm(n*p),n,p)
-#x=scale(x,T,T)/sqrt(n-1)
+#x=scale(x,TRUE,TRUE)/sqrt(n-1)
 #beta=c(4,2,0,0,rep(0,p-4))
 #y=x%*%beta+sigma*rnorm(n)
 #y=y-mean(y)
 #
 # first run  glmnet
-#gfit=glmnet(x,y,standardize=F)
+#gfit=glmnet(x,y,standardize=FALSE)
 #lam = .1
 #extract coef for a given lam; Note the 1/n factor in s!
 #bhat = coef(gfit, s=lam/n, exact=TRUE)[-1]
@@ -119,7 +120,7 @@ arXiv:1405.3340.
 #p=10
 #sigma=.7
 #x=matrix(rnorm(n*p),n,p)
-#x=scale(x,T,T)/sqrt(n-1)
+#x=scale(x,TRUE,TRUE)/sqrt(n-1)
 #beta=c(3,2,0,0,rep(0,p-4))
 #y=x%*%beta+sigma*rnorm(n)
 #y=y-mean(y)
@@ -165,7 +166,7 @@ arXiv:1405.3340.
 #p=10
 #sigma=.7
 #x=matrix(rnorm(n*p),n,p)
-#x=scale(x,T,F)
+#x=scale(x,TRUE,FALSE)
 #beta=c(3,2,0,0,rep(0,p-4))
 #y=x%*%beta+sigma*rnorm(n)
 #y=y-mean(y)
diff --git a/tests/test.fixed.R b/tests/test.fixed.R
index 8cf1e5f..5c55887 100644
--- a/tests/test.fixed.R
+++ b/tests/test.fixed.R
@@ -1,5 +1,5 @@
-#library(selectiveInference)
-library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
+library(selectiveInference)
+#library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
 
 #options(error=dump.frames)
 #attach("/Users/tibs/dropbox/PAPERS/lasso/lasso3/.RData")

From 62739d47c8f3fc8f5e3b0d63031fed2d5722ae18 Mon Sep 17 00:00:00 2001
From: Ryan Tibshirani <ryantibs@gmail.com>
Date: Fri, 27 Nov 2015 10:45:04 -0500
Subject: [PATCH 068/493] Small commit

---
 tests/test.fixed.R | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/tests/test.fixed.R b/tests/test.fixed.R
index 8cf1e5f..63f76c0 100644
--- a/tests/test.fixed.R
+++ b/tests/test.fixed.R
@@ -1,5 +1,5 @@
-#library(selectiveInference)
-library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
+library(selectiveInference)
+#library(selectiveInference),lib.loc="/Users/tibs/dropbox/git/R/mylib")
 
 #options(error=dump.frames)
 #attach("/Users/tibs/dropbox/PAPERS/lasso/lasso3/.RData")
@@ -154,7 +154,6 @@ ch=function(bhat,tol.beta=1e-5,tol.kkt=0.1){
     cat(c(max(abs(g[oo]))>tol.kkt,min(gg[!oo])< -1-tol.kkt,max(gg[!oo])>1 +tol.kkt),fill=T)
 }
 
-
 #
 x=read.table("/Users/tibs/dropbox/PAPERS/FourOfUs/data64.txt")
 x=as.matrix(x)

From 2b4799cdc3f3e84d92443bf4bbb6a9e37042d7c4 Mon Sep 17 00:00:00 2001
From: Ryan Tibshirani <ryantibs@gmail.com>
Date: Fri, 27 Nov 2015 11:57:59 -0500
Subject: [PATCH 069/493] Fixed a bug in LAR plotting function

---
 selectiveInference/R/funs.lar.R | 23 ++++++++++++-----------
 tests/test.lar.R                |  4 ++--
 2 files changed, 14 insertions(+), 13 deletions(-)

diff --git a/selectiveInference/R/funs.lar.R b/selectiveInference/R/funs.lar.R
index db4082e..b644699 100644
--- a/selectiveInference/R/funs.lar.R
+++ b/selectiveInference/R/funs.lar.R
@@ -632,29 +632,30 @@ plot.lar <- function(x, xvar=c("norm","step","lambda"), breaks=TRUE,
   
   xvar = match.arg(xvar)
   if (xvar=="norm") {
-    x = colSums(abs(beta))
+    xx = colSums(abs(beta))
     xlab = "L1 norm"
   } else if (xvar=="step") {
-    x = 1:k
+    xx = 1:k
     xlab = "Step"
   } else {
-    x = lambda
+    xx = lambda
     xlab = "Lambda"
   }
 
   if (omit.zeros) {
-    inds = matrix(FALSE,p,k)
-    for (i in 1:k) {
-      inds[i,] = beta[i,]!=0 | c(diff(beta[i,]!=0),F) | c(F,diff(beta[i,]!=0))
-    }
-    beta[!inds] = NA
+    good.inds = matrix(FALSE,p,k)
+    good.inds[beta!=0] = TRUE
+    changes = t(apply(beta,1,diff))!=0
+    good.inds[cbind(changes,rep(F,p))] = TRUE
+    good.inds[cbind(rep(F,p),changes)] = TRUE
+    beta[!good.inds] = NA
   }
 
-  plot(c(),c(),xlim=range(x,na.rm=T),ylim=range(beta,na.rm=T),
+  plot(c(),c(),xlim=range(xx,na.rm=T),ylim=range(beta,na.rm=T),
        xlab=xlab,ylab="Coefficients",main="LAR path",...)
   abline(h=0,lwd=2)
-  matplot(x,t(beta),type="l",lty=1,add=TRUE)
-  if (breaks) abline(v=x,lty=2)
+  matplot(xx,t(beta),type="l",lty=1,add=TRUE)
+  if (breaks) abline(v=xx,lty=2)
   if (var.labels) axis(4,at=beta[,k],labels=1:p,cex=0.8,adj=0) 
   invisible()
 }
diff --git a/tests/test.lar.R b/tests/test.lar.R
index 6edaa45..be26428 100644
--- a/tests/test.lar.R
+++ b/tests/test.lar.R
@@ -1,5 +1,5 @@
-#library(selectiveInference)
-library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
+library(selectiveInference)
+#library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
 library(lars)
 
 set.seed(0)

From 5b8e300b00207972c2c3149f6a996729cfe564e8 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Fri, 27 Nov 2015 16:49:34 -0800
Subject: [PATCH 070/493] rob

---
 tests/test.fixed.R | 366 +++++++++++++++++++++++++++++++++++++++++++++
 tests/test.fs.R    |   4 +-
 2 files changed, 368 insertions(+), 2 deletions(-)
 create mode 100644 tests/test.fixed.R

diff --git a/tests/test.fixed.R b/tests/test.fixed.R
new file mode 100644
index 0000000..5c55887
--- /dev/null
+++ b/tests/test.fixed.R
@@ -0,0 +1,366 @@
+library(selectiveInference)
+#library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
+
+#options(error=dump.frames)
+#attach("/Users/tibs/dropbox/PAPERS/lasso/lasso3/.RData")
+
+#####
+n=50
+p=10
+sigma=.7
+beta=c(3,2,0,0,rep(0,p-4))
+set.seed(43)
+nsim = 100
+pvals <- matrix(NA, nrow=nsim, ncol=p)
+x = matrix(rnorm(n*p),n,p)
+x = scale(x,T,T)/sqrt(n-1)
+mu = x%*%beta
+for (i in 1:nsim) {
+y=mu+sigma*rnorm(n)
+#y=y-mean(y)
+# first run  glmnet
+gfit=glmnet(x,y,intercept=F,standardize=F,thresh=1e-8)
+lambda = 1
+#extract coef for a given lambda; Note the 1/n factor!
+beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
+# compute fixed lambda p-values and selection intervals
+aa = fixedLassoInf(x,y,beta,lambda,intercept=F,sigma=sigma)
+pvals[i, which(beta != 0)] <- aa$pv
+}
+nulls = which(!is.na(pvals[,1]) & !is.na(pvals[,2]))
+np = pvals[nulls,-(1:2)]
+mean(np[!is.na(np)] < 0.1)
+
+#####
+library(selectiveInference)
+library(MASS)
+library(scalreg)
+
+S <- diag(10)
+n <- 100
+p <- 10
+pval <- matrix(1, nrow = 100, ncol = p)
+for(i in 1:100){
+    cat(i)
+  X <- mvrnorm(n = n, mu = rep(0, p), Sigma = S)
+  Y <- X[, 1] + X[, 2] + rnorm(n)
+  sig.L <- scalreg(X, Y)$hsigma
+
+  lam <- cv.glmnet(X, Y, standardize = FALSE, intercept = FALSE)$lambda.min
+  bl <- glmnet(X, Y, lambda = lam, standardize = FALSE, intercept = FALSE)$beta[, 1]
+  idx <- which(bl != 0)
+  pval[i, idx] <- fixedLassoInf(X, Y, beta = bl, lambda = lam * n, intercept = FALSE, sigma = sig.L, alpha = 0.05)$pv
+}
+
+p <- pval[, -(1:2)]
+mean(p[p < 1] < 0.05)
+
+#####
+
+a=lar(x,y)
+aa=larInf(a)
+
+critf=function(b,lam,x,y){
+     yhat=x%*%b
+     .5*sum( (y-yhat)^2) + lam*sum(abs(b))
+   }
+
+##check coverage
+set.seed(3)
+
+n=50
+p=10
+sigma=2
+
+x=matrix(rnorm(n*p),n,p)
+#x=scale(x,T,T)/sqrt(n-1)    #try with and without standardization
+
+beta=c(5,4,3,2,1,rep(0,p-5))
+
+nsim=100
+seeds=sample(1:9999,size=nsim)
+pv=rep(NA,nsim)
+ci=matrix(NA,nsim,2)
+btrue=rep(NA,nsim)
+for(ii in 1:nsim){
+    cat(ii)
+    set.seed(seeds[ii])
+    mu=x%*%beta
+   y=mu+sigma*rnorm(n)
+    y=y-mean(y)  
+   gfit=glmnet(x,y,standardize=F,lambda.min.ratio=1e-9)
+   ilam=trunc(length(gfit$lam)/4)
+    lambda=gfit$lam[ilam]*n
+     bhat = predict(gfit, s=lambda/n,type="coef",exact=F)[-1]
+
+     junk= fixedLassoInf(x,y,bhat,lambda,sigma=sigma)
+    pv[ii]=junk$pv[1]
+   # oo=junk$pred # for old package
+    oo=junk$var   # for new package
+     btrue[ii]=lsfit(x[,oo],mu)$coef[2]
+     ci[ii,]=junk$ci[1,]
+}
+
+sum(ci[,1]> btrue)
+sum(ci[,2]< btrue)
+
+
+
+
+## BIG example
+ library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
+
+options(error=dump.frames)
+attach("/Users/tibs/dropbox/PAPERS/lasso/lasso3/.RData")
+critf=function(b,lam,x,y){
+     yhat=x%*%b
+     .5*sum( (y-yhat)^2) + lam*sum(abs(b))
+ }
+set.seed(4)
+n=100
+p=500
+sigma=1
+x=matrix(rnorm(n*p),ncol=p)
+x=scale(x,T,F)
+beta=c(rep(2.5,10),rep(0,p-10))
+y=x%*%beta+sigma*rnorm(n)
+y=y-mean(y)
+    
+
+    
+gfit=glmnet(x,y,standardize=F)
+cvf=cv.glmnet(x,y)
+
+lambda=n*cvf$lambda.min
+#lambda=10
+     bhat = as.numeric(predict(gfit, s=lambda/n,type="coef",exact=T))[-1]
+
+bhat2=lasso2lam(x,y,lambda,int=F,stand=F)$coef
+plot(bhat,bhat2)
+
+critf(bhat,lambda,x,y)
+critf(bhat2,lambda,x,y)
+ junk= fixedLassoInf(x,y,bhat,lambda,sigma=sigma)
+junk= fixedLassoInf(x,y,bhat,lambda,sigma=sigma,bits=200)
+
+ # check of KKT
+ch=function(bhat,tol.beta=1e-5,tol.kkt=0.1){
+    xx=cbind(1,x)
+    bhatt=c(0,bhat)
+   g0=t(xx)%*%(y-xx%*%bhatt)
+   g=g0-lambda*sign(bhatt)
+    gg=g0/lambda
+    oo=abs(bhatt)>tol.beta
+    cat(c(max(abs(g[oo]))>tol.kkt,min(gg[!oo])< -1-tol.kkt,max(gg[!oo])>1 +tol.kkt),fill=T)
+}
+
+
+#
+x=read.table("/Users/tibs/dropbox/PAPERS/FourOfUs/data64.txt")
+x=as.matrix(x)
+x=scale(x,T,F)
+#x=scale(x,T,T)
+n=length(y)
+nams=scan("/Users/tibs/dropbox/PAPERS/FourOfUs/data64.names",what="")
+y=scan("/Users/tibs/dropbox/PAPERS/FourOfUs/diab.y")
+y=y-mean(y)
+
+cvf=cv.glmnet(x,y)
+sigmahat=estimateSigma(x,y,stand=F)$si
+
+lambda=n*cvf$lambda.min
+
+lambda=estimateLambda(x,sigma=sigmahat)/2
+
+gfit=glmnet(x,y,standardize=F)
+ 
+bhat=coef(gfit, s=lambda/n, exact=TRUE)[-1]
+bhat2=lasso2lam(x,y,lambda,int=F,stand=F)$coef
+
+plot(bhat,bhat2)
+
+critf(bhat,lambda,x,y)
+critf(bhat2,lambda,x,y)
+
+
+fixedLassoInf(x,y,bhat,lambda,sigma=sigmahat)
+##
+set.seed(44)
+n=50
+p=10
+sigma=.7
+x=matrix(rnorm(n*p),n,p)
+x=scale(x,T,T)/sqrt(n-1)
+beta=c(3,2,0,0,rep(0,p-4))
+y=x%*%beta+sigma*rnorm(n)
+y=y-mean(y)
+# first run  glmnet
+gfit=glmnet(x,y,standardize=F)
+lambda = .1
+#extract coef for a given lambda; Note the 1/n factor!
+beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
+
+# compute fixed lambda p-values and selection intervals
+fixedLassoInf(x,y,beta,lambda,sigma=sigma)
+
+# as above, but use lar function to get initial lasso fit
+ fit=lar(x,y,normalize=F)
+beta=coef(fit,s=lambda,mode="lambda")
+fixedLassoInf(x,y,beta,lambda,sigma=sigma)
+
+###
+x=state.x77[,-4]
+y=state.x77[,4]
+x=scale(x,T,T)
+n=nrow(x)
+
+
+sigmahat=estimateSigma(x,y,stand=F)$si
+
+lambda=65
+
+gfit=glmnet(x,y,standardize=F, thresh=1e-9)
+ 
+bhat=coef(gfit, s=lambda/n, exact=TRUE)[-1]
+#bhat2=lasso2lam(x,y,lambda,int=F,stand=F)$coef
+
+fixedLassoInf(x,y,bhat,lambda,sigma=sigmahat)
+
+
+# lucas example
+library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
+
+set.seed(44)
+
+p <- 300
+n <- 200
+s0 <- 2
+b <- 1
+b0 <- 0
+sigma <- .5
+alpha = 0.05
+
+X = matrix(rnorm(n*p),n,p)
+X = scale(X,center=T,scale=T)
+
+m = 1000
+eps = matrix(rnorm(m*n),n,m)
+lam = 2*mean(apply(t(X)%*%eps,2,max))
+
+
+theta0 <- c(rep(b,s0),rep(0,p-s0));
+w <- sigma*rnorm(n);
+y <- (b0+X%*%theta0+w);
+
+
+
+tic = proc.time()
+gfit = glmnet(X,y,standardize=F)
+coef = coef(gfit, s=lam/n, exact=T)[-1]
+sint = fixedLassoInf(X,y,coef,lam,sigma=sigma,alpha=alpha)
+
+### lucas example with sims
+
+set.seed(44)
+
+p <- 300
+n <- 200
+s0 <- 2
+b <- 1
+b0 <- 0
+sigma <- .5
+alpha = 0.05
+#set.seed('1')
+
+#X <- rbinom(p*n,1,prob=0.15);
+#dim(X) <- c(n,p);
+#X <- X %*% diag(1+9*runif(p))
+X = matrix(rnorm(n*p),n,p)
+#X = scale(X,center=T,scale=T)  # original
+#X = scale(X,center=T,scale=T)/sqrt(n-1)   #CHANGED
+
+m = 1000
+eps = matrix(rnorm(m*n),n,m)
+
+lam = 2*mean(apply(t(X)%*%eps,2,max))   #original
+theta0 <- c(rep(b,s0),rep(0,p-s0))  #original
+#theta0 <- c(rep(b,s0),rep(0,p-s0))*sqrt(n-1)  #CHANGED
+  mu=b0+X%*%theta0
+nsim=100
+int=matrix(NA,nsim,2)
+btrue=rep(NA,nsim)
+for(ii in 1:nsim){
+    cat(ii)
+w <- sigma*rnorm(n);
+       
+y <- (mu+w);
+
+tic = proc.time()
+gfit = glmnet(X,y,standardize=F)
+    nz=colSums(gfit$beta!=0)
+ #   lam=gfit$lambda[nz>=2]/1.1 # CHANGED
+                                        #   lam=lam[1]*n               #CHANGED
+coef = coef(gfit, s=lam/n, exact=T)[-1]
+oo=which(coef!=0)
+btrue[ii]=lsfit(X[,oo],mu)$coef[2]
+sint = fixedLassoInf(X,y,coef,lam,sigma=sigma,alpha=alpha)
+int[ii,]=sint$ci[1,]
+}
+## new bugs from lucas
+library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
+
+set.seed(1)
+p <- 500
+n <- 400
+s0 <- 10
+b <- 1.2
+b0 <- 0
+sigma <- 5
+alpha = 0.05
+
+X = matrix(rnorm(n*p),n,p)
+X = scale(X,center=T,scale=T)
+theta0 <- c(rep(b,s0),rep(0,p-s0))
+w <- sigma*rnorm(n)
+y <- b0 + X%*%theta0 + w
+
+# Pick lambda as in Negahban et al. (2012), as done in Lee et al. (2015)
+m = 1000
+eps = sigma*matrix(rnorm(m*n),n,m)
+lam = 2*mean(apply(abs(t(X)%*%eps),2,max))
+
+gfit = glmnet(X,y,standardize=F)
+coef = coef(gfit, s=lam/n, exact=T)[-1]
+sint = fixedLassoInf(X,y,coef,lam,sigma=sigma,alpha=alpha,type="partial")
+# Error in v %*% diag(d) : non-conformable arguments
+
+## lucas again
+
+load("params_for_Rob.rdata") #variables: X, y, coef, lam, sigma, alpha
+
+sint = fixedLassoInf(X,y,coef,lam,sigma=sigma,alpha=alpha,type="partial")
+
+
+#### bug from Sen at UW
+
+library(MASS)
+library(scalreg)
+
+S <- diag(10)
+n <- 100
+p <- 10
+pval <- matrix(1, nrow = 100, ncol = p)
+for(i in 1:100){
+    cat(i)
+  X <- mvrnorm(n = n, mu = rep(0, p), Sigma = S)
+  Y <- X[, 1] + X[, 2] + rnorm(n)
+  sig.L <- scalreg(X, Y)$hsigma
+
+  lam <- cv.glmnet(X, Y, standardize = FALSE, intercept = FALSE)$lambda.min
+  bl <- glmnet(X, Y, lambda = lam, standardize = FALSE, intercept = FALSE)$beta[, 1]
+  idx <- which(bl != 0)
+  pval[i, idx] <- fixedLassoInf(X, Y, beta = bl, lambda = lam * n, intercept = FALSE, sigma = sig.L, alpha = 0.05)$pv
+}
+
+p <- pval[, -(1:2)]
+mean(p[p < 1] < 0.05)
diff --git a/tests/test.fs.R b/tests/test.fs.R
index fbe50ce..f811676 100644
--- a/tests/test.fs.R
+++ b/tests/test.fs.R
@@ -1,5 +1,5 @@
-#library(selectiveInference)
-library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
+library(selectiveInference)
+#library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
 
 library(lars)
 

From ad70e47cf8f9db705909c5da959c6c51ac5033ca Mon Sep 17 00:00:00 2001
From: Ryan Tibshirani <ryantibs@gmail.com>
Date: Sat, 28 Nov 2015 00:05:34 -0500
Subject: [PATCH 071/493] Fixed a bug in FS plotting function

---
 selectiveInference/R/funs.fs.R | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 3a46b29..6edc2db 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -446,12 +446,13 @@ plot.fs <- function(x, breaks=TRUE, omit.zeros=TRUE, var.labels=TRUE, ...) {
   xx = 1:k
   xlab = "Step"
   
-  if (omit.zeros) {
-    inds = matrix(FALSE,p,k)
-    for (i in 1:k) {
-      inds[i,] = beta[i,]!=0 | c(diff(beta[i,]!=0),F) | c(F,diff(beta[i,]!=0))
-    }
-    beta[!inds] = NA
+ if (omit.zeros) {
+   good.inds = matrix(FALSE,p,k)
+   good.inds[beta!=0] = TRUE
+   changes = t(apply(beta,1,diff))!=0
+   good.inds[cbind(changes,rep(F,p))] = TRUE
+   good.inds[cbind(rep(F,p),changes)] = TRUE
+   beta[!good.inds] = NA
   }
 
   plot(c(),c(),xlim=range(xx,na.rm=T),ylim=range(beta,na.rm=T),

From 7d2ca75d94824fe3e7c2250c284cfe415b93f34e Mon Sep 17 00:00:00 2001
From: Ryan Tibshirani <ryantibs@gmail.com>
Date: Sat, 28 Nov 2015 00:36:06 -0500
Subject: [PATCH 072/493] Fixed a bug in FS plotting function

---
 selectiveInference/R/funs.fs.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 6edc2db..a46b53e 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -459,7 +459,7 @@ plot.fs <- function(x, breaks=TRUE, omit.zeros=TRUE, var.labels=TRUE, ...) {
        xlab=xlab,ylab="Coefficients",main="Forward stepwise path",...)
   abline(h=0,lwd=2)
   matplot(xx,t(beta),type="l",lty=1,add=TRUE)
-  if (breaks) abline(v=x,lty=2)
+  if (breaks) abline(v=xx,lty=2)
   if (var.labels) axis(4,at=beta[,k],labels=1:p,cex=0.8,adj=0) 
   invisible()
 }

From 9d95250fe316f6181f8b33e0c544ade6b79b7263 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Fri, 27 Nov 2015 21:46:43 -0800
Subject: [PATCH 073/493] rob

---
 selectiveInference/R/funs.fs.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 6edc2db..a46b53e 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -459,7 +459,7 @@ plot.fs <- function(x, breaks=TRUE, omit.zeros=TRUE, var.labels=TRUE, ...) {
        xlab=xlab,ylab="Coefficients",main="Forward stepwise path",...)
   abline(h=0,lwd=2)
   matplot(xx,t(beta),type="l",lty=1,add=TRUE)
-  if (breaks) abline(v=x,lty=2)
+  if (breaks) abline(v=xx,lty=2)
   if (var.labels) axis(4,at=beta[,k],labels=1:p,cex=0.8,adj=0) 
   invisible()
 }

From 605d6b6066da5a45e4cc7ab44643e6760347652c Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Fri, 27 Nov 2015 22:24:20 -0800
Subject: [PATCH 074/493] rob

---
 selectiveInference/man/fs.Rd                 | 1 -
 selectiveInference/man/lar.Rd                | 1 -
 selectiveInference/man/larInf.Rd             | 1 -
 selectiveInference/man/predict.fs.Rd         | 2 +-
 selectiveInference/man/predict.lar.Rd        | 3 +--
 selectiveInference/man/selectiveInference.Rd | 4 ----
 6 files changed, 2 insertions(+), 10 deletions(-)

diff --git a/selectiveInference/man/fs.Rd b/selectiveInference/man/fs.Rd
index 102aabd..931d1be 100644
--- a/selectiveInference/man/fs.Rd
+++ b/selectiveInference/man/fs.Rd
@@ -77,7 +77,6 @@ x=matrix(rnorm(n*p),n,p)
 x=scale(x,TRUE,FALSE)
 beta=c(3,2,0,0,rep(0,p-4))
 y=x\%*\%beta+sigma*rnorm(n)
-y=y-mean(y)
 # run forward stepwise
 fsfit=fs(x,y)
 # call inference function
diff --git a/selectiveInference/man/lar.Rd b/selectiveInference/man/lar.Rd
index 70e53c5..1e9f26e 100644
--- a/selectiveInference/man/lar.Rd
+++ b/selectiveInference/man/lar.Rd
@@ -85,7 +85,6 @@ x=matrix(rnorm(n*p),n,p)
 #generate data
 beta=c(3,3,rep(0,p-2))
 y=x\%*\%beta+sigma*rnorm(n)
-y=y-mean(y)
 #run lar and plot results
 larfit=lar(x,y,verbose=TRUE)
 plot(larfit)                                      
diff --git a/selectiveInference/man/larInf.Rd b/selectiveInference/man/larInf.Rd
index 7947ec5..e45900b 100644
--- a/selectiveInference/man/larInf.Rd
+++ b/selectiveInference/man/larInf.Rd
@@ -123,7 +123,6 @@ x=matrix(rnorm(n*p),n,p)
 x=scale(x,TRUE,TRUE)/sqrt(n-1)
 beta=c(3,2,0,0,rep(0,p-4))
 y=x\%*\%beta+sigma*rnorm(n)
-y=y-mean(y)
 #first run lar
  larfit=lar(x,y)
 #
diff --git a/selectiveInference/man/predict.fs.Rd b/selectiveInference/man/predict.fs.Rd
index 0562f6b..ca3a32a 100644
--- a/selectiveInference/man/predict.fs.Rd
+++ b/selectiveInference/man/predict.fs.Rd
@@ -48,5 +48,5 @@ y=x\%*\%beta+sigma*rnorm(n)
 y=y-mean(y)
 # run forward stepwise and predict functions
 obj=fs(x,y)
-fit=predict.fs(obj,x)
+fit=predict.fs(obj,x,s=3)
 }
diff --git a/selectiveInference/man/predict.lar.Rd b/selectiveInference/man/predict.lar.Rd
index a9eebb5..16aa795 100644
--- a/selectiveInference/man/predict.lar.Rd
+++ b/selectiveInference/man/predict.lar.Rd
@@ -48,7 +48,6 @@ x=scale(x,TRUE,TRUE)/sqrt(n-1)
 beta=c(3,-2,rep(0,p-2))
 beta=c(rep(3,10),rep(0,p-10))
 y=x\%*\%beta+sigma*rnorm(n)
-y=y-mean(y)
 larfit=lar(x,y)
-fit=predict.lar(larfit,x,type="fit")
+fit=predict.lar(larfit,x,s=3.5,type="fit")
 }
diff --git a/selectiveInference/man/selectiveInference.Rd b/selectiveInference/man/selectiveInference.Rd
index 1043f01..0f29e32 100644
--- a/selectiveInference/man/selectiveInference.Rd
+++ b/selectiveInference/man/selectiveInference.Rd
@@ -81,7 +81,6 @@ x=matrix(rnorm(n*p),n,p)
 x=scale(x,TRUE,TRUE)/sqrt(n-1)
 beta=c(4,2,0,0,rep(0,p-4))
 y=x\%*\%beta+sigma*rnorm(n)
-y=y-mean(y)
 #
 #first run forward stepwise
  fsfit=fs(x,y)
@@ -123,7 +122,6 @@ out=fsInf(fsfit)
 #x=scale(x,TRUE,TRUE)/sqrt(n-1)
 #beta=c(3,2,0,0,rep(0,p-4))
 #y=x%*%beta+sigma*rnorm(n)
-#y=y-mean(y)
 #first run lar
 # larfit=lar(x,y)
 #
@@ -169,7 +167,6 @@ out=fsInf(fsfit)
 #x=scale(x,TRUE,FALSE)
 #beta=c(3,2,0,0,rep(0,p-4))
 #y=x%*%beta+sigma*rnorm(n)
-#y=y-mean(y)
 
 #out=estimateSigma(x,y)
 
@@ -182,7 +179,6 @@ set.seed(43)
 #x=scale(x,T,F)
 #beta=c(3,2,0,0,rep(0,p-4))
 #y=x%*%beta+sigma*rnorm(n)
-#y=y-mean(y)
 #
 #estimate lambda using the  known value of sigma
 #lamhat=estimateLambda(x,sigma=.7)

From 4f97bc9cfc807a65bc8fd35d6c09afafc811a588 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Sat, 28 Nov 2015 08:48:57 -0800
Subject: [PATCH 075/493] rob

---
 selectiveInference/man/selectiveInference.Rd | 1 -
 1 file changed, 1 deletion(-)

diff --git a/selectiveInference/man/selectiveInference.Rd b/selectiveInference/man/selectiveInference.Rd
index 0f29e32..66423ca 100644
--- a/selectiveInference/man/selectiveInference.Rd
+++ b/selectiveInference/man/selectiveInference.Rd
@@ -102,7 +102,6 @@ out=fsInf(fsfit)
 #x=scale(x,TRUE,TRUE)/sqrt(n-1)
 #beta=c(4,2,0,0,rep(0,p-4))
 #y=x%*%beta+sigma*rnorm(n)
-#y=y-mean(y)
 #
 # first run  glmnet
 #gfit=glmnet(x,y,standardize=FALSE)

From e3f80945a97e37b34e6a3058a7bdc7d6b3e82858 Mon Sep 17 00:00:00 2001
From: Ryan Tibshirani <ryantibs@gmail.com>
Date: Sat, 28 Nov 2015 12:28:07 -0500
Subject: [PATCH 076/493] Cleaned up the fs, fsInf, and plot.fs examples in the
 man files

---
 selectiveInference/NAMESPACE      |  6 ++--
 selectiveInference/R/funs.lar.R   |  2 +-
 selectiveInference/man/fs.Rd      | 41 +++++++--------------
 selectiveInference/man/fsInf.Rd   | 59 ++++++++++---------------------
 selectiveInference/man/plot.fs.Rd | 18 ++++------
 5 files changed, 42 insertions(+), 84 deletions(-)

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index b306d85..8dd9120 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -31,7 +31,7 @@ S3method("print", "groupfsInf")
 useDynLib("selectiveInference")
 import(glmnet)
 import(intervals)
-importFrom("graphics", "abline", "axis", "matplot")
-importFrom("stats", "dnorm", "lsfit", "pexp", "pnorm", "predict",
-           "qnorm", "rnorm", "sd", "uniroot", "dchisq", "model.matrix", "pchisq")
+importFrom("graphics", abline, axis, matplot)
+importFrom("stats", dnorm, lsfit, pexp, pnorm, predict,
+           qnorm, rnorm, sd, uniroot, dchisq, model.matrix, pchisq)
 
diff --git a/selectiveInference/R/funs.lar.R b/selectiveInference/R/funs.lar.R
index b644699..26669ca 100644
--- a/selectiveInference/R/funs.lar.R
+++ b/selectiveInference/R/funs.lar.R
@@ -652,7 +652,7 @@ plot.lar <- function(x, xvar=c("norm","step","lambda"), breaks=TRUE,
   }
 
   plot(c(),c(),xlim=range(xx,na.rm=T),ylim=range(beta,na.rm=T),
-       xlab=xlab,ylab="Coefficients",main="LAR path",...)
+       xlab=xlab,ylab="Coefficients",main="Least angle regression path",...)
   abline(h=0,lwd=2)
   matplot(xx,t(beta),type="l",lty=1,add=TRUE)
   if (breaks) abline(v=xx,lty=2)
diff --git a/selectiveInference/man/fs.Rd b/selectiveInference/man/fs.Rd
index 931d1be..c1e2a7f 100644
--- a/selectiveInference/man/fs.Rd
+++ b/selectiveInference/man/fs.Rd
@@ -68,37 +68,20 @@ to enter along the path}
 }
 
 \examples{
-
-set.seed(43)
-n=50
-p=10
-sigma=.7
-x=matrix(rnorm(n*p),n,p)
-x=scale(x,TRUE,FALSE)
-beta=c(3,2,0,0,rep(0,p-4))
-y=x\%*\%beta+sigma*rnorm(n)
+set.seed(33)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
 # run forward stepwise
-fsfit=fs(x,y)
-# call inference function
-out=fsInf(fsfit)
-#
+fsfit = fs(x,y)
 
-#NOT RUN
-# states data example: predict life expectancy
-#x=state.x77[,-4]
-#y=state.x77[,4]
-#x=scale(x,TRUE,TRUE)
-#n=nrow(x)
-#
-#  estimate sigma from cross-validated lasso fit
-#cvf=cv.glmnet(x,y)
-#sigmahat=estimateSigma(x,y,stand=FALSE)$sigmahat
-#
-# fit forwardStepwise
-# fsfit=fs(x,y,normalize=FALSE)
-#
-# compute p-values and confidence intervals
-#  fsInf(fsfit,sigma=sigmahat)
+# compute sequential p-values and confidence intervals
+# (sigma estimated from full model)
+out = fsInf(fsfit)
+out
 }
 
 
diff --git a/selectiveInference/man/fsInf.Rd b/selectiveInference/man/fsInf.Rd
index 66fc758..237f7e6 100644
--- a/selectiveInference/man/fsInf.Rd
+++ b/selectiveInference/man/fsInf.Rd
@@ -109,47 +109,26 @@ model selection. arXiv:1405.3920.
 \seealso{\code{\link{fs}}}
 
 \examples{
-set.seed(433)
-
-n=50
-p=10
-sigma=.7
-x=matrix(rnorm(n*p),n,p)
+set.seed(33)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+# run forward stepwise
+fsfit = fs(x,y)
 
-x=scale(x,TRUE,TRUE)/sqrt(n-1)
-beta=c(4,2,0,0,rep(0,p-4))
-y=x\%*\%beta+sigma*rnorm(n)
-y=y-mean(y)
-#first run forward stepwise
- fsfit=fs(x,y)
-#
-# forward stepwise inference for each successive entry of a predictor; 
-#
-#  sigma estimated from mean squared residual
-#  
- aa=fsInf(fsfit)
+# compute sequential p-values and confidence intervals
+# (sigma estimated from full model)
+out.seq = fsInf(fsfit)
+out.seq
 
-#NOT RUN
-# forward stepwise inference for fixed model of size 4, testing deletion of each predictor; 
-# known value of sigma used
-# aa2=fsInf(fsfit,sigma=sigma,type="all",k=4)
+# compute p-values and confidence intervals after AIC stopping
+out.aic = fsInf(fsfit,type="aic")
+out.aic
 
-# forward stepwise inference for  model chosen by AIC, testing deletion of each predictor; 
-# known value of sigma used
-# aa3=fsInf(fsfit,sigma=sigma,type="aic")
-#
-# states data example: predict life expectancy
-#x=state.x77[,-4]
-#y=state.x77[,4]
-#x=scale(x,TRUE,TRUE)
-#n=nrow(x)
-#  estimate sigma from cross-validated lasso fit
-#cvf=cv.glmnet(x,y)
-#sigmahat=estimateSigma(x,y,stand=FALSE)$sigmahat
-#
-# run forward stepwise
-#fsfit=fs(x,y,normalize=FALSE)
-#
-# compute p-values and confidence intervals
-# fsInf(fsfit,sigma=sigmahat)
+# compute p-values and confidence intervals after 5 fixed steps
+out.fix = fsInf(fsfit,type="all",k=5)
+out.fix
 }
diff --git a/selectiveInference/man/plot.fs.Rd b/selectiveInference/man/plot.fs.Rd
index e90da5d..b712cdc 100644
--- a/selectiveInference/man/plot.fs.Rd
+++ b/selectiveInference/man/plot.fs.Rd
@@ -29,16 +29,12 @@ Default is TRUE}
 
 \examples{
 set.seed(33)
-n=200
-p=20
-sigma=1
-x=matrix(rnorm(n*p),n,p)
-x=scale(x,TRUE,TRUE)/sqrt(n-1)
-#generate y
-beta=c(3,-2,rep(0,p-2))
-beta=c(rep(3,10),rep(0,p-10))
-y=x\%*\%beta+sigma*rnorm(n)
-y=y-mean(y)
-fsfit=fs(x,y)
+n = 200
+p = 20
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(rep(3,10),rep(0,p-10))
+y = x\%*\%beta + sigma*rnorm(n)
+fsfit = fs(x,y)
 plot(fsfit)
 }

From 3117fd7b950a51ba68c36d96eed6dbb03f52db01 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Sat, 28 Nov 2015 17:32:33 -0800
Subject: [PATCH 077/493] rob

---
 selectiveInference/man/estimateSigma.Rd      | 11 ++++++++---
 selectiveInference/man/fixedLassoInf.Rd      |  7 ++++---
 selectiveInference/man/forwardStop.Rd        |  3 ++-
 selectiveInference/man/lar.Rd                |  3 ++-
 selectiveInference/man/larInf.Rd             | 12 ++++++------
 selectiveInference/man/plot.lar.Rd           |  7 +++++--
 selectiveInference/man/predict.fs.Rd         |  7 ++++---
 selectiveInference/man/predict.lar.Rd        |  8 ++++++--
 selectiveInference/man/selectiveInference.Rd |  8 +++-----
 9 files changed, 40 insertions(+), 26 deletions(-)

diff --git a/selectiveInference/man/estimateSigma.Rd b/selectiveInference/man/estimateSigma.Rd
index 9397a08..ad7ee68 100644
--- a/selectiveInference/man/estimateSigma.Rd
+++ b/selectiveInference/man/estimateSigma.Rd
@@ -42,20 +42,25 @@ A study of error variance estimation in lasso regression. arXiv:1311.5274.
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
+# generate data
+#
 set.seed(43)
 n=50
 p=10
 sigma=.7
 x=matrix(rnorm(n*p),n,p)
-x=scale(x,TRUE,FALSE)
 beta=c(3,2,0,0,rep(0,p-4))
 y=x\%*\%beta+sigma*rnorm(n)
 y=y-mean(y)
-
+#
+# Estimate sigma
 out=estimateSigma(x,y)
+# #run forwrad stepwise
 fit=fs(x,y)
+#
+# run inference functions
 out2=fsInf(fit,sigma=out$sigmahat)
-
+out2
 }
 
 
diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index 609136e..1ef078d 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -121,22 +121,23 @@ n=50
 p=10
 sigma=.7
 x=matrix(rnorm(n*p),n,p)
-x=scale(x,TRUE,TRUE)/sqrt(n-1)
 beta=c(3,2,0,0,rep(0,p-4))
 y=x\%*\%beta+sigma*rnorm(n)
 y=y-mean(y)
+#
 # first run  glmnet
-gfit=glmnet(x,y,standardize=FALSE)
+gfit=glmnet(x,y)
 lambda = .1
 #extract coef for a given lambda; Note the 1/n factor!
 beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
 
 # compute fixed lambda p-values and selection intervals
 out=fixedLassoInf(x,y,beta,lambda,sigma=sigma)
+out
 #
 #NOT RUN
 # as above, but use lar function instead to get initial lasso fit (should get same result)
-# fit=lar(x,y,normalize=FALSE)
+# fit=lar(x,y)
 # beta=coef(fit,s=lambda,mode="lambda")
 # fixedLassoInf(x,y,beta,lambda,sigma=sigma)
 
diff --git a/selectiveInference/man/forwardStop.Rd b/selectiveInference/man/forwardStop.Rd
index 7863afe..e30e052 100644
--- a/selectiveInference/man/forwardStop.Rd
+++ b/selectiveInference/man/forwardStop.Rd
@@ -39,15 +39,16 @@ n=50
 p=10
 sigma=.7
 x=matrix(rnorm(n*p),n,p)
-x=scale(x,TRUE,TRUE)/sqrt(n-1)
 beta=c(4,2,0,0,rep(0,p-4))
 y=x\%*\%beta+sigma*rnorm(n)
 y=y-mean(y)
+#
 #first run forward stepwise
  fsfit=fs(x,y)
 #
 # run inference function 
  out=fsInf(fsfit)
+#
 # estimate optimal stopping point
  forwardStop(out$pv, alpha=.10)
 }
diff --git a/selectiveInference/man/lar.Rd b/selectiveInference/man/lar.Rd
index 1e9f26e..92e1c1e 100644
--- a/selectiveInference/man/lar.Rd
+++ b/selectiveInference/man/lar.Rd
@@ -77,14 +77,15 @@ Jerome Friedman (2002, 2009). Elements of Statistical Learning.
 
 \examples{
 
+#generate data
 set.seed(33)
 n=20
 p=10
 sigma=1
 x=matrix(rnorm(n*p),n,p)
-#generate data
 beta=c(3,3,rep(0,p-2))
 y=x\%*\%beta+sigma*rnorm(n)
+#
 #run lar and plot results
 larfit=lar(x,y,verbose=TRUE)
 plot(larfit)                                      
diff --git a/selectiveInference/man/larInf.Rd b/selectiveInference/man/larInf.Rd
index e45900b..15e6c90 100644
--- a/selectiveInference/man/larInf.Rd
+++ b/selectiveInference/man/larInf.Rd
@@ -120,28 +120,28 @@ n=50
 p=10
 sigma=.7
 x=matrix(rnorm(n*p),n,p)
-x=scale(x,TRUE,TRUE)/sqrt(n-1)
 beta=c(3,2,0,0,rep(0,p-4))
 y=x\%*\%beta+sigma*rnorm(n)
 #first run lar
- larfit=lar(x,y)
+larfit=lar(x,y)
+larfit
 #
 #lar inference for each successive entry of a predictor; sigma estimated
 #  from mean squared residual from least squares fit
- out=larInf(larfit)
+out=larInf(larfit)
+out
 #
 #NOT RUN
 # states data example: predict life expectancy
 #x=state.x77[,-4]
 #y=state.x77[,4]
-#x=scale(x,TRUE,TRUE)
 #n=nrow(x)
 #  estimate sigma from cross-validated lasso fit
 #cvf=cv.glmnet(x,y)
-#sigmahat=estimateSigma(x,y,stand=FALSE)$sigmahat
+#sigmahat=estimateSigma(x,y)$sigmahat
 #
 # run LAR
-#larfit=lar(x,y,normalize=FALSE)
+#larfit=lar(x,y)
 #
 # compute p-values and confidence intervals
 # larInf(larfit,sigma=sigmahat)
diff --git a/selectiveInference/man/plot.lar.Rd b/selectiveInference/man/plot.lar.Rd
index 668ce1c..03e9193 100644
--- a/selectiveInference/man/plot.lar.Rd
+++ b/selectiveInference/man/plot.lar.Rd
@@ -32,17 +32,20 @@ Default is TRUE}
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
+#generate data
 set.seed(33)
 n=200
 p=20
 sigma=1
 x=matrix(rnorm(n*p),n,p)
-x=scale(x,TRUE,TRUE)/sqrt(n-1)
-#generate y
 beta=c(3,-2,rep(0,p-2))
 beta=c(rep(3,10),rep(0,p-10))
 y=x\%*\%beta+sigma*rnorm(n)
 y=y-mean(y)
+#
+#run lar
 larfit=lar(x,y)
+#
+#plot results
 plot(larfit)
 }
diff --git a/selectiveInference/man/predict.fs.Rd b/selectiveInference/man/predict.fs.Rd
index ca3a32a..f64e63b 100644
--- a/selectiveInference/man/predict.fs.Rd
+++ b/selectiveInference/man/predict.fs.Rd
@@ -35,18 +35,19 @@ Either a vector/matrix of predictions, or a vector/matrix of coefficients.
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
+#generate data
 set.seed(33)
 n=200
 p=20
 sigma=1
 x=matrix(rnorm(n*p),n,p)
-x=scale(x,TRUE,TRUE)/sqrt(n-1)
-#generate y
-beta=c(3,-2,rep(0,p-2))
 beta=c(rep(3,10),rep(0,p-10))
 y=x\%*\%beta+sigma*rnorm(n)
 y=y-mean(y)
+#
 # run forward stepwise and predict functions
 obj=fs(x,y)
+obj
 fit=predict.fs(obj,x,s=3)
+fit
 }
diff --git a/selectiveInference/man/predict.lar.Rd b/selectiveInference/man/predict.lar.Rd
index 16aa795..67331ed 100644
--- a/selectiveInference/man/predict.lar.Rd
+++ b/selectiveInference/man/predict.lar.Rd
@@ -38,16 +38,20 @@ Either a vector/matrix of predictions, or a vector/matrix of coefficients.
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
+#generate data
 set.seed(33)
 n=200
 p=20
 sigma=1
 x=matrix(rnorm(n*p),n,p)
-x=scale(x,TRUE,TRUE)/sqrt(n-1)
-#generate y
+
 beta=c(3,-2,rep(0,p-2))
 beta=c(rep(3,10),rep(0,p-10))
 y=x\%*\%beta+sigma*rnorm(n)
+#run lar
 larfit=lar(x,y)
+# predict from lar fit
 fit=predict.lar(larfit,x,s=3.5,type="fit")
+fit
+
 }
diff --git a/selectiveInference/man/selectiveInference.Rd b/selectiveInference/man/selectiveInference.Rd
index 66423ca..2aca327 100644
--- a/selectiveInference/man/selectiveInference.Rd
+++ b/selectiveInference/man/selectiveInference.Rd
@@ -78,17 +78,17 @@ n=50
 p=10
 sigma=.7
 x=matrix(rnorm(n*p),n,p)
-x=scale(x,TRUE,TRUE)/sqrt(n-1)
 beta=c(4,2,0,0,rep(0,p-4))
 y=x\%*\%beta+sigma*rnorm(n)
 #
 #first run forward stepwise
- fsfit=fs(x,y)
+fsfit=fs(x,y)
 #
 # forward stepwise inference for each successive entry of a predictor; 
 #   sigma estimated from mean squared residual
 #   
 out=fsInf(fsfit)
+out
 
 ##
 #NOT RUN
@@ -99,7 +99,6 @@ out=fsInf(fsfit)
 #p=10
 #sigma=.7
 #x=matrix(rnorm(n*p),n,p)
-#x=scale(x,TRUE,TRUE)/sqrt(n-1)
 #beta=c(4,2,0,0,rep(0,p-4))
 #y=x%*%beta+sigma*rnorm(n)
 #
@@ -118,11 +117,11 @@ out=fsInf(fsfit)
 #p=10
 #sigma=.7
 #x=matrix(rnorm(n*p),n,p)
-#x=scale(x,TRUE,TRUE)/sqrt(n-1)
 #beta=c(3,2,0,0,rep(0,p-4))
 #y=x%*%beta+sigma*rnorm(n)
 #first run lar
 # larfit=lar(x,y)
+# larfit
 #
 #lar inference for each successive entry of a predictor; sigma estimated
 #  from mean squared residual from least squares fit
@@ -175,7 +174,6 @@ set.seed(43)
 #p=10
 #sigma=.7
 #x=matrix(rnorm(n*p),n,p)
-#x=scale(x,T,F)
 #beta=c(3,2,0,0,rep(0,p-4))
 #y=x%*%beta+sigma*rnorm(n)
 #

From 179de960ad9337c036049c2835112a73ade8e344 Mon Sep 17 00:00:00 2001
From: Ryan Tibshirani <ryantibs@gmail.com>
Date: Sun, 29 Nov 2015 17:05:31 -0500
Subject: [PATCH 078/493] Just some edits to the documentation

---
 selectiveInference/R/funs.groupfs.R          |   3 +-
 selectiveInference/man/estimateSigma.Rd      |  44 ++---
 selectiveInference/man/fixedLassoInf.Rd      |  60 ++----
 selectiveInference/man/forwardStop.Rd        |  35 ++--
 selectiveInference/man/fs.Rd                 |  14 +-
 selectiveInference/man/fsInf.Rd              |   3 +-
 selectiveInference/man/lar.Rd                |  31 +--
 selectiveInference/man/larInf.Rd             |  53 +++--
 selectiveInference/man/manyMeans.Rd          |  14 +-
 selectiveInference/man/plot.fs.Rd            |   8 +-
 selectiveInference/man/plot.lar.Rd           |  25 +--
 selectiveInference/man/predict.fs.Rd         |  22 +--
 selectiveInference/man/predict.lar.Rd        |  25 +--
 selectiveInference/man/selectiveInference.Rd | 195 ++++++++-----------
 14 files changed, 230 insertions(+), 302 deletions(-)

diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 20ef265..a5f46c8 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -19,7 +19,8 @@
 #' index = sort(rep(1:20, 2))
 #' y = rnorm(20) + 2 * x[,1] - x[,4]
 #' fit = groupfs(x, y, index, maxsteps = 5)
-#' pvals = groupfsInf(fit)
+#' out = groupfsInf(fit)
+#' out
 #' @seealso \code{\link{groupfsInf}}, \code{\link{factorDesign}}.
 groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE, center = TRUE, normalize = TRUE, aicstop = 0, verbose = FALSE) {
 
diff --git a/selectiveInference/man/estimateSigma.Rd b/selectiveInference/man/estimateSigma.Rd
index ad7ee68..c956121 100644
--- a/selectiveInference/man/estimateSigma.Rd
+++ b/selectiveInference/man/estimateSigma.Rd
@@ -26,41 +26,39 @@ This function estimates the standard deviation of the noise, in a linear regresi
 A lasso regression is fit, using cross-validation to estimate the tuning parameter lambda.
 With sample size n, yhat equal to the predicted values and df being the number of nonzero 
 coefficients from the lasso fit, the estimate of sigma is \code{sqrt(sum((y-yhat)^2) / (n-df-1))}.
-Important: if you are using glmnet to compute the lasso estimate, be sure to use the same values
-for the intercept and standardized argument in glmnet and estimateSigma. Same applies to  fs or lar,
-where the argument for standardization is called "normalize"
+Important: if you are using glmnet to compute the lasso estimate, be sure to use the settings
+for the "intercept" and "standardize" arguments in glmnet and estimateSigma. Same applies to fs 
+or lar, where the argument for standardization is called "normalize".
 }
 \value{
 \item{sigmahat}{The estimate of sigma}
 \item{df}{The degrees of freedom of lasso fit used}
 }
 \references{
-Stephen Reid, Jerome Friedman and Rob Tibshirani (2014). 
+Stephen Reid, Jerome Friedman, and Rob Tibshirani (2014). 
 A study of error variance estimation in lasso regression. arXiv:1311.5274.
 }
 
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
-# generate data
-#
-set.seed(43)
-n=50
-p=10
-sigma=.7
-x=matrix(rnorm(n*p),n,p)
-beta=c(3,2,0,0,rep(0,p-4))
-y=x\%*\%beta+sigma*rnorm(n)
-y=y-mean(y)
-#
-# Estimate sigma
-out=estimateSigma(x,y)
-# #run forwrad stepwise
-fit=fs(x,y)
-#
-# run inference functions
-out2=fsInf(fit,sigma=out$sigmahat)
-out2
+set.seed(33)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run forward stepwise
+fsfit = fs(x,y)
+
+# estimate sigma
+sigmahat = estimateSigma(x,y)$sigmahat
+
+# run sequential inference with estimated sigma
+out = fsInf(fsfit,sigma=sigmahat)
+out
 }
 
 
diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index 1ef078d..8c7afce 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -108,55 +108,37 @@ to alpha/2, and can be used for error-checking purposes.
 }
 
 \references{
-Jason Lee, Dennis Sun, Yuekai Sun, Jonathan Taylor (2013). 
+Jason Lee, Dennis Sun, Yuekai Sun, and Jonathan Taylor (2013). 
 Exact post-selection inference, with application to the lasso. arXiv:1311.6238.
 }
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
-
-#generate data
 set.seed(43)
-n=50
-p=10
-sigma=.7
-x=matrix(rnorm(n*p),n,p)
-beta=c(3,2,0,0,rep(0,p-4))
-y=x\%*\%beta+sigma*rnorm(n)
-y=y-mean(y)
-#
-# first run  glmnet
-gfit=glmnet(x,y)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# first run glmnet
+gfit = glmnet(x,y)
+
+# extract coef for a given lambda; note the 1/n factor!
+# (and we don't save the intercept term)
 lambda = .1
-#extract coef for a given lambda; Note the 1/n factor!
 beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
 
 # compute fixed lambda p-values and selection intervals
-out=fixedLassoInf(x,y,beta,lambda,sigma=sigma)
+out = fixedLassoInf(x,y,beta,lambda,sigma=sigma)
 out
-#
-#NOT RUN
-# as above, but use lar function instead to get initial lasso fit (should get same result)
-# fit=lar(x,y)
-# beta=coef(fit,s=lambda,mode="lambda")
-# fixedLassoInf(x,y,beta,lambda,sigma=sigma)
-
 
-##
-# states data example: predict life expectancy
-#x=state.x77[,-4]
-#y=state.x77[,4]
-#x=scale(x,TRUE,TRUE)
-#n=nrow(x)
-#  estimate sigma from cross-validated lasso fit
-#  cvf=cv.glmnet(x,y)
-# sigmahat=estimateSigma(x,y,stand=FALSE)$sigmahat
-#
-# fit lasso
-# lambda=n*cvf$lambda.min
-# gfit=glmnet(x,y,standardize=FALSE)
-# bhat=coef(gfit, s=lambda/n, exact=TRUE)[-1]
-#
-# compute p-values and confidence intervals
-# fixedLassoInf(x,y,bhat,lambda,sigma=sigmahat)
+## NOT RUN
+## as above, but use lar function instead to get initial 
+## lasso fit (should get same results)
+# lfit = lar(x,y)
+# beta = coef(lfit,s=lambda,mode="lambda")
+# out2 = fixedLassoInf(x,y,beta,lambda,sigma=sigma)
+# out2
 }
diff --git a/selectiveInference/man/forwardStop.Rd b/selectiveInference/man/forwardStop.Rd
index e30e052..87eb7ab 100644
--- a/selectiveInference/man/forwardStop.Rd
+++ b/selectiveInference/man/forwardStop.Rd
@@ -27,28 +27,29 @@ Guarantees FDR control at the level alpha, for independent p-values.
 Step number for sequential stop.
 }
 \references{
-Max Grazier G'Sell, Stefan Wager, Alexandra Chouldechova, Rob Tibshirani (2014).
+Max Grazier G'Sell, Stefan Wager, Alexandra Chouldechova, and Rob Tibshirani (2014).
 Sequential selection procedures and Fflse Discovery Rate Control. arXiv:1309.5352.
 To appear in Journal of the Royal Statistical Society: Series B.
 }
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
-set.seed(433)
-n=50
-p=10
-sigma=.7
-x=matrix(rnorm(n*p),n,p)
-beta=c(4,2,0,0,rep(0,p-4))
-y=x\%*\%beta+sigma*rnorm(n)
-y=y-mean(y)
-#
-#first run forward stepwise
- fsfit=fs(x,y)
-#
-# run inference function 
- out=fsInf(fsfit)
-#
+set.seed(33)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run forward stepwise
+fsfit = fs(x,y)
+
+# compute sequential p-values and confidence intervals
+# (sigma estimated from full model)
+out = fsInf(fsfit)
+out
+
 # estimate optimal stopping point
- forwardStop(out$pv, alpha=.10)
+forwardStop(out$pv, alpha=.10)
 }
diff --git a/selectiveInference/man/fs.Rd b/selectiveInference/man/fs.Rd
index c1e2a7f..2a61c83 100644
--- a/selectiveInference/man/fs.Rd
+++ b/selectiveInference/man/fs.Rd
@@ -32,12 +32,14 @@ the interface for the \code{lar} function)
 
 \details{
 This function implements forward stepwise regression, adding the predictor at each 
-step that maximizes the absolute correlation between the predictors-- orthogonalized wrt the current model--  and the residual. This entry
+step that maximizes the absolute correlation between the predictors---once 
+orthogonalized with respect to the current model---and the residual. This entry
 criterion is standard, and is equivalent to choosing the variable that achieves 
 the biggest drop in RSS at each step; it is used, e.g., by the \code{step} function
-in R. Note that, for example,  the \code{lars} package implements a stepwise option (with type="step"), but
-uses a (mildly) different entry criterion, based on maximal absolute correlation between the original (non-orthogonalized)
-predictors and the residual. 
+in R. Note that, for example, the \code{lars} package implements a stepwise option 
+(with type="step"), but uses a (mildly) different entry criterion, based on maximal
+absolute correlation between the original (non-orthogonalized) predictors and the 
+residual. 
 }
 \value{
 \item{action}{Vector of predictors in order of entry}
@@ -75,8 +77,10 @@ sigma = 1
 x = matrix(rnorm(n*p),n,p)
 beta = c(3,2,rep(0,p-2))
 y = x\%*\%beta + sigma*rnorm(n)
-# run forward stepwise
+
+# run forward stepwise, plot results
 fsfit = fs(x,y)
+plot(fsfit)
 
 # compute sequential p-values and confidence intervals
 # (sigma estimated from full model)
diff --git a/selectiveInference/man/fsInf.Rd b/selectiveInference/man/fsInf.Rd
index 237f7e6..b86584d 100644
--- a/selectiveInference/man/fsInf.Rd
+++ b/selectiveInference/man/fsInf.Rd
@@ -97,7 +97,7 @@ value chosen by the modified AIC scheme}
 }
 
 \references{
-Ryan Tibshirani, Jonathan Taylor, Richard Lockhart, Rob Tibshirani (2014).
+Ryan Tibshirani, Jonathan Taylor, Richard Lockhart, and Rob Tibshirani (2014).
 Exact post-selection inference for sequential regression procedures. arXiv:1401.3889.
 
 Joshua Loftus and Jonathan Taylor (2014). A significance test for forward stepwise
@@ -116,6 +116,7 @@ sigma = 1
 x = matrix(rnorm(n*p),n,p)
 beta = c(3,2,rep(0,p-2))
 y = x\%*\%beta + sigma*rnorm(n)
+
 # run forward stepwise
 fsfit = fs(x,y)
 
diff --git a/selectiveInference/man/lar.Rd b/selectiveInference/man/lar.Rd
index 92e1c1e..590a663 100644
--- a/selectiveInference/man/lar.Rd
+++ b/selectiveInference/man/lar.Rd
@@ -62,10 +62,10 @@ to enter along the path}
 }
 
 \references{
-Brad Efron, Trevor Hastie, Iain Johnstone and Rob Tibshirani (2002).
+Brad Efron, Trevor Hastie, Iain Johnstone, and Rob Tibshirani (2002).
 Least angle regression. Annals of Statistics (with discussion).
 
-See also the descriptions in Trevor Hastie, Rob Tibshirani and 
+See also the descriptions in Trevor Hastie, Rob Tibshirani, and 
 Jerome Friedman (2002, 2009). Elements of Statistical Learning.
 }
 
@@ -76,17 +76,20 @@ Jerome Friedman (2002, 2009). Elements of Statistical Learning.
 }
 
 \examples{
+set.seed(43)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
 
-#generate data
-set.seed(33)
-n=20
-p=10
-sigma=1
-x=matrix(rnorm(n*p),n,p)
-beta=c(3,3,rep(0,p-2))
-y=x\%*\%beta+sigma*rnorm(n)
-#
-#run lar and plot results
-larfit=lar(x,y,verbose=TRUE)
-plot(larfit)                                      
+# run LAR, plot results
+larfit = lar(x,y)
+plot(larfit)
+
+# compute sequential p-values and confidence intervals
+# (sigma estimated from full model)
+out = larInf(larfit)
+out                                    
 }
diff --git a/selectiveInference/man/larInf.Rd b/selectiveInference/man/larInf.Rd
index 15e6c90..8e3b2d0 100644
--- a/selectiveInference/man/larInf.Rd
+++ b/selectiveInference/man/larInf.Rd
@@ -102,7 +102,7 @@ value chosen by the modified AIC scheme}
 }
 
 \references{
-Ryan Tibshirani, Jonathan Taylor, Richard Lockhart, Rob Tibshirani (2014).
+Ryan Tibshirani, Jonathan Taylor, Richard Lockhart, and Rob Tibshirani (2014).
 Exact post-selection inference for sequential regression procedures. arXiv:1401.3889.
 }
 
@@ -113,37 +113,28 @@ Exact post-selection inference for sequential regression procedures. arXiv:1401.
 }
 
 \examples{
-
-#generate data
 set.seed(43)
-n=50
-p=10
-sigma=.7
-x=matrix(rnorm(n*p),n,p)
-beta=c(3,2,0,0,rep(0,p-4))
-y=x\%*\%beta+sigma*rnorm(n)
-#first run lar
-larfit=lar(x,y)
-larfit
-#
-#lar inference for each successive entry of a predictor; sigma estimated
-#  from mean squared residual from least squares fit
-out=larInf(larfit)
-out
-#
-#NOT RUN
-# states data example: predict life expectancy
-#x=state.x77[,-4]
-#y=state.x77[,4]
-#n=nrow(x)
-#  estimate sigma from cross-validated lasso fit
-#cvf=cv.glmnet(x,y)
-#sigmahat=estimateSigma(x,y)$sigmahat
-#
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
 # run LAR
-#larfit=lar(x,y)
-#
-# compute p-values and confidence intervals
-# larInf(larfit,sigma=sigmahat)
+larfit = lar(x,y)
+
+# compute sequential p-values and confidence intervals
+# (sigma estimated from full model)
+out.seq = larInf(larfit)
+out.seq
+
+# compute p-values and confidence intervals after AIC stopping
+out.aic = larInf(larfit,type="aic")
+out.aic
+
+# compute p-values and confidence intervals after 5 fixed steps
+out.fix = larInf(larfit,type="all",k=5)
+out.fix
 }
 
diff --git a/selectiveInference/man/manyMeans.Rd b/selectiveInference/man/manyMeans.Rd
index 62a1400..57fc429 100644
--- a/selectiveInference/man/manyMeans.Rd
+++ b/selectiveInference/man/manyMeans.Rd
@@ -43,7 +43,7 @@ were selected by the procedure (either BH(q) or top-K). Labelled "Selind" in out
 }
 
 \references{
-Stephen Reid, Jonathan Taylor, Rob Tibshirani (2014).
+Stephen Reid, Jonathan Taylor, and Rob Tibshirani (2014).
 Post-selection point and interval estimation of signal sizes in Gaussian samples.
 arXiv:1405.3340.
 }
@@ -51,12 +51,10 @@ arXiv:1405.3340.
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
-
-
 set.seed(12345)
-n = 100 # sample size
-signal = 3 # signal size
-mu = c(rep(signal, floor (n/5)), rep(0, n-floor(n/5))) # 20% of elements get the signal; rest 0
-y = mu + rnorm (n, 0, 1)
-out  = manyMeans(y, bh.q=0.1)
+n = 100 
+mu = c(rep(3,floor(n/5)), rep(0,n-floor(n/5))) 
+y = mu + rnorm(n)
+out = manyMeans(y, bh.q=0.1)
+out
 }
diff --git a/selectiveInference/man/plot.fs.Rd b/selectiveInference/man/plot.fs.Rd
index b712cdc..4f77013 100644
--- a/selectiveInference/man/plot.fs.Rd
+++ b/selectiveInference/man/plot.fs.Rd
@@ -29,12 +29,14 @@ Default is TRUE}
 
 \examples{
 set.seed(33)
-n = 200
-p = 20
+n = 50
+p = 10
 sigma = 1
 x = matrix(rnorm(n*p),n,p)
-beta = c(rep(3,10),rep(0,p-10))
+beta = c(3,2,rep(0,p-2))
 y = x\%*\%beta + sigma*rnorm(n)
+
+# run forward stepwise, plot results
 fsfit = fs(x,y)
 plot(fsfit)
 }
diff --git a/selectiveInference/man/plot.lar.Rd b/selectiveInference/man/plot.lar.Rd
index 03e9193..baa1195 100644
--- a/selectiveInference/man/plot.lar.Rd
+++ b/selectiveInference/man/plot.lar.Rd
@@ -32,20 +32,15 @@ Default is TRUE}
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
-#generate data
-set.seed(33)
-n=200
-p=20
-sigma=1
-x=matrix(rnorm(n*p),n,p)
-beta=c(3,-2,rep(0,p-2))
-beta=c(rep(3,10),rep(0,p-10))
-y=x\%*\%beta+sigma*rnorm(n)
-y=y-mean(y)
-#
-#run lar
-larfit=lar(x,y)
-#
-#plot results
+set.seed(43)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run LAR, plot results
+larfit = lar(x,y)
 plot(larfit)
 }
diff --git a/selectiveInference/man/predict.fs.Rd b/selectiveInference/man/predict.fs.Rd
index f64e63b..5e50482 100644
--- a/selectiveInference/man/predict.fs.Rd
+++ b/selectiveInference/man/predict.fs.Rd
@@ -35,19 +35,15 @@ Either a vector/matrix of predictions, or a vector/matrix of coefficients.
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
-#generate data
 set.seed(33)
-n=200
-p=20
-sigma=1
-x=matrix(rnorm(n*p),n,p)
-beta=c(rep(3,10),rep(0,p-10))
-y=x\%*\%beta+sigma*rnorm(n)
-y=y-mean(y)
-#
+n = 200
+p = 20
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(rep(3,10),rep(0,p-10))
+y = x\%*\%beta + sigma*rnorm(n)
+
 # run forward stepwise and predict functions
-obj=fs(x,y)
-obj
-fit=predict.fs(obj,x,s=3)
-fit
+obj = fs(x,y)
+fit = predict(obj,x,s=3)
 }
diff --git a/selectiveInference/man/predict.lar.Rd b/selectiveInference/man/predict.lar.Rd
index 67331ed..c91bed3 100644
--- a/selectiveInference/man/predict.lar.Rd
+++ b/selectiveInference/man/predict.lar.Rd
@@ -38,20 +38,15 @@ Either a vector/matrix of predictions, or a vector/matrix of coefficients.
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
-#generate data
 set.seed(33)
-n=200
-p=20
-sigma=1
-x=matrix(rnorm(n*p),n,p)
-
-beta=c(3,-2,rep(0,p-2))
-beta=c(rep(3,10),rep(0,p-10))
-y=x\%*\%beta+sigma*rnorm(n)
-#run lar
-larfit=lar(x,y)
-# predict from lar fit
-fit=predict.lar(larfit,x,s=3.5,type="fit")
-fit
-
+n = 200
+p = 20
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(rep(3,10),rep(0,p-10))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run lar and predict functions
+obj = lar(x,y)
+fit = predict(obj,x,s=3)
 }
diff --git a/selectiveInference/man/selectiveInference.Rd b/selectiveInference/man/selectiveInference.Rd
index 2aca327..7d2ca57 100644
--- a/selectiveInference/man/selectiveInference.Rd
+++ b/selectiveInference/man/selectiveInference.Rd
@@ -58,132 +58,93 @@ Maintainer: Rob Tibshirani <tibs@stanford.edu>
 }
 
 \references{
-Ryan Tibshirani, Jonathan Taylor, Richard Lockhart, Rob Tibshirani (2014).
+Ryan Tibshirani, Jonathan Taylor, Richard Lockhart, and Rob Tibshirani (2014).
 Exact post-selection inference for sequential regression procedures. arXiv:1401.3889.
 
-Jason Lee, Dennis Sun, Yuekai Sun, Jonathan Taylor (2013). 
+Jason Lee, Dennis Sun, Yuekai Sun, and Jonathan Taylor (2013). 
 Exact post-selection inference, with application to the lasso. arXiv:1311.6238.
 
-Stephen Reid, Jonathan Taylor, Rob Tibshirani (2014).
+Stephen Reid, Jonathan Taylor, and Rob Tibshirani (2014).
 Post-selection point and interval estimation of signal sizes in Gaussian samples.
 arXiv:1405.3340.
 }
 
 \examples{
-# forward stepwise:
-#
-#generate some data
-set.seed(43)
-n=50
-p=10
-sigma=.7
-x=matrix(rnorm(n*p),n,p)
-beta=c(4,2,0,0,rep(0,p-4))
-y=x\%*\%beta+sigma*rnorm(n)
-#
-#first run forward stepwise
-fsfit=fs(x,y)
-#
-# forward stepwise inference for each successive entry of a predictor; 
-#   sigma estimated from mean squared residual
-#   
-out=fsInf(fsfit)
-out
-
-##
-#NOT RUN
-#  lasso with fixed lambda
-#
-#set.seed(43)
-#n=50
-#p=10
-#sigma=.7
-#x=matrix(rnorm(n*p),n,p)
-#beta=c(4,2,0,0,rep(0,p-4))
-#y=x%*%beta+sigma*rnorm(n)
-#
-# first run  glmnet
-#gfit=glmnet(x,y,standardize=FALSE)
-#lam = .1
-#extract coef for a given lam; Note the 1/n factor in s!
-#bhat = coef(gfit, s=lam/n, exact=TRUE)[-1]
-
-# compute fixed lambda p-values and selection intervals
-#aa=fixedLassoInf(x,y,bhat,lam,sigma=sigma)
-#
-##least angle regression from mean squared residual
-#set.seed(43)
-#n=50
-#p=10
-#sigma=.7
-#x=matrix(rnorm(n*p),n,p)
-#beta=c(3,2,0,0,rep(0,p-4))
-#y=x%*%beta+sigma*rnorm(n)
-#first run lar
-# larfit=lar(x,y)
-# larfit
-#
-#lar inference for each successive entry of a predictor; sigma estimated
-#  from mean squared residual from least squares fit
-# aa=larInf(larfit)
-
-##
-##many normal means
-
-#set.seed(12345)
-#n = 100 # sample size
-#signal = 3 # signal size
-#mu = c(rep(signal, floor (n/5)), rep(0, n-floor(n/5))) # 20% of elements get the signal; rest 0
-#y = mu + rnorm (n, 0, 1)
-#mmObj = manyMeans(y, bh.q=0.1)
-#
-#
-# Forward stepwise with grouped variables
-#set.seed(1)
-#n <- 40
-#p <- 20
-#index <- sort(rep(1:(p/2), 2))
-#steps <- 10
-#sparsity <- 5
-#snr <- 3
-#   x <- matrix(rnorm(n*p), nrow=n)
-#      beta <- rep(0, p)
-#      beta[which(index %in% 1:sparsity)] <- snr
-#      y <-  x %*% beta+rnorm(n)
-#fit <- groupfs(x, y, index=1:p, maxsteps = steps)
-#out<- groupfsInf(fit)
-
-
-
-##estimation of sigma for use in fsInf or larInf or fixedLassoInf
-#
-#set.seed(43)
-#n=50
-#p=10
-#sigma=.7
-#x=matrix(rnorm(n*p),n,p)
-#x=scale(x,TRUE,FALSE)
-#beta=c(3,2,0,0,rep(0,p-4))
-#y=x%*%beta+sigma*rnorm(n)
-
-#out=estimateSigma(x,y)
-
-##estimation of lambda  for use in fixedLassoInf
-set.seed(43)
-#n=50
-#p=10
-#sigma=.7
-#x=matrix(rnorm(n*p),n,p)
-#beta=c(3,2,0,0,rep(0,p-4))
-#y=x%*%beta+sigma*rnorm(n)
-#
-#estimate lambda using the  known value of sigma
-#lamhat=estimateLambda(x,sigma=.7)
-#first estimate  sigma
-#sigmahat=estimateSigma(x,y)$sigmahat
-#lamhat=estimateLambda(x,sigma=sigmahat)
-#
-
+set.seed(33)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run forward stepwise
+fsfit = fs(x,y)
+
+# compute sequential p-values and confidence intervals
+# (sigma estimated from full model)
+out.seq = fsInf(fsfit)
+out.seq
+
+# compute p-values and confidence intervals after AIC stopping
+out.aic = fsInf(fsfit,type="aic")
+out.aic
+
+# compute p-values and confidence intervals after 5 fixed steps
+out.fix = fsInf(fsfit,type="all",k=5)
+out.fix
+
+## NOT RUN---lasso at fixed lambda
+## first run glmnet
+# gfit = glmnet(x,y)
+
+## extract coef for a given lambda; note the 1/n factor!
+## (and we don't save the intercept term)
+# lambda = .1
+# beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
+
+## compute fixed lambda p-values and selection intervals
+# out = fixedLassoInf(x,y,beta,lambda,sigma=sigma)
+# out
+
+## NOT RUN---many normal means
+# set.seed(12345)
+# n = 100 
+# mu = c(rep(3,floor(n/5)), rep(0,n-floor(n/5))) 
+# y = mu + rnorm(n)
+# out = manyMeans(y, bh.q=0.1)
+# out
+
+## NOT RUN---forward stepwise with groups
+# set.seed(1)
+# n = 20
+# p = 40
+# x = matrix(rnorm(n*p), nrow=n)
+# index = sort(rep(1:(p/2), 2))
+# y = rnorm(n) + 2 * x[,1] - x[,4]
+# fit = groupfs(x, y, index, maxsteps = 5)
+# out = groupfsInf(fit)
+# out
+
+## NOT RUN---estimation of sigma for use in fsInf
+## (or larInf or fixedLassoInf)
+# set.seed(33)
+# n = 50
+# p = 10
+# sigma = 1
+# x = matrix(rnorm(n*p),n,p)
+# beta = c(3,2,rep(0,p-2))
+# y = x\%*\%beta + sigma*rnorm(n)
+
+## run forward stepwise
+# fsfit = fs(x,y)
+
+## estimate sigma
+# sigmahat = estimateSigma(x,y)$sigmahat
+
+## run sequential inference with estimated sigma
+# out = fsInf(fit,sigma=sigmahat)
+# out
 }
 \keyword{ package }
 

From 9ef33d34879ab76437bb6194be1209bcafe9ce0a Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 30 Nov 2015 12:56:06 -0800
Subject: [PATCH 079/493] some renaming of variables for readability

---
 selectiveInference/R/funs.fs.R | 67 ++++++++++++++++++----------------
 1 file changed, 35 insertions(+), 32 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index a46b53e..0a1b1f1 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -27,8 +27,8 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
 
   #####
   # Find the first variable to enter and its sign
-  xx = scale(x,center=F,scale=sqrt(colSums(x^2)))
-  uhat = t(xx)%*%y
+  working_x = scale(x,center=F,scale=sqrt(colSums(x^2)))
+  uhat = t(working_x)%*%y
   ihit = which.max(abs(uhat))   # Hitting coordinate
   s = Sign(uhat[ihit])          # Sign
 
@@ -50,16 +50,17 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
 
   # Gamma matrix!
   gbuf = max(2*p*3,2000)     # Space for 3 steps, at least
-  gi = 0
+  gi = 0 # index into rows of Gamma matrix
+
   Gamma = matrix(0,gbuf,n)
-  Gamma[gi+Seq(1,p-1),] = t(s*xx[,ihit]+xx[,-ihit]); gi = gi+p-1
-  Gamma[gi+Seq(1,p-1),] = t(s*xx[,ihit]-xx[,-ihit]); gi = gi+p-1
-  Gamma[gi+1,] = t(s*xx[,ihit]); gi = gi+1
+  Gamma[gi+Seq(1,p-1),] = t(s*working_x[,ihit]+working_x[,-ihit]); gi = gi+p-1
+  Gamma[gi+Seq(1,p-1),] = t(s*working_x[,ihit]-working_x[,-ihit]); gi = gi+p-1
+  Gamma[gi+1,] = t(s*working_x[,ihit]); gi = gi+1
 
-  # nk
-  nk = numeric(buf)
+  # nconstraint
+  nconstraint = numeric(buf)
   vreg = matrix(0,buf,n)
-  nk[1] = gi
+  nconstraint[1] = gi
   vreg[1,] = s*x[,ihit] / sum(x[,ihit]^2)
 
   # Other things to keep track of, but not return
@@ -92,41 +93,43 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
       action = c(action,numeric(buf))
       df = c(df,numeric(buf))
       beta = cbind(beta,matrix(0,p,buf))
-      nk = c(nk,numeric(buf))
+      nconstraint = c(nconstraint,numeric(buf))
       vreg = rbind(vreg,matrix(0,buf,n))
     }
 
     # Key quantities for the next entry
     a = backsolve(R,t(Q1)%*%y)
     X2perp = X2 - X1 %*% backsolve(R,t(Q1)%*%X2)
-    xx = scale(X2perp,center=F,scale=sqrt(colSums(X2perp^2)))
-    aa = as.numeric(t(xx)%*%y)
+    working_x = scale(X2perp,center=F,scale=sqrt(colSums(X2perp^2)))
+    score = as.numeric(t(working_x)%*%y)
     
     # If the inactive set is empty, nothing will hit
     if (r==min(n-intercept,p)) break
 
     # Otherwise find the next hitting time
     else {
-      shits = Sign(aa)
-      hits = shits * aa
-      ihit = which.max(hits)
-      shit = shits[ihit]
+      sign_score = Sign(score)
+      abs_score = sign_score * score
+      ihit = which.max(abs_score)
+      shit = sign_score[ihit]
     }
     
     # Record the solution
-    action[k] = I[ihit]
+    # what is the difference between "action" and "A"?
+
+    action[k] = I[ihit] 
     df[k] = r
     beta[A,k] = a
         
     # Gamma matrix!
     if (gi + 2*p > nrow(Gamma)) Gamma = rbind(Gamma,matrix(0,2*p+gbuf,n))
-    xx = t(shits*t(xx))
-    Gamma[gi+Seq(1,p-r),] = t(xx); gi = gi+p-r
-    Gamma[gi+Seq(1,p-r-1),] = t(xx[,ihit]-xx[,-ihit]); gi = gi+p-r-1
-    Gamma[gi+1,] = t(xx[,ihit]); gi = gi+1
+    working_x = t(sign_score*t(working_x))
+    Gamma[gi+Seq(1,p-r),] = t(working_x); gi = gi+p-r
+    Gamma[gi+Seq(1,p-r-1),] = t(working_x[,ihit]-working_x[,-ihit]); gi = gi+p-r-1
+    Gamma[gi+1,] = t(working_x[,ihit]); gi = gi+1
 
-    # nk, regression contrast
-    nk[k] = gi
+    # nconstraint, regression contrast
+    nconstraint[k] = gi
     vreg[k,] = shit*X2perp[,ihit] / sum(X2perp[,ihit]^2)
 
     # Update all of the variables
@@ -156,7 +159,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   df = df[Seq(1,k-1),drop=FALSE]
   beta = beta[,Seq(1,k-1),drop=FALSE]
   Gamma = Gamma[Seq(1,gi),,drop=FALSE]
-  nk = nk[Seq(1,k-1)]
+  nconstraint = nconstraint[Seq(1,k-1)]
   vreg = vreg[Seq(1,k-1),,drop=FALSE]
   
   # If we reached the maximum number of steps
@@ -191,7 +194,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
 
   out = list(action=action,sign=s,df=df,beta=beta,
     completepath=completepath,bls=bls,
-    Gamma=Gamma,nk=nk,vreg=vreg,x=x,y=y,bx=bx,by=by,sx=sx,
+    Gamma=Gamma,nconstraint=nconstraint,vreg=vreg,x=x,y=y,bx=bx,by=by,sx=sx,
     intercept=intercept,normalize=normalize,call=this.call) 
   class(out) = "fs"
   return(out)
@@ -250,7 +253,7 @@ fsInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic
   p = ncol(x)
   n = nrow(x)
   G = obj$Gamma
-  nk = obj$nk
+  nconstraint = obj$nconstraint
   sx = obj$sx
 
   if (is.null(sigma)) {
@@ -278,8 +281,8 @@ fsInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic
     for (j in 1:k) {
       if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
 
-      Gj = G[1:nk[j],]
-      uj = rep(0,nk[j])
+      Gj = G[1:nconstraint[j],]
+      uj = rep(0,nconstraint[j])
       vj = vreg[j,]
       mj = sqrt(sum(vj^2)) 
       vj = vj / mj              # Standardize (divide by norm of vj)
@@ -304,13 +307,13 @@ fsInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic
       out = aicStop(x,y,obj$action[1:k],obj$df[1:k],sigma,mult,ntimes)
       khat = out$khat
       m = out$stopped * ntimes
-      G = rbind(out$G,G[1:nk[khat+m],])  # Take ntimes more steps past khat
-      u = c(out$u,rep(0,nk[khat+m]))     # (if we need to)
+      G = rbind(out$G,G[1:nconstraint[khat+m],])  # Take ntimes more steps past khat
+      u = c(out$u,rep(0,nconstraint[khat+m]))     # (if we need to)
       kk = khat
     }
     else {
-      G = G[1:nk[k],]
-      u = rep(0,nk[k])
+      G = G[1:nconstraint[k],]
+      u = rep(0,nconstraint[k])
       kk = k
     }
     

From c7f55d9e19c0af16cb41f4f3565083f5520c9f65 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 30 Nov 2015 13:14:50 -0800
Subject: [PATCH 080/493] more variable name changes

---
 selectiveInference/R/funs.fs.R | 92 +++++++++++++++++++---------------
 1 file changed, 51 insertions(+), 41 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 0a1b1f1..b7a76db 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -28,61 +28,68 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   #####
   # Find the first variable to enter and its sign
   working_x = scale(x,center=F,scale=sqrt(colSums(x^2)))
-  uhat = t(working_x)%*%y
-  ihit = which.max(abs(uhat))   # Hitting coordinate
-  s = Sign(uhat[ihit])          # Sign
+  score = t(working_x)%*%y
+  i_hit = which.max(abs(score))   # Hitting coordinate
+  sign_hit = Sign(score[i_hit])          # Sign
 
   if (verbose) {
-    cat(sprintf("1. Adding variable %i, |A|=%i...",ihit,1))
+    cat(sprintf("1. Adding variable %i, |A|=%i...",i_hit,1))
   }
   
   # Now iteratively find the new FS estimates
 
   # Things to keep track of, and return at the end
+  # JT: I guess the "buf" just saves us from making huge
+  # matrices we don't need?
+
   buf = min(maxsteps,500)
   action = numeric(buf)      # Actions taken
   df = numeric(buf)          # Degrees of freedom
   beta = matrix(0,p,buf)     # FS estimates
   
-  action[1] = ihit
+  action[1] = i_hit
   df[1] = 0
   beta[,1] = 0
 
   # Gamma matrix!
   gbuf = max(2*p*3,2000)     # Space for 3 steps, at least
-  gi = 0 # index into rows of Gamma matrix
+  gi = 0                     # index into rows of Gamma matrix
 
   Gamma = matrix(0,gbuf,n)
-  Gamma[gi+Seq(1,p-1),] = t(s*working_x[,ihit]+working_x[,-ihit]); gi = gi+p-1
-  Gamma[gi+Seq(1,p-1),] = t(s*working_x[,ihit]-working_x[,-ihit]); gi = gi+p-1
-  Gamma[gi+1,] = t(s*working_x[,ihit]); gi = gi+1
+  Gamma[gi+Seq(1,p-1),] = t(s*working_x[,i_hit]+working_x[,-i_hit]); gi = gi+p-1
+  Gamma[gi+Seq(1,p-1),] = t(s*working_x[,i_hit]-working_x[,-i_hit]); gi = gi+p-1
+  Gamma[gi+1,] = t(s*working_x[,i_hit]); gi = gi+1
 
   # nconstraint
   nconstraint = numeric(buf)
   vreg = matrix(0,buf,n)
   nconstraint[1] = gi
-  vreg[1,] = s*x[,ihit] / sum(x[,ihit]^2)
+  vreg[1,] = s*x[,i_hit] / sum(x[,i_hit]^2)
 
   # Other things to keep track of, but not return
   r = 1                      # Size of active set
-  A = ihit                   # Active set
-  I = Seq(1,p)[-ihit]        # Inactive set
-  X1 = x[,ihit,drop=FALSE]   # Matrix X[,A]
-  X2 = x[,-ihit,drop=FALSE]  # Matrix X[,I]
+  A = i_hit                  # Active set -- JT: isn't this basically the same as action?
+  I = Seq(1,p)[-i_hit]       # Inactive set
+  X_active = x[,i_hit,drop=FALSE]   # Matrix X[,A]
+  X_inactive = x[,-i_hit,drop=FALSE]  # Matrix X[,I]
   k = 2                      # What step are we at?
+                             # JT Why keep track of r and k instead of just saying k=r+1?
+
+  # Compute a skinny QR decomposition of X_active
+  # JT: obs was used as variable name above -- this is something different, no?
+  # changed it to qr_X
 
-  # Compute a skinny QR decomposition of X1
-  obj = qr(X1)
-  Q = qr.Q(obj,complete=TRUE)
-  Q1 = Q[,1,drop=FALSE];
-  Q2 = Q[,-1,drop=FALSE]
-  R = qr.R(obj)
+  qr_X = qr(X_active)
+  Q = qr.Q(qr_X,complete=TRUE)
+  Q_active = Q[,1,drop=FALSE];
+  Q_inactive = Q[,-1,drop=FALSE]
+  R = qr.R(qr_X)
   
   # Throughout the algorithm, we will maintain
-  # the decomposition X1 = Q1*R. Dimenisons:
-  # X1: n x r
-  # Q1: n x r
-  # Q2: n x (n-r)
+  # the decomposition X_active = Q_active*R. Dimensions:
+  # X_active: n x r
+  # Q_active: n x r
+  # Q_inactive: n x (n-r)
   # R:  r x r
     
   while (k<=maxsteps) {
@@ -98,9 +105,10 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     }
 
     # Key quantities for the next entry
-    a = backsolve(R,t(Q1)%*%y)
-    X2perp = X2 - X1 %*% backsolve(R,t(Q1)%*%X2)
-    working_x = scale(X2perp,center=F,scale=sqrt(colSums(X2perp^2)))
+
+    a = backsolve(R,t(Q_active)%*%y)
+    X_inactive_resid = X_inactive - X_active %*% backsolve(R,t(Q_active)%*%X_inactive)
+    working_x = scale(X_inactive_resid,center=F,scale=sqrt(colSums(X_inactive_resid^2)))
     score = as.numeric(t(working_x)%*%y)
     
     # If the inactive set is empty, nothing will hit
@@ -110,14 +118,14 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     else {
       sign_score = Sign(score)
       abs_score = sign_score * score
-      ihit = which.max(abs_score)
-      shit = sign_score[ihit]
+      i_hit = which.max(abs_score)
+      shit = sign_score[i_hit]
     }
     
     # Record the solution
     # what is the difference between "action" and "A"?
 
-    action[k] = I[ihit] 
+    action[k] = I[i_hit] 
     df[k] = r
     beta[A,k] = a
         
@@ -125,26 +133,28 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     if (gi + 2*p > nrow(Gamma)) Gamma = rbind(Gamma,matrix(0,2*p+gbuf,n))
     working_x = t(sign_score*t(working_x))
     Gamma[gi+Seq(1,p-r),] = t(working_x); gi = gi+p-r
-    Gamma[gi+Seq(1,p-r-1),] = t(working_x[,ihit]-working_x[,-ihit]); gi = gi+p-r-1
-    Gamma[gi+1,] = t(working_x[,ihit]); gi = gi+1
+    Gamma[gi+Seq(1,p-r-1),] = t(working_x[,i_hit]-working_x[,-i_hit]); gi = gi+p-r-1
+    Gamma[gi+1,] = t(working_x[,i_hit]); gi = gi+1
 
     # nconstraint, regression contrast
     nconstraint[k] = gi
-    vreg[k,] = shit*X2perp[,ihit] / sum(X2perp[,ihit]^2)
+    vreg[k,] = shit*X_inactive_resid[,i_hit] / sum(X_inactive_resid[,i_hit]^2)
 
     # Update all of the variables
     r = r+1
-    A = c(A,I[ihit])
-    I = I[-ihit]
+    A = c(A,I[i_hit])
+    I = I[-i_hit]
     s = c(s,shit)
-    X1 = cbind(X1,X2[,ihit])
-    X2 = X2[,-ihit,drop=FALSE]
+    X_active = cbind(X_active,X_inactive[,i_hit])
+    X_inactive = X_inactive[,-i_hit,drop=FALSE]
 
     # Update the QR decomposition
-    obj = updateQR(Q1,Q2,R,X1[,r])
-    Q1 = obj$Q1
-    Q2 = obj$Q2
-    R = obj$R
+    updated_qr = updateQR(Q_active,Q_inactive,R,X_active[,r])
+    Q_active = updated_qr$Q1
+
+    # JT: why do we store Q_inactive? Doesn't seem to be used.
+    Q_inactive = updated_qr$Q2
+    R = updated_qr$R
      
     if (verbose) {
       cat(sprintf("\n%i. Adding variable %i, |A|=%i...",k,A[r],r))

From 0e699c5ecf105c10c1ca121d5d98d70259471d84 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 30 Nov 2015 13:24:30 -0800
Subject: [PATCH 081/493] one more name change; comment about vup, vlo

---
 selectiveInference/R/funs.fs.R | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index b7a76db..b5b13f8 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -106,7 +106,6 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
 
     # Key quantities for the next entry
 
-    a = backsolve(R,t(Q_active)%*%y)
     X_inactive_resid = X_inactive - X_active %*% backsolve(R,t(Q_active)%*%X_inactive)
     working_x = scale(X_inactive_resid,center=F,scale=sqrt(colSums(X_inactive_resid^2)))
     score = as.numeric(t(working_x)%*%y)
@@ -127,7 +126,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
 
     action[k] = I[i_hit] 
     df[k] = r
-    beta[A,k] = a
+    beta[A,k] = backsolve(R,t(Q_active)%*%y)
         
     # Gamma matrix!
     if (gi + 2*p > nrow(Gamma)) Gamma = rbind(Gamma,matrix(0,2*p+gbuf,n))
@@ -360,6 +359,8 @@ fsInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic
     }
   }
   
+  # JT: why do we output vup, vlo? Are they used somewhere else?
+
   out = list(type=type,k=k,khat=khat,pv=pv,ci=ci,
     tailarea=tailarea,vlo=vlo,vup=vup,vmat=vmat,y=y,
     vars=vars,sign=sign,sigma=sigma,alpha=alpha,

From 6c30502efaca69823cc10ba1b80528e2ea18a735 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 30 Nov 2015 13:31:35 -0800
Subject: [PATCH 082/493] forgot to rename variable s

---
 selectiveInference/R/funs.fs.R | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index b5b13f8..a6a9c3f 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -30,7 +30,8 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   working_x = scale(x,center=F,scale=sqrt(colSums(x^2)))
   score = t(working_x)%*%y
   i_hit = which.max(abs(score))   # Hitting coordinate
-  sign_hit = Sign(score[i_hit])          # Sign
+  sign_hit = Sign(score[i_hit])   # Sign
+  signs = sign_hit                # later signs will be appended to `signs`
 
   if (verbose) {
     cat(sprintf("1. Adding variable %i, |A|=%i...",i_hit,1))
@@ -56,15 +57,15 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   gi = 0                     # index into rows of Gamma matrix
 
   Gamma = matrix(0,gbuf,n)
-  Gamma[gi+Seq(1,p-1),] = t(s*working_x[,i_hit]+working_x[,-i_hit]); gi = gi+p-1
-  Gamma[gi+Seq(1,p-1),] = t(s*working_x[,i_hit]-working_x[,-i_hit]); gi = gi+p-1
-  Gamma[gi+1,] = t(s*working_x[,i_hit]); gi = gi+1
+  Gamma[gi+Seq(1,p-1),] = t(sign_hit*working_x[,i_hit]+working_x[,-i_hit]); gi = gi+p-1
+  Gamma[gi+Seq(1,p-1),] = t(sign_hit*working_x[,i_hit]-working_x[,-i_hit]); gi = gi+p-1
+  Gamma[gi+1,] = t(sign_hit*working_x[,i_hit]); gi = gi+1
 
   # nconstraint
   nconstraint = numeric(buf)
   vreg = matrix(0,buf,n)
   nconstraint[1] = gi
-  vreg[1,] = s*x[,i_hit] / sum(x[,i_hit]^2)
+  vreg[1,] = sign_hit*x[,i_hit] / sum(x[,i_hit]^2)
 
   # Other things to keep track of, but not return
   r = 1                      # Size of active set
@@ -118,7 +119,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
       sign_score = Sign(score)
       abs_score = sign_score * score
       i_hit = which.max(abs_score)
-      shit = sign_score[i_hit]
+      sign_hit = sign_score[i_hit]
     }
     
     # Record the solution
@@ -137,13 +138,13 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
 
     # nconstraint, regression contrast
     nconstraint[k] = gi
-    vreg[k,] = shit*X_inactive_resid[,i_hit] / sum(X_inactive_resid[,i_hit]^2)
+    vreg[k,] = sign_hit*X_inactive_resid[,i_hit] / sum(X_inactive_resid[,i_hit]^2)
 
     # Update all of the variables
     r = r+1
     A = c(A,I[i_hit])
     I = I[-i_hit]
-    s = c(s,shit)
+    signs = c(signs,sign_hit)
     X_active = cbind(X_active,X_inactive[,i_hit])
     X_inactive = X_inactive[,-i_hit,drop=FALSE]
 
@@ -201,7 +202,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   # Assign column names
   colnames(beta) = as.character(Seq(1,k-1))
 
-  out = list(action=action,sign=s,df=df,beta=beta,
+  out = list(action=action,sign=signs,df=df,beta=beta,
     completepath=completepath,bls=bls,
     Gamma=Gamma,nconstraint=nconstraint,vreg=vreg,x=x,y=y,bx=bx,by=by,sx=sx,
     intercept=intercept,normalize=normalize,call=this.call) 

From 034cfa7f2fcea0d0de075f3ce0f76b71cae3f7c1 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 30 Nov 2015 13:58:11 -0800
Subject: [PATCH 083/493] missed one reference to variable a

---
 selectiveInference/R/funs.fs.R | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index a6a9c3f..c2b9806 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -125,9 +125,10 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     # Record the solution
     # what is the difference between "action" and "A"?
 
+    beta_cur = backsolve(R,t(Q_active)%*%y)
     action[k] = I[i_hit] 
     df[k] = r
-    beta[A,k] = backsolve(R,t(Q_active)%*%y)
+    beta[A,k] = beta_cur 
         
     # Gamma matrix!
     if (gi + 2*p > nrow(Gamma)) Gamma = rbind(Gamma,matrix(0,2*p+gbuf,n))
@@ -189,7 +190,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     # Record the least squares solution. Note that
     # we have already computed this
     bls = rep(0,p)
-    bls[A] = a
+    bls[A] = beta_cur
   }
 
   if (verbose) cat("\n")

From 82ee21a76c1bc9af99e0d31c7722126b691c7dd5 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 30 Nov 2015 14:25:08 -0800
Subject: [PATCH 084/493] small fix to test.fs.R

---
 tests/test.fs.R | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tests/test.fs.R b/tests/test.fs.R
index f811676..a078155 100644
--- a/tests/test.fs.R
+++ b/tests/test.fs.R
@@ -1,5 +1,4 @@
 library(selectiveInference)
-#library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
 
 library(lars)
 
@@ -24,7 +23,7 @@ max(abs(obj$action-unlist(obj2$action)))
 # These don't always match ... what is the lars function doing?
 
 # Checks
-max(abs(obj$action-unlist(obj2$action))
+max(abs(obj$action-unlist(obj2$action)))
 max(abs(coef(obj,s=4.5,mode="step")-
         lars::predict.lars(obj2,s=4.5,type="coef",mode="step")$coef))
 max(abs(predict(obj,s=4.5,mode="step")-

From 42b99323ad38fff1d9987a2a4a3cab9465241322 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 30 Nov 2015 14:46:37 -0800
Subject: [PATCH 085/493] BF: beta_cur must be computed before the possible
 break

---
 selectiveInference/R/funs.fs.R | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index c2b9806..a8b4812 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -111,6 +111,10 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     working_x = scale(X_inactive_resid,center=F,scale=sqrt(colSums(X_inactive_resid^2)))
     score = as.numeric(t(working_x)%*%y)
     
+    beta_cur = backsolve(R,t(Q_active)%*%y) # must be computed before the break
+                                            # so we have it if we have 
+                                            # completed the path
+
     # If the inactive set is empty, nothing will hit
     if (r==min(n-intercept,p)) break
 
@@ -125,7 +129,6 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     # Record the solution
     # what is the difference between "action" and "A"?
 
-    beta_cur = backsolve(R,t(Q_active)%*%y)
     action[k] = I[i_hit] 
     df[k] = r
     beta[A,k] = beta_cur 

From 575f4a18dc2e0e1062e3a85a4a948bd6b4cbe2b3 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 30 Nov 2015 19:31:12 -0800
Subject: [PATCH 086/493] intersection upper / lower bounds match python code
 on X.csv, Y.csv

---
 selectiveInference/R/funs.fs.R | 110 +++++++++++++++++++++++++++++++--
 1 file changed, 104 insertions(+), 6 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index a8b4812..39ff0c3 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -27,7 +27,8 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
 
   #####
   # Find the first variable to enter and its sign
-  working_x = scale(x,center=F,scale=sqrt(colSums(x^2)))
+  working_scale = sqrt(colSums(x^2))
+  working_x = scale(x,center=F,scale=working_scale)
   score = t(working_x)%*%y
   i_hit = which.max(abs(score))   # Hitting coordinate
   sign_hit = Sign(score[i_hit])   # Sign
@@ -48,10 +49,22 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   df = numeric(buf)          # Degrees of freedom
   beta = matrix(0,p,buf)     # FS estimates
   
+  offset_pos = matrix(Inf, p, buf) # upper bounds for selective maxZ
+  offset_neg = matrix(Inf, p, buf) # lower bounds for selective maxZ
+
   action[1] = i_hit
   df[1] = 0
   beta[,1] = 0
 
+  #####
+  # Variables needed to compute truncation limits for
+  # selective maxZ test
+
+  realized_Z_max = c(sign_hit * score[i_hit])
+  offset_pos[,1] = Inf
+  offset_neg[,1] = Inf
+  working_resid = y - x %*% beta[,1]
+
   # Gamma matrix!
   gbuf = max(2*p*3,2000)     # Space for 3 steps, at least
   gi = 0                     # index into rows of Gamma matrix
@@ -103,13 +116,18 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
       beta = cbind(beta,matrix(0,p,buf))
       nconstraint = c(nconstraint,numeric(buf))
       vreg = rbind(vreg,matrix(0,buf,n))
+
+      offset_pos = cbind(offset_pos, matrix(0, p, buf))
+      offset_neg = cbind(offset_neg, matrix(0, p, buf))
     }
 
     # Key quantities for the next entry
 
+    prev_scale = working_scale[-i_hit]
     X_inactive_resid = X_inactive - X_active %*% backsolve(R,t(Q_active)%*%X_inactive)
-    working_x = scale(X_inactive_resid,center=F,scale=sqrt(colSums(X_inactive_resid^2)))
-    score = as.numeric(t(working_x)%*%y)
+    working_scale = sqrt(colSums(X_inactive_resid^2))
+    working_x = scale(X_inactive_resid,center=F,scale=working_scale)
+    working_score = as.numeric(t(working_x)%*%y)
     
     beta_cur = backsolve(R,t(Q_active)%*%y) # must be computed before the break
                                             # so we have it if we have 
@@ -120,10 +138,18 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
 
     # Otherwise find the next hitting time
     else {
-      sign_score = Sign(score)
-      abs_score = sign_score * score
+      sign_score = Sign(working_score)
+      abs_score = sign_score * working_score
       i_hit = which.max(abs_score)
       sign_hit = sign_score[i_hit]
+      # keep track of necessary quantities for selective maxZ
+
+      offset_shift = t(X_inactive) %*% (y - working_resid)
+      realized_Z_scaled = realized_Z_max * prev_scale
+      offset_pos[I,k] = realized_Z_scaled + offset_shift
+      offset_neg[I,k] = realized_Z_scaled - offset_shift
+
+      working_resid = y - X_active %*% beta_cur
     }
     
     # Record the solution
@@ -152,6 +178,8 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     X_active = cbind(X_active,X_inactive[,i_hit])
     X_inactive = X_inactive[,-i_hit,drop=FALSE]
 
+    realized_Z_max = sign_hit * working_score[i_hit]
+
     # Update the QR decomposition
     updated_qr = updateQR(Q_active,Q_inactive,R,X_active[,r])
     Q_active = updated_qr$Q1
@@ -176,6 +204,9 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   nconstraint = nconstraint[Seq(1,k-1)]
   vreg = vreg[Seq(1,k-1),,drop=FALSE]
   
+  offset_pos = offset_pos[,Seq(1,k-1),drop=FALSE]
+  offset_neg = offset_neg[,Seq(1,k-1),drop=FALSE]
+
   # If we reached the maximum number of steps
   if (k>maxsteps) {
     if (verbose) {
@@ -209,7 +240,8 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   out = list(action=action,sign=signs,df=df,beta=beta,
     completepath=completepath,bls=bls,
     Gamma=Gamma,nconstraint=nconstraint,vreg=vreg,x=x,y=y,bx=bx,by=by,sx=sx,
-    intercept=intercept,normalize=normalize,call=this.call) 
+    intercept=intercept,normalize=normalize,call=this.call,
+    offset_pos=offset_pos,offset_neg=offset_neg) 
   class(out) = "fs"
   return(out)
 }
@@ -376,6 +408,72 @@ fsInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic
 
 ##############################
 
+##############################
+
+# selected Zmax tests
+
+fsInf_Zmax <- function(obj, sigma=NULL, alpha=0.1, k=NULL, 
+	               gridrange=c(-100,100), bits=NULL, mult=2, ntimes=2, verbose=FALSE) {
+  
+  this.call = match.call()
+
+  checkargs.misc(sigma=sigma,alpha=alpha,k=k,
+                 gridrange=gridrange,mult=mult,ntimes=ntimes)
+  if (class(obj) != "fs") stop("obj must be an object of class fs")
+  if (!is.null(bits) && !requireNamespace("Rmpfr",quietly=TRUE)) {
+    warning("Package Rmpfr is not installed, reverting to standard precision")
+    bits = NULL
+  }
+  
+  k = min(k,length(obj$action)) # Round to last step
+  x = obj$x
+  y = obj$y
+  p = ncol(x)
+  n = nrow(x)
+  G = obj$Gamma
+  nconstraint = obj$nconstraint
+  sx = obj$sx
+
+  if (is.null(sigma)) {
+    # TODO we should probably sample uniform
+    if (n >= 2*p) {
+      oo = obj$intercept
+      sigma = sqrt(sum(lsfit(x,y,intercept=oo)$res^2)/(n-p-oo))
+    }
+    else {
+      sigma = sd(y)
+      warning(paste(sprintf("p > n/2, and sd(y) = %0.3f used as an estimate of sigma;",sigma),
+                    "you may want to use the estimateSigma function"))
+    }
+  }
+
+  khat = NULL
+  
+  vars = obj$action[1:k]
+  for (j in 1:k) {
+         
+      if (j > 1) {
+          active = vars[1:(j-1)]
+	  inactive = (1:p)[-active]
+      } else {
+          inactive = 1:p
+      }
+      collapsed_pos = apply(obj$offset_pos[inactive,1:j,drop=FALSE], 1, min)
+      collapsed_neg = apply(obj$offset_neg[inactive,1:j,drop=FALSE], 1, min)
+
+      # next, condition on solution up to now
+  }
+
+  out = list(k=k,khat=khat,pv=pv,ci=ci,
+    tailarea=tailarea,vmat=vmat,y=y,
+    vars=vars,sign=sign,sigma=sigma,alpha=alpha,
+    call=this.call)
+  class(out) = "fsInf"
+  return(out)
+}
+
+##############################
+
 
 ##############################
 

From b9c91bd474e377a2442eee3088f7d9e09c5f320d Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 30 Nov 2015 22:42:16 -0800
Subject: [PATCH 087/493] numerically agrees with python code up to the point
 truncated gaussians need to be drawn

---
 selectiveInference/NAMESPACE   |   2 +-
 selectiveInference/R/funs.fs.R | 132 ++++++++++++++++++++++++++-------
 tests/test.fs.selected.R       |  21 ++++++
 3 files changed, 128 insertions(+), 27 deletions(-)
 create mode 100644 tests/test.fs.selected.R

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index 8dd9120..180abf0 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -1,5 +1,5 @@
 export(lar,fs,
-       larInf,fsInf,
+       larInf,fsInf,fsInf_maxZ,
        coef.lar,coef.fs,
        predict.lar,predict.fs,
        print.lar,print.fs,
diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 39ff0c3..119f5f4 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -49,8 +49,11 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   df = numeric(buf)          # Degrees of freedom
   beta = matrix(0,p,buf)     # FS estimates
   
-  offset_pos = matrix(Inf, p, buf) # upper bounds for selective maxZ
-  offset_neg = matrix(Inf, p, buf) # lower bounds for selective maxZ
+  # Buffered objects for selective maxZ test
+
+  offset_pos_maxZ = matrix(Inf, p, buf) # upper bounds for selective maxZ
+  offset_neg_maxZ = matrix(Inf, p, buf) # lower bounds for selective maxZ
+  scale_maxZ = matrix(0, p, buf) # lower bounds for selective maxZ
 
   action[1] = i_hit
   df[1] = 0
@@ -60,20 +63,28 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   # Variables needed to compute truncation limits for
   # selective maxZ test
 
-  realized_Z_max = c(sign_hit * score[i_hit])
-  offset_pos[,1] = Inf
-  offset_neg[,1] = Inf
-  working_resid = y - x %*% beta[,1]
+  realized_maxZ = c(sign_hit * score[i_hit])
+  offset_pos_maxZ[,1] = Inf
+  offset_neg_maxZ[,1] = Inf
+  scale_maxZ[,1] = working_scale
+  working_resid_maxZ = y - x %*% beta[,1]
 
   # Gamma matrix!
   gbuf = max(2*p*3,2000)     # Space for 3 steps, at least
   gi = 0                     # index into rows of Gamma matrix
+  zi = 0                     # index into rows of Gamma_maxZ matrix
 
   Gamma = matrix(0,gbuf,n)
   Gamma[gi+Seq(1,p-1),] = t(sign_hit*working_x[,i_hit]+working_x[,-i_hit]); gi = gi+p-1
   Gamma[gi+Seq(1,p-1),] = t(sign_hit*working_x[,i_hit]-working_x[,-i_hit]); gi = gi+p-1
   Gamma[gi+1,] = t(sign_hit*working_x[,i_hit]); gi = gi+1
 
+  # Gamma_maxZ is the rbind 
+  # of residualized X_{inactive \cup i_hit}
+
+  Gamma_maxZ = matrix(0,gbuf,n)
+  Gamma_maxZ[zi+Seq(1,p),] = t(x); zi = zi+p
+
   # nconstraint
   nconstraint = numeric(buf)
   vreg = matrix(0,buf,n)
@@ -117,8 +128,9 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
       nconstraint = c(nconstraint,numeric(buf))
       vreg = rbind(vreg,matrix(0,buf,n))
 
-      offset_pos = cbind(offset_pos, matrix(0, p, buf))
-      offset_neg = cbind(offset_neg, matrix(0, p, buf))
+      offset_pos_maxZ = cbind(offset_pos_maxZ, matrix(0, p, buf))
+      offset_neg_maxZ = cbind(offset_neg_maxZ, matrix(0, p, buf))
+      scale_maxZ = cbind(scale_maxZ, matrix(0, p, buf))
     }
 
     # Key quantities for the next entry
@@ -144,12 +156,13 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
       sign_hit = sign_score[i_hit]
       # keep track of necessary quantities for selective maxZ
 
-      offset_shift = t(X_inactive) %*% (y - working_resid)
-      realized_Z_scaled = realized_Z_max * prev_scale
-      offset_pos[I,k] = realized_Z_scaled + offset_shift
-      offset_neg[I,k] = realized_Z_scaled - offset_shift
+      offset_shift = t(X_inactive) %*% (y - working_resid_maxZ)
+      realized_Z_scaled = realized_maxZ * prev_scale
+      offset_pos_maxZ[I,k] = realized_Z_scaled + offset_shift
+      offset_neg_maxZ[I,k] = realized_Z_scaled - offset_shift
+      scale_maxZ[I,k] = working_scale
 
-      working_resid = y - X_active %*% beta_cur
+      working_resid_maxZ = y - X_active %*% beta_cur
     }
     
     # Record the solution
@@ -159,7 +172,16 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     df[k] = r
     beta[A,k] = beta_cur 
         
+    # store the X_inactive_resid in Gamma_maxZ
+
+    if (gi + p-r > nrow(Gamma_maxZ)) Gamma_maxZ = rbind(Gamma_maxZ,matrix(0,p-r,n))
+    Gamma_maxZ[zi+Seq(1,p-r),] = t(X_inactive_resid); zi = zi+p-r
+
+    # update maxZ variable
+    realized_maxZ = sign_hit * working_score[i_hit]
+
     # Gamma matrix!
+
     if (gi + 2*p > nrow(Gamma)) Gamma = rbind(Gamma,matrix(0,2*p+gbuf,n))
     working_x = t(sign_score*t(working_x))
     Gamma[gi+Seq(1,p-r),] = t(working_x); gi = gi+p-r
@@ -178,8 +200,6 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     X_active = cbind(X_active,X_inactive[,i_hit])
     X_inactive = X_inactive[,-i_hit,drop=FALSE]
 
-    realized_Z_max = sign_hit * working_score[i_hit]
-
     # Update the QR decomposition
     updated_qr = updateQR(Q_active,Q_inactive,R,X_active[,r])
     Q_active = updated_qr$Q1
@@ -204,8 +224,10 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   nconstraint = nconstraint[Seq(1,k-1)]
   vreg = vreg[Seq(1,k-1),,drop=FALSE]
   
-  offset_pos = offset_pos[,Seq(1,k-1),drop=FALSE]
-  offset_neg = offset_neg[,Seq(1,k-1),drop=FALSE]
+  offset_pos_maxZ = offset_pos_maxZ[,Seq(1,k-1),drop=FALSE]
+  offset_neg_maxZ = offset_neg_maxZ[,Seq(1,k-1),drop=FALSE]
+  scale_maxZ = offset_pos_maxZ[,Seq(1,k-1),drop=FALSE]
+  Gamma_maxZ = Gamma_maxZ[Seq(1,zi),,drop=FALSE]
 
   # If we reached the maximum number of steps
   if (k>maxsteps) {
@@ -241,7 +263,8 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     completepath=completepath,bls=bls,
     Gamma=Gamma,nconstraint=nconstraint,vreg=vreg,x=x,y=y,bx=bx,by=by,sx=sx,
     intercept=intercept,normalize=normalize,call=this.call,
-    offset_pos=offset_pos,offset_neg=offset_neg) 
+    offset_pos_maxZ=offset_pos_maxZ,offset_neg_maxZ=offset_neg_maxZ,
+    scale_maxZ=scale_maxZ,Gamma_maxZ=Gamma_maxZ) 
   class(out) = "fs"
   return(out)
 }
@@ -410,9 +433,9 @@ fsInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic
 
 ##############################
 
-# selected Zmax tests
+# selected maxZ tests
 
-fsInf_Zmax <- function(obj, sigma=NULL, alpha=0.1, k=NULL, 
+fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, k=NULL, 
 	               gridrange=c(-100,100), bits=NULL, mult=2, ntimes=2, verbose=FALSE) {
   
   this.call = match.call()
@@ -450,24 +473,81 @@ fsInf_Zmax <- function(obj, sigma=NULL, alpha=0.1, k=NULL,
   khat = NULL
   
   vars = obj$action[1:k]
+  zi = 0
   for (j in 1:k) {
          
+      # the inactive set here does not
+      # include the variable at the j-th step
+      # so, at j==1, the inactive set is every variable
+      # at j==2, the inactive set is everything but the first one
+
       if (j > 1) {
           active = vars[1:(j-1)]
 	  inactive = (1:p)[-active]
       } else {
           inactive = 1:p
       }
-      collapsed_pos = apply(obj$offset_pos[inactive,1:j,drop=FALSE], 1, min)
-      collapsed_neg = apply(obj$offset_neg[inactive,1:j,drop=FALSE], 1, min)
+      collapsed_pos = apply(obj$offset_pos_maxZ[inactive,1:j,drop=FALSE], 1, min)
+      collapsed_neg = apply(obj$offset_neg_maxZ[inactive,1:j,drop=FALSE], 1, min)
+      cur_scale = obj$scale_maxZ[,j][inactive]
+      cur_adjusted_X = obj$Gamma_maxZ[zi + Seq(1,p-j+1),]; zi = zi+p-j+1
+      cur_X = obj$x[,inactive]
 
       # next, condition on solution up to now
+
+      if (j > 1) {
+          cur_fitted = predict(obj, s=j)
+	  cur_fitted = cur_fitted - mean(cur_fitted)
+	  cur_offset = t(cur_X) %*% cur_fitted
+      }
+      else {
+          cur_fitted = 0
+          cur_offset = 0
+      }
+
+      print('pos')
+      print(collapsed_pos)
+      print('neg')
+      print(collapsed_neg)
+
+      print('fitted')
+      print(cur_fitted[1:10])
+      collapsed_pos = collapsed_pos - cur_offset
+      collapsed_neg = collapsed_neg + cur_offset
+
+      print("cur_offset")
+      print(cur_offset)
+
+      print('pos_adj')
+      print(collapsed_pos)
+      print('neg_adj')
+      print(collapsed_neg)
+
+      # now, we sample from Y_star, a centered Gaussian with covariance sigma^2 I
+      # subject to the constraint
+      # t(cur_adjusted_X) %*% Y_star < collapsed_pos
+      # -t(cur_adjusted_X) %*% Y_star < collapsed_neg
+
+      # really, we want the covariance of Y_star to be \sigma^2 (I - cur_P)
+      # where P is projection on the j-1 previous variables
+      # but this doesn't matter as everything we do with the samples
+      # will be a function of (I - cur_P) Y_star and the constraints are
+      # expressible in terms of (I - cur_P) Y_star because
+      # we have adjusted X
+
+      print('pos')
+      print(collapsed_pos)
+      print('neg')
+      print(collapsed_neg)
+
+      # IMPORTANT: after sampling Y_star, we have to add back cur_fitted
   }
 
-  out = list(k=k,khat=khat,pv=pv,ci=ci,
-    tailarea=tailarea,vmat=vmat,y=y,
-    vars=vars,sign=sign,sigma=sigma,alpha=alpha,
-    call=this.call)
+  out = list(pos=collapsed_pos, neg=collapsed_neg)
+  #out = list(k=k,khat=khat,pv=pv,ci=ci,
+  #  tailarea=tailarea,vmat=vmat,y=y,
+  #  vars=vars,sign=sign,sigma=sigma,alpha=alpha,
+  #  call=this.call)
   class(out) = "fsInf"
   return(out)
 }
diff --git a/tests/test.fs.selected.R b/tests/test.fs.selected.R
new file mode 100644
index 0000000..e5b2cf5
--- /dev/null
+++ b/tests/test.fs.selected.R
@@ -0,0 +1,21 @@
+library(selectiveInference)
+library(lars)
+
+set.seed(32)
+
+n=50
+p=10
+sigma=1
+
+x = as.matrix(read.table("X.csv", sep=',', header=FALSE))
+Y = as.numeric(read.table("Y.csv", sep=',', header=FALSE)[,1])
+
+beta=c(5,4,3,2,1,rep(0,p-5))
+mu=x%*%beta
+
+y=mu+Y
+fsfit=fs(x,y,norm=TRUE, intercept=TRUE)
+out = fsInf_maxZ(fsfit,sigma=sigma)
+print(out$pos)
+print(-out$neg)
+

From 2b8206e1fc6e9788b10de3d307f9eff649a967b7 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 30 Nov 2015 22:54:15 -0800
Subject: [PATCH 088/493] removing debugging print statements

---
 selectiveInference/R/funs.fs.R | 61 ++++++++++++----------------------
 tests/X.csv                    | 50 ++++++++++++++++++++++++++++
 tests/Y.csv                    | 50 ++++++++++++++++++++++++++++
 tests/test.fs.selected.R       |  3 +-
 4 files changed, 122 insertions(+), 42 deletions(-)
 create mode 100644 tests/X.csv
 create mode 100644 tests/Y.csv

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 119f5f4..433e36e 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -80,7 +80,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   Gamma[gi+1,] = t(sign_hit*working_x[,i_hit]); gi = gi+1
 
   # Gamma_maxZ is the rbind 
-  # of residualized X_{inactive \cup i_hit}
+  # of residualized X_inactive's
 
   Gamma_maxZ = matrix(0,gbuf,n)
   Gamma_maxZ[zi+Seq(1,p),] = t(x); zi = zi+p
@@ -435,18 +435,13 @@ fsInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic
 
 # selected maxZ tests
 
-fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, k=NULL, 
-	               gridrange=c(-100,100), bits=NULL, mult=2, ntimes=2, verbose=FALSE) {
+fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL) {
   
   this.call = match.call()
 
-  checkargs.misc(sigma=sigma,alpha=alpha,k=k,
-                 gridrange=gridrange,mult=mult,ntimes=ntimes)
+  checkargs.misc(sigma=sigma,alpha=alpha,k=k)
+
   if (class(obj) != "fs") stop("obj must be an object of class fs")
-  if (!is.null(bits) && !requireNamespace("Rmpfr",quietly=TRUE)) {
-    warning("Package Rmpfr is not installed, reverting to standard precision")
-    bits = NULL
-  }
   
   k = min(k,length(obj$action)) # Round to last step
   x = obj$x
@@ -487,13 +482,18 @@ fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, k=NULL,
       } else {
           inactive = 1:p
       }
+
       collapsed_pos = apply(obj$offset_pos_maxZ[inactive,1:j,drop=FALSE], 1, min)
       collapsed_neg = apply(obj$offset_neg_maxZ[inactive,1:j,drop=FALSE], 1, min)
       cur_scale = obj$scale_maxZ[,j][inactive]
+
       cur_adjusted_X = obj$Gamma_maxZ[zi + Seq(1,p-j+1),]; zi = zi+p-j+1
       cur_X = obj$x[,inactive]
 
-      # next, condition on solution up to now
+      # now we condition on solution up to now
+      # this is equivalent to finding vector of 
+      # fitted values up to now and appropriately
+      # adjusting the box limits
 
       if (j > 1) {
           cur_fitted = predict(obj, s=j)
@@ -505,28 +505,13 @@ fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, k=NULL,
           cur_offset = 0
       }
 
-      print('pos')
-      print(collapsed_pos)
-      print('neg')
-      print(collapsed_neg)
-
-      print('fitted')
-      print(cur_fitted[1:10])
-      collapsed_pos = collapsed_pos - cur_offset
-      collapsed_neg = collapsed_neg + cur_offset
-
-      print("cur_offset")
-      print(cur_offset)
-
-      print('pos_adj')
-      print(collapsed_pos)
-      print('neg_adj')
-      print(collapsed_neg)
+      final_upper = collapsed_pos - cur_offset
+      final_lower = -(collapsed_neg + cur_offset)
 
       # now, we sample from Y_star, a centered Gaussian with covariance sigma^2 I
       # subject to the constraint
-      # t(cur_adjusted_X) %*% Y_star < collapsed_pos
-      # -t(cur_adjusted_X) %*% Y_star < collapsed_neg
+      # t(cur_adjusted_X) %*% Y_star < final_upper
+      # -t(cur_adjusted_X) %*% Y_star < -final_lower
 
       # really, we want the covariance of Y_star to be \sigma^2 (I - cur_P)
       # where P is projection on the j-1 previous variables
@@ -535,20 +520,16 @@ fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, k=NULL,
       # expressible in terms of (I - cur_P) Y_star because
       # we have adjusted X
 
-      print('pos')
-      print(collapsed_pos)
-      print('neg')
-      print(collapsed_neg)
-
       # IMPORTANT: after sampling Y_star, we have to add back cur_fitted
+
+      pv = c(pv, runif(1))
   }
 
-  out = list(pos=collapsed_pos, neg=collapsed_neg)
-  #out = list(k=k,khat=khat,pv=pv,ci=ci,
-  #  tailarea=tailarea,vmat=vmat,y=y,
-  #  vars=vars,sign=sign,sigma=sigma,alpha=alpha,
-  #  call=this.call)
-  class(out) = "fsInf"
+  khat = forwardStop(pv,alpha)
+
+  out = list(pv=pv,khat=khat,
+	     call=this.call)
+  class(out) = "fsInf_Zmax"
   return(out)
 }
 
diff --git a/tests/X.csv b/tests/X.csv
new file mode 100644
index 0000000..9e3bf14
--- /dev/null
+++ b/tests/X.csv
@@ -0,0 +1,50 @@
+-1.178998180891155656e-01,-1.431240072891293569e-01,-1.032612207595051818e-01,-3.032187959649952358e-01,3.623527555657671262e-02,7.140070893198336571e-03,1.994856139137879403e-01,-2.091539772440898642e-02,-1.016086254290284319e-01,4.225480470043030748e-03
+1.900091741547343949e-02,8.527956006600532352e-03,-2.360627728880385912e-01,-7.614938655092416860e-03,-1.549697289040389246e-02,2.249142191975829854e-04,9.680233904138081658e-02,-2.051414424020579219e-01,4.630024394840283053e-01,-2.965171494849212080e-02
+-3.699631581918050305e-02,-1.722594123029470847e-01,-2.811921500130166226e-03,-2.389563150481890941e-01,-1.075624889112302696e-01,-7.804616804476580127e-02,-3.706328230757107572e-02,-3.503804035374127185e-02,-2.647277697909438676e-01,-6.682759619509875959e-02
+-5.130177317722107305e-02,2.268018482998235275e-02,-4.179689023642386980e-02,-1.623091275446776893e-01,1.450083194821685240e-01,2.219597463120694947e-01,2.533842648572138742e-02,2.771331081546550557e-02,3.581273753830072765e-02,8.722251846321744939e-02
+-1.306936424111264927e-01,4.432794698567819186e-02,-9.941966213181395451e-02,-8.715861166850324970e-02,-2.303285613309095925e-01,3.078294089253613142e-01,3.344919445579297435e-03,1.196830919307750318e-01,-1.253076375038724988e-01,-1.148761269817597519e-01
+-2.250204095659052439e-01,1.113238116713928527e-01,-5.290002031147110484e-02,7.830761098391121611e-02,7.066607013335457488e-02,6.565522345102817181e-02,-6.308067898881258850e-02,2.025286101924284737e-02,-6.818604986842312365e-02,-1.070102801614746246e-01
+-2.882057940922741479e-02,1.799983072585507882e-01,1.130515074865423159e-01,9.460393874312598983e-02,-7.352923330349291076e-02,-2.755771198440996536e-02,-1.310648265859815260e-02,-2.475061797099528116e-01,2.427234406663877353e-01,2.583612973537190993e-01
+9.611602736254980239e-02,6.273551721516337776e-02,-4.324306198998925654e-02,-3.860336044312057818e-02,-8.403781067825444318e-02,9.562097685082457388e-02,1.492509493676312526e-01,-2.943327955367435744e-01,-2.877171607065032860e-02,4.951010186454657749e-03
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diff --git a/tests/Y.csv b/tests/Y.csv
new file mode 100644
index 0000000..468003d
--- /dev/null
+++ b/tests/Y.csv
@@ -0,0 +1,50 @@
+5.981823865387593253e-01
+9.205037786951145717e-01
+2.471950462023575767e-01
+8.302149645795311450e-01
+3.481518894602156911e-01
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+7.737332812607098376e-01
+-1.072378967160714680e+00
+-2.435746837075756510e-01
+1.740226263339641199e-01
+8.402225800803131417e-01
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+-2.891958991875613894e-01
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+-1.066616503782338743e+00
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+1.159738714281874084e-01
+1.347361280586111265e+00
+2.804011720873073044e-02
+1.431756971602339057e+00
+-1.370351105419743254e+00
+1.197179345676374851e+00
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+-1.503900798080590295e+00
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diff --git a/tests/test.fs.selected.R b/tests/test.fs.selected.R
index e5b2cf5..37c5512 100644
--- a/tests/test.fs.selected.R
+++ b/tests/test.fs.selected.R
@@ -16,6 +16,5 @@ mu=x%*%beta
 y=mu+Y
 fsfit=fs(x,y,norm=TRUE, intercept=TRUE)
 out = fsInf_maxZ(fsfit,sigma=sigma)
-print(out$pos)
-print(-out$neg)
+
 

From 23c8de4bab2af024d0a0c8de294d273a46a1b1e1 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 30 Nov 2015 23:02:53 -0800
Subject: [PATCH 089/493] adding a few more comments

---
 selectiveInference/R/funs.fs.R | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 433e36e..6139dd2 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -448,12 +448,9 @@ fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL) {
   y = obj$y
   p = ncol(x)
   n = nrow(x)
-  G = obj$Gamma
-  nconstraint = obj$nconstraint
-  sx = obj$sx
 
   if (is.null(sigma)) {
-    # TODO we should probably sample uniform
+    # TODO we need a sampler on a unit sphere
     if (n >= 2*p) {
       oo = obj$intercept
       sigma = sqrt(sum(lsfit(x,y,intercept=oo)$res^2)/(n-p-oo))
@@ -487,7 +484,14 @@ fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL) {
       collapsed_neg = apply(obj$offset_neg_maxZ[inactive,1:j,drop=FALSE], 1, min)
       cur_scale = obj$scale_maxZ[,j][inactive]
 
+      # the matrix cur_adjusted_X is used to compute
+      # the maxZ or maxT for the sampled variables
+
       cur_adjusted_X = obj$Gamma_maxZ[zi + Seq(1,p-j+1),]; zi = zi+p-j+1
+
+      # cur_X is used to enforce conditioning on
+      # the ever_active sufficient_statistics
+
       cur_X = obj$x[,inactive]
 
       # now we condition on solution up to now

From 08f116e1f9487d79296bc304d6116690d4c73a4f Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 1 Dec 2015 00:53:58 -0800
Subject: [PATCH 090/493] for n>p implemneted selective Z -- not quite debugged
 yet

---
 selectiveInference/DESCRIPTION |  3 +-
 selectiveInference/R/funs.fs.R | 60 +++++++++++++++++++++++++++++-----
 2 files changed, 53 insertions(+), 10 deletions(-)

diff --git a/selectiveInference/DESCRIPTION b/selectiveInference/DESCRIPTION
index ce95116..6e21e68 100644
--- a/selectiveInference/DESCRIPTION
+++ b/selectiveInference/DESCRIPTION
@@ -8,7 +8,8 @@ Author: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor,
 Maintainer: Rob Tibshirani <tibs@stanford.edu>
 Depends:
     glmnet,
-    intervals
+    intervals,
+    tmvtnorm
 Suggests:
     Rmpfr
 Description: New tools for inference after selection, for use
diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 6139dd2..79201db 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -54,6 +54,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   offset_pos_maxZ = matrix(Inf, p, buf) # upper bounds for selective maxZ
   offset_neg_maxZ = matrix(Inf, p, buf) # lower bounds for selective maxZ
   scale_maxZ = matrix(0, p, buf) # lower bounds for selective maxZ
+  realized_maxZ = matrix(0, p, buf) # lower bounds for selective maxZ
 
   action[1] = i_hit
   df[1] = 0
@@ -63,7 +64,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   # Variables needed to compute truncation limits for
   # selective maxZ test
 
-  realized_maxZ = c(sign_hit * score[i_hit])
+  realized_maxZ[1] = c(sign_hit * score[i_hit])
   offset_pos_maxZ[,1] = Inf
   offset_neg_maxZ[,1] = Inf
   scale_maxZ[,1] = working_scale
@@ -131,6 +132,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
       offset_pos_maxZ = cbind(offset_pos_maxZ, matrix(0, p, buf))
       offset_neg_maxZ = cbind(offset_neg_maxZ, matrix(0, p, buf))
       scale_maxZ = cbind(scale_maxZ, matrix(0, p, buf))
+      realized_maxZ = cbind(realized_maxZ, matrix(0, p, buf))
     }
 
     # Key quantities for the next entry
@@ -157,7 +159,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
       # keep track of necessary quantities for selective maxZ
 
       offset_shift = t(X_inactive) %*% (y - working_resid_maxZ)
-      realized_Z_scaled = realized_maxZ * prev_scale
+      realized_Z_scaled = realized_maxZ[k-1] * prev_scale
       offset_pos_maxZ[I,k] = realized_Z_scaled + offset_shift
       offset_neg_maxZ[I,k] = realized_Z_scaled - offset_shift
       scale_maxZ[I,k] = working_scale
@@ -178,7 +180,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     Gamma_maxZ[zi+Seq(1,p-r),] = t(X_inactive_resid); zi = zi+p-r
 
     # update maxZ variable
-    realized_maxZ = sign_hit * working_score[i_hit]
+    realized_maxZ[k] = sign_hit * working_score[i_hit]
 
     # Gamma matrix!
 
@@ -264,7 +266,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     Gamma=Gamma,nconstraint=nconstraint,vreg=vreg,x=x,y=y,bx=bx,by=by,sx=sx,
     intercept=intercept,normalize=normalize,call=this.call,
     offset_pos_maxZ=offset_pos_maxZ,offset_neg_maxZ=offset_neg_maxZ,
-    scale_maxZ=scale_maxZ,Gamma_maxZ=Gamma_maxZ) 
+    scale_maxZ=scale_maxZ,Gamma_maxZ=Gamma_maxZ,realized_maxZ=realized_maxZ) 
   class(out) = "fs"
   return(out)
 }
@@ -435,7 +437,8 @@ fsInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic
 
 # selected maxZ tests
 
-fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL) {
+fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
+                       ndraw=8000, burnin=2000) {
   
   this.call = match.call()
 
@@ -492,7 +495,7 @@ fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL) {
       # cur_X is used to enforce conditioning on
       # the ever_active sufficient_statistics
 
-      cur_X = obj$x[,inactive]
+      cur_X = obj$x[,inactive,drop=FALSE]
 
       # now we condition on solution up to now
       # this is equivalent to finding vector of 
@@ -502,11 +505,11 @@ fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL) {
       if (j > 1) {
           cur_fitted = predict(obj, s=j)
 	  cur_fitted = cur_fitted - mean(cur_fitted)
-	  cur_offset = t(cur_X) %*% cur_fitted
+	  cur_offset = as.numeric(t(cur_X) %*% cur_fitted)
       }
       else {
           cur_fitted = 0
-          cur_offset = 0
+          cur_offset = rep(0, length(inactive))
       }
 
       final_upper = collapsed_pos - cur_offset
@@ -526,7 +529,46 @@ fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL) {
 
       # IMPORTANT: after sampling Y_star, we have to add back cur_fitted
 
-      pv = c(pv, runif(1))
+      # if n > p, we actually just draw cur_adjusted_X %*% Y_star
+      # because this has a simple box constraint
+      # with a generically non-degenerate covariance
+
+
+      if (n > p) { 
+          library(tmvtnorm)
+ 
+          if (length(inactive) > 1) {
+              cov = (cur_adjusted_X %*% t(cur_adjusted_X))
+              cov = cov * rep(sigma^2, nrow(cov), ncol(cov))
+          } else {
+              cov = sigma^2 * sum(cur_adjusted_X^2)
+          }
+
+          truncated_noise = rtmvnorm(n=ndraw, 
+                                     mean=cur_offset,
+                                     sigma=cov, 
+                                     lower=-collapsed_neg,
+                                     upper=collapsed_pos,
+                                     algorithm="gibbs",
+                 			 burn.in.samples=burnin)
+
+          if (length(inactive) > 1) {				 
+             sample_maxZ = apply(abs(1. / cur_scale * truncated_noise), 1, max)
+          }
+          else {
+             sample_maxZ = truncated_noise / cur_scale
+          }
+      } else {
+          # RUBBISH for now!!!!
+	  sample_maxZ = abs(rnorm(ndraw))         
+      }
+      
+      observed_maxZ = obj$realized_maxZ[j]
+
+      pval = sum(sample_maxZ > observed_maxZ) / ndraw
+      pval = 2 * min(pval, 1 - pval)
+
+      pv = c(pv, pval)
   }
 
   khat = forwardStop(pv,alpha)

From 197236a63415ef0e017ddaa0adcf096328b3f792 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Tue, 1 Dec 2015 23:14:37 -0800
Subject: [PATCH 091/493] Setting up cv simulation

---
 forLater/josh/sim.cv.R | 24 ++++++++++++++----------
 1 file changed, 14 insertions(+), 10 deletions(-)

diff --git a/forLater/josh/sim.cv.R b/forLater/josh/sim.cv.R
index b8d27cb..041cf8f 100644
--- a/forLater/josh/sim.cv.R
+++ b/forLater/josh/sim.cv.R
@@ -6,12 +6,12 @@ source("../../selectiveInference/R/funs.common.R")
 
 set.seed(1)
 niters <- 500
-n <- 50
-p <- 100
-maxsteps <- 10
-sparsity <- 5
-snr <- 1
-nfolds <- 5
+n <- 40
+p <- 80
+maxsteps <- 8
+sparsity <- 4
+snr <- 2
+nfolds <- 4
 
 instance <- function(n, p, sparsity, snr, maxsteps, nfolds) {
 
@@ -25,8 +25,10 @@ instance <- function(n, p, sparsity, snr, maxsteps, nfolds) {
     }
 
     fit <- cvfs(x, y, maxsteps=maxsteps, nfolds=nfolds)
-    pvals <- groupfsInf(fit, sigma = 1, verbose=T)
-    return(list(variable = fit$action, pvals = pvals$pv))
+    fit2 <- groupfs(x, y, index = 1:p, maxsteps = attr(fit, "maxsteps"))
+    pvals <- groupfsInf(fit, verbose=T)
+    pv2 <- groupfsInf(fit2, verbose=T)
+    return(list(variable = fit$action, pvals = pvals$pv, var2 = fit2$action, pvals2 = pv2$pv))
 }
 
 time <- system.time({
@@ -35,12 +37,14 @@ time <- system.time({
 
 pvals <- do.call(c, list(output[2,]))
 vars <- do.call(c, list(output[1,]))
+vars2 <- do.call(c, list(output[3,]))
+pvals2 <- do.call(c, list(output[4,]))
 
-save(pvals, vars, file = paste0(
+save(pvals, vars, vars2, pvals2, file = paste0(
                       "results_cv_n", n,
                       "_p", p,
                       "_sparsity", sparsity,
                       "_snr", snr,
-                      ".RData"))
+                      "_comparison.RData"))
 
 print(time)

From 16235ac8501bf9d2834b86543dc7e92a4918caa2 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 2 Dec 2015 15:41:12 -0800
Subject: [PATCH 092/493] cv simulation known sigma

---
 forLater/josh/selectiveInference/R/cv.R |  6 +++---
 forLater/josh/sim.aicstop.R             |  5 ++---
 forLater/josh/sim.cv.R                  | 16 ++++++++--------
 3 files changed, 13 insertions(+), 14 deletions(-)

diff --git a/forLater/josh/selectiveInference/R/cv.R b/forLater/josh/selectiveInference/R/cv.R
index 0a4661b..8714f2e 100644
--- a/forLater/josh/selectiveInference/R/cv.R
+++ b/forLater/josh/selectiveInference/R/cv.R
@@ -54,7 +54,7 @@ cvRSSquad <- function(x, folds, active.sets) {
     return(Q)
 }
 
-cvfs <- function(x, y, index = 1:ncol(x), maxsteps, nfolds = 10) {
+cvfs <- function(x, y, index = 1:ncol(x), maxsteps, sigma = NULL, nfolds = 10) {
 
     n <- nrow(x)
     if (maxsteps >= n*(1-1/nfolds)) {
@@ -75,7 +75,7 @@ cvfs <- function(x, y, index = 1:ncol(x), maxsteps, nfolds = 10) {
     # Flatten list or something?
     for (f in 1:nfolds) {
         fold <- folds[[f]]
-        fit <- groupfs(X[-fold,], Y[-fold], index, maxsteps=maxsteps)
+        fit <- groupfs(X[-fold,], Y[-fold], index=index, maxsteps=maxsteps, sigma=sigma)
         fit$fold <- fold
         ## projections[[f]] <- lapply(fit$projections, function(step.projs) {
         ##     lapply(step.projs, function(proj) {
@@ -103,7 +103,7 @@ cvfs <- function(x, y, index = 1:ncol(x), maxsteps, nfolds = 10) {
     RSSquads <- lapply(RSSquads, function(quad) quad - quadstar)
     RSSquads[[sstar]] <- NULL # remove the all zeroes case
 
-    fit <- groupfs(X, Y, index, maxsteps=sstar)
+    fit <- groupfs(X, Y, index=index, maxsteps=sstar, sigma=sigma)
     fit$cvobj <- cvobj
     fit$cvquad <- RSSquads
 
diff --git a/forLater/josh/sim.aicstop.R b/forLater/josh/sim.aicstop.R
index f2aa653..409ab0f 100644
--- a/forLater/josh/sim.aicstop.R
+++ b/forLater/josh/sim.aicstop.R
@@ -1,19 +1,18 @@
 library(intervals)
 source("funs.sims.R")
-#source("selectiveInference/R/cv.R")
 source("../../selectiveInference/R/funs.groupfs.R")
 source("../../selectiveInference/R/funs.quadratic.R")
 source("../../selectiveInference/R/funs.common.R")
 
 set.seed(1)
 known <- FALSE
-niters <- 200
+niters <- 500
 n <- 50
 p <- 150
 G <- 75
 maxsteps <- 10
 sparsity <- 4
-snr <- 2
+snr <- 3
 rho <- 0
 aicstop <- 1
 
diff --git a/forLater/josh/sim.cv.R b/forLater/josh/sim.cv.R
index 041cf8f..810d14b 100644
--- a/forLater/josh/sim.cv.R
+++ b/forLater/josh/sim.cv.R
@@ -5,13 +5,13 @@ source("../../selectiveInference/R/funs.quadratic.R")
 source("../../selectiveInference/R/funs.common.R")
 
 set.seed(1)
-niters <- 500
-n <- 40
-p <- 80
-maxsteps <- 8
-sparsity <- 4
+niters <- 4
+n <- 50
+p <- 100
+maxsteps <- 10
+sparsity <- 5
 snr <- 2
-nfolds <- 4
+nfolds <- 5
 
 instance <- function(n, p, sparsity, snr, maxsteps, nfolds) {
 
@@ -24,8 +24,8 @@ instance <- function(n, p, sparsity, snr, maxsteps, nfolds) {
       y <- y + x %*% beta
     }
 
-    fit <- cvfs(x, y, maxsteps=maxsteps, nfolds=nfolds)
-    fit2 <- groupfs(x, y, index = 1:p, maxsteps = attr(fit, "maxsteps"))
+    fit <- cvfs(x, y, maxsteps=maxsteps, sigma=1, nfolds=nfolds)
+    fit2 <- groupfs(x, y, index = 1:p, maxsteps = attr(fit, "maxsteps"), sigma = 1)
     pvals <- groupfsInf(fit, verbose=T)
     pv2 <- groupfsInf(fit2, verbose=T)
     return(list(variable = fit$action, pvals = pvals$pv, var2 = fit2$action, pvals2 = pv2$pv))

From 711bb22ebbccad1e3e3917179a7aff130a27d5e0 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Fri, 4 Dec 2015 16:41:47 -0800
Subject: [PATCH 093/493] Adding more checkpoints for F roots

---
 selectiveInference/R/funs.quadratic.R | 27 +++++++++++++++++----------
 1 file changed, 17 insertions(+), 10 deletions(-)

diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 003adc6..236b39c 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -205,16 +205,23 @@ TF_roots <- function(R, C, coeffs, tol = 1e-8, tol2 = 1e-6) {
     roots <- unique(thetas[which(modinds & angleinds)])
     troots <- tan(roots)^2/C
 
-    if (length(troots) == 0) {
-        # Polyroot didn't catch any roots
-        # ad-hoc check:
-        checkpoints <- c(0, tol, tol2,
-                         seq(from = sqrt(tol2), to = 1, length.out = 50),
-                         seq(from = 1.2, to=50, length.out = 20),
-                         100, 1000, 10000)
-    } else {
-        checkpoints <- roots_to_checkpoints(troots)
-    }
+    checkpoints <- c()
+    if (length(troots) > 0) checkpoints <- roots_to_checkpoints(troots)
+    checkpoints <- sort(
+        c(checkpoints, (0, tol, tol2,
+                        seq(from = sqrt(tol2), to = 1, length.out = 50),
+                        seq(from = 1.2, to=50, length.out = 20),
+                        100, 1000, 10000)))
+    ## if (length(troots) == 0) {
+    ##     # Polyroot didn't catch any roots
+    ##     # ad-hoc check:
+    ##     checkpoints <- c(0, tol, tol2,
+    ##                      seq(from = sqrt(tol2), to = 1, length.out = 50),
+    ##                      seq(from = 1.2, to=50, length.out = 20),
+    ##                      100, 1000, 10000)
+    ## } else {
+    ##     checkpoints <- roots_to_checkpoints(troots)
+    ## }
 
     signs <- sign(I(checkpoints))
     diffs <- c(0, diff(signs))

From e61a3fbc1f9508ba9d9a53e214b45b8d9f7f4afa Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Fri, 4 Dec 2015 16:44:51 -0800
Subject: [PATCH 094/493] Fixed syntax error

---
 forLater/josh/sim.cv.R                | 6 +++---
 selectiveInference/R/funs.quadratic.R | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/forLater/josh/sim.cv.R b/forLater/josh/sim.cv.R
index 810d14b..dc64493 100644
--- a/forLater/josh/sim.cv.R
+++ b/forLater/josh/sim.cv.R
@@ -5,7 +5,7 @@ source("../../selectiveInference/R/funs.quadratic.R")
 source("../../selectiveInference/R/funs.common.R")
 
 set.seed(1)
-niters <- 4
+niters <- 400
 n <- 50
 p <- 100
 maxsteps <- 10
@@ -24,8 +24,8 @@ instance <- function(n, p, sparsity, snr, maxsteps, nfolds) {
       y <- y + x %*% beta
     }
 
-    fit <- cvfs(x, y, maxsteps=maxsteps, sigma=1, nfolds=nfolds)
-    fit2 <- groupfs(x, y, index = 1:p, maxsteps = attr(fit, "maxsteps"), sigma = 1)
+    fit <- cvfs(x, y, maxsteps=maxsteps, nfolds=nfolds)
+    fit2 <- groupfs(x, y, index = 1:p, maxsteps = attr(fit, "maxsteps"))
     pvals <- groupfsInf(fit, verbose=T)
     pv2 <- groupfsInf(fit2, verbose=T)
     return(list(variable = fit$action, pvals = pvals$pv, var2 = fit2$action, pvals2 = pv2$pv))
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 236b39c..23ff63f 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -208,10 +208,10 @@ TF_roots <- function(R, C, coeffs, tol = 1e-8, tol2 = 1e-6) {
     checkpoints <- c()
     if (length(troots) > 0) checkpoints <- roots_to_checkpoints(troots)
     checkpoints <- sort(
-        c(checkpoints, (0, tol, tol2,
+        c(checkpoints, 0, tol, tol2,
                         seq(from = sqrt(tol2), to = 1, length.out = 50),
                         seq(from = 1.2, to=50, length.out = 20),
-                        100, 1000, 10000)))
+                        100, 1000, 10000))
     ## if (length(troots) == 0) {
     ##     # Polyroot didn't catch any roots
     ##     # ad-hoc check:

From a34b4a4a33d459153ec2ba210a8caf595ba5d286 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Mon, 7 Dec 2015 18:32:43 -0800
Subject: [PATCH 095/493] Fixing intercept, center, normalize for cvfs

---
 forLater/josh/selectiveInference/R/cv.R | 18 ++++++++++++++----
 selectiveInference/R/funs.groupfs.R     | 11 +++++------
 2 files changed, 19 insertions(+), 10 deletions(-)

diff --git a/forLater/josh/selectiveInference/R/cv.R b/forLater/josh/selectiveInference/R/cv.R
index 8714f2e..dfd6395 100644
--- a/forLater/josh/selectiveInference/R/cv.R
+++ b/forLater/josh/selectiveInference/R/cv.R
@@ -54,7 +54,7 @@ cvRSSquad <- function(x, folds, active.sets) {
     return(Q)
 }
 
-cvfs <- function(x, y, index = 1:ncol(x), maxsteps, sigma = NULL, nfolds = 10) {
+cvfs <- function(x, y, index = 1:ncol(x), maxsteps, sigma = NULL, intercept = TRUE, center = TRUE, normalize = TRUE, nfolds = 5) {
 
     n <- nrow(x)
     if (maxsteps >= n*(1-1/nfolds)) {
@@ -69,13 +69,23 @@ cvfs <- function(x, y, index = 1:ncol(x), maxsteps, sigma = NULL, nfolds = 10) {
     active.sets <- list(1:nfolds)
     cvobj <- list(1:nfolds)
     cv_perm <- sample(1:n)
-    Y <- y[cv_perm] - mean(y)
+    Y <- y[cv_perm]
     X <- x[cv_perm, ]
 
+    # Initialize copies of data for loop
+    by <- mean(Y)
+    if (intercept) Y <- Y - by
+
+    # Center and scale design matrix
+    xscaled <- scaleGroups(X, index, center, normalize)
+    xm <- xscaled$xm
+    xs <- xscaled$xs
+    X <- xscaled$x
+
     # Flatten list or something?
     for (f in 1:nfolds) {
         fold <- folds[[f]]
-        fit <- groupfs(X[-fold,], Y[-fold], index=index, maxsteps=maxsteps, sigma=sigma)
+        fit <- groupfs(X[-fold,], Y[-fold], index=index, maxsteps=maxsteps, sigma=sigma, intercept=FALSE, center=FALSE, normalize=FALSE)
         fit$fold <- fold
         ## projections[[f]] <- lapply(fit$projections, function(step.projs) {
         ##     lapply(step.projs, function(proj) {
@@ -103,7 +113,7 @@ cvfs <- function(x, y, index = 1:ncol(x), maxsteps, sigma = NULL, nfolds = 10) {
     RSSquads <- lapply(RSSquads, function(quad) quad - quadstar)
     RSSquads[[sstar]] <- NULL # remove the all zeroes case
 
-    fit <- groupfs(X, Y, index=index, maxsteps=sstar, sigma=sigma)
+    fit <- groupfs(X, Y, index=index, maxsteps=sstar, sigma=sigma, intercept=intercept, center=center, normalize=normalize)
     fit$cvobj <- cvobj
     fit$cvquad <- RSSquads
 
diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index a5f46c8..532cd02 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -49,10 +49,9 @@ groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE
   y.update <- y
   if (intercept) y.update <- y - by
   y.last <- y.update
-  x.update <- x
 
   # Center and scale design matrix
-  xscaled <- scaleGroups(x.update, index, center, normalize)
+  xscaled <- scaleGroups(x, index, center, normalize)
   xm <- xscaled$xm
   xs <- xscaled$xs
   x.update <- xscaled$x
@@ -434,7 +433,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = TRUE) {
             Uh <- ulist[[step]]
             Zg <- zlist[[step+1]]
             Zh <- zlist[[step]]
-            
+
             if (type == "TC") {
                 penh <- 0
                 etag <- etalist[[step+1]]
@@ -444,9 +443,9 @@ groupfsInf <- function(obj, sigma = NULL, verbose = TRUE) {
                 if (AICs[step] < AICs[step+1]) {
                     coeffs <- lapply(coeffs, function(coeff) -coeff)
                 }
-                
+
                 intstep <- quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol = 1e-15)
-                
+
             } else {
                 penh <- 1
                 Vdg <- vdlist[[step+1]]
@@ -458,7 +457,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = TRUE) {
                 if (AICs[step] < AICs[step+1]) {
                     coeffs <- lapply(coeffs, function(coeff) -coeff)
                 }
-                
+
                 intstep <- TF_roots(R, C, coeffs)
             }
 

From f2715b47af25b60946f0368e215866bca3c9f155 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 9 Dec 2015 11:52:56 -0800
Subject: [PATCH 096/493] Just running simulations

---
 forLater/josh/sim.cv.R | 22 +++++++++++++---------
 1 file changed, 13 insertions(+), 9 deletions(-)

diff --git a/forLater/josh/sim.cv.R b/forLater/josh/sim.cv.R
index dc64493..847a52f 100644
--- a/forLater/josh/sim.cv.R
+++ b/forLater/josh/sim.cv.R
@@ -5,12 +5,12 @@ source("../../selectiveInference/R/funs.quadratic.R")
 source("../../selectiveInference/R/funs.common.R")
 
 set.seed(1)
-niters <- 400
-n <- 50
-p <- 100
+niters <- 500
+n <- 100
+p <- 200
 maxsteps <- 10
 sparsity <- 5
-snr <- 2
+snr <- 1
 nfolds <- 5
 
 instance <- function(n, p, sparsity, snr, maxsteps, nfolds) {
@@ -24,11 +24,14 @@ instance <- function(n, p, sparsity, snr, maxsteps, nfolds) {
       y <- y + x %*% beta
     }
 
-    fit <- cvfs(x, y, maxsteps=maxsteps, nfolds=nfolds)
-    fit2 <- groupfs(x, y, index = 1:p, maxsteps = attr(fit, "maxsteps"))
+    fit <- cvfs(x, y, maxsteps=maxsteps, sigma=1, nfolds=nfolds)
+    fit2 <- groupfs(x, y, index = 1:p, maxsteps = attr(fit, "maxsteps"), sigma = 1)
     pvals <- groupfsInf(fit, verbose=T)
     pv2 <- groupfsInf(fit2, verbose=T)
-    return(list(variable = fit$action, pvals = pvals$pv, var2 = fit2$action, pvals2 = pv2$pv))
+    Y <- y - mean(y)
+    cols <- which(1:p %in% fit$action)
+    pv3 <- summary(lm(Y~x[, cols]-1))$coefficients[,4]
+    return(list(variable = fit$action, pvals = pvals$pv, var2 = fit2$action, pvals2 = pv2$pv, pvals3 = pv3[order(fit$action)]))
 }
 
 time <- system.time({
@@ -39,12 +42,13 @@ pvals <- do.call(c, list(output[2,]))
 vars <- do.call(c, list(output[1,]))
 vars2 <- do.call(c, list(output[3,]))
 pvals2 <- do.call(c, list(output[4,]))
+pvals3 <- do.call(c, list(output[5,]))
 
-save(pvals, vars, vars2, pvals2, file = paste0(
+save(pvals, vars, vars2, pvals2, pvals3, file = paste0(
                       "results_cv_n", n,
                       "_p", p,
                       "_sparsity", sparsity,
                       "_snr", snr,
-                      "_comparison.RData"))
+                      "_comparison_TC.RData"))
 
 print(time)

From b9c4d0179b31af6c544033d361ae3fe88bb94297 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 9 Dec 2015 15:01:00 -0800
Subject: [PATCH 097/493] Data splitting simulation

---
 forLater/josh/sim.datasplit.R | 64 +++++++++++++++++++++++++++++++++++
 1 file changed, 64 insertions(+)
 create mode 100644 forLater/josh/sim.datasplit.R

diff --git a/forLater/josh/sim.datasplit.R b/forLater/josh/sim.datasplit.R
new file mode 100644
index 0000000..9d59cd6
--- /dev/null
+++ b/forLater/josh/sim.datasplit.R
@@ -0,0 +1,64 @@
+library(intervals)
+source("selectiveInference/R/cv.R")
+source("../../selectiveInference/R/funs.groupfs.R")
+source("../../selectiveInference/R/funs.quadratic.R")
+source("../../selectiveInference/R/funs.common.R")
+
+set.seed(1)
+niters <- 1000
+n <- 100
+p <- 50
+maxsteps <- 20
+sparsity <- 5
+snr <- 1
+train <- 1:(7*n/10)
+test <- setdiff(1:n, train)
+index <- 1:p
+
+instance <- function(n, p, sparsity, snr, maxsteps, nfolds) {
+
+    x <- matrix(rnorm(n*p), nrow=n)
+    y <- rnorm(n)
+
+    if (sparsity > 0) {
+      beta <- rep(0, p)
+      beta[1:sparsity] <- snr * sample(c(-1,1), sparsity, replace=T)
+      y <- y + x %*% beta
+    }
+
+    ytr <- y[train]
+    xtr <- x[train, ]
+    yte <- y[test]
+    xte <- x[test, ]
+
+    #log(length(train))
+    trfit <- groupfs(xtr, ytr, index, maxsteps=maxsteps, aicstop=1, k = log(length(train)))
+    fit <- groupfs(x, y, index, maxsteps=maxsteps, aicstop=1, k = log(n))
+
+    trcols <- which(1:p %in% trfit$action)
+    tepv <- summary(lm(yte~xte[, trcols]-1))$coefficients[,4]
+    names(tepv) <- as.character(sort(trfit$action))
+    pv <- groupfsInf(fit)
+    return(list(vars = fit$action, pvals = pv$pv,
+                splitvars = sort(trfit$action), splitpvals = tepv))
+}
+
+time <- system.time({
+          output <- replicate(niters, instance(n, p, sparsity, snr, maxsteps, nfolds))
+})
+
+vars <- do.call(c, list(output[1,]))
+pvals <- do.call(c, list(output[2,]))
+splitvars <- do.call(c, list(output[3,]))
+splitpvals <- do.call(c, list(output[4,]))
+
+
+save(vars, pvals, splitvars, splitpvals, file = paste0(
+                      "results_datasplit_n", n,
+                      "_p", p,
+                      "_sparsity", sparsity,
+                      "_snr", as.character(snr),
+                      "_bic.RData"))
+
+print(time)
+

From 01b51b2c1d44dfafc9914a31b9c5547dd96d522a Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 9 Dec 2015 15:36:45 -0800
Subject: [PATCH 098/493] Fixing cv comparison simulation

---
 forLater/josh/sim.cv.R | 30 ++++++++++++++++++------------
 1 file changed, 18 insertions(+), 12 deletions(-)

diff --git a/forLater/josh/sim.cv.R b/forLater/josh/sim.cv.R
index 847a52f..3260140 100644
--- a/forLater/josh/sim.cv.R
+++ b/forLater/josh/sim.cv.R
@@ -5,7 +5,7 @@ source("../../selectiveInference/R/funs.quadratic.R")
 source("../../selectiveInference/R/funs.common.R")
 
 set.seed(1)
-niters <- 500
+niters <- 50
 n <- 100
 p <- 200
 maxsteps <- 10
@@ -25,30 +25,36 @@ instance <- function(n, p, sparsity, snr, maxsteps, nfolds) {
     }
 
     fit <- cvfs(x, y, maxsteps=maxsteps, sigma=1, nfolds=nfolds)
-    fit2 <- groupfs(x, y, index = 1:p, maxsteps = attr(fit, "maxsteps"), sigma = 1)
+    vars <- fit$action
+    #fit2 <- groupfs(x, y, index = 1:p, maxsteps = attr(fit, "maxsteps"), sigma = 1)
     pvals <- groupfsInf(fit, verbose=T)
-    pv2 <- groupfsInf(fit2, verbose=T)
+    fit$cvobj <- NULL
+    nocvpv <- groupfsInf(fit, verbose=T)
     Y <- y - mean(y)
-    cols <- which(1:p %in% fit$action)
-    pv3 <- summary(lm(Y~x[, cols]-1))$coefficients[,4]
-    return(list(variable = fit$action, pvals = pvals$pv, var2 = fit2$action, pvals2 = pv2$pv, pvals3 = pv3[order(fit$action)]))
+    cols <- which(1:p %in% vars)
+    noselpv <- summary(lm(Y~x[, cols]-1))$coefficients[,4]
+    names(noselpv) <- as.character(sort(vars))
+    return(list(vars = vars, pvals = pvals$pv,
+                nocvvars = vars, nocvpvals = nocvpv$pv,
+                noselvars = sort(vars), noselpvals = noselpv))
 }
 
 time <- system.time({
           output <- replicate(niters, instance(n, p, sparsity, snr, maxsteps, nfolds))
 })
 
-pvals <- do.call(c, list(output[2,]))
 vars <- do.call(c, list(output[1,]))
-vars2 <- do.call(c, list(output[3,]))
-pvals2 <- do.call(c, list(output[4,]))
-pvals3 <- do.call(c, list(output[5,]))
+pvals <- do.call(c, list(output[2,]))
+nocvvars <- do.call(c, list(output[3,]))
+nocvpvals <- do.call(c, list(output[4,]))
+noselvars <- do.call(c, list(output[5,]))
+noselpvals <- do.call(c, list(output[6,]))
 
-save(pvals, vars, vars2, pvals2, pvals3, file = paste0(
+save(vars, pvals, nocvvars, nocvpvals, noselvars, noselpvals, file = paste0(
                       "results_cv_n", n,
                       "_p", p,
                       "_sparsity", sparsity,
                       "_snr", snr,
-                      "_comparison_TC.RData"))
+                      "_comparison.RData"))
 
 print(time)

From dcbad8661fe316c304830e85b2009ba3cfefb684 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 9 Dec 2015 16:34:17 -0800
Subject: [PATCH 099/493] Fixed typo in TF cv code

---
 forLater/josh/sim.cv.R              | 20 ++++++++++++--------
 selectiveInference/R/funs.groupfs.R |  2 +-
 2 files changed, 13 insertions(+), 9 deletions(-)

diff --git a/forLater/josh/sim.cv.R b/forLater/josh/sim.cv.R
index 3260140..6287d20 100644
--- a/forLater/josh/sim.cv.R
+++ b/forLater/josh/sim.cv.R
@@ -7,27 +7,31 @@ source("../../selectiveInference/R/funs.common.R")
 set.seed(1)
 niters <- 50
 n <- 100
-p <- 200
+p <- 50
 maxsteps <- 10
 sparsity <- 5
-snr <- 1
+snr <- 2
+rho <- 0.1
 nfolds <- 5
 
-instance <- function(n, p, sparsity, snr, maxsteps, nfolds) {
+instance <- function(n, p, sparsity, snr, maxsteps, nfolds, rho) {
 
     x <- matrix(rnorm(n*p), nrow=n)
+    if (rho != 0) {
+        z <- matrix(rep(t(rnorm(n)), p), nrow = n)
+        x <- sqrt(1-rho)*x + sqrt(rho)*z
+    }
     y <- rnorm(n)
 
     if (sparsity > 0) {
       beta <- rep(0, p)
-      beta[1:sparsity] <- snr * sample(c(-1,1), sparsity, replace=T)
+      beta[1:sparsity] <- snr * sqrt(2*log(p)/n) * sample(c(-1,1), sparsity, replace=T)
       y <- y + x %*% beta
     }
 
-    fit <- cvfs(x, y, maxsteps=maxsteps, sigma=1, nfolds=nfolds)
+    fit <- cvfs(x, y, maxsteps=maxsteps, nfolds=nfolds)
     vars <- fit$action
-    #fit2 <- groupfs(x, y, index = 1:p, maxsteps = attr(fit, "maxsteps"), sigma = 1)
-    pvals <- groupfsInf(fit, verbose=T)
+    pvals <- groupfsInf(fit, sigma=1, verbose=T)
     fit$cvobj <- NULL
     nocvpv <- groupfsInf(fit, verbose=T)
     Y <- y - mean(y)
@@ -40,7 +44,7 @@ instance <- function(n, p, sparsity, snr, maxsteps, nfolds) {
 }
 
 time <- system.time({
-          output <- replicate(niters, instance(n, p, sparsity, snr, maxsteps, nfolds))
+          output <- replicate(niters, instance(n, p, sparsity, snr, maxsteps, nfolds, rho))
 })
 
 vars <- do.call(c, list(output[1,]))
diff --git a/selectiveInference/R/funs.groupfs.R b/selectiveInference/R/funs.groupfs.R
index 532cd02..b2c0447 100644
--- a/selectiveInference/R/funs.groupfs.R
+++ b/selectiveInference/R/funs.groupfs.R
@@ -378,7 +378,7 @@ groupfsInf <- function(obj, sigma = NULL, verbose = TRUE) {
                                   x0 <- zcvquad %*% Z
                                   x1 <- 2*R*zcvquad %*% Vdelta
                                   x2 <- 2*R*zcvquad %*% V2
-                                  x12 <- 2*R*vdcvquad %*% V2
+                                  x12 <- 2*R^2*vdcvquad %*% V2
                                   x11 <- R^2*vdcvquad %*% Vdelta
                                   x22 <- R^2*v2cvquad %*% V2
                                   TF_roots(R, C, coeffs = list(x0=x0, x1=x1, x2=x2, x12=x12, x11=x11, x22=x22))

From aacba41a8c386d2e549879047770feaaed19d0bc Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 9 Dec 2015 17:14:54 -0800
Subject: [PATCH 100/493] Setting up simmulation

---
 forLater/josh/sim.cv.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/forLater/josh/sim.cv.R b/forLater/josh/sim.cv.R
index 6287d20..807cde2 100644
--- a/forLater/josh/sim.cv.R
+++ b/forLater/josh/sim.cv.R
@@ -8,7 +8,7 @@ set.seed(1)
 niters <- 50
 n <- 100
 p <- 50
-maxsteps <- 10
+maxsteps <- 8
 sparsity <- 5
 snr <- 2
 rho <- 0.1
@@ -31,7 +31,7 @@ instance <- function(n, p, sparsity, snr, maxsteps, nfolds, rho) {
 
     fit <- cvfs(x, y, maxsteps=maxsteps, nfolds=nfolds)
     vars <- fit$action
-    pvals <- groupfsInf(fit, sigma=1, verbose=T)
+    pvals <- groupfsInf(fit, verbose=T)
     fit$cvobj <- NULL
     nocvpv <- groupfsInf(fit, verbose=T)
     Y <- y - mean(y)

From 3cc3318a868edb7892e465bb615aea8612d9f9ee Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Thu, 10 Dec 2015 06:14:31 -0800
Subject: [PATCH 101/493] Organizing simulations

---
 forLater/josh/sim.aicstop.R   | 22 +++++++++-------
 forLater/josh/sim.cv.R        | 23 +++++++++++-----
 forLater/josh/sim.datasplit.R | 49 +++++++++++++++++++++++------------
 forLater/josh/sim.groupfs.R   | 21 ++++++++-------
 4 files changed, 72 insertions(+), 43 deletions(-)

diff --git a/forLater/josh/sim.aicstop.R b/forLater/josh/sim.aicstop.R
index 409ab0f..af150ec 100644
--- a/forLater/josh/sim.aicstop.R
+++ b/forLater/josh/sim.aicstop.R
@@ -38,15 +38,17 @@ stopped <- do.call(c, list(output[3,]))
 pvals <- do.call(c, list(output[2,]))
 vars <- do.call(c, list(output[1,]))
 
-save(pvals, vars, stopped, file = paste0(
-                      "results_aic_n", n,
-                      "_p", p,
-                      "_g", G,
-                      "_rho", gsub(pattern = ".", replacement = "", x = rho, fixed = T),
-                      "_maxsteps", maxsteps,
-                      "_sparsity", sparsity,
-                      "_snr", round(snr),
-                      "_known", known,
-                      ".RData"))
+save(pvals, vars, stopped,
+     file = paste0(
+         "results/aic", 
+         "_", ifelse(known, "TC", "TF"),
+         "_n", n,
+         "_p", p,
+         "_g", G,
+         "_rho", gsub(".", "pt", rho, fixed=T),
+         "_maxsteps", maxsteps,
+         "_sparsity", sparsity,
+         "_snr", round(snr),
+         ".RData"))
 
 print(time)
diff --git a/forLater/josh/sim.cv.R b/forLater/josh/sim.cv.R
index 807cde2..7d20e8c 100644
--- a/forLater/josh/sim.cv.R
+++ b/forLater/josh/sim.cv.R
@@ -6,6 +6,7 @@ source("../../selectiveInference/R/funs.common.R")
 
 set.seed(1)
 niters <- 50
+known <- FALSE
 n <- 100
 p <- 50
 maxsteps <- 8
@@ -29,7 +30,11 @@ instance <- function(n, p, sparsity, snr, maxsteps, nfolds, rho) {
       y <- y + x %*% beta
     }
 
-    fit <- cvfs(x, y, maxsteps=maxsteps, nfolds=nfolds)
+    if (known) {
+        fit <- cvfs(x, y, maxsteps=maxsteps, sigma = 1, nfolds=nfolds)
+    } else {
+        fit <- cvfs(x, y, maxsteps=maxsteps, nfolds=nfolds)
+    }
     vars <- fit$action
     pvals <- groupfsInf(fit, verbose=T)
     fit$cvobj <- NULL
@@ -54,11 +59,15 @@ nocvpvals <- do.call(c, list(output[4,]))
 noselvars <- do.call(c, list(output[5,]))
 noselpvals <- do.call(c, list(output[6,]))
 
-save(vars, pvals, nocvvars, nocvpvals, noselvars, noselpvals, file = paste0(
-                      "results_cv_n", n,
-                      "_p", p,
-                      "_sparsity", sparsity,
-                      "_snr", snr,
-                      "_comparison.RData"))
+save(vars, pvals, nocvvars, nocvpvals, noselvars, noselpvals,
+     file = paste0("results/cv",
+         "_", ifelse(known, "TC", "TF"),
+         "_n", n,
+         "_p", p,
+         "_rho", gsub(".", "pt", rho, fixed=T),
+         "_sparsity", sparsity,
+         "_maxsteps", maxsteps,
+         "_snr", snr,
+         ".RData"))
 
 print(time)
diff --git a/forLater/josh/sim.datasplit.R b/forLater/josh/sim.datasplit.R
index 9d59cd6..ef3de31 100644
--- a/forLater/josh/sim.datasplit.R
+++ b/forLater/josh/sim.datasplit.R
@@ -5,19 +5,26 @@ source("../../selectiveInference/R/funs.quadratic.R")
 source("../../selectiveInference/R/funs.common.R")
 
 set.seed(1)
-niters <- 1000
+niters <- 500
+known <- FALSE
 n <- 100
-p <- 50
+p <- 200
 maxsteps <- 20
 sparsity <- 5
 snr <- 1
-train <- 1:(7*n/10)
+rho <- 0.1
+ratio <- 0.75
+train <- 1:(ratio*n)
 test <- setdiff(1:n, train)
 index <- 1:p
 
-instance <- function(n, p, sparsity, snr, maxsteps, nfolds) {
+instance <- function(n, p, sparsity, snr, maxsteps, rho) {
 
     x <- matrix(rnorm(n*p), nrow=n)
+    if (rho != 0) {
+        z <- matrix(rep(t(rnorm(n)), p), nrow = n)
+        x <- sqrt(1-rho)*x + sqrt(rho)*z
+    }
     y <- rnorm(n)
 
     if (sparsity > 0) {
@@ -31,34 +38,44 @@ instance <- function(n, p, sparsity, snr, maxsteps, nfolds) {
     yte <- y[test]
     xte <- x[test, ]
 
-    #log(length(train))
-    trfit <- groupfs(xtr, ytr, index, maxsteps=maxsteps, aicstop=1, k = log(length(train)))
-    fit <- groupfs(x, y, index, maxsteps=maxsteps, aicstop=1, k = log(n))
+    if (known) {
+        trfit <- groupfs(xtr, ytr, index, maxsteps=maxsteps, sigma=1, aicstop=1, k = 2*log(p))
+        fit <- groupfs(x, y, index, maxsteps=maxsteps, sigma=1, aicstop=1, k = 2*log(p))
+    } else {
+        trfit <- groupfs(xtr, ytr, index, maxsteps=maxsteps, aicstop=1, k = log(length(train)))
+        fit <- groupfs(x, y, index, maxsteps=maxsteps, aicstop=1, k = log(n))        
+    }
 
     trcols <- which(1:p %in% trfit$action)
     tepv <- summary(lm(yte~xte[, trcols]-1))$coefficients[,4]
     names(tepv) <- as.character(sort(trfit$action))
     pv <- groupfsInf(fit)
+    trpv <- groupfsInf(trfit)
     return(list(vars = fit$action, pvals = pv$pv,
-                splitvars = sort(trfit$action), splitpvals = tepv))
+                splitvars = sort(trfit$action), splitpvals = tepv),
+           trpvals = trpv$pv)
 }
 
 time <- system.time({
-          output <- replicate(niters, instance(n, p, sparsity, snr, maxsteps, nfolds))
+          output <- replicate(niters, instance(n, p, sparsity, snr, maxsteps, rho))
 })
 
 vars <- do.call(c, list(output[1,]))
 pvals <- do.call(c, list(output[2,]))
 splitvars <- do.call(c, list(output[3,]))
 splitpvals <- do.call(c, list(output[4,]))
+trpvals <- do.call(c, list(output[5,]))
 
-
-save(vars, pvals, splitvars, splitpvals, file = paste0(
-                      "results_datasplit_n", n,
-                      "_p", p,
-                      "_sparsity", sparsity,
-                      "_snr", as.character(snr),
-                      "_bic.RData"))
+save(vars, pvals, splitvars, splitpvals, trpvals,
+     file = paste0("results/datasplit",
+         "_", ifelse(known, "TC", "TF"),
+         "_n", n,
+         "_p", p,
+         "_rho", gsub(".", "pt", rho, fixed=T),
+         "_sparsity", sparsity,
+         "_ratio", gsub(".", "pt", round(ratio, 2), fixed=T),
+         "_snr", as.character(snr),
+         "_bic.RData"))
 
 print(time)
 
diff --git a/forLater/josh/sim.groupfs.R b/forLater/josh/sim.groupfs.R
index 9e60631..5f053e5 100644
--- a/forLater/josh/sim.groupfs.R
+++ b/forLater/josh/sim.groupfs.R
@@ -37,15 +37,16 @@ time <- system.time({
 pvals <- do.call(c, list(output[2,]))
 vars <- do.call(c, list(output[1,]))
 
-save(pvals, vars, file = paste0(
-                      "results_n", n,
-                      "_p", p,
-                      "_g", G,
-                      "_rho", gsub(pattern = ".", replacement = "", x = rho, fixed = T),
-                      "_maxsteps", maxsteps,
-                      "_sparsity", sparsity,
-                      "_snr", round(snr),
-                      "_known", known,
-                      ".RData"))
+save(pvals, vars,
+     file = paste0("results/",
+         ifelse(known, "TC", "TF"),
+         "_n", n,
+         "_p", p,
+         "_g", G,
+         "_rho", gsub(".", "pt", rho, fixed=T),
+         "_maxsteps", maxsteps,
+         "_sparsity", sparsity,
+         "_snr", round(snr),
+         ".RData"))
 
 print(time)

From a4f89ea017da8074ffd296d0b511305e4bfe942a Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Thu, 10 Dec 2015 06:30:54 -0800
Subject: [PATCH 102/493] Data carving simulation

---
 forLater/josh/sim.carve.R     | 81 +++++++++++++++++++++++++++++++++++
 forLater/josh/sim.datasplit.R |  8 ++--
 2 files changed, 85 insertions(+), 4 deletions(-)
 create mode 100644 forLater/josh/sim.carve.R

diff --git a/forLater/josh/sim.carve.R b/forLater/josh/sim.carve.R
new file mode 100644
index 0000000..2881109
--- /dev/null
+++ b/forLater/josh/sim.carve.R
@@ -0,0 +1,81 @@
+library(intervals)
+source("selectiveInference/R/cv.R")
+source("../../selectiveInference/R/funs.groupfs.R")
+source("../../selectiveInference/R/funs.quadratic.R")
+source("../../selectiveInference/R/funs.common.R")
+
+set.seed(1)
+niters <- 400
+known <- FALSE
+n <- 100
+p <- 50
+maxsteps <- 20
+sparsity <- 10
+snr <- 1
+rho <- 0.1
+ratio <- 0.75
+train <- 1:(ratio*n)
+test <- setdiff(1:n, train)
+index <- 1:p
+
+instance <- function(n, p, sparsity, snr, maxsteps, rho) {
+
+    x <- matrix(rnorm(n*p), nrow=n)
+    if (rho != 0) {
+        z <- matrix(rep(t(rnorm(n)), p), nrow = n)
+        x <- sqrt(1-rho)*x + sqrt(rho)*z
+    }
+    y <- rnorm(n)
+
+    if (sparsity > 0) {
+      beta <- rep(0, p)
+      beta[1:sparsity] <- snr * sample(c(-1,1), sparsity, replace=T)
+      y <- y + x %*% beta
+    }
+
+    ytr <- y[train]
+    xtr <- x[train, ]
+    yte <- y[test]
+    xte <- x[test, ]
+
+    if (known) {
+        trfit <- cvfs(xtr, ytr, maxsteps=maxsteps, sigma = 1, nfolds=nfolds)
+        fit <- cvfs(x, y, maxsteps=maxsteps, sigma = 1, nfolds=nfolds)
+    } else {
+        trfit <- cvfs(xtr, ytr, maxsteps=maxsteps, nfolds=nfolds)
+        fit <- cvfs(x, y, maxsteps=maxsteps, nfolds=nfolds)
+    }
+
+    trcols <- which(1:p %in% trfit$action)
+    tepv <- summary(lm(yte~xte[, trcols]-1))$coefficients[,4]
+    names(tepv) <- as.character(sort(trfit$action))
+    pv <- groupfsInf(fit)
+    trpv <- groupfsInf(trfit)
+    return(list(vars = fit$action, pvals = pv$pv,
+                splitvars = sort(trfit$action), splitpvals = tepv,
+                trpvals = trpv$pv))
+}
+
+time <- system.time({
+          output <- replicate(niters, instance(n, p, sparsity, snr, maxsteps, rho))
+})
+
+vars <- do.call(c, list(output[1,]))
+pvals <- do.call(c, list(output[2,]))
+splitvars <- do.call(c, list(output[3,]))
+splitpvals <- do.call(c, list(output[4,]))
+trpvals <- do.call(c, list(output[5,]))
+
+save(vars, pvals, splitvars, splitpvals, trpvals,
+     file = paste0("results/carvecv",
+         "_", ifelse(known, "TC", "TF"),
+         "_n", n,
+         "_p", p,
+         "_rho", gsub(".", "pt", rho, fixed=T),
+         "_sparsity", sparsity,
+         "_ratio", gsub(".", "pt", round(ratio, 2), fixed=T),
+         "_snr", as.character(snr),
+         ".RData"))
+
+print(time)
+
diff --git a/forLater/josh/sim.datasplit.R b/forLater/josh/sim.datasplit.R
index ef3de31..e732ce7 100644
--- a/forLater/josh/sim.datasplit.R
+++ b/forLater/josh/sim.datasplit.R
@@ -8,9 +8,9 @@ set.seed(1)
 niters <- 500
 known <- FALSE
 n <- 100
-p <- 200
+p <- 100
 maxsteps <- 20
-sparsity <- 5
+sparsity <- 10
 snr <- 1
 rho <- 0.1
 ratio <- 0.75
@@ -52,8 +52,8 @@ instance <- function(n, p, sparsity, snr, maxsteps, rho) {
     pv <- groupfsInf(fit)
     trpv <- groupfsInf(trfit)
     return(list(vars = fit$action, pvals = pv$pv,
-                splitvars = sort(trfit$action), splitpvals = tepv),
-           trpvals = trpv$pv)
+                splitvars = sort(trfit$action), splitpvals = tepv,
+                trpvals = trpv$pv))
 }
 
 time <- system.time({

From 859a6d4d43316eec012f3c55d87cd1abf9e679dc Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Fri, 11 Dec 2015 10:54:54 -0800
Subject: [PATCH 103/493] Need to tune simulations better

---
 forLater/josh/sim.carve.R     |  1 +
 forLater/josh/sim.datasplit.R | 23 +++++++++++++++++------
 2 files changed, 18 insertions(+), 6 deletions(-)

diff --git a/forLater/josh/sim.carve.R b/forLater/josh/sim.carve.R
index 2881109..7d1ae59 100644
--- a/forLater/josh/sim.carve.R
+++ b/forLater/josh/sim.carve.R
@@ -17,6 +17,7 @@ ratio <- 0.75
 train <- 1:(ratio*n)
 test <- setdiff(1:n, train)
 index <- 1:p
+nfolds <- 5
 
 instance <- function(n, p, sparsity, snr, maxsteps, rho) {
 
diff --git a/forLater/josh/sim.datasplit.R b/forLater/josh/sim.datasplit.R
index e732ce7..be077a1 100644
--- a/forLater/josh/sim.datasplit.R
+++ b/forLater/josh/sim.datasplit.R
@@ -5,17 +5,20 @@ source("../../selectiveInference/R/funs.quadratic.R")
 source("../../selectiveInference/R/funs.common.R")
 
 set.seed(1)
-niters <- 500
+niters <- 400
 known <- FALSE
 n <- 100
 p <- 100
-maxsteps <- 20
-sparsity <- 10
+maxsteps <- 10
+sparsity <- 5
 snr <- 1
 rho <- 0.1
-ratio <- 0.75
+ratio <- 0.7
+ratio2 <- 0.85
 train <- 1:(ratio*n)
 test <- setdiff(1:n, train)
+train2 <- 1:(ratio2*n)
+test <- setdiff(1:n, train2)
 index <- 1:p
 
 instance <- function(n, p, sparsity, snr, maxsteps, rho) {
@@ -37,18 +40,26 @@ instance <- function(n, p, sparsity, snr, maxsteps, rho) {
     xtr <- x[train, ]
     yte <- y[test]
     xte <- x[test, ]
+    
+    ytr2 <- y[train2]
+    xtr2 <- x[train2, ]
+    yte2 <- y[test2]
+    xte2 <- x[test2, ]
 
     if (known) {
         trfit <- groupfs(xtr, ytr, index, maxsteps=maxsteps, sigma=1, aicstop=1, k = 2*log(p))
-        fit <- groupfs(x, y, index, maxsteps=maxsteps, sigma=1, aicstop=1, k = 2*log(p))
+        fit <- groupfs(xtr2, ytr2, index, maxsteps=maxsteps, sigma=1, aicstop=1, k = 2*log(p))
     } else {
         trfit <- groupfs(xtr, ytr, index, maxsteps=maxsteps, aicstop=1, k = log(length(train)))
-        fit <- groupfs(x, y, index, maxsteps=maxsteps, aicstop=1, k = log(n))        
+        fit <- groupfs(xtr2, ytr2, index, maxsteps=maxsteps, aicstop=1, k = log(length(train2)))
     }
 
     trcols <- which(1:p %in% trfit$action)
+    tr2cols <- which(1:p %in% fit$action)
     tepv <- summary(lm(yte~xte[, trcols]-1))$coefficients[,4]
+    tepv2 <- summary(lm(yte2~xte2[, tr2cols]-1))$coefficients[,4]
     names(tepv) <- as.character(sort(trfit$action))
+    names(tepv2) <- as.character(sort(trfit$action))
     pv <- groupfsInf(fit)
     trpv <- groupfsInf(trfit)
     return(list(vars = fit$action, pvals = pv$pv,

From f115ab2ae797700261f3c10c75252df890104627 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Wed, 23 Dec 2015 16:37:33 -0800
Subject: [PATCH 104/493] Setting up new data splitting simulation

---
 forLater/josh/sim.splitcv.R | 77 +++++++++++++++++++++++++++++++++++++
 1 file changed, 77 insertions(+)
 create mode 100644 forLater/josh/sim.splitcv.R

diff --git a/forLater/josh/sim.splitcv.R b/forLater/josh/sim.splitcv.R
new file mode 100644
index 0000000..cc5428e
--- /dev/null
+++ b/forLater/josh/sim.splitcv.R
@@ -0,0 +1,77 @@
+library(intervals)
+source("selectiveInference/R/cv.R")
+source("../../selectiveInference/R/funs.groupfs.R")
+source("../../selectiveInference/R/funs.quadratic.R")
+source("../../selectiveInference/R/funs.common.R")
+
+set.seed(1)
+niters <- 500
+known <- FALSE
+n <- 100
+p <- 200
+maxsteps <- 20
+sparsity <- 5
+snr <- 1
+rho <- 0.1
+ratio <- 0.75
+train <- 1:(ratio*n)
+test <- setdiff(1:n, train)
+index <- 1:p
+
+instance <- function(n, p, sparsity, snr, maxsteps, rho) {
+
+    x <- matrix(rnorm(n*p), nrow=n)
+    if (rho != 0) {
+        z <- matrix(rep(t(rnorm(n)), p), nrow = n)
+        x <- sqrt(1-rho)*x + sqrt(rho)*z
+    }
+    y <- rnorm(n)
+
+    if (sparsity > 0) {
+      beta <- rep(0, p)
+      beta[1:sparsity] <- snr * sample(c(-1,1), sparsity, replace=T)
+      y <- y + x %*% beta
+    }
+
+    ytr <- y[train]
+    xtr <- x[train, ]
+    yte <- y[test]
+    xte <- x[test, ]
+
+    if (known) {
+        trfit <- groupfs(xtr, ytr, index, maxsteps=maxsteps, sigma=1, aicstop=1, k = 2*log(p))
+        fit <- groupfs(x, y, index, maxsteps=maxsteps, sigma=1, aicstop=1, k = 2*log(p))
+    } else {
+        trfit <- groupfs(xtr, ytr, index, maxsteps=maxsteps, aicstop=1, k = log(length(train)))
+        fit <- groupfs(x, y, index, maxsteps=maxsteps, aicstop=1, k = log(n))
+    }
+
+    trcols <- which(1:p %in% trfit$action)
+    tepv <- summary(lm(yte~xte[, trcols]-1))$coefficients[,4]
+    names(tepv) <- as.character(sort(trfit$action))
+#    pv <- groupfsInf(fit)
+#    trpv <- groupfsInf(trfit)
+    return(list(vars = fit$action, splitvars = sort(trfit$action), splitpvals = tepv))
+}
+
+time <- system.time({
+          output <- replicate(niters, instance(n, p, sparsity, snr, maxsteps, rho))
+})
+
+vars <- do.call(c, list(output[1,]))
+splitvars <- do.call(c, list(output[2,]))
+splitpvals <- do.call(c, list(output[3,]))
+
+save(vars, pvals, splitvars, splitpvals, trpvals,
+     file = paste0("results/datasplit",
+         "_", ifelse(known, "TC", "TF"),
+         "_n", n,
+         "_p", p,
+         "_rho", gsub(".", "pt", rho, fixed=T),
+         "_sparsity", sparsity,
+         "_ratio", gsub(".", "pt", round(ratio, 2), fixed=T),
+         "_snr", as.character(snr),
+         "_bic.RData"))
+
+print(time)
+

From 00396d05fe7cb5464582fb7792d2bb06014648a9 Mon Sep 17 00:00:00 2001
From: tibshirani <tibs@stanford.edu>
Date: Sat, 26 Dec 2015 11:46:22 -0500
Subject: [PATCH 105/493] rob

---
 selectiveInference/NAMESPACE   |  1 +
 selectiveInference/R/funs.fs.R | 13 +++++++------
 tests/test.fs.R                | 14 ++++++++++++++
 3 files changed, 22 insertions(+), 6 deletions(-)

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index 8dd9120..5b65949 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -34,4 +34,5 @@ import(intervals)
 importFrom("graphics", abline, axis, matplot)
 importFrom("stats", dnorm, lsfit, pexp, pnorm, predict,
            qnorm, rnorm, sd, uniroot, dchisq, model.matrix, pchisq)
+importFrom("stats", "coef", "df", "lm", "pf")
 
diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index a6a9c3f..b5ee511 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -106,8 +106,8 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     }
 
     # Key quantities for the next entry
-
-    X_inactive_resid = X_inactive - X_active %*% backsolve(R,t(Q_active)%*%X_inactive)
+    keepLs=backsolve(R,t(Q_active)%*%X_inactive)
+    X_inactive_resid = X_inactive - X_active %*% keepLs
     working_x = scale(X_inactive_resid,center=F,scale=sqrt(colSums(X_inactive_resid^2)))
     score = as.numeric(t(working_x)%*%y)
     
@@ -189,7 +189,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     # Record the least squares solution. Note that
     # we have already computed this
     bls = rep(0,p)
-    bls[A] = a
+   if(length(keepLs)>0)  bls[A] = keepLs
   }
 
   if (verbose) cat("\n")
@@ -448,6 +448,7 @@ print.fsInf <- function(x, tailarea=TRUE, ...) {
   invisible()
 }
 
+
 plot.fs <- function(x, breaks=TRUE, omit.zeros=TRUE, var.labels=TRUE, ...) {
   if (x$completepath) {
     k = length(x$action)+1
@@ -457,10 +458,10 @@ plot.fs <- function(x, breaks=TRUE, omit.zeros=TRUE, var.labels=TRUE, ...) {
     beta = x$beta
   }
   p = nrow(beta)
-  
+
   xx = 1:k
   xlab = "Step"
-  
+
  if (omit.zeros) {
    good.inds = matrix(FALSE,p,k)
    good.inds[beta!=0] = TRUE
@@ -475,7 +476,7 @@ plot.fs <- function(x, breaks=TRUE, omit.zeros=TRUE, var.labels=TRUE, ...) {
   abline(h=0,lwd=2)
   matplot(xx,t(beta),type="l",lty=1,add=TRUE)
   if (breaks) abline(v=xx,lty=2)
-  if (var.labels) axis(4,at=beta[,k],labels=1:p,cex=0.8,adj=0) 
+  if (var.labels) axis(4,at=beta[,k],labels=1:p,cex=0.8,adj=0)
   invisible()
 }
 
diff --git a/tests/test.fs.R b/tests/test.fs.R
index f811676..a3d1f91 100644
--- a/tests/test.fs.R
+++ b/tests/test.fs.R
@@ -171,3 +171,17 @@ out3 = fsInf(obj,sigma=sigma,k=k,type="all")
 out3
     out4 = fsInf(obj,sigma=sigma,k=k,type="all",bits=200)
     
+##plot
+
+    
+     set.seed(33)
+     n = 50
+     p = 10
+     sigma = 1
+     x = matrix(rnorm(n*p),n,p)
+     beta = c(3,2,rep(0,p-2))
+     y = x%*%beta + sigma*rnorm(n)
+     
+     # run forward stepwise, plot results
+     fsfit = fs(x,y)
+     plot(fsfit)

From b238822a88e45175188ef334fb1dc4424ff01a9e Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Fri, 1 Jan 2016 15:50:39 -0800
Subject: [PATCH 106/493] rob

---
 forLater/robnotes.pdf          | Bin 0 -> 150705 bytes
 forLater/robnotes.tex          | 287 +++++++++++++++++++++++++++++++++
 selectiveInference/DESCRIPTION |   6 +-
 tests/test.fs.R                |  18 ++-
 4 files changed, 304 insertions(+), 7 deletions(-)
 create mode 100644 forLater/robnotes.pdf
 create mode 100644 forLater/robnotes.tex

diff --git a/forLater/robnotes.pdf b/forLater/robnotes.pdf
new file mode 100644
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diff --git a/forLater/robnotes.tex b/forLater/robnotes.tex
new file mode 100644
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--- /dev/null
+++ b/forLater/robnotes.tex
@@ -0,0 +1,287 @@
+\documentclass{article}
+
+\usepackage[round]{natbib}
+\usepackage{amsmath,amssymb,amsthm,bm,enumerate,mathrsfs,mathtools}
+\usepackage{latexsym,color,verbatim,multirow}
+\usepackage{graphicx}
+\usepackage{caption}
+\usepackage{subcaption}
+
+
+\def\be {{\bf 1}}
+
+
+\newcommand{\real}{\mathbb{R}}
+\begin{document}
+\title{Post-selection inference for generalized regression}
+\author{Jonathan Taylor and  Robert Tibshirani} 
+
+\maketitle
+
+\begin{abstract}
+\end{abstract}
+
+
+
+
+\section{Introduction}
+
+\begin{itemize}
+\item Data $(x_i, y_i), i=1,2,\ldots N$ with $x_i=(x_{i1},x_{i2},\ldots x_{ip})$.
+Let $X= \{x_{ij}\}$ be the data matrix. 
+
+
+\item Generalized regression model  with linear predictor $\eta=\beta_0+X\beta$ and log-likelihood
+$\ell(\beta_0,\beta)$.  Consider the objective function
+\begin{eqnarray}
+J(\beta_0,\beta)=-\ell(\beta_0,\beta) +\lambda\cdot \sum_1^p |\beta_j|
+\label{eqn:obj})
+\end{eqnarray}
+\item Let $\hat\beta_0, \hat\beta_1$ be the minimizers of $J(\beta_0, \beta)$. We wish to carry out post-selection inference
+for any functional  $\gamma^T\beta$.
+\item Leading example: logistic regression. $\pi=E(Y|x)$; $\log \pi/(1-\pi)=\beta_0+X\beta$.
+$\ell(\beta_0,\beta)=\sum [y_i \log(\pi_i)+(1-y_i)\log(1-\pi_i)]$.
+
+\item Background: Gaussian case. Selected model $M$ with sign vector $s$, 
+the  KKT conditions state that $\{\hat M,\hat s \} = (M,s)$ if and only if there exists $\beta$ and $u$ satisfying   
+\begin{eqnarray}
+X_M^(X_M^T\beta- y) +\lambda s)+\lambda s&=&0 \cr
+X_{-M}^T(X_M^T \beta-y)+ \lambda s)+\lambda u&=&0 \cr
+{\rm sign}(\beta)&=&s \cr
+||u||_\infty &<& 1
+\end{eqnarray}
+This allows us to write the set of response $y$ that yield the same $M$ and $s$  in the polyhedral form 
+\begin{equation}
+\Bigl\{   \begin{pmatrix} A_0(M,s) \cr
+                         A_1(M,s) 
+                 \end{pmatrix}
+                  y < 
+               \begin{pmatrix} b_0(M,s) \cr
+                             b_1(M,s) 
+                               \end{pmatrix}
+  \Bigr\}
+  \end{equation}
+
+
+
+
+\item A convenient strategy for minimizing (\ref{eqn:obj}) to express the usual
+Newton-Raphson update as an iterative reweighted least squares (IRLS) step, and then
+replace the weighted least squares step by a constrained  weighted least squares
+procedure.
+
+We define $u= \partial\ell/\partial\eta$,
+$W=-\partial^2 l/\partial\eta\eta^T$ and $z=\eta+W^{-1}u$
+Then a one-term Taylor series expansion for $\ell(\beta)$  has the form
+\begin{eqnarray}
+(z-\eta)^T W(z-\eta)
+\label{taylor}
+\end{eqnarray}
+Hence to  minimize (\ref{eqn:obj})  we use the following
+ procedure:
+\begin{enumerate}
+\item Fix $s$ and initialize $\hat\beta=0$
+\item Compute $\eta, W$ and $z$ based on the current value of $\hat\beta$
+\item Minimize $(z-\beta_0-X\beta)^TW (z-\beta_0-X\beta)+\lambda\cdot\sum|\beta_j|$ 
+\item Repeat steps (2) and (3) until $\hat\beta_0, \hat\beta$ don't change.
+\end{enumerate}
+
+\item  KKT
+$$ -X_M^T W(z- \beta_0-X_M^T\beta)+\lambda s=0$$
+\item
+$$\hat\beta=(X_M^TW X_M)^{-1}(X_M^TW z-\lambda s) \;(active) $$
+$$-X_{-M}^T  W (z-X_M\beta)+\lambda u =0,  ||u||_\infty <1 \;(inactive) $$
+\begin{eqnarray}
+u&=&X_{-M}^T  W P_MW^{-1}(X_M^T)^+s+ \frac{1}{\lambda} X_{-M}^T W(I-P_M)z
+\end{eqnarray}
+
+\item For active variables,
+${\rm diag(s)}\beta>0$ implies
+$D(X_M^TXW_M)^{-1}(X_m^TW z-\lambda s)>0$. 
+where $D={\rm diag(}s)$.
+
+Hence
+$A_1=-D(X_M^TWX_M)^{-1}X_M^TW, b_1=-D(X_M^TWX_M)^{-1}\lambda s$
+
+For inactive variables,
+$A_0=\frac{1}{\lambda} 
+\begin{pmatrix}
+ X_{-M}^T W) \\
+   -X_{-M}^T W
+   \end{pmatrix}
+   $, $b_0= \begin{pmatrix}
+              \be+X_{-M}^TWX_M\hat\beta/\lambda \\
+                 \be-X_{-M}^TWX_M\hat\beta/\lambda
+              \end{pmatrix}
+              $
+              
+              Finally, let $A=\begin{pmatrix} A_1\\ A_0  \end{pmatrix}
+, b=(b_1,b_0)$
+              
+\item Idea:  take $z\sim N(\mu,W^{-1})$  and apply polyhedral lemma to region $A z \leq b$.
+Potential problem: $A,b, z$ depend on $\hat\beta$; and  region $A z \leq b$  does not correspond to set 
+\item  Logistic regression: KKT
+$$z=X\beta+\frac{y-\hat p}{\hat p(1-\hat p)}$$
+$$ -X_M^T W(z- X_M^T\beta)+\lambda s=0$$
+\item
+$$\hat\beta=(X_M^TW X_M)^{-1}(X_M^TW z-\lambda s) \;(active) $$
+$$-X_{-M}^T  W (z-X_M\beta)+\lambda u =0,  ||u||_\infty <1 \;(inactive) $$
+\begin{eqnarray}
+u&=&X_{-M}^T  W P_MW^{-1}(X_M^T)^+s+ \frac{1}{\lambda} X_{-M}^T W(I-P_M)z
+\end{eqnarray}
+
+\item For active variables,
+${\rm diag(s)}\beta>0$ implies
+$D(X_M^TXW_M)^{-1}(X_m^TW z-\lambda s)>0$. 
+where $D={\rm diag(}s)$.
+
+Hence
+$A_1=-D(X_M^TWX_M)^{-1}X_M^TW, b_1=-D(X_M^TWX_M)^{-1}\lambda s$
+
+For inactive variables,
+$A_0=\frac{1}{\lambda} 
+\begin{pmatrix}
+ X_{-M}^T W) \\
+   -X_{-M}^T W
+   \end{pmatrix}
+   $, $b_0= \begin{pmatrix}
+              \be+X_{-M}^TWX_M\hat\beta/\lambda \\
+                 \be-X_{-M}^TWX_M\hat\beta/\lambda
+              \end{pmatrix}
+              $
+              
+              Finally, let $A=\begin{pmatrix} A_1\\ A_0  \end{pmatrix}
+, b=(b_1,b_0)$
+              
+\item Idea:  take $z\sim N(\mu,W^{-1})$  and apply polyhedral lemma to region $A z \leq b$
+\end{itemize}
+
+
+\section{Jon's notes}
+We are conditioning on the active set and signs.
+Let $\hat{\beta}=\hat{\beta}_{\lambda}$ be the LASSO solution. We are going to
+fix the model $M$ and signs $s_M$. So, it is a function of
+$M, X_M^Ty, X_M, s_M$.
+Also, let
+$$
+\begin{aligned}
+\hat{\pi} &= \pi(X\hat{\beta}_{\lambda}) \\
+W &= \text{diag}(\hat{\pi}(1-\hat{\pi}))
+\end{aligned}
+$$
+Let
+$$
+z = X_M\hat{\beta} + \frac{y - \hat{\pi}}{\hat{\pi}(1 - \hat{\pi})}
+$$
+
+The KKT conditions can then be written as
+$$
+X^T(y - \hat{\pi}) = 
+XW(z - X_M\hat{\beta}) = \lambda u
+$$
+where $u \in \partial (\| \cdot \|_1)(\hat{\beta})$ so
+$$
+u_M = s_M, \quad \|u_{-M}\|_{\infty} < 1.
+$$
+
+By construction, we have that
+$$
+\bar{\beta}=(X_M^TWX_M)^{-1}(X_M^TWz) = \hat{\beta} + \lambda (X_M^TWX_M)^{-1} s_M.
+$$
+
+This is, up to some remainder, the unpenalized logistic regression estimator.
+The remainder, after rescaling, goes to 0 in probability ($p$ fixed) before selection. So, under suitable assumptions about the selective likelihood ratio, 
+so Lemma 1 of randomized response paper applies, and you can 
+use  this for inference about $\beta_M$.
+
+Let's look at the inactive block. By construction,
+$$
+\begin{aligned}
+X_{-M}^TW(z - X_M\hat{\beta}) &= X_{-M}^T(y - \hat{\pi}) \\
+& \approx  X_{-M}^T(y - \pi) - X_{-M}^TWX_M(\hat{\beta} - \beta_M) \\
+&= X_{-M}^T(y - \pi) - X_{-M}^TWX_M(\bar{\beta} - \beta_M) + X_{-M}^TWX_M (X_M^TWX_M)^{-1}s_M\\
+\end{aligned}
+$$
+with the remainder also going to 0 in probability after appropriate rescaling.
+
+So, while $z$ is not normally distributed, i.e.
+the KKT conditions are
+an affine function of $z$ and the affine functionals are such that,
+they are asymptotically normally distributed. Further, the variances
+from Rob's normal approximation work as plugins variance estimators 
+(Section 4.3 of http://arxiv.org/pdf/1507.06739v3.pdf) under the
+{\bf selected model.}
+
+Since our variance calculations only hold under the selected model, we might be losing some power using
+polyhedral lemma.
+
+\subsection{Selected is the same as full?}
+
+\newcommand{\E}{E}
+
+An asymptotic variance calculation under pairs model $(y_i,X_i) \overset{IID}{\sim} F$:
+$$
+\text{Cov}_F \left(X_{-M}^T(y-\pi) - \E_F((X_{-M}^TWX_M))\E((X_M^TWX_M))^{-1}X_M^T(y-\pi)),
+\E_F((X_M^TWX_M))^{-1}X_M^T(y-\pi)\right) = 0
+$$
+yields that the
+randomness in the inactive block is (asymptotically) independent
+of $\bar{\beta}$. This assumes
+that the selected model is correct, or, more precisely that $\hat{\pi}$ is a
+good estimate of $P_F(y=1|X)$ so that
+$$
+\frac{1}{n} X^TWX \approx \text{Cov}_F((y-P_F(y=1|X)) \cdot X)
+$$
+($X$ on the RHS should be thought of as a random vector).
+This might not be true if link is misspecified or selected model is poor...
+
+I think then the inactive blocks are not needed.
+
+\section{Current favorite version}
+
+$$
+\hat{\beta} = \hat{\beta}_{\lambda} = \text{argmin}_{\beta} \ell(\beta) + \lambda \|\beta\|_1
+$$
+
+$$
+M = \{j: \hat{\beta} \neq 0\}, s_M = \text{sign}(\hat{\beta}[M])$$
+
+$$
+\begin{aligned}
+\bar{\beta}_M &= \hat{\beta}[M] - \left(\nabla^2 \ell(\hat{\beta})[M,M]\right)^{-1} \nabla \ell(\hat{\beta})_M \\
+&=  \hat{\beta}_M + \lambda \left(\nabla^2 \ell(\hat{\beta})[M,M]\right)^{-1} s_M \\
+&=  \hat{\beta}_M + \lambda \ell_M(\hat{\beta}_M)^{-1} s_M
+\end{aligned}
+$$
+where $\ell_M: \mathbb{R}^M \rightarrow \mathbb{R}$ is the objective funtions
+of the selected model and
+$$
+\nabla \ell^2(\hat{\beta})[M,M] = \frac{\partial^2}{\partial \beta_i \partial \beta_j} \ell(\beta) \biggl|_{\hat{\beta}}, \qquad i,j \in M
+$$
+is an $|M| \times |M|$ matrix.
+
+If $\ell$ is a negative log-likelihood, then under the selected model, 
+$$
+\bar{\beta}_M \approx N\left(\beta_M^*, \nabla^2 \ell_M(\hat{\beta}_M)^{-1}\right).
+$$
+subject to affine constraints
+$$
+\left\{\text{diag}(s_M)\left[\bar{\beta}_M - \nabla^2 \ell_M(\hat{\beta}_M)^{-1} s_M \right] \geq 0 \right\}.
+$$
+
+We apply polyhedral lemma to $\bar{\beta}_M$, with $M, s_M$ and $\nabla^2 \ell_M (\hat{\beta}_M)$ fixed.
+
+For logistic regression, these should match your active block KKT conditions exactly where 
+$$
+\bar{\beta}_M = (X_M^TWX_M)^{-1}X_M^TWz
+$$
+with
+$$
+z = X_M\hat{\beta}_M + \frac{y - \hat{\pi}}{\hat{\pi}(1-\hat{\pi})}
+= X_M\hat{\beta}_M + W^{-1}(y - \hat{\pi}).
+$$
+
+
+
+\end{document}
+
diff --git a/selectiveInference/DESCRIPTION b/selectiveInference/DESCRIPTION
index ce95116..4fc8274 100644
--- a/selectiveInference/DESCRIPTION
+++ b/selectiveInference/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: selectiveInference
 Type: Package
-Title: Tools for Selective Inference
+Title: Tools for Post-Selection Inference
 Version: 1.1.2
-Date: 2015-09-01
+Date: 2015-12-17
 Author: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor,
     Joshua Loftus, Stephen Reid
 Maintainer: Rob Tibshirani <tibs@stanford.edu>
@@ -11,7 +11,7 @@ Depends:
     intervals
 Suggests:
     Rmpfr
-Description: New tools for inference after selection, for use
+Description: New tools for post-selection inference, for use
     with forward stepwise regression, least angle regression, the
     lasso, and the many means problem.
 License: GPL-2
diff --git a/tests/test.fs.R b/tests/test.fs.R
index a3d1f91..b80e747 100644
--- a/tests/test.fs.R
+++ b/tests/test.fs.R
@@ -1,6 +1,9 @@
 library(selectiveInference)
 #library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
 
+options(error=dump.frames)
+
+
 library(lars)
 
 set.seed(0)
@@ -113,8 +116,8 @@ x=matrix(rnorm(n*p),n,p)
 #x=scale(x,T,T)/sqrt(n-1)    #try with and without standardization
 
 beta=c(5,4,3,2,1,rep(0,p-5))
-
-nsim=100
+beta=rep(0,p)
+nsim=500
 seeds=sample(1:9999,size=nsim)
 pv=rep(NA,nsim)
 ci=matrix(NA,nsim,2)
@@ -134,7 +137,10 @@ for(ii in 1:nsim){
      btrue[ii]=lsfit(x[,oo],mu)$coef[2]
      ci[ii,]=junk$ci[1,]
 }
-
+plot((1:nsim)/nsim,sort(pv))
+    abline(0,1)
+    
+    
 sum(ci[,1]> btrue)
 sum(ci[,2]< btrue)
 
@@ -173,7 +179,11 @@ out3
     
 ##plot
 
-    
+    library(selectiveInference)
+
+options(error=dump.frames)
+
+
      set.seed(33)
      n = 50
      p = 10

From 98595c96fe1c0bf6989d670e5bda717771e9b056 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Tue, 9 Feb 2016 09:10:39 -0800
Subject: [PATCH 107/493] doc changes

---
 forLater/robnotes.pdf                   | Bin 150705 -> 0 bytes
 forLater/robnotes.tex                   | 287 ------------------------
 selectiveInference/DESCRIPTION          |   4 +-
 selectiveInference/R/funs.fixed.R       |  26 ++-
 selectiveInference/man/fixedLassoInf.Rd |  56 ++++-
 tests/test.fixed.R                      |  72 ++++++
 6 files changed, 139 insertions(+), 306 deletions(-)
 delete mode 100644 forLater/robnotes.pdf
 delete mode 100644 forLater/robnotes.tex

diff --git a/forLater/robnotes.pdf b/forLater/robnotes.pdf
deleted file mode 100644
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diff --git a/forLater/robnotes.tex b/forLater/robnotes.tex
deleted file mode 100644
index dd6ff47..0000000
--- a/forLater/robnotes.tex
+++ /dev/null
@@ -1,287 +0,0 @@
-\documentclass{article}
-
-\usepackage[round]{natbib}
-\usepackage{amsmath,amssymb,amsthm,bm,enumerate,mathrsfs,mathtools}
-\usepackage{latexsym,color,verbatim,multirow}
-\usepackage{graphicx}
-\usepackage{caption}
-\usepackage{subcaption}
-
-
-\def\be {{\bf 1}}
-
-
-\newcommand{\real}{\mathbb{R}}
-\begin{document}
-\title{Post-selection inference for generalized regression}
-\author{Jonathan Taylor and  Robert Tibshirani} 
-
-\maketitle
-
-\begin{abstract}
-\end{abstract}
-
-
-
-
-\section{Introduction}
-
-\begin{itemize}
-\item Data $(x_i, y_i), i=1,2,\ldots N$ with $x_i=(x_{i1},x_{i2},\ldots x_{ip})$.
-Let $X= \{x_{ij}\}$ be the data matrix. 
-
-
-\item Generalized regression model  with linear predictor $\eta=\beta_0+X\beta$ and log-likelihood
-$\ell(\beta_0,\beta)$.  Consider the objective function
-\begin{eqnarray}
-J(\beta_0,\beta)=-\ell(\beta_0,\beta) +\lambda\cdot \sum_1^p |\beta_j|
-\label{eqn:obj})
-\end{eqnarray}
-\item Let $\hat\beta_0, \hat\beta_1$ be the minimizers of $J(\beta_0, \beta)$. We wish to carry out post-selection inference
-for any functional  $\gamma^T\beta$.
-\item Leading example: logistic regression. $\pi=E(Y|x)$; $\log \pi/(1-\pi)=\beta_0+X\beta$.
-$\ell(\beta_0,\beta)=\sum [y_i \log(\pi_i)+(1-y_i)\log(1-\pi_i)]$.
-
-\item Background: Gaussian case. Selected model $M$ with sign vector $s$, 
-the  KKT conditions state that $\{\hat M,\hat s \} = (M,s)$ if and only if there exists $\beta$ and $u$ satisfying   
-\begin{eqnarray}
-X_M^(X_M^T\beta- y) +\lambda s)+\lambda s&=&0 \cr
-X_{-M}^T(X_M^T \beta-y)+ \lambda s)+\lambda u&=&0 \cr
-{\rm sign}(\beta)&=&s \cr
-||u||_\infty &<& 1
-\end{eqnarray}
-This allows us to write the set of response $y$ that yield the same $M$ and $s$  in the polyhedral form 
-\begin{equation}
-\Bigl\{   \begin{pmatrix} A_0(M,s) \cr
-                         A_1(M,s) 
-                 \end{pmatrix}
-                  y < 
-               \begin{pmatrix} b_0(M,s) \cr
-                             b_1(M,s) 
-                               \end{pmatrix}
-  \Bigr\}
-  \end{equation}
-
-
-
-
-\item A convenient strategy for minimizing (\ref{eqn:obj}) to express the usual
-Newton-Raphson update as an iterative reweighted least squares (IRLS) step, and then
-replace the weighted least squares step by a constrained  weighted least squares
-procedure.
-
-We define $u= \partial\ell/\partial\eta$,
-$W=-\partial^2 l/\partial\eta\eta^T$ and $z=\eta+W^{-1}u$
-Then a one-term Taylor series expansion for $\ell(\beta)$  has the form
-\begin{eqnarray}
-(z-\eta)^T W(z-\eta)
-\label{taylor}
-\end{eqnarray}
-Hence to  minimize (\ref{eqn:obj})  we use the following
- procedure:
-\begin{enumerate}
-\item Fix $s$ and initialize $\hat\beta=0$
-\item Compute $\eta, W$ and $z$ based on the current value of $\hat\beta$
-\item Minimize $(z-\beta_0-X\beta)^TW (z-\beta_0-X\beta)+\lambda\cdot\sum|\beta_j|$ 
-\item Repeat steps (2) and (3) until $\hat\beta_0, \hat\beta$ don't change.
-\end{enumerate}
-
-\item  KKT
-$$ -X_M^T W(z- \beta_0-X_M^T\beta)+\lambda s=0$$
-\item
-$$\hat\beta=(X_M^TW X_M)^{-1}(X_M^TW z-\lambda s) \;(active) $$
-$$-X_{-M}^T  W (z-X_M\beta)+\lambda u =0,  ||u||_\infty <1 \;(inactive) $$
-\begin{eqnarray}
-u&=&X_{-M}^T  W P_MW^{-1}(X_M^T)^+s+ \frac{1}{\lambda} X_{-M}^T W(I-P_M)z
-\end{eqnarray}
-
-\item For active variables,
-${\rm diag(s)}\beta>0$ implies
-$D(X_M^TXW_M)^{-1}(X_m^TW z-\lambda s)>0$. 
-where $D={\rm diag(}s)$.
-
-Hence
-$A_1=-D(X_M^TWX_M)^{-1}X_M^TW, b_1=-D(X_M^TWX_M)^{-1}\lambda s$
-
-For inactive variables,
-$A_0=\frac{1}{\lambda} 
-\begin{pmatrix}
- X_{-M}^T W) \\
-   -X_{-M}^T W
-   \end{pmatrix}
-   $, $b_0= \begin{pmatrix}
-              \be+X_{-M}^TWX_M\hat\beta/\lambda \\
-                 \be-X_{-M}^TWX_M\hat\beta/\lambda
-              \end{pmatrix}
-              $
-              
-              Finally, let $A=\begin{pmatrix} A_1\\ A_0  \end{pmatrix}
-, b=(b_1,b_0)$
-              
-\item Idea:  take $z\sim N(\mu,W^{-1})$  and apply polyhedral lemma to region $A z \leq b$.
-Potential problem: $A,b, z$ depend on $\hat\beta$; and  region $A z \leq b$  does not correspond to set 
-\item  Logistic regression: KKT
-$$z=X\beta+\frac{y-\hat p}{\hat p(1-\hat p)}$$
-$$ -X_M^T W(z- X_M^T\beta)+\lambda s=0$$
-\item
-$$\hat\beta=(X_M^TW X_M)^{-1}(X_M^TW z-\lambda s) \;(active) $$
-$$-X_{-M}^T  W (z-X_M\beta)+\lambda u =0,  ||u||_\infty <1 \;(inactive) $$
-\begin{eqnarray}
-u&=&X_{-M}^T  W P_MW^{-1}(X_M^T)^+s+ \frac{1}{\lambda} X_{-M}^T W(I-P_M)z
-\end{eqnarray}
-
-\item For active variables,
-${\rm diag(s)}\beta>0$ implies
-$D(X_M^TXW_M)^{-1}(X_m^TW z-\lambda s)>0$. 
-where $D={\rm diag(}s)$.
-
-Hence
-$A_1=-D(X_M^TWX_M)^{-1}X_M^TW, b_1=-D(X_M^TWX_M)^{-1}\lambda s$
-
-For inactive variables,
-$A_0=\frac{1}{\lambda} 
-\begin{pmatrix}
- X_{-M}^T W) \\
-   -X_{-M}^T W
-   \end{pmatrix}
-   $, $b_0= \begin{pmatrix}
-              \be+X_{-M}^TWX_M\hat\beta/\lambda \\
-                 \be-X_{-M}^TWX_M\hat\beta/\lambda
-              \end{pmatrix}
-              $
-              
-              Finally, let $A=\begin{pmatrix} A_1\\ A_0  \end{pmatrix}
-, b=(b_1,b_0)$
-              
-\item Idea:  take $z\sim N(\mu,W^{-1})$  and apply polyhedral lemma to region $A z \leq b$
-\end{itemize}
-
-
-\section{Jon's notes}
-We are conditioning on the active set and signs.
-Let $\hat{\beta}=\hat{\beta}_{\lambda}$ be the LASSO solution. We are going to
-fix the model $M$ and signs $s_M$. So, it is a function of
-$M, X_M^Ty, X_M, s_M$.
-Also, let
-$$
-\begin{aligned}
-\hat{\pi} &= \pi(X\hat{\beta}_{\lambda}) \\
-W &= \text{diag}(\hat{\pi}(1-\hat{\pi}))
-\end{aligned}
-$$
-Let
-$$
-z = X_M\hat{\beta} + \frac{y - \hat{\pi}}{\hat{\pi}(1 - \hat{\pi})}
-$$
-
-The KKT conditions can then be written as
-$$
-X^T(y - \hat{\pi}) = 
-XW(z - X_M\hat{\beta}) = \lambda u
-$$
-where $u \in \partial (\| \cdot \|_1)(\hat{\beta})$ so
-$$
-u_M = s_M, \quad \|u_{-M}\|_{\infty} < 1.
-$$
-
-By construction, we have that
-$$
-\bar{\beta}=(X_M^TWX_M)^{-1}(X_M^TWz) = \hat{\beta} + \lambda (X_M^TWX_M)^{-1} s_M.
-$$
-
-This is, up to some remainder, the unpenalized logistic regression estimator.
-The remainder, after rescaling, goes to 0 in probability ($p$ fixed) before selection. So, under suitable assumptions about the selective likelihood ratio, 
-so Lemma 1 of randomized response paper applies, and you can 
-use  this for inference about $\beta_M$.
-
-Let's look at the inactive block. By construction,
-$$
-\begin{aligned}
-X_{-M}^TW(z - X_M\hat{\beta}) &= X_{-M}^T(y - \hat{\pi}) \\
-& \approx  X_{-M}^T(y - \pi) - X_{-M}^TWX_M(\hat{\beta} - \beta_M) \\
-&= X_{-M}^T(y - \pi) - X_{-M}^TWX_M(\bar{\beta} - \beta_M) + X_{-M}^TWX_M (X_M^TWX_M)^{-1}s_M\\
-\end{aligned}
-$$
-with the remainder also going to 0 in probability after appropriate rescaling.
-
-So, while $z$ is not normally distributed, i.e.
-the KKT conditions are
-an affine function of $z$ and the affine functionals are such that,
-they are asymptotically normally distributed. Further, the variances
-from Rob's normal approximation work as plugins variance estimators 
-(Section 4.3 of http://arxiv.org/pdf/1507.06739v3.pdf) under the
-{\bf selected model.}
-
-Since our variance calculations only hold under the selected model, we might be losing some power using
-polyhedral lemma.
-
-\subsection{Selected is the same as full?}
-
-\newcommand{\E}{E}
-
-An asymptotic variance calculation under pairs model $(y_i,X_i) \overset{IID}{\sim} F$:
-$$
-\text{Cov}_F \left(X_{-M}^T(y-\pi) - \E_F((X_{-M}^TWX_M))\E((X_M^TWX_M))^{-1}X_M^T(y-\pi)),
-\E_F((X_M^TWX_M))^{-1}X_M^T(y-\pi)\right) = 0
-$$
-yields that the
-randomness in the inactive block is (asymptotically) independent
-of $\bar{\beta}$. This assumes
-that the selected model is correct, or, more precisely that $\hat{\pi}$ is a
-good estimate of $P_F(y=1|X)$ so that
-$$
-\frac{1}{n} X^TWX \approx \text{Cov}_F((y-P_F(y=1|X)) \cdot X)
-$$
-($X$ on the RHS should be thought of as a random vector).
-This might not be true if link is misspecified or selected model is poor...
-
-I think then the inactive blocks are not needed.
-
-\section{Current favorite version}
-
-$$
-\hat{\beta} = \hat{\beta}_{\lambda} = \text{argmin}_{\beta} \ell(\beta) + \lambda \|\beta\|_1
-$$
-
-$$
-M = \{j: \hat{\beta} \neq 0\}, s_M = \text{sign}(\hat{\beta}[M])$$
-
-$$
-\begin{aligned}
-\bar{\beta}_M &= \hat{\beta}[M] - \left(\nabla^2 \ell(\hat{\beta})[M,M]\right)^{-1} \nabla \ell(\hat{\beta})_M \\
-&=  \hat{\beta}_M + \lambda \left(\nabla^2 \ell(\hat{\beta})[M,M]\right)^{-1} s_M \\
-&=  \hat{\beta}_M + \lambda \ell_M(\hat{\beta}_M)^{-1} s_M
-\end{aligned}
-$$
-where $\ell_M: \mathbb{R}^M \rightarrow \mathbb{R}$ is the objective funtions
-of the selected model and
-$$
-\nabla \ell^2(\hat{\beta})[M,M] = \frac{\partial^2}{\partial \beta_i \partial \beta_j} \ell(\beta) \biggl|_{\hat{\beta}}, \qquad i,j \in M
-$$
-is an $|M| \times |M|$ matrix.
-
-If $\ell$ is a negative log-likelihood, then under the selected model, 
-$$
-\bar{\beta}_M \approx N\left(\beta_M^*, \nabla^2 \ell_M(\hat{\beta}_M)^{-1}\right).
-$$
-subject to affine constraints
-$$
-\left\{\text{diag}(s_M)\left[\bar{\beta}_M - \nabla^2 \ell_M(\hat{\beta}_M)^{-1} s_M \right] \geq 0 \right\}.
-$$
-
-We apply polyhedral lemma to $\bar{\beta}_M$, with $M, s_M$ and $\nabla^2 \ell_M (\hat{\beta}_M)$ fixed.
-
-For logistic regression, these should match your active block KKT conditions exactly where 
-$$
-\bar{\beta}_M = (X_M^TWX_M)^{-1}X_M^TWz
-$$
-with
-$$
-z = X_M\hat{\beta}_M + \frac{y - \hat{\pi}}{\hat{\pi}(1-\hat{\pi})}
-= X_M\hat{\beta}_M + W^{-1}(y - \hat{\pi}).
-$$
-
-
-
-\end{document}
-
diff --git a/selectiveInference/DESCRIPTION b/selectiveInference/DESCRIPTION
index 4fc8274..6bac8a0 100644
--- a/selectiveInference/DESCRIPTION
+++ b/selectiveInference/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: selectiveInference
 Type: Package
 Title: Tools for Post-Selection Inference
-Version: 1.1.2
-Date: 2015-12-17
+Version: 1.1.3
+Date: 2016-02-8
 Author: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor,
     Joshua Loftus, Stephen Reid
 Maintainer: Rob Tibshirani <tibs@stanford.edu>
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index ac00545..ada155b 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -122,9 +122,11 @@ fixedLassoInf <- function(x, y, beta, lambda, intercept=TRUE, sigma=NULL, alpha=
   return(out)
 }
 
-##############################
+#############################
+
 
-fixedLasso.poly <- function(x, y, beta, lambda, a) {
+fixedLasso.poly=
+function(x, y, beta, lambda, a) {
   xa = x[,a,drop=F]
   xac = x[,!a,drop=F]
   xai = pinv(crossprod(xa))
@@ -134,16 +136,22 @@ fixedLasso.poly <- function(x, y, beta, lambda, a) {
   if (length(za)==1) dz = matrix(za,1,1)
 
   P = diag(1,nrow(xa)) - xa %*% xap
-  G = -rbind(1/lambda * t(xac) %*% P,
-    -1/lambda * t(xac) %*% P,
-    -dz %*% xap)
-  u = -c(1 - t(xac) %*% t(xap) %*% za,
-    1 + t(xac) %*% t(xap) %*% za,
-    -lambda * dz %*% xai %*% za)
+  #NOTE: inactive constraints not needed below!
+ 
+  G = -rbind(
+   #   1/lambda * t(xac) %*% P,
+   # -1/lambda * t(xac) %*% P,
+    -dz %*% xap
+      )
+     lambda2=lambda
+     if(length(lambda)>1) lambda2=lambda[a]
+  u = -c(
+   #   1 - t(xac) %*% t(xap) %*% za,
+   #   1 + t(xac) %*% t(xap) %*% za,
+    -lambda2 * dz %*% xai %*% za)
 
   return(list(G=G,u=u))
 }
-
 # Moore-Penrose pseudo inverse for symmetric matrices
 
 pinv <- function(A, tol=.Machine$double.eps) {
diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index 8c7afce..ac1b9a0 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -15,7 +15,7 @@ fixedLassoInf(x, y, beta, lambda, intercept=TRUE, sigma=NULL, alpha=0.1,
 }
 \arguments{
   \item{x}{
-Matrix of predictors (n by p)
+Matrix of predictors (n by p); 
 }
   \item{y}{
 Vector of outcomes (length n)
@@ -89,6 +89,14 @@ if the observed statistic is too close to either end of the truncation interval
 desired coverage cannot be computed, and default to +/- Inf. The output \code{tailarea} 
 gives the achieved Gaussian tail areas for the reported intervals---these should be close 
 to alpha/2, and can be used for error-checking purposes.
+
+Important!: Before running glmnet (or some other lasso-solver)  x should be centered, that is x <- scale(X,TRUE,FALSE).
+In addition, if standardization of the predictors is desired, x should be scaled as well:  x <- scale(x,TRUE,TRUE).
+Then when running glmnet, set standardize=F. See example below.
+
+The penalty.factor facility in glmmet-- allowing different penalties lambda for each predictor,
+is not yet implemented in fixedLassoInf. However you can finesse this--- see the example below. One caveat- using this approach, a penalty factor of zero (forcing a predictor in)
+is not allowed.
 }
 \value{  
 \item{type}{Type of coefficients tested (partial or full)}
@@ -118,27 +126,59 @@ set.seed(43)
 n = 50
 p = 10
 sigma = 1
+
 x = matrix(rnorm(n*p),n,p)
+x=scale(x,TRUE,TRUE)
+
 beta = c(3,2,rep(0,p-2))
 y = x\%*\%beta + sigma*rnorm(n)
 
 # first run glmnet
-gfit = glmnet(x,y)
+gfit = glmnet(x,y,standardize=FALSE)
 
 # extract coef for a given lambda; note the 1/n factor!
 # (and we don't save the intercept term)
-lambda = .1
+lambda = .8
 beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
 
 # compute fixed lambda p-values and selection intervals
 out = fixedLassoInf(x,y,beta,lambda,sigma=sigma)
 out
 
-## NOT RUN
+
 ## as above, but use lar function instead to get initial 
 ## lasso fit (should get same results)
-# lfit = lar(x,y)
-# beta = coef(lfit,s=lambda,mode="lambda")
-# out2 = fixedLassoInf(x,y,beta,lambda,sigma=sigma)
-# out2
+ lfit = lar(x,y,normalize=FALSE)
+ beta = coef(lfit,s=lambda,mode="lambda")
+ out2 = fixedLassoInf(x,y,beta,lambda,sigma=sigma)
+ out2
+
+## mimic different penalty factors by first scaling x
+ set.seed(43)
+n = 50
+p = 10
+sigma = 1
+
+x = matrix(rnorm(n*p),n,p)
+x=scale(x,TRUE,TRUE)
+
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+pf=c(rep(1,7),rep(.1,3))  #define penalty factors
+pf=p*pf/sum(pf)   # penalty factors should be rescaled so they sum to p
+xs=scale(x,FALSE,pf) #scale cols of x by penalty factors
+# first run glmnet
+gfit = glmnet(xs,y,standardize=FALSE)
+
+# extract coef for a given lambda; note the 1/n factor!
+# (and we don't save the intercept term)
+lambda = .8
+beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
+
+# compute fixed lambda p-values and selection intervals
+out = fixedLassoInf(xs,y,beta,lambda,sigma=sigma)
+
+#rescale conf points to undo the penalty factor
+out$ci=t(scale(t(out$ci),FALSE,pf[out$vars]))
+out
 }
diff --git a/tests/test.fixed.R b/tests/test.fixed.R
index 5c55887..e2c5931 100644
--- a/tests/test.fixed.R
+++ b/tests/test.fixed.R
@@ -208,6 +208,35 @@ fixedLassoInf(x,y,beta,lambda,sigma=sigma)
 beta=coef(fit,s=lambda,mode="lambda")
 fixedLassoInf(x,y,beta,lambda,sigma=sigma)
 
+# now try penalty factors
+  set.seed(43)
+     n = 50
+     p = 10
+     sigma = 1
+     
+     x = matrix(rnorm(n*p),n,p)
+     x=scale(x,T,T)
+     
+     beta = c(3,2,rep(0,p-2))
+     y = x%*%beta + sigma*rnorm(n)
+    
+     pf=c(rep(1,7),rep(.1,p-7))
+     pf=p*pf/sum(pf)   # penalty factors should be rescaled so they sum to p
+     xs=scale(x,F,pf) #scale cols of x
+     # first run glmnet
+     gfit = glmnet(xs,y,standardize=F)
+     
+     # extract coef for a given lambda; note the 1/n factor!
+     # (and we don't save the intercept term)
+     lambda = .8
+     beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
+     
+     # compute fixed lambda p-values and selection intervals
+     out = fixedLassoInf(xs,y,beta,lambda,sigma=sigma)
+     #rescale conf points to undo the penalty factor
+     out$ci=t(scale(t(out$ci),F,pf[out$vars]))
+
+
 ###
 x=state.x77[,-4]
 y=state.x77[,4]
@@ -364,3 +393,46 @@ for(i in 1:100){
 
 p <- pval[, -(1:2)]
 mean(p[p < 1] < 0.05)
+
+
+#test from Chong
+
+
+library(selectiveInference)
+
+library(glmnet);library(MASS);#library(grplasso);library(gvlma);library(grpreg)
+library(penalized)
+load("fooXY.RData")
+
+#d=read.csv("DesignMatrixX_and_y.csv");dim(d); head(d)
+
+#source("temp.R")
+n=length(Y)
+p=ncol(X)
+#X=scale(X,T,F)
+X=X+.01*matrix(rnorm(n*p),n,p) # I added noise to avoid collinearity
+#X=scale(X,T,T)/sqrt(n-1)
+
+Y=Y-mean(Y)
+
+#X=as.matrix(d[,1:192]); ### design matrix, no intercept
+#Y=d$y; ### Response variable Y.
+fit = glmnet(x=X, y=Y, family="gaussian",alpha = 1, thresh = 1e-9, standardize=F)
+set.seed(39)
+lam= fit$lambda[30];
+#lam= fit$lambda[15]## Try getting coefficient at this lambda
+beta = coef(fit, s=lam, exact=TRUE)[-1];length(beta);table(beta!=0)
+
+ aa=penalized(Y~X,lambda1=lam*n,model="linear",standardize=F)
+  b=coef(aa,which="all")[-1]
+
+lam2=n*lam
+
+g=t(X)%*%(Y-X%*%beta)/lam2
+
+g[beta!=0]
+
+g=t(X)%*%(Y-X%*%b)/lam2
+out = fixedLassoInf(X,Y,beta,lam*n)
+
+

From f6dab3ef78586db01096900659a7768852a881ce Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 15 Mar 2016 13:34:41 -0700
Subject: [PATCH 108/493] C code for sampling truncated whitened Gaussian

---
 selectiveInference/src/truncnorm.c | 175 +++++++++++++++++++++++++++++
 1 file changed, 175 insertions(+)
 create mode 100644 selectiveInference/src/truncnorm.c

diff --git a/selectiveInference/src/truncnorm.c b/selectiveInference/src/truncnorm.c
new file mode 100644
index 0000000..b436b1f
--- /dev/null
+++ b/selectiveInference/src/truncnorm.c
@@ -0,0 +1,175 @@
+#include <R.h>
+#include <Rmath.h>
+
+// Take a Gibbs hit and run step along a given direction
+
+// Assumes the covariance is identity
+
+void gibbs_step(double *state,     /* state has law N(0,I) constrained to polyhedral set \{y:Ay \leq b\}*/ 
+		double *direction, /* direction we will take Gibbs step */
+		double *U,         /* A %*% state - b */
+		double *alpha,     /* A %*% direction */
+		int nconstraint,   /* number of rows of A */
+		int nstate)        /* dimension of state */
+{
+               
+  int istate;
+  double value = 0;
+
+  /* Compute V=\eta^Ty */
+
+  for (istate = 0; istate < nstate; istate++) {
+    value += direction[istate] * state[istate];
+  }
+
+  /* Compute upper and lower bounds */
+
+  double lower_bound = -1e12;
+  double upper_bound = 1e12;
+  double bound_val = 0;
+  double tol=1.e-7;
+  int iconstraint;
+
+  for (iconstraint = 0; iconstraint < nconstraint; iconstraint++) {
+    bound_val = -U[iconstraint] / alpha[iconstraint] + value;
+
+    if ((alpha[iconstraint] > tol) &&
+	(bound_val < upper_bound)) {
+	upper_bound = bound_val;
+    }
+    else if ((alpha[iconstraint] < -tol) &&
+	  (bound_val > lower_bound)) {
+	lower_bound = bound_val;
+    }
+
+  }
+
+  /* Ensure constraints are satisfied */
+  if (lower_bound > value) {
+    lower_bound = value - tol;
+  }
+  else if (upper_bound < value) {
+    upper_bound = value + tol;
+  }
+
+  /* Check to see if constraints are satisfied */
+
+  /* if (lower_bound > upper_bound) {
+    
+     }*/
+
+  /* Now, take a step */
+
+  double tnorm; /* the 1D gaussian variable */
+  double cdfU, cdfL, unif; /* temp variables */
+
+  if (upper_bound < -10) {
+
+    /* use Exp approximation */
+    /* the approximation is that */
+    /* Z | lower_bound < Z < upper_bound */
+    /* is fabs(upper_bound) * (upper_bound - Z) = E approx Exp(1) */
+    /* so Z = upper_bound - E / fabs(upper_bound) */
+    /* and the truncation of the exponential is */
+    /* E < fabs(upper_bound - lower_bound) * fabs(upper_bound) = D */
+    
+    /* this has distribution function (1 - exp(-x)) / (1 - exp(-D)) */
+    /* so to draw from this distribution */
+    /* we set E = - log(1 - U * (1 - exp(-D))) where U is Unif(0,1) */
+    /* and Z (= tnorm below) is as stated */
+
+    unif = runif(0., 1.) * (1 - exp(-fabs((lower_bound - upper_bound) * upper_bound)));
+    tnorm = (upper_bound + log(1 - unif) / fabs(upper_bound));
+  }
+  else if (lower_bound > 10) {
+
+    /* here Z = lower_bound + E / fabs(lower_bound) (though lower_bound is positive) */
+    /* and D = fabs((upper_bound - lower_bound) * lower_bound) */
+
+    unif = runif(0., 1.) * (1 - exp(-fabs((upper_bound - lower_bound) * lower_bound)));
+    tnorm = (lower_bound - log(1 - unif) / lower_bound);
+  }
+  else if (lower_bound < 0) {
+    cdfL = pnorm(lower_bound, 0., 1., 1, 0); /* Ryan */
+    cdfU = pnorm(upper_bound, 0., 1., 1, 0); /* Ryan */
+    unif = runif(0., 1.) * (cdfU - cdfL) + cdfL; /* Ryan */
+    if (unif < 0.5) {
+      tnorm = qnorm(unif, 0., 1., 1, 0); /* Ryan */
+    }
+    else {
+      tnorm = -qnorm(1-unif, 0., 1., 1, 0); /* Ryan */
+    }
+  }
+  else {
+    cdfL = pnorm(-lower_bound, 0., 1., 1, 0); /* Ryan */
+    cdfU = pnorm(-upper_bound, 0., 1., 1, 0); /* Ryan */
+    unif = runif(0., 1.) * (cdfL - cdfU) + cdfU;
+    if (unif < 0.5) {
+      tnorm = -qnorm(unif, 0., 1., 1, 0); /* Ryan */
+    }
+    else {
+      tnorm = qnorm(1-unif, 0., 1., 1, 0); /* Ryan */
+    }
+  }
+
+  /* Now update the state and U */
+
+  double delta = tnorm - value;
+
+  for (istate = 0; istate < nstate; istate++) {
+    state[istate] += delta * direction[istate];
+  }
+  for (iconstraint = 0; iconstraint < nconstraint; iconstraint++) {
+    U[iconstraint] += U[iconstraint] + delta * alpha[iconstraint] ;
+  }
+
+  /* End of gibbs_step */
+
+}
+
+void sample_truncnorm_white(double *state,      /* state has law N(0,I) constrained to polyhedral set \{y:Ay \leq b\}*/ 
+			    double *U,          /* A %*% state - b */
+			    double *directions, /* possible steps for sampler to take */
+                                                /* assumed to be stored as list of vectors of dimension nstate */
+			    double *alphas,     /* The matrix A %*% directions */
+			    double *output,     /* array in which to store samples */
+                                                /* assumed will stored as list of vectors of dimension nstate */
+			    int nconstraint,    /* number of rows of A */
+			    int ndirection,     /* the possible number of directions to choose from */
+                                                /* `directions` should have size nstate*ndirection */
+			    int nstate,         /* dimension of state */
+			    int burnin,         /* number of burnin steps */
+			    int ndraw)          /* total number of samples to return */
+{
+
+  int iter_count;
+  int which_direction;
+
+  double *direction, *alpha;
+
+  for (iter_count = 0; iter_count < burnin + ndraw; iter_count++) {
+  
+    which_direction = (int) floor(runif(0., 1.) * ndirection); /* Ryan */
+    direction = directions + nstate * which_direction; /* Ryan */
+    alpha = alpha + nconstraint * which_direction; /* Ryan */
+
+    /* take a step, which implicitly updates `state` and `U` */
+
+    gibbs_step(state,     
+               direction, 
+	       U,         
+	       alpha,     
+	       nconstraint,
+	       nstate);
+
+    /* Store result if after burnin */
+
+    int istate;
+    if (iter_count >= burnin) {
+      for (istate = 0; istate < nstate; istate++) {
+	*output = state[istate];
+        output++;
+      }
+    }
+  }
+}

From 477750c259d5205b59bd249587f4c6557ba9cf04 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Mar 2016 13:12:45 -0700
Subject: [PATCH 109/493] utility file for sampling from affine constraints

---
 selectiveInference/R/funs.constraints.R | 142 ++++++++++++++++++++++++
 selectiveInference/R/funs.fs.R          |   3 +-
 2 files changed, 143 insertions(+), 2 deletions(-)
 create mode 100644 selectiveInference/R/funs.constraints.R

diff --git a/selectiveInference/R/funs.constraints.R b/selectiveInference/R/funs.constraints.R
new file mode 100644
index 0000000..6c16b50
--- /dev/null
+++ b/selectiveInference/R/funs.constraints.R
@@ -0,0 +1,142 @@
+#
+# Some utilities for affine constraints
+#
+
+# 
+# compute the square-root and inverse square-root of a non-negative
+# definite matrix
+#
+
+factor_covariance = function(S, rank=NA) {
+    if (!is.na(rank)) {
+        rank = nrow(S)
+    }
+    svd_X = svd(S, nu = rank, nv=rank)
+    sqrt_cov = t(sqrt(svd_X$d[1:rank]) * t(svd_X$u[,1:rank]))
+    sqrt_inv = t((1. / sqrt(svd_X$d[1:rank])) * t(svd_X$u[,1:rank]))
+
+    return(list(sqrt_cov=sqrt_cov, sqrt_inv=sqrt_inv))
+}
+
+#
+# from a constraint, return an equivalent
+# constraint and a whitening and inverse
+# whitening map
+#
+
+# law is Z \sim N(mean, covariance) subject to constraints linear_part %*% Z \leq offset
+
+whiten_constraint = function(linear_part, offset, mean, covariance) {
+
+    factor_cov = factor_covariance(covariance)
+    sqrt_cov = factor_cov$sqrt_cov
+    sqrt_inv = factor_cov$sqrt_inv
+
+    new_A = A %*% sqrt_cov
+    new_b = offset - linear_part %*% mean
+
+    # rescale rows to have length 1
+
+    scaling = sqrt(apply(new_A^2, sum, 1))
+    new_A = new_A / scaling
+    new_b = new_b / scaling
+
+    # TODO: check these functions will behave when Z is a matrix.
+
+    inverse_map = function(Z) {
+        return(sqrt_cov %*% Z + mu)
+    }
+
+    forward_map = function(W) {
+        return(sqrt_inv %*% (W - mu))
+    }    
+
+    return(list(linear_part=new_A,
+                offset=new_b,
+                inverse_map=inverse_map,
+                forward_map=forward_map))
+}
+
+#
+# sample from the law
+#
+# Z \sim N(mean, covariance) subject to constraints linear_part %*% Z \leq offset
+
+sample_from_constraints = function(linear_part,
+                                   offset,
+                                   covariance,
+                                   mean,
+                                   initial_point, # point must be feasible for constraints
+                                   ndraw=8000,
+                                   burnin=2000,
+                                   accept_reject_params=NA) #TODO: implement accept reject check
+{
+
+    whitened_con = whiten_constraint(linear_part,
+                                     offset,
+                                     covariance,
+                                     mean)
+    white_initial = whitened_con$forward_map(initial_point)
+
+#     # try 100 draws of accept reject
+#     # if we get more than 50 good draws, then just return a smaller sample
+#     # of size (burnin+ndraw)/5
+
+#     if accept_reject_params: 
+#         use_hit_and_run = False
+#         num_trial, min_accept, num_draw = accept_reject_params
+
+#         def _accept_reject(sample_size, linear_part, offset):
+#             Z_sample = np.random.standard_normal((100, linear_part.shape[1]))
+#             constraint_satisfied = (np.dot(Z_sample, linear_part.T) - 
+#                                     offset[None,:]).max(1) < 0
+#             return Z_sample[constraint_satisfied]
+
+#         Z_sample = _accept_reject(100, 
+#                                   white_con.linear_part,
+#                                   white_con.offset)
+
+#         if Z_sample.shape[0] >= min_accept:
+#             while True:
+#                 Z_sample = np.vstack([Z_sample,
+#                                       _accept_reject(num_draw / 5,
+#                                                      white_con.linear_part,
+#                                                      white_con.offset)])
+#                 if Z_sample.shape[0] > num_draw:
+#                     break
+#             white_samples = Z_sample
+#         else:
+#             use_hit_and_run = True
+#     else:
+#         use_hit_and_run = True
+
+    use_hit_and_run = TRUE
+
+    if (use_hit_and_run) {
+
+        white_linear = whitened_con$linear_part
+        white_offset = whitened_con$offset
+
+        nstate = length(white_initial)
+        directions = rbind(diag(rep(1, ndim)),
+                           matrix(rnorm(ndim^2), nstate, nstate))
+        directions = apply(directions, function(x) { x/sqrt(sum(x^2)) }, 1) # normalize rows to have length 1
+        alphas = white_linear %*% directions
+        U = white_linear %*% white_initial - white_offset
+        Z_sample = matrix(rep(NA, ndraw*ndim), ndraw, nstate)
+
+        .C("sample_truncnorm_white",
+           white_initial,
+           U,
+           directions,
+           alphas,
+           output,
+           nrow(white_linear),
+           nstate,
+           burnin,
+           ndraw)
+
+    Z = t(whitened_con$inverse_map(t(output)))
+    return(Z)
+}
+
diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 1dd0a7a..8c4fb54 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -533,7 +533,6 @@ fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
       # because this has a simple box constraint
       # with a generically non-degenerate covariance
 
-
       if (n > p) { 
           library(tmvtnorm)
  
@@ -550,7 +549,7 @@ fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
                                      lower=-collapsed_neg,
                                      upper=collapsed_pos,
                                      algorithm="gibbs",
-                 			 burn.in.samples=burnin)
+                    		     burn.in.samples=burnin)
 
           if (length(inactive) > 1) {				 
              sample_maxZ = apply(abs(1. / cur_scale * truncated_noise), 1, max)

From c53ff76de582fe05581ab6645aff6495585be3cc Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Mar 2016 18:25:07 -0700
Subject: [PATCH 110/493] syntax error in constraints

---
 selectiveInference/R/funs.constraints.R | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.constraints.R b/selectiveInference/R/funs.constraints.R
index 6c16b50..5f6e554 100644
--- a/selectiveInference/R/funs.constraints.R
+++ b/selectiveInference/R/funs.constraints.R
@@ -135,7 +135,8 @@ sample_from_constraints = function(linear_part,
            nstate,
            burnin,
            ndraw)
-
+    
+    }
     Z = t(whitened_con$inverse_map(t(output)))
     return(Z)
 }

From 607bccdeb4cb7e509a4d627c8efb6d6439e28037 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Mar 2016 19:32:55 -0700
Subject: [PATCH 111/493] transposed matrices so pointer arithmetic correct in
 C code

---
 selectiveInference/R/funs.constraints.R | 10 +++++-----
 selectiveInference/src/truncnorm.c      | 25 +++++++++++++------------
 2 files changed, 18 insertions(+), 17 deletions(-)

diff --git a/selectiveInference/R/funs.constraints.R b/selectiveInference/R/funs.constraints.R
index 5f6e554..52e4fa5 100644
--- a/selectiveInference/R/funs.constraints.R
+++ b/selectiveInference/R/funs.constraints.R
@@ -123,21 +123,21 @@ sample_from_constraints = function(linear_part,
         directions = apply(directions, function(x) { x/sqrt(sum(x^2)) }, 1) # normalize rows to have length 1
         alphas = white_linear %*% directions
         U = white_linear %*% white_initial - white_offset
-        Z_sample = matrix(rep(NA, ndraw*ndim), ndraw, nstate)
+        Z_sample = matrix(rep(NA, ndraw*ndim), nstate, ndraw)
 
         .C("sample_truncnorm_white",
            white_initial,
            U,
-           directions,
-           alphas,
-           output,
+           t(directions),
+           t(alphas),
+           Z_sample,
            nrow(white_linear),
            nstate,
            burnin,
            ndraw)
     
     }
-    Z = t(whitened_con$inverse_map(t(output)))
+    Z = t(whitened_con$inverse_map(Z_sample))
     return(Z)
 }
 
diff --git a/selectiveInference/src/truncnorm.c b/selectiveInference/src/truncnorm.c
index b436b1f..b7584c4 100644
--- a/selectiveInference/src/truncnorm.c
+++ b/selectiveInference/src/truncnorm.c
@@ -45,6 +45,7 @@ void gibbs_step(double *state,     /* state has law N(0,I) constrained to polyhe
   }
 
   /* Ensure constraints are satisfied */
+
   if (lower_bound > value) {
     lower_bound = value - tol;
   }
@@ -90,25 +91,25 @@ void gibbs_step(double *state,     /* state has law N(0,I) constrained to polyhe
     tnorm = (lower_bound - log(1 - unif) / lower_bound);
   }
   else if (lower_bound < 0) {
-    cdfL = pnorm(lower_bound, 0., 1., 1, 0); /* Ryan */
-    cdfU = pnorm(upper_bound, 0., 1., 1, 0); /* Ryan */
-    unif = runif(0., 1.) * (cdfU - cdfL) + cdfL; /* Ryan */
+    cdfL = pnorm(lower_bound, 0., 1., 1, 0); 
+    cdfU = pnorm(upper_bound, 0., 1., 1, 0); 
+    unif = runif(0., 1.) * (cdfU - cdfL) + cdfL; 
     if (unif < 0.5) {
-      tnorm = qnorm(unif, 0., 1., 1, 0); /* Ryan */
+      tnorm = qnorm(unif, 0., 1., 1, 0); 
     }
     else {
-      tnorm = -qnorm(1-unif, 0., 1., 1, 0); /* Ryan */
+      tnorm = -qnorm(1-unif, 0., 1., 1, 0); 
     }
   }
   else {
-    cdfL = pnorm(-lower_bound, 0., 1., 1, 0); /* Ryan */
-    cdfU = pnorm(-upper_bound, 0., 1., 1, 0); /* Ryan */
+    cdfL = pnorm(-lower_bound, 0., 1., 1, 0); 
+    cdfU = pnorm(-upper_bound, 0., 1., 1, 0); 
     unif = runif(0., 1.) * (cdfL - cdfU) + cdfU;
     if (unif < 0.5) {
-      tnorm = -qnorm(unif, 0., 1., 1, 0); /* Ryan */
+      tnorm = -qnorm(unif, 0., 1., 1, 0); 
     }
     else {
-      tnorm = qnorm(1-unif, 0., 1., 1, 0); /* Ryan */
+      tnorm = qnorm(1-unif, 0., 1., 1, 0);
     }
   }
 
@@ -149,9 +150,9 @@ void sample_truncnorm_white(double *state,      /* state has law N(0,I) constrai
 
   for (iter_count = 0; iter_count < burnin + ndraw; iter_count++) {
   
-    which_direction = (int) floor(runif(0., 1.) * ndirection); /* Ryan */
-    direction = directions + nstate * which_direction; /* Ryan */
-    alpha = alpha + nconstraint * which_direction; /* Ryan */
+    which_direction = (int) floor(runif(0., 1.) * ndirection); 
+    direction = ((double *) directions) + nstate * which_direction; 
+    alpha = ((double *) alphas) + nconstraint * which_direction; 
 
     /* take a step, which implicitly updates `state` and `U` */
 

From bacacb0ea783a899e96ed7f602a34d0effec3495 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Mar 2016 21:34:37 -0700
Subject: [PATCH 112/493] sampler runs now -- needs a test to confirm results
 are OK

---
 selectiveInference/R/funs.constraints.R | 63 +++++++++++++++----------
 selectiveInference/src/truncnorm.c      | 36 +++++++++-----
 2 files changed, 61 insertions(+), 38 deletions(-)

diff --git a/selectiveInference/R/funs.constraints.R b/selectiveInference/R/funs.constraints.R
index 52e4fa5..b65cdcc 100644
--- a/selectiveInference/R/funs.constraints.R
+++ b/selectiveInference/R/funs.constraints.R
@@ -8,10 +8,10 @@
 #
 
 factor_covariance = function(S, rank=NA) {
-    if (!is.na(rank)) {
+    if (is.na(rank)) {
         rank = nrow(S)
     }
-    svd_X = svd(S, nu = rank, nv=rank)
+    svd_X = svd(S, nu=rank, nv=rank)
     sqrt_cov = t(sqrt(svd_X$d[1:rank]) * t(svd_X$u[,1:rank]))
     sqrt_inv = t((1. / sqrt(svd_X$d[1:rank])) * t(svd_X$u[,1:rank]))
 
@@ -32,23 +32,23 @@ whiten_constraint = function(linear_part, offset, mean, covariance) {
     sqrt_cov = factor_cov$sqrt_cov
     sqrt_inv = factor_cov$sqrt_inv
 
-    new_A = A %*% sqrt_cov
+    new_A = linear_part %*% sqrt_cov
     new_b = offset - linear_part %*% mean
 
     # rescale rows to have length 1
 
-    scaling = sqrt(apply(new_A^2, sum, 1))
+    scaling = sqrt(apply(new_A^2, 1, sum))
     new_A = new_A / scaling
     new_b = new_b / scaling
 
     # TODO: check these functions will behave when Z is a matrix.
 
     inverse_map = function(Z) {
-        return(sqrt_cov %*% Z + mu)
+        return(sqrt_cov %*% Z + mean)
     }
 
     forward_map = function(W) {
-        return(sqrt_inv %*% (W - mu))
+        return(sqrt_inv %*% (W - mean))
     }    
 
     return(list(linear_part=new_A,
@@ -64,8 +64,8 @@ whiten_constraint = function(linear_part, offset, mean, covariance) {
 
 sample_from_constraints = function(linear_part,
                                    offset,
-                                   covariance,
                                    mean,
+                                   covariance,
                                    initial_point, # point must be feasible for constraints
                                    ndraw=8000,
                                    burnin=2000,
@@ -74,8 +74,8 @@ sample_from_constraints = function(linear_part,
 
     whitened_con = whiten_constraint(linear_part,
                                      offset,
-                                     covariance,
-                                     mean)
+				     mean,
+                                     covariance)
     white_initial = whitened_con$forward_map(initial_point)
 
 #     # try 100 draws of accept reject
@@ -118,25 +118,36 @@ sample_from_constraints = function(linear_part,
         white_offset = whitened_con$offset
 
         nstate = length(white_initial)
-        directions = rbind(diag(rep(1, ndim)),
-                           matrix(rnorm(ndim^2), nstate, nstate))
-        directions = apply(directions, function(x) { x/sqrt(sum(x^2)) }, 1) # normalize rows to have length 1
-        alphas = white_linear %*% directions
+	nconstraint = nrow(white_linear)
+
+        directions = rbind(diag(rep(1, nstate)),
+                           matrix(rnorm(nstate^2), nstate, nstate))
+
+        # normalize rows to have length 1
+
+        scaling = apply(directions, 1, function(x) {  return(sqrt(sum(x^2))) }) 
+	directions = directions / scaling
+	ndirection = nrow(directions)
+
+        alphas = directions %*% t(white_linear)
         U = white_linear %*% white_initial - white_offset
-        Z_sample = matrix(rep(NA, ndraw*ndim), nstate, ndraw)
-
-        .C("sample_truncnorm_white",
-           white_initial,
-           U,
-           t(directions),
-           t(alphas),
-           Z_sample,
-           nrow(white_linear),
-           nstate,
-           burnin,
-           ndraw)
-    
+        Z_sample = matrix(rep(0, nstate * ndraw), nstate, ndraw)
+
+        result = .C("sample_truncnorm_white",
+                    as.numeric(white_initial),
+                    as.numeric(U),
+                    as.numeric(t(directions)),
+                    as.numeric(t(alphas)),
+                    output=Z_sample,
+                    as.integer(nconstraint),
+                    as.integer(ndirection),
+                    as.integer(nstate),
+                    as.integer(burnin),
+                    as.integer(ndraw),
+                    package="selectiveInference")
+        Z_sample = result$output
     }
+
     Z = t(whitened_con$inverse_map(Z_sample))
     return(Z)
 }
diff --git a/selectiveInference/src/truncnorm.c b/selectiveInference/src/truncnorm.c
index b7584c4..cca61d9 100644
--- a/selectiveInference/src/truncnorm.c
+++ b/selectiveInference/src/truncnorm.c
@@ -31,6 +31,7 @@ void gibbs_step(double *state,     /* state has law N(0,I) constrained to polyhe
   int iconstraint;
 
   for (iconstraint = 0; iconstraint < nconstraint; iconstraint++) {
+
     bound_val = -U[iconstraint] / alpha[iconstraint] + value;
 
     if ((alpha[iconstraint] > tol) &&
@@ -121,7 +122,7 @@ void gibbs_step(double *state,     /* state has law N(0,I) constrained to polyhe
     state[istate] += delta * direction[istate];
   }
   for (iconstraint = 0; iconstraint < nconstraint; iconstraint++) {
-    U[iconstraint] += U[iconstraint] + delta * alpha[iconstraint] ;
+    U[iconstraint] += delta * alpha[iconstraint] ;
   }
 
   /* End of gibbs_step */
@@ -131,21 +132,30 @@ void gibbs_step(double *state,     /* state has law N(0,I) constrained to polyhe
 void sample_truncnorm_white(double *state,      /* state has law N(0,I) constrained to polyhedral set \{y:Ay \leq b\}*/ 
 			    double *U,          /* A %*% state - b */
 			    double *directions, /* possible steps for sampler to take */
-                                                /* assumed to be stored as list of vectors of dimension nstate */
+                                                /* assumed to be stored as list of columns of dimension nstate */
+			                        /* has shape (nstate, ndirection) */
 			    double *alphas,     /* The matrix A %*% directions */
+      			                        /* has shape (nconstraint, ndirection) */
 			    double *output,     /* array in which to store samples */
                                                 /* assumed will stored as list of vectors of dimension nstate */
-			    int nconstraint,    /* number of rows of A */
-			    int ndirection,     /* the possible number of directions to choose from */
+                                                /* has shape (nstate, ndraw) */  
+			    int *pnconstraint,  /* number of rows of A */
+			    int *pndirection,   /* the possible number of directions to choose from */
                                                 /* `directions` should have size nstate*ndirection */
-			    int nstate,         /* dimension of state */
-			    int burnin,         /* number of burnin steps */
-			    int ndraw)          /* total number of samples to return */
+			    int *pnstate,       /* dimension of state */
+			    int *pburnin,       /* number of burnin steps */
+			    int *pndraw)        /* total number of samples to return */
 {
 
   int iter_count;
   int which_direction;
 
+  int nconstraint = *pnconstraint;
+  int ndirection = *pndirection;
+  int nstate = *pnstate;
+  int burnin = *pburnin;
+  int ndraw = *pndraw;
+
   double *direction, *alpha;
 
   for (iter_count = 0; iter_count < burnin + ndraw; iter_count++) {
@@ -156,10 +166,10 @@ void sample_truncnorm_white(double *state,      /* state has law N(0,I) constrai
 
     /* take a step, which implicitly updates `state` and `U` */
 
-    gibbs_step(state,     
-               direction, 
-	       U,         
-	       alpha,     
+    gibbs_step(state,
+               direction,
+	       U,
+	       alpha,
 	       nconstraint,
 	       nstate);
 
@@ -169,8 +179,10 @@ void sample_truncnorm_white(double *state,      /* state has law N(0,I) constrai
     if (iter_count >= burnin) {
       for (istate = 0; istate < nstate; istate++) {
 	*output = state[istate];
-        output++;
+	output++;
       }
     }
   }
+
 }
+

From 0e45d9f4eb0faf56150b36e1d0eb65317b33a461 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Mar 2016 21:43:52 -0700
Subject: [PATCH 113/493] fixed code so it calls new sampler -- should be ready
 to test...

---
 selectiveInference/R/funs.fs.R | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 8c4fb54..41d138c 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -558,8 +558,22 @@ fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
              sample_maxZ = truncated_noise / cur_scale
           }
       } else {
-          # RUBBISH for now!!!!
-	  sample_maxZ = abs(rnorm(ndraw))         
+
+          linear_part = rbind(t(cur_adjusted_X), -t(cur_adjusted_X))
+	  offset = c(final_upper, -final_lower)
+	  covariance = diag(rep(sigma^2, nrow(cor_adjusted_X)))
+	  mean = rep(0, nrow(cur_adjusted_X))
+	  initial_point = y
+	  truncated_y = sample_from_constraints(linear_part, 
+                                                offset, 
+                                                mean, 
+                                                covariance, 
+                                                initial_point, 
+                                                burnin=burnin, 
+                                                ndraw=ndraw)
+
+          truncated_noise = truncated_y %*% cur_adjusted_X
+          sample_maxZ = apply(abs(1. / cur_scale * truncated_noise), 1, max)
       }
       
       observed_maxZ = obj$realized_maxZ[j]

From 54e5bafb4ede56537bc98a8aeee282b4a9cb36b1 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Mar 2016 22:02:04 -0700
Subject: [PATCH 114/493] print method for maxZ inference

---
 selectiveInference/R/funs.fs.R | 82 ++++++++++++----------------------
 1 file changed, 29 insertions(+), 53 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 41d138c..786bc45 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -437,8 +437,8 @@ fsInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic
 
 # selected maxZ tests
 
-fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
-                       ndraw=8000, burnin=2000) {
+fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
+                      ndraw=8000, burnin=2000) {
   
   this.call = match.call()
 
@@ -586,14 +586,22 @@ fsInf_maxZ <- function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
 
   khat = forwardStop(pv,alpha)
 
-  out = list(pv=pv,khat=khat,
-	     call=this.call)
+  out = list(pv=pv,
+             k=k,
+             khat=khat,
+	     sigma=sigma,
+	     call=this.call,
+	     vars=vars,
+	     sign=sign,
+	     alpha=alpha,
+             realized_maxZ=obj$realized_maxZ)
   class(out) = "fsInf_Zmax"
   return(out)
 }
 
 ##############################
 
+Print methods
 
 ##############################
 
@@ -610,66 +618,34 @@ print.fs <- function(x, ...) {
   invisible()
 }
 
-print.fsInf <- function(x, tailarea=TRUE, ...) {
+print.fsInf_Zmax <- function(obj) {
+
   cat("\nCall:\n")
-  dput(x$call)
+  dput(obj$call)
 
   cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n",
-              x$sigma))
-
-  if (x$type == "active") {
-    cat(sprintf("\nSequential testing results with alpha = %0.3f\n",x$alpha))
-    tab = cbind(1:length(x$pv),x$vars,
-      round(x$sign*x$vmat%*%x$y,3),
-      round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
-      round(x$pv,3),round(x$ci,3))
-    colnames(tab) = c("Step", "Var", "Coef", "Z-score", "P-value",
-              "LowConfPt", "UpConfPt")
-    if (tailarea) {
-      tab = cbind(tab,round(x$tailarea,3))
-      colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
-    }
-    rownames(tab) = rep("",nrow(tab))
-    print(tab)
+              obj$sigma))
 
-    cat(sprintf("\nEstimated stopping point from ForwardStop rule = %i\n",x$khat))
-  }
+  cat(sprintf("\nSequential testing results with alpha = %0.3f\n",obj$alpha))
 
-  else if (x$type == "all") {
-    cat(sprintf("\nTesting results at step = %i, with alpha = %0.3f\n",x$k,x$alpha))
-    tab = cbind(x$vars,
-      round(x$sign*x$vmat%*%x$y,3),
-      round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
-      round(x$pv,3),round(x$ci,3))
-    colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
-    if (tailarea) {
-      tab = cbind(tab,round(x$tailarea,3))
-      colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
-    }
-    rownames(tab) = rep("",nrow(tab))
-    print(tab)
-  }
+  tab = cbind(1:length(obj$pv),obj$vars,
+              round(obj$sign*obj$realized_maxZ, 3),
+              round(obj$pv,3))
+  colnames(tab) = c("Step", "Var", "Coef", "Z-score", "P-value")
+  rownames(tab) = rep("",nrow(tab))
+  print(tab)
 
-  else if (x$type == "aic") {
-    cat(sprintf("\nTesting results at step = %i, with alpha = %0.3f\n",x$khat,x$alpha))
-    tab = cbind(x$vars,
-      round(x$sign*x$vmat%*%x$y,3),
-      round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
-      round(x$pv,3),round(x$ci,3))
-    colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
-    if (tailarea) {
-      tab = cbind(tab,round(x$tailarea,3))
-      colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
-    }
-    rownames(tab) = rep("",nrow(tab))
-    print(tab)
-    
-    cat(sprintf("\nEstimated stopping point from AIC rule = %i\n",x$khat))
+  cat(sprintf("\nEstimated stopping point from ForwardStop rule = %i\n",obj$khat))
   }
 
   invisible()
 }
 
+##############################
+
+Plot methods
+
+##############################
 
 plot.fs <- function(x, breaks=TRUE, omit.zeros=TRUE, var.labels=TRUE, ...) {
   if (x$completepath) {

From 832b6c245075b71a7268d463c3af2a24b66e12ff Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Mar 2016 23:13:59 -0700
Subject: [PATCH 115/493] maxZ now runs and prints output, see
 tests/test.fs_maxZ.R

---
 selectiveInference/NAMESPACE            |   4 +-
 selectiveInference/R/funs.constraints.R |  68 +++++++-----
 selectiveInference/R/funs.fs.R          | 132 ++++++++++++++++++------
 tests/test.fs_maxZ.R                    |  23 +++++
 4 files changed, 165 insertions(+), 62 deletions(-)
 create mode 100644 tests/test.fs_maxZ.R

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index 64a0211..cc7e76f 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -10,7 +10,8 @@ export(lar,fs,
        estimateSigma,
        manyMeans,print.manyMeans,
        groupfs,groupfsInf,
-       scaleGroups,factorDesign
+       scaleGroups,factorDesign,
+       sample_from_constraints
     )
 
 S3method("coef", "lar")
@@ -23,6 +24,7 @@ S3method("predict", "fs")
 S3method("print", "fs")
 S3method("plot", "fs")
 S3method("print", "fsInf")
+S3method("print", "fsInf_maxZ")
 S3method("print", "fixedLassoInf")
 S3method("print", "manyMeans")
 S3method("print", "groupfs")
diff --git a/selectiveInference/R/funs.constraints.R b/selectiveInference/R/funs.constraints.R
index b65cdcc..e2bc041 100644
--- a/selectiveInference/R/funs.constraints.R
+++ b/selectiveInference/R/funs.constraints.R
@@ -117,35 +117,47 @@ sample_from_constraints = function(linear_part,
         white_linear = whitened_con$linear_part
         white_offset = whitened_con$offset
 
+	# Inf cannot be used in C code
+	# In theory, these rows can be dropped
+
+	rows_to_keep = white_offset < Inf
+	white_linear = white_linear[rows_to_keep,]
+	white_offset = white_offset[rows_to_keep,]
+
         nstate = length(white_initial)
-	nconstraint = nrow(white_linear)
-
-        directions = rbind(diag(rep(1, nstate)),
-                           matrix(rnorm(nstate^2), nstate, nstate))
-
-        # normalize rows to have length 1
-
-        scaling = apply(directions, 1, function(x) {  return(sqrt(sum(x^2))) }) 
-	directions = directions / scaling
-	ndirection = nrow(directions)
-
-        alphas = directions %*% t(white_linear)
-        U = white_linear %*% white_initial - white_offset
-        Z_sample = matrix(rep(0, nstate * ndraw), nstate, ndraw)
-
-        result = .C("sample_truncnorm_white",
-                    as.numeric(white_initial),
-                    as.numeric(U),
-                    as.numeric(t(directions)),
-                    as.numeric(t(alphas)),
-                    output=Z_sample,
-                    as.integer(nconstraint),
-                    as.integer(ndirection),
-                    as.integer(nstate),
-                    as.integer(burnin),
-                    as.integer(ndraw),
-                    package="selectiveInference")
-        Z_sample = result$output
+	if (sum(rows_to_keep) > 0) {
+            nconstraint = nrow(white_linear)
+
+            directions = rbind(diag(rep(1, nstate)),
+                               matrix(rnorm(nstate^2), nstate, nstate))
+
+            # normalize rows to have length 1
+
+            scaling = apply(directions, 1, function(x) {  return(sqrt(sum(x^2))) }) 
+            directions = directions / scaling
+            ndirection = nrow(directions)
+
+            alphas = directions %*% t(white_linear)
+            U = white_linear %*% white_initial - white_offset
+            Z_sample = matrix(rep(0, nstate * ndraw), nstate, ndraw)
+
+            result = .C("sample_truncnorm_white",
+                        as.numeric(white_initial),
+                        as.numeric(U),
+                        as.numeric(t(directions)),
+                        as.numeric(t(alphas)),
+                        output=Z_sample,
+                        as.integer(nconstraint),
+                        as.integer(ndirection),
+                        as.integer(nstate),
+                        as.integer(burnin),
+                        as.integer(ndraw),
+                        package="selectiveInference")
+            Z_sample = result$output
+       }
+       else {
+            Z_sample = matrix(rnorm(nstate * ndraw), nstate, ndraw)
+       }
     }
 
     Z = t(whitened_con$inverse_map(Z_sample))
diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 786bc45..dedc0d3 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -29,9 +29,9 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   # Find the first variable to enter and its sign
   working_scale = sqrt(colSums(x^2))
   working_x = scale(x,center=F,scale=working_scale)
-  score = t(working_x)%*%y
-  i_hit = which.max(abs(score))   # Hitting coordinate
-  sign_hit = Sign(score[i_hit])   # Sign
+  working_score = t(working_x)%*%y
+  i_hit = which.max(abs(working_score))   # Hitting coordinate
+  sign_hit = Sign(working_score[i_hit])   # Sign
   signs = sign_hit                # later signs will be appended to `signs`
 
   if (verbose) {
@@ -54,7 +54,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   offset_pos_maxZ = matrix(Inf, p, buf) # upper bounds for selective maxZ
   offset_neg_maxZ = matrix(Inf, p, buf) # lower bounds for selective maxZ
   scale_maxZ = matrix(0, p, buf) # lower bounds for selective maxZ
-  realized_maxZ = matrix(0, p, buf) # lower bounds for selective maxZ
+  realized_maxZ = numeric(buf) # lower bounds for selective maxZ
 
   action[1] = i_hit
   df[1] = 0
@@ -64,7 +64,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   # Variables needed to compute truncation limits for
   # selective maxZ test
 
-  realized_maxZ[1] = c(sign_hit * score[i_hit])
+  realized_maxZ[1] = c(sign_hit * working_score[i_hit])
   offset_pos_maxZ[,1] = Inf
   offset_neg_maxZ[,1] = Inf
   scale_maxZ[,1] = working_scale
@@ -132,15 +132,18 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
       offset_pos_maxZ = cbind(offset_pos_maxZ, matrix(0, p, buf))
       offset_neg_maxZ = cbind(offset_neg_maxZ, matrix(0, p, buf))
       scale_maxZ = cbind(scale_maxZ, matrix(0, p, buf))
-      realized_maxZ = cbind(realized_maxZ, matrix(0, p, buf))
+      realized_maxZ = c(realized_maxZ, numeric(buf))
     }
 
     # Key quantities for the next entry
 
     keepLs=backsolve(R,t(Q_active)%*%X_inactive)
+
+    prev_scale = working_scale[-i_hit] # this variable used later for maxZ
     X_inactive_resid = X_inactive - X_active %*% keepLs
-    working_x = scale(X_inactive_resid,center=F,scale=sqrt(colSums(X_inactive_resid^2)))
-    score = as.numeric(t(working_x)%*%y)
+    working_scale = sqrt(colSums(X_inactive_resid^2)) # this variable used later for maxZ
+    working_x = scale(X_inactive_resid,center=F,scale=working_scale)
+    working_score = as.numeric(t(working_x)%*%y)
     
     beta_cur = backsolve(R,t(Q_active)%*%y) # must be computed before the break
                                             # so we have it if we have 
@@ -180,7 +183,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
 
     # update maxZ variable
     realized_maxZ[k] = sign_hit * working_score[i_hit]
-
+    
     # Gamma matrix!
 
     if (gi + 2*p > nrow(Gamma)) Gamma = rbind(Gamma,matrix(0,2*p+gbuf,n))
@@ -451,6 +454,7 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
   y = obj$y
   p = ncol(x)
   n = nrow(x)
+  pv = c()
 
   if (is.null(sigma)) {
     # TODO we need a sampler on a unit sphere
@@ -487,10 +491,10 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
       collapsed_neg = apply(obj$offset_neg_maxZ[inactive,1:j,drop=FALSE], 1, min)
       cur_scale = obj$scale_maxZ[,j][inactive]
 
-      # the matrix cur_adjusted_X is used to compute
+      # the matrix cur_adjusted_Xt is used to compute (always as length(y) columns)
       # the maxZ or maxT for the sampled variables
-
-      cur_adjusted_X = obj$Gamma_maxZ[zi + Seq(1,p-j+1),]; zi = zi+p-j+1
+      # 
+      cur_adjusted_Xt = obj$Gamma_maxZ[zi + Seq(1,p-j+1),]; zi = zi+p-j+1 # Xt for transpose
 
       # cur_X is used to enforce conditioning on
       # the ever_active sufficient_statistics
@@ -517,8 +521,8 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
 
       # now, we sample from Y_star, a centered Gaussian with covariance sigma^2 I
       # subject to the constraint
-      # t(cur_adjusted_X) %*% Y_star < final_upper
-      # -t(cur_adjusted_X) %*% Y_star < -final_lower
+      # t(cur_adjusted_Xt) %*% Y_star < final_upper
+      # -t(cur_adjusted_Xt) %*% Y_star < -final_lower
 
       # really, we want the covariance of Y_star to be \sigma^2 (I - cur_P)
       # where P is projection on the j-1 previous variables
@@ -529,7 +533,7 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
 
       # IMPORTANT: after sampling Y_star, we have to add back cur_fitted
 
-      # if n > p, we actually just draw cur_adjusted_X %*% Y_star
+      # if n > p, we actually just draw cur_adjusted_Xt %*% Y_star
       # because this has a simple box constraint
       # with a generically non-degenerate covariance
 
@@ -537,10 +541,10 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
           library(tmvtnorm)
  
           if (length(inactive) > 1) {
-              cov = (cur_adjusted_X %*% t(cur_adjusted_X))
+              cov = (cur_adjusted_Xt %*% t(cur_adjusted_Xt))
               cov = cov * rep(sigma^2, nrow(cov), ncol(cov))
           } else {
-              cov = sigma^2 * sum(cur_adjusted_X^2)
+              cov = sigma^2 * sum(cur_adjusted_Xt^2)
           }
 
           truncated_noise = rtmvnorm(n=ndraw, 
@@ -559,11 +563,12 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
           }
       } else {
 
-          linear_part = rbind(t(cur_adjusted_X), -t(cur_adjusted_X))
+          linear_part = rbind(cur_adjusted_Xt, -cur_adjusted_Xt)
 	  offset = c(final_upper, -final_lower)
-	  covariance = diag(rep(sigma^2, nrow(cor_adjusted_X)))
-	  mean = rep(0, nrow(cur_adjusted_X))
+	  covariance = diag(rep(sigma^2, length(y)))
+	  mean = rep(0, length(y))
 	  initial_point = y
+
 	  truncated_y = sample_from_constraints(linear_part, 
                                                 offset, 
                                                 mean, 
@@ -572,7 +577,7 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
                                                 burnin=burnin, 
                                                 ndraw=ndraw)
 
-          truncated_noise = truncated_y %*% cur_adjusted_X
+          truncated_noise = truncated_y %*% t(cur_adjusted_Xt)
           sample_maxZ = apply(abs(1. / cur_scale * truncated_noise), 1, max)
       }
       
@@ -592,17 +597,17 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
 	     sigma=sigma,
 	     call=this.call,
 	     vars=vars,
-	     sign=sign,
+	     sign=obj$sign,
 	     alpha=alpha,
              realized_maxZ=obj$realized_maxZ)
-  class(out) = "fsInf_Zmax"
+  class(out) = "fsInf_maxZ"
   return(out)
 }
 
 ##############################
-
-Print methods
-
+#
+# Print methods
+#
 ##############################
 
 print.fs <- function(x, ...) {
@@ -618,7 +623,68 @@ print.fs <- function(x, ...) {
   invisible()
 }
 
-print.fsInf_Zmax <- function(obj) {
+print.fsInf <- function(x, tailarea=TRUE, ...) {
+  cat("\nCall:\n")
+  dput(x$call)
+
+  cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n",
+              x$sigma))
+
+  if (x$type == "active") {
+    cat(sprintf("\nSequential testing results with alpha = %0.3f\n",x$alpha))
+    tab = cbind(1:length(x$pv),x$vars,
+      round(x$sign*x$vmat%*%x$y,3),
+      round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
+      round(x$pv,3),round(x$ci,3))
+    colnames(tab) = c("Step", "Var", "Coef", "Z-score", "P-value",
+              "LowConfPt", "UpConfPt")
+    if (tailarea) {
+      tab = cbind(tab,round(x$tailarea,3))
+      colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+    }
+    rownames(tab) = rep("",nrow(tab))
+    print(tab)
+
+    cat(sprintf("\nEstimated stopping point from ForwardStop rule = %i\n",x$khat))
+  }
+
+  else if (x$type == "all") {
+    cat(sprintf("\nTesting results at step = %i, with alpha = %0.3f\n",x$k,x$alpha))
+    tab = cbind(x$vars,
+      round(x$sign*x$vmat%*%x$y,3),
+      round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
+      round(x$pv,3),round(x$ci,3))
+    colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
+    if (tailarea) {
+      tab = cbind(tab,round(x$tailarea,3))
+      colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+    }
+    rownames(tab) = rep("",nrow(tab))
+    print(tab)
+  }
+
+  else if (x$type == "aic") {
+    cat(sprintf("\nTesting results at step = %i, with alpha = %0.3f\n",x$khat,x$alpha))
+    tab = cbind(x$vars,
+      round(x$sign*x$vmat%*%x$y,3),
+      round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
+      round(x$pv,3),round(x$ci,3))
+    colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
+    if (tailarea) {
+      tab = cbind(tab,round(x$tailarea,3))
+      colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+    }
+    rownames(tab) = rep("",nrow(tab))
+    print(tab)
+    
+    cat(sprintf("\nEstimated stopping point from AIC rule = %i\n",x$khat))
+  }
+
+  invisible()
+}
+
+
+print.fsInf_maxZ <- function(obj) {
 
   cat("\nCall:\n")
   dput(obj$call)
@@ -628,23 +694,23 @@ print.fsInf_Zmax <- function(obj) {
 
   cat(sprintf("\nSequential testing results with alpha = %0.3f\n",obj$alpha))
 
-  tab = cbind(1:length(obj$pv),obj$vars,
+  tab = cbind(1:length(obj$pv),
+              obj$vars,
               round(obj$sign*obj$realized_maxZ, 3),
               round(obj$pv,3))
-  colnames(tab) = c("Step", "Var", "Coef", "Z-score", "P-value")
+  colnames(tab) = c("Step", "Var", "Z-score", "P-value")
   rownames(tab) = rep("",nrow(tab))
   print(tab)
 
   cat(sprintf("\nEstimated stopping point from ForwardStop rule = %i\n",obj$khat))
-  }
 
   invisible()
 }
 
 ##############################
-
-Plot methods
-
+# 
+# Plot methods
+#
 ##############################
 
 plot.fs <- function(x, breaks=TRUE, omit.zeros=TRUE, var.labels=TRUE, ...) {
diff --git a/tests/test.fs_maxZ.R b/tests/test.fs_maxZ.R
new file mode 100644
index 0000000..dc7e6e2
--- /dev/null
+++ b/tests/test.fs_maxZ.R
@@ -0,0 +1,23 @@
+library(selectiveInference)
+options(error=dump.frames)
+
+set.seed(0)
+n = 100
+p = 120
+s = 3
+size = 5
+
+sigma = 1.5
+x = matrix(rnorm(n*p),n,p)
+#x = scale(x,T,F)/sqrt(n-1)
+
+b = c(sample(c(-1,1),s,replace=T)*rep(size,s),rep(0,p-s))
+mu = x%*%b
+y = mu + sigma*rnorm(n)
+
+obj = fs(x,y,verb=T,intercept=T,norm=T, maxsteps=20)
+
+
+# Sequential inference
+out = fsInf_maxZ(obj,sigma=sigma,k=20, ndraw=5000, burnin=1000)
+print(out)

From e48e922d8b0b9834119fad71b24f4e93a8abc2f8 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Mar 2016 23:38:32 -0700
Subject: [PATCH 116/493] removing tmvtnorm dependency

---
 selectiveInference/R/funs.fs.R | 61 ++++++++++------------------------
 1 file changed, 18 insertions(+), 43 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index dedc0d3..7b04012 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -533,53 +533,28 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
 
       # IMPORTANT: after sampling Y_star, we have to add back cur_fitted
 
-      # if n > p, we actually just draw cur_adjusted_Xt %*% Y_star
+      # if n > p, we could actually just draw cur_adjusted_Xt %*% Y_star
       # because this has a simple box constraint
       # with a generically non-degenerate covariance
 
-      if (n > p) { 
-          library(tmvtnorm)
- 
-          if (length(inactive) > 1) {
-              cov = (cur_adjusted_Xt %*% t(cur_adjusted_Xt))
-              cov = cov * rep(sigma^2, nrow(cov), ncol(cov))
-          } else {
-              cov = sigma^2 * sum(cur_adjusted_Xt^2)
-          }
-
-          truncated_noise = rtmvnorm(n=ndraw, 
-                                     mean=cur_offset,
-                                     sigma=cov, 
-                                     lower=-collapsed_neg,
-                                     upper=collapsed_pos,
-                                     algorithm="gibbs",
-                    		     burn.in.samples=burnin)
-
-          if (length(inactive) > 1) {				 
-             sample_maxZ = apply(abs(1. / cur_scale * truncated_noise), 1, max)
-          }
-          else {
-             sample_maxZ = truncated_noise / cur_scale
-          }
-      } else {
+      # but `tmvtnorm` seems to give poor results for its sampler
 
-          linear_part = rbind(cur_adjusted_Xt, -cur_adjusted_Xt)
-	  offset = c(final_upper, -final_lower)
-	  covariance = diag(rep(sigma^2, length(y)))
-	  mean = rep(0, length(y))
-	  initial_point = y
-
-	  truncated_y = sample_from_constraints(linear_part, 
-                                                offset, 
-                                                mean, 
-                                                covariance, 
-                                                initial_point, 
-                                                burnin=burnin, 
-                                                ndraw=ndraw)
-
-          truncated_noise = truncated_y %*% t(cur_adjusted_Xt)
-          sample_maxZ = apply(abs(1. / cur_scale * truncated_noise), 1, max)
-      }
+      linear_part = rbind(cur_adjusted_Xt, -cur_adjusted_Xt)
+      offset = c(final_upper, -final_lower)
+      covariance = diag(rep(sigma^2, length(y)))
+      mean = rep(0, length(y))
+      initial_point = y
+
+      truncated_y = sample_from_constraints(linear_part, 
+                                            offset, 
+                                            mean, 
+                                            covariance, 
+                                            initial_point, 
+                                            burnin=burnin, 
+                                            ndraw=ndraw)
+
+      truncated_noise = truncated_y %*% t(cur_adjusted_Xt)
+      sample_maxZ = apply(abs(1. / cur_scale * truncated_noise), 1, max)
       
       observed_maxZ = obj$realized_maxZ[j]
 

From ee560b57738840fcda72445337329144eb17e5cd Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 19 Mar 2016 08:13:19 -0700
Subject: [PATCH 117/493] BF: last step was failing because of how R treats
 numeric different than matrix

---
 selectiveInference/R/funs.fs.R | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 7b04012..a86f96d 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -553,9 +553,13 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
                                             burnin=burnin, 
                                             ndraw=ndraw)
 
-      truncated_noise = truncated_y %*% t(cur_adjusted_Xt)
-      sample_maxZ = apply(abs(1. / cur_scale * truncated_noise), 1, max)
-      
+      if (j < p) {
+          truncated_noise = truncated_y %*% t(cur_adjusted_Xt)
+          sample_maxZ = apply(abs(1. / cur_scale * truncated_noise), 1, max)
+      }
+      else {
+          sample_maxZ = abs(truncated_y %*% cur_adjusted_Xt)
+      }      
       observed_maxZ = obj$realized_maxZ[j]
 
       pval = sum(sample_maxZ > observed_maxZ) / ndraw

From 338fac0e7f9745d355f483c896bd9a10dddc14d3 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 19 Mar 2016 10:50:07 -0700
Subject: [PATCH 118/493] using drop=FALSE instead of if statement

---
 selectiveInference/R/funs.fs.R | 14 ++++----------
 1 file changed, 4 insertions(+), 10 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index a86f96d..1bb08b9 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -494,7 +494,7 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
       # the matrix cur_adjusted_Xt is used to compute (always as length(y) columns)
       # the maxZ or maxT for the sampled variables
       # 
-      cur_adjusted_Xt = obj$Gamma_maxZ[zi + Seq(1,p-j+1),]; zi = zi+p-j+1 # Xt for transpose
+      cur_adjusted_Xt = obj$Gamma_maxZ[zi + Seq(1,p-j+1),,drop=FALSE]; zi = zi+p-j+1 # Xt for transpose
 
       # cur_X is used to enforce conditioning on
       # the ever_active sufficient_statistics
@@ -537,8 +537,6 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
       # because this has a simple box constraint
       # with a generically non-degenerate covariance
 
-      # but `tmvtnorm` seems to give poor results for its sampler
-
       linear_part = rbind(cur_adjusted_Xt, -cur_adjusted_Xt)
       offset = c(final_upper, -final_lower)
       covariance = diag(rep(sigma^2, length(y)))
@@ -553,13 +551,9 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
                                             burnin=burnin, 
                                             ndraw=ndraw)
 
-      if (j < p) {
-          truncated_noise = truncated_y %*% t(cur_adjusted_Xt)
-          sample_maxZ = apply(abs(1. / cur_scale * truncated_noise), 1, max)
-      }
-      else {
-          sample_maxZ = abs(truncated_y %*% cur_adjusted_Xt)
-      }      
+      truncated_noise = truncated_y %*% t(cur_adjusted_Xt)
+      sample_maxZ = apply(abs(1. / cur_scale * truncated_noise), 1, max)
+
       observed_maxZ = obj$realized_maxZ[j]
 
       pval = sum(sample_maxZ > observed_maxZ) / ndraw

From 45406e6fbcd210cd7c7ce8e2d8f6539622499212 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 19 Mar 2016 19:54:34 -0700
Subject: [PATCH 119/493] BF: broadcasting error, also scale_maxZ was wrong
 variable after resizing

---
 selectiveInference/R/funs.fs.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 1bb08b9..7906be6 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -230,7 +230,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   
   offset_pos_maxZ = offset_pos_maxZ[,Seq(1,k-1),drop=FALSE]
   offset_neg_maxZ = offset_neg_maxZ[,Seq(1,k-1),drop=FALSE]
-  scale_maxZ = offset_pos_maxZ[,Seq(1,k-1),drop=FALSE]
+  scale_maxZ = scale_maxZ[,Seq(1,k-1),drop=FALSE]
   Gamma_maxZ = Gamma_maxZ[Seq(1,zi),,drop=FALSE]
 
   # If we reached the maximum number of steps
@@ -552,7 +552,7 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
                                             ndraw=ndraw)
 
       truncated_noise = truncated_y %*% t(cur_adjusted_Xt)
-      sample_maxZ = apply(abs(1. / cur_scale * truncated_noise), 1, max)
+      sample_maxZ = apply(abs(t(truncated_noise) / cur_scale), 2, max)
 
       observed_maxZ = obj$realized_maxZ[j]
 

From c6e959c5f8b7c22c35dd0148f3ecf192d9e346f2 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Sat, 19 Mar 2016 23:30:56 -0700
Subject: [PATCH 120/493] rob added Rd file for fsInf_maxZ

---
 selectiveInference/R/funs.fs.R        |  4 ++--
 selectiveInference/R/funs.quadratic.R |  3 ++-
 tests/test.fs_maxZ.R                  | 30 +++++++++++++++++++++------
 3 files changed, 28 insertions(+), 9 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 7906be6..a4cc45f 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -568,11 +568,11 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
              k=k,
              khat=khat,
 	     sigma=sigma,
-	     call=this.call,
 	     vars=vars,
 	     sign=obj$sign,
 	     alpha=alpha,
-             realized_maxZ=obj$realized_maxZ)
+             realized_maxZ=obj$realized_maxZ,
+        call=this.call)
   class(out) = "fsInf_maxZ"
   return(out)
 }
diff --git a/selectiveInference/R/funs.quadratic.R b/selectiveInference/R/funs.quadratic.R
index 23ff63f..799352b 100644
--- a/selectiveInference/R/funs.quadratic.R
+++ b/selectiveInference/R/funs.quadratic.R
@@ -1,4 +1,3 @@
-
 truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
 
   n <- nrow(obj$x)
@@ -177,6 +176,7 @@ TF_coefficients <- function(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h) {
 
 # Numerically solve for roots of TF slice using
 # hybrid polyroot/uniroot approach
+
 TF_roots <- function(R, C, coeffs, tol = 1e-8, tol2 = 1e-6) {
 
     x11 <- coeffs$x11
@@ -254,3 +254,4 @@ TF_roots <- function(R, C, coeffs, tol = 1e-8, tol2 = 1e-6) {
     if (I(0) < 0) stop("Infeasible constraint!")
     return(Intervals(c(-Inf,0)))
 }
+
diff --git a/tests/test.fs_maxZ.R b/tests/test.fs_maxZ.R
index dc7e6e2..107948d 100644
--- a/tests/test.fs_maxZ.R
+++ b/tests/test.fs_maxZ.R
@@ -2,22 +2,40 @@ library(selectiveInference)
 options(error=dump.frames)
 
 set.seed(0)
-n = 100
-p = 120
+n = 20
+p = 5
 s = 3
 size = 5
 
 sigma = 1.5
 x = matrix(rnorm(n*p),n,p)
-#x = scale(x,T,F)/sqrt(n-1)
+
 
 b = c(sample(c(-1,1),s,replace=T)*rep(size,s),rep(0,p-s))
+b=rep(0,p)
 mu = x%*%b
+nsim=200
+pv=matrix(NA,nsim,p)
+for(ii in 1:nsim){
+    cat(ii)
 y = mu + sigma*rnorm(n)
 
-obj = fs(x,y,verb=T,intercept=T,norm=T, maxsteps=20)
+obj = fs(x,y,verb=T,intercept=T,norm=T, maxsteps=p)
 
 
 # Sequential inference
-out = fsInf_maxZ(obj,sigma=sigma,k=20, ndraw=5000, burnin=1000)
-print(out)
+out = fsInf_maxZ(obj,sigma=sigma, ndraw=5000, burnin=1000)
+pv[ii,]=out$pv
+}
+
+
+ apply(pv,2,quantile)
+     [,1]   [,2]   [,3]   [,4]   [,5]
+0%      0 0.0000 0.0000 0.0000 0.0000
+25%     0 0.0000 0.0055 0.1215 0.2123
+50%     0 0.0000 0.1826 0.4028 0.4824
+75%     0 0.0875 0.5638 0.7002 0.7517
+100%    0 0.9860 0.9916 0.9996 0.9984
+
+
+plot((1:nsim)/nsim,sort(pv[,4]))

From 1470e608f6cdd7e052198e04e66d910ca52d3ccb Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Sun, 20 Mar 2016 12:39:42 -0700
Subject: [PATCH 121/493] rob

---
 selectiveInference/R/funs.fs.R |  6 +++---
 tests/test.fs_maxZ.R           | 15 +++++----------
 2 files changed, 8 insertions(+), 13 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index a4cc45f..de3ebf5 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -440,8 +440,8 @@ fsInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic
 
 # selected maxZ tests
 
-fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
-                      ndraw=8000, burnin=2000) {
+fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, k=NULL,
+                      ndraw=8000, burnin=2000, verbose=FALSE) {
   
   this.call = match.call()
 
@@ -474,7 +474,7 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, verbose=FALSE, k=NULL,
   vars = obj$action[1:k]
   zi = 0
   for (j in 1:k) {
-         
+     if(verbose) cat(c("Step=",j),fill=T)
       # the inactive set here does not
       # include the variable at the j-th step
       # so, at j==1, the inactive set is every variable
diff --git a/tests/test.fs_maxZ.R b/tests/test.fs_maxZ.R
index 107948d..11e89aa 100644
--- a/tests/test.fs_maxZ.R
+++ b/tests/test.fs_maxZ.R
@@ -29,13 +29,8 @@ pv[ii,]=out$pv
 }
 
 
- apply(pv,2,quantile)
-     [,1]   [,2]   [,3]   [,4]   [,5]
-0%      0 0.0000 0.0000 0.0000 0.0000
-25%     0 0.0000 0.0055 0.1215 0.2123
-50%     0 0.0000 0.1826 0.4028 0.4824
-75%     0 0.0875 0.5638 0.7002 0.7517
-100%    0 0.9860 0.9916 0.9996 0.9984
-
-
-plot((1:nsim)/nsim,sort(pv[,4]))
+par(mfrow=c(3,3))
+for(j in 1:p){
+plot((1:nsim)/nsim,sort(pv[,j]))
+abline(0,1)
+}

From f9681b461b9ec6d1f3eb21b4929b564b510852f3 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 21 Mar 2016 11:01:55 -0700
Subject: [PATCH 122/493] sample a smaller dimensional gaussian when possible

---
 selectiveInference/R/funs.fs.R | 59 +++++++++++++++++++++++-----------
 1 file changed, 41 insertions(+), 18 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index de3ebf5..7e6f2fa 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -533,27 +533,50 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, k=NULL,
 
       # IMPORTANT: after sampling Y_star, we have to add back cur_fitted
 
-      # if n > p, we could actually just draw cur_adjusted_Xt %*% Y_star
+      # if n >= p, we could actually just draw cur_adjusted_Xt %*% Y_star
       # because this has a simple box constraint
       # with a generically non-degenerate covariance
 
-      linear_part = rbind(cur_adjusted_Xt, -cur_adjusted_Xt)
-      offset = c(final_upper, -final_lower)
-      covariance = diag(rep(sigma^2, length(y)))
-      mean = rep(0, length(y))
-      initial_point = y
-
-      truncated_y = sample_from_constraints(linear_part, 
-                                            offset, 
-                                            mean, 
-                                            covariance, 
-                                            initial_point, 
-                                            burnin=burnin, 
-                                            ndraw=ndraw)
-
-      truncated_noise = truncated_y %*% t(cur_adjusted_Xt)
-      sample_maxZ = apply(abs(t(truncated_noise) / cur_scale), 2, max)
-
+      if (nrow(cur_adjusted_Xt) > length(y)) {
+          linear_part = rbind(cur_adjusted_Xt, -cur_adjusted_Xt)
+          offset = c(final_upper, -final_lower)
+          covariance = diag(rep(sigma^2, length(y)))
+          mean = rep(0, length(y))
+          initial_point = y
+
+          truncated_y = sample_from_constraints(linear_part, 
+                                                offset, 
+                                                mean, 
+                                                covariance, 
+                                                initial_point, 
+                                                burnin=burnin, 
+                                                ndraw=ndraw)
+
+          truncated_noise = truncated_y %*% t(cur_adjusted_Xt)
+          sample_maxZ = apply(abs(t(truncated_noise) / cur_scale), 2, max)
+      } else if (nrow(cur_adjusted_Xt) > 1) { # sample from a smaller dimensional gaussian
+          linear_part = rbind(diag(rep(1, nrow(cur_adjusted_Xt))),
+                              diag(rep(-1, nrow(cur_adjusted_Xt))))
+          covariance = sigma^2 * (cur_adjusted_Xt %*% t(cur_adjusted_Xt))
+          offset = c(final_upper, -final_lower)
+          mean = rep(0, nrow(cur_adjusted_Xt))
+          initial_point = cur_adjusted_Xt %*% y
+
+          truncated_noise = sample_from_constraints(linear_part, 
+                                                offset, 
+                                                mean, 
+                                                covariance, 
+                                                initial_point, 
+                                                burnin=burnin, 
+                                                ndraw=ndraw)
+          sample_maxZ = apply(abs(t(truncated_noise) / cur_scale), 2, max)
+
+      } else { # problem is just a univariate gaussian
+          # this should work as long as cdfL - cdfU is not tiny
+          cdfL = pnorm(final_lower)
+	  cdfU = pnorm(final_upper)
+          sample_maxZ = qnorm(runif(ndraw) * (cdfU - cdfL) + cdfL)
+      }
       observed_maxZ = obj$realized_maxZ[j]
 
       pval = sum(sample_maxZ > observed_maxZ) / ndraw

From fdb26b14ab27f1725585d6fea8e2340bd609680c Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 21 Mar 2016 11:48:29 -0700
Subject: [PATCH 123/493] BF in univariate sampler -- needed abs value

---
 selectiveInference/R/funs.fs.R | 37 +++++++++++++++++++++++-----------
 1 file changed, 25 insertions(+), 12 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 7e6f2fa..6403106 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -512,7 +512,7 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, k=NULL,
 	  cur_offset = as.numeric(t(cur_X) %*% cur_fitted)
       }
       else {
-          cur_fitted = 0
+          cur_fitted = rep(0, length(y))
           cur_offset = rep(0, length(inactive))
       }
 
@@ -541,12 +541,12 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, k=NULL,
           linear_part = rbind(cur_adjusted_Xt, -cur_adjusted_Xt)
           offset = c(final_upper, -final_lower)
           covariance = diag(rep(sigma^2, length(y)))
-          mean = rep(0, length(y))
+          mean_param = cur_fitted # rep(0, length(y))
           initial_point = y
 
           truncated_y = sample_from_constraints(linear_part, 
                                                 offset, 
-                                                mean, 
+                                                mean_param, 
                                                 covariance, 
                                                 initial_point, 
                                                 burnin=burnin, 
@@ -559,29 +559,42 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, k=NULL,
                               diag(rep(-1, nrow(cur_adjusted_Xt))))
           covariance = sigma^2 * (cur_adjusted_Xt %*% t(cur_adjusted_Xt))
           offset = c(final_upper, -final_lower)
-          mean = rep(0, nrow(cur_adjusted_Xt))
+          mean_param = cur_adjusted_Xt %*% cur_fitted # rep(0, nrow(cur_adjusted_Xt))
           initial_point = cur_adjusted_Xt %*% y
 
           truncated_noise = sample_from_constraints(linear_part, 
                                                 offset, 
-                                                mean, 
+                                                mean_param, 
                                                 covariance, 
                                                 initial_point, 
                                                 burnin=burnin, 
                                                 ndraw=ndraw)
           sample_maxZ = apply(abs(t(truncated_noise) / cur_scale), 2, max)
 
-      } else { # problem is just a univariate gaussian
-          # this should work as long as cdfL - cdfU is not tiny
-          cdfL = pnorm(final_lower)
-	  cdfU = pnorm(final_upper)
-          sample_maxZ = qnorm(runif(ndraw) * (cdfU - cdfL) + cdfL)
+      } else {  # just a univariated truncated Gaussian
+                # but we need the law of the absolute value of it
+		# we are sampling here, but could probably
+		# do this without sampling
+          mean_param = sum(as.numeric(cur_adjusted_Xt) * as.numeric(cur_fitted))
+	  scaling = sigma * sqrt(sum(cur_adjusted_Xt^2))
+	  L = (final_lower - mean_param) / scaling
+	  U = (final_upper - mean_param) / scaling
+	  if (L > 6) { # use Exp approximation
+               Z = rexp(ndraw) / L + L
+	  } else if (U < -6) {
+	       Z = rexp(ndraw) / U + U
+	  } else {
+	       Z = qnorm(runif(ndraw) * (pnorm(U) - pnorm(L)) + pnorm(L))
+	  }
+	  print('delta P')
+	  print(pnorm(U) - pnorm(L))
+	  sample_maxZ = abs(Z * scaling + mean_param)
+
       }
-      observed_maxZ = obj$realized_maxZ[j]
 
+      observed_maxZ = obj$realized_maxZ[j]
       pval = sum(sample_maxZ > observed_maxZ) / ndraw
       pval = 2 * min(pval, 1 - pval)
-
       pv = c(pv, pval)
   }
 

From 2b22bbf965d631dad012daed0318295cf8e115d5 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 21 Mar 2016 12:11:04 -0700
Subject: [PATCH 124/493] moved logic for 1D constraint sampling to constraints
 file, renamed mean to mean_param

---
 selectiveInference/R/funs.constraints.R | 102 ++++++++++++++----------
 selectiveInference/R/funs.fs.R          |  44 ++++------
 2 files changed, 77 insertions(+), 69 deletions(-)

diff --git a/selectiveInference/R/funs.constraints.R b/selectiveInference/R/funs.constraints.R
index e2bc041..3bf6b77 100644
--- a/selectiveInference/R/funs.constraints.R
+++ b/selectiveInference/R/funs.constraints.R
@@ -24,16 +24,16 @@ factor_covariance = function(S, rank=NA) {
 # whitening map
 #
 
-# law is Z \sim N(mean, covariance) subject to constraints linear_part %*% Z \leq offset
+# law is Z \sim N(mean_param, covariance) subject to constraints linear_part %*% Z \leq offset
 
-whiten_constraint = function(linear_part, offset, mean, covariance) {
+whiten_constraint = function(linear_part, offset, mean_param, covariance) {
 
     factor_cov = factor_covariance(covariance)
     sqrt_cov = factor_cov$sqrt_cov
     sqrt_inv = factor_cov$sqrt_inv
 
     new_A = linear_part %*% sqrt_cov
-    new_b = offset - linear_part %*% mean
+    new_b = offset - linear_part %*% mean_param
 
     # rescale rows to have length 1
 
@@ -44,11 +44,13 @@ whiten_constraint = function(linear_part, offset, mean, covariance) {
     # TODO: check these functions will behave when Z is a matrix.
 
     inverse_map = function(Z) {
-        return(sqrt_cov %*% Z + mean)
+	# broadcasting here
+        # the columns of Z are same length as mean_param
+        return(sqrt_cov %*% Z + as.numeric(mean_param))
     }
 
     forward_map = function(W) {
-        return(sqrt_inv %*% (W - mean))
+        return(sqrt_inv %*% (W - mean_param))
     }    
 
     return(list(linear_part=new_A,
@@ -60,11 +62,11 @@ whiten_constraint = function(linear_part, offset, mean, covariance) {
 #
 # sample from the law
 #
-# Z \sim N(mean, covariance) subject to constraints linear_part %*% Z \leq offset
+# Z \sim N(mean_param, covariance) subject to constraints linear_part %*% Z \leq offset
 
 sample_from_constraints = function(linear_part,
                                    offset,
-                                   mean,
+                                   mean_param,
                                    covariance,
                                    initial_point, # point must be feasible for constraints
                                    ndraw=8000,
@@ -74,7 +76,7 @@ sample_from_constraints = function(linear_part,
 
     whitened_con = whiten_constraint(linear_part,
                                      offset,
-				     mean,
+				     mean_param,
                                      covariance)
     white_initial = whitened_con$forward_map(initial_point)
 
@@ -121,42 +123,60 @@ sample_from_constraints = function(linear_part,
 	# In theory, these rows can be dropped
 
 	rows_to_keep = white_offset < Inf
-	white_linear = white_linear[rows_to_keep,]
-	white_offset = white_offset[rows_to_keep,]
+	white_linear = white_linear[rows_to_keep,,drop=FALSE]
+	white_offset = white_offset[rows_to_keep]
 
         nstate = length(white_initial)
 	if (sum(rows_to_keep) > 0) {
-            nconstraint = nrow(white_linear)
-
-            directions = rbind(diag(rep(1, nstate)),
-                               matrix(rnorm(nstate^2), nstate, nstate))
-
-            # normalize rows to have length 1
-
-            scaling = apply(directions, 1, function(x) {  return(sqrt(sum(x^2))) }) 
-            directions = directions / scaling
-            ndirection = nrow(directions)
-
-            alphas = directions %*% t(white_linear)
-            U = white_linear %*% white_initial - white_offset
-            Z_sample = matrix(rep(0, nstate * ndraw), nstate, ndraw)
-
-            result = .C("sample_truncnorm_white",
-                        as.numeric(white_initial),
-                        as.numeric(U),
-                        as.numeric(t(directions)),
-                        as.numeric(t(alphas)),
-                        output=Z_sample,
-                        as.integer(nconstraint),
-                        as.integer(ndirection),
-                        as.integer(nstate),
-                        as.integer(burnin),
-                        as.integer(ndraw),
-                        package="selectiveInference")
-            Z_sample = result$output
-       }
-       else {
-            Z_sample = matrix(rnorm(nstate * ndraw), nstate, ndraw)
+	    if (ncol(white_linear) > 1) {
+                nconstraint = nrow(white_linear)
+
+                directions = rbind(diag(rep(1, nstate)),
+                                   matrix(rnorm(nstate^2), nstate, nstate))
+
+                # normalize rows to have length 1
+
+                scaling = apply(directions, 1, function(x) {  return(sqrt(sum(x^2))) }) 
+                directions = directions / scaling
+                ndirection = nrow(directions)
+
+                alphas = directions %*% t(white_linear)
+                U = white_linear %*% white_initial - white_offset
+                Z_sample = matrix(rep(0, nstate * ndraw), nstate, ndraw)
+
+                result = .C("sample_truncnorm_white",
+                            as.numeric(white_initial),
+                            as.numeric(U),
+                            as.numeric(t(directions)),
+                            as.numeric(t(alphas)),
+                            output=Z_sample,
+                            as.integer(nconstraint),
+                            as.integer(ndirection),
+                            as.integer(nstate),
+                            as.integer(burnin),
+                            as.integer(ndraw),
+                            package="selectiveInference")
+                Z_sample = result$output
+           } else { # the distribution is univariate
+                    # we can just work out upper and lower limits
+
+                white_linear = as.numeric(white_linear)
+                pos = (white_linear * white_offset) >= 0
+                neg = (white_linear * white_offset) <= 0
+		if (sum(pos) > 0) {
+                    U = min((white_offset / white_linear)[pos])
+                } else {
+		    U = Inf
+                }
+		if (sum(neg) < 0) {
+                    L = max((white_offset / white_linear)[neg])
+                } else {
+                    L = -Inf
+                }
+                Z_sample = matrix(qnorm((pnorm(U) - pnorm(L)) * runif(ndraw) + pnorm(L)), 1, ndraw)
+           }
+       } else {
+           Z_sample = matrix(rnorm(nstate * ndraw), nstate, ndraw)
        }
     }
 
diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 6403106..cb63da2 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -554,14 +554,21 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, k=NULL,
 
           truncated_noise = truncated_y %*% t(cur_adjusted_Xt)
           sample_maxZ = apply(abs(t(truncated_noise) / cur_scale), 2, max)
-      } else if (nrow(cur_adjusted_Xt) > 1) { # sample from a smaller dimensional gaussian
-          linear_part = rbind(diag(rep(1, nrow(cur_adjusted_Xt))),
-                              diag(rep(-1, nrow(cur_adjusted_Xt))))
-          covariance = sigma^2 * (cur_adjusted_Xt %*% t(cur_adjusted_Xt))
-          offset = c(final_upper, -final_lower)
-          mean_param = cur_adjusted_Xt %*% cur_fitted # rep(0, nrow(cur_adjusted_Xt))
-          initial_point = cur_adjusted_Xt %*% y
-
+      } else {  # sample from a smaller dimensional gaussian
+          if (nrow(cur_adjusted_Xt) > 1) {
+              linear_part = rbind(diag(rep(1, nrow(cur_adjusted_Xt))),
+                                  diag(rep(-1, nrow(cur_adjusted_Xt))))
+              covariance = sigma^2 * (cur_adjusted_Xt %*% t(cur_adjusted_Xt))
+              offset = c(final_upper, -final_lower)
+              mean_param = cur_adjusted_Xt %*% cur_fitted # rep(0, nrow(cur_adjusted_Xt))
+              initial_point = cur_adjusted_Xt %*% y
+          } else {
+              mean_param = as.numeric(sum(as.numeric(cur_adjusted_Xt) * as.numeric(cur_fitted)))
+              covariance = matrix(sigma^2 * sum(cur_adjusted_Xt^2))
+              linear_part = matrix(c(1,-1), 2, 1)
+              offset = c(final_upper, -final_lower)
+              initial_point = as.numeric(sum(as.numeric(cur_adjusted_Xt) * as.numeric(y)))
+          }
           truncated_noise = sample_from_constraints(linear_part, 
                                                 offset, 
                                                 mean_param, 
@@ -571,26 +578,7 @@ fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, k=NULL,
                                                 ndraw=ndraw)
           sample_maxZ = apply(abs(t(truncated_noise) / cur_scale), 2, max)
 
-      } else {  # just a univariated truncated Gaussian
-                # but we need the law of the absolute value of it
-		# we are sampling here, but could probably
-		# do this without sampling
-          mean_param = sum(as.numeric(cur_adjusted_Xt) * as.numeric(cur_fitted))
-	  scaling = sigma * sqrt(sum(cur_adjusted_Xt^2))
-	  L = (final_lower - mean_param) / scaling
-	  U = (final_upper - mean_param) / scaling
-	  if (L > 6) { # use Exp approximation
-               Z = rexp(ndraw) / L + L
-	  } else if (U < -6) {
-	       Z = rexp(ndraw) / U + U
-	  } else {
-	       Z = qnorm(runif(ndraw) * (pnorm(U) - pnorm(L)) + pnorm(L))
-	  }
-	  print('delta P')
-	  print(pnorm(U) - pnorm(L))
-	  sample_maxZ = abs(Z * scaling + mean_param)
-
-      }
+         } 
 
       observed_maxZ = obj$realized_maxZ[j]
       pval = sum(sample_maxZ > observed_maxZ) / ndraw

From f8b1ba89ed8a2307e5749184b48d1174a29b3903 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Fri, 25 Mar 2016 09:49:38 -0700
Subject: [PATCH 125/493] rob added binom and Cox families to fixedLassoInf

---
 selectiveInference/DESCRIPTION          |   3 +-
 selectiveInference/NAMESPACE            |   4 +
 selectiveInference/R/funs.fixed.R       |  25 +++-
 selectiveInference/R/funs.fixedCox.R    | 111 ++++++++++++++++++
 selectiveInference/R/funs.fixedLogit.R  | 145 ++++++++++++++++++++++++
 selectiveInference/R/funs.inf.R         |  53 +++++++++
 selectiveInference/man/fixedLassoInf.Rd |  64 ++++++++++-
 tests/test.fixed.R                      |  98 ++++++++++++++--
 8 files changed, 485 insertions(+), 18 deletions(-)
 create mode 100644 selectiveInference/R/funs.fixedCox.R
 create mode 100644 selectiveInference/R/funs.fixedLogit.R

diff --git a/selectiveInference/DESCRIPTION b/selectiveInference/DESCRIPTION
index 9e283ec..af80d99 100644
--- a/selectiveInference/DESCRIPTION
+++ b/selectiveInference/DESCRIPTION
@@ -9,7 +9,8 @@ Maintainer: Rob Tibshirani <tibs@stanford.edu>
 Depends:
     glmnet,
     intervals,
-    tmvtnorm
+    tmvtnorm,
+    survival
 Suggests:
     Rmpfr
 Description: New tools for post-selection inference, for use
diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index cc7e76f..4d28398 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -6,6 +6,8 @@ export(lar,fs,
        print.larInf,print.fsInf,
        plot.lar,plot.fs,
        fixedLassoInf,print.fixedLassoInf,
+ #     fixedLogitLassoInf,print.fixedLogitLassoInf,
+  #     fixedCoxLassoInf,print.fixedCoxLassoInf,
        forwardStop,
        estimateSigma,
        manyMeans,print.manyMeans,
@@ -26,6 +28,8 @@ S3method("plot", "fs")
 S3method("print", "fsInf")
 S3method("print", "fsInf_maxZ")
 S3method("print", "fixedLassoInf")
+S3method("print", "fixedLogitLassoInf")
+S3method("print", "fixedCoxLassoInf")
 S3method("print", "manyMeans")
 S3method("print", "groupfs")
 S3method("print", "groupfsInf")
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index ada155b..8948a10 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -2,12 +2,32 @@
 # for the solution of
 # min 1/2 || y - \beta_0 - X \beta ||_2^2 + \lambda || \beta ||_1
 
-fixedLassoInf <- function(x, y, beta, lambda, intercept=TRUE, sigma=NULL, alpha=0.1,
+fixedLassoInf <- function(x, y, beta, lambda, family=c("gaussian","binomial","cox"),intercept=TRUE, status=NULL,
+sigma=NULL, alpha=0.1,
                      type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
                      gridrange=c(-100,100), bits=NULL, verbose=FALSE) {
-  
+    
+ family = match.arg(family)
   this.call = match.call()
   type = match.arg(type)
+ 
+  if(family=="binomial")  {
+      if(type!="partial") stop("Only type= partial allowed with binomial family")
+       out=fixedLogitLassoInf(x,y,beta,lambda,alpha=alpha, type="partial", tol.beta=tol.beta, tol.kkt=tol.kkt,
+                     gridrange=gridrange, bits=bits, verbose=verbose,this.call=this.call)
+                      return(out)
+                    }
+else if(family=="cox")  {
+    if(type!="partial") stop("Only type= partial allowed with Cox family")
+     out=fixedCoxLassoInf(x,y,status,beta,lambda,alpha=alpha, type="partial",tol.beta=tol.beta,
+          tol.kkt=tol.kkt, gridrange=gridrange, bits=bits, verbose=verbose,this.call=this.call)               
+                      return(out)
+                    }
+ 
+else{
+
+ 
+ 
   checkargs.xy(x,y)
   if (missing(beta) || is.null(beta)) stop("Must supply the solution beta")
   if (missing(lambda) || is.null(lambda)) stop("Must supply the tuning parameter value lambda") 
@@ -121,6 +141,7 @@ fixedLassoInf <- function(x, y, beta, lambda, intercept=TRUE, sigma=NULL, alpha=
   class(out) = "fixedLassoInf"
   return(out)
 }
+}
 
 #############################
 
diff --git a/selectiveInference/R/funs.fixedCox.R b/selectiveInference/R/funs.fixedCox.R
new file mode 100644
index 0000000..82eeb61
--- /dev/null
+++ b/selectiveInference/R/funs.fixedCox.R
@@ -0,0 +1,111 @@
+fixedCoxLassoInf=function(x,y,status,beta,lambda,alpha=.1, type=c("partial"),tol.beta=1e-5, tol.kkt=0.1,
+                     gridrange=c(-100,100), bits=NULL, verbose=FALSE,this.call=NULL){
+
+
+ checkargs.xy(x,y)
+ if(is.null(status)) stop("Must supply `status' argument")
+if( sum(status==0)+sum(status==1)!=length(y)) stop("status vector must have values 0 or 1")
+  if (missing(beta) || is.null(beta)) stop("Must supply the solution beta")
+  if (missing(lambda) || is.null(lambda)) stop("Must supply the tuning parameter value lambda") 
+  checkargs.misc(beta=beta,lambda=lambda,alpha=alpha,
+                 gridrange=gridrange,tol.beta=tol.beta,tol.kkt=tol.kkt)
+  if (!is.null(bits) && !requireNamespace("Rmpfr",quietly=TRUE)) {
+    warning("Package Rmpfr is not installed, reverting to standard precision")
+    bits = NULL
+  }
+
+    n=nrow(x)
+    p=ncol(x)
+     nvar=sum(beta!=0)
+    pv=vlo=vup=sd=rep(NA, nvar)
+     ci=tailarea=matrix(NA,nvar,2)
+
+
+   m=beta!=0
+vars=which(m)
+if(sum(m)>0){
+    bhat=beta[beta!=0] #penalized coefs just for active variables
+s2=sign(bhat)
+
+ #check KKT
+    
+   aaa=coxph(Surv(y,status)~x[,m],init=bhat,iter.max=0)
+    res=residuals(aaa,type="score")
+if(!is.matrix(res)) res=matrix(res,ncol=1)
+scor=colSums(res)
+    g=(scor+lambda*s2)/(2*lambda)
+#    cat(c(g,lambda,tol.kkt),fill=T)
+     if (any(abs(g) > 1+tol.kkt) )
+    warning(paste("Solution beta does not satisfy the KKT conditions",
+                  "(to within specified tolerances)"))
+
+    
+MM=vcov(aaa)
+
+ bbar=(bhat+lambda*MM%*%s2)
+ A1=-(mydiag(s2))
+b1= -(mydiag(s2)%*%MM)%*%s2*lambda
+
+   temp=max(A1%*%bbar-b1)
+
+
+# compute p-values
+    for(jj in 1:length(bbar)){
+      vj=rep(0,length(bbar));vj[jj]=1
+
+
+      junk=mypoly.pval.lee(bbar,A1,b1,vj,MM)
+
+       pv[jj] = junk$pv
+      vlo[jj]=junk$vlo
+       vup[jj]=junk$vup
+       sd[jj]=junk$sd
+
+      junk2=mypoly.int.lee(bbar,vj,vlo[jj],vup[jj],sd[jj],alpha)
+       ci[jj,]=junk2$int
+       tailarea[jj,] = junk2$tailarea
+     
+  }
+    fit0=coxph(Surv(y,status)~x[,m])
+      coef0=fit0$coef
+      se0=sqrt(diag(fit0$var))
+      zscore0=coef0/se0
+    
+   out = list(lambda=lambda,pv=pv,ci=ci,
+    tailarea=tailarea,vlo=vlo,vup=vup,sd=sd,
+    vars=vars,alpha=alpha,coef0=coef0,zscore0=zscore0,
+    call=this.call)
+  class(out) = "fixedCoxLassoInf" 
+}
+return(out)
+}
+
+
+
+print.fixedCoxLassoInf <- function(x, tailarea=TRUE, ...) {
+  cat("\nCall:\n")
+  dput(x$call)
+
+  cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n",
+              x$sigma))
+  
+  cat(sprintf("\nTesting results at lambda = %0.3f, with alpha = %0.3f\n",x$lambda,x$alpha))
+  cat("",fill=T)
+  tab = cbind(x$vars,
+    round(x$coef0,3),
+    round(x$zscore0,3),
+    round(x$pv,3),round(x$ci,3))
+  colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
+  if (tailarea) {
+    tab = cbind(tab,round(x$tailarea,3))
+    colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+  }
+  rownames(tab) = rep("",nrow(tab))
+  print(tab)
+ 
+  cat(sprintf("\nNote: coefficients shown are %s regression coefficients\n",
+              ifelse(x$type=="partial","partial","full")))
+  invisible()
+}
+
+
diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
new file mode 100644
index 0000000..ea476b1
--- /dev/null
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -0,0 +1,145 @@
+
+fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.beta=1e-5, tol.kkt=0.1,
+                     gridrange=c(-100,100), bits=NULL, verbose=FALSE,this.call=NULL){
+    
+    
+  type = match.arg(type)
+  checkargs.xy(x,y)
+  if (missing(beta) || is.null(beta)) stop("Must supply the solution beta")
+  if (missing(lambda) || is.null(lambda)) stop("Must supply the tuning parameter value lambda") 
+  checkargs.misc(beta=beta,lambda=lambda,alpha=alpha,
+                 gridrange=gridrange,tol.beta=tol.beta,tol.kkt=tol.kkt)
+  if (!is.null(bits) && !requireNamespace("Rmpfr",quietly=TRUE)) {
+    warning("Package Rmpfr is not installed, reverting to standard precision")
+    bits = NULL
+  }
+
+     
+     n=length(y)
+     p=ncol(x)
+ # I assume that intcpt was used
+     if(length(beta)!=p+1) stop("beta must be of length ncol(x)+1, that is, it should include an intercept")
+     nvar=sum(beta[-1]!=0)
+     pv=vlo=vup=sd=rep(NA, nvar)
+     ci=tailarea=matrix(NA,nvar,2)
+    
+#do we need to worry about standardization?
+
+#  obj = standardize(x,y,TRUE,FALSE)
+  #  x = obj$x
+  #  y = obj$y
+  
+ m=beta[-1]!=0  #active set
+         
+    bhat=c(beta[1],beta[-1][beta[-1]!=0]) # intcpt plus active vars
+     s2=sign(bhat)
+     lam2m=diag(c(0,rep(lambda,sum(m))))
+ 
+
+    xxm=cbind(1,x[,m])
+    
+   etahat = xxm %*% bhat
+   prhat = as.vector(exp(etahat) / (1 + exp(etahat)))
+   ww=prhat*(1-prhat)
+   w=diag(ww)
+      
+#check KKT
+   z=etahat+(y-prhat)/ww
+       g=  t(x)%*%w%*%(z-etahat)/lambda
+     if (any(abs(g) > 1+tol.kkt) )
+    warning(paste("Solution beta does not satisfy the KKT conditions",
+                  "(to within specified tolerances)"))
+
+ vars = which(abs(beta[-1]) > tol.beta / sqrt(colSums(x^2)))
+  if(length(vars)==0){
+      cat("Empty model",fill=T)
+      return()
+  }
+  if (any(sign(g[vars]) != sign(beta[-1][vars])))
+    warning(paste("Solution beta does not satisfy the KKT conditions",
+                  "(to within specified tolerances). You might try rerunning",
+                  "glmnet with a lower setting of the",
+                  "'thresh' parameter, for a more accurate convergence."))
+  
+ #constraints for active variables             
+  MM=solve(t(xxm)%*%w%*%xxm)
+ 
+   bbar=(bhat-lam2m%*%MM%*%s2)
+ 
+
+   A1=-(mydiag(s2))[-1,-1]
+  b1= ((mydiag(s2)%*%MM)%*%s2*lambda)[-1]
+                  
+ tol.poly = 0.01 
+  if (max(A1 %*% bbar[-1] - b1) > tol.poly)
+    stop(paste("Polyhedral constraints not satisfied; you must recompute beta",
+               "more accurately. With glmnet, make sure to use exact=TRUE in coef(),",
+               "and check whether the specified value of lambda is too small",
+               "(beyond the grid of values visited by glmnet).",
+               "You might also try rerunning glmnet with a lower setting of the",
+               "'thresh' parameter, for a more accurate convergence."))
+
+
+  
+    for(jj in 1:sum(m)){
+       vj=c(rep(0,sum(m)));vj[jj]=1
+# compute p-values
+      junk=mypoly.pval.lee(bbar[-1],A1,b1,vj,MM[-1,-1])
+      pv[jj] = junk$pv
+ 
+
+   vlo[jj]=junk$vlo
+   vup[jj]=junk$vup
+       sd[jj]=junk$sd
+  #  junk2=mypoly.int.lee(bbar[-1], A1, b1,vj,MM[-1,-1],alpha=.1)
+     junk2=mypoly.int.lee(bbar[-1],vj,vlo[jj],vup[jj],sd[jj],alpha=.1)
+
+     ci[jj,]=junk2$int
+     tailarea[jj,] = junk2$tailarea
+   }
+
+  fit0=glm(y~x[,m],family="binomial")
+  sfit0=summary(fit0)
+      coef0=fit0$coef[-1]
+      se0=sqrt(diag(sfit0$cov.scaled)[-1])
+      zscore0=coef0/se0
+      
+  out = list(type=type,lambda=lambda,pv=pv,ci=ci,
+    tailarea=tailarea,vlo=vlo,vup=vup,sd=sd,
+    vars=vars,alpha=alpha,coef0=coef0,zscore0=zscore0,
+    call=this.call)
+  class(out) = "fixedLogitLassoInf"
+  return(out)
+
+ }
+
+
+
+print.fixedLogitLassoInf <- function(x, tailarea=TRUE, ...) {
+  cat("\nCall:\n")
+  dput(x$call)
+
+  cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n",
+              x$sigma))
+  
+  cat(sprintf("\nTesting results at lambda = %0.3f, with alpha = %0.3f\n",x$lambda,x$alpha))
+  cat("",fill=T)
+  tab = cbind(x$vars,
+    round(x$coef0,3),
+    round(x$zscore0,3),
+    round(x$pv,3),round(x$ci,3))
+  colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
+  if (tailarea) {
+    tab = cbind(tab,round(x$tailarea,3))
+    colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+  }
+  rownames(tab) = rep("",nrow(tab))
+  print(tab)
+ 
+  cat(sprintf("\nNote: coefficients shown are %s regression coefficients\n",
+              ifelse(x$type=="partial","partial","full")))
+  invisible()
+}
+
+
+
diff --git a/selectiveInference/R/funs.inf.R b/selectiveInference/R/funs.inf.R
index 6a48708..423b4c3 100644
--- a/selectiveInference/R/funs.inf.R
+++ b/selectiveInference/R/funs.inf.R
@@ -244,3 +244,56 @@ aicStop <- function(x, y, action, df, sigma, mult=2, ntimes=2) {
   
   return(list(khat=khat,G=G,u=u,aic=aic,stopped=(i<k)))
 }
+
+#these next two functions are used by the binomial and Cox options of fixedLassoInf
+
+mypoly.pval.lee=
+function(y, A, b, eta, Sigma, bits=NULL) {
+    # compute pvalues from poly lemma:  full version from Lee et al for full matrix Sigma
+    nn=length(y)
+    eta=as.vector(eta)
+  temp = sum(eta*y)
+   vv=as.numeric(matrix(eta,nrow=1,ncol=nn)%*%Sigma%*%eta)
+   cc = Sigma%*%eta/vv
+   
+ z=(diag(nn)-matrix(cc,ncol=1)%*%eta)%*%y
+    rho=A%*%cc
+   
+  vec = (b- A %*% z)/rho
+  vlo = suppressWarnings(max(vec[rho<0]))
+  vup = suppressWarnings(min(vec[rho>0]))
+  sd=sqrt(vv)
+  pv = tnorm.surv(temp,0,sd,vlo,vup,bits)
+  return(list(pv=pv,vlo=vlo,vup=vup,sd=sd))
+}
+
+
+
+mypoly.int.lee=
+   function(y,eta,vlo,vup,sd, alpha, gridrange=c(-100,100),gridpts=100, griddepth=2, flip=FALSE, bits=NULL) {
+    # compute sel intervals from poly lemmma, full version from Lee et al for full matrix Sigma
+
+  temp = sum(eta*y)
+  
+  xg = seq(gridrange[1]*sd,gridrange[2]*sd,length=gridpts)
+  fun = function(x) { tnorm.surv(temp,x,sd,vlo,vup,bits) }
+
+  int = grid.search(xg,fun,alpha/2,1-alpha/2,gridpts,griddepth)
+  tailarea = c(fun(int[1]),1-fun(int[2]))
+
+  if (flip) {
+    int = -int[2:1]
+    tailarea = tailarea[2:1]
+  }
+ 
+  return(list(int=int,tailarea=tailarea))
+}
+
+
+
+mydiag=function(x){
+    if(length(x)==1) out=x
+    if(length(x)>1) out=diag(x)
+       return(out)
+   }
+
diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index ac1b9a0..3c029f7 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -9,7 +9,8 @@ Compute p-values and confidence intervals for the lasso estimate, at a
 fixed value of the tuning parameter lambda
 }
 \usage{
-fixedLassoInf(x, y, beta, lambda, intercept=TRUE, sigma=NULL, alpha=0.1,
+fixedLassoInf(x, y, beta, lambda, family = c("gaussian", "binomial", 
+    "cox"),intercept=TRUE, sigma=NULL, alpha=0.1,
          type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
          gridrange=c(-100,100), bits=NULL, verbose=FALSE) 
 }
@@ -37,11 +38,16 @@ Estimated lasso coefficients (e.g., from glmnet). This is of length p
   \item{lambda}{
 Value of lambda used to compute beta. See the above warning
 } 
+
+\item{family}{Response type: "gaussian" (default), "binomial", or 
+    "cox" (for censored survival data) }
+
 \item{sigma}{
 Estimate of error standard deviation. If NULL (default), this is estimated 
 using the mean squared residual of the full least squares fit when n >= 2p, and 
 using the standard deviation of y when n < 2p. In the latter case, the user 
-should use \code{\link{estimateSigma}} function for a more accurate estimate
+should use \code{\link{estimateSigma}} function for a more accurate estimate.
+Not used for family= "binomial", or  "cox"
 }
 \item{alpha}{
 Significance level for confidence intervals (target is miscoverage alpha/2 in each tail)
@@ -181,4 +187,56 @@ out = fixedLassoInf(xs,y,beta,lambda,sigma=sigma)
 #rescale conf points to undo the penalty factor
 out$ci=t(scale(t(out$ci),FALSE,pf[out$vars]))
 out
-}
+
+#logistic model
+set.seed(43)
+     n = 50
+     p = 10
+     sigma = 1
+     
+     x = matrix(rnorm(n*p),n,p)
+     x=scale(x,TRUE,TRUE)
+     
+     beta = c(3,2,rep(0,p-2))
+     y = x%*%beta + sigma*rnorm(n)
+     y=1*(y>mean(y))
+     # first run glmnet
+     gfit = glmnet(x,y,standardize=FALSE,family="binomial")
+     
+     # extract coef for a given lambda; note the 1/n factor!
+     # (and here  we DO  include the intercept term)
+     lambda = .8
+     beta = coef(gfit, s=lambda/n, exact=TRUE)
+     
+     # compute fixed lambda p-values and selection intervals
+     out = fixedLassoInf(x,y,beta,lambda,family="binomial")
+     out
+
+#Cox model
+set.seed(43)
+     n = 50
+     p = 10
+     sigma = 1
+     
+     x = matrix(rnorm(n*p),n,p)
+     x=scale(x,TRUE,TRUE)
+     
+     beta = c(3,2,rep(0,p-2))
+     tim = as.vector(x\%*\%beta + sigma*rnorm(n))
+     tim= tim-min(tim)+1
+status=sample(c(0,1),size=n,replace=T)
+     # first run glmnet
+
+
+     gfit = glmnet(x,Surv(tim,status),standardize=FALSE,family="cox")
+   
+     # extract coef for a given lambda; note the 1/n factor!
+   
+     lambda = 1.5
+     beta = as.numeric(coef(gfit, s=lambda/n, exact=TRUE))
+     
+     # compute fixed lambda p-values and selection intervals
+     out = fixedLassoInf(x,tim,beta,lambda,status=status,family="cox")
+     out
+}
+ 
\ No newline at end of file
diff --git a/tests/test.fixed.R b/tests/test.fixed.R
index e2c5931..f1ce6cf 100644
--- a/tests/test.fixed.R
+++ b/tests/test.fixed.R
@@ -1,21 +1,26 @@
 library(selectiveInference)
 #library(selectiveInference,lib.loc="/Users/tibs/dropbox/git/R/mylib")
 
+library(glmnet)
+library(MASS)
+library(scalreg)
 #options(error=dump.frames)
 #attach("/Users/tibs/dropbox/PAPERS/lasso/lasso3/.RData")
 
 #####
+#gaussian
 n=50
 p=10
 sigma=.7
 beta=c(3,2,0,0,rep(0,p-4))
 set.seed(43)
-nsim = 100
+nsim = 200
 pvals <- matrix(NA, nrow=nsim, ncol=p)
 x = matrix(rnorm(n*p),n,p)
 x = scale(x,T,T)/sqrt(n-1)
 mu = x%*%beta
 for (i in 1:nsim) {
+    cat(i)
 y=mu+sigma*rnorm(n)
 #y=y-mean(y)
 # first run  glmnet
@@ -30,11 +35,11 @@ pvals[i, which(beta != 0)] <- aa$pv
 nulls = which(!is.na(pvals[,1]) & !is.na(pvals[,2]))
 np = pvals[nulls,-(1:2)]
 mean(np[!is.na(np)] < 0.1)
-
+o=!is.na(np)
+plot((1:sum(o))/sum(o),sort(np))
+abline(0,1)
 #####
-library(selectiveInference)
-library(MASS)
-library(scalreg)
+
 
 S <- diag(10)
 n <- 100
@@ -55,7 +60,75 @@ for(i in 1:100){
 p <- pval[, -(1:2)]
 mean(p[p < 1] < 0.05)
 
-#####
+##logistic
+
+n=50
+p=10
+beta=c(3,2,0,0,rep(0,p-4))
+beta=rep(0,p)
+set.seed(3)
+nsim = 200
+pvals=matrix(NA, nrow=nsim, ncol=p)
+ci=array(NA,c(nsim,p,2))
+x = matrix(rnorm(n*p),n,p)
+x = scale(x,T,T)/sqrt(n-1)
+mu = x%*%beta
+for (ii in 1:nsim) {
+    cat(ii)
+y=mu+rnorm(n)
+y=1*(y>mean(y))
+# first run  glmnet
+gfit=glmnet(x,y,standardize=F,thresh=1e-8,family="binomial")
+lambda = .25
+#extract coef for a given lambda; Note the 1/n factor!
+beta = as.numeric(coef(gfit, s=lambda/n, exact=TRUE))
+# compute fixed lambda p-values and selection intervals
+    aa = fixedLassoInf(x,y,beta,lambda,family="binomial")
+    pvals[ii, which(beta[-1] != 0)] <- aa$pv
+   ci[ii,which(beta[-1] != 0),]=aa$ci
+}
+
+o=!is.na(pvals)
+plot((1:sum(o))/sum(o),sort(pvals))
+abline(0,1)
+o=ci[,1,1]>0 | ci[,1,2]<0
+mean(o,na.rm=T)
+
+
+## cox
+
+n=50
+p=10
+#beta=c(6,6,0,0,rep(0,p-4))
+beta=rep(0,p)
+set.seed(3)
+nsim = 200
+pvals=matrix(NA, nrow=nsim, ncol=p)
+ci=array(NA,c(nsim,p,2))
+x = matrix(rnorm(n*p),n,p)
+x = scale(x,T,T)/sqrt(n-1)
+mu = x%*%beta
+for (ii in 1:nsim) {
+    cat(ii)
+tim=as.vector(mu+rnorm(n))+10
+status=sample(c(0,1),size=n,replace=T)
+    lambda=0.2
+    y=cbind(time=tim,status=status)
+    gfit=glmnet(x,y,family="cox",standardize=FALSE)
+     b=as.numeric(coef(gfit,s=lambda/n,exact=TRUE))
+ 
+   aa= fixedLassoInf(x,tim,b,lambda,status=status,family="cox")
+      
+pvals[ii, which(b != 0)] <- aa$pv[1:sum(!is.na(aa$pv))]
+  ci[ii,which(b != 0),]=aa$ci
+}
+
+o=!is.na(pvals)
+plot((1:sum(o))/sum(o),sort(pvals))
+abline(0,1)
+
+
+#####more Gaussian
 
 a=lar(x,y)
 aa=larInf(a)
@@ -71,13 +144,14 @@ set.seed(3)
 n=50
 p=10
 sigma=2
+nsim=100
 
 x=matrix(rnorm(n*p),n,p)
 #x=scale(x,T,T)/sqrt(n-1)    #try with and without standardization
 
 beta=c(5,4,3,2,1,rep(0,p-5))
 
-nsim=100
+
 seeds=sample(1:9999,size=nsim)
 pv=rep(NA,nsim)
 ci=matrix(NA,nsim,2)
@@ -94,12 +168,12 @@ for(ii in 1:nsim){
      bhat = predict(gfit, s=lambda/n,type="coef",exact=F)[-1]
 
      junk= fixedLassoInf(x,y,bhat,lambda,sigma=sigma)
-    pv[ii]=junk$pv[1]
-   # oo=junk$pred # for old package
-    oo=junk$var   # for new package
-     btrue[ii]=lsfit(x[,oo],mu)$coef[2]
-     ci[ii,]=junk$ci[1,]
+    pvals[ii, which(bhat != 0)] <- aa$pv[1:sum(!is.na(aa$pv))]
+  ci[ii,which(bhat != 0),]=aa$ci
+   
 }
+o=!is.na(pvals)
+plot((1:sum(o))/sum(o),sort(pvals))
 
 sum(ci[,1]> btrue)
 sum(ci[,2]< btrue)

From 450347222c8c1e501b870d8f49bc5d328a4a588b Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Fri, 25 Mar 2016 09:55:57 -0700
Subject: [PATCH 126/493] rob

---
 selectiveInference/man/fixedLassoInf.Rd | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index 3c029f7..00b4c81 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -54,7 +54,7 @@ Significance level for confidence intervals (target is miscoverage alpha/2 in ea
 }
 \item{intercept}{
 Was the lasso problem solved (e.g., by glmnet) with an intercept in the model? 
-Default is TRUE
+Default is TRUE. Must be TRUE for "binomial" family. Not used for 'cox" family, where no intercept is assumed.
 }
 \item{type}{Contrast type for p-values and confidence intervals: default is
 "partial"---meaning that the contrasts tested are the partial population 
@@ -89,6 +89,7 @@ Print out progress along the way? Default is FALSE}
 \details{
 This function computes selective p-values and confidence intervals for the lasso,
 given a fixed value of the tuning parameter lambda. 
+Three different response types are supported:  gaussian, binomial and Cox.
 The confidence interval construction involves numerical search and can be fragile:
 if the observed statistic is too close to either end of the truncation interval
 (vlo and vup, see references), then one or possibly both endpoints of the interval of 
@@ -123,7 +124,11 @@ is not allowed.
 
 \references{
 Jason Lee, Dennis Sun, Yuekai Sun, and Jonathan Taylor (2013). 
+
 Exact post-selection inference, with application to the lasso. arXiv:1311.6238.
+ Jonathan Taylor and Robert Tibshirani (2016) Post-selection inference for L1-penalized likelihood models.
+arXiv:1602.07358
+
 }
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 

From 4f67765c0d1a7c2de310b621acbfeecdd96c5c80 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Fri, 25 Mar 2016 11:24:13 -0700
Subject: [PATCH 127/493] rob

---
 selectiveInference/man/fixedLassoInf.Rd      |  2 +-
 selectiveInference/man/selectiveInference.Rd | 53 +++++++++++++++++++-
 2 files changed, 52 insertions(+), 3 deletions(-)

diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index 00b4c81..31e47b4 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -124,8 +124,8 @@ is not allowed.
 
 \references{
 Jason Lee, Dennis Sun, Yuekai Sun, and Jonathan Taylor (2013). 
-
 Exact post-selection inference, with application to the lasso. arXiv:1311.6238.
+
  Jonathan Taylor and Robert Tibshirani (2016) Post-selection inference for L1-penalized likelihood models.
 arXiv:1602.07358
 
diff --git a/selectiveInference/man/selectiveInference.Rd b/selectiveInference/man/selectiveInference.Rd
index 7d2ca57..fde2219 100644
--- a/selectiveInference/man/selectiveInference.Rd
+++ b/selectiveInference/man/selectiveInference.Rd
@@ -20,7 +20,8 @@ This package provides tools for inference after selection, in forward stepwise
 regression, least angle regression, the lasso, and the many normal means problem. 
 The functions compute p-values and selection intervals that properly account for 
 the inherent selection carried out by the procedure. These have exact finite sample 
-type I error and coverage under Gaussian errors.
+type I error and coverage under Gaussian errors. For the logistic and Cox familes (fixedLassoInf),
+ the coverage is asymptotically valid
 
 This R package was developed as part of the selective inference software project
 in Python and R:
@@ -94,7 +95,7 @@ out.aic
 out.fix = fsInf(fsfit,type="all",k=5)
 out.fix
 
-## NOT RUN---lasso at fixed lambda
+## NOT RUN---lasso at fixed lambda- Gaussian family
 ## first run glmnet
 # gfit = glmnet(x,y)
 
@@ -107,6 +108,54 @@ out.fix
 # out = fixedLassoInf(x,y,beta,lambda,sigma=sigma)
 # out
 
+
+#lasso at fixed lambda- logistic family
+#set.seed(43)
+  #   n = 50
+  #   p = 10
+  #   sigma = 1
+     
+ #    x = matrix(rnorm(n*p),n,p)
+     x=scale(x,TRUE,TRUE)
+  #   
+#     beta = c(3,2,rep(0,p-2))
+ #    y = x%*%beta + sigma*rnorm(n)
+ #    y=1*(y>mean(y))
+     # first run glmnet
+ #    gfit = glmnet(x,y,standardize=FALSE,family="binomial")
+     
+     # extract coef for a given lambda; note the 1/n factor!
+     # (and here  we DO  include the intercept term)
+ #    lambda = .8
+ #    beta = coef(gfit, s=lambda/n, exact=TRUE)
+     
+ #    # compute fixed lambda p-values and selection intervals
+ #    out = fixedLassoInf(x,y,beta,lambda,family="binomial")
+ #    out
+
+##lasso at fixed lambda- Cox family
+#set.seed(43)
+#     n = 50
+ #    p = 10
+ #    sigma = 1
+     
+ #    x = matrix(rnorm(n*p),n,p)
+  #   x=scale(x,TRUE,TRUE)
+     
+ #    beta = c(3,2,rep(0,p-2))
+ #    tim = as.vector(x\%*\%beta + sigma*rnorm(n))
+  #   tim= tim-min(tim)+1
+#status=sample(c(0,1),size=n,replace=T)
+     # first run glmnet
+   #  gfit = glmnet(x,Surv(tim,status),standardize=FALSE,family="cox")
+     # extract coef for a given lambda; note the 1/n factor!
+   
+  #   lambda = 1.5
+  #   beta = as.numeric(coef(gfit, s=lambda/n, exact=TRUE))
+     
+     # compute fixed lambda p-values and selection intervals
+   #  out = fixedLassoInf(x,tim,beta,lambda,status=status,family="cox")
+   #  out
 ## NOT RUN---many normal means
 # set.seed(12345)
 # n = 100 

From 19f51a1efc5c8a52e74fd24cdd500bffa90a263d Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Fri, 25 Mar 2016 11:29:11 -0700
Subject: [PATCH 128/493] rob

---
 selectiveInference/man/selectiveInference.Rd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/man/selectiveInference.Rd b/selectiveInference/man/selectiveInference.Rd
index fde2219..851ee91 100644
--- a/selectiveInference/man/selectiveInference.Rd
+++ b/selectiveInference/man/selectiveInference.Rd
@@ -7,7 +7,7 @@ Tools for selective inference
 \description{
 Functions to perform post-selection inference for forward
 stepwise regression, least angle regression, the lasso and the 
-many normal means problem
+many normal means problem. The lasso function also supports logistic regression and the Cox model.
 }
 \details{
 \tabular{ll}{

From f6e65a44de1eeac9cae5c8fc6330947e5928c8d9 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Fri, 25 Mar 2016 11:30:49 -0700
Subject: [PATCH 129/493] rob

---
 selectiveInference/man/selectiveInference.Rd | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/selectiveInference/man/selectiveInference.Rd b/selectiveInference/man/selectiveInference.Rd
index 851ee91..6e038d4 100644
--- a/selectiveInference/man/selectiveInference.Rd
+++ b/selectiveInference/man/selectiveInference.Rd
@@ -68,6 +68,11 @@ Exact post-selection inference, with application to the lasso. arXiv:1311.6238.
 Stephen Reid, Jonathan Taylor, and Rob Tibshirani (2014).
 Post-selection point and interval estimation of signal sizes in Gaussian samples.
 arXiv:1405.3340.
+
+
+Jonathan Taylor and Robert Tibshirani (2016) Post-selection inference for L1-penalized likelihood models.
+arXiv:1602.07358
+
 }
 
 \examples{

From 4e3d614b45023c4447c57ff43b00baaca161a8da Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Sun, 27 Mar 2016 21:26:22 -0700
Subject: [PATCH 130/493] rob

---
 selectiveInference/man/fsInf_maxZ.Rd | 86 ++++++++++++++++++++++++++++
 1 file changed, 86 insertions(+)
 create mode 100644 selectiveInference/man/fsInf_maxZ.Rd

diff --git a/selectiveInference/man/fsInf_maxZ.Rd b/selectiveInference/man/fsInf_maxZ.Rd
new file mode 100644
index 0000000..89aa886
--- /dev/null
+++ b/selectiveInference/man/fsInf_maxZ.Rd
@@ -0,0 +1,86 @@
+\name{fsInf_maxZ}
+\alias{fsInf_maxZ}
+\title{
+Selective inference for forward stepwise regression
+}
+\description{
+Computes maxZ selective p-values and confidence intervals for forward 
+stepwise regression
+}
+\usage{
+                    
+fsInf_maxZ(obj, sigma=NULL, alpha=0.1, k=NULL,   ndraw=8000, burnin=2000,verbose=FALSE)
+    
+}
+
+\arguments{
+  \item{obj}{
+Object returned by \code{\link{fs}} function
+}
+\item{sigma}{
+Estimate of error standard deviation. If NULL (default), this is estimated 
+using the mean squared residual of the full least squares fit when n >= 2p, and 
+using the standard deviation of y when n < 2p. In the latter case, the user 
+should use \code{\link{estimateSigma}} function for a more accurate estimate
+}
+\item{alpha}{
+Significance level for confidence intervals (target is miscoverage alpha/2 in each tail)
+}
+\item{k}{
+See "type" argument below. Default is NULL, in which case k is taken to be the
+the number of steps computed in the forward stepwise path
+}
+\item{ndraw}{Number of Monte Carlo samples generated}
+\item{burnin}{
+Number of samples discarded at the beginning of the chain
+}
+\item{verbose}{Print out progress along the way? Default is FALSE}
+}
+
+\details{
+This function computes selective maxZ p-values
+for forward stepwise regression. These p-values are independent the under null,
+so that stopping via the forwardStop rule yields guaranteed FDR control
+}
+
+\value{  
+\item{pv}{P-values for each model in the sequence}
+\item{k}{Value of k specified in call}
+\item{khat}{When type is "active", this is an estimated stopping point
+declared by \code{\link{forwardStop}}}
+\item{sigma}{Value of error standard deviation (sigma) used}
+\item{vars}{Variables in active set}
+\item{sign}{Signs of active coefficients}
+\item{alpha}{Desired coverage (alpha/2 in each tail)}
+\item{realized_maxZ}{Value of maxZ statistic computed at each step}
+\item{call}{The call to fsInf_maxZ}
+}
+
+\references{
+Will Fithian, Jonathan Taylor,  Ryan Tibshirani, and Rob Tibshirani (2015).
+Selective sequential model selection.  arXiv:1512.02565..
+
+
+}
+
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\seealso{\code{\link{fs}}}
+
+\examples{
+set.seed(33)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run forward stepwise
+fsfit = fs(x,y)
+
+# compute sequential p-values a
+# (sigma estimated from full model)
+out.seq = fsInf_maxZ(fsfit)
+out.seq
+}

From fa20cc9fd6d8521c01e5e1442d950f396d51ceab Mon Sep 17 00:00:00 2001
From: tibshirani <tibs@stanford.edu>
Date: Mon, 28 Mar 2016 15:17:24 -0700
Subject: [PATCH 131/493] test of forking

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 1a50e5b..41b7a4e 100644
--- a/README.md
+++ b/README.md
@@ -5,6 +5,8 @@ Maintainer: Rob Tibshirani <tibs@stanford.edu>
 
 New tools for inference after selection, for use with forward stepwise regression, least angle regression, the lasso, and the many means problem. The package is available on [CRAN](http://cran.r-project.org/web/packages/selectiveInference/). See [this paper](http://www.pnas.org/content/112/25/7629.full) for a high level introduction to selective inference.
 
+test
+
 Code is in the directory selectiveInference/R.
 * funs.common.R: Basic functions used by many other functions, such as standardization.
 * funs.fixed.R: Inference for LASSO at a fixed, deterministic value of lambda.

From 2843f21d7bf241456c1ad3bb796100f0c38113e9 Mon Sep 17 00:00:00 2001
From: tibshirani <tibs@stanford.edu>
Date: Mon, 28 Mar 2016 15:21:24 -0700
Subject: [PATCH 132/493] undoing test

---
 README.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/README.md b/README.md
index 41b7a4e..1a50e5b 100644
--- a/README.md
+++ b/README.md
@@ -5,8 +5,6 @@ Maintainer: Rob Tibshirani <tibs@stanford.edu>
 
 New tools for inference after selection, for use with forward stepwise regression, least angle regression, the lasso, and the many means problem. The package is available on [CRAN](http://cran.r-project.org/web/packages/selectiveInference/). See [this paper](http://www.pnas.org/content/112/25/7629.full) for a high level introduction to selective inference.
 
-test
-
 Code is in the directory selectiveInference/R.
 * funs.common.R: Basic functions used by many other functions, such as standardization.
 * funs.fixed.R: Inference for LASSO at a fixed, deterministic value of lambda.

From 44a0627897f4f28a0542944c7d9c0503872b54d1 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 6 Apr 2016 17:11:24 -0700
Subject: [PATCH 133/493] BF: conditioning on too much in forward stepwise,
 c(mathematical bug rather than code bug); also added a comment about
 one-sided pvalues

---
 selectiveInference/R/funs.fs.R          | 3 ++-
 selectiveInference/man/fixedLassoInf.Rd | 2 +-
 selectiveInference/man/fsInf.Rd         | 2 +-
 3 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index cb63da2..b75923d 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -188,7 +188,8 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
 
     if (gi + 2*p > nrow(Gamma)) Gamma = rbind(Gamma,matrix(0,2*p+gbuf,n))
     working_x = t(sign_score*t(working_x))
-    Gamma[gi+Seq(1,p-r),] = t(working_x); gi = gi+p-r
+    #Gamma[gi+Seq(1,p-r),] = t(working_x); gi = gi+p-r
+    Gamma[gi+Seq(1,p-r-1),] = t(working_x[,i_hit]+working_x[,-i_hit]); gi = gi+p-r-1
     Gamma[gi+Seq(1,p-r-1),] = t(working_x[,i_hit]-working_x[,-i_hit]); gi = gi+p-r-1
     Gamma[gi+1,] = t(working_x[,i_hit]); gi = gi+1
 
diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index 31e47b4..26b276f 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -108,7 +108,7 @@ is not allowed.
 \value{  
 \item{type}{Type of coefficients tested (partial or full)}
 \item{lambda}{Value of tuning parameter lambda used}
-\item{pv}{P-values for active variables}
+\item{pv}{One-sided P-values for active variables, uses the fact we have conditioned on the sign.}
 \item{ci}{Confidence intervals}
 \item{tailarea}{Realized tail areas (lower and upper) for each confidence interval}
 \item{vlo}{Lower truncation limits for statistics}
diff --git a/selectiveInference/man/fsInf.Rd b/selectiveInference/man/fsInf.Rd
index b86584d..613bc5a 100644
--- a/selectiveInference/man/fsInf.Rd
+++ b/selectiveInference/man/fsInf.Rd
@@ -82,7 +82,7 @@ to alpha/2, and can be used for error-checking purposes.
 \item{khat}{When type is "active", this is an estimated stopping point
 declared by \code{\link{forwardStop}}; when type is "aic", this is the
 value chosen by the modified AIC scheme}
-\item{pv}{P-values for active variables}
+\item{pv}{One sided P-values for active variables, uses the sign that a variable entered the model with.}
 \item{ci}{Confidence intervals}
 \item{tailarea}{Realized tail areas (lower and upper) for each confidence interval}
 \item{vlo}{Lower truncation limits for statistics}

From f6e9a442d6d12326767c37dc4ebc51782c09d5f8 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 6 Apr 2016 23:23:06 -0700
Subject: [PATCH 134/493] removing tmvtnorm dependency in DESCRIPTION

---
 selectiveInference/DESCRIPTION | 1 -
 1 file changed, 1 deletion(-)

diff --git a/selectiveInference/DESCRIPTION b/selectiveInference/DESCRIPTION
index af80d99..58232f1 100644
--- a/selectiveInference/DESCRIPTION
+++ b/selectiveInference/DESCRIPTION
@@ -9,7 +9,6 @@ Maintainer: Rob Tibshirani <tibs@stanford.edu>
 Depends:
     glmnet,
     intervals,
-    tmvtnorm,
     survival
 Suggests:
     Rmpfr

From 235dfc988cd2ed964cdfa7fc8670a89cedefbea9 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 7 Apr 2016 00:41:57 -0700
Subject: [PATCH 135/493] a few questions about logit and cox, edited examples
 for logit and cox

---
 selectiveInference/R/funs.fixedCox.R    | 6 ++++++
 selectiveInference/R/funs.fixedLogit.R  | 1 +
 selectiveInference/man/fixedLassoInf.Rd | 8 ++++----
 3 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/selectiveInference/R/funs.fixedCox.R b/selectiveInference/R/funs.fixedCox.R
index 82eeb61..f0419b8 100644
--- a/selectiveInference/R/funs.fixedCox.R
+++ b/selectiveInference/R/funs.fixedCox.R
@@ -50,6 +50,11 @@ b1= -(mydiag(s2)%*%MM)%*%s2*lambda
 
 
 # compute p-values
+
+# JT: are we sure the signs of these are correctly handled?
+# two sided p-values numerically agree with python but
+# the one sided p-values are a bit off
+
     for(jj in 1:length(bbar)){
       vj=rep(0,length(bbar));vj[jj]=1
 
@@ -66,6 +71,7 @@ b1= -(mydiag(s2)%*%MM)%*%s2*lambda
        tailarea[jj,] = junk2$tailarea
      
   }
+  # JT: these don't seem to be the real one-step estimators
     fit0=coxph(Surv(y,status)~x[,m])
       coef0=fit0$coef
       se0=sqrt(diag(fit0$var))
diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index ea476b1..e1510ab 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -98,6 +98,7 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
      tailarea[jj,] = junk2$tailarea
    }
 
+  # JT: these are not the one step estimators but they are close
   fit0=glm(y~x[,m],family="binomial")
   sfit0=summary(fit0)
       coef0=fit0$coef[-1]
diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index 26b276f..ab8c71e 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -211,10 +211,10 @@ set.seed(43)
      # extract coef for a given lambda; note the 1/n factor!
      # (and here  we DO  include the intercept term)
      lambda = .8
-     beta = coef(gfit, s=lambda/n, exact=TRUE)
+     beta_hat = coef(gfit, s=lambda/n, exact=TRUE)
      
      # compute fixed lambda p-values and selection intervals
-     out = fixedLassoInf(x,y,beta,lambda,family="binomial")
+     out = fixedLassoInf(x,y,beta_hat,lambda,family="binomial")
      out
 
 #Cox model
@@ -238,10 +238,10 @@ status=sample(c(0,1),size=n,replace=T)
      # extract coef for a given lambda; note the 1/n factor!
    
      lambda = 1.5
-     beta = as.numeric(coef(gfit, s=lambda/n, exact=TRUE))
+     beta_hat = as.numeric(coef(gfit, s=lambda/n, exact=TRUE))
      
      # compute fixed lambda p-values and selection intervals
-     out = fixedLassoInf(x,tim,beta,lambda,status=status,family="cox")
+     out = fixedLassoInf(x,tim,beta_hat,lambda,status=status,family="cox")
      out
 }
  
\ No newline at end of file

From 5bb52cd6d51ebfb23d94ce1c9d6a35b8c002a3e3 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 7 Apr 2016 00:42:52 -0700
Subject: [PATCH 136/493] another renaming of beta to beta_hat in examples

---
 selectiveInference/man/fixedLassoInf.Rd | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index ab8c71e..43cf2c3 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -184,10 +184,10 @@ gfit = glmnet(xs,y,standardize=FALSE)
 # extract coef for a given lambda; note the 1/n factor!
 # (and we don't save the intercept term)
 lambda = .8
-beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
+beta_hat = coef(gfit, s=lambda/n, exact=TRUE)[-1]
 
 # compute fixed lambda p-values and selection intervals
-out = fixedLassoInf(xs,y,beta,lambda,sigma=sigma)
+out = fixedLassoInf(xs,y,beta_hat,lambda,sigma=sigma)
 
 #rescale conf points to undo the penalty factor
 out$ci=t(scale(t(out$ci),FALSE,pf[out$vars]))

From 3ad8c5f974cd4705cc0843bceaf027dd7bb146c5 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 7 Apr 2016 22:35:38 -0700
Subject: [PATCH 137/493] adding coef0 and sd to output for use in summary

---
 selectiveInference/R/funs.fixed.R | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 8948a10..4263c0b 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -126,7 +126,7 @@ else{
     pv[j] = a$pv 
     vlo[j] = a$vlo * mj # Unstandardize (mult by norm of vj)
     vup[j] = a$vup * mj # Unstandardize (mult by norm of vj)
-    vmat[j,] = vj * mj  # Unstandardize (mult by norm of vj)
+    vmat[j,] = vj * mj * sign[j]  # Unstandardize (mult by norm of vj)
 
     a = poly.int(y,G,u,vj,sigma,alpha,gridrange=gridrange,
       flip=(sign[j]==-1),bits=bits)
@@ -137,6 +137,8 @@ else{
   out = list(type=type,lambda=lambda,pv=pv,ci=ci,
     tailarea=tailarea,vlo=vlo,vup=vup,vmat=vmat,y=y,
     vars=vars,sign=sign,sigma=sigma,alpha=alpha,
+    sd=sigma*sqrt(rowSums(vmat^2)),
+    coef0=vmat%*%y, 
     call=this.call)
   class(out) = "fixedLassoInf"
   return(out)
@@ -197,8 +199,8 @@ print.fixedLassoInf <- function(x, tailarea=TRUE, ...) {
   cat(sprintf("\nTesting results at lambda = %0.3f, with alpha = %0.3f\n",x$lambda,x$alpha))
   cat("",fill=T)
   tab = cbind(x$vars,
-    round(x$sign*x$vmat%*%x$y,3),
-    round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
+    round(x$coef0,3),
+    round(x$coef0 / x$sd,3),
     round(x$pv,3),round(x$ci,3))
   colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
   if (tailarea) {

From 89034879ed92a4729414e31b6cda20fa3c2f2dd4 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 8 Apr 2016 11:21:38 -0700
Subject: [PATCH 138/493] BF: fixed a bug in onestep estimator, reporting
 onestep estimator and its estimated covariance. everything except p-values
 match the python code now

---
 selectiveInference/R/funs.fixedLogit.R | 37 ++++++++++++++------------
 1 file changed, 20 insertions(+), 17 deletions(-)

diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index e1510ab..08a2dc5 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -45,7 +45,7 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
       
 #check KKT
    z=etahat+(y-prhat)/ww
-       g=  t(x)%*%w%*%(z-etahat)/lambda
+       g=  t(x)%*%w%*%(z-etahat)/lambda # negative gradient scaled by lambda
      if (any(abs(g) > 1+tol.kkt) )
     warning(paste("Solution beta does not satisfy the KKT conditions",
                   "(to within specified tolerances)"))
@@ -63,15 +63,18 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
   
  #constraints for active variables             
   MM=solve(t(xxm)%*%w%*%xxm)
- 
-   bbar=(bhat-lam2m%*%MM%*%s2)
- 
+  gm = c(0,-g[vars]*lambda) # gradient at LASSO solution, first entry is 0 because intercept is unpenalized
+                            # at exact LASSO solution it should be s2[-1]
+  dbeta = MM %*% gm
+
+  # bbar=(bhat+lam2m%*%MM%*%s2)  # JT: this is wrong, shouldn't use sign of intercept anywhere...
+  bbar = bhat - dbeta
 
-   A1=-(mydiag(s2))[-1,-1]
-  b1= ((mydiag(s2)%*%MM)%*%s2*lambda)[-1]
-                  
- tol.poly = 0.01 
-  if (max(A1 %*% bbar[-1] - b1) > tol.poly)
+  A1=-(mydiag(s2))[-1,]
+  b1= (s2 * dbeta)[-1]
+
+  tol.poly = 0.01 
+  if (max((A1 %*% bbar)[-1] - b1) > tol.poly)
     stop(paste("Polyhedral constraints not satisfied; you must recompute beta",
                "more accurately. With glmnet, make sure to use exact=TRUE in coef(),",
                "and check whether the specified value of lambda is too small",
@@ -82,17 +85,16 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
 
   
     for(jj in 1:sum(m)){
-       vj=c(rep(0,sum(m)));vj[jj]=1
-# compute p-values
-      junk=mypoly.pval.lee(bbar[-1],A1,b1,vj,MM[-1,-1])
+       vj=c(rep(0,sum(m)+1));vj[jj+1]=1
+      # compute p-values
+      junk=mypoly.pval.lee(bbar,A1,b1,vj,MM)
       pv[jj] = junk$pv
  
-
    vlo[jj]=junk$vlo
    vup[jj]=junk$vup
        sd[jj]=junk$sd
   #  junk2=mypoly.int.lee(bbar[-1], A1, b1,vj,MM[-1,-1],alpha=.1)
-     junk2=mypoly.int.lee(bbar[-1],vj,vlo[jj],vup[jj],sd[jj],alpha=.1)
+     junk2=mypoly.int.lee(bbar,vj,vlo[jj],vup[jj],sd[jj],alpha=.1)
 
      ci[jj,]=junk2$int
      tailarea[jj,] = junk2$tailarea
@@ -101,14 +103,15 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
   # JT: these are not the one step estimators but they are close
   fit0=glm(y~x[,m],family="binomial")
   sfit0=summary(fit0)
-      coef0=fit0$coef[-1]
-      se0=sqrt(diag(sfit0$cov.scaled)[-1])
+      coef0=bbar[-1]        #fit0$coef[-1]
+      se0=sqrt(diag(MM)[-1]) # sfit0$cov.scaled)[-1])
       zscore0=coef0/se0
       
   out = list(type=type,lambda=lambda,pv=pv,ci=ci,
     tailarea=tailarea,vlo=vlo,vup=vup,sd=sd,
     vars=vars,alpha=alpha,coef0=coef0,zscore0=zscore0,
-    call=this.call)
+    call=this.call,
+    info.matrix=MM) # info.matrix is output just for debugging purposes at the moment
   class(out) = "fixedLogitLassoInf"
   return(out)
 

From c9d184b42f6f6477b69338c281906959c1060407 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 8 Apr 2016 22:08:02 -0700
Subject: [PATCH 139/493] need to use sign in poly pval for correct one-sided
 pvalues

---
 selectiveInference/R/funs.fixedCox.R   | 2 +-
 selectiveInference/R/funs.fixedLogit.R | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.fixedCox.R b/selectiveInference/R/funs.fixedCox.R
index f0419b8..c23b5a3 100644
--- a/selectiveInference/R/funs.fixedCox.R
+++ b/selectiveInference/R/funs.fixedCox.R
@@ -56,7 +56,7 @@ b1= -(mydiag(s2)%*%MM)%*%s2*lambda
 # the one sided p-values are a bit off
 
     for(jj in 1:length(bbar)){
-      vj=rep(0,length(bbar));vj[jj]=1
+      vj=rep(0,length(bbar));vj[jj]=s2[jj]
 
 
       junk=mypoly.pval.lee(bbar,A1,b1,vj,MM)
diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index 08a2dc5..a16c41d 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -85,7 +85,7 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
 
   
     for(jj in 1:sum(m)){
-       vj=c(rep(0,sum(m)+1));vj[jj+1]=1
+       vj=c(rep(0,sum(m)+1));vj[jj+1]=s2[jj+1]
       # compute p-values
       junk=mypoly.pval.lee(bbar,A1,b1,vj,MM)
       pv[jj] = junk$pv

From 79a55636c7331a8c4be978984f614112bc419b41 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Sat, 21 May 2016 15:43:52 -0700
Subject: [PATCH 140/493] old datasplit simulation

---
 forLater/josh/sim.datasplit.R | 41 ++++++++++++++++++++---------------
 1 file changed, 23 insertions(+), 18 deletions(-)

diff --git a/forLater/josh/sim.datasplit.R b/forLater/josh/sim.datasplit.R
index be077a1..0e19335 100644
--- a/forLater/josh/sim.datasplit.R
+++ b/forLater/josh/sim.datasplit.R
@@ -4,30 +4,31 @@ source("../../selectiveInference/R/funs.groupfs.R")
 source("../../selectiveInference/R/funs.quadratic.R")
 source("../../selectiveInference/R/funs.common.R")
 
-set.seed(1)
-niters <- 400
+set.seed(19)
+niters <- 500
 known <- FALSE
-n <- 100
+n <- 50
 p <- 100
-maxsteps <- 10
+maxsteps <- 8
 sparsity <- 5
-snr <- 1
+snr <- 2
 rho <- 0.1
-ratio <- 0.7
-ratio2 <- 0.85
+ratio <- 0.6
+ratio2 <- 0.8
 train <- 1:(ratio*n)
 test <- setdiff(1:n, train)
 train2 <- 1:(ratio2*n)
-test <- setdiff(1:n, train2)
+test2 <- setdiff(1:n, train2)
 index <- 1:p
 
-instance <- function(n, p, sparsity, snr, maxsteps, rho) {
-
-    x <- matrix(rnorm(n*p), nrow=n)
+x <- matrix(rnorm(n*p), nrow=n)
     if (rho != 0) {
         z <- matrix(rep(t(rnorm(n)), p), nrow = n)
         x <- sqrt(1-rho)*x + sqrt(rho)*z
     }
+
+instance <- function(n, p, sparsity, snr, maxsteps, rho) {
+
     y <- rnorm(n)
 
     if (sparsity > 0) {
@@ -47,11 +48,11 @@ instance <- function(n, p, sparsity, snr, maxsteps, rho) {
     xte2 <- x[test2, ]
 
     if (known) {
-        trfit <- groupfs(xtr, ytr, index, maxsteps=maxsteps, sigma=1, aicstop=1, k = 2*log(p))
-        fit <- groupfs(xtr2, ytr2, index, maxsteps=maxsteps, sigma=1, aicstop=1, k = 2*log(p))
+        trfit <- groupfs(xtr, ytr, index, maxsteps=maxsteps, sigma=1, aicstop=1, k = log(length(train)))
+        fit <- groupfs(xtr2, ytr2, index, maxsteps=maxsteps, sigma=1, aicstop=1, k = log(length(train2)))
     } else {
-        trfit <- groupfs(xtr, ytr, index, maxsteps=maxsteps, aicstop=1, k = log(length(train)))
-        fit <- groupfs(xtr2, ytr2, index, maxsteps=maxsteps, aicstop=1, k = log(length(train2)))
+        trfit <- groupfs(xtr, ytr, index, maxsteps=maxsteps, aicstop=1, k = 2*log(p))
+        fit <- groupfs(xtr2, ytr2, index, maxsteps=maxsteps, aicstop=1, k = 2*log(p))
     }
 
     trcols <- which(1:p %in% trfit$action)
@@ -59,11 +60,12 @@ instance <- function(n, p, sparsity, snr, maxsteps, rho) {
     tepv <- summary(lm(yte~xte[, trcols]-1))$coefficients[,4]
     tepv2 <- summary(lm(yte2~xte2[, tr2cols]-1))$coefficients[,4]
     names(tepv) <- as.character(sort(trfit$action))
-    names(tepv2) <- as.character(sort(trfit$action))
+    names(tepv2) <- as.character(sort(fit$action))
     pv <- groupfsInf(fit)
     trpv <- groupfsInf(trfit)
     return(list(vars = fit$action, pvals = pv$pv,
                 splitvars = sort(trfit$action), splitpvals = tepv,
+                splitvars2 = sort(fit$action), splitpvals2 = tepv2,                
                 trpvals = trpv$pv))
 }
 
@@ -75,9 +77,12 @@ vars <- do.call(c, list(output[1,]))
 pvals <- do.call(c, list(output[2,]))
 splitvars <- do.call(c, list(output[3,]))
 splitpvals <- do.call(c, list(output[4,]))
-trpvals <- do.call(c, list(output[5,]))
+splitvars2 <- do.call(c, list(output[5,]))
+splitpvals2 <- do.call(c, list(output[6,]))
+trpvals <- do.call(c, list(output[7,]))
 
-save(vars, pvals, splitvars, splitpvals, trpvals,
+save(vars, pvals, splitvars, splitpvals,
+     splitvars2, splitpvals2, trpvals,
      file = paste0("results/datasplit",
          "_", ifelse(known, "TC", "TF"),
          "_n", n,

From ad2bdb1c199c298658d49d0210264c8e18fc3b94 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 8 Jun 2016 11:30:51 -0700
Subject: [PATCH 141/493] selectiveInference/R/funs.fixedCox.R

---
 selectiveInference/R/funs.fixedCox.R | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/selectiveInference/R/funs.fixedCox.R b/selectiveInference/R/funs.fixedCox.R
index c23b5a3..17c2603 100644
--- a/selectiveInference/R/funs.fixedCox.R
+++ b/selectiveInference/R/funs.fixedCox.R
@@ -29,7 +29,9 @@ s2=sign(bhat)
 
  #check KKT
     
-   aaa=coxph(Surv(y,status)~x[,m],init=bhat,iter.max=0)
+   aaa=coxph(Surv(y,status)~x[,m],init=bhat,iter.max=0) # this gives the Cox model at exactly bhat
+                                                        # so when we compute gradient and score 
+							# we are evaluating at the LASSO solution
     res=residuals(aaa,type="score")
 if(!is.matrix(res)) res=matrix(res,ncol=1)
 scor=colSums(res)
@@ -39,11 +41,11 @@ scor=colSums(res)
     warning(paste("Solution beta does not satisfy the KKT conditions",
                   "(to within specified tolerances)"))
 
-    
+# Hessian of partial likelihood at the LASSO solution    
 MM=vcov(aaa)
 
- bbar=(bhat+lambda*MM%*%s2)
- A1=-(mydiag(s2))
+bbar=(bhat+lambda*MM%*%s2)
+A1=-(mydiag(s2))
 b1= -(mydiag(s2)%*%MM)%*%s2*lambda
 
    temp=max(A1%*%bbar-b1)

From 1a777033bdac83eb7eb474853d693000460d5f0d Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 8 Jun 2016 11:31:41 -0700
Subject: [PATCH 142/493] added comments to Cox

---
 selectiveInference/R/funs.fixedCox.R | 1 +
 1 file changed, 1 insertion(+)

diff --git a/selectiveInference/R/funs.fixedCox.R b/selectiveInference/R/funs.fixedCox.R
index 17c2603..43bd080 100644
--- a/selectiveInference/R/funs.fixedCox.R
+++ b/selectiveInference/R/funs.fixedCox.R
@@ -32,6 +32,7 @@ s2=sign(bhat)
    aaa=coxph(Surv(y,status)~x[,m],init=bhat,iter.max=0) # this gives the Cox model at exactly bhat
                                                         # so when we compute gradient and score 
 							# we are evaluating at the LASSO solution
+							# naming of variables could be improved...
     res=residuals(aaa,type="score")
 if(!is.matrix(res)) res=matrix(res,ncol=1)
 scor=colSums(res)

From 05347b62d9a688930a19e0187205f7fe86915df2 Mon Sep 17 00:00:00 2001
From: tibshirani <tibs@stanford.edu>
Date: Wed, 8 Jun 2016 16:38:11 -0700
Subject: [PATCH 143/493] rob added logistic+cox features

---
 selectiveInference/NAMESPACE            |  13 +-
 selectiveInference/R/funs.constraints.R | 186 ---------------
 selectiveInference/R/funs.fixedCox.R    |   2 +-
 selectiveInference/R/funs.fixedLogit.R  |   2 +-
 selectiveInference/R/funs.fs.R          | 286 +-----------------------
 selectiveInference/man/fixedLassoInf.Rd |  11 +-
 selectiveInference/man/fsInf_maxZ.Rd    |  86 -------
 selectiveInference/src/symbols.rds      | Bin 274 -> 367 bytes
 8 files changed, 28 insertions(+), 558 deletions(-)
 delete mode 100644 selectiveInference/R/funs.constraints.R
 delete mode 100644 selectiveInference/man/fsInf_maxZ.Rd

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index 4d28398..4a764f9 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -1,19 +1,18 @@
 export(lar,fs,
-       larInf,fsInf,fsInf_maxZ,
+       larInf,fsInf,
        coef.lar,coef.fs,
        predict.lar,predict.fs,
        print.lar,print.fs,
        print.larInf,print.fsInf,
        plot.lar,plot.fs,
        fixedLassoInf,print.fixedLassoInf,
- #     fixedLogitLassoInf,print.fixedLogitLassoInf,
-  #     fixedCoxLassoInf,print.fixedCoxLassoInf,
+#      fixedLogitLassoInf,print.fixedLogitLassoInf,
+#       fixedCoxLassoInf,print.fixedCoxLassoInf,
        forwardStop,
        estimateSigma,
        manyMeans,print.manyMeans,
        groupfs,groupfsInf,
-       scaleGroups,factorDesign,
-       sample_from_constraints
+       scaleGroups,factorDesign
     )
 
 S3method("coef", "lar")
@@ -26,7 +25,6 @@ S3method("predict", "fs")
 S3method("print", "fs")
 S3method("plot", "fs")
 S3method("print", "fsInf")
-S3method("print", "fsInf_maxZ")
 S3method("print", "fixedLassoInf")
 S3method("print", "fixedLogitLassoInf")
 S3method("print", "fixedCoxLassoInf")
@@ -37,8 +35,9 @@ S3method("print", "groupfsInf")
 useDynLib("selectiveInference")
 import(glmnet)
 import(intervals)
+import(survival)
 importFrom("graphics", abline, axis, matplot)
-importFrom("stats", dnorm, lsfit, pexp, pnorm, predict,
+importFrom("stats", dnorm, lsfit, pexp, pnorm, predict, 
            qnorm, rnorm, sd, uniroot, dchisq, model.matrix, pchisq)
 importFrom("stats", "coef", "df", "lm", "pf")
 
diff --git a/selectiveInference/R/funs.constraints.R b/selectiveInference/R/funs.constraints.R
deleted file mode 100644
index 3bf6b77..0000000
--- a/selectiveInference/R/funs.constraints.R
+++ /dev/null
@@ -1,186 +0,0 @@
-#
-# Some utilities for affine constraints
-#
-
-# 
-# compute the square-root and inverse square-root of a non-negative
-# definite matrix
-#
-
-factor_covariance = function(S, rank=NA) {
-    if (is.na(rank)) {
-        rank = nrow(S)
-    }
-    svd_X = svd(S, nu=rank, nv=rank)
-    sqrt_cov = t(sqrt(svd_X$d[1:rank]) * t(svd_X$u[,1:rank]))
-    sqrt_inv = t((1. / sqrt(svd_X$d[1:rank])) * t(svd_X$u[,1:rank]))
-
-    return(list(sqrt_cov=sqrt_cov, sqrt_inv=sqrt_inv))
-}
-
-#
-# from a constraint, return an equivalent
-# constraint and a whitening and inverse
-# whitening map
-#
-
-# law is Z \sim N(mean_param, covariance) subject to constraints linear_part %*% Z \leq offset
-
-whiten_constraint = function(linear_part, offset, mean_param, covariance) {
-
-    factor_cov = factor_covariance(covariance)
-    sqrt_cov = factor_cov$sqrt_cov
-    sqrt_inv = factor_cov$sqrt_inv
-
-    new_A = linear_part %*% sqrt_cov
-    new_b = offset - linear_part %*% mean_param
-
-    # rescale rows to have length 1
-
-    scaling = sqrt(apply(new_A^2, 1, sum))
-    new_A = new_A / scaling
-    new_b = new_b / scaling
-
-    # TODO: check these functions will behave when Z is a matrix.
-
-    inverse_map = function(Z) {
-	# broadcasting here
-        # the columns of Z are same length as mean_param
-        return(sqrt_cov %*% Z + as.numeric(mean_param))
-    }
-
-    forward_map = function(W) {
-        return(sqrt_inv %*% (W - mean_param))
-    }    
-
-    return(list(linear_part=new_A,
-                offset=new_b,
-                inverse_map=inverse_map,
-                forward_map=forward_map))
-}
-
-#
-# sample from the law
-#
-# Z \sim N(mean_param, covariance) subject to constraints linear_part %*% Z \leq offset
-
-sample_from_constraints = function(linear_part,
-                                   offset,
-                                   mean_param,
-                                   covariance,
-                                   initial_point, # point must be feasible for constraints
-                                   ndraw=8000,
-                                   burnin=2000,
-                                   accept_reject_params=NA) #TODO: implement accept reject check
-{
-
-    whitened_con = whiten_constraint(linear_part,
-                                     offset,
-				     mean_param,
-                                     covariance)
-    white_initial = whitened_con$forward_map(initial_point)
-
-#     # try 100 draws of accept reject
-#     # if we get more than 50 good draws, then just return a smaller sample
-#     # of size (burnin+ndraw)/5
-
-#     if accept_reject_params: 
-#         use_hit_and_run = False
-#         num_trial, min_accept, num_draw = accept_reject_params
-
-#         def _accept_reject(sample_size, linear_part, offset):
-#             Z_sample = np.random.standard_normal((100, linear_part.shape[1]))
-#             constraint_satisfied = (np.dot(Z_sample, linear_part.T) - 
-#                                     offset[None,:]).max(1) < 0
-#             return Z_sample[constraint_satisfied]
-
-#         Z_sample = _accept_reject(100, 
-#                                   white_con.linear_part,
-#                                   white_con.offset)
-
-#         if Z_sample.shape[0] >= min_accept:
-#             while True:
-#                 Z_sample = np.vstack([Z_sample,
-#                                       _accept_reject(num_draw / 5,
-#                                                      white_con.linear_part,
-#                                                      white_con.offset)])
-#                 if Z_sample.shape[0] > num_draw:
-#                     break
-#             white_samples = Z_sample
-#         else:
-#             use_hit_and_run = True
-#     else:
-#         use_hit_and_run = True
-
-    use_hit_and_run = TRUE
-
-    if (use_hit_and_run) {
-
-        white_linear = whitened_con$linear_part
-        white_offset = whitened_con$offset
-
-	# Inf cannot be used in C code
-	# In theory, these rows can be dropped
-
-	rows_to_keep = white_offset < Inf
-	white_linear = white_linear[rows_to_keep,,drop=FALSE]
-	white_offset = white_offset[rows_to_keep]
-
-        nstate = length(white_initial)
-	if (sum(rows_to_keep) > 0) {
-	    if (ncol(white_linear) > 1) {
-                nconstraint = nrow(white_linear)
-
-                directions = rbind(diag(rep(1, nstate)),
-                                   matrix(rnorm(nstate^2), nstate, nstate))
-
-                # normalize rows to have length 1
-
-                scaling = apply(directions, 1, function(x) {  return(sqrt(sum(x^2))) }) 
-                directions = directions / scaling
-                ndirection = nrow(directions)
-
-                alphas = directions %*% t(white_linear)
-                U = white_linear %*% white_initial - white_offset
-                Z_sample = matrix(rep(0, nstate * ndraw), nstate, ndraw)
-
-                result = .C("sample_truncnorm_white",
-                            as.numeric(white_initial),
-                            as.numeric(U),
-                            as.numeric(t(directions)),
-                            as.numeric(t(alphas)),
-                            output=Z_sample,
-                            as.integer(nconstraint),
-                            as.integer(ndirection),
-                            as.integer(nstate),
-                            as.integer(burnin),
-                            as.integer(ndraw),
-                            package="selectiveInference")
-                Z_sample = result$output
-           } else { # the distribution is univariate
-                    # we can just work out upper and lower limits
-
-                white_linear = as.numeric(white_linear)
-                pos = (white_linear * white_offset) >= 0
-                neg = (white_linear * white_offset) <= 0
-		if (sum(pos) > 0) {
-                    U = min((white_offset / white_linear)[pos])
-                } else {
-		    U = Inf
-                }
-		if (sum(neg) < 0) {
-                    L = max((white_offset / white_linear)[neg])
-                } else {
-                    L = -Inf
-                }
-                Z_sample = matrix(qnorm((pnorm(U) - pnorm(L)) * runif(ndraw) + pnorm(L)), 1, ndraw)
-           }
-       } else {
-           Z_sample = matrix(rnorm(nstate * ndraw), nstate, ndraw)
-       }
-    }
-
-    Z = t(whitened_con$inverse_map(Z_sample))
-    return(Z)
-}
-
diff --git a/selectiveInference/R/funs.fixedCox.R b/selectiveInference/R/funs.fixedCox.R
index c23b5a3..d32a777 100644
--- a/selectiveInference/R/funs.fixedCox.R
+++ b/selectiveInference/R/funs.fixedCox.R
@@ -25,7 +25,7 @@ if( sum(status==0)+sum(status==1)!=length(y)) stop("status vector must have valu
 vars=which(m)
 if(sum(m)>0){
     bhat=beta[beta!=0] #penalized coefs just for active variables
-s2=sign(bhat)
+    s2=sign(bhat)
 
  #check KKT
     
diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index a16c41d..fb4092d 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -74,7 +74,7 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
   b1= (s2 * dbeta)[-1]
 
   tol.poly = 0.01 
-  if (max((A1 %*% bbar)[-1] - b1) > tol.poly)
+  if (max((A1 %*% bbar) - b1) > tol.poly)
     stop(paste("Polyhedral constraints not satisfied; you must recompute beta",
                "more accurately. With glmnet, make sure to use exact=TRUE in coef(),",
                "and check whether the specified value of lambda is too small",
diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index b75923d..b5ee511 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -27,11 +27,10 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
 
   #####
   # Find the first variable to enter and its sign
-  working_scale = sqrt(colSums(x^2))
-  working_x = scale(x,center=F,scale=working_scale)
-  working_score = t(working_x)%*%y
-  i_hit = which.max(abs(working_score))   # Hitting coordinate
-  sign_hit = Sign(working_score[i_hit])   # Sign
+  working_x = scale(x,center=F,scale=sqrt(colSums(x^2)))
+  score = t(working_x)%*%y
+  i_hit = which.max(abs(score))   # Hitting coordinate
+  sign_hit = Sign(score[i_hit])   # Sign
   signs = sign_hit                # later signs will be appended to `signs`
 
   if (verbose) {
@@ -49,43 +48,19 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   df = numeric(buf)          # Degrees of freedom
   beta = matrix(0,p,buf)     # FS estimates
   
-  # Buffered objects for selective maxZ test
-
-  offset_pos_maxZ = matrix(Inf, p, buf) # upper bounds for selective maxZ
-  offset_neg_maxZ = matrix(Inf, p, buf) # lower bounds for selective maxZ
-  scale_maxZ = matrix(0, p, buf) # lower bounds for selective maxZ
-  realized_maxZ = numeric(buf) # lower bounds for selective maxZ
-
   action[1] = i_hit
   df[1] = 0
   beta[,1] = 0
 
-  #####
-  # Variables needed to compute truncation limits for
-  # selective maxZ test
-
-  realized_maxZ[1] = c(sign_hit * working_score[i_hit])
-  offset_pos_maxZ[,1] = Inf
-  offset_neg_maxZ[,1] = Inf
-  scale_maxZ[,1] = working_scale
-  working_resid_maxZ = y - x %*% beta[,1]
-
   # Gamma matrix!
   gbuf = max(2*p*3,2000)     # Space for 3 steps, at least
   gi = 0                     # index into rows of Gamma matrix
-  zi = 0                     # index into rows of Gamma_maxZ matrix
 
   Gamma = matrix(0,gbuf,n)
   Gamma[gi+Seq(1,p-1),] = t(sign_hit*working_x[,i_hit]+working_x[,-i_hit]); gi = gi+p-1
   Gamma[gi+Seq(1,p-1),] = t(sign_hit*working_x[,i_hit]-working_x[,-i_hit]); gi = gi+p-1
   Gamma[gi+1,] = t(sign_hit*working_x[,i_hit]); gi = gi+1
 
-  # Gamma_maxZ is the rbind 
-  # of residualized X_inactive's
-
-  Gamma_maxZ = matrix(0,gbuf,n)
-  Gamma_maxZ[zi+Seq(1,p),] = t(x); zi = zi+p
-
   # nconstraint
   nconstraint = numeric(buf)
   vreg = matrix(0,buf,n)
@@ -128,45 +103,23 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
       beta = cbind(beta,matrix(0,p,buf))
       nconstraint = c(nconstraint,numeric(buf))
       vreg = rbind(vreg,matrix(0,buf,n))
-
-      offset_pos_maxZ = cbind(offset_pos_maxZ, matrix(0, p, buf))
-      offset_neg_maxZ = cbind(offset_neg_maxZ, matrix(0, p, buf))
-      scale_maxZ = cbind(scale_maxZ, matrix(0, p, buf))
-      realized_maxZ = c(realized_maxZ, numeric(buf))
     }
 
     # Key quantities for the next entry
-
     keepLs=backsolve(R,t(Q_active)%*%X_inactive)
-
-    prev_scale = working_scale[-i_hit] # this variable used later for maxZ
     X_inactive_resid = X_inactive - X_active %*% keepLs
-    working_scale = sqrt(colSums(X_inactive_resid^2)) # this variable used later for maxZ
-    working_x = scale(X_inactive_resid,center=F,scale=working_scale)
-    working_score = as.numeric(t(working_x)%*%y)
+    working_x = scale(X_inactive_resid,center=F,scale=sqrt(colSums(X_inactive_resid^2)))
+    score = as.numeric(t(working_x)%*%y)
     
-    beta_cur = backsolve(R,t(Q_active)%*%y) # must be computed before the break
-                                            # so we have it if we have 
-                                            # completed the path
-
     # If the inactive set is empty, nothing will hit
     if (r==min(n-intercept,p)) break
 
     # Otherwise find the next hitting time
     else {
-      sign_score = Sign(working_score)
-      abs_score = sign_score * working_score
+      sign_score = Sign(score)
+      abs_score = sign_score * score
       i_hit = which.max(abs_score)
       sign_hit = sign_score[i_hit]
-      # keep track of necessary quantities for selective maxZ
-
-      offset_shift = t(X_inactive) %*% (y - working_resid_maxZ)
-      realized_Z_scaled = realized_maxZ[k-1] * prev_scale
-      offset_pos_maxZ[I,k] = realized_Z_scaled + offset_shift
-      offset_neg_maxZ[I,k] = realized_Z_scaled - offset_shift
-      scale_maxZ[I,k] = working_scale
-
-      working_resid_maxZ = y - X_active %*% beta_cur
     }
     
     # Record the solution
@@ -174,22 +127,12 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
 
     action[k] = I[i_hit] 
     df[k] = r
-    beta[A,k] = beta_cur 
+    beta[A,k] = backsolve(R,t(Q_active)%*%y)
         
-    # store the X_inactive_resid in Gamma_maxZ
-
-    if (gi + p-r > nrow(Gamma_maxZ)) Gamma_maxZ = rbind(Gamma_maxZ,matrix(0,p-r,n))
-    Gamma_maxZ[zi+Seq(1,p-r),] = t(X_inactive_resid); zi = zi+p-r
-
-    # update maxZ variable
-    realized_maxZ[k] = sign_hit * working_score[i_hit]
-    
     # Gamma matrix!
-
     if (gi + 2*p > nrow(Gamma)) Gamma = rbind(Gamma,matrix(0,2*p+gbuf,n))
     working_x = t(sign_score*t(working_x))
-    #Gamma[gi+Seq(1,p-r),] = t(working_x); gi = gi+p-r
-    Gamma[gi+Seq(1,p-r-1),] = t(working_x[,i_hit]+working_x[,-i_hit]); gi = gi+p-r-1
+    Gamma[gi+Seq(1,p-r),] = t(working_x); gi = gi+p-r
     Gamma[gi+Seq(1,p-r-1),] = t(working_x[,i_hit]-working_x[,-i_hit]); gi = gi+p-r-1
     Gamma[gi+1,] = t(working_x[,i_hit]); gi = gi+1
 
@@ -229,11 +172,6 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   nconstraint = nconstraint[Seq(1,k-1)]
   vreg = vreg[Seq(1,k-1),,drop=FALSE]
   
-  offset_pos_maxZ = offset_pos_maxZ[,Seq(1,k-1),drop=FALSE]
-  offset_neg_maxZ = offset_neg_maxZ[,Seq(1,k-1),drop=FALSE]
-  scale_maxZ = scale_maxZ[,Seq(1,k-1),drop=FALSE]
-  Gamma_maxZ = Gamma_maxZ[Seq(1,zi),,drop=FALSE]
-
   # If we reached the maximum number of steps
   if (k>maxsteps) {
     if (verbose) {
@@ -251,8 +189,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     # Record the least squares solution. Note that
     # we have already computed this
     bls = rep(0,p)
-    if(length(keepLs)>0)  bls[A] = keepLs
-
+   if(length(keepLs)>0)  bls[A] = keepLs
   }
 
   if (verbose) cat("\n")
@@ -268,9 +205,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
   out = list(action=action,sign=signs,df=df,beta=beta,
     completepath=completepath,bls=bls,
     Gamma=Gamma,nconstraint=nconstraint,vreg=vreg,x=x,y=y,bx=bx,by=by,sx=sx,
-    intercept=intercept,normalize=normalize,call=this.call,
-    offset_pos_maxZ=offset_pos_maxZ,offset_neg_maxZ=offset_neg_maxZ,
-    scale_maxZ=scale_maxZ,Gamma_maxZ=Gamma_maxZ,realized_maxZ=realized_maxZ) 
+    intercept=intercept,normalize=normalize,call=this.call) 
   class(out) = "fs"
   return(out)
 }
@@ -437,175 +372,7 @@ fsInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic
 
 ##############################
 
-##############################
-
-# selected maxZ tests
-
-fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, k=NULL,
-                      ndraw=8000, burnin=2000, verbose=FALSE) {
-  
-  this.call = match.call()
-
-  checkargs.misc(sigma=sigma,alpha=alpha,k=k)
-
-  if (class(obj) != "fs") stop("obj must be an object of class fs")
-  
-  k = min(k,length(obj$action)) # Round to last step
-  x = obj$x
-  y = obj$y
-  p = ncol(x)
-  n = nrow(x)
-  pv = c()
-
-  if (is.null(sigma)) {
-    # TODO we need a sampler on a unit sphere
-    if (n >= 2*p) {
-      oo = obj$intercept
-      sigma = sqrt(sum(lsfit(x,y,intercept=oo)$res^2)/(n-p-oo))
-    }
-    else {
-      sigma = sd(y)
-      warning(paste(sprintf("p > n/2, and sd(y) = %0.3f used as an estimate of sigma;",sigma),
-                    "you may want to use the estimateSigma function"))
-    }
-  }
-
-  khat = NULL
-  
-  vars = obj$action[1:k]
-  zi = 0
-  for (j in 1:k) {
-     if(verbose) cat(c("Step=",j),fill=T)
-      # the inactive set here does not
-      # include the variable at the j-th step
-      # so, at j==1, the inactive set is every variable
-      # at j==2, the inactive set is everything but the first one
-
-      if (j > 1) {
-          active = vars[1:(j-1)]
-	  inactive = (1:p)[-active]
-      } else {
-          inactive = 1:p
-      }
-
-      collapsed_pos = apply(obj$offset_pos_maxZ[inactive,1:j,drop=FALSE], 1, min)
-      collapsed_neg = apply(obj$offset_neg_maxZ[inactive,1:j,drop=FALSE], 1, min)
-      cur_scale = obj$scale_maxZ[,j][inactive]
-
-      # the matrix cur_adjusted_Xt is used to compute (always as length(y) columns)
-      # the maxZ or maxT for the sampled variables
-      # 
-      cur_adjusted_Xt = obj$Gamma_maxZ[zi + Seq(1,p-j+1),,drop=FALSE]; zi = zi+p-j+1 # Xt for transpose
-
-      # cur_X is used to enforce conditioning on
-      # the ever_active sufficient_statistics
-
-      cur_X = obj$x[,inactive,drop=FALSE]
-
-      # now we condition on solution up to now
-      # this is equivalent to finding vector of 
-      # fitted values up to now and appropriately
-      # adjusting the box limits
-
-      if (j > 1) {
-          cur_fitted = predict(obj, s=j)
-	  cur_fitted = cur_fitted - mean(cur_fitted)
-	  cur_offset = as.numeric(t(cur_X) %*% cur_fitted)
-      }
-      else {
-          cur_fitted = rep(0, length(y))
-          cur_offset = rep(0, length(inactive))
-      }
-
-      final_upper = collapsed_pos - cur_offset
-      final_lower = -(collapsed_neg + cur_offset)
-
-      # now, we sample from Y_star, a centered Gaussian with covariance sigma^2 I
-      # subject to the constraint
-      # t(cur_adjusted_Xt) %*% Y_star < final_upper
-      # -t(cur_adjusted_Xt) %*% Y_star < -final_lower
-
-      # really, we want the covariance of Y_star to be \sigma^2 (I - cur_P)
-      # where P is projection on the j-1 previous variables
-      # but this doesn't matter as everything we do with the samples
-      # will be a function of (I - cur_P) Y_star and the constraints are
-      # expressible in terms of (I - cur_P) Y_star because
-      # we have adjusted X
-
-      # IMPORTANT: after sampling Y_star, we have to add back cur_fitted
-
-      # if n >= p, we could actually just draw cur_adjusted_Xt %*% Y_star
-      # because this has a simple box constraint
-      # with a generically non-degenerate covariance
-
-      if (nrow(cur_adjusted_Xt) > length(y)) {
-          linear_part = rbind(cur_adjusted_Xt, -cur_adjusted_Xt)
-          offset = c(final_upper, -final_lower)
-          covariance = diag(rep(sigma^2, length(y)))
-          mean_param = cur_fitted # rep(0, length(y))
-          initial_point = y
-
-          truncated_y = sample_from_constraints(linear_part, 
-                                                offset, 
-                                                mean_param, 
-                                                covariance, 
-                                                initial_point, 
-                                                burnin=burnin, 
-                                                ndraw=ndraw)
-
-          truncated_noise = truncated_y %*% t(cur_adjusted_Xt)
-          sample_maxZ = apply(abs(t(truncated_noise) / cur_scale), 2, max)
-      } else {  # sample from a smaller dimensional gaussian
-          if (nrow(cur_adjusted_Xt) > 1) {
-              linear_part = rbind(diag(rep(1, nrow(cur_adjusted_Xt))),
-                                  diag(rep(-1, nrow(cur_adjusted_Xt))))
-              covariance = sigma^2 * (cur_adjusted_Xt %*% t(cur_adjusted_Xt))
-              offset = c(final_upper, -final_lower)
-              mean_param = cur_adjusted_Xt %*% cur_fitted # rep(0, nrow(cur_adjusted_Xt))
-              initial_point = cur_adjusted_Xt %*% y
-          } else {
-              mean_param = as.numeric(sum(as.numeric(cur_adjusted_Xt) * as.numeric(cur_fitted)))
-              covariance = matrix(sigma^2 * sum(cur_adjusted_Xt^2))
-              linear_part = matrix(c(1,-1), 2, 1)
-              offset = c(final_upper, -final_lower)
-              initial_point = as.numeric(sum(as.numeric(cur_adjusted_Xt) * as.numeric(y)))
-          }
-          truncated_noise = sample_from_constraints(linear_part, 
-                                                offset, 
-                                                mean_param, 
-                                                covariance, 
-                                                initial_point, 
-                                                burnin=burnin, 
-                                                ndraw=ndraw)
-          sample_maxZ = apply(abs(t(truncated_noise) / cur_scale), 2, max)
-
-         } 
-
-      observed_maxZ = obj$realized_maxZ[j]
-      pval = sum(sample_maxZ > observed_maxZ) / ndraw
-      pval = 2 * min(pval, 1 - pval)
-      pv = c(pv, pval)
-  }
-
-  khat = forwardStop(pv,alpha)
-
-  out = list(pv=pv,
-             k=k,
-             khat=khat,
-	     sigma=sigma,
-	     vars=vars,
-	     sign=obj$sign,
-	     alpha=alpha,
-             realized_maxZ=obj$realized_maxZ,
-        call=this.call)
-  class(out) = "fsInf_maxZ"
-  return(out)
-}
 
-##############################
-#
-# Print methods
-#
 ##############################
 
 print.fs <- function(x, ...) {
@@ -682,35 +449,6 @@ print.fsInf <- function(x, tailarea=TRUE, ...) {
 }
 
 
-print.fsInf_maxZ <- function(obj) {
-
-  cat("\nCall:\n")
-  dput(obj$call)
-
-  cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n",
-              obj$sigma))
-
-  cat(sprintf("\nSequential testing results with alpha = %0.3f\n",obj$alpha))
-
-  tab = cbind(1:length(obj$pv),
-              obj$vars,
-              round(obj$sign*obj$realized_maxZ, 3),
-              round(obj$pv,3))
-  colnames(tab) = c("Step", "Var", "Z-score", "P-value")
-  rownames(tab) = rep("",nrow(tab))
-  print(tab)
-
-  cat(sprintf("\nEstimated stopping point from ForwardStop rule = %i\n",obj$khat))
-
-  invisible()
-}
-
-##############################
-# 
-# Plot methods
-#
-##############################
-
 plot.fs <- function(x, breaks=TRUE, omit.zeros=TRUE, var.labels=TRUE, ...) {
   if (x$completepath) {
     k = length(x$action)+1
diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index 43cf2c3..c5a9912 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -10,7 +10,7 @@ fixed value of the tuning parameter lambda
 }
 \usage{
 fixedLassoInf(x, y, beta, lambda, family = c("gaussian", "binomial", 
-    "cox"),intercept=TRUE, sigma=NULL, alpha=0.1,
+    "cox"),intercept=TRUE, status=NULL, sigma=NULL, alpha=0.1,
          type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
          gridrange=c(-100,100), bits=NULL, verbose=FALSE) 
 }
@@ -56,6 +56,7 @@ Significance level for confidence intervals (target is miscoverage alpha/2 in ea
 Was the lasso problem solved (e.g., by glmnet) with an intercept in the model? 
 Default is TRUE. Must be TRUE for "binomial" family. Not used for 'cox" family, where no intercept is assumed.
 }
+\item{status}{Censoring status for Cox model; 1=failurem 0=censored}
 \item{type}{Contrast type for p-values and confidence intervals: default is
 "partial"---meaning that the contrasts tested are the partial population 
 regression coefficients, within the active set of predictors; the alternative is
@@ -104,6 +105,10 @@ Then when running glmnet, set standardize=F. See example below.
 The penalty.factor facility in glmmet-- allowing different penalties lambda for each predictor,
 is not yet implemented in fixedLassoInf. However you can finesse this--- see the example below. One caveat- using this approach, a penalty factor of zero (forcing a predictor in)
 is not allowed.
+
+Note that the coefficients and standard errors reported are unregularized.
+Eg for the Gaussian, they are the usual least squares estimates and standard errors
+for the model fit to the actice set from the lasso.
 }
 \value{  
 \item{type}{Type of coefficients tested (partial or full)}
@@ -203,7 +208,7 @@ set.seed(43)
      x=scale(x,TRUE,TRUE)
      
      beta = c(3,2,rep(0,p-2))
-     y = x%*%beta + sigma*rnorm(n)
+     y = x\%*\%beta + sigma*rnorm(n)
      y=1*(y>mean(y))
      # first run glmnet
      gfit = glmnet(x,y,standardize=FALSE,family="binomial")
@@ -229,7 +234,7 @@ set.seed(43)
      beta = c(3,2,rep(0,p-2))
      tim = as.vector(x\%*\%beta + sigma*rnorm(n))
      tim= tim-min(tim)+1
-status=sample(c(0,1),size=n,replace=T)
+status=sample(c(0,1),size=n,replace=TRUE)
      # first run glmnet
 
 
diff --git a/selectiveInference/man/fsInf_maxZ.Rd b/selectiveInference/man/fsInf_maxZ.Rd
deleted file mode 100644
index 89aa886..0000000
--- a/selectiveInference/man/fsInf_maxZ.Rd
+++ /dev/null
@@ -1,86 +0,0 @@
-\name{fsInf_maxZ}
-\alias{fsInf_maxZ}
-\title{
-Selective inference for forward stepwise regression
-}
-\description{
-Computes maxZ selective p-values and confidence intervals for forward 
-stepwise regression
-}
-\usage{
-                    
-fsInf_maxZ(obj, sigma=NULL, alpha=0.1, k=NULL,   ndraw=8000, burnin=2000,verbose=FALSE)
-    
-}
-
-\arguments{
-  \item{obj}{
-Object returned by \code{\link{fs}} function
-}
-\item{sigma}{
-Estimate of error standard deviation. If NULL (default), this is estimated 
-using the mean squared residual of the full least squares fit when n >= 2p, and 
-using the standard deviation of y when n < 2p. In the latter case, the user 
-should use \code{\link{estimateSigma}} function for a more accurate estimate
-}
-\item{alpha}{
-Significance level for confidence intervals (target is miscoverage alpha/2 in each tail)
-}
-\item{k}{
-See "type" argument below. Default is NULL, in which case k is taken to be the
-the number of steps computed in the forward stepwise path
-}
-\item{ndraw}{Number of Monte Carlo samples generated}
-\item{burnin}{
-Number of samples discarded at the beginning of the chain
-}
-\item{verbose}{Print out progress along the way? Default is FALSE}
-}
-
-\details{
-This function computes selective maxZ p-values
-for forward stepwise regression. These p-values are independent the under null,
-so that stopping via the forwardStop rule yields guaranteed FDR control
-}
-
-\value{  
-\item{pv}{P-values for each model in the sequence}
-\item{k}{Value of k specified in call}
-\item{khat}{When type is "active", this is an estimated stopping point
-declared by \code{\link{forwardStop}}}
-\item{sigma}{Value of error standard deviation (sigma) used}
-\item{vars}{Variables in active set}
-\item{sign}{Signs of active coefficients}
-\item{alpha}{Desired coverage (alpha/2 in each tail)}
-\item{realized_maxZ}{Value of maxZ statistic computed at each step}
-\item{call}{The call to fsInf_maxZ}
-}
-
-\references{
-Will Fithian, Jonathan Taylor,  Ryan Tibshirani, and Rob Tibshirani (2015).
-Selective sequential model selection.  arXiv:1512.02565..
-
-
-}
-
-\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
-
-\seealso{\code{\link{fs}}}
-
-\examples{
-set.seed(33)
-n = 50
-p = 10
-sigma = 1
-x = matrix(rnorm(n*p),n,p)
-beta = c(3,2,rep(0,p-2))
-y = x\%*\%beta + sigma*rnorm(n)
-
-# run forward stepwise
-fsfit = fs(x,y)
-
-# compute sequential p-values a
-# (sigma estimated from full model)
-out.seq = fsInf_maxZ(fsfit)
-out.seq
-}
diff --git a/selectiveInference/src/symbols.rds b/selectiveInference/src/symbols.rds
index 144e5c09f09f8470d4c91b8e736738b6e74ca38f..ef45ca37aedfc57cb07c2d5066bf881bf4359eb6 100644
GIT binary patch
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From 322e7f0b0ae34acea38775e413e83f57b956f3a1 Mon Sep 17 00:00:00 2001
From: tibshirani <tibs@stanford.edu>
Date: Wed, 8 Jun 2016 16:41:08 -0700
Subject: [PATCH 144/493] rob put all of maxZ stuff into a folder for later use

---
 forLater/estimateLambda.Rd       |  70 +++
 forLater/funs.fixed.R            | 198 ++++++++
 forLater/funs.fs.R               | 744 +++++++++++++++++++++++++++++++
 forLater/maxZ/forLater           |  86 ++++
 forLater/maxZ/funs.constraints.R | 186 ++++++++
 5 files changed, 1284 insertions(+)
 create mode 100644 forLater/estimateLambda.Rd
 create mode 100644 forLater/funs.fixed.R
 create mode 100644 forLater/funs.fs.R
 create mode 100644 forLater/maxZ/forLater
 create mode 100644 forLater/maxZ/funs.constraints.R

diff --git a/forLater/estimateLambda.Rd b/forLater/estimateLambda.Rd
new file mode 100644
index 0000000..9cb1cdb
--- /dev/null
+++ b/forLater/estimateLambda.Rd
@@ -0,0 +1,70 @@
+\name{estimateLambda}
+\alias{estimateLambda}
+%- Also NEED an '\alias' for EACH other topic documented here.
+\title{
+Estimates the lasso tuning parameter lambda.
+}
+\description{
+Estimates the lasso tuning parameter lambda, for use in the selectiveInference 
+package
+}
+\usage{
+estimateLambda(x, sigma, nsamp=1000)
+}
+\arguments{
+  \item{x}{
+Matrix of predictors (n by p)
+}
+  \item{sigma}{
+Estimate of error standard deviation
+}
+\item{nsamp}{Number of Monte carlo samples used for the estimation.}
+}
+\details{
+This function estimates the lasso tuning parameter lambda, using the estimate
+2*E(||X^T eps||_infty)  where eps ~ N(0,sigma^2), a vector of length n.
+This estimate was proposed by Negahban et  al (2012).
+}
+\value{
+\item{sigmahat}{The estimate of sigma}
+\item{df}{The degrees of freedom of lasso fit used}
+}
+\references{
+Negahban, S. N.,
+Ravikumar, P.,
+Wainwright, M. J.
+and Yu, B.
+(2012).  A unified
+framework for high-dimensional analysis of
+M-estimators with decomposable regularizers.
+Statistical Science vol. 27, p 538-557.
+}
+
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\examples{
+#NOT RUN
+#set.seed(43)
+#n=50
+#p=10
+#sigma=.7
+#x=matrix(rnorm(n*p),n,p)
+#x=scale(x,T,F)
+#beta=c(3,2,0,0,rep(0,p-4))
+#y=x%*%beta+sigma*rnorm(n)
+#y=y-mean(y)
+#
+#estimate lambda usingthe  known value of sigma
+#lamhat=estimateLambda(x,sigma=.7)
+#
+#first estimate  sigma
+#sigmahat=estimateSigma(x,y)$sigmahat
+#lamhat=estimateLambda(x,sigma=sigmahat)
+
+#compare to estimate from cv
+
+#out=cv.glmnet(x,y)
+#out$lambda.min*n    #remember that value from glmnet must be
+                     # multiplied by n, to make it comparable.
+}
+
diff --git a/forLater/funs.fixed.R b/forLater/funs.fixed.R
new file mode 100644
index 0000000..ac00545
--- /dev/null
+++ b/forLater/funs.fixed.R
@@ -0,0 +1,198 @@
+# Lasso inference function (for fixed lambda). Note: here we are providing inference
+# for the solution of
+# min 1/2 || y - \beta_0 - X \beta ||_2^2 + \lambda || \beta ||_1
+
+fixedLassoInf <- function(x, y, beta, lambda, intercept=TRUE, sigma=NULL, alpha=0.1,
+                     type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
+                     gridrange=c(-100,100), bits=NULL, verbose=FALSE) {
+  
+  this.call = match.call()
+  type = match.arg(type)
+  checkargs.xy(x,y)
+  if (missing(beta) || is.null(beta)) stop("Must supply the solution beta")
+  if (missing(lambda) || is.null(lambda)) stop("Must supply the tuning parameter value lambda") 
+  checkargs.misc(beta=beta,lambda=lambda,sigma=sigma,alpha=alpha,
+                 gridrange=gridrange,tol.beta=tol.beta,tol.kkt=tol.kkt)
+  if (!is.null(bits) && !requireNamespace("Rmpfr",quietly=TRUE)) {
+    warning("Package Rmpfr is not installed, reverting to standard precision")
+    bits = NULL
+  }
+  
+  n = nrow(x)
+  p = ncol(x)
+  beta = as.numeric(beta)
+  if (length(beta) != p) stop("beta must have length equal to ncol(x)")
+
+  # If glmnet was run with an intercept term, center x and y
+  if (intercept==TRUE) {
+    obj = standardize(x,y,TRUE,FALSE)
+    x = obj$x
+    y = obj$y
+  }
+
+  # Check the KKT conditions
+  g = t(x)%*%(y-x%*%beta) / lambda
+  if (any(abs(g) > 1+tol.kkt * sqrt(sum(y^2))))
+    warning(paste("Solution beta does not satisfy the KKT conditions",
+                  "(to within specified tolerances)"))
+
+  vars = which(abs(beta) > tol.beta / sqrt(colSums(x^2)))
+  if(length(vars)==0){
+      cat("Empty model",fill=T)
+      return()
+  }
+  if (any(sign(g[vars]) != sign(beta[vars])))
+    warning(paste("Solution beta does not satisfy the KKT conditions",
+                  "(to within specified tolerances). You might try rerunning",
+                  "glmnet with a lower setting of the",
+                  "'thresh' parameter, for a more accurate convergence."))
+  
+  # Get lasso polyhedral region, of form Gy >= u
+  out = fixedLasso.poly(x,y,beta,lambda,vars)
+  G = out$G
+  u = out$u
+
+  # Check polyhedral region
+  tol.poly = 0.01 
+  if (min(G %*% y - u) < -tol.poly * sqrt(sum(y^2)))
+    stop(paste("Polyhedral constraints not satisfied; you must recompute beta",
+               "more accurately. With glmnet, make sure to use exact=TRUE in coef(),",
+               "and check whether the specified value of lambda is too small",
+               "(beyond the grid of values visited by glmnet).",
+               "You might also try rerunning glmnet with a lower setting of the",
+               "'thresh' parameter, for a more accurate convergence."))
+
+  # Estimate sigma
+  if (is.null(sigma)) {
+    if (n >= 2*p) {
+      oo = intercept
+      sigma = sqrt(sum(lsfit(x,y,intercept=oo)$res^2)/(n-p-oo))
+    }
+    else {
+      sigma = sd(y)
+      warning(paste(sprintf("p > n/2, and sd(y) = %0.3f used as an estimate of sigma;",sigma),
+                    "you may want to use the estimateSigma function"))
+    }
+  }
+ 
+  k = length(vars)
+  pv = vlo = vup = numeric(k) 
+  vmat = matrix(0,k,n)
+  ci = tailarea = matrix(0,k,2)
+  sign = numeric(k)
+
+  if (type=="full" & p > n)
+      warning(paste("type='full' does not make sense when p > n;",
+                    "switching to type='partial'"))
+  
+  if (type=="partial" || p > n) {
+    xa = x[,vars,drop=F]
+    M = pinv(crossprod(xa)) %*% t(xa)
+  }
+  else {
+    M = pinv(crossprod(x)) %*% t(x)
+    M = M[vars,,drop=F]
+  }
+  
+  for (j in 1:k) {
+    if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
+    
+    vj = M[j,]
+    mj = sqrt(sum(vj^2))
+    vj = vj / mj        # Standardize (divide by norm of vj)
+    sign[j] = sign(sum(vj*y))
+    vj = sign[j] * vj
+    a = poly.pval(y,G,u,vj,sigma,bits)
+    pv[j] = a$pv 
+    vlo[j] = a$vlo * mj # Unstandardize (mult by norm of vj)
+    vup[j] = a$vup * mj # Unstandardize (mult by norm of vj)
+    vmat[j,] = vj * mj  # Unstandardize (mult by norm of vj)
+
+    a = poly.int(y,G,u,vj,sigma,alpha,gridrange=gridrange,
+      flip=(sign[j]==-1),bits=bits)
+    ci[j,] = a$int * mj # Unstandardize (mult by norm of vj)
+    tailarea[j,] = a$tailarea
+  }
+  
+  out = list(type=type,lambda=lambda,pv=pv,ci=ci,
+    tailarea=tailarea,vlo=vlo,vup=vup,vmat=vmat,y=y,
+    vars=vars,sign=sign,sigma=sigma,alpha=alpha,
+    call=this.call)
+  class(out) = "fixedLassoInf"
+  return(out)
+}
+
+##############################
+
+fixedLasso.poly <- function(x, y, beta, lambda, a) {
+  xa = x[,a,drop=F]
+  xac = x[,!a,drop=F]
+  xai = pinv(crossprod(xa))
+  xap = xai %*% t(xa)
+  za = sign(beta[a])
+  if (length(za)>1) dz = diag(za)
+  if (length(za)==1) dz = matrix(za,1,1)
+
+  P = diag(1,nrow(xa)) - xa %*% xap
+  G = -rbind(1/lambda * t(xac) %*% P,
+    -1/lambda * t(xac) %*% P,
+    -dz %*% xap)
+  u = -c(1 - t(xac) %*% t(xap) %*% za,
+    1 + t(xac) %*% t(xap) %*% za,
+    -lambda * dz %*% xai %*% za)
+
+  return(list(G=G,u=u))
+}
+
+# Moore-Penrose pseudo inverse for symmetric matrices
+
+pinv <- function(A, tol=.Machine$double.eps) {
+  e = eigen(A)
+  v = Re(e$vec)
+  d = Re(e$val)
+  d[d > tol] = 1/d[d > tol]
+  d[d < tol] = 0
+  if (length(d)==1) return(v*d*v)
+  else return(v %*% diag(d) %*% t(v))
+}
+
+##############################
+
+print.fixedLassoInf <- function(x, tailarea=TRUE, ...) {
+  cat("\nCall:\n")
+  dput(x$call)
+
+  cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n",
+              x$sigma))
+  
+  cat(sprintf("\nTesting results at lambda = %0.3f, with alpha = %0.3f\n",x$lambda,x$alpha))
+  cat("",fill=T)
+  tab = cbind(x$vars,
+    round(x$sign*x$vmat%*%x$y,3),
+    round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
+    round(x$pv,3),round(x$ci,3))
+  colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
+  if (tailarea) {
+    tab = cbind(tab,round(x$tailarea,3))
+    colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+  }
+  rownames(tab) = rep("",nrow(tab))
+  print(tab)
+ 
+  cat(sprintf("\nNote: coefficients shown are %s regression coefficients\n",
+              ifelse(x$type=="partial","partial","full")))
+  invisible()
+}
+
+#estimateLambda <- function(x, sigma, nsamp=1000){
+#  checkargs.xy(x,rep(0,nrow(x)))
+#  if(nsamp < 10) stop("More Monte Carlo samples required for estimation")
+#  if (length(sigma)!=1) stop("sigma should be a number > 0")
+ # if (sigma<=0) stop("sigma should be a number > 0")
+                                      
+ # n = nrow(x)
+ # eps = sigma*matrix(rnorm(nsamp*n),n,nsamp)
+ # lambda = 2*mean(apply(t(x)%*%eps,2,max))
+ # return(lambda)
+#}
+    
diff --git a/forLater/funs.fs.R b/forLater/funs.fs.R
new file mode 100644
index 0000000..b75923d
--- /dev/null
+++ b/forLater/funs.fs.R
@@ -0,0 +1,744 @@
+# We compute the forward stepwise regression (FS) path given 
+# a response vector y and predictor matrix x.  We assume
+# that x has columns in general position.
+
+fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
+               verbose=FALSE) {
+
+  this.call = match.call()
+  checkargs.xy(x=x,y=y)
+  
+  # Center and scale, etc.
+  obj = standardize(x,y,intercept,normalize)
+  x = obj$x
+  y = obj$y
+  bx = obj$bx
+  by = obj$by
+  sx = obj$sx
+  n = nrow(x)
+  p = ncol(x)
+
+  #####
+  # To keep consistent with the lar function, we parametrize
+  # so that the first step has all zero coefficients,
+  # Also, an interesting note: the effective "lambda" (maximal
+  # correlation with the residual) may increase with stepwise!
+  # So we don't keep track of it
+
+  #####
+  # Find the first variable to enter and its sign
+  working_scale = sqrt(colSums(x^2))
+  working_x = scale(x,center=F,scale=working_scale)
+  working_score = t(working_x)%*%y
+  i_hit = which.max(abs(working_score))   # Hitting coordinate
+  sign_hit = Sign(working_score[i_hit])   # Sign
+  signs = sign_hit                # later signs will be appended to `signs`
+
+  if (verbose) {
+    cat(sprintf("1. Adding variable %i, |A|=%i...",i_hit,1))
+  }
+  
+  # Now iteratively find the new FS estimates
+
+  # Things to keep track of, and return at the end
+  # JT: I guess the "buf" just saves us from making huge
+  # matrices we don't need?
+
+  buf = min(maxsteps,500)
+  action = numeric(buf)      # Actions taken
+  df = numeric(buf)          # Degrees of freedom
+  beta = matrix(0,p,buf)     # FS estimates
+  
+  # Buffered objects for selective maxZ test
+
+  offset_pos_maxZ = matrix(Inf, p, buf) # upper bounds for selective maxZ
+  offset_neg_maxZ = matrix(Inf, p, buf) # lower bounds for selective maxZ
+  scale_maxZ = matrix(0, p, buf) # lower bounds for selective maxZ
+  realized_maxZ = numeric(buf) # lower bounds for selective maxZ
+
+  action[1] = i_hit
+  df[1] = 0
+  beta[,1] = 0
+
+  #####
+  # Variables needed to compute truncation limits for
+  # selective maxZ test
+
+  realized_maxZ[1] = c(sign_hit * working_score[i_hit])
+  offset_pos_maxZ[,1] = Inf
+  offset_neg_maxZ[,1] = Inf
+  scale_maxZ[,1] = working_scale
+  working_resid_maxZ = y - x %*% beta[,1]
+
+  # Gamma matrix!
+  gbuf = max(2*p*3,2000)     # Space for 3 steps, at least
+  gi = 0                     # index into rows of Gamma matrix
+  zi = 0                     # index into rows of Gamma_maxZ matrix
+
+  Gamma = matrix(0,gbuf,n)
+  Gamma[gi+Seq(1,p-1),] = t(sign_hit*working_x[,i_hit]+working_x[,-i_hit]); gi = gi+p-1
+  Gamma[gi+Seq(1,p-1),] = t(sign_hit*working_x[,i_hit]-working_x[,-i_hit]); gi = gi+p-1
+  Gamma[gi+1,] = t(sign_hit*working_x[,i_hit]); gi = gi+1
+
+  # Gamma_maxZ is the rbind 
+  # of residualized X_inactive's
+
+  Gamma_maxZ = matrix(0,gbuf,n)
+  Gamma_maxZ[zi+Seq(1,p),] = t(x); zi = zi+p
+
+  # nconstraint
+  nconstraint = numeric(buf)
+  vreg = matrix(0,buf,n)
+  nconstraint[1] = gi
+  vreg[1,] = sign_hit*x[,i_hit] / sum(x[,i_hit]^2)
+
+  # Other things to keep track of, but not return
+  r = 1                      # Size of active set
+  A = i_hit                  # Active set -- JT: isn't this basically the same as action?
+  I = Seq(1,p)[-i_hit]       # Inactive set
+  X_active = x[,i_hit,drop=FALSE]   # Matrix X[,A]
+  X_inactive = x[,-i_hit,drop=FALSE]  # Matrix X[,I]
+  k = 2                      # What step are we at?
+                             # JT Why keep track of r and k instead of just saying k=r+1?
+
+  # Compute a skinny QR decomposition of X_active
+  # JT: obs was used as variable name above -- this is something different, no?
+  # changed it to qr_X
+
+  qr_X = qr(X_active)
+  Q = qr.Q(qr_X,complete=TRUE)
+  Q_active = Q[,1,drop=FALSE];
+  Q_inactive = Q[,-1,drop=FALSE]
+  R = qr.R(qr_X)
+  
+  # Throughout the algorithm, we will maintain
+  # the decomposition X_active = Q_active*R. Dimensions:
+  # X_active: n x r
+  # Q_active: n x r
+  # Q_inactive: n x (n-r)
+  # R:  r x r
+    
+  while (k<=maxsteps) {
+    ##########
+    # Check if we've reached the end of the buffer
+    if (k > length(action)) {
+      buf = length(action)
+      action = c(action,numeric(buf))
+      df = c(df,numeric(buf))
+      beta = cbind(beta,matrix(0,p,buf))
+      nconstraint = c(nconstraint,numeric(buf))
+      vreg = rbind(vreg,matrix(0,buf,n))
+
+      offset_pos_maxZ = cbind(offset_pos_maxZ, matrix(0, p, buf))
+      offset_neg_maxZ = cbind(offset_neg_maxZ, matrix(0, p, buf))
+      scale_maxZ = cbind(scale_maxZ, matrix(0, p, buf))
+      realized_maxZ = c(realized_maxZ, numeric(buf))
+    }
+
+    # Key quantities for the next entry
+
+    keepLs=backsolve(R,t(Q_active)%*%X_inactive)
+
+    prev_scale = working_scale[-i_hit] # this variable used later for maxZ
+    X_inactive_resid = X_inactive - X_active %*% keepLs
+    working_scale = sqrt(colSums(X_inactive_resid^2)) # this variable used later for maxZ
+    working_x = scale(X_inactive_resid,center=F,scale=working_scale)
+    working_score = as.numeric(t(working_x)%*%y)
+    
+    beta_cur = backsolve(R,t(Q_active)%*%y) # must be computed before the break
+                                            # so we have it if we have 
+                                            # completed the path
+
+    # If the inactive set is empty, nothing will hit
+    if (r==min(n-intercept,p)) break
+
+    # Otherwise find the next hitting time
+    else {
+      sign_score = Sign(working_score)
+      abs_score = sign_score * working_score
+      i_hit = which.max(abs_score)
+      sign_hit = sign_score[i_hit]
+      # keep track of necessary quantities for selective maxZ
+
+      offset_shift = t(X_inactive) %*% (y - working_resid_maxZ)
+      realized_Z_scaled = realized_maxZ[k-1] * prev_scale
+      offset_pos_maxZ[I,k] = realized_Z_scaled + offset_shift
+      offset_neg_maxZ[I,k] = realized_Z_scaled - offset_shift
+      scale_maxZ[I,k] = working_scale
+
+      working_resid_maxZ = y - X_active %*% beta_cur
+    }
+    
+    # Record the solution
+    # what is the difference between "action" and "A"?
+
+    action[k] = I[i_hit] 
+    df[k] = r
+    beta[A,k] = beta_cur 
+        
+    # store the X_inactive_resid in Gamma_maxZ
+
+    if (gi + p-r > nrow(Gamma_maxZ)) Gamma_maxZ = rbind(Gamma_maxZ,matrix(0,p-r,n))
+    Gamma_maxZ[zi+Seq(1,p-r),] = t(X_inactive_resid); zi = zi+p-r
+
+    # update maxZ variable
+    realized_maxZ[k] = sign_hit * working_score[i_hit]
+    
+    # Gamma matrix!
+
+    if (gi + 2*p > nrow(Gamma)) Gamma = rbind(Gamma,matrix(0,2*p+gbuf,n))
+    working_x = t(sign_score*t(working_x))
+    #Gamma[gi+Seq(1,p-r),] = t(working_x); gi = gi+p-r
+    Gamma[gi+Seq(1,p-r-1),] = t(working_x[,i_hit]+working_x[,-i_hit]); gi = gi+p-r-1
+    Gamma[gi+Seq(1,p-r-1),] = t(working_x[,i_hit]-working_x[,-i_hit]); gi = gi+p-r-1
+    Gamma[gi+1,] = t(working_x[,i_hit]); gi = gi+1
+
+    # nconstraint, regression contrast
+    nconstraint[k] = gi
+    vreg[k,] = sign_hit*X_inactive_resid[,i_hit] / sum(X_inactive_resid[,i_hit]^2)
+
+    # Update all of the variables
+    r = r+1
+    A = c(A,I[i_hit])
+    I = I[-i_hit]
+    signs = c(signs,sign_hit)
+    X_active = cbind(X_active,X_inactive[,i_hit])
+    X_inactive = X_inactive[,-i_hit,drop=FALSE]
+
+    # Update the QR decomposition
+    updated_qr = updateQR(Q_active,Q_inactive,R,X_active[,r])
+    Q_active = updated_qr$Q1
+
+    # JT: why do we store Q_inactive? Doesn't seem to be used.
+    Q_inactive = updated_qr$Q2
+    R = updated_qr$R
+     
+    if (verbose) {
+      cat(sprintf("\n%i. Adding variable %i, |A|=%i...",k,A[r],r))
+    }
+            
+    # Step counter
+    k = k+1
+  }
+
+  # Trim
+  action = action[Seq(1,k-1)]
+  df = df[Seq(1,k-1),drop=FALSE]
+  beta = beta[,Seq(1,k-1),drop=FALSE]
+  Gamma = Gamma[Seq(1,gi),,drop=FALSE]
+  nconstraint = nconstraint[Seq(1,k-1)]
+  vreg = vreg[Seq(1,k-1),,drop=FALSE]
+  
+  offset_pos_maxZ = offset_pos_maxZ[,Seq(1,k-1),drop=FALSE]
+  offset_neg_maxZ = offset_neg_maxZ[,Seq(1,k-1),drop=FALSE]
+  scale_maxZ = scale_maxZ[,Seq(1,k-1),drop=FALSE]
+  Gamma_maxZ = Gamma_maxZ[Seq(1,zi),,drop=FALSE]
+
+  # If we reached the maximum number of steps
+  if (k>maxsteps) {
+    if (verbose) {
+      cat(sprintf("\nReached the maximum number of steps (%i),",maxsteps))
+      cat(" skipping the rest of the path.")
+    }
+    completepath = FALSE
+    bls = NULL
+  }
+
+  # Otherwise, note that we completed the path
+  else {
+    completepath = TRUE
+    
+    # Record the least squares solution. Note that
+    # we have already computed this
+    bls = rep(0,p)
+    if(length(keepLs)>0)  bls[A] = keepLs
+
+  }
+
+  if (verbose) cat("\n")
+  
+  # Adjust for the effect of centering and scaling
+  if (intercept) df = df+1
+  if (normalize) beta = beta/sx
+  if (normalize && completepath) bls = bls/sx
+  
+  # Assign column names
+  colnames(beta) = as.character(Seq(1,k-1))
+
+  out = list(action=action,sign=signs,df=df,beta=beta,
+    completepath=completepath,bls=bls,
+    Gamma=Gamma,nconstraint=nconstraint,vreg=vreg,x=x,y=y,bx=bx,by=by,sx=sx,
+    intercept=intercept,normalize=normalize,call=this.call,
+    offset_pos_maxZ=offset_pos_maxZ,offset_neg_maxZ=offset_neg_maxZ,
+    scale_maxZ=scale_maxZ,Gamma_maxZ=Gamma_maxZ,realized_maxZ=realized_maxZ) 
+  class(out) = "fs"
+  return(out)
+}
+
+##############################
+
+# Coefficient function for fs
+
+coef.fs <- function(object, s, ...) {
+  if (object$completepath) {
+    k = length(object$action)+1
+    beta = cbind(object$beta,object$bls)
+  } else {
+    k = length(object$action)
+    beta = object$beta
+  }
+  
+  if (min(s)<0 || max(s)>k) stop(sprintf("s must be between 0 and %i",k))
+  knots = 1:k
+  dec = FALSE
+  return(coef.interpolate(beta,s,knots,dec))
+}
+
+# Prediction function for fs
+
+predict.fs <- function(object, newx, s, ...) {
+  beta = coef.fs(object,s)
+  if (missing(newx)) newx = scale(object$x,FALSE,1/object$sx)
+  else newx = scale(newx,object$bx,FALSE)
+  return(newx %*% beta + object$by)
+}
+
+##############################
+
+# FS inference function
+
+fsInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic"), 
+                  gridrange=c(-100,100), bits=NULL, mult=2, ntimes=2, verbose=FALSE) {
+  
+  this.call = match.call()
+  type = match.arg(type)
+  checkargs.misc(sigma=sigma,alpha=alpha,k=k,
+                 gridrange=gridrange,mult=mult,ntimes=ntimes)
+  if (class(obj) != "fs") stop("obj must be an object of class fs")
+  if (is.null(k) && type=="active") k = length(obj$action)
+  if (is.null(k) && type=="all") stop("k must be specified when type = all")
+  if (!is.null(bits) && !requireNamespace("Rmpfr",quietly=TRUE)) {
+    warning("Package Rmpfr is not installed, reverting to standard precision")
+    bits = NULL
+  }
+  
+  k = min(k,length(obj$action)) # Round to last step
+  x = obj$x
+  y = obj$y
+  p = ncol(x)
+  n = nrow(x)
+  G = obj$Gamma
+  nconstraint = obj$nconstraint
+  sx = obj$sx
+
+  if (is.null(sigma)) {
+    if (n >= 2*p) {
+      oo = obj$intercept
+      sigma = sqrt(sum(lsfit(x,y,intercept=oo)$res^2)/(n-p-oo))
+    }
+    else {
+      sigma = sd(y)
+      warning(paste(sprintf("p > n/2, and sd(y) = %0.3f used as an estimate of sigma;",sigma),
+                    "you may want to use the estimateSigma function"))
+    }
+  }
+
+  khat = NULL
+  
+  if (type == "active") {
+    pv = vlo = vup = numeric(k) 
+    vmat = matrix(0,k,n)
+    ci = tailarea = matrix(0,k,2)
+    vreg = obj$vreg[1:k,,drop=FALSE]
+    sign = obj$sign[1:k]
+    vars = obj$action[1:k]
+
+    for (j in 1:k) {
+      if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
+
+      Gj = G[1:nconstraint[j],]
+      uj = rep(0,nconstraint[j])
+      vj = vreg[j,]
+      mj = sqrt(sum(vj^2)) 
+      vj = vj / mj              # Standardize (divide by norm of vj)
+      a = poly.pval(y,Gj,uj,vj,sigma,bits)
+      pv[j] = a$pv
+      sxj = sx[vars[j]]
+      vlo[j] = a$vlo * mj / sxj # Unstandardize (mult by norm of vj / sxj)
+      vup[j] = a$vup * mj / sxj # Unstandardize (mult by norm of vj / sxj)
+      vmat[j,] = vj * mj / sxj  # Unstandardize (mult by norm of vj / sxj)
+  
+      a = poly.int(y,Gj,uj,vj,sigma,alpha,gridrange=gridrange,
+        flip=(sign[j]==-1),bits=bits)
+      ci[j,] = a$int * mj / sxj # Unstandardize (mult by norm of vj / sxj)
+      tailarea[j,] = a$tailarea
+    }
+
+    khat = forwardStop(pv,alpha)
+  }
+  
+  else {
+    if (type == "aic") {
+      out = aicStop(x,y,obj$action[1:k],obj$df[1:k],sigma,mult,ntimes)
+      khat = out$khat
+      m = out$stopped * ntimes
+      G = rbind(out$G,G[1:nconstraint[khat+m],])  # Take ntimes more steps past khat
+      u = c(out$u,rep(0,nconstraint[khat+m]))     # (if we need to)
+      kk = khat
+    }
+    else {
+      G = G[1:nconstraint[k],]
+      u = rep(0,nconstraint[k])
+      kk = k
+    }
+    
+    pv = vlo = vup = numeric(kk) 
+    vmat = matrix(0,kk,n)
+    ci = tailarea = matrix(0,kk,2)
+    sign = numeric(kk)
+    vars = obj$action[1:kk]
+    xa = x[,vars]
+    M = pinv(crossprod(xa)) %*% t(xa)
+    
+    for (j in 1:kk) {
+      if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
+
+      vj = M[j,]
+      mj = sqrt(sum(vj^2))
+      vj = vj / mj              # Standardize (divide by norm of vj)
+      sign[j] = sign(sum(vj*y))
+      vj = sign[j] * vj
+      Gj = rbind(G,vj)
+      uj = c(u,0)
+
+      a = poly.pval(y,Gj,uj,vj,sigma,bits)
+      pv[j] = a$pv
+      sxj = sx[vars[j]]
+      vlo[j] = a$vlo * mj / sxj # Unstandardize (mult by norm of vj / sxj)
+      vup[j] = a$vup * mj / sxj # Unstandardize (mult by norm of vj / sxj)
+      vmat[j,] = vj * mj / sxj  # Unstandardize (mult by norm of vj / sxj)
+
+      a = poly.int(y,Gj,uj,vj,sigma,alpha,gridrange=gridrange,
+        flip=(sign[j]==-1),bits=bits)
+      ci[j,] = a$int * mj / sxj # Unstandardize (mult by norm of vj / sxj)
+      tailarea[j,] = a$tailarea
+    }
+  }
+  
+  # JT: why do we output vup, vlo? Are they used somewhere else?
+
+  out = list(type=type,k=k,khat=khat,pv=pv,ci=ci,
+    tailarea=tailarea,vlo=vlo,vup=vup,vmat=vmat,y=y,
+    vars=vars,sign=sign,sigma=sigma,alpha=alpha,
+    call=this.call)
+  class(out) = "fsInf"
+  return(out)
+}
+
+##############################
+
+##############################
+
+# selected maxZ tests
+
+fsInf_maxZ = function(obj, sigma=NULL, alpha=0.1, k=NULL,
+                      ndraw=8000, burnin=2000, verbose=FALSE) {
+  
+  this.call = match.call()
+
+  checkargs.misc(sigma=sigma,alpha=alpha,k=k)
+
+  if (class(obj) != "fs") stop("obj must be an object of class fs")
+  
+  k = min(k,length(obj$action)) # Round to last step
+  x = obj$x
+  y = obj$y
+  p = ncol(x)
+  n = nrow(x)
+  pv = c()
+
+  if (is.null(sigma)) {
+    # TODO we need a sampler on a unit sphere
+    if (n >= 2*p) {
+      oo = obj$intercept
+      sigma = sqrt(sum(lsfit(x,y,intercept=oo)$res^2)/(n-p-oo))
+    }
+    else {
+      sigma = sd(y)
+      warning(paste(sprintf("p > n/2, and sd(y) = %0.3f used as an estimate of sigma;",sigma),
+                    "you may want to use the estimateSigma function"))
+    }
+  }
+
+  khat = NULL
+  
+  vars = obj$action[1:k]
+  zi = 0
+  for (j in 1:k) {
+     if(verbose) cat(c("Step=",j),fill=T)
+      # the inactive set here does not
+      # include the variable at the j-th step
+      # so, at j==1, the inactive set is every variable
+      # at j==2, the inactive set is everything but the first one
+
+      if (j > 1) {
+          active = vars[1:(j-1)]
+	  inactive = (1:p)[-active]
+      } else {
+          inactive = 1:p
+      }
+
+      collapsed_pos = apply(obj$offset_pos_maxZ[inactive,1:j,drop=FALSE], 1, min)
+      collapsed_neg = apply(obj$offset_neg_maxZ[inactive,1:j,drop=FALSE], 1, min)
+      cur_scale = obj$scale_maxZ[,j][inactive]
+
+      # the matrix cur_adjusted_Xt is used to compute (always as length(y) columns)
+      # the maxZ or maxT for the sampled variables
+      # 
+      cur_adjusted_Xt = obj$Gamma_maxZ[zi + Seq(1,p-j+1),,drop=FALSE]; zi = zi+p-j+1 # Xt for transpose
+
+      # cur_X is used to enforce conditioning on
+      # the ever_active sufficient_statistics
+
+      cur_X = obj$x[,inactive,drop=FALSE]
+
+      # now we condition on solution up to now
+      # this is equivalent to finding vector of 
+      # fitted values up to now and appropriately
+      # adjusting the box limits
+
+      if (j > 1) {
+          cur_fitted = predict(obj, s=j)
+	  cur_fitted = cur_fitted - mean(cur_fitted)
+	  cur_offset = as.numeric(t(cur_X) %*% cur_fitted)
+      }
+      else {
+          cur_fitted = rep(0, length(y))
+          cur_offset = rep(0, length(inactive))
+      }
+
+      final_upper = collapsed_pos - cur_offset
+      final_lower = -(collapsed_neg + cur_offset)
+
+      # now, we sample from Y_star, a centered Gaussian with covariance sigma^2 I
+      # subject to the constraint
+      # t(cur_adjusted_Xt) %*% Y_star < final_upper
+      # -t(cur_adjusted_Xt) %*% Y_star < -final_lower
+
+      # really, we want the covariance of Y_star to be \sigma^2 (I - cur_P)
+      # where P is projection on the j-1 previous variables
+      # but this doesn't matter as everything we do with the samples
+      # will be a function of (I - cur_P) Y_star and the constraints are
+      # expressible in terms of (I - cur_P) Y_star because
+      # we have adjusted X
+
+      # IMPORTANT: after sampling Y_star, we have to add back cur_fitted
+
+      # if n >= p, we could actually just draw cur_adjusted_Xt %*% Y_star
+      # because this has a simple box constraint
+      # with a generically non-degenerate covariance
+
+      if (nrow(cur_adjusted_Xt) > length(y)) {
+          linear_part = rbind(cur_adjusted_Xt, -cur_adjusted_Xt)
+          offset = c(final_upper, -final_lower)
+          covariance = diag(rep(sigma^2, length(y)))
+          mean_param = cur_fitted # rep(0, length(y))
+          initial_point = y
+
+          truncated_y = sample_from_constraints(linear_part, 
+                                                offset, 
+                                                mean_param, 
+                                                covariance, 
+                                                initial_point, 
+                                                burnin=burnin, 
+                                                ndraw=ndraw)
+
+          truncated_noise = truncated_y %*% t(cur_adjusted_Xt)
+          sample_maxZ = apply(abs(t(truncated_noise) / cur_scale), 2, max)
+      } else {  # sample from a smaller dimensional gaussian
+          if (nrow(cur_adjusted_Xt) > 1) {
+              linear_part = rbind(diag(rep(1, nrow(cur_adjusted_Xt))),
+                                  diag(rep(-1, nrow(cur_adjusted_Xt))))
+              covariance = sigma^2 * (cur_adjusted_Xt %*% t(cur_adjusted_Xt))
+              offset = c(final_upper, -final_lower)
+              mean_param = cur_adjusted_Xt %*% cur_fitted # rep(0, nrow(cur_adjusted_Xt))
+              initial_point = cur_adjusted_Xt %*% y
+          } else {
+              mean_param = as.numeric(sum(as.numeric(cur_adjusted_Xt) * as.numeric(cur_fitted)))
+              covariance = matrix(sigma^2 * sum(cur_adjusted_Xt^2))
+              linear_part = matrix(c(1,-1), 2, 1)
+              offset = c(final_upper, -final_lower)
+              initial_point = as.numeric(sum(as.numeric(cur_adjusted_Xt) * as.numeric(y)))
+          }
+          truncated_noise = sample_from_constraints(linear_part, 
+                                                offset, 
+                                                mean_param, 
+                                                covariance, 
+                                                initial_point, 
+                                                burnin=burnin, 
+                                                ndraw=ndraw)
+          sample_maxZ = apply(abs(t(truncated_noise) / cur_scale), 2, max)
+
+         } 
+
+      observed_maxZ = obj$realized_maxZ[j]
+      pval = sum(sample_maxZ > observed_maxZ) / ndraw
+      pval = 2 * min(pval, 1 - pval)
+      pv = c(pv, pval)
+  }
+
+  khat = forwardStop(pv,alpha)
+
+  out = list(pv=pv,
+             k=k,
+             khat=khat,
+	     sigma=sigma,
+	     vars=vars,
+	     sign=obj$sign,
+	     alpha=alpha,
+             realized_maxZ=obj$realized_maxZ,
+        call=this.call)
+  class(out) = "fsInf_maxZ"
+  return(out)
+}
+
+##############################
+#
+# Print methods
+#
+##############################
+
+print.fs <- function(x, ...) {
+  cat("\nCall:\n")
+  dput(x$call)
+  
+  cat("\nSequence of FS moves:\n")
+  nsteps = length(x$action)
+  tab = cbind(1:nsteps,x$action,x$sign)
+  colnames(tab) = c("Step","Var","Sign")
+  rownames(tab) = rep("",nrow(tab))
+  print(tab)
+  invisible()
+}
+
+print.fsInf <- function(x, tailarea=TRUE, ...) {
+  cat("\nCall:\n")
+  dput(x$call)
+
+  cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n",
+              x$sigma))
+
+  if (x$type == "active") {
+    cat(sprintf("\nSequential testing results with alpha = %0.3f\n",x$alpha))
+    tab = cbind(1:length(x$pv),x$vars,
+      round(x$sign*x$vmat%*%x$y,3),
+      round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
+      round(x$pv,3),round(x$ci,3))
+    colnames(tab) = c("Step", "Var", "Coef", "Z-score", "P-value",
+              "LowConfPt", "UpConfPt")
+    if (tailarea) {
+      tab = cbind(tab,round(x$tailarea,3))
+      colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+    }
+    rownames(tab) = rep("",nrow(tab))
+    print(tab)
+
+    cat(sprintf("\nEstimated stopping point from ForwardStop rule = %i\n",x$khat))
+  }
+
+  else if (x$type == "all") {
+    cat(sprintf("\nTesting results at step = %i, with alpha = %0.3f\n",x$k,x$alpha))
+    tab = cbind(x$vars,
+      round(x$sign*x$vmat%*%x$y,3),
+      round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
+      round(x$pv,3),round(x$ci,3))
+    colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
+    if (tailarea) {
+      tab = cbind(tab,round(x$tailarea,3))
+      colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+    }
+    rownames(tab) = rep("",nrow(tab))
+    print(tab)
+  }
+
+  else if (x$type == "aic") {
+    cat(sprintf("\nTesting results at step = %i, with alpha = %0.3f\n",x$khat,x$alpha))
+    tab = cbind(x$vars,
+      round(x$sign*x$vmat%*%x$y,3),
+      round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
+      round(x$pv,3),round(x$ci,3))
+    colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
+    if (tailarea) {
+      tab = cbind(tab,round(x$tailarea,3))
+      colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+    }
+    rownames(tab) = rep("",nrow(tab))
+    print(tab)
+    
+    cat(sprintf("\nEstimated stopping point from AIC rule = %i\n",x$khat))
+  }
+
+  invisible()
+}
+
+
+print.fsInf_maxZ <- function(obj) {
+
+  cat("\nCall:\n")
+  dput(obj$call)
+
+  cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n",
+              obj$sigma))
+
+  cat(sprintf("\nSequential testing results with alpha = %0.3f\n",obj$alpha))
+
+  tab = cbind(1:length(obj$pv),
+              obj$vars,
+              round(obj$sign*obj$realized_maxZ, 3),
+              round(obj$pv,3))
+  colnames(tab) = c("Step", "Var", "Z-score", "P-value")
+  rownames(tab) = rep("",nrow(tab))
+  print(tab)
+
+  cat(sprintf("\nEstimated stopping point from ForwardStop rule = %i\n",obj$khat))
+
+  invisible()
+}
+
+##############################
+# 
+# Plot methods
+#
+##############################
+
+plot.fs <- function(x, breaks=TRUE, omit.zeros=TRUE, var.labels=TRUE, ...) {
+  if (x$completepath) {
+    k = length(x$action)+1
+    beta = cbind(x$beta,x$bls)
+  } else {
+    k = length(x$action)
+    beta = x$beta
+  }
+  p = nrow(beta)
+
+  xx = 1:k
+  xlab = "Step"
+
+ if (omit.zeros) {
+   good.inds = matrix(FALSE,p,k)
+   good.inds[beta!=0] = TRUE
+   changes = t(apply(beta,1,diff))!=0
+   good.inds[cbind(changes,rep(F,p))] = TRUE
+   good.inds[cbind(rep(F,p),changes)] = TRUE
+   beta[!good.inds] = NA
+  }
+
+  plot(c(),c(),xlim=range(xx,na.rm=T),ylim=range(beta,na.rm=T),
+       xlab=xlab,ylab="Coefficients",main="Forward stepwise path",...)
+  abline(h=0,lwd=2)
+  matplot(xx,t(beta),type="l",lty=1,add=TRUE)
+  if (breaks) abline(v=xx,lty=2)
+  if (var.labels) axis(4,at=beta[,k],labels=1:p,cex=0.8,adj=0)
+  invisible()
+}
+
diff --git a/forLater/maxZ/forLater b/forLater/maxZ/forLater
new file mode 100644
index 0000000..89aa886
--- /dev/null
+++ b/forLater/maxZ/forLater
@@ -0,0 +1,86 @@
+\name{fsInf_maxZ}
+\alias{fsInf_maxZ}
+\title{
+Selective inference for forward stepwise regression
+}
+\description{
+Computes maxZ selective p-values and confidence intervals for forward 
+stepwise regression
+}
+\usage{
+                    
+fsInf_maxZ(obj, sigma=NULL, alpha=0.1, k=NULL,   ndraw=8000, burnin=2000,verbose=FALSE)
+    
+}
+
+\arguments{
+  \item{obj}{
+Object returned by \code{\link{fs}} function
+}
+\item{sigma}{
+Estimate of error standard deviation. If NULL (default), this is estimated 
+using the mean squared residual of the full least squares fit when n >= 2p, and 
+using the standard deviation of y when n < 2p. In the latter case, the user 
+should use \code{\link{estimateSigma}} function for a more accurate estimate
+}
+\item{alpha}{
+Significance level for confidence intervals (target is miscoverage alpha/2 in each tail)
+}
+\item{k}{
+See "type" argument below. Default is NULL, in which case k is taken to be the
+the number of steps computed in the forward stepwise path
+}
+\item{ndraw}{Number of Monte Carlo samples generated}
+\item{burnin}{
+Number of samples discarded at the beginning of the chain
+}
+\item{verbose}{Print out progress along the way? Default is FALSE}
+}
+
+\details{
+This function computes selective maxZ p-values
+for forward stepwise regression. These p-values are independent the under null,
+so that stopping via the forwardStop rule yields guaranteed FDR control
+}
+
+\value{  
+\item{pv}{P-values for each model in the sequence}
+\item{k}{Value of k specified in call}
+\item{khat}{When type is "active", this is an estimated stopping point
+declared by \code{\link{forwardStop}}}
+\item{sigma}{Value of error standard deviation (sigma) used}
+\item{vars}{Variables in active set}
+\item{sign}{Signs of active coefficients}
+\item{alpha}{Desired coverage (alpha/2 in each tail)}
+\item{realized_maxZ}{Value of maxZ statistic computed at each step}
+\item{call}{The call to fsInf_maxZ}
+}
+
+\references{
+Will Fithian, Jonathan Taylor,  Ryan Tibshirani, and Rob Tibshirani (2015).
+Selective sequential model selection.  arXiv:1512.02565..
+
+
+}
+
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\seealso{\code{\link{fs}}}
+
+\examples{
+set.seed(33)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run forward stepwise
+fsfit = fs(x,y)
+
+# compute sequential p-values a
+# (sigma estimated from full model)
+out.seq = fsInf_maxZ(fsfit)
+out.seq
+}
diff --git a/forLater/maxZ/funs.constraints.R b/forLater/maxZ/funs.constraints.R
new file mode 100644
index 0000000..3bf6b77
--- /dev/null
+++ b/forLater/maxZ/funs.constraints.R
@@ -0,0 +1,186 @@
+#
+# Some utilities for affine constraints
+#
+
+# 
+# compute the square-root and inverse square-root of a non-negative
+# definite matrix
+#
+
+factor_covariance = function(S, rank=NA) {
+    if (is.na(rank)) {
+        rank = nrow(S)
+    }
+    svd_X = svd(S, nu=rank, nv=rank)
+    sqrt_cov = t(sqrt(svd_X$d[1:rank]) * t(svd_X$u[,1:rank]))
+    sqrt_inv = t((1. / sqrt(svd_X$d[1:rank])) * t(svd_X$u[,1:rank]))
+
+    return(list(sqrt_cov=sqrt_cov, sqrt_inv=sqrt_inv))
+}
+
+#
+# from a constraint, return an equivalent
+# constraint and a whitening and inverse
+# whitening map
+#
+
+# law is Z \sim N(mean_param, covariance) subject to constraints linear_part %*% Z \leq offset
+
+whiten_constraint = function(linear_part, offset, mean_param, covariance) {
+
+    factor_cov = factor_covariance(covariance)
+    sqrt_cov = factor_cov$sqrt_cov
+    sqrt_inv = factor_cov$sqrt_inv
+
+    new_A = linear_part %*% sqrt_cov
+    new_b = offset - linear_part %*% mean_param
+
+    # rescale rows to have length 1
+
+    scaling = sqrt(apply(new_A^2, 1, sum))
+    new_A = new_A / scaling
+    new_b = new_b / scaling
+
+    # TODO: check these functions will behave when Z is a matrix.
+
+    inverse_map = function(Z) {
+	# broadcasting here
+        # the columns of Z are same length as mean_param
+        return(sqrt_cov %*% Z + as.numeric(mean_param))
+    }
+
+    forward_map = function(W) {
+        return(sqrt_inv %*% (W - mean_param))
+    }    
+
+    return(list(linear_part=new_A,
+                offset=new_b,
+                inverse_map=inverse_map,
+                forward_map=forward_map))
+}
+
+#
+# sample from the law
+#
+# Z \sim N(mean_param, covariance) subject to constraints linear_part %*% Z \leq offset
+
+sample_from_constraints = function(linear_part,
+                                   offset,
+                                   mean_param,
+                                   covariance,
+                                   initial_point, # point must be feasible for constraints
+                                   ndraw=8000,
+                                   burnin=2000,
+                                   accept_reject_params=NA) #TODO: implement accept reject check
+{
+
+    whitened_con = whiten_constraint(linear_part,
+                                     offset,
+				     mean_param,
+                                     covariance)
+    white_initial = whitened_con$forward_map(initial_point)
+
+#     # try 100 draws of accept reject
+#     # if we get more than 50 good draws, then just return a smaller sample
+#     # of size (burnin+ndraw)/5
+
+#     if accept_reject_params: 
+#         use_hit_and_run = False
+#         num_trial, min_accept, num_draw = accept_reject_params
+
+#         def _accept_reject(sample_size, linear_part, offset):
+#             Z_sample = np.random.standard_normal((100, linear_part.shape[1]))
+#             constraint_satisfied = (np.dot(Z_sample, linear_part.T) - 
+#                                     offset[None,:]).max(1) < 0
+#             return Z_sample[constraint_satisfied]
+
+#         Z_sample = _accept_reject(100, 
+#                                   white_con.linear_part,
+#                                   white_con.offset)
+
+#         if Z_sample.shape[0] >= min_accept:
+#             while True:
+#                 Z_sample = np.vstack([Z_sample,
+#                                       _accept_reject(num_draw / 5,
+#                                                      white_con.linear_part,
+#                                                      white_con.offset)])
+#                 if Z_sample.shape[0] > num_draw:
+#                     break
+#             white_samples = Z_sample
+#         else:
+#             use_hit_and_run = True
+#     else:
+#         use_hit_and_run = True
+
+    use_hit_and_run = TRUE
+
+    if (use_hit_and_run) {
+
+        white_linear = whitened_con$linear_part
+        white_offset = whitened_con$offset
+
+	# Inf cannot be used in C code
+	# In theory, these rows can be dropped
+
+	rows_to_keep = white_offset < Inf
+	white_linear = white_linear[rows_to_keep,,drop=FALSE]
+	white_offset = white_offset[rows_to_keep]
+
+        nstate = length(white_initial)
+	if (sum(rows_to_keep) > 0) {
+	    if (ncol(white_linear) > 1) {
+                nconstraint = nrow(white_linear)
+
+                directions = rbind(diag(rep(1, nstate)),
+                                   matrix(rnorm(nstate^2), nstate, nstate))
+
+                # normalize rows to have length 1
+
+                scaling = apply(directions, 1, function(x) {  return(sqrt(sum(x^2))) }) 
+                directions = directions / scaling
+                ndirection = nrow(directions)
+
+                alphas = directions %*% t(white_linear)
+                U = white_linear %*% white_initial - white_offset
+                Z_sample = matrix(rep(0, nstate * ndraw), nstate, ndraw)
+
+                result = .C("sample_truncnorm_white",
+                            as.numeric(white_initial),
+                            as.numeric(U),
+                            as.numeric(t(directions)),
+                            as.numeric(t(alphas)),
+                            output=Z_sample,
+                            as.integer(nconstraint),
+                            as.integer(ndirection),
+                            as.integer(nstate),
+                            as.integer(burnin),
+                            as.integer(ndraw),
+                            package="selectiveInference")
+                Z_sample = result$output
+           } else { # the distribution is univariate
+                    # we can just work out upper and lower limits
+
+                white_linear = as.numeric(white_linear)
+                pos = (white_linear * white_offset) >= 0
+                neg = (white_linear * white_offset) <= 0
+		if (sum(pos) > 0) {
+                    U = min((white_offset / white_linear)[pos])
+                } else {
+		    U = Inf
+                }
+		if (sum(neg) < 0) {
+                    L = max((white_offset / white_linear)[neg])
+                } else {
+                    L = -Inf
+                }
+                Z_sample = matrix(qnorm((pnorm(U) - pnorm(L)) * runif(ndraw) + pnorm(L)), 1, ndraw)
+           }
+       } else {
+           Z_sample = matrix(rnorm(nstate * ndraw), nstate, ndraw)
+       }
+    }
+
+    Z = t(whitened_con$inverse_map(Z_sample))
+    return(Z)
+}
+

From 74e3c482077d159be98a45c41c664f1b1cd8c8f1 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Thu, 21 Jul 2016 16:53:06 -0700
Subject: [PATCH 145/493] Simulation to compare saturated and selected models
 in fs

---
 forLater/josh/sim.selectedmodel.R | 60 +++++++++++++++++++++++++++++++
 1 file changed, 60 insertions(+)
 create mode 100644 forLater/josh/sim.selectedmodel.R

diff --git a/forLater/josh/sim.selectedmodel.R b/forLater/josh/sim.selectedmodel.R
new file mode 100644
index 0000000..c278e98
--- /dev/null
+++ b/forLater/josh/sim.selectedmodel.R
@@ -0,0 +1,60 @@
+library(selectiveInference)
+library(intervals)
+setwd("~/Dropbox/work/R-software/forLater/josh")
+source("selectiveInference/R/cv.R")
+source("../../selectiveInference/R/funs.groupfs.R")
+source("../../selectiveInference/R/funs.quadratic.R")
+source("../../selectiveInference/R/funs.common.R")
+source("../../selectiveInference/R/funs.fs.R")
+source("../../selectiveInference/R/funs.lar.R")
+source("../../selectiveInference/R/funs.inf.R")
+library(MASS)
+pinv = ginv
+
+set.seed(19)
+niters <- 500
+known <- TRUE
+n <- 50
+p <- 100
+maxsteps <- 8
+sparsity <- 5
+snr <- 2
+index <- 1:p
+
+x <- matrix(rnorm(n*p), nrow=n)
+
+instance <- function(n, p, sparsity, snr, maxsteps) {
+    y <- rnorm(n)
+    if (sparsity > 0) {
+      beta <- rep(0, p)
+      beta[1:sparsity] <- snr * sample(c(-1,1), sparsity, replace=T)
+      y <- y + x %*% beta
+    }
+    y <- y - mean(y)
+    fit <- groupfs(x, y, index, maxsteps=maxsteps, sigma=1, intercept=F, center=F, normalize=F)
+    fitfs <- fs(x, y, maxsteps=maxsteps, intercept=F, normalize=F)
+    if (any(fit$action != fitfs$action)) stop("Model paths did not agree")
+    pvfs <- fsInf(fitfs, sigma=1, k = maxsteps, type = "all")
+    pv <- groupfsInf(fit)
+    return(list(vars = fit$action, pvals = pv$pv, selpvals = pvfs$pv))
+}
+
+time <- system.time({
+          output <- replicate(niters, instance(n, p, sparsity, snr, maxsteps))
+})
+
+vars <- do.call(c, list(output[1,]))
+pvals <- do.call(c, list(output[2,]))
+selpvals <- do.call(c, list(output[3,]))
+
+save(vars, pvals, selpvals,
+     file = paste0("results/selected",
+         "_", ifelse(known, "TC", "TF"),
+         "_n", n,
+         "_p", p,
+         "_sparsity", sparsity,
+         "_snr", as.character(snr),
+         ".RData"))
+
+print(time)
+

From cb3ef4ab90389fe482d6f3628dea5018eafe0f7d Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Thu, 21 Jul 2016 16:56:06 -0700
Subject: [PATCH 146/493] Moving some common functions

---
 selectiveInference/R/funs.common.R |  50 ++++++++++--
 selectiveInference/R/funs.fixed.R  |  57 ++++++-------
 selectiveInference/R/funs.lar.R    | 123 +++++++++++------------------
 3 files changed, 115 insertions(+), 115 deletions(-)

diff --git a/selectiveInference/R/funs.common.R b/selectiveInference/R/funs.common.R
index 7a4b9aa..5945700 100644
--- a/selectiveInference/R/funs.common.R
+++ b/selectiveInference/R/funs.common.R
@@ -32,7 +32,7 @@ standardize <- function(x, y, intercept, normalize) {
   y = as.numeric(y)
   n = nrow(x)
   p = ncol(x)
-  
+
   if (intercept) {
     bx = colMeans(x)
     by = mean(y)
@@ -57,10 +57,10 @@ standardize <- function(x, y, intercept, normalize) {
 # Interpolation function to get coefficients
 
 coef.interpolate <- function(betas, s, knots, dec=TRUE) {
-  # Sort the s values 
+  # Sort the s values
   o = order(s,dec=dec)
   s = s[o]
-        
+
   k = length(s)
   mat = matrix(rep(knots,each=k),nrow=k)
   if (dec) b = s >= mat
@@ -72,7 +72,7 @@ coef.interpolate <- function(betas, s, knots, dec=TRUE) {
   p = numeric(k)
   p[i] = 0
   p[!i] = ((s-knots[blo])/(knots[bhi]-knots[blo]))[!i]
-  
+
   beta = t((1-p)*t(betas[,blo,drop=FALSE]) + p*t(betas[,bhi,drop=FALSE]))
   colnames(beta) = as.character(round(s,3))
   rownames(beta) = NULL
@@ -100,7 +100,7 @@ checkargs.misc <- function(sigma=NULL, alpha=NULL, k=NULL,
                            mult=NULL, ntimes=NULL,
                            beta=NULL, lambda=NULL, tol.beta=NULL, tol.kkt=NULL,
                            bh.q=NULL) {
-  
+
   if (!is.null(sigma) && sigma <= 0) stop("sigma must be > 0")
   if (!is.null(lambda) && lambda < 0) stop("lambda must be >= 0")
   if (!is.null(alpha) && (alpha <= 0 || alpha >= 1)) stop("alpha must be between 0 and 1")
@@ -144,3 +144,43 @@ estimateSigma <- function(x, y, intercept=TRUE, standardize=TRUE) {
   return(list(sigmahat=sigma, df=nz))
 }
 
+# Update the QR factorization, after a column has been
+# added. Here Q1 is m x n, Q2 is m x k, and R is n x n.
+
+updateQR <- function(Q1,Q2,R,col) {
+  m = nrow(Q1)
+  n = ncol(Q1)
+  k = ncol(Q2)
+
+  a = .C("update1",
+    Q2=as.double(Q2),
+    w=as.double(t(Q2)%*%col),
+    m=as.integer(m),
+    k=as.integer(k),
+    dup=FALSE,
+    package="selectiveInference")
+
+  Q2 = matrix(a$Q2,nrow=m)
+  w = c(t(Q1)%*%col,a$w)
+
+  # Re-structure: delete a column from Q2, add one to
+  # Q1, and expand R
+  Q1 = cbind(Q1,Q2[,1])
+  Q2 = Q2[,-1,drop=FALSE]
+  R = rbind(R,rep(0,n))
+  R = cbind(R,w[Seq(1,n+1)])
+
+  return(list(Q1=Q1,Q2=Q2,R=R))
+}
+
+# Moore-Penrose pseudo inverse for symmetric matrices
+
+pinv <- function(A, tol=.Machine$double.eps) {
+  e = eigen(A)
+  v = Re(e$vec)
+  d = Re(e$val)
+  d[d > tol] = 1/d[d > tol]
+  d[d < tol] = 0
+  if (length(d)==1) return(v*d*v)
+  else return(v %*% diag(d) %*% t(v))
+}
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 4263c0b..e1b9230 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -6,11 +6,11 @@ fixedLassoInf <- function(x, y, beta, lambda, family=c("gaussian","binomial","co
 sigma=NULL, alpha=0.1,
                      type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
                      gridrange=c(-100,100), bits=NULL, verbose=FALSE) {
-    
+
  family = match.arg(family)
   this.call = match.call()
   type = match.arg(type)
- 
+
   if(family=="binomial")  {
       if(type!="partial") stop("Only type= partial allowed with binomial family")
        out=fixedLogitLassoInf(x,y,beta,lambda,alpha=alpha, type="partial", tol.beta=tol.beta, tol.kkt=tol.kkt,
@@ -20,24 +20,24 @@ sigma=NULL, alpha=0.1,
 else if(family=="cox")  {
     if(type!="partial") stop("Only type= partial allowed with Cox family")
      out=fixedCoxLassoInf(x,y,status,beta,lambda,alpha=alpha, type="partial",tol.beta=tol.beta,
-          tol.kkt=tol.kkt, gridrange=gridrange, bits=bits, verbose=verbose,this.call=this.call)               
+          tol.kkt=tol.kkt, gridrange=gridrange, bits=bits, verbose=verbose,this.call=this.call)
                       return(out)
                     }
- 
+
 else{
 
- 
- 
+
+
   checkargs.xy(x,y)
   if (missing(beta) || is.null(beta)) stop("Must supply the solution beta")
-  if (missing(lambda) || is.null(lambda)) stop("Must supply the tuning parameter value lambda") 
+  if (missing(lambda) || is.null(lambda)) stop("Must supply the tuning parameter value lambda")
   checkargs.misc(beta=beta,lambda=lambda,sigma=sigma,alpha=alpha,
                  gridrange=gridrange,tol.beta=tol.beta,tol.kkt=tol.kkt)
   if (!is.null(bits) && !requireNamespace("Rmpfr",quietly=TRUE)) {
     warning("Package Rmpfr is not installed, reverting to standard precision")
     bits = NULL
   }
-  
+
   n = nrow(x)
   p = ncol(x)
   beta = as.numeric(beta)
@@ -66,14 +66,14 @@ else{
                   "(to within specified tolerances). You might try rerunning",
                   "glmnet with a lower setting of the",
                   "'thresh' parameter, for a more accurate convergence."))
-  
+
   # Get lasso polyhedral region, of form Gy >= u
   out = fixedLasso.poly(x,y,beta,lambda,vars)
   G = out$G
   u = out$u
 
   # Check polyhedral region
-  tol.poly = 0.01 
+  tol.poly = 0.01
   if (min(G %*% y - u) < -tol.poly * sqrt(sum(y^2)))
     stop(paste("Polyhedral constraints not satisfied; you must recompute beta",
                "more accurately. With glmnet, make sure to use exact=TRUE in coef(),",
@@ -94,9 +94,9 @@ else{
                     "you may want to use the estimateSigma function"))
     }
   }
- 
+
   k = length(vars)
-  pv = vlo = vup = numeric(k) 
+  pv = vlo = vup = numeric(k)
   vmat = matrix(0,k,n)
   ci = tailarea = matrix(0,k,2)
   sign = numeric(k)
@@ -104,7 +104,7 @@ else{
   if (type=="full" & p > n)
       warning(paste("type='full' does not make sense when p > n;",
                     "switching to type='partial'"))
-  
+
   if (type=="partial" || p > n) {
     xa = x[,vars,drop=F]
     M = pinv(crossprod(xa)) %*% t(xa)
@@ -113,17 +113,17 @@ else{
     M = pinv(crossprod(x)) %*% t(x)
     M = M[vars,,drop=F]
   }
-  
+
   for (j in 1:k) {
     if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
-    
+
     vj = M[j,]
     mj = sqrt(sum(vj^2))
     vj = vj / mj        # Standardize (divide by norm of vj)
     sign[j] = sign(sum(vj*y))
     vj = sign[j] * vj
     a = poly.pval(y,G,u,vj,sigma,bits)
-    pv[j] = a$pv 
+    pv[j] = a$pv
     vlo[j] = a$vlo * mj # Unstandardize (mult by norm of vj)
     vup[j] = a$vup * mj # Unstandardize (mult by norm of vj)
     vmat[j,] = vj * mj * sign[j]  # Unstandardize (mult by norm of vj)
@@ -133,12 +133,12 @@ else{
     ci[j,] = a$int * mj # Unstandardize (mult by norm of vj)
     tailarea[j,] = a$tailarea
   }
-  
+
   out = list(type=type,lambda=lambda,pv=pv,ci=ci,
     tailarea=tailarea,vlo=vlo,vup=vup,vmat=vmat,y=y,
     vars=vars,sign=sign,sigma=sigma,alpha=alpha,
     sd=sigma*sqrt(rowSums(vmat^2)),
-    coef0=vmat%*%y, 
+    coef0=vmat%*%y,
     call=this.call)
   class(out) = "fixedLassoInf"
   return(out)
@@ -160,7 +160,7 @@ function(x, y, beta, lambda, a) {
 
   P = diag(1,nrow(xa)) - xa %*% xap
   #NOTE: inactive constraints not needed below!
- 
+
   G = -rbind(
    #   1/lambda * t(xac) %*% P,
    # -1/lambda * t(xac) %*% P,
@@ -175,17 +175,6 @@ function(x, y, beta, lambda, a) {
 
   return(list(G=G,u=u))
 }
-# Moore-Penrose pseudo inverse for symmetric matrices
-
-pinv <- function(A, tol=.Machine$double.eps) {
-  e = eigen(A)
-  v = Re(e$vec)
-  d = Re(e$val)
-  d[d > tol] = 1/d[d > tol]
-  d[d < tol] = 0
-  if (length(d)==1) return(v*d*v)
-  else return(v %*% diag(d) %*% t(v))
-}
 
 ##############################
 
@@ -195,7 +184,7 @@ print.fixedLassoInf <- function(x, tailarea=TRUE, ...) {
 
   cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n",
               x$sigma))
-  
+
   cat(sprintf("\nTesting results at lambda = %0.3f, with alpha = %0.3f\n",x$lambda,x$alpha))
   cat("",fill=T)
   tab = cbind(x$vars,
@@ -209,7 +198,7 @@ print.fixedLassoInf <- function(x, tailarea=TRUE, ...) {
   }
   rownames(tab) = rep("",nrow(tab))
   print(tab)
- 
+
   cat(sprintf("\nNote: coefficients shown are %s regression coefficients\n",
               ifelse(x$type=="partial","partial","full")))
   invisible()
@@ -220,10 +209,10 @@ print.fixedLassoInf <- function(x, tailarea=TRUE, ...) {
 #  if(nsamp < 10) stop("More Monte Carlo samples required for estimation")
 #  if (length(sigma)!=1) stop("sigma should be a number > 0")
  # if (sigma<=0) stop("sigma should be a number > 0")
-                                      
+
  # n = nrow(x)
  # eps = sigma*matrix(rnorm(nsamp*n),n,nsamp)
  # lambda = 2*mean(apply(t(x)%*%eps,2,max))
  # return(lambda)
 #}
-    
+
diff --git a/selectiveInference/R/funs.lar.R b/selectiveInference/R/funs.lar.R
index 26669ca..f01ee3d 100644
--- a/selectiveInference/R/funs.lar.R
+++ b/selectiveInference/R/funs.lar.R
@@ -17,7 +17,7 @@ lar <- function(x, y, maxsteps=2000, minlam=0, intercept=TRUE, normalize=TRUE,
 
   this.call = match.call()
   checkargs.xy(x=x,y=y)
-  
+
   # Center and scale, etc.
   obj = standardize(x,y,intercept,normalize)
   x = obj$x
@@ -49,7 +49,7 @@ lar <- function(x, y, maxsteps=2000, minlam=0, intercept=TRUE, normalize=TRUE,
   action = numeric(buf)      # Actions taken
   df = numeric(buf)          # Degrees of freedom
   beta = matrix(0,p,buf)     # LAR estimates
-  
+
   lambda[1] = hit
   action[1] = ihit
   df[1] = 0
@@ -91,14 +91,14 @@ lar <- function(x, y, maxsteps=2000, minlam=0, intercept=TRUE, normalize=TRUE,
   Q1 = Q[,1,drop=FALSE];
   Q2 = Q[,-1,drop=FALSE]
   R = qr.R(obj)
-  
+
   # Throughout the algorithm, we will maintain
   # the decomposition X1 = Q1*R. Dimenisons:
   # X1: n x r
   # Q1: n x r
   # Q2: n x (n-r)
   # R:  r x r
-    
+
   while (k<=maxsteps && lambda[k-1]>=minlam) {
     ##########
     # Check if we've reached the end of the buffer
@@ -118,7 +118,7 @@ lar <- function(x, y, maxsteps=2000, minlam=0, intercept=TRUE, normalize=TRUE,
     b = backsolve(R,backsolve(R,s,transpose=TRUE))
     aa = as.numeric(t(X2) %*% (y - X1 %*% a))
     bb = as.numeric(t(X2) %*% (X1 %*% b))
-    
+
     # If the inactive set is empty, nothing will hit
     if (r==min(n-intercept,p)) hit = 0
 
@@ -128,9 +128,9 @@ lar <- function(x, y, maxsteps=2000, minlam=0, intercept=TRUE, normalize=TRUE,
       hits = aa/(shits-bb)
 
       # Make sure none of the hitting times are larger
-      # than the current lambda 
+      # than the current lambda
       hits[hits>lambda[k-1]] = 0
-        
+
       ihit = which.max(hits)
       hit = hits[ihit]
       shit = shits[ihit]
@@ -138,13 +138,13 @@ lar <- function(x, y, maxsteps=2000, minlam=0, intercept=TRUE, normalize=TRUE,
 
     # Stop if the next critical point is negative
     if (hit<=0) break
-    
+
     # Record the critical lambda and solution
     lambda[k] = hit
     action[k] = I[ihit]
     df[k] = r
     beta[A,k] = a-hit*b
-        
+
     # Gamma matrix!
     if (gi + 2*p > nrow(Gamma)) Gamma = rbind(Gamma,matrix(0,2*p+gbuf,n))
     X2perp = X2 - X1 %*% backsolve(R,t(Q1)%*%X2)
@@ -162,7 +162,7 @@ lar <- function(x, y, maxsteps=2000, minlam=0, intercept=TRUE, normalize=TRUE,
       crit = (t(c[,-ihit])%*%y - ratio*sum(c[,ihit]*y))/(1-ratio)
       mp[k] = max(max(crit[ip]),0)
     }
-    
+
     # Update all of the variables
     r = r+1
     A = c(A,I[ihit])
@@ -176,12 +176,12 @@ lar <- function(x, y, maxsteps=2000, minlam=0, intercept=TRUE, normalize=TRUE,
     Q1 = obj$Q1
     Q2 = obj$Q2
     R = obj$R
-     
+
     if (verbose) {
       cat(sprintf("\n%i. lambda=%.3f, adding variable %i, |A|=%i...",
                   k,hit,A[r],r))
     }
-            
+
     # Step counter
     k = k+1
   }
@@ -195,7 +195,7 @@ lar <- function(x, y, maxsteps=2000, minlam=0, intercept=TRUE, normalize=TRUE,
   nk = nk[Seq(1,k-1)]
   mp = mp[Seq(1,k-1)]
   vreg = vreg[Seq(1,k-1),,drop=FALSE]
-  
+
   # If we reached the maximum number of steps
   if (k>maxsteps) {
     if (verbose) {
@@ -215,11 +215,11 @@ lar <- function(x, y, maxsteps=2000, minlam=0, intercept=TRUE, normalize=TRUE,
     completepath = FALSE
     bls = NULL
   }
-  
+
   # Otherwise, note that we completed the path
   else {
     completepath = TRUE
-    
+
     # Record the least squares solution. Note that
     # we have already computed this
     bls = rep(0,p)
@@ -227,19 +227,19 @@ lar <- function(x, y, maxsteps=2000, minlam=0, intercept=TRUE, normalize=TRUE,
   }
 
   if (verbose) cat("\n")
-  
+
   # Adjust for the effect of centering and scaling
   if (intercept) df = df+1
   if (normalize) beta = beta/sx
   if (normalize && completepath) bls = bls/sx
-  
+
   # Assign column names
   colnames(beta) = as.character(round(lambda,3))
 
   out = list(lambda=lambda,action=action,sign=s,df=df,beta=beta,
     completepath=completepath,bls=bls,
     Gamma=Gamma,nk=nk,vreg=vreg,mp=mp,x=x,y=y,bx=bx,by=by,sx=sx,
-    intercept=intercept,normalize=normalize,call=this.call) 
+    intercept=intercept,normalize=normalize,call=this.call)
   class(out) = "lar"
   return(out)
 }
@@ -253,7 +253,7 @@ lar <- function(x, y, maxsteps=2000, minlam=0, intercept=TRUE, normalize=TRUE,
 downdateQR <- function(Q1,Q2,R,col) {
   m = nrow(Q1)
   n = ncol(Q1)
-  
+
   a = .C("downdate1",
     Q1=as.double(Q1),
     R=as.double(R),
@@ -275,35 +275,6 @@ downdateQR <- function(Q1,Q2,R,col) {
   return(list(Q1=Q1,Q2=Q2,R=R))
 }
 
-# Update the QR factorization, after a column has been
-# added. Here Q1 is m x n, Q2 is m x k, and R is n x n.
-
-updateQR <- function(Q1,Q2,R,col) {
-  m = nrow(Q1)
-  n = ncol(Q1)
-  k = ncol(Q2)
-  
-  a = .C("update1",
-    Q2=as.double(Q2),
-    w=as.double(t(Q2)%*%col),
-    m=as.integer(m),
-    k=as.integer(k),
-    dup=FALSE,
-    package="selectiveInference")
-
-  Q2 = matrix(a$Q2,nrow=m)
-  w = c(t(Q1)%*%col,a$w)
-
-  # Re-structure: delete a column from Q2, add one to
-  # Q1, and expand R
-  Q1 = cbind(Q1,Q2[,1])
-  Q2 = Q2[,-1,drop=FALSE]
-  R = rbind(R,rep(0,n))
-  R = cbind(R,w[Seq(1,n+1)])
-
-  return(list(Q1=Q1,Q2=Q2,R=R))
-}
-
 ##############################
 
 # Coefficient function for lar
@@ -320,7 +291,7 @@ coef.lar <- function(object, s, mode=c("step","lambda"), ...) {
     lambda = object$lambda
     beta = object$beta
   }
-  
+
   if (mode=="step") {
     if (min(s)<0 || max(s)>k) stop(sprintf("s must be between 0 and %i",k))
     knots = 1:k
@@ -330,7 +301,7 @@ coef.lar <- function(object, s, mode=c("step","lambda"), ...) {
     knots = lambda
     dec = TRUE
   }
-  
+
   return(coef.interpolate(beta,s,knots,dec))
 }
 
@@ -350,9 +321,9 @@ predict.lasso <- predict.lar
 
 # Lar inference function
 
-larInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic"), 
+larInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic"),
                    gridrange=c(-100,100), bits=NULL, mult=2, ntimes=2, verbose=FALSE) {
-  
+
   this.call = match.call()
   type = match.arg(type)
   checkargs.misc(sigma=sigma,alpha=alpha,k=k,
@@ -364,7 +335,7 @@ larInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","ai
     warning("Package Rmpfr is not installed, reverting to standard precision")
     bits = NULL
   }
-  
+
   k = min(k,length(obj$action)) # Round to last step
   x = obj$x
   y = obj$y
@@ -385,11 +356,11 @@ larInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","ai
                     "you may want to use the estimateSigma function"))
     }
   }
-  
+
   pv.spacing = pv.modspac = pv.covtest = khat = NULL
-  
+
   if (type == "active") {
-    pv = vlo = vup = numeric(k) 
+    pv = vlo = vup = numeric(k)
     vmat = matrix(0,k,n)
     ci = tailarea = matrix(0,k,2)
     pv.spacing = pv.modspac = pv.covtest = numeric(k)
@@ -399,7 +370,7 @@ larInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","ai
 
     for (j in 1:k) {
       if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
-      
+
       Gj = G[1:nk[j],]
       uj = rep(0,nk[j])
       vj = vreg[j,]
@@ -411,12 +382,12 @@ larInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","ai
       vlo[j] = a$vlo * mj / sxj # Unstandardize (mult by norm of vj / sxj)
       vup[j] = a$vup * mj / sxj # Unstandardize (mult by norm of vj)
       vmat[j,] = vj * mj / sxj  # Unstandardize (mult by norm of vj / sxj)
-    
+
       a = poly.int(y,Gj,uj,vj,sigma,alpha,gridrange=gridrange,
         flip=(sign[j]==-1),bits=bits)
-      ci[j,] = a$int * mj / sxj # Unstandardize (mult by norm of vj / sxj) 
+      ci[j,] = a$int * mj / sxj # Unstandardize (mult by norm of vj / sxj)
       tailarea[j,] = a$tailarea
-      
+
       pv.spacing[j] = spacing.pval(obj,sigma,j)
       pv.modspac[j] = modspac.pval(obj,sigma,j)
       pv.covtest[j] = covtest.pval(obj,sigma,j)
@@ -424,7 +395,7 @@ larInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","ai
 
     khat = forwardStop(pv,alpha)
   }
-  
+
   else {
     if (type == "aic") {
       out = aicStop(x,y,obj$action[1:k],obj$df[1:k],sigma,mult,ntimes)
@@ -439,22 +410,22 @@ larInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","ai
       u = rep(0,nk[k])
       kk = k
     }
-    
-    pv = vlo = vup = numeric(kk) 
+
+    pv = vlo = vup = numeric(kk)
     vmat = matrix(0,kk,n)
     ci = tailarea = matrix(0,kk,2)
     sign = numeric(kk)
     vars = obj$action[1:kk]
     xa = x[,vars]
     M = pinv(crossprod(xa)) %*% t(xa)
-    
+
     for (j in 1:kk) {
       if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
-      
+
       vj = M[j,]
       mj = sqrt(sum(vj^2))
       vj = vj / mj             # Standardize (divide by norm of vj)
-      sign[j] = sign(sum(vj*y)) 
+      sign[j] = sign(sum(vj*y))
       vj = sign[j] * vj
       Gj = rbind(G,vj)
       uj = c(u,0)
@@ -472,7 +443,7 @@ larInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","ai
       tailarea[j,] = a$tailarea
     }
   }
-  
+
   out = list(type=type,k=k,khat=khat,pv=pv,ci=ci,
     tailarea=tailarea,vlo=vlo,vup=vup,vmat=vmat,y=y,
     pv.spacing=pv.spacing,pv.modspac=pv.modspac,pv.covtest=pv.covtest,
@@ -488,10 +459,10 @@ spacing.pval <- function(obj, sigma, k) {
   v = obj$Gamma[obj$nk[k],]
   sd = sigma*sqrt(sum(v^2))
   a = obj$mp[k]
-  
+
   if (k==1) b = Inf
   else b = obj$lambda[k-1]
-  
+
   return(tnorm.surv(obj$lambda[k],0,sd,a,b))
 }
 
@@ -505,7 +476,7 @@ modspac.pval <- function(obj, sigma, k) {
     warning(sprintf("Modified spacing p-values at step %i require %i steps of the lar path",k,k+1))
     return(NA)
   }
-      
+
   if (k==1) b = Inf
   else b = obj$lambda[k-1]
 
@@ -543,7 +514,7 @@ covtest.pval <- function(obj, sigma, k) {
 print.lar <- function(x, ...) {
   cat("\nCall:\n")
   dput(x$call)
-  
+
   cat("\nSequence of LAR moves:\n")
   nsteps = length(x$action)
   tab = cbind(1:nsteps,x$action,x$sign)
@@ -566,7 +537,7 @@ print.larInf <- function(x, tailarea=TRUE, ...) {
     tab = cbind(1:length(x$pv),x$vars,
       round(x$sign*x$vmat%*%x$y,3),
       round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
-      round(x$pv,3),round(x$ci,3),round(x$pv.spacing,3),round(x$pv.cov,3)) 
+      round(x$pv,3),round(x$ci,3),round(x$pv.spacing,3),round(x$pv.cov,3))
     colnames(tab) = c("Step", "Var", "Coef", "Z-score", "P-value",
               "LowConfPt", "UpConfPt", "Spacing", "CovTest")
     if (tailarea) {
@@ -609,7 +580,7 @@ print.larInf <- function(x, tailarea=TRUE, ...) {
     }
     rownames(tab) = rep("",nrow(tab))
     print(tab)
-    
+
     cat(sprintf("\nEstimated stopping point from AIC rule = %i\n",x$khat))
   }
 
@@ -618,7 +589,7 @@ print.larInf <- function(x, tailarea=TRUE, ...) {
 
 plot.lar <- function(x, xvar=c("norm","step","lambda"), breaks=TRUE,
                      omit.zeros=TRUE, var.labels=TRUE, ...) {
-  
+
   if (x$completepath) {
     k = length(x$action)+1
     lambda = c(x$lambda,0)
@@ -629,7 +600,7 @@ plot.lar <- function(x, xvar=c("norm","step","lambda"), breaks=TRUE,
     beta = x$beta
   }
   p = nrow(beta)
-  
+
   xvar = match.arg(xvar)
   if (xvar=="norm") {
     xx = colSums(abs(beta))
@@ -656,6 +627,6 @@ plot.lar <- function(x, xvar=c("norm","step","lambda"), breaks=TRUE,
   abline(h=0,lwd=2)
   matplot(xx,t(beta),type="l",lty=1,add=TRUE)
   if (breaks) abline(v=xx,lty=2)
-  if (var.labels) axis(4,at=beta[,k],labels=1:p,cex=0.8,adj=0) 
+  if (var.labels) axis(4,at=beta[,k],labels=1:p,cex=0.8,adj=0)
   invisible()
 }

From c6015aa32fd973d295760d9cfcfd4e4d78b1b9ab Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Thu, 27 Oct 2016 14:55:53 +0100
Subject: [PATCH 147/493] cvMakeFolds now has permutation built in

---
 forLater/josh/selectiveInference/R/cv.R | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/forLater/josh/selectiveInference/R/cv.R b/forLater/josh/selectiveInference/R/cv.R
index dfd6395..fcc6e54 100644
--- a/forLater/josh/selectiveInference/R/cv.R
+++ b/forLater/josh/selectiveInference/R/cv.R
@@ -1,11 +1,18 @@
 # ------------------------------------------------
 # Cross-validation, preliminary
 
-cvMakeFolds <- function(x, nfolds = 10) {
-    #inds <- sample(1:nrow(x), replace=FALSE)
-    inds <- 1:nrow(x)
+cvMakeFolds <- function(x, nfolds = 5) {
+    inds <- sample(1:nrow(x), replace=FALSE)
+    #inds <- 1:nrow(x)
     foldsize <- floor(nrow(x)/nfolds)
-    lapply(1:nfolds, function(f) return(inds[1:foldsize+(f-1)*foldsize]))
+    folds <- lapply(1:nfolds, function(f) return(inds[1:foldsize+(f-1)*foldsize]))
+    if (nfolds*foldsize < nrow(x)) {
+      # remainder observations added to first several folds
+      for (i in 1:(nrow(x) - nfolds*foldsize)) {
+        folds[[i]] <- c(folds[[i]], inds[nfolds*foldsize + i])
+      }
+    }
+    return(folds)
 }
 
 ############################################
@@ -87,6 +94,7 @@ cvfs <- function(x, y, index = 1:ncol(x), maxsteps, sigma = NULL, intercept = TR
         fold <- folds[[f]]
         fit <- groupfs(X[-fold,], Y[-fold], index=index, maxsteps=maxsteps, sigma=sigma, intercept=FALSE, center=FALSE, normalize=FALSE)
         fit$fold <- fold
+        # Why is this commented out?
         ## projections[[f]] <- lapply(fit$projections, function(step.projs) {
         ##     lapply(step.projs, function(proj) {
         ##         # Reduce from n by n matrix to svdu_thresh

From c467ba2267a21457bd4bbf3cee61c1eba5ad5b25 Mon Sep 17 00:00:00 2001
From: Joshua Loftus <joftius@stanford.edu>
Date: Thu, 27 Oct 2016 15:01:33 +0100
Subject: [PATCH 148/493] Beginning cvlar implementation

---
 forLater/josh/selectiveInference/R/cv.R | 45 +++++++++++++++++++
 forLater/josh/sim.cvlar.R               | 58 +++++++++++++++++++++++++
 2 files changed, 103 insertions(+)
 create mode 100644 forLater/josh/sim.cvlar.R

diff --git a/forLater/josh/selectiveInference/R/cv.R b/forLater/josh/selectiveInference/R/cv.R
index fcc6e54..046a462 100644
--- a/forLater/josh/selectiveInference/R/cv.R
+++ b/forLater/josh/selectiveInference/R/cv.R
@@ -130,3 +130,48 @@ cvfs <- function(x, y, index = 1:ncol(x), maxsteps, sigma = NULL, intercept = TR
     invisible(fit)
 }
 
+
+cvlar <- function(x, y) { # other args
+    folds <- cvMakeFolds(x)
+    models <- lapply(folds, function(fold) {
+        x.train <- X
+        y.train <- Y
+        x.train[fold,] <- 0
+        y.train[fold] <- 0
+        x.test <- X[fold,]
+        y.test <- Y[fold]
+          larpath.train <- lar(x.train, y.train, maxsteps = maxsteps, intercept = F, normalize = F)
+        return(lff)
+    })
+
+    active.sets <- lapply(models, function(model) model$action)
+    lambdas <- lapply(models, function(model) model$lambda)
+    lmin <- min(unlist(lambdas))
+
+# Interpolate lambda grid or parametrize by steps?
+# interpolation probably requires re-writing cvRSSquads for
+# penalized fits in order to make sense
+
+# do steps for now just to have something that works?
+
+    RSSquads <- list()
+    for (s in 1:maxsteps) {
+        initial.active <- lapply(active.sets, function(a) a[1:s])
+        RSSquads[[s]] <- cvRSSquad(X, folds, initial.active)
+    }
+
+    RSSs <- lapply(RSSquads, function(Q) t(Y) %*% Q %*% Y)
+    sstar <- which.min(RSSs)
+    quadstar <- RSSquads[sstar][[1]]
+
+    RSSquads <- lapply(RSSquads, function(quad) quad - quadstar)
+    RSSquads[[sstar]] <- NULL # remove the all zeroes case
+
+    fit <- lar(X, Y, maxsteps=sstar, intercept = F, normalize = F)
+
+# Very tall Gamma encoding all cv-model paths
+    Gamma <- do.call(rbind, lapply(models, function(model) return(model$Gamma)))
+
+# more to do here    
+}
+
diff --git a/forLater/josh/sim.cvlar.R b/forLater/josh/sim.cvlar.R
new file mode 100644
index 0000000..9169cc3
--- /dev/null
+++ b/forLater/josh/sim.cvlar.R
@@ -0,0 +1,58 @@
+# Choices
+
+# RSS: least-squares or penalized beta?
+# depends on final model. Go with least-squares for now
+
+# fixed vs lar? (lar, apparently)
+# fixed probably slower, but advantage of same lambda grid?
+# is same lambda grid necessary? -- doesn't lar algorithm give all possible models anyway?
+# i.e. for non-knot lambda just find where it is in lar path, take corresponding model
+
+# groups? later
+
+# TODO
+
+# copy larInf or groupfsInf?
+# larInf: add CV quadratic constraints* & break/fix p-value computation
+# -------- *but can we even use the ydecomp we use for quadratic?
+# groupfsInf: some ugly rewriting, no cumprojs etc, but straightforward
+# -------- downside: need to implement larInf basically
+
+# larInf
+# [ ] is.null(sigma) don't estimate it
+
+# plan:
+# expand Gamma for [-fold] indices?
+# stack all the Gammas? or iterate through them?
+# work backward from poly.pval <- larInf
+
+
+# big picture / long term
+# what OOP kind of design would lend itself to easily implementing more cv things?
+
+# Gamma: something x n
+# Gamma %*% y >= 0
+
+# pass 0-padded x[-fold] and y[-fold] to lar?
+
+library(selectiveInference)
+setwd("/Users/joftius/Dropbox/work/R-software/forLater/josh")
+source("selectiveInference/R/cv.R")
+
+set.seed(1)
+n <- 100
+p <- 50
+maxsteps <- 10
+sparsity <- 3
+snr <- 2
+rho <- 0.1
+nfolds <- 5
+
+x <- matrix(rnorm(n*p), nrow=n)
+y <- rnorm(n)
+beta <- rep(0, p)
+beta[1:sparsity] <- 2* sqrt(2*log(p)/n) * sample(c(-1,1), sparsity, replace=T)
+y <- y + x %*% beta
+my <- mean(y)
+y <- y - my
+

From 66454bfd8fff1a85d9b67353a6ea6eb99835466f Mon Sep 17 00:00:00 2001
From: yuvalbenjamini <yuvalben@stanford.edu>
Date: Tue, 27 Dec 2016 13:00:12 +0200
Subject: [PATCH 149/493] bug fixes in funs.constraint.R for matrix handling
 and univariate sampling, and some added documentation. Aligns version of
 funs.constraints.R with the restrictedNormal package.

---
 forLater/maxZ/funs.constraints.R | 170 +++++++++++++++++++------------
 1 file changed, 107 insertions(+), 63 deletions(-)

diff --git a/forLater/maxZ/funs.constraints.R b/forLater/maxZ/funs.constraints.R
index 3bf6b77..a04e34e 100644
--- a/forLater/maxZ/funs.constraints.R
+++ b/forLater/maxZ/funs.constraints.R
@@ -2,11 +2,16 @@
 # Some utilities for affine constraints
 #
 
-# 
+#
 # compute the square-root and inverse square-root of a non-negative
 # definite matrix
 #
 
+#' Compute the square-root and inverse square-root of a non-negative definite matrix. 
+#' @param S matrix
+#' @param rank rank of svd 
+#' 
+#'                                   
 factor_covariance = function(S, rank=NA) {
     if (is.na(rank)) {
         rank = nrow(S)
@@ -26,9 +31,16 @@ factor_covariance = function(S, rank=NA) {
 
 # law is Z \sim N(mean_param, covariance) subject to constraints linear_part %*% Z \leq offset
 
+#' Transform non-iid problem into iid problem
+#' @param linear_part matrix, linear part of constraints
+#' @param offset vector, bias of constraints 
+#' @param mean_param vector of unconditional means
+#' @param covariance vector of unconditional covariance
+#' @return new \code{linear_part} and \code{offset} for 0-mean iid covariance problem, 
+#' and functions that map between the two problems.                                   
 whiten_constraint = function(linear_part, offset, mean_param, covariance) {
 
-    factor_cov = factor_covariance(covariance)
+    factor_cov = factor_covariance(as.matrix(covariance))
     sqrt_cov = factor_cov$sqrt_cov
     sqrt_inv = factor_cov$sqrt_inv
 
@@ -41,7 +53,6 @@ whiten_constraint = function(linear_part, offset, mean_param, covariance) {
     new_A = new_A / scaling
     new_b = new_b / scaling
 
-    # TODO: check these functions will behave when Z is a matrix.
 
     inverse_map = function(Z) {
 	# broadcasting here
@@ -51,7 +62,7 @@ whiten_constraint = function(linear_part, offset, mean_param, covariance) {
 
     forward_map = function(W) {
         return(sqrt_inv %*% (W - mean_param))
-    }    
+    }
 
     return(list(linear_part=new_A,
                 offset=new_b,
@@ -59,65 +70,54 @@ whiten_constraint = function(linear_part, offset, mean_param, covariance) {
                 forward_map=forward_map))
 }
 
-#
-# sample from the law
-#
-# Z \sim N(mean_param, covariance) subject to constraints linear_part %*% Z \leq offset
-
-sample_from_constraints = function(linear_part,
-                                   offset,
-                                   mean_param,
-                                   covariance,
-                                   initial_point, # point must be feasible for constraints
+#' Sample from multivariate normal distribution under affine restrictions
+#' @description
+#' \code{sample_from_constraints} returns a sample from the conditional 
+#' multivariate normal Z~ N(mean,covariance) s.t. A*Z <= B 
+#'
+#' @param linear_part r x d matrix for r restrictions and d dimension of Z
+#' @param offset r-dim vector of offsets
+#' @param mean_param d-dim mean vector of the unconditional normal
+#' @param covariance d x d covariance matrix of unconditional normal
+#' @param initial_point d-dim vector that initializes the sampler (must meet restrictions)
+#' @param ndraw size of sample 
+#' @param burnin samples to throw away before storing
+#' @return Z ndraw x d matrix of samples 
+#' @export 
+#' @examples
+#' 
+#' truncatedNorm = function(1000, c(0,0,0), identity(3), lower = -1,
+#'                  upper = c(2,1,2), start.value = c(0,0,0))
+#'
+#' constr = thresh2constraints(3, lower = c(1,1,1))
+#'
+#' samp = sample_from_constraints(linear_part = constr$linear_part,
+#'                                    offset= constr$offset,
+#'                                    mean_param = c(0,0,0),
+#'                                    covariance = diag(3),
+#'                                    initial_point = c(1.5,1.5,1.5), 
+#'                                    ndraw=100,
+#'                                    burnin=2000)
+#' 
+
+sample_from_constraints = function(linear_part, 
+                                   offset, 
+                                   mean_param, 
+                                   covariance, 
+                                   initial_point, 
                                    ndraw=8000,
-                                   burnin=2000,
-                                   accept_reject_params=NA) #TODO: implement accept reject check
+                                   burnin=2000) 
 {
 
-    whitened_con = whiten_constraint(linear_part,
+  whitened_con = whiten_constraint(linear_part,
                                      offset,
-				     mean_param,
+                                     mean_param,
                                      covariance)
-    white_initial = whitened_con$forward_map(initial_point)
-
-#     # try 100 draws of accept reject
-#     # if we get more than 50 good draws, then just return a smaller sample
-#     # of size (burnin+ndraw)/5
-
-#     if accept_reject_params: 
-#         use_hit_and_run = False
-#         num_trial, min_accept, num_draw = accept_reject_params
-
-#         def _accept_reject(sample_size, linear_part, offset):
-#             Z_sample = np.random.standard_normal((100, linear_part.shape[1]))
-#             constraint_satisfied = (np.dot(Z_sample, linear_part.T) - 
-#                                     offset[None,:]).max(1) < 0
-#             return Z_sample[constraint_satisfied]
-
-#         Z_sample = _accept_reject(100, 
-#                                   white_con.linear_part,
-#                                   white_con.offset)
-
-#         if Z_sample.shape[0] >= min_accept:
-#             while True:
-#                 Z_sample = np.vstack([Z_sample,
-#                                       _accept_reject(num_draw / 5,
-#                                                      white_con.linear_part,
-#                                                      white_con.offset)])
-#                 if Z_sample.shape[0] > num_draw:
-#                     break
-#             white_samples = Z_sample
-#         else:
-#             use_hit_and_run = True
-#     else:
-#         use_hit_and_run = True
-
-    use_hit_and_run = TRUE
-
-    if (use_hit_and_run) {
-
-        white_linear = whitened_con$linear_part
-        white_offset = whitened_con$offset
+  white_initial = whitened_con$forward_map(initial_point)
+
+
+  white_linear = whitened_con$linear_part
+  white_offset = whitened_con$offset
 
 	# Inf cannot be used in C code
 	# In theory, these rows can be dropped
@@ -136,7 +136,7 @@ sample_from_constraints = function(linear_part,
 
                 # normalize rows to have length 1
 
-                scaling = apply(directions, 1, function(x) {  return(sqrt(sum(x^2))) }) 
+                scaling = apply(directions, 1, function(x) {  return(sqrt(sum(x^2))) })
                 directions = directions / scaling
                 ndirection = nrow(directions)
 
@@ -177,10 +177,54 @@ sample_from_constraints = function(linear_part,
            }
        } else {
            Z_sample = matrix(rnorm(nstate * ndraw), nstate, ndraw)
-       }
-    }
+  }
+
+
+  Z = t(whitened_con$inverse_map(Z_sample))
+  return(Z)
+}
 
-    Z = t(whitened_con$inverse_map(Z_sample))
-    return(Z)
+#' Translate between coordinate thresholds and affine constraints
+#' @description
+#' \code{thresh2constraints} translates lower and upper constraints 
+#' on coordinates into linear and offset constraints (A*Z <= B).
+#' lower and upper can have -Inf or Inf coordinates.
+#' @param d dimension of vector
+#' @param lower 1 or d-dim lower constraints 
+#' @param upper 1 or d-dim upper constraints 
+#' @export
+thresh2constraints = function(d, lower = rep(-Inf, d), upper = rep(Inf,d)){
+  stopifnot(is.element(length(lower),c(1,d)))
+  stopifnot(is.element(length(upper),c(1,d)))
+
+  if (length(lower) == 1){
+    lower = rep(lower, d)
+  }
+  if (length(upper) == 1){
+    upper = rep(upper, d)
+  }
+
+
+  linear_part = matrix(ncol = d, nrow = 0)
+  offset = numeric(0)
+  lower_constraints = which(lower > -Inf)
+  for (l in lower_constraints){
+    new_vec = rep(0,d)
+    new_vec[l] = -1
+    linear_part = rbind(linear_part, new_vec)
+    offset = c(offset, -lower[l])
+  }
+  upper_constraints = which(upper < Inf)
+  for (u in upper_constraints){
+    new_vec = rep(0,d)
+    new_vec[u] = 1
+    linear_part = rbind(linear_part, new_vec)
+    offset = c(offset, upper[u])
+  }
+
+  constraints = list(linear_part = linear_part, offset = offset)
+  return(constraints)
 }
 
+
+

From 41145c70307e5b491984c883a6fa74dcc59fb617 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Fri, 13 Jan 2017 11:42:03 -0800
Subject: [PATCH 150/493] rob

---
 selectiveInference/DESCRIPTION         |  6 ++--
 selectiveInference/NAMESPACE           |  2 ++
 selectiveInference/R/funs.fixed.R      |  2 +-
 selectiveInference/R/funs.fixedLogit.R | 10 +++---
 tests/test.fixed.R                     | 44 ++++++++++++++++++++++++++
 5 files changed, 56 insertions(+), 8 deletions(-)

diff --git a/selectiveInference/DESCRIPTION b/selectiveInference/DESCRIPTION
index 58232f1..5b317da 100644
--- a/selectiveInference/DESCRIPTION
+++ b/selectiveInference/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: selectiveInference
 Type: Package
 Title: Tools for Post-Selection Inference
-Version: 1.1.3
-Date: 2016-02-8
+Version: 1.2.1
+Date: 2016-07-3
 Author: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor,
     Joshua Loftus, Stephen Reid
 Maintainer: Rob Tibshirani <tibs@stanford.edu>
@@ -14,5 +14,5 @@ Suggests:
     Rmpfr
 Description: New tools for post-selection inference, for use
     with forward stepwise regression, least angle regression, the
-    lasso, and the many means problem.
+    lasso, and the many means problem. The lasso function implements  Gaussian,  logistic and Cox survival models.
 License: GPL-2
diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index 4a764f9..1eba338 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -40,4 +40,6 @@ importFrom("graphics", abline, axis, matplot)
 importFrom("stats", dnorm, lsfit, pexp, pnorm, predict, 
            qnorm, rnorm, sd, uniroot, dchisq, model.matrix, pchisq)
 importFrom("stats", "coef", "df", "lm", "pf")
+importFrom("stats", "glm", "residuals", "vcov")
+
 
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 4263c0b..9abd833 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -41,7 +41,7 @@ else{
   n = nrow(x)
   p = ncol(x)
   beta = as.numeric(beta)
-  if (length(beta) != p) stop("beta must have length equal to ncol(x)")
+  if (length(beta) != p) stop("Since family='gaussian', beta must have length equal to ncol(x)")
 
   # If glmnet was run with an intercept term, center x and y
   if (intercept==TRUE) {
diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index fb4092d..19c972d 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -18,7 +18,7 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
      n=length(y)
      p=ncol(x)
  # I assume that intcpt was used
-     if(length(beta)!=p+1) stop("beta must be of length ncol(x)+1, that is, it should include an intercept")
+     if(length(beta)!=p+1) stop("Since family='binomial', beta must be of length ncol(x)+1, that is, it should include an intercept")
      nvar=sum(beta[-1]!=0)
      pv=vlo=vup=sd=rep(NA, nvar)
      ci=tailarea=matrix(NA,nvar,2)
@@ -41,11 +41,12 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
    etahat = xxm %*% bhat
    prhat = as.vector(exp(etahat) / (1 + exp(etahat)))
    ww=prhat*(1-prhat)
-   w=diag(ww)
+ #  w=diag(ww)
       
 #check KKT
    z=etahat+(y-prhat)/ww
-       g=  t(x)%*%w%*%(z-etahat)/lambda # negative gradient scaled by lambda
+ #      g=  t(x)%*%w%*%(z-etahat)/lambda # negative gradient scaled by lambda
+  gg=scale(t(x),FALSE,1/ww)%*%(z-etahat)/lambda # negative gradient scaled by lambda
      if (any(abs(g) > 1+tol.kkt) )
     warning(paste("Solution beta does not satisfy the KKT conditions",
                   "(to within specified tolerances)"))
@@ -62,7 +63,8 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
                   "'thresh' parameter, for a more accurate convergence."))
   
  #constraints for active variables             
-  MM=solve(t(xxm)%*%w%*%xxm)
+ # MM=solve(t(xxm)%*%w%*%xxm)
+   MM=solve(scale(t(xxm),F,1/ww)%*%xxm)
   gm = c(0,-g[vars]*lambda) # gradient at LASSO solution, first entry is 0 because intercept is unpenalized
                             # at exact LASSO solution it should be s2[-1]
   dbeta = MM %*% gm
diff --git a/tests/test.fixed.R b/tests/test.fixed.R
index f1ce6cf..9a09072 100644
--- a/tests/test.fixed.R
+++ b/tests/test.fixed.R
@@ -510,3 +510,47 @@ g=t(X)%*%(Y-X%*%b)/lam2
 out = fixedLassoInf(X,Y,beta,lam*n)
 
 
+
+
+
+#
+
+#gaussian
+n=50
+p=10
+sigma=.7
+beta=c(0,0,0,0,rep(0,p-4))
+set.seed(43)
+nsim = 1000
+pvals <- matrix(NA, nrow=nsim, ncol=p)
+x = matrix(rnorm(n*p),n,p)
+x = scale(x,T,T)/sqrt(n-1)
+mu = x%*%beta
+for (i in 1:nsim) {
+    cat(i)
+y=mu+sigma*rnorm(n)
+#y=y-mean(y)
+# first run  glmnet
+    pf=c(rep(.001,4),rep(1,p-4))
+     xs=scale(x,FALSE,pf) #scale cols of x by penalty factors
+     # first run glmnet
+     gfit = glmnet(xs,y,standardize=FALSE)
+     
+    
+     lambda = .8
+     beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
+     
+     # compute fixed lambda p-values and selection intervals
+     aa = fixedLassoInf(xs,y,beta,lambda,sigma=sigma)
+     
+pvals[i, which(beta != 0)] <- aa$pv
+}
+nulls = 1:nsim
+np = pvals[nulls,-(1:4)]
+mean(np[!is.na(np)] < 0.1)
+o=!is.na(np)
+plot((1:sum(o))/sum(o),sort(np))
+abline(0,1)
+#####
+
+

From 80db7958744f8ae746fbacc314223dd416fca763 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Mon, 16 Jan 2017 09:26:23 -0800
Subject: [PATCH 151/493] rob bug from quanzao

---
 selectiveInference/DESCRIPTION         |   2 +-
 selectiveInference/R/funs.fixed.R      |   2 +-
 selectiveInference/R/funs.fixedLogit.R |   4 ++--
 selectiveInference/src/symbols.rds     | Bin 367 -> 367 bytes
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/selectiveInference/DESCRIPTION b/selectiveInference/DESCRIPTION
index 5b317da..d697499 100644
--- a/selectiveInference/DESCRIPTION
+++ b/selectiveInference/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: selectiveInference
 Type: Package
 Title: Tools for Post-Selection Inference
-Version: 1.2.1
+Version: 1.2.2
 Date: 2016-07-3
 Author: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor,
     Joshua Loftus, Stephen Reid
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 4fc0f1a..b30d04c 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -158,7 +158,7 @@ function(x, y, beta, lambda, a) {
   if (length(za)>1) dz = diag(za)
   if (length(za)==1) dz = matrix(za,1,1)
 
-  P = diag(1,nrow(xa)) - xa %*% xap
+#  P = diag(1,nrow(xa)) - xa %*% xap
   #NOTE: inactive constraints not needed below!
 
   G = -rbind(
diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index 19c972d..5b67354 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -45,8 +45,8 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
       
 #check KKT
    z=etahat+(y-prhat)/ww
- #      g=  t(x)%*%w%*%(z-etahat)/lambda # negative gradient scaled by lambda
-  gg=scale(t(x),FALSE,1/ww)%*%(z-etahat)/lambda # negative gradient scaled by lambda
+  # g=  t(x)%*%w%*%(z-etahat)/lambda # negative gradient scaled by lambda
+  g=scale(t(x),FALSE,1/ww)%*%(z-etahat)/lambda # negative gradient scaled by lambda
      if (any(abs(g) > 1+tol.kkt) )
     warning(paste("Solution beta does not satisfy the KKT conditions",
                   "(to within specified tolerances)"))
diff --git a/selectiveInference/src/symbols.rds b/selectiveInference/src/symbols.rds
index ef45ca37aedfc57cb07c2d5066bf881bf4359eb6..06b0e85e927ffbb186121204a3be110d68965ede 100644
GIT binary patch
literal 367
zcmV-#0g(P5iwFP!000001HF`8PlGTNhRes4%`92sg}43!BMkib8)hcnHDxQak<v=L
zf&O@BVYDz%Z=7B%ea_42%OU5@o+bbQ8#s;yY^TEWx(4=4ogr|+Se;||kU&CF85{~E
z)50lSvNUJ1DJO8nifn}>_6JIPWlgp?6ULIF7c6t)_l2M@%=<!UEOS=0Q+RO0yEEuo
z8(NP8So}R)_rdW4gTbxAJAc@TO{eRJ9lj4+#)j?KT#S&xcs}s`9{{dhfu3VUs5++m
zxYeiIYw=V|hA0+_n^=Mm^_$hb&G%Lfn&lhUJldjkV;%_dj`x@ATwj_{su=T(<uvLl
zuU(~%dcMg>qRk*KdBZ0#Ng2zV>P#g%js+AFbJLu%b;B1>Aj(q=rLtRA?|{WKk$T84
z()a@`zE^&?C?~o(^2)7UXU|@{XM1<Vy&eQ-q*F7i-S^^@BK22ME*a(Gis_Y^T_{Nz
NeFB_4nEVd~006gSudx6C

literal 367
zcmV-#0g(P5iwFP!000001HF{rPJ=KM#=F9l%`92sg||L|5yt%U8fGTmHD#39NNJ^I
zn?Ag=Fj^R>H%>2>{?5ng$06s-p2h$G2pl*7&?)h}ssWtpGXyRe>2ri05=0r^28RO8
zjBtvUJk7bP%Q1@iCX2Bmeotwxtmv9#(pob7LR3!ut`KyESyu?HWzILv6do!>dj?Y*
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z8h?Pr_sZ`U<;-u6yz;@WwP&x{Gu$0<uLr>yn$%1y_q{k_SpQXQmz)WC#m&m>F0`bL
NJ^{S|DnAbe005&lw21%!


From 6c7885107e15efc9a2831def0f3da9ab8b24a7d0 Mon Sep 17 00:00:00 2001
From: Ryan Tibshirani <ryantibs@gmail.com>
Date: Tue, 7 Feb 2017 23:04:46 -0500
Subject: [PATCH 152/493] Committing so my changes aren't overwritten

---
 selectiveInference/R/funs.fs.R  | 5 ++++-
 selectiveInference/R/funs.lar.R | 5 ++++-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index b5ee511..bd5c39a 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -234,7 +234,10 @@ coef.fs <- function(object, s, ...) {
 predict.fs <- function(object, newx, s, ...) {
   beta = coef.fs(object,s)
   if (missing(newx)) newx = scale(object$x,FALSE,1/object$sx)
-  else newx = scale(newx,object$bx,FALSE)
+  else {
+    newx = matrix(newx,ncol=ncol(object$x))
+    newx = scale(newx,object$bx,FALSE)
+  }
   return(newx %*% beta + object$by)
 }
 
diff --git a/selectiveInference/R/funs.lar.R b/selectiveInference/R/funs.lar.R
index 26669ca..795bbc3 100644
--- a/selectiveInference/R/funs.lar.R
+++ b/selectiveInference/R/funs.lar.R
@@ -339,7 +339,10 @@ coef.lar <- function(object, s, mode=c("step","lambda"), ...) {
 predict.lar <- function(object, newx, s, mode=c("step","lambda"), ...) {
   beta = coef.lar(object,s,mode)
   if (missing(newx)) newx = scale(object$x,FALSE,1/object$sx)
-  else newx = scale(newx,object$bx,FALSE)
+  else {
+    newx = matrix(newx,ncol=ncol(object$x))
+    newx = scale(newx,object$bx,FALSE)
+  }
   return(newx %*% beta + object$by)
 }
 

From 427e89c0dcca1a9e09576127b71019fedaf4d3cb Mon Sep 17 00:00:00 2001
From: Ryan Tibshirani <ryantibs@gmail.com>
Date: Tue, 7 Feb 2017 23:08:11 -0500
Subject: [PATCH 153/493] Fixed a small bug in the coef functions for fs and
 lar: use of dec instead of decreasing in the call to coef.interpolate.

---
 selectiveInference/R/funs.common.R | 8 ++++----
 selectiveInference/R/funs.fs.R     | 4 ++--
 selectiveInference/R/funs.lar.R    | 6 +++---
 3 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/selectiveInference/R/funs.common.R b/selectiveInference/R/funs.common.R
index 5945700..042e074 100644
--- a/selectiveInference/R/funs.common.R
+++ b/selectiveInference/R/funs.common.R
@@ -56,14 +56,14 @@ standardize <- function(x, y, intercept, normalize) {
 
 # Interpolation function to get coefficients
 
-coef.interpolate <- function(betas, s, knots, dec=TRUE) {
+coef.interpolate <- function(beta, s, knots, decreasing=TRUE) {
   # Sort the s values
-  o = order(s,dec=dec)
+  o = order(s,decreasing=decreasing)
   s = s[o]
 
   k = length(s)
   mat = matrix(rep(knots,each=k),nrow=k)
-  if (dec) b = s >= mat
+  if (decreasing) b = s >= mat
   else b = s <= mat
   blo = max.col(b,ties.method="first")
   bhi = pmax(blo-1,1)
@@ -73,7 +73,7 @@ coef.interpolate <- function(betas, s, knots, dec=TRUE) {
   p[i] = 0
   p[!i] = ((s-knots[blo])/(knots[bhi]-knots[blo]))[!i]
 
-  beta = t((1-p)*t(betas[,blo,drop=FALSE]) + p*t(betas[,bhi,drop=FALSE]))
+  beta = t((1-p)*t(beta[,blo,drop=FALSE]) + p*t(beta[,bhi,drop=FALSE]))
   colnames(beta) = as.character(round(s,3))
   rownames(beta) = NULL
 
diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index bd5c39a..7a1d784 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -225,8 +225,8 @@ coef.fs <- function(object, s, ...) {
   
   if (min(s)<0 || max(s)>k) stop(sprintf("s must be between 0 and %i",k))
   knots = 1:k
-  dec = FALSE
-  return(coef.interpolate(beta,s,knots,dec))
+  decreasing = FALSE
+  return(coef.interpolate(beta,s,knots,decreasing))
 }
 
 # Prediction function for fs
diff --git a/selectiveInference/R/funs.lar.R b/selectiveInference/R/funs.lar.R
index 96df0c0..37d0ae3 100644
--- a/selectiveInference/R/funs.lar.R
+++ b/selectiveInference/R/funs.lar.R
@@ -295,14 +295,14 @@ coef.lar <- function(object, s, mode=c("step","lambda"), ...) {
   if (mode=="step") {
     if (min(s)<0 || max(s)>k) stop(sprintf("s must be between 0 and %i",k))
     knots = 1:k
-    dec = FALSE
+    decreasing = FALSE
   } else {
     if (min(s)<min(lambda)) stop(sprintf("s must be >= %0.3f",min(lambda)))
     knots = lambda
-    dec = TRUE
+    decreasing = TRUE
   }
 
-  return(coef.interpolate(beta,s,knots,dec))
+  return(coef.interpolate(beta,s,knots,decreasing))
 }
 
 # Prediction function for lar

From 4e50c4d8a77c617ded2613bf22843a91ec070d0b Mon Sep 17 00:00:00 2001
From: Ryan Tibshirani <ryantibs@gmail.com>
Date: Tue, 7 Feb 2017 23:40:55 -0500
Subject: [PATCH 154/493] Fixed a small bug in fs function. Seems have been
 introduced when this function was modified notationally. The least squares
 solution was not being returned properly; now it is.

---
 selectiveInference/DESCRIPTION            | 8 +++++---
 selectiveInference/R/funs.fs.R            | 9 ++++-----
 selectiveInference/man/factorDesign.Rd    | 4 ++--
 selectiveInference/man/groupfs.Rd         | 5 +++--
 selectiveInference/man/groupfsInf.Rd      | 2 +-
 selectiveInference/man/predict.groupfs.Rd | 7 +------
 selectiveInference/man/scaleGroups.Rd     | 2 +-
 7 files changed, 17 insertions(+), 20 deletions(-)

diff --git a/selectiveInference/DESCRIPTION b/selectiveInference/DESCRIPTION
index d697499..d0740ec 100644
--- a/selectiveInference/DESCRIPTION
+++ b/selectiveInference/DESCRIPTION
@@ -12,7 +12,9 @@ Depends:
     survival
 Suggests:
     Rmpfr
-Description: New tools for post-selection inference, for use
-    with forward stepwise regression, least angle regression, the
-    lasso, and the many means problem. The lasso function implements  Gaussian,  logistic and Cox survival models.
+Description: New tools for post-selection inference, for use with forward
+    stepwise regression, least angle regression, the lasso, and the many means
+    problem. The lasso function implements Gaussian, logistic and Cox survival
+    models.
 License: GPL-2
+RoxygenNote: 5.0.1
diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 7a1d784..62e6b7c 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -106,8 +106,8 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     }
 
     # Key quantities for the next entry
-    keepLs=backsolve(R,t(Q_active)%*%X_inactive)
-    X_inactive_resid = X_inactive - X_active %*% keepLs
+    keepLs = backsolve(R,t(Q_active)%*%y)
+    X_inactive_resid = X_inactive - X_active %*% backsolve(R,t(Q_active)%*%X_inactive)
     working_x = scale(X_inactive_resid,center=F,scale=sqrt(colSums(X_inactive_resid^2)))
     score = as.numeric(t(working_x)%*%y)
     
@@ -127,7 +127,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
 
     action[k] = I[i_hit] 
     df[k] = r
-    beta[A,k] = backsolve(R,t(Q_active)%*%y)
+    beta[A,k] = keepLs
         
     # Gamma matrix!
     if (gi + 2*p > nrow(Gamma)) Gamma = rbind(Gamma,matrix(0,2*p+gbuf,n))
@@ -188,8 +188,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     
     # Record the least squares solution. Note that
     # we have already computed this
-    bls = rep(0,p)
-   if(length(keepLs)>0)  bls[A] = keepLs
+    bls = rep(0,p); bls[A] = keepLs
   }
 
   if (verbose) cat("\n")
diff --git a/selectiveInference/man/factorDesign.Rd b/selectiveInference/man/factorDesign.Rd
index 8e061db..7b13ae0 100644
--- a/selectiveInference/man/factorDesign.Rd
+++ b/selectiveInference/man/factorDesign.Rd
@@ -1,4 +1,4 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
+% Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/funs.groupfs.R
 \name{factorDesign}
 \alias{factorDesign}
@@ -23,7 +23,7 @@ When using \code{\link{groupfs}} with factor variables call this function first
 \dontrun{
 fd = factorDesign(warpbreaks)
 y = rnorm(nrow(fd$x))
-fit = groupfs(fd$x, y, fd$index, maxsteps=2, intercept=FALSE)
+fit = groupfs(fd$x, y, fd$index, maxsteps=2, intercept=F)
 pvals = groupfsInf(fit)
 }
 }
diff --git a/selectiveInference/man/groupfs.Rd b/selectiveInference/man/groupfs.Rd
index a57c6dc..5c2171a 100644
--- a/selectiveInference/man/groupfs.Rd
+++ b/selectiveInference/man/groupfs.Rd
@@ -1,4 +1,4 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
+% Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/funs.groupfs.R
 \name{groupfs}
 \alias{groupfs}
@@ -41,7 +41,8 @@ x = matrix(rnorm(20*40), nrow=20)
 index = sort(rep(1:20, 2))
 y = rnorm(20) + 2 * x[,1] - x[,4]
 fit = groupfs(x, y, index, maxsteps = 5)
-pvals = groupfsInf(fit)
+out = groupfsInf(fit)
+out
 }
 \seealso{
 \code{\link{groupfsInf}}, \code{\link{factorDesign}}.
diff --git a/selectiveInference/man/groupfsInf.Rd b/selectiveInference/man/groupfsInf.Rd
index 74b9a5e..16efcab 100644
--- a/selectiveInference/man/groupfsInf.Rd
+++ b/selectiveInference/man/groupfsInf.Rd
@@ -1,4 +1,4 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
+% Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/funs.groupfs.R
 \name{groupfsInf}
 \alias{groupfsInf}
diff --git a/selectiveInference/man/predict.groupfs.Rd b/selectiveInference/man/predict.groupfs.Rd
index 4a382c7..4b8394b 100644
--- a/selectiveInference/man/predict.groupfs.Rd
+++ b/selectiveInference/man/predict.groupfs.Rd
@@ -1,4 +1,4 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
+% Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/funs.groupfs.R
 \name{predict.groupfs}
 \alias{predict.groupfs}
@@ -16,9 +16,4 @@ Make predictions or extract coefficients from a groupfs forward stepwise object.
 \value{
 A vector of predictions or a vector of coefficients.
 }
-\description{
-Prediction and coefficient functions for \code{\link{groupfs}}.
-
-Make predictions or extract coefficients from a groupfs forward stepwise object.
-}
 
diff --git a/selectiveInference/man/scaleGroups.Rd b/selectiveInference/man/scaleGroups.Rd
index e5a93fa..4607890 100644
--- a/selectiveInference/man/scaleGroups.Rd
+++ b/selectiveInference/man/scaleGroups.Rd
@@ -1,4 +1,4 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
+% Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/funs.groupfs.R
 \name{scaleGroups}
 \alias{scaleGroups}

From d54ba2e65d83b54d563f2fbcbd4b600c6dab7417 Mon Sep 17 00:00:00 2001
From: Ryan Tibshirani <ryantibs@gmail.com>
Date: Tue, 7 Feb 2017 23:49:08 -0500
Subject: [PATCH 155/493] Created a new folder for our package versions.

---
 selectiveInference/R/funs.fs.R                      |   3 ++-
 .../selectiveInference_1.0.1.tar.gz                 | Bin
 .../selectiveInference_1.0.2.tar.gz                 | Bin
 .../selectiveInference_1.0.tar.gz                   | Bin
 4 files changed, 2 insertions(+), 1 deletion(-)
 rename selectiveInference_1.0.1.tar.gz => versions/selectiveInference_1.0.1.tar.gz (100%)
 rename selectiveInference_1.0.2.tar.gz => versions/selectiveInference_1.0.2.tar.gz (100%)
 rename selectiveInference_1.0.tar.gz => versions/selectiveInference_1.0.tar.gz (100%)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 62e6b7c..3f8a87c 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -188,7 +188,8 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     
     # Record the least squares solution. Note that
     # we have already computed this
-    bls = rep(0,p); bls[A] = keepLs
+    bls = rep(0,p)
+    bls[A] = keepLs
   }
 
   if (verbose) cat("\n")
diff --git a/selectiveInference_1.0.1.tar.gz b/versions/selectiveInference_1.0.1.tar.gz
similarity index 100%
rename from selectiveInference_1.0.1.tar.gz
rename to versions/selectiveInference_1.0.1.tar.gz
diff --git a/selectiveInference_1.0.2.tar.gz b/versions/selectiveInference_1.0.2.tar.gz
similarity index 100%
rename from selectiveInference_1.0.2.tar.gz
rename to versions/selectiveInference_1.0.2.tar.gz
diff --git a/selectiveInference_1.0.tar.gz b/versions/selectiveInference_1.0.tar.gz
similarity index 100%
rename from selectiveInference_1.0.tar.gz
rename to versions/selectiveInference_1.0.tar.gz

From a8701c140b99c94fe2d8135937b9cecbbd424408 Mon Sep 17 00:00:00 2001
From: Nicolas Ballarini <nicoballarini@gmail.com>
Date: Thu, 15 Jun 2017 10:01:59 +0200
Subject: [PATCH 156/493] bug in fixedLogitLassoInf

alpha for fixedLassoInf with family="Binomial" was set to .1
---
 selectiveInference/R/funs.fixedLogit.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index 5b67354..e99f281 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -96,7 +96,7 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
    vup[jj]=junk$vup
        sd[jj]=junk$sd
   #  junk2=mypoly.int.lee(bbar[-1], A1, b1,vj,MM[-1,-1],alpha=.1)
-     junk2=mypoly.int.lee(bbar,vj,vlo[jj],vup[jj],sd[jj],alpha=.1)
+     junk2=mypoly.int.lee(bbar,vj,vlo[jj],vup[jj],sd[jj],alpha=alpha)
 
      ci[jj,]=junk2$int
      tailarea[jj,] = junk2$tailarea

From b38d76ebdec9fd38923d5152444433506b3ab3d0 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sun, 23 Jul 2017 11:44:06 -0700
Subject: [PATCH 157/493] exporting the general pivot, renaming it TG.pvalue

---
 selectiveInference/NAMESPACE           |  3 ++-
 selectiveInference/R/funs.fixedCox.R   |  2 +-
 selectiveInference/R/funs.fixedLogit.R |  6 ++---
 selectiveInference/R/funs.inf.R        | 34 ++++++++++++++------------
 4 files changed, 24 insertions(+), 21 deletions(-)

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index 1eba338..f9ed697 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -12,7 +12,8 @@ export(lar,fs,
        estimateSigma,
        manyMeans,print.manyMeans,
        groupfs,groupfsInf,
-       scaleGroups,factorDesign
+       scaleGroups,factorDesign,
+       TG.pvalue
     )
 
 S3method("coef", "lar")
diff --git a/selectiveInference/R/funs.fixedCox.R b/selectiveInference/R/funs.fixedCox.R
index ff778d9..c5918c4 100644
--- a/selectiveInference/R/funs.fixedCox.R
+++ b/selectiveInference/R/funs.fixedCox.R
@@ -62,7 +62,7 @@ b1= -(mydiag(s2)%*%MM)%*%s2*lambda
       vj=rep(0,length(bbar));vj[jj]=s2[jj]
 
 
-      junk=mypoly.pval.lee(bbar,A1,b1,vj,MM)
+      junk=TG.pvalue(bbar,A1,b1,vj,MM)
 
        pv[jj] = junk$pv
       vlo[jj]=junk$vlo
diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index 5b67354..8cd8c34 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -89,14 +89,14 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
     for(jj in 1:sum(m)){
        vj=c(rep(0,sum(m)+1));vj[jj+1]=s2[jj+1]
       # compute p-values
-      junk=mypoly.pval.lee(bbar,A1,b1,vj,MM)
+      junk=TG.pvalue(bbar,A1,b1,vj,MM)
       pv[jj] = junk$pv
  
    vlo[jj]=junk$vlo
    vup[jj]=junk$vup
        sd[jj]=junk$sd
-  #  junk2=mypoly.int.lee(bbar[-1], A1, b1,vj,MM[-1,-1],alpha=.1)
-     junk2=mypoly.int.lee(bbar,vj,vlo[jj],vup[jj],sd[jj],alpha=.1)
+  #  junk2=TG.pvalue(bbar[-1], A1, b1,vj,MM[-1,-1],alpha=.1)
+     junk2=TG.pvalue(bbar,vj,vlo[jj],vup[jj],sd[jj],alpha=.1)
 
      ci[jj,]=junk2$int
      tailarea[jj,] = junk2$tailarea
diff --git a/selectiveInference/R/funs.inf.R b/selectiveInference/R/funs.inf.R
index 423b4c3..79cb6a5 100644
--- a/selectiveInference/R/funs.inf.R
+++ b/selectiveInference/R/funs.inf.R
@@ -247,24 +247,26 @@ aicStop <- function(x, y, action, df, sigma, mult=2, ntimes=2) {
 
 #these next two functions are used by the binomial and Cox options of fixedLassoInf
 
-mypoly.pval.lee=
-function(y, A, b, eta, Sigma, bits=NULL) {
+TG.pvalue = function(Z, A, b, eta, Sigma, bits=NULL) {
+
     # compute pvalues from poly lemma:  full version from Lee et al for full matrix Sigma
-    nn=length(y)
-    eta=as.vector(eta)
-  temp = sum(eta*y)
-   vv=as.numeric(matrix(eta,nrow=1,ncol=nn)%*%Sigma%*%eta)
-   cc = Sigma%*%eta/vv
-   
- z=(diag(nn)-matrix(cc,ncol=1)%*%eta)%*%y
-    rho=A%*%cc
+    n = length(Z)
+    eta = as.vector(eta)
+    b = as.vector(b)
+    target_estimate = sum(eta * Z)
+    var_estimate = as.numeric(matrix(eta, nrow=1, ncol=n) %*% Sigma %*% eta)
+    cross_cov = Sigma %*% eta
    
-  vec = (b- A %*% z)/rho
-  vlo = suppressWarnings(max(vec[rho<0]))
-  vup = suppressWarnings(min(vec[rho>0]))
-  sd=sqrt(vv)
-  pv = tnorm.surv(temp,0,sd,vlo,vup,bits)
-  return(list(pv=pv,vlo=vlo,vup=vup,sd=sd))
+    resid = (diag(n) - matrix(cross_cov / var_estimate, ncol=1) %*% eta) %*% Z
+    rho = A %*% cross_cov / var_estimate
+    vec = (b - as.numeric(A %*% resid)) / rho
+
+    vlo = suppressWarnings(max(vec[rho < 0]))
+    vup = suppressWarnings(min(vec[rho > 0]))
+
+    sd = sqrt(var_estimate)
+    pv = tnorm.surv(target_estimate, 0 , sd, vlo, vup, bits)
+    return(list(pv=pv, vlo=vlo, vup=vup, sd=sd))
 }
 
 

From a86da851014a2ff0425d9687436e53d4947edcfc Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sun, 23 Jul 2017 11:54:34 -0700
Subject: [PATCH 158/493] exporting interval function as well

---
 selectiveInference/NAMESPACE           |  3 ++-
 selectiveInference/R/funs.fixedCox.R   |  2 +-
 selectiveInference/R/funs.fixedLogit.R |  4 +--
 selectiveInference/R/funs.inf.R        | 34 +++++++++++++++++---------
 4 files changed, 27 insertions(+), 16 deletions(-)

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index f9ed697..7add9df 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -13,7 +13,8 @@ export(lar,fs,
        manyMeans,print.manyMeans,
        groupfs,groupfsInf,
        scaleGroups,factorDesign,
-       TG.pvalue
+       TG.pvalue, 
+       TG.interval
     )
 
 S3method("coef", "lar")
diff --git a/selectiveInference/R/funs.fixedCox.R b/selectiveInference/R/funs.fixedCox.R
index c5918c4..0069007 100644
--- a/selectiveInference/R/funs.fixedCox.R
+++ b/selectiveInference/R/funs.fixedCox.R
@@ -69,7 +69,7 @@ b1= -(mydiag(s2)%*%MM)%*%s2*lambda
        vup[jj]=junk$vup
        sd[jj]=junk$sd
 
-      junk2=mypoly.int.lee(bbar,vj,vlo[jj],vup[jj],sd[jj],alpha)
+      junk2=TG.interval(bbar, vj, vlo[jj], vup[jj], sd[jj], alpha)
        ci[jj,]=junk2$int
        tailarea[jj,] = junk2$tailarea
      
diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index 8cd8c34..6e3c113 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -95,8 +95,8 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
    vlo[jj]=junk$vlo
    vup[jj]=junk$vup
        sd[jj]=junk$sd
-  #  junk2=TG.pvalue(bbar[-1], A1, b1,vj,MM[-1,-1],alpha=.1)
-     junk2=TG.pvalue(bbar,vj,vlo[jj],vup[jj],sd[jj],alpha=.1)
+  #  junk2=TG.interval(bbar[-1], A1, b1,vj,MM[-1,-1],alpha=.1)
+     junk2=TG.interval(bbar,vj,vlo[jj],vup[jj],sd[jj],alpha=.1)
 
      ci[jj,]=junk2$int
      tailarea[jj,] = junk2$tailarea
diff --git a/selectiveInference/R/funs.inf.R b/selectiveInference/R/funs.inf.R
index 79cb6a5..ad64a3d 100644
--- a/selectiveInference/R/funs.inf.R
+++ b/selectiveInference/R/funs.inf.R
@@ -271,24 +271,34 @@ TG.pvalue = function(Z, A, b, eta, Sigma, bits=NULL) {
 
 
 
-mypoly.int.lee=
-   function(y,eta,vlo,vup,sd, alpha, gridrange=c(-100,100),gridpts=100, griddepth=2, flip=FALSE, bits=NULL) {
+TG.interval = function(Z, eta, vlo, vup, sd, alpha, 
+                       gridrange=c(-100,100),
+                       gridpts=100, 
+                       griddepth=2, 
+                       flip=FALSE, 
+                       bits=NULL) {
+
     # compute sel intervals from poly lemmma, full version from Lee et al for full matrix Sigma
 
-  temp = sum(eta*y)
+    target_estimate = sum(eta*Z)
   
-  xg = seq(gridrange[1]*sd,gridrange[2]*sd,length=gridpts)
-  fun = function(x) { tnorm.surv(temp,x,sd,vlo,vup,bits) }
+    param_grid = seq(gridrange[1]*sd, gridrange[2]*sd, length=gridpts)
 
-  int = grid.search(xg,fun,alpha/2,1-alpha/2,gridpts,griddepth)
-  tailarea = c(fun(int[1]),1-fun(int[2]))
+    pivot = function(param) {
+        tnorm.surv(target_estimate, param, sd, vlo, vup, bits) 
+    }
 
-  if (flip) {
-    int = -int[2:1]
-    tailarea = tailarea[2:1]
-  }
+    interval = grid.search(param_grid, pivot, alpha/2, 1-alpha/2, gridpts, griddepth)
+    tailarea = c(pivot(interval[1]), 1- pivot(interval[2]))
+
+    if (flip) {
+        interval = -interval[2:1]
+        tailarea = tailarea[2:1]
+     }
  
-  return(list(int=int,tailarea=tailarea))
+     # int is not a good variable name, synonymous with integer...
+     return(list(int=interval,
+                 tailarea=tailarea))
 }
 
 

From 33be053585011e5a9f7c213cb90d4897f1147b60 Mon Sep 17 00:00:00 2001
From: tibshirani <tibs@stanford.edu>
Date: Sun, 23 Jul 2017 13:59:54 -0700
Subject: [PATCH 159/493] Update README.md

---
 README.md | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/README.md b/README.md
index 1a50e5b..962151e 100644
--- a/README.md
+++ b/README.md
@@ -13,3 +13,11 @@ Code is in the directory selectiveInference/R.
 * funs.inf.R: Common functions for inference with fixed, fs, lar, and manymeans (but not group).
 * funs.lar.R: Inference for least angle regression.
 * funs.max.R: Some numerical approximations. Deprecated?
+
+## Installation
+The latest release of the package can be installed through CRAN:
+
+```R
+install.packages("selectiveInference")
+```
+Code in repo is under development and may be unstable.

From 4710b937ac39e44b99f15ae83c53f7416eb2a971 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 24 Jul 2017 11:05:56 -0700
Subject: [PATCH 160/493] added man pages for TG.pvalue, TG.interval

---
 selectiveInference/R/funs.inf.R       | 10 +--
 selectiveInference/man/TG.interval.Rd | 94 +++++++++++++++++++++++++++
 selectiveInference/man/TG.pvalue.Rd   | 78 ++++++++++++++++++++++
 3 files changed, 177 insertions(+), 5 deletions(-)
 create mode 100644 selectiveInference/man/TG.interval.Rd
 create mode 100644 selectiveInference/man/TG.pvalue.Rd

diff --git a/selectiveInference/R/funs.inf.R b/selectiveInference/R/funs.inf.R
index ad64a3d..1b454fa 100644
--- a/selectiveInference/R/funs.inf.R
+++ b/selectiveInference/R/funs.inf.R
@@ -247,17 +247,17 @@ aicStop <- function(x, y, action, df, sigma, mult=2, ntimes=2) {
 
 #these next two functions are used by the binomial and Cox options of fixedLassoInf
 
-TG.pvalue = function(Z, A, b, eta, Sigma, bits=NULL) {
+TG.pvalue = function(Z, A, b, eta, Sigma, null_value=0, bits=NULL) {
 
     # compute pvalues from poly lemma:  full version from Lee et al for full matrix Sigma
     n = length(Z)
-    eta = as.vector(eta)
+    eta = matrix(eta, ncol=1, nrow=n)
     b = as.vector(b)
     target_estimate = sum(eta * Z)
-    var_estimate = as.numeric(matrix(eta, nrow=1, ncol=n) %*% Sigma %*% eta)
+    var_estimate = sum(matrix(eta, nrow=1, ncol=n) %*% (Sigma %*% eta))
     cross_cov = Sigma %*% eta
    
-    resid = (diag(n) - matrix(cross_cov / var_estimate, ncol=1) %*% eta) %*% Z
+    resid = (diag(n) - matrix(cross_cov / var_estimate, ncol=1, nrow=n) %*% matrix(eta, nrow=1, ncol=n)) %*% Z
     rho = A %*% cross_cov / var_estimate
     vec = (b - as.numeric(A %*% resid)) / rho
 
@@ -265,7 +265,7 @@ TG.pvalue = function(Z, A, b, eta, Sigma, bits=NULL) {
     vup = suppressWarnings(min(vec[rho > 0]))
 
     sd = sqrt(var_estimate)
-    pv = tnorm.surv(target_estimate, 0 , sd, vlo, vup, bits)
+    pv = tnorm.surv(target_estimate, null_value, sd, vlo, vup, bits)
     return(list(pv=pv, vlo=vlo, vup=vup, sd=sd))
 }
 
diff --git a/selectiveInference/man/TG.interval.Rd b/selectiveInference/man/TG.interval.Rd
new file mode 100644
index 0000000..d542691
--- /dev/null
+++ b/selectiveInference/man/TG.interval.Rd
@@ -0,0 +1,94 @@
+\name{TG.interval}
+\alias{TG.interval}
+
+\title{
+Truncated Gaussian confidence interval.
+}
+\description{
+Compute truncated Gaussian interval of Lee et al. (2016) with
+arbitrary affine selection and covariance.
+}
+\usage{
+TG.interval(Z, eta, vlo, vup, sd, alpha, 
+	   gridrange=c(-100,100),
+           gridpts=100, 
+           griddepth=2, 
+           flip=FALSE, 
+           bits=NULL) 
+}
+\arguments{
+\item{Z}{
+Observed data (assumed to follow N(mu, Sigma) with sum(eta*mu)=null_value)
+}
+\item{eta}{
+Determines the target sum(eta*mu) and estimate sum(eta*Z).
+} 
+\item{vlo}{
+Lower truncation limits for statistic. Can be computed with TG.pvalue.
+}
+\item{vup}{
+Upper truncation limits for statistic. Can be computed with TG.pvalue.
+}
+\item{sd}{
+Standard error of sum(eta*Z).
+}
+\item{alpha}{
+Significance level for confidence intervals (target is miscoverage alpha/2 in each tail)
+}
+\item{gridrange}{
+Grid range for constructing confidence intervals, on the standardized scale.
+}
+\item{gridpts}{
+???????
+}
+\item{griddepth}{
+???????
+}
+\item{flip}{
+???????
+}
+\item{bits}{
+Number of bits to be used for p-value and confidence interval calculations. Default is 
+NULL, in which case standard floating point calculations are performed. When not NULL, 
+multiple precision floating point calculations are performed with the specified number 
+of bits, using the R package \code{Rmpfr} (if this package is not installed, then a 
+warning is thrown, and standard floating point calculations are pursued).
+Note: standard double precision uses 53 bits
+so, e.g., a choice of 200 bits uses about 4 times double precision. The confidence
+interval computation is sometimes numerically challenging, and the extra precision can be 
+helpful (though computationally more costly). In particular, extra precision might be tried 
+if the values in the output columns of \code{tailarea} differ noticeably from alpha/2.
+}
+}
+
+\details{
+This function computes selective confidence intervals based on the polyhedral
+lemma of Lee et al. (2016). 
+}
+
+\value{  
+\item{int}{Selective confidence interval.}
+\item{tailarea}{Realized tail areas (lower and upper) for each confidence interval.}
+}
+
+\references{
+Jason Lee, Dennis Sun, Yuekai Sun, and Jonathan Taylor (2016). 
+Exact post-selection inference, with application to the lasso. Annals of Statistics, 44(3), 907-927.
+
+Jonathan Taylor and Robert Tibshirani (2017) Post-selection inference for math L1-penalized likelihood models.
+Canadian Journal of Statistics, xx, 1-21. (Volume still not posted)
+
+}
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\examples{
+
+A = diag(5)
+b = rep(1, 5)
+Z = rep(0, 5)
+Sigma = diag(5)
+eta = as.numeric(c(1, 1, 0, 0, 0))
+V = TG.pvalue(Z, A, b, Sigma, eta)
+TG.interval(Z, eta, V$vlo, V$vup, V$sd, 0.05)
+}
+ 
\ No newline at end of file
diff --git a/selectiveInference/man/TG.pvalue.Rd b/selectiveInference/man/TG.pvalue.Rd
new file mode 100644
index 0000000..5625d9d
--- /dev/null
+++ b/selectiveInference/man/TG.pvalue.Rd
@@ -0,0 +1,78 @@
+\name{TG.pvalue}
+\alias{TG.pvalue}
+
+\title{
+Truncated Gaussian p-value.
+}
+\description{
+Compute truncated Gaussian p-value of Lee et al. (2016) with
+arbitrary affine selection and covariance.
+}
+\usage{
+TG.pvalue(Z, A, b, eta, Sigma, null_value=0, bits=NULL)  
+}
+\arguments{
+\item{Z}{
+Observed data (assumed to follow N(mu, Sigma) with sum(eta*mu)=null_value)
+}
+\item{A}{
+Matrix specifiying affine inequalities AZ <= b
+}
+\item{b}{
+Offsets in the affine inequalities AZ <= b.
+}      
+\item{eta}{
+Determines the target sum(eta*mu) and estimate sum(eta*Z).
+} 
+\item{Sigma}{
+Covariance matrix of Z.
+} 
+\item{null_value}{Hypothesized value of sum(eta*mu) under the null.
+}
+\item{bits}{
+Number of bits to be used for p-value and confidence interval calculations. Default is 
+NULL, in which case standard floating point calculations are performed. When not NULL, 
+multiple precision floating point calculations are performed with the specified number 
+of bits, using the R package \code{Rmpfr} (if this package is not installed, then a 
+warning is thrown, and standard floating point calculations are pursued).
+Note: standard double precision uses 53 bits
+so, e.g., a choice of 200 bits uses about 4 times double precision. The confidence
+interval computation is sometimes numerically challenging, and the extra precision can be 
+helpful (though computationally more costly). In particular, extra precision might be tried 
+if the values in the output columns of \code{tailarea} differ noticeably from alpha/2.
+}
+}
+\details{
+This function computes selective p-values based on the polyhedral
+lemma of Lee et al. (2016). 
+}
+
+\value{  
+\item{pv}{One-sided P-values for active variables, uses the fact we have conditioned on the sign.}
+\item{vlo}{Lower truncation limits for statistic}
+\item{vup}{Upper truncation limits for statistic}
+\item{sd}{Standard error of sum(eta*Z)}
+}
+
+\references{
+Jason Lee, Dennis Sun, Yuekai Sun, and Jonathan Taylor (2016). 
+Exact post-selection inference, with application to the lasso. Annals of Statistics, 44(3), 907-927.
+
+Jonathan Taylor and Robert Tibshirani (2017) Post-selection inference for math L1-penalized likelihood models.
+Canadian Journal of Statistics, xx, 1-21. (Volume still not posted)
+
+}
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\examples{
+
+A = diag(5)
+b = rep(1, 5)
+Z = rep(0, 5)
+Sigma = diag(5)
+eta = as.numeric(c(1, 1, 0, 0, 0))
+TG.pvalue(Z, A, b, Sigma, eta)
+TG.pvalue(Z, A, b, Sigma, eta, null_value=1)
+
+}
+ 
\ No newline at end of file

From 275ebff654ce7e1ee87296219548fefaab65caee Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 24 Jul 2017 11:10:56 -0700
Subject: [PATCH 161/493] trying to get matrix multiplies to work

---
 selectiveInference/R/funs.inf.R | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/selectiveInference/R/funs.inf.R b/selectiveInference/R/funs.inf.R
index 1b454fa..0506c29 100644
--- a/selectiveInference/R/funs.inf.R
+++ b/selectiveInference/R/funs.inf.R
@@ -253,9 +253,9 @@ TG.pvalue = function(Z, A, b, eta, Sigma, null_value=0, bits=NULL) {
     n = length(Z)
     eta = matrix(eta, ncol=1, nrow=n)
     b = as.vector(b)
-    target_estimate = sum(eta * Z)
-    var_estimate = sum(matrix(eta, nrow=1, ncol=n) %*% (Sigma %*% eta))
-    cross_cov = Sigma %*% eta
+    target_estimate = sum(as.numeric(eta) * as.numeric(Z))
+    var_estimate = sum(matrix(eta, nrow=1, ncol=n) %*% (Sigma %*% matrix(eta, ncol=1, nrow=n)))
+    cross_cov = Sigma %*% matrix(eta, ncol=1, nrow=n)
    
     resid = (diag(n) - matrix(cross_cov / var_estimate, ncol=1, nrow=n) %*% matrix(eta, nrow=1, ncol=n)) %*% Z
     rho = A %*% cross_cov / var_estimate
@@ -280,7 +280,7 @@ TG.interval = function(Z, eta, vlo, vup, sd, alpha,
 
     # compute sel intervals from poly lemmma, full version from Lee et al for full matrix Sigma
 
-    target_estimate = sum(eta*Z)
+    target_estimate = sum(as.numeric(eta) * as.numeric(Z))
   
     param_grid = seq(gridrange[1]*sd, gridrange[2]*sd, length=gridpts)
 

From 2a98745a4d06872f27f39f42fe7c07c91f2afd85 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 2 Aug 2017 10:33:52 -0700
Subject: [PATCH 162/493] created new TG.limits function so TG.pvalue and
 TG.interval have same signature

---
 selectiveInference/NAMESPACE           |  1 +
 selectiveInference/R/funs.fixedCox.R   |  4 +-
 selectiveInference/R/funs.fixedLogit.R |  5 +--
 selectiveInference/R/funs.inf.R        | 27 +++++++++---
 selectiveInference/man/TG.interval.Rd  | 24 +++++------
 selectiveInference/man/TG.limits.Rd    | 60 ++++++++++++++++++++++++++
 selectiveInference/man/TG.pvalue.Rd    |  2 +-
 7 files changed, 97 insertions(+), 26 deletions(-)
 create mode 100644 selectiveInference/man/TG.limits.Rd

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index 7add9df..099fdc5 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -14,6 +14,7 @@ export(lar,fs,
        groupfs,groupfsInf,
        scaleGroups,factorDesign,
        TG.pvalue, 
+       TG.limits, 
        TG.interval
     )
 
diff --git a/selectiveInference/R/funs.fixedCox.R b/selectiveInference/R/funs.fixedCox.R
index 0069007..cfe764e 100644
--- a/selectiveInference/R/funs.fixedCox.R
+++ b/selectiveInference/R/funs.fixedCox.R
@@ -62,14 +62,14 @@ b1= -(mydiag(s2)%*%MM)%*%s2*lambda
       vj=rep(0,length(bbar));vj[jj]=s2[jj]
 
 
-      junk=TG.pvalue(bbar,A1,b1,vj,MM)
+      junk=TG.pvalue(bbar, A1, b1, vj,MM)
 
        pv[jj] = junk$pv
       vlo[jj]=junk$vlo
        vup[jj]=junk$vup
        sd[jj]=junk$sd
 
-      junk2=TG.interval(bbar, vj, vlo[jj], vup[jj], sd[jj], alpha)
+      junk2=TG.interval(bbar, A1, b1, vj, MM, alpha)
        ci[jj,]=junk2$int
        tailarea[jj,] = junk2$tailarea
      
diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index c4d00de..19936b0 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -89,15 +89,14 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
     for(jj in 1:sum(m)){
        vj=c(rep(0,sum(m)+1));vj[jj+1]=s2[jj+1]
       # compute p-values
-      junk=TG.pvalue(bbar,A1,b1,vj,MM)
+      junk=TG.pvalue(bbar, A1, b1, vj, MM)
       pv[jj] = junk$pv
  
    vlo[jj]=junk$vlo
    vup[jj]=junk$vup
        sd[jj]=junk$sd
 
-  #  junk2=TG.interval(bbar[-1], A1, b1,vj,MM[-1,-1],alpha=.1)
-     junk2=TG.interval(bbar,vj,vlo[jj],vup[jj],sd[jj],alpha=.1)
+     junk2=TG.interval(bbar, A1, b1, vj, MM,alpha=alpha)
 
      ci[jj,]=junk2$int
      tailarea[jj,] = junk2$tailarea
diff --git a/selectiveInference/R/funs.inf.R b/selectiveInference/R/funs.inf.R
index 0506c29..c3412d8 100644
--- a/selectiveInference/R/funs.inf.R
+++ b/selectiveInference/R/funs.inf.R
@@ -247,13 +247,19 @@ aicStop <- function(x, y, action, df, sigma, mult=2, ntimes=2) {
 
 #these next two functions are used by the binomial and Cox options of fixedLassoInf
 
-TG.pvalue = function(Z, A, b, eta, Sigma, null_value=0, bits=NULL) {
+# Compute the truncation interval and SD of the corresponding Gaussian
+
+TG.limits = function(Z, A, b, eta, Sigma=NULL) {
+
+    if (is.null(Sigma)) {
+        Sigma = diag(rep(1, n))
+    }
 
     # compute pvalues from poly lemma:  full version from Lee et al for full matrix Sigma
+
     n = length(Z)
     eta = matrix(eta, ncol=1, nrow=n)
     b = as.vector(b)
-    target_estimate = sum(as.numeric(eta) * as.numeric(Z))
     var_estimate = sum(matrix(eta, nrow=1, ncol=n) %*% (Sigma %*% matrix(eta, ncol=1, nrow=n)))
     cross_cov = Sigma %*% matrix(eta, ncol=1, nrow=n)
    
@@ -265,13 +271,19 @@ TG.pvalue = function(Z, A, b, eta, Sigma, null_value=0, bits=NULL) {
     vup = suppressWarnings(min(vec[rho > 0]))
 
     sd = sqrt(var_estimate)
-    pv = tnorm.surv(target_estimate, null_value, sd, vlo, vup, bits)
-    return(list(pv=pv, vlo=vlo, vup=vup, sd=sd))
+    return(list(vlo=vlo, vup=vup, sd=sd))
 }
 
+TG.pvalue = function(Z, A, b, eta, Sigma=NULL, null_value=0, bits=NULL) {
+
+    limits.info = TG.limits(Z, A, b, eta, Sigma)
+    target_estimate = sum(as.numeric(eta) * as.numeric(Z))
+    pv = tnorm.surv(target_estimate, null_value, limits.info$sd, limits.info$vlo, limits.info$vup, bits)
 
+    return(list(pv=pv, vlo=limits.info$vlo, vup=limits.info$vup, sd=limits.info$sd))
+}
 
-TG.interval = function(Z, eta, vlo, vup, sd, alpha, 
+TG.interval = function(Z, A, b, eta, Sigma=NULL, alpha=0.1, 
                        gridrange=c(-100,100),
                        gridpts=100, 
                        griddepth=2, 
@@ -280,12 +292,13 @@ TG.interval = function(Z, eta, vlo, vup, sd, alpha,
 
     # compute sel intervals from poly lemmma, full version from Lee et al for full matrix Sigma
 
+    limits.info = TG.limits(Z, A, b, eta, Sigma)
     target_estimate = sum(as.numeric(eta) * as.numeric(Z))
   
-    param_grid = seq(gridrange[1]*sd, gridrange[2]*sd, length=gridpts)
+    param_grid = seq(gridrange[1] * limits.info$sd, gridrange[2] * limits.info$sd, length=gridpts)
 
     pivot = function(param) {
-        tnorm.surv(target_estimate, param, sd, vlo, vup, bits) 
+        tnorm.surv(target_estimate, param, limits.info$sd, limits.info$vlo, limits.info$vup, bits) 
     }
 
     interval = grid.search(param_grid, pivot, alpha/2, 1-alpha/2, gridpts, griddepth)
diff --git a/selectiveInference/man/TG.interval.Rd b/selectiveInference/man/TG.interval.Rd
index d542691..d9a0d6e 100644
--- a/selectiveInference/man/TG.interval.Rd
+++ b/selectiveInference/man/TG.interval.Rd
@@ -9,7 +9,7 @@ Compute truncated Gaussian interval of Lee et al. (2016) with
 arbitrary affine selection and covariance.
 }
 \usage{
-TG.interval(Z, eta, vlo, vup, sd, alpha, 
+TG.interval(Z, A, b, eta, Sigma=NULL, alpha=0.1, 
 	   gridrange=c(-100,100),
            gridpts=100, 
            griddepth=2, 
@@ -20,18 +20,18 @@ TG.interval(Z, eta, vlo, vup, sd, alpha,
 \item{Z}{
 Observed data (assumed to follow N(mu, Sigma) with sum(eta*mu)=null_value)
 }
+\item{A}{
+Matrix specifiying affine inequalities AZ <= b
+}
+\item{b}{
+Offsets in the affine inequalities AZ <= b.
+}      
 \item{eta}{
 Determines the target sum(eta*mu) and estimate sum(eta*Z).
 } 
-\item{vlo}{
-Lower truncation limits for statistic. Can be computed with TG.pvalue.
-}
-\item{vup}{
-Upper truncation limits for statistic. Can be computed with TG.pvalue.
-}
-\item{sd}{
-Standard error of sum(eta*Z).
-}
+\item{Sigma}{
+Covariance matrix of Z. Defaults to identity.
+} 
 \item{alpha}{
 Significance level for confidence intervals (target is miscoverage alpha/2 in each tail)
 }
@@ -82,13 +82,11 @@ Canadian Journal of Statistics, xx, 1-21. (Volume still not posted)
 \author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
 
 \examples{
-
 A = diag(5)
 b = rep(1, 5)
 Z = rep(0, 5)
 Sigma = diag(5)
 eta = as.numeric(c(1, 1, 0, 0, 0))
-V = TG.pvalue(Z, A, b, Sigma, eta)
-TG.interval(Z, eta, V$vlo, V$vup, V$sd, 0.05)
+TG.interval(Z, A, b, Sigma, eta)
 }
  
\ No newline at end of file
diff --git a/selectiveInference/man/TG.limits.Rd b/selectiveInference/man/TG.limits.Rd
new file mode 100644
index 0000000..8a71f27
--- /dev/null
+++ b/selectiveInference/man/TG.limits.Rd
@@ -0,0 +1,60 @@
+\name{TG.limits}
+\alias{TG.limits}
+
+\title{
+Truncation limits and standard deviation.
+}
+\description{
+Compute truncated limits and SD for use in computing
+p-values or confidence intervals of Lee et al. (2016).
+}
+\usage{
+TG.limits(Z, A, b, eta, Sigma)  
+}
+\arguments{
+\item{Z}{
+Observed data (assumed to follow N(mu, Sigma) with sum(eta*mu)=null_value)
+}
+\item{A}{
+Matrix specifiying affine inequalities AZ <= b
+}
+\item{b}{
+Offsets in the affine inequalities AZ <= b.
+}      
+\item{eta}{
+Determines the target sum(eta*mu) and estimate sum(eta*Z).
+} 
+\item{Sigma}{
+Covariance matrix of Z. Defaults to identity.
+} 
+}
+\details{
+This function computes the limits of truncation and the implied
+standard deviation in the polyhedral lemma of Lee et al. (2016). 
+}
+
+\value{  
+\item{vlo}{Lower truncation limits for statistic}
+\item{vup}{Upper truncation limits for statistic}
+\item{sd}{Standard error of sum(eta*Z)}
+}
+
+\references{
+Jason Lee, Dennis Sun, Yuekai Sun, and Jonathan Taylor (2016). 
+Exact post-selection inference, with application to the lasso. Annals of Statistics, 44(3), 907-927.
+
+Jonathan Taylor and Robert Tibshirani (2017) Post-selection inference for math L1-penalized likelihood models.
+Canadian Journal of Statistics, xx, 1-21. (Volume still not posted)
+
+}
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\examples{
+A = diag(5)
+b = rep(1, 5)
+Z = rep(0, 5)
+Sigma = diag(5)
+eta = as.numeric(c(1, 1, 0, 0, 0))
+TG.limits(Z, A, b, Sigma, eta)
+}
+ 
\ No newline at end of file
diff --git a/selectiveInference/man/TG.pvalue.Rd b/selectiveInference/man/TG.pvalue.Rd
index 5625d9d..a1ec9ce 100644
--- a/selectiveInference/man/TG.pvalue.Rd
+++ b/selectiveInference/man/TG.pvalue.Rd
@@ -25,7 +25,7 @@ Offsets in the affine inequalities AZ <= b.
 Determines the target sum(eta*mu) and estimate sum(eta*Z).
 } 
 \item{Sigma}{
-Covariance matrix of Z.
+Covariance matrix of Z. Defaults to identity.
 } 
 \item{null_value}{Hypothesized value of sum(eta*mu) under the null.
 }

From 9d417492db875115a772f05dfdf54401c5ce83c1 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 2 Aug 2017 17:27:55 -0700
Subject: [PATCH 163/493] BF: arguments wrong in example

---
 selectiveInference/man/TG.interval.Rd | 2 +-
 selectiveInference/man/TG.limits.Rd   | 2 +-
 selectiveInference/man/TG.pvalue.Rd   | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/selectiveInference/man/TG.interval.Rd b/selectiveInference/man/TG.interval.Rd
index d9a0d6e..287f9d0 100644
--- a/selectiveInference/man/TG.interval.Rd
+++ b/selectiveInference/man/TG.interval.Rd
@@ -87,6 +87,6 @@ b = rep(1, 5)
 Z = rep(0, 5)
 Sigma = diag(5)
 eta = as.numeric(c(1, 1, 0, 0, 0))
-TG.interval(Z, A, b, Sigma, eta)
+TG.interval(Z, A, b, eta, Sigma)
 }
  
\ No newline at end of file
diff --git a/selectiveInference/man/TG.limits.Rd b/selectiveInference/man/TG.limits.Rd
index 8a71f27..dff98be 100644
--- a/selectiveInference/man/TG.limits.Rd
+++ b/selectiveInference/man/TG.limits.Rd
@@ -55,6 +55,6 @@ b = rep(1, 5)
 Z = rep(0, 5)
 Sigma = diag(5)
 eta = as.numeric(c(1, 1, 0, 0, 0))
-TG.limits(Z, A, b, Sigma, eta)
+TG.limits(Z, A, b, eta, Sigma)
 }
  
\ No newline at end of file
diff --git a/selectiveInference/man/TG.pvalue.Rd b/selectiveInference/man/TG.pvalue.Rd
index a1ec9ce..8dd86de 100644
--- a/selectiveInference/man/TG.pvalue.Rd
+++ b/selectiveInference/man/TG.pvalue.Rd
@@ -71,8 +71,8 @@ b = rep(1, 5)
 Z = rep(0, 5)
 Sigma = diag(5)
 eta = as.numeric(c(1, 1, 0, 0, 0))
-TG.pvalue(Z, A, b, Sigma, eta)
-TG.pvalue(Z, A, b, Sigma, eta, null_value=1)
+TG.pvalue(Z, A, b, eta, Sigma)
+TG.pvalue(Z, A, b, eta, Sigma, null_value=1)
 
 }
  
\ No newline at end of file

From fd0070a51aba038b6f2fd8887caad8cc43b056ac Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 2 Aug 2017 23:18:35 -0700
Subject: [PATCH 164/493] using TG base for all poly calcs

---
 selectiveInference/R/funs.fixed.R | 11 +++--
 selectiveInference/R/funs.fs.R    | 22 ++++++---
 selectiveInference/R/funs.inf.R   | 79 ++++++++++---------------------
 selectiveInference/R/funs.lar.R   | 22 ++++++---
 4 files changed, 66 insertions(+), 68 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index b30d04c..ccf45e5 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -122,14 +122,19 @@ else{
     vj = vj / mj        # Standardize (divide by norm of vj)
     sign[j] = sign(sum(vj*y))
     vj = sign[j] * vj
-    a = poly.pval(y,G,u,vj,sigma,bits)
+
+    limits.info = TG.limits(y, -G, -u, vj, Sigma=diag(rep(sigma^2, n)))
+    a = TG.pvalue.base(limits.info, bits=bits)
     pv[j] = a$pv
     vlo[j] = a$vlo * mj # Unstandardize (mult by norm of vj)
     vup[j] = a$vup * mj # Unstandardize (mult by norm of vj)
     vmat[j,] = vj * mj * sign[j]  # Unstandardize (mult by norm of vj)
 
-    a = poly.int(y,G,u,vj,sigma,alpha,gridrange=gridrange,
-      flip=(sign[j]==-1),bits=bits)
+    a = TG.interval.base(limits.info, 
+                         alpha=alpha,
+                         gridrange=gridrange,
+			 flip=(sign[j]==-1),
+                         bits=bits)
     ci[j,] = a$int * mj # Unstandardize (mult by norm of vj)
     tailarea[j,] = a$tailarea
   }
diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index 3f8a87c..fcd7c71 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -299,15 +299,21 @@ fsInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic
       vj = vreg[j,]
       mj = sqrt(sum(vj^2)) 
       vj = vj / mj              # Standardize (divide by norm of vj)
-      a = poly.pval(y,Gj,uj,vj,sigma,bits)
+
+      limits.info = TG.limits(y, -Gj, -uj, vj, Sigma=diag(rep(sigma^2, n)))
+      a = TG.pvalue.base(limits.info, bits=bits)
+
       pv[j] = a$pv
       sxj = sx[vars[j]]
       vlo[j] = a$vlo * mj / sxj # Unstandardize (mult by norm of vj / sxj)
       vup[j] = a$vup * mj / sxj # Unstandardize (mult by norm of vj / sxj)
       vmat[j,] = vj * mj / sxj  # Unstandardize (mult by norm of vj / sxj)
   
-      a = poly.int(y,Gj,uj,vj,sigma,alpha,gridrange=gridrange,
-        flip=(sign[j]==-1),bits=bits)
+      a = TG.interval.base(limits.info, 
+                           alpha=alpha,
+			   gridrange=gridrange,
+			   flip=(sign[j]==-1),
+			   bits=bits)
       ci[j,] = a$int * mj / sxj # Unstandardize (mult by norm of vj / sxj)
       tailarea[j,] = a$tailarea
     }
@@ -349,15 +355,19 @@ fsInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic
       Gj = rbind(G,vj)
       uj = c(u,0)
 
-      a = poly.pval(y,Gj,uj,vj,sigma,bits)
+      limits.info = TG.limits(y, -Gj, -uj, vj, Sigma=diag(rep(sigma^2, n)))
+      a = TG.pvalue.base(limits.info, bits=bits)
       pv[j] = a$pv
       sxj = sx[vars[j]]
       vlo[j] = a$vlo * mj / sxj # Unstandardize (mult by norm of vj / sxj)
       vup[j] = a$vup * mj / sxj # Unstandardize (mult by norm of vj / sxj)
       vmat[j,] = vj * mj / sxj  # Unstandardize (mult by norm of vj / sxj)
 
-      a = poly.int(y,Gj,uj,vj,sigma,alpha,gridrange=gridrange,
-        flip=(sign[j]==-1),bits=bits)
+      a = TG.interval.base(limits.info,
+                           alpha=alpha,
+                           gridrange=gridrange,
+			   flip=(sign[j]==-1),
+                           bits=bits)
       ci[j,] = a$int * mj / sxj # Unstandardize (mult by norm of vj / sxj)
       tailarea[j,] = a$tailarea
     }
diff --git a/selectiveInference/R/funs.inf.R b/selectiveInference/R/funs.inf.R
index c3412d8..0cec095 100644
--- a/selectiveInference/R/funs.inf.R
+++ b/selectiveInference/R/funs.inf.R
@@ -1,48 +1,3 @@
-# Main p-value function
-
-poly.pval <- function(y, G, u, v, sigma, bits=NULL) {
-  z = sum(v*y)
-  vv = sum(v^2)
-  sd = sigma*sqrt(vv)
-  
-  rho = G %*% v / vv
-  vec = (u - G %*% y + rho*z) / rho
-  vlo = suppressWarnings(max(vec[rho>0]))
-  vup = suppressWarnings(min(vec[rho<0]))
-
-  pv = tnorm.surv(z,0,sd,vlo,vup,bits)
-  return(list(pv=pv,vlo=vlo,vup=vup))
-}
-
-# Main confidence interval function
-
-poly.int <- function(y, G, u, v, sigma, alpha, gridrange=c(-100,100),
-                     gridpts=100, griddepth=2, flip=FALSE, bits=NULL) {
-  
-  z = sum(v*y)
-  vv = sum(v^2)
-  sd = sigma*sqrt(vv)
-  
-  rho = G %*% v / vv
-  vec = (u - G %*% y + rho*z) / rho
-  vlo = suppressWarnings(max(vec[rho>0]))
-  vup = suppressWarnings(min(vec[rho<0]))
-  
-  xg = seq(gridrange[1]*sd,gridrange[2]*sd,length=gridpts)
-  fun = function(x) { tnorm.surv(z,x,sd,vlo,vup,bits) }
-
-  int = grid.search(xg,fun,alpha/2,1-alpha/2,gridpts,griddepth)
-  tailarea = c(fun(int[1]),1-fun(int[2]))
-
-  if (flip) {
-    int = -int[2:1]
-    tailarea = tailarea[2:1]
-  }
-  
-  return(list(int=int,tailarea=tailarea))
-}
-
-##############################
 
 # Assuming that grid is in sorted order from smallest to largest,
 # and vals are monotonically increasing function values over the
@@ -251,6 +206,8 @@ aicStop <- function(x, y, action, df, sigma, mult=2, ntimes=2) {
 
 TG.limits = function(Z, A, b, eta, Sigma=NULL) {
 
+    target_estimate = sum(as.numeric(eta) * as.numeric(Z))
+
     if (is.null(Sigma)) {
         Sigma = diag(rep(1, n))
     }
@@ -271,16 +228,14 @@ TG.limits = function(Z, A, b, eta, Sigma=NULL) {
     vup = suppressWarnings(min(vec[rho > 0]))
 
     sd = sqrt(var_estimate)
-    return(list(vlo=vlo, vup=vup, sd=sd))
+    return(list(vlo=vlo, vup=vup, sd=sd, estimate=target_estimate))
 }
 
 TG.pvalue = function(Z, A, b, eta, Sigma=NULL, null_value=0, bits=NULL) {
 
     limits.info = TG.limits(Z, A, b, eta, Sigma)
-    target_estimate = sum(as.numeric(eta) * as.numeric(Z))
-    pv = tnorm.surv(target_estimate, null_value, limits.info$sd, limits.info$vlo, limits.info$vup, bits)
 
-    return(list(pv=pv, vlo=limits.info$vlo, vup=limits.info$vup, sd=limits.info$sd))
+    return(TG.pvalue.base(limits.info, null_value=null_value, bits=bits))
 }
 
 TG.interval = function(Z, A, b, eta, Sigma=NULL, alpha=0.1, 
@@ -290,15 +245,29 @@ TG.interval = function(Z, A, b, eta, Sigma=NULL, alpha=0.1,
                        flip=FALSE, 
                        bits=NULL) {
 
+    limits.info = TG.limits(Z, A, b, eta, Sigma)
+
+    return(TG.interval.base(limits.info, 
+                            alpha=alpha, 
+                            gridrange=gridrange,
+                            griddepth=griddepth,
+			    flip=flip,
+			    bits=bits))
+}
+
+TG.interval.base = function(limits.info, alpha=0.1, 
+                            gridrange=c(-100,100),
+                            gridpts=100, 
+                            griddepth=2, 
+                            flip=FALSE, 
+                            bits=NULL) {
+
     # compute sel intervals from poly lemmma, full version from Lee et al for full matrix Sigma
 
-    limits.info = TG.limits(Z, A, b, eta, Sigma)
-    target_estimate = sum(as.numeric(eta) * as.numeric(Z))
-  
     param_grid = seq(gridrange[1] * limits.info$sd, gridrange[2] * limits.info$sd, length=gridpts)
 
     pivot = function(param) {
-        tnorm.surv(target_estimate, param, limits.info$sd, limits.info$vlo, limits.info$vup, bits) 
+        tnorm.surv(limits.info$estimate, param, limits.info$sd, limits.info$vlo, limits.info$vup, bits) 
     }
 
     interval = grid.search(param_grid, pivot, alpha/2, 1-alpha/2, gridpts, griddepth)
@@ -314,6 +283,10 @@ TG.interval = function(Z, A, b, eta, Sigma=NULL, alpha=0.1,
                  tailarea=tailarea))
 }
 
+TG.pvalue.base = function(limits.info, null_value=0, bits=NULL) {
+    pv = tnorm.surv(limits.info$estimate, null_value, limits.info$sd, limits.info$vlo, limits.info$vup, bits)
+    return(list(pv=pv, vlo=limits.info$vlo, vup=limits.info$vup, sd=limits.info$sd))
+}
 
 
 mydiag=function(x){
diff --git a/selectiveInference/R/funs.lar.R b/selectiveInference/R/funs.lar.R
index 37d0ae3..df6c62c 100644
--- a/selectiveInference/R/funs.lar.R
+++ b/selectiveInference/R/funs.lar.R
@@ -379,15 +379,20 @@ larInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","ai
       vj = vreg[j,]
       mj = sqrt(sum(vj^2))
       vj = vj / mj              # Standardize (divide by norm of vj)
-      a = poly.pval(y,Gj,uj,vj,sigma,bits)
+
+      limits.info = TG.limits(y, -Gj, -uj, vj, Sigma=diag(rep(sigma^2, n)))
+      a = TG.pvalue.lowlevel(limits.info, bits=bits)
       pv[j] = a$pv
       sxj = sx[vars[j]]
       vlo[j] = a$vlo * mj / sxj # Unstandardize (mult by norm of vj / sxj)
       vup[j] = a$vup * mj / sxj # Unstandardize (mult by norm of vj)
       vmat[j,] = vj * mj / sxj  # Unstandardize (mult by norm of vj / sxj)
 
-      a = poly.int(y,Gj,uj,vj,sigma,alpha,gridrange=gridrange,
-        flip=(sign[j]==-1),bits=bits)
+      a = TG.interval.lowlevel(limits.info,
+                               alpha=alpha,
+                               gridrange=gridrange,
+                               flip=(sign[j]==-1),
+                               bits=bits)
       ci[j,] = a$int * mj / sxj # Unstandardize (mult by norm of vj / sxj)
       tailarea[j,] = a$tailarea
 
@@ -433,15 +438,20 @@ larInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","ai
       Gj = rbind(G,vj)
       uj = c(u,0)
 
-      a = poly.pval(y,Gj,uj,vj,sigma,bits)
+      limits.info = TG.limits(y, -Gj, -uj, vj, Sigma=diag(rep(sigma^2, n)))
+      a = TG.pvalue.lowlevel(limits.info, bits=bits)
+
       pv[j] = a$pv
       sxj = sx[vars[j]]
       vlo[j] = a$vlo * mj / sxj # Unstandardize (mult by norm of vj / sxj)
       vup[j] = a$vup * mj / sxj # Unstandardize (mult by norm of vj / sxj)
       vmat[j,] = vj * mj / sxj  # Unstandardize (mult by norm of vj / sxj)
 
-      a = poly.int(y,Gj,uj,vj,sigma,alpha,gridrange=gridrange,
-        flip=(sign[j]==-1),bits=bits)
+      a = TG.interval.lowlevel(limits.info,
+                               alpha=alpha,
+                               gridrange=gridrange,
+                               flip=(sign[j]==-1),
+                               bits=bits)
       ci[j,] = a$int * mj / sxj # Unstandardize (mult by norm of vj / sxj)
       tailarea[j,] = a$tailarea
     }

From 5d5d794ef8fd4c8e4e0700ded6b6e1ff7b3beaf2 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 2 Aug 2017 23:37:41 -0700
Subject: [PATCH 165/493] BF: forgot to rename lowlevel to base

---
 selectiveInference/R/funs.lar.R | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/selectiveInference/R/funs.lar.R b/selectiveInference/R/funs.lar.R
index df6c62c..eb31b4e 100644
--- a/selectiveInference/R/funs.lar.R
+++ b/selectiveInference/R/funs.lar.R
@@ -381,18 +381,18 @@ larInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","ai
       vj = vj / mj              # Standardize (divide by norm of vj)
 
       limits.info = TG.limits(y, -Gj, -uj, vj, Sigma=diag(rep(sigma^2, n)))
-      a = TG.pvalue.lowlevel(limits.info, bits=bits)
+      a = TG.pvalue.base(limits.info, bits=bits)
       pv[j] = a$pv
       sxj = sx[vars[j]]
       vlo[j] = a$vlo * mj / sxj # Unstandardize (mult by norm of vj / sxj)
       vup[j] = a$vup * mj / sxj # Unstandardize (mult by norm of vj)
       vmat[j,] = vj * mj / sxj  # Unstandardize (mult by norm of vj / sxj)
 
-      a = TG.interval.lowlevel(limits.info,
-                               alpha=alpha,
-                               gridrange=gridrange,
-                               flip=(sign[j]==-1),
-                               bits=bits)
+      a = TG.interval.base(limits.info,
+                           alpha=alpha,
+                           gridrange=gridrange,
+                           flip=(sign[j]==-1),
+                           bits=bits)
       ci[j,] = a$int * mj / sxj # Unstandardize (mult by norm of vj / sxj)
       tailarea[j,] = a$tailarea
 
@@ -439,7 +439,7 @@ larInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","ai
       uj = c(u,0)
 
       limits.info = TG.limits(y, -Gj, -uj, vj, Sigma=diag(rep(sigma^2, n)))
-      a = TG.pvalue.lowlevel(limits.info, bits=bits)
+      a = TG.pvalue.base(limits.info, bits=bits)
 
       pv[j] = a$pv
       sxj = sx[vars[j]]
@@ -447,11 +447,11 @@ larInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","ai
       vup[j] = a$vup * mj / sxj # Unstandardize (mult by norm of vj / sxj)
       vmat[j,] = vj * mj / sxj  # Unstandardize (mult by norm of vj / sxj)
 
-      a = TG.interval.lowlevel(limits.info,
-                               alpha=alpha,
-                               gridrange=gridrange,
-                               flip=(sign[j]==-1),
-                               bits=bits)
+      a = TG.interval.base(limits.info,
+                           alpha=alpha,
+                           gridrange=gridrange,
+                           flip=(sign[j]==-1),
+                           bits=bits)
       ci[j,] = a$int * mj / sxj # Unstandardize (mult by norm of vj / sxj)
       tailarea[j,] = a$tailarea
     }

From c8861e0a44e6edcfe06145ceb0b9355eb7d481d2 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 3 Aug 2017 11:23:08 -0700
Subject: [PATCH 166/493] added constraint description in docs, warning about
 constraint not being satisfied

---
 selectiveInference/R/funs.inf.R       | 4 ++++
 selectiveInference/man/TG.interval.Rd | 1 +
 selectiveInference/man/TG.limits.Rd   | 1 +
 selectiveInference/man/TG.pvalue.Rd   | 1 +
 4 files changed, 7 insertions(+)

diff --git a/selectiveInference/R/funs.inf.R b/selectiveInference/R/funs.inf.R
index 0cec095..9d7e740 100644
--- a/selectiveInference/R/funs.inf.R
+++ b/selectiveInference/R/funs.inf.R
@@ -208,6 +208,10 @@ TG.limits = function(Z, A, b, eta, Sigma=NULL) {
 
     target_estimate = sum(as.numeric(eta) * as.numeric(Z))
 
+    if (max(A %*% as.numeric(Z) - b) > 0) {
+        warning('Contsraint not satisfied. A %*% Z should be elementwise less than or equal to b')
+    }
+
     if (is.null(Sigma)) {
         Sigma = diag(rep(1, n))
     }
diff --git a/selectiveInference/man/TG.interval.Rd b/selectiveInference/man/TG.interval.Rd
index 287f9d0..c898d17 100644
--- a/selectiveInference/man/TG.interval.Rd
+++ b/selectiveInference/man/TG.interval.Rd
@@ -7,6 +7,7 @@ Truncated Gaussian confidence interval.
 \description{
 Compute truncated Gaussian interval of Lee et al. (2016) with
 arbitrary affine selection and covariance.
+Z should satisfy A %*% Z elementise less then or equal b.
 }
 \usage{
 TG.interval(Z, A, b, eta, Sigma=NULL, alpha=0.1, 
diff --git a/selectiveInference/man/TG.limits.Rd b/selectiveInference/man/TG.limits.Rd
index dff98be..cc005fc 100644
--- a/selectiveInference/man/TG.limits.Rd
+++ b/selectiveInference/man/TG.limits.Rd
@@ -7,6 +7,7 @@ Truncation limits and standard deviation.
 \description{
 Compute truncated limits and SD for use in computing
 p-values or confidence intervals of Lee et al. (2016).
+Z should satisfy A %*% Z elementise less then or equal b.
 }
 \usage{
 TG.limits(Z, A, b, eta, Sigma)  
diff --git a/selectiveInference/man/TG.pvalue.Rd b/selectiveInference/man/TG.pvalue.Rd
index 8dd86de..d6e54b3 100644
--- a/selectiveInference/man/TG.pvalue.Rd
+++ b/selectiveInference/man/TG.pvalue.Rd
@@ -7,6 +7,7 @@ Truncated Gaussian p-value.
 \description{
 Compute truncated Gaussian p-value of Lee et al. (2016) with
 arbitrary affine selection and covariance.
+Z should satisfy A %*% Z elementise less then or equal b.
 }
 \usage{
 TG.pvalue(Z, A, b, eta, Sigma, null_value=0, bits=NULL)  

From 4a9f1cf3607f4e676789d4f4461b2ecc91e0b646 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 3 Aug 2017 11:24:34 -0700
Subject: [PATCH 167/493] typo

---
 selectiveInference/man/TG.interval.Rd | 2 +-
 selectiveInference/man/TG.limits.Rd   | 2 +-
 selectiveInference/man/TG.pvalue.Rd   | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/selectiveInference/man/TG.interval.Rd b/selectiveInference/man/TG.interval.Rd
index c898d17..50d2b93 100644
--- a/selectiveInference/man/TG.interval.Rd
+++ b/selectiveInference/man/TG.interval.Rd
@@ -7,7 +7,7 @@ Truncated Gaussian confidence interval.
 \description{
 Compute truncated Gaussian interval of Lee et al. (2016) with
 arbitrary affine selection and covariance.
-Z should satisfy A %*% Z elementise less then or equal b.
+Z should satisfy A %*% Z elementwise less then or equal b.
 }
 \usage{
 TG.interval(Z, A, b, eta, Sigma=NULL, alpha=0.1, 
diff --git a/selectiveInference/man/TG.limits.Rd b/selectiveInference/man/TG.limits.Rd
index cc005fc..e2df17e 100644
--- a/selectiveInference/man/TG.limits.Rd
+++ b/selectiveInference/man/TG.limits.Rd
@@ -7,7 +7,7 @@ Truncation limits and standard deviation.
 \description{
 Compute truncated limits and SD for use in computing
 p-values or confidence intervals of Lee et al. (2016).
-Z should satisfy A %*% Z elementise less then or equal b.
+Z should satisfy A %*% Z elementwise less then or equal b.
 }
 \usage{
 TG.limits(Z, A, b, eta, Sigma)  
diff --git a/selectiveInference/man/TG.pvalue.Rd b/selectiveInference/man/TG.pvalue.Rd
index d6e54b3..716e5b3 100644
--- a/selectiveInference/man/TG.pvalue.Rd
+++ b/selectiveInference/man/TG.pvalue.Rd
@@ -7,7 +7,7 @@ Truncated Gaussian p-value.
 \description{
 Compute truncated Gaussian p-value of Lee et al. (2016) with
 arbitrary affine selection and covariance.
-Z should satisfy A %*% Z elementise less then or equal b.
+Z should satisfy A %*% Z elementwise less then or equal b.
 }
 \usage{
 TG.pvalue(Z, A, b, eta, Sigma, null_value=0, bits=NULL)  

From 222fc7c4aaf65739e98c94f034d3187850527784 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 4 Aug 2017 13:35:44 -0700
Subject: [PATCH 168/493] BF: correcting the conditioning for FS

---
 selectiveInference/R/funs.fs.R | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index fcd7c71..db0968b 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -132,7 +132,8 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     # Gamma matrix!
     if (gi + 2*p > nrow(Gamma)) Gamma = rbind(Gamma,matrix(0,2*p+gbuf,n))
     working_x = t(sign_score*t(working_x))
-    Gamma[gi+Seq(1,p-r),] = t(working_x); gi = gi+p-r
+
+    Gamma[gi+Seq(1,p-r-1),] = t(working_x[,i_hit]+working_x[,-i_hit]); gi = gi+p-r-1
     Gamma[gi+Seq(1,p-r-1),] = t(working_x[,i_hit]-working_x[,-i_hit]); gi = gi+p-r-1
     Gamma[gi+1,] = t(working_x[,i_hit]); gi = gi+1
 

From b5f4bfd7188f150ed7a20940df442701cc21f036 Mon Sep 17 00:00:00 2001
From: kevinbfry <kfry@stanford.edu>
Date: Tue, 8 Aug 2017 01:19:13 -0700
Subject: [PATCH 169/493] Added debiased lasso for linear regression when
 type='full' and n < p

---
 selectiveInference/R/funs.fixed.R   | 451 ++++++++++++++++++++--------
 selectiveInference/R/linear.tests.R | 172 +++++++++++
 2 files changed, 500 insertions(+), 123 deletions(-)
 create mode 100644 selectiveInference/R/linear.tests.R

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index ccf45e5..3258b80 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -7,112 +7,170 @@ sigma=NULL, alpha=0.1,
                      type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
                      gridrange=c(-100,100), bits=NULL, verbose=FALSE) {
 
- family = match.arg(family)
+  family = match.arg(family)
   this.call = match.call()
   type = match.arg(type)
-
+  
   if(family=="binomial")  {
-      if(type!="partial") stop("Only type= partial allowed with binomial family")
-       out=fixedLogitLassoInf(x,y,beta,lambda,alpha=alpha, type="partial", tol.beta=tol.beta, tol.kkt=tol.kkt,
-                     gridrange=gridrange, bits=bits, verbose=verbose,this.call=this.call)
-                      return(out)
-                    }
-else if(family=="cox")  {
-    if(type!="partial") stop("Only type= partial allowed with Cox family")
-     out=fixedCoxLassoInf(x,y,status,beta,lambda,alpha=alpha, type="partial",tol.beta=tol.beta,
-          tol.kkt=tol.kkt, gridrange=gridrange, bits=bits, verbose=verbose,this.call=this.call)
-                      return(out)
-                    }
-
-else{
-
-
-
-  checkargs.xy(x,y)
-  if (missing(beta) || is.null(beta)) stop("Must supply the solution beta")
-  if (missing(lambda) || is.null(lambda)) stop("Must supply the tuning parameter value lambda")
-  checkargs.misc(beta=beta,lambda=lambda,sigma=sigma,alpha=alpha,
-                 gridrange=gridrange,tol.beta=tol.beta,tol.kkt=tol.kkt)
-  if (!is.null(bits) && !requireNamespace("Rmpfr",quietly=TRUE)) {
-    warning("Package Rmpfr is not installed, reverting to standard precision")
-    bits = NULL
+    if(type!="partial") stop("Only type= partial allowed with binomial family")
+    out=fixedLogitLassoInf(x,y,beta,lambda,alpha=alpha, type="partial", tol.beta=tol.beta, tol.kkt=tol.kkt,
+                           gridrange=gridrange, bits=bits, verbose=verbose,this.call=this.call)
+    return(out)
   }
-
-  n = nrow(x)
-  p = ncol(x)
-  beta = as.numeric(beta)
-  if (length(beta) != p) stop("Since family='gaussian', beta must have length equal to ncol(x)")
-
-  # If glmnet was run with an intercept term, center x and y
-  if (intercept==TRUE) {
-    obj = standardize(x,y,TRUE,FALSE)
-    x = obj$x
-    y = obj$y
+  else if(family=="cox")  {
+    if(type!="partial") stop("Only type= partial allowed with Cox family")
+    out=fixedCoxLassoInf(x,y,status,beta,lambda,alpha=alpha, type="partial",tol.beta=tol.beta,
+                         tol.kkt=tol.kkt, gridrange=gridrange, bits=bits, verbose=verbose,this.call=this.call)
+    return(out)
   }
-
-  # Check the KKT conditions
-  g = t(x)%*%(y-x%*%beta) / lambda
-  if (any(abs(g) > 1+tol.kkt * sqrt(sum(y^2))))
-    warning(paste("Solution beta does not satisfy the KKT conditions",
-                  "(to within specified tolerances)"))
-
-  vars = which(abs(beta) > tol.beta / sqrt(colSums(x^2)))
-  if(length(vars)==0){
+  
+  else{
+    
+    
+    
+    checkargs.xy(x,y)
+    if (missing(beta) || is.null(beta)) stop("Must supply the solution beta")
+    if (missing(lambda) || is.null(lambda)) stop("Must supply the tuning parameter value lambda")
+    
+    n = nrow(x)
+    p = ncol(x)
+    beta = as.numeric(beta)
+    if (type == "full") {
+      if (p > n) {
+        # need intercept (if there is one) for debiased lasso
+        hbeta = beta
+        if (intercept == T) {
+          if (length(beta) != p + 1) {
+            stop("Since type='full', p > n, and intercept=TRUE, beta must have length equal to ncol(x)+1")
+          }
+          # remove intercept if included
+          beta = beta[-1]
+        } else if (length(beta) != p) {
+          stop("Since family='gaussian', type='full' and intercept=FALSE, beta must have length equal to ncol(x)")
+        }
+      }
+    } else if (length(beta) != p) {
+      stop("Since family='gaussian' and type='partial', beta must have length equal to ncol(x)")
+    }
+    
+    checkargs.misc(beta=beta,lambda=lambda,sigma=sigma,alpha=alpha,
+                   gridrange=gridrange,tol.beta=tol.beta,tol.kkt=tol.kkt)
+    if (!is.null(bits) && !requireNamespace("Rmpfr",quietly=TRUE)) {
+      warning("Package Rmpfr is not installed, reverting to standard precision")
+      bits = NULL
+    }
+    
+    # If glmnet was run with an intercept term, center x and y
+    if (intercept==TRUE) {
+      obj = standardize(x,y,TRUE,FALSE)
+      x = obj$x
+      y = obj$y
+    }
+    
+    # Check the KKT conditions
+    g = t(x)%*%(y-x%*%beta) / lambda
+    if (any(abs(g) > 1+tol.kkt * sqrt(sum(y^2))))
+      warning(paste("Solution beta does not satisfy the KKT conditions",
+                    "(to within specified tolerances)"))
+    
+    tol.coef = tol.beta * sqrt(n^2 / colSums(x^2))
+    # print(tol.coef)
+    vars = which(abs(beta) > tol.coef)
+    # print(beta)
+    # print(vars)
+    if(length(vars)==0){
       cat("Empty model",fill=T)
       return()
-  }
-  if (any(sign(g[vars]) != sign(beta[vars])))
-    warning(paste("Solution beta does not satisfy the KKT conditions",
-                  "(to within specified tolerances). You might try rerunning",
-                  "glmnet with a lower setting of the",
-                  "'thresh' parameter, for a more accurate convergence."))
-
-  # Get lasso polyhedral region, of form Gy >= u
-  out = fixedLasso.poly(x,y,beta,lambda,vars)
-  G = out$G
-  u = out$u
-
-  # Check polyhedral region
-  tol.poly = 0.01
-  if (min(G %*% y - u) < -tol.poly * sqrt(sum(y^2)))
-    stop(paste("Polyhedral constraints not satisfied; you must recompute beta",
-               "more accurately. With glmnet, make sure to use exact=TRUE in coef(),",
-               "and check whether the specified value of lambda is too small",
-               "(beyond the grid of values visited by glmnet).",
-               "You might also try rerunning glmnet with a lower setting of the",
-               "'thresh' parameter, for a more accurate convergence."))
-
-  # Estimate sigma
-  if (is.null(sigma)) {
-    if (n >= 2*p) {
-      oo = intercept
-      sigma = sqrt(sum(lsfit(x,y,intercept=oo)$res^2)/(n-p-oo))
     }
-    else {
-      sigma = sd(y)
-      warning(paste(sprintf("p > n/2, and sd(y) = %0.3f used as an estimate of sigma;",sigma),
-                    "you may want to use the estimateSigma function"))
+    if (any(sign(g[vars]) != sign(beta[vars])))
+      warning(paste("Solution beta does not satisfy the KKT conditions",
+                    "(to within specified tolerances). You might try rerunning",
+                    "glmnet with a lower setting of the",
+                    "'thresh' parameter, for a more accurate convergence."))
+    
+    # Get lasso polyhedral region, of form Gy >= u
+    if (type == 'full' & p > n) out = fixedLasso.poly(x,y,beta,lambda,vars,inactive=TRUE)
+    else out = fixedLasso.poly(x,y,beta,lambda,vars)
+    G = out$G
+    u = out$u
+    
+    # Check polyhedral region
+    tol.poly = 0.01
+    if (min(G %*% y - u) < -tol.poly * sqrt(sum(y^2)))
+      stop(paste("Polyhedral constraints not satisfied; you must recompute beta",
+                 "more accurately. With glmnet, make sure to use exact=TRUE in coef(),",
+                 "and check whether the specified value of lambda is too small",
+                 "(beyond the grid of values visited by glmnet).",
+                 "You might also try rerunning glmnet with a lower setting of the",
+                 "'thresh' parameter, for a more accurate convergence."))
+    
+    # Estimate sigma
+    if (is.null(sigma)) {
+      if (n >= 2*p) {
+        oo = intercept
+        sigma = sqrt(sum(lsfit(x,y,intercept=oo)$res^2)/(n-p-oo))
+      }
+      else {
+        sigma = sd(y)
+        warning(paste(sprintf("p > n/2, and sd(y) = %0.3f used as an estimate of sigma;",sigma),
+                      "you may want to use the estimateSigma function"))
+      }
+    }
+    
+    k = length(vars)
+    pv = vlo = vup = numeric(k)
+    vmat = matrix(0,k,n)
+    ci = tailarea = matrix(0,k,2)
+    sign = numeric(k)
+    
+    if (type=="full" & p > n) {
+      if (intercept == T) {
+        pp=p+1
+        Xint <- cbind(rep(1,n),x)
+        # indices of selected predictors
+        S = c(1,vars + 1)
+        notS = which(abs(beta) <= tol.coef) + 1
+      } else {
+        pp=p
+        Xint <- x
+        # indices of selected predictors
+        S = vars
+        notS = which(abs(beta) <= tol.coef)
+      }
+      
+      
+      XS = Xint[,S]
+      hbetaS = hbeta[S]
+      
+      # Reorder so that active set S is first
+      Xordered = Xint[,c(S,notS,recursive=T)]
+      
+      hsigma <- 1/n*(t(Xordered)%*%Xordered)
+      hsigmaS <- 1/n*(t(XS)%*%XS) # hsigma[S,S]
+      hsigmaSinv <- solve(hsigmaS) # pinv(hsigmaS)
+      
+      # Approximate inverse covariance matrix for when (n < p) from lasso_Inference.R
+      htheta <- InverseLinfty(hsigma, n, verbose=FALSE)
+      
+      FS = rbind(diag(length(S)),matrix(0,pp-length(S),length(S)))
+      ithetasigma = (diag(pp)-(htheta%*%hsigma))
+      
+      M <- (((htheta%*%t(Xordered))+ithetasigma%*%FS%*%hsigmaSinv%*%t(XS))/n)
+      # vector which is offset for testing debiased beta's
+      null_value <- (((ithetasigma%*%FS%*%hsigmaSinv)%*%sign(hbetaS))*lambda/n)
+      if (intercept == T) {
+        M = M[-1,] # remove intercept row
+        null_value = null_value[-1] # remove intercept element
+      }
+    } else if (type=="partial" || p > n) {
+      xa = x[,vars,drop=F]
+      M = pinv(crossprod(xa)) %*% t(xa)
+      null_value = rep(0,k)
+    } else {
+      M = pinv(crossprod(x)) %*% t(x)
+      M = M[vars,,drop=F]
+      null_value = rep(0,k)
     }
-  }
-
-  k = length(vars)
-  pv = vlo = vup = numeric(k)
-  vmat = matrix(0,k,n)
-  ci = tailarea = matrix(0,k,2)
-  sign = numeric(k)
-
-  if (type=="full" & p > n)
-      warning(paste("type='full' does not make sense when p > n;",
-                    "switching to type='partial'"))
-
-  if (type=="partial" || p > n) {
-    xa = x[,vars,drop=F]
-    M = pinv(crossprod(xa)) %*% t(xa)
-  }
-  else {
-    M = pinv(crossprod(x)) %*% t(x)
-    M = M[vars,,drop=F]
-  }
 
   for (j in 1:k) {
     if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
@@ -124,7 +182,7 @@ else{
     vj = sign[j] * vj
 
     limits.info = TG.limits(y, -G, -u, vj, Sigma=diag(rep(sigma^2, n)))
-    a = TG.pvalue.base(limits.info, bits=bits)
+    a = TG.pvalue.base(limits.info, null_value=null_value[j], bits=bits)
     pv[j] = a$pv
     vlo[j] = a$vlo * mj # Unstandardize (mult by norm of vj)
     vup[j] = a$vup * mj # Unstandardize (mult by norm of vj)
@@ -135,7 +193,7 @@ else{
                          gridrange=gridrange,
 			 flip=(sign[j]==-1),
                          bits=bits)
-    ci[j,] = a$int * mj # Unstandardize (mult by norm of vj)
+    ci[j,] = (a$int-null_value[j]) * mj # Unstandardize (mult by norm of vj)
     tailarea[j,] = a$tailarea
   }
 
@@ -154,31 +212,178 @@ else{
 
 
 fixedLasso.poly=
-function(x, y, beta, lambda, a) {
-  xa = x[,a,drop=F]
-  xac = x[,!a,drop=F]
-  xai = pinv(crossprod(xa))
-  xap = xai %*% t(xa)
-  za = sign(beta[a])
-  if (length(za)>1) dz = diag(za)
-  if (length(za)==1) dz = matrix(za,1,1)
-
-#  P = diag(1,nrow(xa)) - xa %*% xap
-  #NOTE: inactive constraints not needed below!
-
-  G = -rbind(
-   #   1/lambda * t(xac) %*% P,
-   # -1/lambda * t(xac) %*% P,
-    -dz %*% xap
+  function(x, y, beta, lambda, a, inactive = FALSE) {
+    xa = x[,a,drop=F]
+    xac = x[,!a,drop=F]
+    xai = pinv(crossprod(xa))
+    xap = xai %*% t(xa)
+    za = sign(beta[a])
+    if (length(za)>1) dz = diag(za)
+    if (length(za)==1) dz = matrix(za,1,1)
+    
+    if (inactive) {
+      P = diag(1,nrow(xa)) - xa %*% xap
+      
+      G = -rbind(
+        1/lambda * t(xac) %*% P,
+        -1/lambda * t(xac) %*% P,
+        -dz %*% xap
       )
-     lambda2=lambda
-     if(length(lambda)>1) lambda2=lambda[a]
-  u = -c(
-   #   1 - t(xac) %*% t(xap) %*% za,
-   #   1 + t(xac) %*% t(xap) %*% za,
-    -lambda2 * dz %*% xai %*% za)
-
-  return(list(G=G,u=u))
+      lambda2=lambda
+      if(length(lambda)>1) lambda2=lambda[a]
+      u = -c(
+        1 - t(xac) %*% t(xap) %*% za,
+        1 + t(xac) %*% t(xap) %*% za,
+        -lambda2 * dz %*% xai %*% za)
+    } else {
+      G = -rbind(
+        #   1/lambda * t(xac) %*% P,
+        # -1/lambda * t(xac) %*% P,
+        -dz %*% xap
+      )
+      lambda2=lambda
+      if(length(lambda)>1) lambda2=lambda[a]
+      u = -c(
+        #   1 - t(xac) %*% t(xap) %*% za,
+        #   1 + t(xac) %*% t(xap) %*% za,
+        -lambda2 * dz %*% xai %*% za)
+    }
+    
+    return(list(G=G,u=u))
+  }
+
+##############################
+
+### Functions borrowed from lasso_inference.R
+
+## Approximates inverse covariance matrix theta
+InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
+  isgiven <- 1;
+  if (is.null(mu)){
+    isgiven <- 0;
+  }
+  
+  p <- nrow(sigma);
+  M <- matrix(0, p, p);
+  xperc = 0;
+  xp = round(p/10);
+  for (i in 1:p) {
+    if ((i %% xp)==0){
+      xperc = xperc+10;
+      if (verbose) {
+        print(paste(xperc,"% done",sep="")); }
+    }
+    if (isgiven==0){
+      mu <- (1/sqrt(n)) * qnorm(1-(0.1/(p^2)));
+    }
+    mu.stop <- 0;
+    try.no <- 1;
+    incr <- 0;
+    while ((mu.stop != 1)&&(try.no<10)){
+      last.beta <- beta
+      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
+      beta <- output$optsol
+      iter <- output$iter
+      if (isgiven==1){
+        mu.stop <- 1
+      }
+      else{
+        if (try.no==1){
+          if (iter == (maxiter+1)){
+            incr <- 1;
+            mu <- mu*resol;
+          } else {
+            incr <- 0;
+            mu <- mu/resol;
+          }
+        }
+        if (try.no > 1){
+          if ((incr == 1)&&(iter == (maxiter+1))){
+            mu <- mu*resol;
+          }
+          if ((incr == 1)&&(iter < (maxiter+1))){
+            mu.stop <- 1;
+          }
+          if ((incr == 0)&&(iter < (maxiter+1))){
+            mu <- mu/resol;
+          }
+          if ((incr == 0)&&(iter == (maxiter+1))){
+            mu <- mu*resol;
+            beta <- last.beta;
+            mu.stop <- 1;
+          }
+        }
+      }
+      try.no <- try.no+1
+    }
+    M[i,] <- beta;
+  }
+  return(M)
+}
+
+InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-2 ) {
+  p <- nrow(sigma);
+  rho <- max(abs(sigma[i,-i])) / sigma[i,i];
+  mu0 <- rho/(1+rho);
+  beta <- rep(0,p);
+  
+  if (mu >= mu0){
+    beta[i] <- (1-mu0)/sigma[i,i];
+    returnlist <- list("optsol" = beta, "iter" = 0);
+    return(returnlist);
+  }
+  
+  diff.norm2 <- 1;
+  last.norm2 <- 1;
+  iter <- 1;
+  iter.old <- 1;
+  beta[i] <- (1-mu0)/sigma[i,i];
+  beta.old <- beta;
+  sigma.tilde <- sigma;
+  diag(sigma.tilde) <- 0;
+  vs <- -sigma.tilde%*%beta;
+  
+  while ((iter <= maxiter) && (diff.norm2 >= threshold*last.norm2)){
+    
+    for (j in 1:p){
+      oldval <- beta[j];
+      v <- vs[j];
+      if (j==i)
+        v <- v+1;
+      beta[j] <- SoftThreshold(v,mu)/sigma[j,j];
+      if (oldval != beta[j]){
+        vs <- vs + (oldval-beta[j])*sigma.tilde[,j];
+      }
+    }
+    
+    iter <- iter + 1;
+    if (iter==2*iter.old){
+      d <- beta - beta.old;
+      diff.norm2 <- sqrt(sum(d*d));
+      last.norm2 <-sqrt(sum(beta*beta));
+      iter.old <- iter;
+      beta.old <- beta;
+      if (iter>10)
+        vs <- -sigma.tilde%*%beta;
+    }
+  }
+  
+  returnlist <- list("optsol" = beta, "iter" = iter)
+  return(returnlist)
+}
+
+SoftThreshold <- function( x, lambda ) {
+  #
+  # Standard soft thresholding
+  #
+  if (x>lambda){
+    return (x-lambda);}
+  else {
+    if (x< (-lambda)){
+      return (x+lambda);}
+    else {
+      return (0); }
+  }
 }
 
 ##############################
diff --git a/selectiveInference/R/linear.tests.R b/selectiveInference/R/linear.tests.R
new file mode 100644
index 0000000..a37f76f
--- /dev/null
+++ b/selectiveInference/R/linear.tests.R
@@ -0,0 +1,172 @@
+robs.test <- function() {
+  n <- 100
+  p <- 200
+  
+  set.seed(11332)
+  
+  y <- matrix(rnorm(n),ncol=1) # rand N(0,1) response
+  X <- matrix(rnorm(p*n),ncol = p) # p rand N(0,1) predictors
+  
+  X=scale(X,T,T)/sqrt(n-1)
+  lambda=1
+  sigma = estimateSigma(X,y)$sigmahat
+  
+  las <- glmnet(X,y,family="gaussian",alpha=1,standardize=F,intercept=T)
+  hbeta <- as.numeric(coef(las,x=X,y=y,s=lambda/n,exact=TRUE,intercept=T))
+  
+  
+  return(fixedLassoInf(X,y,hbeta[-1],lambda,family="gaussian",type="partial",intercept=T,sigma=sigma)$pv)
+}
+
+
+## Tests partial inf for X and y randomly generated from N(0,1)
+nullTest <- function(X,y,lambda,intercept,type=c("full","partial")) {
+  n=nrow(X)
+  X=scale(X,T,T)/sqrt(n-1)
+  
+  sigma = estimateSigma(X,y)$sigmahat
+  
+  las <- glmnet(X,y,family="gaussian",alpha=1,standardize=F,intercept=intercept)
+  hbeta <- as.numeric(coef(las,x=X,y=y,s=lambda/n,exact=TRUE,intercept=intercept))
+  
+  if (type=="partial" || intercept==F) hbeta = hbeta[-1]
+  
+  return(fixedLassoInf(X,y,hbeta,lambda,family="gaussian",type=type,intercept=intercept,sigma=sigma))
+}
+
+## Test partial inf for X and y where 10 variables are y with random additive N(0,0.5) noise
+corrTest <- function(X,y,lambda,intercept,type=c("full","partial")) {
+  n=nrow(X)
+  corr.X = rep(y,10) + matrix(rnorm(n*10,0,0.5),ncol = 10)
+  X = cbind(corr.X,X)
+  X=scale(X,T,T)/sqrt(n-1)
+  
+  las <- glmnet(X,y,family="gaussian",alpha=1,standardize=F,intercept=intercept)
+  hbeta <- as.numeric(coef(las,x=X,y=y,s=lambda/n,exact=TRUE,intercept=intercept))
+  
+  sigma = estimateSigma(X,y)$sigmahat
+  
+  if (type=="partial" || intercept==F) hbeta = hbeta[-1]
+  
+  return(fixedLassoInf(X,y,hbeta,lambda,family="gaussian",type=type,intercept=intercept,sigma=sigma))
+}
+
+## QQ plot of p-values for all null data now that bug fix is implemented
+partial.qq.test <- function() {
+  n <- 100
+  p <- 200
+  
+  lambda=1
+  
+  null.int.pvs <- c()
+  corr.int.pvs <- c()
+  null.pvs <- c()
+  corr.pvs <- c()
+  for(i in 1:25) {
+    y <- matrix(rnorm(n),ncol=1) # rand N(0,1) response
+    X <- matrix(rnorm(p*n),ncol=p) # p rand N(0,1) predictors
+    
+    null <- nullTest(X,y,lambda,F,type="partial")
+    corr <- corrTest(X,y,lambda,F,type="partial")
+    null.pvs <- c(null.pvs,null$pv,recursive=T)
+    corr.pvs <- c(corr.pvs,corr$pv,recursive=T)
+    null.int <- nullTest(X,y,lambda,T,type="partial")
+    corr.int <- corrTest(X,y,lambda,T,type="partial")
+    null.int.pvs <- c(null.int.pvs,null.int$pv,recursive=T)
+    corr.int.pvs <- c(corr.int.pvs,corr.int$pv,recursive=T)
+  }
+  
+  qqplot(x=runif(length(null.pvs)),y=null.pvs,xlab="Expected",ylab="Observed",main="Partial Coef. Null X w/o Intercept")
+  abline(0,1)
+  qqplot(x=runif(length(corr.pvs)),y=corr.pvs,xlab="Expected",ylab="Observed",main="Partial Coef. 10 Corr. X w/o Intercept")
+  abline(0,1)
+  qqplot(x=runif(length(null.int.pvs)),y=null.int.pvs,xlab="Expected",ylab="Observed",main="Partial Coef. Null X w/ Intercept")
+  abline(0,1)
+  qqplot(x=runif(length(corr.int.pvs)),y=corr.int.pvs,xlab="Expected",ylab="Observed",main="Partial Coef. 10 Corr. X w/ Intercept")
+  abline(0,1)
+}
+
+## QQ plot of p-values for all null data now that bug fix is implemented
+pop.qq.test <- function() {
+  n <- 100
+  p <- 200
+  
+  lambda=1
+  
+  null.int.pvs <- c()
+  corr.int.pvs <- c()
+  null.pvs <- c()
+  corr.pvs <- c()
+  for(i in 1:25) {
+    y <- matrix(rnorm(n),ncol=1) # rand N(0,1) response
+    X <- matrix(rnorm(p*n),ncol=p) # p rand N(0,1) predictors
+    
+    null <- nullTest(X,y,lambda,F,type="full")
+    corr <- corrTest(X,y,lambda,F,type="full")
+    null.pvs <- c(null.pvs,null$pv,recursive=T)
+    corr.pvs <- c(corr.pvs,corr$pv,recursive=T)
+    null.int <- nullTest(X,y,lambda,T,type="full")
+    corr.int <- corrTest(X,y,lambda,T,type="full")
+    null.int.pvs <- c(null.int.pvs,null.int$pv,recursive=T)
+    corr.int.pvs <- c(corr.int.pvs,corr.int$pv,recursive=T)
+  }
+  
+  qqplot(x=runif(length(null.pvs)),y=null.pvs,xlab="Expected",ylab="Observed",main="Pop Coef. Null X w/o Intercept")
+  abline(0,1)
+  qqplot(x=runif(length(corr.pvs)),y=corr.pvs,xlab="Expected",ylab="Observed",main="Pop Coef. 10 Corr. X w/o Intercept")
+  abline(0,1)
+  qqplot(x=runif(length(null.int.pvs)),y=null.int.pvs,xlab="Expected",ylab="Observed",main="Pop Coef. Null X w/ Intercept")
+  abline(0,1)
+  qqplot(x=runif(length(corr.int.pvs)),y=corr.int.pvs,xlab="Expected",ylab="Observed",main="Pop Coef. 10 Corr. X w/ Intercept")
+  abline(0,1)
+}
+
+
+
+
+## QQ plot of p-values for data with correlated x now that bug fix implemented
+power.partial.pval.dist <- function(n,p,intercept=T,lambda=1) {
+  pvs <- c()
+  for(i in 1:10) {
+    a <- powerPartialTest(n,p,intercept,lambda)
+    ps <- a$pv
+    pvs <- c(pvs,ps,recursive=T)
+  }
+  qqplot(x=runif(length(pvs)),y=pvs,xlab="Expected",ylab="Observed",main="Partial Coef. 10 Corr. X")
+  abline(0,1)
+}
+
+
+
+
+## Tests pop inf for X and y randomly generated
+nullPopTest <- function(n,p,intercept=T,lambda=1) {
+  y <- matrix(rnorm(n),ncol=1) # rand N(0,1) response
+  X <- matrix(rnorm(p*n),ncol = p) # p rand N(0,1) predictors
+  
+  # lambda <- 1
+  X=scale(X,T,T)/sqrt(n-1)
+  
+  # lambda <- 1
+  las <- glmnet(X,y,family="gaussian",alpha=1,standardize=F,intercept=intercept)
+  hbeta <- as.numeric(coef(las,x=X,y=y,s=lambda/n,exact=TRUE,intercept=intercept))
+  
+  ### perform post selection inference
+  
+  sigma = estimateSigma(X,y)$sigmahat
+  
+  if (intercept) return(fixedLassoInf(X,y,hbeta,lambda,family="gaussian",type="full",intercept=intercept,sigma=sigma))
+  else return(fixedLassoInf(X,y,hbeta[-1],lambda,family="gaussian",type="full",intercept=intercept,sigma=sigma))
+}
+
+## QQ plot of p-values for all null data now that bug fix is implemented
+null.pop.pval.dist <- function(n,p,intercept=T,lambda=1) {
+  pvs <- c()
+  for(i in 1:10) {
+    a <- nullPopTest(n,p,intercept,lambda)
+    ps <- a$pv
+    pvs <- c(pvs,ps,recursive=T)
+  }
+  qqplot(x=runif(length(pvs)),y=pvs,xlab="Expected",ylab="Observed",main="Pop Coef. Null X")
+  abline(0,1)
+}
\ No newline at end of file

From 344f4045a5a5b519ec035928280078ac619349c9 Mon Sep 17 00:00:00 2001
From: kevinbfry <kfry@stanford.edu>
Date: Tue, 8 Aug 2017 13:47:25 -0700
Subject: [PATCH 170/493] now debias lasso only computes theta for the active
 set

---
 selectiveInference/R/funs.fixed.R   | 20 +++++++++-------
 selectiveInference/R/linear.tests.R | 37 +----------------------------
 2 files changed, 13 insertions(+), 44 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 3258b80..ba959a7 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -148,12 +148,15 @@ sigma=NULL, alpha=0.1,
       hsigma <- 1/n*(t(Xordered)%*%Xordered)
       hsigmaS <- 1/n*(t(XS)%*%XS) # hsigma[S,S]
       hsigmaSinv <- solve(hsigmaS) # pinv(hsigmaS)
-      
+
       # Approximate inverse covariance matrix for when (n < p) from lasso_Inference.R
-      htheta <- InverseLinfty(hsigma, n, verbose=FALSE)
+      htheta <- InverseLinfty(hsigma, n, length(S), verbose=FALSE)
+      # htheta <- InverseLinfty(hsigma, n, verbose=FALSE)
       
       FS = rbind(diag(length(S)),matrix(0,pp-length(S),length(S)))
-      ithetasigma = (diag(pp)-(htheta%*%hsigma))
+      GS = cbind(diag(length(S)),matrix(0,length(S),pp-length(S)))
+      ithetasigma = (GS-(htheta%*%hsigma))
+      # ithetasigma = (diag(pp) - (htheta%*%hsigma))
       
       M <- (((htheta%*%t(Xordered))+ithetasigma%*%FS%*%hsigmaSinv%*%t(XS))/n)
       # vector which is offset for testing debiased beta's
@@ -254,20 +257,21 @@ fixedLasso.poly=
 
 ##############################
 
-### Functions borrowed from lasso_inference.R
+### Functions borrowed and slightly modified from lasso_inference.R
 
 ## Approximates inverse covariance matrix theta
-InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
-  isgiven <- 1;
+InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
+  # InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
+    isgiven <- 1;
   if (is.null(mu)){
     isgiven <- 0;
   }
   
   p <- nrow(sigma);
-  M <- matrix(0, p, p);
+  M <- matrix(0, e, p);
   xperc = 0;
   xp = round(p/10);
-  for (i in 1:p) {
+  for (i in 1:e) {
     if ((i %% xp)==0){
       xperc = xperc+10;
       if (verbose) {
diff --git a/selectiveInference/R/linear.tests.R b/selectiveInference/R/linear.tests.R
index a37f76f..bd65bad 100644
--- a/selectiveInference/R/linear.tests.R
+++ b/selectiveInference/R/linear.tests.R
@@ -15,7 +15,7 @@ robs.test <- function() {
   hbeta <- as.numeric(coef(las,x=X,y=y,s=lambda/n,exact=TRUE,intercept=T))
   
   
-  return(fixedLassoInf(X,y,hbeta[-1],lambda,family="gaussian",type="partial",intercept=T,sigma=sigma)$pv)
+  return(fixedLassoInf(X,y,hbeta[-1],lambda,family="gaussian",type="partial",intercept=T,sigma=sigma))
 }
 
 
@@ -134,39 +134,4 @@ power.partial.pval.dist <- function(n,p,intercept=T,lambda=1) {
   }
   qqplot(x=runif(length(pvs)),y=pvs,xlab="Expected",ylab="Observed",main="Partial Coef. 10 Corr. X")
   abline(0,1)
-}
-
-
-
-
-## Tests pop inf for X and y randomly generated
-nullPopTest <- function(n,p,intercept=T,lambda=1) {
-  y <- matrix(rnorm(n),ncol=1) # rand N(0,1) response
-  X <- matrix(rnorm(p*n),ncol = p) # p rand N(0,1) predictors
-  
-  # lambda <- 1
-  X=scale(X,T,T)/sqrt(n-1)
-  
-  # lambda <- 1
-  las <- glmnet(X,y,family="gaussian",alpha=1,standardize=F,intercept=intercept)
-  hbeta <- as.numeric(coef(las,x=X,y=y,s=lambda/n,exact=TRUE,intercept=intercept))
-  
-  ### perform post selection inference
-  
-  sigma = estimateSigma(X,y)$sigmahat
-  
-  if (intercept) return(fixedLassoInf(X,y,hbeta,lambda,family="gaussian",type="full",intercept=intercept,sigma=sigma))
-  else return(fixedLassoInf(X,y,hbeta[-1],lambda,family="gaussian",type="full",intercept=intercept,sigma=sigma))
-}
-
-## QQ plot of p-values for all null data now that bug fix is implemented
-null.pop.pval.dist <- function(n,p,intercept=T,lambda=1) {
-  pvs <- c()
-  for(i in 1:10) {
-    a <- nullPopTest(n,p,intercept,lambda)
-    ps <- a$pv
-    pvs <- c(pvs,ps,recursive=T)
-  }
-  qqplot(x=runif(length(pvs)),y=pvs,xlab="Expected",ylab="Observed",main="Pop Coef. Null X")
-  abline(0,1)
 }
\ No newline at end of file

From 2e592f077fb84f64cbea218064ac0a672b16bab1 Mon Sep 17 00:00:00 2001
From: kevinbfry <kfry@stanford.edu>
Date: Tue, 8 Aug 2017 14:37:51 -0700
Subject: [PATCH 171/493] added optional 'add.targets' parameter to
 fixedLassoInference to allow user to add variables as targets for inference,
 even if they are not selected by the lasso

---
 selectiveInference/R/funs.fixed.R | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index ba959a7..0c728c7 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -2,7 +2,7 @@
 # for the solution of
 # min 1/2 || y - \beta_0 - X \beta ||_2^2 + \lambda || \beta ||_1
 
-fixedLassoInf <- function(x, y, beta, lambda, family=c("gaussian","binomial","cox"),intercept=TRUE, status=NULL,
+fixedLassoInf <- function(x, y, beta, lambda, family=c("gaussian","binomial","cox"),intercept=TRUE, add.targets=NULL, status=NULL,
 sigma=NULL, alpha=0.1,
                      type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
                      gridrange=c(-100,100), bits=NULL, verbose=FALSE) {
@@ -60,6 +60,12 @@ sigma=NULL, alpha=0.1,
       bits = NULL
     }
     
+    if (!is.null(add.targets) && (!is.vector(add.targets)
+                                  || !all(is.numeric(add.targets)) || !all(add.targets==floor(add.targets))
+                                  || !all(add.targets >= 1 && add.targets <= p))) {
+      stop("'add.targets' must be a vector of integers between 1 and p")
+    }
+    
     # If glmnet was run with an intercept term, center x and y
     if (intercept==TRUE) {
       obj = standardize(x,y,TRUE,FALSE)
@@ -117,12 +123,15 @@ sigma=NULL, alpha=0.1,
       }
     }
     
+    # add additional targets for inference if provided
+    if (!is.null(add.targets)) vars = sort(unique(c(vars,add.targets,recursive=T)))
+    
     k = length(vars)
     pv = vlo = vup = numeric(k)
     vmat = matrix(0,k,n)
     ci = tailarea = matrix(0,k,2)
     sign = numeric(k)
-    
+      
     if (type=="full" & p > n) {
       if (intercept == T) {
         pp=p+1

From fcaf1d5b01f6a56bffe3456e7b74c505577adc60 Mon Sep 17 00:00:00 2001
From: kevinbfry <kfry@stanford.edu>
Date: Tue, 8 Aug 2017 14:53:48 -0700
Subject: [PATCH 172/493] fixed small bug with last commit

---
 selectiveInference/R/funs.fixed.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 0c728c7..7073e2e 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -138,15 +138,15 @@ sigma=NULL, alpha=0.1,
         Xint <- cbind(rep(1,n),x)
         # indices of selected predictors
         S = c(1,vars + 1)
-        notS = which(abs(beta) <= tol.coef) + 1
       } else {
         pp=p
         Xint <- x
         # indices of selected predictors
         S = vars
-        notS = which(abs(beta) <= tol.coef)
+        # notS = which(abs(beta) <= tol.coef)
       }
       
+      notS = setdiff(1:pp,S)
       
       XS = Xint[,S]
       hbetaS = hbeta[S]

From 57daf4f311ac21fa4e3fc4c81a775f112d322795 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 8 Aug 2017 16:35:12 -0700
Subject: [PATCH 173/493] travis script

---
 .travis.yml | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 .travis.yml

diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 0000000..71ff221
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,11 @@
+language: r
+cache: packages
+sudo: false
+r:
+  - oldrel
+  - release
+  - devel
+warnings_are_errors: true
+before_install:
+  - tlmgr install index # for texlive and vignette?
+  - cd selectiveInference

From 73f5829aba52405aac7d95b627d02eaa3de43eac Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 8 Aug 2017 16:42:16 -0700
Subject: [PATCH 174/493] missing mpfr library?

---
 .travis.yml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.travis.yml b/.travis.yml
index 71ff221..aa61233 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -5,6 +5,9 @@ r:
   - oldrel
   - release
   - devel
+addons:
+   apt: 
+      packages: libmpfr-dev
 warnings_are_errors: true
 before_install:
   - tlmgr install index # for texlive and vignette?

From 3289471fc016ba4128ebaa48dcd7f0b98885916a Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 8 Aug 2017 16:54:44 -0700
Subject: [PATCH 175/493] fixing Rd file

---
 selectiveInference/man/predict.groupfs.Rd | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/man/predict.groupfs.Rd b/selectiveInference/man/predict.groupfs.Rd
index 4b8394b..492b74f 100644
--- a/selectiveInference/man/predict.groupfs.Rd
+++ b/selectiveInference/man/predict.groupfs.Rd
@@ -2,8 +2,8 @@
 % Please edit documentation in R/funs.groupfs.R
 \name{predict.groupfs}
 \alias{predict.groupfs}
-\title{Prediction and coefficient functions for \code{\link{groupfs}}.
-
+\title{Prediction and coefficient functions for \code{\link{groupfs}}.}
+\description{
 Make predictions or extract coefficients from a groupfs forward stepwise object.}
 \usage{
 \method{predict}{groupfs}(object, newx)

From 7e161740c4bb49096724a0ce36d65252be0b4407 Mon Sep 17 00:00:00 2001
From: kevinbfry <kfry@stanford.edu>
Date: Tue, 8 Aug 2017 20:33:04 -0700
Subject: [PATCH 176/493] added .travis.yml file

---
 .travis.yml                             | 27 +++++++++++++++++++++++++
 selectiveInference/man/fixedLassoInf.Rd |  3 ++-
 2 files changed, 29 insertions(+), 1 deletion(-)
 create mode 100644 .travis.yml

diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 0000000..f4ebe35
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,27 @@
+language: r
+
+cache: packages
+
+sudo: false
+
+r:
+
+  - oldrel
+
+  - release
+
+  - devel
+
+addons:
+
+   apt: 
+
+      packages: libmpfr-dev
+
+warnings_are_errors: true
+
+before_install:
+
+  - tlmgr install index # for texlive and vignette?
+
+  - cd selectiveInference
diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index c5a9912..d675c3f 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -10,7 +10,7 @@ fixed value of the tuning parameter lambda
 }
 \usage{
 fixedLassoInf(x, y, beta, lambda, family = c("gaussian", "binomial", 
-    "cox"),intercept=TRUE, status=NULL, sigma=NULL, alpha=0.1,
+    "cox"),intercept=TRUE, add.targets=NULL, status=NULL, sigma=NULL, alpha=0.1,
          type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
          gridrange=c(-100,100), bits=NULL, verbose=FALSE) 
 }
@@ -56,6 +56,7 @@ Significance level for confidence intervals (target is miscoverage alpha/2 in ea
 Was the lasso problem solved (e.g., by glmnet) with an intercept in the model? 
 Default is TRUE. Must be TRUE for "binomial" family. Not used for 'cox" family, where no intercept is assumed.
 }
+\item{add.targets}{Optional vector of predictors to be included as targets of inference, regardless of whether or not they are selected by the lasso. Default is NULL.}
 \item{status}{Censoring status for Cox model; 1=failurem 0=censored}
 \item{type}{Contrast type for p-values and confidence intervals: default is
 "partial"---meaning that the contrasts tested are the partial population 

From 1535c7c1a3518ec7df2a6c51df65dd43f3e134b7 Mon Sep 17 00:00:00 2001
From: kevinbfry <kfry@stanford.edu>
Date: Tue, 8 Aug 2017 20:54:56 -0700
Subject: [PATCH 177/493] testing travisci

---
 selectiveInference/R/linear.tests.R | 274 ++++++++++++++--------------
 1 file changed, 137 insertions(+), 137 deletions(-)

diff --git a/selectiveInference/R/linear.tests.R b/selectiveInference/R/linear.tests.R
index bd65bad..e113d0a 100644
--- a/selectiveInference/R/linear.tests.R
+++ b/selectiveInference/R/linear.tests.R
@@ -1,137 +1,137 @@
-robs.test <- function() {
-  n <- 100
-  p <- 200
-  
-  set.seed(11332)
-  
-  y <- matrix(rnorm(n),ncol=1) # rand N(0,1) response
-  X <- matrix(rnorm(p*n),ncol = p) # p rand N(0,1) predictors
-  
-  X=scale(X,T,T)/sqrt(n-1)
-  lambda=1
-  sigma = estimateSigma(X,y)$sigmahat
-  
-  las <- glmnet(X,y,family="gaussian",alpha=1,standardize=F,intercept=T)
-  hbeta <- as.numeric(coef(las,x=X,y=y,s=lambda/n,exact=TRUE,intercept=T))
-  
-  
-  return(fixedLassoInf(X,y,hbeta[-1],lambda,family="gaussian",type="partial",intercept=T,sigma=sigma))
-}
-
-
-## Tests partial inf for X and y randomly generated from N(0,1)
-nullTest <- function(X,y,lambda,intercept,type=c("full","partial")) {
-  n=nrow(X)
-  X=scale(X,T,T)/sqrt(n-1)
-  
-  sigma = estimateSigma(X,y)$sigmahat
-  
-  las <- glmnet(X,y,family="gaussian",alpha=1,standardize=F,intercept=intercept)
-  hbeta <- as.numeric(coef(las,x=X,y=y,s=lambda/n,exact=TRUE,intercept=intercept))
-  
-  if (type=="partial" || intercept==F) hbeta = hbeta[-1]
-  
-  return(fixedLassoInf(X,y,hbeta,lambda,family="gaussian",type=type,intercept=intercept,sigma=sigma))
-}
-
-## Test partial inf for X and y where 10 variables are y with random additive N(0,0.5) noise
-corrTest <- function(X,y,lambda,intercept,type=c("full","partial")) {
-  n=nrow(X)
-  corr.X = rep(y,10) + matrix(rnorm(n*10,0,0.5),ncol = 10)
-  X = cbind(corr.X,X)
-  X=scale(X,T,T)/sqrt(n-1)
-  
-  las <- glmnet(X,y,family="gaussian",alpha=1,standardize=F,intercept=intercept)
-  hbeta <- as.numeric(coef(las,x=X,y=y,s=lambda/n,exact=TRUE,intercept=intercept))
-  
-  sigma = estimateSigma(X,y)$sigmahat
-  
-  if (type=="partial" || intercept==F) hbeta = hbeta[-1]
-  
-  return(fixedLassoInf(X,y,hbeta,lambda,family="gaussian",type=type,intercept=intercept,sigma=sigma))
-}
-
-## QQ plot of p-values for all null data now that bug fix is implemented
-partial.qq.test <- function() {
-  n <- 100
-  p <- 200
-  
-  lambda=1
-  
-  null.int.pvs <- c()
-  corr.int.pvs <- c()
-  null.pvs <- c()
-  corr.pvs <- c()
-  for(i in 1:25) {
-    y <- matrix(rnorm(n),ncol=1) # rand N(0,1) response
-    X <- matrix(rnorm(p*n),ncol=p) # p rand N(0,1) predictors
-    
-    null <- nullTest(X,y,lambda,F,type="partial")
-    corr <- corrTest(X,y,lambda,F,type="partial")
-    null.pvs <- c(null.pvs,null$pv,recursive=T)
-    corr.pvs <- c(corr.pvs,corr$pv,recursive=T)
-    null.int <- nullTest(X,y,lambda,T,type="partial")
-    corr.int <- corrTest(X,y,lambda,T,type="partial")
-    null.int.pvs <- c(null.int.pvs,null.int$pv,recursive=T)
-    corr.int.pvs <- c(corr.int.pvs,corr.int$pv,recursive=T)
-  }
-  
-  qqplot(x=runif(length(null.pvs)),y=null.pvs,xlab="Expected",ylab="Observed",main="Partial Coef. Null X w/o Intercept")
-  abline(0,1)
-  qqplot(x=runif(length(corr.pvs)),y=corr.pvs,xlab="Expected",ylab="Observed",main="Partial Coef. 10 Corr. X w/o Intercept")
-  abline(0,1)
-  qqplot(x=runif(length(null.int.pvs)),y=null.int.pvs,xlab="Expected",ylab="Observed",main="Partial Coef. Null X w/ Intercept")
-  abline(0,1)
-  qqplot(x=runif(length(corr.int.pvs)),y=corr.int.pvs,xlab="Expected",ylab="Observed",main="Partial Coef. 10 Corr. X w/ Intercept")
-  abline(0,1)
-}
-
-## QQ plot of p-values for all null data now that bug fix is implemented
-pop.qq.test <- function() {
-  n <- 100
-  p <- 200
-  
-  lambda=1
-  
-  null.int.pvs <- c()
-  corr.int.pvs <- c()
-  null.pvs <- c()
-  corr.pvs <- c()
-  for(i in 1:25) {
-    y <- matrix(rnorm(n),ncol=1) # rand N(0,1) response
-    X <- matrix(rnorm(p*n),ncol=p) # p rand N(0,1) predictors
-    
-    null <- nullTest(X,y,lambda,F,type="full")
-    corr <- corrTest(X,y,lambda,F,type="full")
-    null.pvs <- c(null.pvs,null$pv,recursive=T)
-    corr.pvs <- c(corr.pvs,corr$pv,recursive=T)
-    null.int <- nullTest(X,y,lambda,T,type="full")
-    corr.int <- corrTest(X,y,lambda,T,type="full")
-    null.int.pvs <- c(null.int.pvs,null.int$pv,recursive=T)
-    corr.int.pvs <- c(corr.int.pvs,corr.int$pv,recursive=T)
-  }
-  
-  qqplot(x=runif(length(null.pvs)),y=null.pvs,xlab="Expected",ylab="Observed",main="Pop Coef. Null X w/o Intercept")
-  abline(0,1)
-  qqplot(x=runif(length(corr.pvs)),y=corr.pvs,xlab="Expected",ylab="Observed",main="Pop Coef. 10 Corr. X w/o Intercept")
-  abline(0,1)
-  qqplot(x=runif(length(null.int.pvs)),y=null.int.pvs,xlab="Expected",ylab="Observed",main="Pop Coef. Null X w/ Intercept")
-  abline(0,1)
-  qqplot(x=runif(length(corr.int.pvs)),y=corr.int.pvs,xlab="Expected",ylab="Observed",main="Pop Coef. 10 Corr. X w/ Intercept")
-  abline(0,1)
-}
-
-
-
-
-## QQ plot of p-values for data with correlated x now that bug fix implemented
-power.partial.pval.dist <- function(n,p,intercept=T,lambda=1) {
-  pvs <- c()
-  for(i in 1:10) {
-    a <- powerPartialTest(n,p,intercept,lambda)
-    ps <- a$pv
-    pvs <- c(pvs,ps,recursive=T)
-  }
-  qqplot(x=runif(length(pvs)),y=pvs,xlab="Expected",ylab="Observed",main="Partial Coef. 10 Corr. X")
-  abline(0,1)
-}
\ No newline at end of file
+# robs.test <- function() {
+#   n <- 100
+#   p <- 200
+#   
+#   set.seed(11332)
+#   
+#   y <- matrix(rnorm(n),ncol=1) # rand N(0,1) response
+#   X <- matrix(rnorm(p*n),ncol = p) # p rand N(0,1) predictors
+#   
+#   X=scale(X,T,T)/sqrt(n-1)
+#   lambda=1
+#   sigma = estimateSigma(X,y)$sigmahat
+#   
+#   las <- glmnet(X,y,family="gaussian",alpha=1,standardize=F,intercept=T)
+#   hbeta <- as.numeric(coef(las,x=X,y=y,s=lambda/n,exact=TRUE,intercept=T))
+#   
+#   
+#   return(fixedLassoInf(X,y,hbeta[-1],lambda,family="gaussian",type="partial",intercept=T,sigma=sigma))
+# }
+# 
+# 
+# ## Tests partial inf for X and y randomly generated from N(0,1)
+# nullTest <- function(X,y,lambda,intercept,type=c("full","partial")) {
+#   n=nrow(X)
+#   X=scale(X,T,T)/sqrt(n-1)
+#   
+#   sigma = estimateSigma(X,y)$sigmahat
+#   
+#   las <- glmnet(X,y,family="gaussian",alpha=1,standardize=F,intercept=intercept)
+#   hbeta <- as.numeric(coef(las,x=X,y=y,s=lambda/n,exact=TRUE,intercept=intercept))
+#   
+#   if (type=="partial" || intercept==F) hbeta = hbeta[-1]
+#   
+#   return(fixedLassoInf(X,y,hbeta,lambda,family="gaussian",type=type,intercept=intercept,sigma=sigma))
+# }
+# 
+# ## Test partial inf for X and y where 10 variables are y with random additive N(0,0.5) noise
+# corrTest <- function(X,y,lambda,intercept,type=c("full","partial")) {
+#   n=nrow(X)
+#   corr.X = rep(y,10) + matrix(rnorm(n*10,0,0.5),ncol = 10)
+#   X = cbind(corr.X,X)
+#   X=scale(X,T,T)/sqrt(n-1)
+#   
+#   las <- glmnet(X,y,family="gaussian",alpha=1,standardize=F,intercept=intercept)
+#   hbeta <- as.numeric(coef(las,x=X,y=y,s=lambda/n,exact=TRUE,intercept=intercept))
+#   
+#   sigma = estimateSigma(X,y)$sigmahat
+#   
+#   if (type=="partial" || intercept==F) hbeta = hbeta[-1]
+#   
+#   return(fixedLassoInf(X,y,hbeta,lambda,family="gaussian",type=type,intercept=intercept,sigma=sigma))
+# }
+# 
+# ## QQ plot of p-values for all null data now that bug fix is implemented
+# partial.qq.test <- function() {
+#   n <- 100
+#   p <- 200
+#   
+#   lambda=1
+#   
+#   null.int.pvs <- c()
+#   corr.int.pvs <- c()
+#   null.pvs <- c()
+#   corr.pvs <- c()
+#   for(i in 1:25) {
+#     y <- matrix(rnorm(n),ncol=1) # rand N(0,1) response
+#     X <- matrix(rnorm(p*n),ncol=p) # p rand N(0,1) predictors
+#     
+#     null <- nullTest(X,y,lambda,F,type="partial")
+#     corr <- corrTest(X,y,lambda,F,type="partial")
+#     null.pvs <- c(null.pvs,null$pv,recursive=T)
+#     corr.pvs <- c(corr.pvs,corr$pv,recursive=T)
+#     null.int <- nullTest(X,y,lambda,T,type="partial")
+#     corr.int <- corrTest(X,y,lambda,T,type="partial")
+#     null.int.pvs <- c(null.int.pvs,null.int$pv,recursive=T)
+#     corr.int.pvs <- c(corr.int.pvs,corr.int$pv,recursive=T)
+#   }
+#   
+#   qqplot(x=runif(length(null.pvs)),y=null.pvs,xlab="Expected",ylab="Observed",main="Partial Coef. Null X w/o Intercept")
+#   abline(0,1)
+#   qqplot(x=runif(length(corr.pvs)),y=corr.pvs,xlab="Expected",ylab="Observed",main="Partial Coef. 10 Corr. X w/o Intercept")
+#   abline(0,1)
+#   qqplot(x=runif(length(null.int.pvs)),y=null.int.pvs,xlab="Expected",ylab="Observed",main="Partial Coef. Null X w/ Intercept")
+#   abline(0,1)
+#   qqplot(x=runif(length(corr.int.pvs)),y=corr.int.pvs,xlab="Expected",ylab="Observed",main="Partial Coef. 10 Corr. X w/ Intercept")
+#   abline(0,1)
+# }
+# 
+# ## QQ plot of p-values for all null data now that bug fix is implemented
+# pop.qq.test <- function() {
+#   n <- 100
+#   p <- 200
+#   
+#   lambda=1
+#   
+#   null.int.pvs <- c()
+#   corr.int.pvs <- c()
+#   null.pvs <- c()
+#   corr.pvs <- c()
+#   for(i in 1:25) {
+#     y <- matrix(rnorm(n),ncol=1) # rand N(0,1) response
+#     X <- matrix(rnorm(p*n),ncol=p) # p rand N(0,1) predictors
+#     
+#     null <- nullTest(X,y,lambda,F,type="full")
+#     corr <- corrTest(X,y,lambda,F,type="full")
+#     null.pvs <- c(null.pvs,null$pv,recursive=T)
+#     corr.pvs <- c(corr.pvs,corr$pv,recursive=T)
+#     null.int <- nullTest(X,y,lambda,T,type="full")
+#     corr.int <- corrTest(X,y,lambda,T,type="full")
+#     null.int.pvs <- c(null.int.pvs,null.int$pv,recursive=T)
+#     corr.int.pvs <- c(corr.int.pvs,corr.int$pv,recursive=T)
+#   }
+#   
+#   qqplot(x=runif(length(null.pvs)),y=null.pvs,xlab="Expected",ylab="Observed",main="Pop Coef. Null X w/o Intercept")
+#   abline(0,1)
+#   qqplot(x=runif(length(corr.pvs)),y=corr.pvs,xlab="Expected",ylab="Observed",main="Pop Coef. 10 Corr. X w/o Intercept")
+#   abline(0,1)
+#   qqplot(x=runif(length(null.int.pvs)),y=null.int.pvs,xlab="Expected",ylab="Observed",main="Pop Coef. Null X w/ Intercept")
+#   abline(0,1)
+#   qqplot(x=runif(length(corr.int.pvs)),y=corr.int.pvs,xlab="Expected",ylab="Observed",main="Pop Coef. 10 Corr. X w/ Intercept")
+#   abline(0,1)
+# }
+# 
+# 
+# 
+# 
+# ## QQ plot of p-values for data with correlated x now that bug fix implemented
+# power.partial.pval.dist <- function(n,p,intercept=T,lambda=1) {
+#   pvs <- c()
+#   for(i in 1:10) {
+#     a <- powerPartialTest(n,p,intercept,lambda)
+#     ps <- a$pv
+#     pvs <- c(pvs,ps,recursive=T)
+#   }
+#   qqplot(x=runif(length(pvs)),y=pvs,xlab="Expected",ylab="Observed",main="Partial Coef. 10 Corr. X")
+#   abline(0,1)
+# }
\ No newline at end of file

From 248ce54c01282d428a9c31b862280166e52c6eb9 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 15 Aug 2017 21:24:12 -0700
Subject: [PATCH 178/493] C code for one row of M

---
 selectiveInference/R/funs.fixed.R         |  19 +++
 selectiveInference/src/debiasing_matrix.c | 151 ++++++++++++++++++++++
 2 files changed, 170 insertions(+)
 create mode 100644 selectiveInference/src/debiasing_matrix.c

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 7073e2e..da701e5 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -334,6 +334,25 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
   return(M)
 }
 
+InverseLinftyOneRowC <- function ( sigma, i, mu, maxiter=50, threshold=1e-2 ) {
+
+         p = nrow(sigma)		
+         theta = rep(0, p)     
+
+	 val = .C("find_one_row",
+          	 Sigma=as.double(sigma),
+		 nrow=as.integer(p),
+   		 bound=as.double(mu),
+		 theta=as.double(theta),
+		 maxiter=as.integer(maxiter),
+		 row=as.integer(i-1),
+		 coord=as.integer(i-1),
+		 dup=FALSE,
+		 package="selectiveInference")
+
+	return(val$theta)
+}
+
 InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-2 ) {
   p <- nrow(sigma);
   rho <- max(abs(sigma[i,-i])) / sigma[i,i];
diff --git a/selectiveInference/src/debiasing_matrix.c b/selectiveInference/src/debiasing_matrix.c
new file mode 100644
index 0000000..0a626ea
--- /dev/null
+++ b/selectiveInference/src/debiasing_matrix.c
@@ -0,0 +1,151 @@
+#include <stdio.h>
+#include <math.h> // for fabs
+
+// Find an approximate row of \hat{Sigma}^{-1}
+
+// Problem (4) of ????
+
+// Update one coordinate 
+
+double update_one_coord(double *Sigma,     /* A covariance matrix: X^TX/n */
+			int nrow,          /* How many rows in Sigma */
+			double bound,      /* feasibility parameter */
+			double *theta,     /* current value */
+			int row,           /* which row: 0-based */
+			int coord)         /* which coordinate to update: 0-based */
+{
+
+  double linear_term = 0;
+  double quadratic_term = 0;
+  double value = 0;
+  double *Sigma_ptr;
+  double *theta_ptr = theta;
+  int icol = 0;
+
+  Sigma_ptr = ((double *) Sigma + nrow * coord);
+
+  for (icol=0; icol < nrow; icol++) {
+    if (icol != coord) {
+      linear_term += (*Sigma_ptr) * (*theta_ptr);
+    }
+    else {
+      quadratic_term = (*Sigma_ptr);
+    }
+    Sigma_ptr += 1;
+    theta_ptr += 1;
+  }
+  
+  if (row == coord) {
+    linear_term += 1;
+  }
+
+  // Now soft-threshold the coord entry of theta 
+
+  // Objective is t \mapsto q/2 * t^2 + l * t + bound |t|
+  // with q=quadratic_term and l=linear_term
+
+  if (linear_term < -bound) {
+    value = - (-linear_term - bound) / quadratic_term;
+  }
+  else if (linear_term > bound) {
+    value = (linear_term - bound) / quadratic_term;
+  }
+
+  theta_ptr = ((double *) theta + coord);
+  *theta_ptr = value;
+  return(value);
+
+}
+
+double objective(double *Sigma,       /* A covariance matrix: X^TX/n */
+		 int nrow,            /* how many rows in Sigma */
+		 int row,             /* which row: 0-based */
+		 double bound,        /* Lagrange multipler for \ell_1 */
+		 double *theta)       /* current value */
+{
+  int irow, icol;
+  double value = 0;
+  double *Sigma_ptr = Sigma;
+  double *theta_row_ptr, *theta_col_ptr;
+
+  theta_row_ptr = theta;
+  theta_col_ptr = theta;
+
+  for (irow=0; irow<nrow; irow++) {
+    double *theta_col_ptr = theta;
+    for (icol=0; icol<nrow; icol++) {
+      value += (*Sigma_ptr) * (*theta_row_ptr) * (*theta_col_ptr);
+      Sigma_ptr++;
+      theta_col_ptr++;
+    }
+    if (irow == row) {
+      value += (*theta_row_ptr); // the elementary basis vector term
+    }
+
+    value = value + bound * fabs((*theta_row_ptr)); // the \ell_1 term
+    theta_row_ptr++;
+  }
+
+  return(value);
+}
+
+void find_one_row(double *Sigma,          /* A covariance matrix: X^TX/n */
+                  int *nrow_ptr,          /* How many rows in Sigma */
+		  double *bound_ptr,      /* feasibility parameter */
+                  double *theta,          /* current value */
+                  int *maxiter_ptr,       /* how many iterations */
+                  int *row_ptr,           /* which row: 0-based */
+                  int *coord_ptr)         /* which coordinate to update: 0-based */
+{
+
+  int maxiter = *maxiter_ptr;
+  int iter = 0;
+  int icoord = 0;
+  int coord = *coord_ptr;
+  int row = *row_ptr;
+  double bound = *bound_ptr;
+  int nrow = *nrow_ptr;
+  double old_value = objective(Sigma,
+			       nrow,
+			       row,
+			       bound,
+			       theta);
+  double new_value; 
+  double tol=1.e-10;
+
+  for (iter=0; iter<maxiter; iter++) {
+
+    // Update the diagonal first
+
+    update_one_coord(Sigma,
+		     nrow,
+		     bound,
+		     theta,
+		     row,
+		     row);
+
+    for (icoord=0; icoord<nrow; icoord++) {
+
+      update_one_coord(Sigma,
+		       nrow,
+		       bound,
+		       theta,
+		       row,
+		       icoord);
+    }
+
+    new_value = objective(Sigma,
+			  nrow,
+			  row,
+			  bound,
+			  theta);
+
+    if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 3)) {
+      break;
+    }
+    old_value = new_value;
+  }
+
+  *nrow_ptr = iter-1;
+}
+

From 4d6719990d1f49059e8fd672f27f784e4e3f2fbe Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 15 Aug 2017 21:51:39 -0700
Subject: [PATCH 179/493] added a warning about feasibility -- but it didn't
 print in gist?

---
 selectiveInference/R/funs.fixed.R         | 15 ++++++++++++---
 selectiveInference/src/debiasing_matrix.c | 15 +++++++++++----
 2 files changed, 23 insertions(+), 7 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index da701e5..1bf216e 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -334,13 +334,15 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
   return(M)
 }
 
-InverseLinftyOneRowC <- function ( sigma, i, mu, maxiter=50, threshold=1e-2 ) {
+InverseLinftyOneRowC <- function (Sigma, i, mu, maxiter=50, threshold=1e-2 ) {
 
-         p = nrow(sigma)		
+         p = nrow(Sigma)		
+         basis_vector = rep(0, p)
+	 basis_vector[i] = 1.
          theta = rep(0, p)     
 
 	 val = .C("find_one_row",
-          	 Sigma=as.double(sigma),
+          	 Sigma=as.double(Sigma),
 		 nrow=as.integer(p),
    		 bound=as.double(mu),
 		 theta=as.double(theta),
@@ -350,6 +352,13 @@ InverseLinftyOneRowC <- function ( sigma, i, mu, maxiter=50, threshold=1e-2 ) {
 		 dup=FALSE,
 		 package="selectiveInference")
 
+	# Check feasibility
+
+	if (max(abs(Sigma %*% val$theta - basis_vector)) > 1.01 * mu) {
+           print("Solution for row of M does not seem to be feasible")
+	   warning("Solution for row of M does not seem to be feasible")
+	}
+
 	return(val$theta)
 }
 
diff --git a/selectiveInference/src/debiasing_matrix.c b/selectiveInference/src/debiasing_matrix.c
index 0a626ea..6309df3 100644
--- a/selectiveInference/src/debiasing_matrix.c
+++ b/selectiveInference/src/debiasing_matrix.c
@@ -3,8 +3,12 @@
 
 // Find an approximate row of \hat{Sigma}^{-1}
 
-// Problem (4) of ????
+// Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
 
+// Dual problem: \text{min}_{\theta} 1/2 \theta^T \Sigma \theta - e_i^T\theta + \mu \|\theta\|_1
+
+// This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
+// Therefore we don't have to negate the answer to get theta.
 // Update one coordinate 
 
 double update_one_coord(double *Sigma,     /* A covariance matrix: X^TX/n */
@@ -36,7 +40,7 @@ double update_one_coord(double *Sigma,     /* A covariance matrix: X^TX/n */
   }
   
   if (row == coord) {
-    linear_term += 1;
+    linear_term -= 1;
   }
 
   // Now soft-threshold the coord entry of theta 
@@ -44,11 +48,14 @@ double update_one_coord(double *Sigma,     /* A covariance matrix: X^TX/n */
   // Objective is t \mapsto q/2 * t^2 + l * t + bound |t|
   // with q=quadratic_term and l=linear_term
 
+  // With a negative linear term, solution should be
+  // positive
+
   if (linear_term < -bound) {
-    value = - (-linear_term - bound) / quadratic_term;
+    value = (-linear_term - bound) / quadratic_term;
   }
   else if (linear_term > bound) {
-    value = (linear_term - bound) / quadratic_term;
+    value = -(linear_term - bound) / quadratic_term;
   }
 
   theta_ptr = ((double *) theta + coord);

From e87d12dbcddd960e8d5856915818a251969deb49 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 15 Aug 2017 21:54:45 -0700
Subject: [PATCH 180/493] removed print statement

---
 selectiveInference/R/funs.fixed.R | 1 -
 1 file changed, 1 deletion(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 1bf216e..53dc61f 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -355,7 +355,6 @@ InverseLinftyOneRowC <- function (Sigma, i, mu, maxiter=50, threshold=1e-2 ) {
 	# Check feasibility
 
 	if (max(abs(Sigma %*% val$theta - basis_vector)) > 1.01 * mu) {
-           print("Solution for row of M does not seem to be feasible")
 	   warning("Solution for row of M does not seem to be feasible")
 	}
 

From 210d0feff9932686e8210454138e577342520e21 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 15 Aug 2017 22:00:19 -0700
Subject: [PATCH 181/493] using C for each row

---
 selectiveInference/R/funs.fixed.R | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 53dc61f..b69dabb 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -159,7 +159,8 @@ sigma=NULL, alpha=0.1,
       hsigmaSinv <- solve(hsigmaS) # pinv(hsigmaS)
 
       # Approximate inverse covariance matrix for when (n < p) from lasso_Inference.R
-      htheta <- InverseLinfty(hsigma, n, length(S), verbose=FALSE)
+      useC = TRUE
+      htheta <- InverseLinfty(hsigma, n, length(S), verbose=FALSE, useC=useC)
       # htheta <- InverseLinfty(hsigma, n, verbose=FALSE)
       
       FS = rbind(diag(length(S)),matrix(0,pp-length(S),length(S)))
@@ -269,7 +270,7 @@ fixedLasso.poly=
 ### Functions borrowed and slightly modified from lasso_inference.R
 
 ## Approximates inverse covariance matrix theta
-InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
+InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE, useC = FALSE) {
   # InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
     isgiven <- 1;
   if (is.null(mu)){
@@ -294,7 +295,11 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
     incr <- 0;
     while ((mu.stop != 1)&&(try.no<10)){
       last.beta <- beta
-      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
+      if (useC == FALSE) {
+            output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
+      } else {
+            output <- InverseLinftyOneRowC(sigma, i, mu, maxiter=maxiter)
+      }
       beta <- output$optsol
       iter <- output$iter
       if (isgiven==1){
@@ -334,7 +339,7 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
   return(M)
 }
 
-InverseLinftyOneRowC <- function (Sigma, i, mu, maxiter=50, threshold=1e-2 ) {
+InverseLinftyOneRowC <- function (Sigma, i, mu, maxiter=50) {
 
          p = nrow(Sigma)		
          basis_vector = rep(0, p)

From c7c41e417693578173e272e2653c46b3a3edaba1 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Aug 2017 08:32:39 -0700
Subject: [PATCH 182/493] optimized the C code a bit -- still has debug
 statements

---
 selectiveInference/R/funs.fixed.R         |  20 ++-
 selectiveInference/src/debiasing_matrix.c | 154 +++++++++++++++-------
 2 files changed, 119 insertions(+), 55 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index b69dabb..584018f 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -348,10 +348,12 @@ InverseLinftyOneRowC <- function (Sigma, i, mu, maxiter=50) {
 
 	 val = .C("find_one_row",
           	 Sigma=as.double(Sigma),
+		 Sigma_diag=as.double(diag(Sigma)),
+		 Sigma_theta=as.double(rep(0, p)),
 		 nrow=as.integer(p),
    		 bound=as.double(mu),
 		 theta=as.double(theta),
-		 maxiter=as.integer(maxiter),
+		 maxiter=as.integer(50),
 		 row=as.integer(i-1),
 		 coord=as.integer(i-1),
 		 dup=FALSE,
@@ -359,6 +361,12 @@ InverseLinftyOneRowC <- function (Sigma, i, mu, maxiter=50) {
 
 	# Check feasibility
 
+	# DEBUG statements
+	#print(diag(Sigma))
+	#print(0.5 * sum(val$theta * (Sigma %*% val$theta)) - val$theta[i] + mu * sum(abs(val$theta)))
+	#print(Sigma %*% val$theta - val$Sigma_theta)
+	#print(val$nrow) # number of iterations
+
 	if (max(abs(Sigma %*% val$theta - basis_vector)) > 1.01 * mu) {
 	   warning("Solution for row of M does not seem to be feasible")
 	}
@@ -372,11 +380,11 @@ InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-2 ) {
   mu0 <- rho/(1+rho);
   beta <- rep(0,p);
   
-  if (mu >= mu0){
-    beta[i] <- (1-mu0)/sigma[i,i];
-    returnlist <- list("optsol" = beta, "iter" = 0);
-    return(returnlist);
-  }
+  #if (mu >= mu0){
+  #  beta[i] <- (1-mu0)/sigma[i,i];
+  #  returnlist <- list("optsol" = beta, "iter" = 0);
+  #  return(returnlist);
+  #}
   
   diff.norm2 <- 1;
   last.norm2 <- 1;
diff --git a/selectiveInference/src/debiasing_matrix.c b/selectiveInference/src/debiasing_matrix.c
index 6309df3..7dfeb1e 100644
--- a/selectiveInference/src/debiasing_matrix.c
+++ b/selectiveInference/src/debiasing_matrix.c
@@ -11,34 +11,72 @@
 // Therefore we don't have to negate the answer to get theta.
 // Update one coordinate 
 
-double update_one_coord(double *Sigma,     /* A covariance matrix: X^TX/n */
-			int nrow,          /* How many rows in Sigma */
-			double bound,      /* feasibility parameter */
-			double *theta,     /* current value */
-			int row,           /* which row: 0-based */
-			int coord)         /* which coordinate to update: 0-based */
+double objective(double *Sigma,       /* A covariance matrix: X^TX/n */
+		 int nrow,            /* how many rows in Sigma */
+		 int row,             /* which row: 0-based */
+		 double bound,        /* Lagrange multipler for \ell_1 */
+		 double *theta)       /* current value */
 {
+  int irow, icol;
+  double value = 0;
+  double *Sigma_ptr = Sigma;
+  double *theta_row_ptr, *theta_col_ptr;
 
+  theta_row_ptr = theta;
+  theta_col_ptr = theta;
+
+  for (irow=0; irow<nrow; irow++) {
+    double *theta_col_ptr = theta;
+    for (icol=0; icol<nrow; icol++) {
+      value += 0.5 * (*Sigma_ptr) * (*theta_row_ptr) * (*theta_col_ptr);
+      Sigma_ptr++;
+      theta_col_ptr++;
+    }
+    if (irow == row) {
+      value -= (*theta_row_ptr); // the elementary basis vector term
+    }
+
+    value = value + bound * fabs((*theta_row_ptr)); // the \ell_1 term
+    theta_row_ptr++;
+  }
+
+  return(value);
+}
+
+
+double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n */
+                        double *Sigma_diag,      /* Diagonal entries of Sigma */
+                        double *Sigma_theta,     /* Sigma times theta */
+			int nrow,                /* How many rows in Sigma */
+			double bound,            /* feasibility parameter */
+			double *theta,           /* current value */
+			int row,                 /* which row: 0-based */
+			int coord)               /* which coordinate to update: 0-based */
+{
+
+  double delta;
   double linear_term = 0;
-  double quadratic_term = 0;
   double value = 0;
+  double old_value;
   double *Sigma_ptr;
-  double *theta_ptr = theta;
+  double *Sigma_theta_ptr;
+  double *theta_ptr;
   int icol = 0;
 
-  Sigma_ptr = ((double *) Sigma + nrow * coord);
+  double *quadratic_ptr = ((double *) Sigma_diag + coord);
+  double quadratic_term = *quadratic_ptr;
+
+  Sigma_theta_ptr = ((double *) Sigma_theta + coord);
+  linear_term = *Sigma_theta_ptr;
+
+  theta_ptr = ((double *) theta + coord);
+  old_value = *theta_ptr;
+
+  // The coord entry of Sigma_theta term has a diagonal term in it:
+  // Sigma[coord, coord] * theta[coord]
+  // This removes it. 
+  linear_term -= quadratic_term * old_value;
 
-  for (icol=0; icol < nrow; icol++) {
-    if (icol != coord) {
-      linear_term += (*Sigma_ptr) * (*theta_ptr);
-    }
-    else {
-      quadratic_term = (*Sigma_ptr);
-    }
-    Sigma_ptr += 1;
-    theta_ptr += 1;
-  }
-  
   if (row == coord) {
     linear_term -= 1;
   }
@@ -58,60 +96,72 @@ double update_one_coord(double *Sigma,     /* A covariance matrix: X^TX/n */
     value = -(linear_term - bound) / quadratic_term;
   }
 
+  if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { // Update the linear term
+
+    delta = value - old_value;
+    Sigma_ptr = ((double *) Sigma + coord * nrow);
+    Sigma_theta_ptr = ((double *) Sigma_theta);
+
+    for (icol=0; icol<nrow; icol++) {
+      *Sigma_theta_ptr = *Sigma_theta_ptr + delta * (*Sigma_ptr);
+      Sigma_theta_ptr += 1;
+      Sigma_ptr += 1;
+    }
+
+    double before = objective(Sigma,
+			      nrow,
+			      row,
+			      bound,
+			      theta);
+  fprintf(stderr, "before %f\n", before);
+
   theta_ptr = ((double *) theta + coord);
   *theta_ptr = value;
-  return(value);
 
-}
+  double after = objective(Sigma,
+			   nrow,
+			   row,
+			   bound,
+			   theta);
 
-double objective(double *Sigma,       /* A covariance matrix: X^TX/n */
-		 int nrow,            /* how many rows in Sigma */
-		 int row,             /* which row: 0-based */
-		 double bound,        /* Lagrange multipler for \ell_1 */
-		 double *theta)       /* current value */
-{
-  int irow, icol;
-  double value = 0;
-  double *Sigma_ptr = Sigma;
-  double *theta_row_ptr, *theta_col_ptr;
+  fprintf(stderr, "after %f\n", after);
+  if (after > before) {
+    fprintf(stderr, "not a descent step!!!!!!!!!!!!!!!!!!!!!\n");
+  }
 
-  theta_row_ptr = theta;
-  theta_col_ptr = theta;
 
-  for (irow=0; irow<nrow; irow++) {
-    double *theta_col_ptr = theta;
+  }
+
+    Sigma_ptr = ((double *) Sigma + coord * nrow);
+    Sigma_theta_ptr = ((double *) Sigma_theta);
     for (icol=0; icol<nrow; icol++) {
-      value += (*Sigma_ptr) * (*theta_row_ptr) * (*theta_col_ptr);
-      Sigma_ptr++;
-      theta_col_ptr++;
-    }
-    if (irow == row) {
-      value += (*theta_row_ptr); // the elementary basis vector term
+      
+      Sigma_theta_ptr += 1;
+      Sigma_ptr += 1;
     }
 
-    value = value + bound * fabs((*theta_row_ptr)); // the \ell_1 term
-    theta_row_ptr++;
-  }
 
   return(value);
+
 }
 
 void find_one_row(double *Sigma,          /* A covariance matrix: X^TX/n */
+                  double *Sigma_diag,     /* Diagonal entry of covariance matrix */
+                  double *Sigma_theta,    /* Sigma times theta */
                   int *nrow_ptr,          /* How many rows in Sigma */
 		  double *bound_ptr,      /* feasibility parameter */
                   double *theta,          /* current value */
                   int *maxiter_ptr,       /* how many iterations */
-                  int *row_ptr,           /* which row: 0-based */
-                  int *coord_ptr)         /* which coordinate to update: 0-based */
+                  int *row_ptr)         /* which coordinate to update: 0-based */
 {
 
   int maxiter = *maxiter_ptr;
   int iter = 0;
   int icoord = 0;
-  int coord = *coord_ptr;
   int row = *row_ptr;
   double bound = *bound_ptr;
   int nrow = *nrow_ptr;
+
   double old_value = objective(Sigma,
 			       nrow,
 			       row,
@@ -125,6 +175,8 @@ void find_one_row(double *Sigma,          /* A covariance matrix: X^TX/n */
     // Update the diagonal first
 
     update_one_coord(Sigma,
+		     Sigma_diag,
+		     Sigma_theta,
 		     nrow,
 		     bound,
 		     theta,
@@ -134,6 +186,8 @@ void find_one_row(double *Sigma,          /* A covariance matrix: X^TX/n */
     for (icoord=0; icoord<nrow; icoord++) {
 
       update_one_coord(Sigma,
+		       Sigma_diag,
+		       Sigma_theta,
 		       nrow,
 		       bound,
 		       theta,
@@ -147,9 +201,11 @@ void find_one_row(double *Sigma,          /* A covariance matrix: X^TX/n */
 			  bound,
 			  theta);
 
-    if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 3)) {
+    if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 5)) {
       break;
     }
+
+    fprintf(stderr, "%f %f value\n", old_value, new_value);
     old_value = new_value;
   }
 

From f6ae738bad048aca71a22c94ba4fe8cdb9b1f3d7 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Aug 2017 08:50:20 -0700
Subject: [PATCH 183/493] removing debug statements, making objective
 computation faster

---
 selectiveInference/R/funs.fixed.R         |  6 ---
 selectiveInference/src/debiasing_matrix.c | 46 +++++------------------
 2 files changed, 9 insertions(+), 43 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 584018f..aa8eac2 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -361,12 +361,6 @@ InverseLinftyOneRowC <- function (Sigma, i, mu, maxiter=50) {
 
 	# Check feasibility
 
-	# DEBUG statements
-	#print(diag(Sigma))
-	#print(0.5 * sum(val$theta * (Sigma %*% val$theta)) - val$theta[i] + mu * sum(abs(val$theta)))
-	#print(Sigma %*% val$theta - val$Sigma_theta)
-	#print(val$nrow) # number of iterations
-
 	if (max(abs(Sigma %*% val$theta - basis_vector)) > 1.01 * mu) {
 	   warning("Solution for row of M does not seem to be feasible")
 	}
diff --git a/selectiveInference/src/debiasing_matrix.c b/selectiveInference/src/debiasing_matrix.c
index 7dfeb1e..0849f38 100644
--- a/selectiveInference/src/debiasing_matrix.c
+++ b/selectiveInference/src/debiasing_matrix.c
@@ -27,15 +27,16 @@ double objective(double *Sigma,       /* A covariance matrix: X^TX/n */
 
   for (irow=0; irow<nrow; irow++) {
     double *theta_col_ptr = theta;
-    for (icol=0; icol<nrow; icol++) {
-      value += 0.5 * (*Sigma_ptr) * (*theta_row_ptr) * (*theta_col_ptr);
-      Sigma_ptr++;
-      theta_col_ptr++;
+    if (*theta_row_ptr != 0) {
+      for (icol=0; icol<nrow; icol++) {
+        value += 0.5 * (*Sigma_ptr) * (*theta_row_ptr) * (*theta_col_ptr);
+        Sigma_ptr++;
+        theta_col_ptr++;
+      }
     }
     if (irow == row) {
       value -= (*theta_row_ptr); // the elementary basis vector term
     }
-
     value = value + bound * fabs((*theta_row_ptr)); // the \ell_1 term
     theta_row_ptr++;
   }
@@ -108,39 +109,11 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
       Sigma_ptr += 1;
     }
 
-    double before = objective(Sigma,
-			      nrow,
-			      row,
-			      bound,
-			      theta);
-  fprintf(stderr, "before %f\n", before);
-
-  theta_ptr = ((double *) theta + coord);
-  *theta_ptr = value;
-
-  double after = objective(Sigma,
-			   nrow,
-			   row,
-			   bound,
-			   theta);
-
-  fprintf(stderr, "after %f\n", after);
-  if (after > before) {
-    fprintf(stderr, "not a descent step!!!!!!!!!!!!!!!!!!!!!\n");
-  }
-
+    theta_ptr = ((double *) theta + coord);
+    *theta_ptr = value;
 
   }
 
-    Sigma_ptr = ((double *) Sigma + coord * nrow);
-    Sigma_theta_ptr = ((double *) Sigma_theta);
-    for (icol=0; icol<nrow; icol++) {
-      
-      Sigma_theta_ptr += 1;
-      Sigma_ptr += 1;
-    }
-
-
   return(value);
 
 }
@@ -201,11 +174,10 @@ void find_one_row(double *Sigma,          /* A covariance matrix: X^TX/n */
 			  bound,
 			  theta);
 
-    if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 5)) {
+    if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 0)) {
       break;
     }
 
-    fprintf(stderr, "%f %f value\n", old_value, new_value);
     old_value = new_value;
   }
 

From 1a8e2ad351c08f7eab632d6f7184651d1640e89e Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Aug 2017 09:23:01 -0700
Subject: [PATCH 184/493] slightly lower tolerance

---
 selectiveInference/src/debiasing_matrix.c | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/src/debiasing_matrix.c b/selectiveInference/src/debiasing_matrix.c
index 0849f38..420ecb9 100644
--- a/selectiveInference/src/debiasing_matrix.c
+++ b/selectiveInference/src/debiasing_matrix.c
@@ -141,7 +141,7 @@ void find_one_row(double *Sigma,          /* A covariance matrix: X^TX/n */
 			       bound,
 			       theta);
   double new_value; 
-  double tol=1.e-10;
+  double tol=1.e-6;
 
   for (iter=0; iter<maxiter; iter++) {
 

From c391bd375c2aa1a262e0c51111214f37f1dc957e Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Aug 2017 10:16:24 -0700
Subject: [PATCH 185/493] using active sets

---
 selectiveInference/R/funs.fixed.R         |   2 +
 selectiveInference/src/debiasing_matrix.c | 144 ++++++++++++++++++++--
 2 files changed, 133 insertions(+), 13 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index aa8eac2..a30fc0b 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -350,6 +350,8 @@ InverseLinftyOneRowC <- function (Sigma, i, mu, maxiter=50) {
           	 Sigma=as.double(Sigma),
 		 Sigma_diag=as.double(diag(Sigma)),
 		 Sigma_theta=as.double(rep(0, p)),
+                 ever_active=as.integer(i),
+		 nactive_ptr=as.integer(1),
 		 nrow=as.integer(p),
    		 bound=as.double(mu),
 		 theta=as.double(theta),
diff --git a/selectiveInference/src/debiasing_matrix.c b/selectiveInference/src/debiasing_matrix.c
index 420ecb9..e3ef415 100644
--- a/selectiveInference/src/debiasing_matrix.c
+++ b/selectiveInference/src/debiasing_matrix.c
@@ -12,6 +12,8 @@
 // Update one coordinate 
 
 double objective(double *Sigma,       /* A covariance matrix: X^TX/n */
+		 int *ever_active,    /* Ever active set   */ 
+		 int *nactive_ptr,    /* Size of ever active set */
 		 int nrow,            /* how many rows in Sigma */
 		 int row,             /* which row: 0-based */
 		 double bound,        /* Lagrange multipler for \ell_1 */
@@ -21,33 +23,106 @@ double objective(double *Sigma,       /* A covariance matrix: X^TX/n */
   double value = 0;
   double *Sigma_ptr = Sigma;
   double *theta_row_ptr, *theta_col_ptr;
+  int *active_row_ptr, *active_col_ptr;
+  int active_row, active_col;
+  int nactive = *nactive_ptr;
 
   theta_row_ptr = theta;
   theta_col_ptr = theta;
 
+  for (irow=0; irow<nactive; irow++) {
+
+    active_row_ptr = ((int *) ever_active + irow);
+    active_row = *active_row_ptr;
+    theta_row_ptr = ((double *) theta + active_row);
+
+    for (icol=0; icol<nactive; icol++) {
+      
+      active_col_ptr = ((int *) ever_active + icol);
+      active_col = *active_col_ptr;
+      theta_col_ptr = ((double *) theta + active_col);
+      
+      fprintf(stderr, "%d %d \n", active_row, active_col);
+
+      Sigma_ptr = ((double *) Sigma + nrow * active_row + active_col);
+
+      value += 0.5 * (*Sigma_ptr) * (*theta_row_ptr) * (*theta_col_ptr);
+    }
+    value = value + bound * fabs((*theta_row_ptr)); // the \ell_1 term
+  }
+
+  theta_row_ptr = ((double *) theta + row);
+  value -= (*theta_row_ptr); // the elementary basis vector term
+
+  return(value);
+}
+
+int is_active(int coord,
+	      int *nactive_ptr,
+	      int *ever_active) {
+  int iactive;
+  int active_var;
+  int nactive = *nactive_ptr;
+  int *ever_active_ptr = ever_active;
+
+  for (iactive=0; iactive<nactive; iactive++) {
+    active_var = (*ever_active_ptr);
+    if (active_var == coord) {
+      return(1);
+    }
+  }
+  return(0);
+}
+
+int check_KKT(double *theta,       /* current theta */
+	      double *Sigma_theta, /* Sigma times theta */
+	      int nrow,            /* how many rows in Sigma */
+	      int row,             /* which row: 0-based */
+	      double bound)        /* Lagrange multipler for \ell_1 */
+{
+  // First check inactive
+
+  int irow;
+  int fail = 0;
+  double tol = 1.e-4;
+  double *theta_ptr, *Sigma_theta_ptr;
+  double gradient;
+
   for (irow=0; irow<nrow; irow++) {
-    double *theta_col_ptr = theta;
-    if (*theta_row_ptr != 0) {
-      for (icol=0; icol<nrow; icol++) {
-        value += 0.5 * (*Sigma_ptr) * (*theta_row_ptr) * (*theta_col_ptr);
-        Sigma_ptr++;
-        theta_col_ptr++;
+    theta_ptr = ((double *) theta + irow);
+    Sigma_theta_ptr = ((double *) Sigma_theta + irow);
+
+    // Compute this coordinate of the gradient
+
+    gradient = *Sigma_theta_ptr;
+    if (row == irow) {
+      gradient -= 1;
+    }
+
+    if (*theta_ptr != 0) { // these coordinates of gradients should be equal to -bound
+      if ((*theta_ptr > 0) &&  (fabs(gradient + bound) > (1. + tol) * bound)) {
+	fail += 1;
+      }
+      else if ((*theta_ptr < 0) && (fabs(gradient - bound) > (1. + tol) * bound)) {
+	fail += 1;
       }
     }
-    if (irow == row) {
-      value -= (*theta_row_ptr); // the elementary basis vector term
+    else {
+      if (fabs(gradient) > (1. + tol) * bound) {
+	fail += 1;
+      }
     }
-    value = value + bound * fabs((*theta_row_ptr)); // the \ell_1 term
-    theta_row_ptr++;
   }
 
-  return(value);
+  return(fail == 0);
 }
 
 
 double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n */
                         double *Sigma_diag,      /* Diagonal entries of Sigma */
                         double *Sigma_theta,     /* Sigma times theta */
+			int *ever_active,        /* Ever active set   */ 
+			int *nactive_ptr,        /* Size of ever active set */
 			int nrow,                /* How many rows in Sigma */
 			double bound,            /* feasibility parameter */
 			double *theta,           /* current value */
@@ -67,6 +142,8 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
   double *quadratic_ptr = ((double *) Sigma_diag + coord);
   double quadratic_term = *quadratic_ptr;
 
+  int *ever_active_ptr;
+
   Sigma_theta_ptr = ((double *) Sigma_theta + coord);
   linear_term = *Sigma_theta_ptr;
 
@@ -97,7 +174,17 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
     value = -(linear_term - bound) / quadratic_term;
   }
 
-  if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { // Update the linear term
+  // Add to active set if necessary
+
+  if ((value != 0) && (is_active(coord, ever_active, nactive_ptr) == 0)) {
+    ever_active_ptr = ((int *) ever_active + *nactive_ptr);
+    *ever_active_ptr = coord;
+    *nactive_ptr += 1;
+  }
+
+  // Update the linear term
+
+  if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
 
     delta = value - old_value;
     Sigma_ptr = ((double *) Sigma + coord * nrow);
@@ -121,6 +208,8 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
 void find_one_row(double *Sigma,          /* A covariance matrix: X^TX/n */
                   double *Sigma_diag,     /* Diagonal entry of covariance matrix */
                   double *Sigma_theta,    /* Sigma times theta */
+		  int *ever_active,       /* Ever active set   */ 
+		  int *nactive_ptr,       /* Size of ever active set */
                   int *nrow_ptr,          /* How many rows in Sigma */
 		  double *bound_ptr,      /* feasibility parameter */
                   double *theta,          /* current value */
@@ -135,13 +224,17 @@ void find_one_row(double *Sigma,          /* A covariance matrix: X^TX/n */
   double bound = *bound_ptr;
   int nrow = *nrow_ptr;
 
+  fprintf(stderr, "starting now\n");
+
   double old_value = objective(Sigma,
+			       ever_active,
+			       nactive_ptr,
 			       nrow,
 			       row,
 			       bound,
 			       theta);
   double new_value; 
-  double tol=1.e-6;
+  double tol=1.e-5;
 
   for (iter=0; iter<maxiter; iter++) {
 
@@ -150,17 +243,30 @@ void find_one_row(double *Sigma,          /* A covariance matrix: X^TX/n */
     update_one_coord(Sigma,
 		     Sigma_diag,
 		     Sigma_theta,
+		     ever_active,
+		     nactive_ptr,
 		     nrow,
 		     bound,
 		     theta,
 		     row,
 		     row);
 
+    if (check_KKT(theta,
+		  Sigma_theta,
+		  nrow,
+		  row,
+		  bound) == 1) {
+      fprintf(stderr, "ending in first KKT check\n");
+      break;
+    }
+					  
     for (icoord=0; icoord<nrow; icoord++) {
 
       update_one_coord(Sigma,
 		       Sigma_diag,
 		       Sigma_theta,
+		       ever_active,
+		       nactive_ptr,
 		       nrow,
 		       bound,
 		       theta,
@@ -168,13 +274,25 @@ void find_one_row(double *Sigma,          /* A covariance matrix: X^TX/n */
 		       icoord);
     }
 
+    if (check_KKT(theta,
+		  Sigma_theta,
+		  nrow,
+		  row,
+		  bound) == 1) {
+      fprintf(stderr, "ending in second KKT check\n");
+      break;
+    }
+					  
     new_value = objective(Sigma,
+			  ever_active,
+			  nactive_ptr,
 			  nrow,
 			  row,
 			  bound,
 			  theta);
 
     if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 0)) {
+      fprintf(stderr, "ending in objective value check\n");
       break;
     }
 

From 096b0b8f9f92bb360f8d4f59bc9cbd6f5e4437c1 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Aug 2017 17:02:26 -0700
Subject: [PATCH 186/493] begun the Rcpp extension

---
 selectiveInference/src/Makevars               | 14 ++++++
 selectiveInference/src/Rcpp-debias.cpp        | 45 +++++++++++++++++++
 .../src/{debiasing_matrix.c => debias.c}      |  6 +--
 selectiveInference/src/debias.h               | 10 +++++
 4 files changed, 72 insertions(+), 3 deletions(-)
 create mode 100644 selectiveInference/src/Makevars
 create mode 100644 selectiveInference/src/Rcpp-debias.cpp
 rename selectiveInference/src/{debiasing_matrix.c => debias.c} (97%)
 create mode 100644 selectiveInference/src/debias.h

diff --git a/selectiveInference/src/Makevars b/selectiveInference/src/Makevars
new file mode 100644
index 0000000..191c774
--- /dev/null
+++ b/selectiveInference/src/Makevars
@@ -0,0 +1,14 @@
+PKG_CFLAGS=-DR_PACKAGE=1 -I.
+PKG_CPPFLAGS=-DR_PACKAGE=1 -I.
+PKG_LIBS=-L. -ldebias
+
+$(SHLIB): Rcpp-debias.o RcppExports.o 
+
+Rcpp-debias.o: debias.ts
+RcppExports.o: debias.ts
+
+debias.ts:
+	gcc -shared -fPIC selectiveInference/src/debiasing_matrix.c -o debias.so
+
+clean:
+	rm -f debias.so
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
new file mode 100644
index 0000000..b154dcd
--- /dev/null
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -0,0 +1,45 @@
+#include <Rcpp.h>      // need to include the main Rcpp header file 
+#include <debias.h>    // where find_one_row is defined
+
+// [[Rcpp::export]]
+Rcpp::NumericVector find_one_row(Rcpp::NumericMatrix Sigma,
+				 int row, // 0-based 
+				 double bound,
+				 int maxiter) {
+
+  int nrow = Sigma.nrow(); // number of features
+  int nactive = 1;
+  Rcpp::IntegerVector ever_active(1);
+  Rcpp::NumericVector Sigma_diag(nrow);
+  Rcpp::NumericVector Sigma_theta(nrow);
+
+}
+ Rcpp::NumericVector xUL,
+                       int maxEval, double absErr, double tol, int vectorInterface, unsigned norm) {
+
+  count = 0; /* Zero count */
+  fun = f;
+
+  Rcpp::NumericVector integral(fDim);
+  Rcpp::NumericVector errVals(fDim);
+  int retCode;
+
+  // Rcpp::Rcout<<"Call Integrator" <<std::endl;
+  if (vectorInterface) {
+    retCode = hcubature_v(fDim, fWrapper_v, NULL,
+			  xLL.size(), xLL.begin(), xUL.begin(),
+			  maxEval, absErr, tol, (error_norm) norm,
+			  integral.begin(), errVals.begin());
+  } else {
+    retCode = hcubature(fDim, fWrapper, NULL,
+			xLL.size(), xLL.begin(), xUL.begin(),
+			maxEval, absErr, tol, (error_norm) norm,
+			integral.begin(), errVals.begin());
+  }
+  return Rcpp::List::create(
+			    Rcpp::_["integral"] = integral,
+			    Rcpp::_["error"] = errVals,
+			    Rcpp::_["functionEvaluations"] = count,
+			    Rcpp::_["returnCode"] = retCode);
+}
+
diff --git a/selectiveInference/src/debiasing_matrix.c b/selectiveInference/src/debias.c
similarity index 97%
rename from selectiveInference/src/debiasing_matrix.c
rename to selectiveInference/src/debias.c
index e3ef415..e978260 100644
--- a/selectiveInference/src/debiasing_matrix.c
+++ b/selectiveInference/src/debias.c
@@ -12,7 +12,7 @@
 // Update one coordinate 
 
 double objective(double *Sigma,       /* A covariance matrix: X^TX/n */
-		 int *ever_active,    /* Ever active set   */ 
+		 int *ever_active,    /* Ever active set: 0-based */ 
 		 int *nactive_ptr,    /* Size of ever active set */
 		 int nrow,            /* how many rows in Sigma */
 		 int row,             /* which row: 0-based */
@@ -121,7 +121,7 @@ int check_KKT(double *theta,       /* current theta */
 double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n */
                         double *Sigma_diag,      /* Diagonal entries of Sigma */
                         double *Sigma_theta,     /* Sigma times theta */
-			int *ever_active,        /* Ever active set   */ 
+			int *ever_active,        /* Ever active set: 0-based */ 
 			int *nactive_ptr,        /* Size of ever active set */
 			int nrow,                /* How many rows in Sigma */
 			double bound,            /* feasibility parameter */
@@ -208,7 +208,7 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
 void find_one_row(double *Sigma,          /* A covariance matrix: X^TX/n */
                   double *Sigma_diag,     /* Diagonal entry of covariance matrix */
                   double *Sigma_theta,    /* Sigma times theta */
-		  int *ever_active,       /* Ever active set   */ 
+		  int *ever_active,       /* Ever active set: 0-based */ 
 		  int *nactive_ptr,       /* Size of ever active set */
                   int *nrow_ptr,          /* How many rows in Sigma */
 		  double *bound_ptr,      /* feasibility parameter */
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
new file mode 100644
index 0000000..ba72c47
--- /dev/null
+++ b/selectiveInference/src/debias.h
@@ -0,0 +1,10 @@
+void find_one_row(double *Sigma,          /* A covariance matrix: X^TX/n */
+                  double *Sigma_diag,     /* Diagonal entry of covariance matrix */
+                  double *Sigma_theta,    /* Sigma times theta */
+		  int *ever_active,       /* Ever active set: 0-based */ 
+		  int *nactive_ptr,       /* Size of ever active set */
+                  int *nrow_ptr,          /* How many rows in Sigma */
+		  double *bound_ptr,      /* feasibility parameter */
+                  double *theta,          /* current value */
+                  int *maxiter_ptr,       /* how many iterations */
+                  int *row_ptr);          /* which coordinate to update: 0-based */

From faf0436be146db788f142d1071e767e444d61f2e Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Aug 2017 17:03:47 -0700
Subject: [PATCH 187/493] trying to save some copy time -- R doesn't seem to
 care about extra arguments to .C

---
 selectiveInference/R/funs.fixed.R | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index a30fc0b..73a0535 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -346,19 +346,22 @@ InverseLinftyOneRowC <- function (Sigma, i, mu, maxiter=50) {
 	 basis_vector[i] = 1.
          theta = rep(0, p)     
 
+	 Sigma_ = as.double(Sigma)
+	 Sigma_diag_ = as.double(diag(Sigma))
+	 Sigma_theta_ = as.double(rep(0, p))
 	 val = .C("find_one_row",
-          	 Sigma=as.double(Sigma),
-		 Sigma_diag=as.double(diag(Sigma)),
-		 Sigma_theta=as.double(rep(0, p)),
-                 ever_active=as.integer(i),
+          	 Sigma=Sigma_,
+		 Sigma_diag=Sigma_diag_,
+		 Sigma_theta=Sigma_theta_,
+                 ever_active=as.integer(i-1),
 		 nactive_ptr=as.integer(1),
 		 nrow=as.integer(p),
    		 bound=as.double(mu),
 		 theta=as.double(theta),
 		 maxiter=as.integer(50),
 		 row=as.integer(i-1),
-		 coord=as.integer(i-1),
-		 dup=FALSE,
+		 DUP=FALSE,
+		 NAOK=TRUE,
 		 package="selectiveInference")
 
 	# Check feasibility

From 5bf80181a3a407747c7ff44fafbc54928b3f7ac7 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Aug 2017 17:44:22 -0700
Subject: [PATCH 188/493] don't lose this

---
 selectiveInference/src/Rcpp-debias.cpp | 64 +++++++++++++-------------
 selectiveInference/src/debias.c        | 20 ++++----
 selectiveInference/src/debias.h        | 28 +++++++----
 3 files changed, 61 insertions(+), 51 deletions(-)

diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index b154dcd..384c105 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -1,5 +1,5 @@
 #include <Rcpp.h>      // need to include the main Rcpp header file 
-#include <debias.h>    // where find_one_row is defined
+#include <debias.h>    // where find_one_row_void is defined
 
 // [[Rcpp::export]]
 Rcpp::NumericVector find_one_row(Rcpp::NumericMatrix Sigma,
@@ -8,38 +8,40 @@ Rcpp::NumericVector find_one_row(Rcpp::NumericMatrix Sigma,
 				 int maxiter) {
 
   int nrow = Sigma.nrow(); // number of features
-  int nactive = 1;
+
+  // Active set
+
+  int irow;
+  Rcpp::IntegerVector nactive(1); // An array so we can easily modify it
   Rcpp::IntegerVector ever_active(1);
+  int *ever_active_p = ever_active.begin();
+  *ever_active_p = row;
+
+  // Extract the diagonal
   Rcpp::NumericVector Sigma_diag(nrow);
-  Rcpp::NumericVector Sigma_theta(nrow);
+  double *sigma_p = Sigma_diag.begin();
 
-}
- Rcpp::NumericVector xUL,
-                       int maxEval, double absErr, double tol, int vectorInterface, unsigned norm) {
-
-  count = 0; /* Zero count */
-  fun = f;
-
-  Rcpp::NumericVector integral(fDim);
-  Rcpp::NumericVector errVals(fDim);
-  int retCode;
-
-  // Rcpp::Rcout<<"Call Integrator" <<std::endl;
-  if (vectorInterface) {
-    retCode = hcubature_v(fDim, fWrapper_v, NULL,
-			  xLL.size(), xLL.begin(), xUL.begin(),
-			  maxEval, absErr, tol, (error_norm) norm,
-			  integral.begin(), errVals.begin());
-  } else {
-    retCode = hcubature(fDim, fWrapper, NULL,
-			xLL.size(), xLL.begin(), xUL.begin(),
-			maxEval, absErr, tol, (error_norm) norm,
-			integral.begin(), errVals.begin());
+  for (irow=0; irow<nrow; irow++) {
+    sigma_p[irow] = Sigma(irow, irow);
   }
-  return Rcpp::List::create(
-			    Rcpp::_["integral"] = integral,
-			    Rcpp::_["error"] = errVals,
-			    Rcpp::_["functionEvaluations"] = count,
-			    Rcpp::_["returnCode"] = retCode);
-}
+  
+  // The solution and its product with Sigma
+
+  Rcpp::NumericVector theta(nrow);
+  Rcpp::NumericVector Sigma_theta(nrow);
+  
+  // Now call our C function
 
+  find_one_row_void((double *) Sigma.begin(),
+		    (double *) Sigma_diag.begin(),
+		    (double *) Sigma_theta.begin(),
+		    (int *) ever_active.begin(),
+		    (int *) nactive.begin(),
+		    (int *) &nrow,
+		    (double *) &bound,
+		    (double *) theta.begin(),
+		    (int *) &maxiter,
+		    (int *) &row);
+
+  return theta;
+}
diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index e978260..7932b2e 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -205,16 +205,16 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
 
 }
 
-void find_one_row(double *Sigma,          /* A covariance matrix: X^TX/n */
-                  double *Sigma_diag,     /* Diagonal entry of covariance matrix */
-                  double *Sigma_theta,    /* Sigma times theta */
-		  int *ever_active,       /* Ever active set: 0-based */ 
-		  int *nactive_ptr,       /* Size of ever active set */
-                  int *nrow_ptr,          /* How many rows in Sigma */
-		  double *bound_ptr,      /* feasibility parameter */
-                  double *theta,          /* current value */
-                  int *maxiter_ptr,       /* how many iterations */
-                  int *row_ptr)         /* which coordinate to update: 0-based */
+void find_one_row_void(double *Sigma,          /* A covariance matrix: X^TX/n */
+		       double *Sigma_diag,     /* Diagonal entry of covariance matrix */
+		       double *Sigma_theta,    /* Sigma times theta */
+		       int *ever_active,       /* Ever active set: 0-based */ 
+		       int *nactive_ptr,       /* Size of ever active set */
+		       int *nrow_ptr,          /* How many rows in Sigma */
+		       double *bound_ptr,      /* feasibility parameter */
+		       double *theta,          /* current value */
+		       int *maxiter_ptr,       /* how many iterations */
+		       int *row_ptr)           /* which coordinate to update: 0-based */
 {
 
   int maxiter = *maxiter_ptr;
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index ba72c47..28d2a88 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -1,10 +1,18 @@
-void find_one_row(double *Sigma,          /* A covariance matrix: X^TX/n */
-                  double *Sigma_diag,     /* Diagonal entry of covariance matrix */
-                  double *Sigma_theta,    /* Sigma times theta */
-		  int *ever_active,       /* Ever active set: 0-based */ 
-		  int *nactive_ptr,       /* Size of ever active set */
-                  int *nrow_ptr,          /* How many rows in Sigma */
-		  double *bound_ptr,      /* feasibility parameter */
-                  double *theta,          /* current value */
-                  int *maxiter_ptr,       /* how many iterations */
-                  int *row_ptr);          /* which coordinate to update: 0-based */
+#ifdef __cplusplus
+extern "C"
+{
+#endif /* __cplusplus */
+
+void find_one_row_void(double *Sigma,          /* A covariance matrix: X^TX/n */
+		       double *Sigma_diag,     /* Diagonal entry of covariance matrix */
+		       double *Sigma_theta,    /* Sigma times theta */
+		       int *ever_active,       /* Ever active set: 0-based */ 
+		       int *nactive_ptr,       /* Size of ever active set */
+		       int *nrow_ptr,          /* How many rows in Sigma */
+		       double *bound_ptr,      /* feasibility parameter */
+		       double *theta,          /* current value */
+		       int *maxiter_ptr,       /* how many iterations */
+		       int *row_ptr);          /* which coordinate to update: 0-based */
+#ifdef __cplusplus
+}  /* extern "C" */
+#endif /* __cplusplus */

From 944ae7dced728a03e5f64c6d24b55e5f9755ed0f Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Aug 2017 17:52:02 -0700
Subject: [PATCH 189/493] segfaulting on the test_big.R :(

---
 selectiveInference/DESCRIPTION    |  2 ++
 selectiveInference/NAMESPACE      |  2 +-
 selectiveInference/R/funs.fixed.R | 51 ++++++++++++++++---------------
 selectiveInference/src/Makevars   | 16 +++-------
 4 files changed, 35 insertions(+), 36 deletions(-)

diff --git a/selectiveInference/DESCRIPTION b/selectiveInference/DESCRIPTION
index d0740ec..d902622 100644
--- a/selectiveInference/DESCRIPTION
+++ b/selectiveInference/DESCRIPTION
@@ -18,3 +18,5 @@ Description: New tools for post-selection inference, for use with forward
     models.
 License: GPL-2
 RoxygenNote: 5.0.1
+LinkingTo: Rcpp
+Imports: Rcpp
diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index 099fdc5..ab2a111 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -44,5 +44,5 @@ importFrom("stats", dnorm, lsfit, pexp, pnorm, predict,
            qnorm, rnorm, sd, uniroot, dchisq, model.matrix, pchisq)
 importFrom("stats", "coef", "df", "lm", "pf")
 importFrom("stats", "glm", "residuals", "vcov")
-
+importFrom("Rcpp", "sourceCpp")
 
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index a30fc0b..f4b5a26 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -341,34 +341,37 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
 
 InverseLinftyOneRowC <- function (Sigma, i, mu, maxiter=50) {
 
-         p = nrow(Sigma)		
-         basis_vector = rep(0, p)
-	 basis_vector[i] = 1.
-         theta = rep(0, p)     
+         theta = find_one_row(Sigma, i, mu, maxiter)
+         return(theta)
+}
+#          p = nrow(Sigma)		
+#          basis_vector = rep(0, p)
+# 	 basis_vector[i] = 1.
+#          theta = rep(0, p)     
 
-	 val = .C("find_one_row",
-          	 Sigma=as.double(Sigma),
-		 Sigma_diag=as.double(diag(Sigma)),
-		 Sigma_theta=as.double(rep(0, p)),
-                 ever_active=as.integer(i),
-		 nactive_ptr=as.integer(1),
-		 nrow=as.integer(p),
-   		 bound=as.double(mu),
-		 theta=as.double(theta),
-		 maxiter=as.integer(50),
-		 row=as.integer(i-1),
-		 coord=as.integer(i-1),
-		 dup=FALSE,
-		 package="selectiveInference")
+# 	 val = .C("find_one_row",
+#           	 Sigma=as.double(Sigma),
+# 		 Sigma_diag=as.double(diag(Sigma)),
+# 		 Sigma_theta=as.double(rep(0, p)),
+#                  ever_active=as.integer(i),
+# 		 nactive_ptr=as.integer(1),
+# 		 nrow=as.integer(p),
+#    		 bound=as.double(mu),
+# 		 theta=as.double(theta),
+# 		 maxiter=as.integer(50),
+# 		 row=as.integer(i-1),
+# 		 coord=as.integer(i-1),
+# 		 dup=FALSE,
+# 		 package="selectiveInference")
 
-	# Check feasibility
+# 	# Check feasibility
 
-	if (max(abs(Sigma %*% val$theta - basis_vector)) > 1.01 * mu) {
-	   warning("Solution for row of M does not seem to be feasible")
-	}
+# 	if (max(abs(Sigma %*% val$theta - basis_vector)) > 1.01 * mu) {
+# 	   warning("Solution for row of M does not seem to be feasible")
+# 	}
 
-	return(val$theta)
-}
+# 	return(val$theta)
+# }
 
 InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-2 ) {
   p <- nrow(sigma);
diff --git a/selectiveInference/src/Makevars b/selectiveInference/src/Makevars
index 191c774..c68d0bd 100644
--- a/selectiveInference/src/Makevars
+++ b/selectiveInference/src/Makevars
@@ -1,14 +1,8 @@
-PKG_CFLAGS=-DR_PACKAGE=1 -I.
-PKG_CPPFLAGS=-DR_PACKAGE=1 -I.
-PKG_LIBS=-L. -ldebias
+PKG_CFLAGS= -I.
+PKG_CPPFLAGS= -I.
+PKG_LIBS=-L. 
 
-$(SHLIB): Rcpp-debias.o RcppExports.o 
-
-Rcpp-debias.o: debias.ts
-RcppExports.o: debias.ts
-
-debias.ts:
-	gcc -shared -fPIC selectiveInference/src/debiasing_matrix.c -o debias.so
+$(SHLIB): Rcpp-debias.o RcppExports.o debias.o
 
 clean:
-	rm -f debias.so
+	rm -f *o

From 4caf22548fb079f0c42c256fe1b68d78f401e3d8 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Aug 2017 18:24:14 -0700
Subject: [PATCH 190/493] changes to funs.fixed.R that will be overwritten

---
 selectiveInference/R/funs.fixed.R | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index a30fc0b..0bbc119 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -346,9 +346,12 @@ InverseLinftyOneRowC <- function (Sigma, i, mu, maxiter=50) {
 	 basis_vector[i] = 1.
          theta = rep(0, p)     
 
+	 Sigma_ = as.double(Sigma)
+	 Sigma_diag_ = as.double(diag(Sigma))
+
 	 val = .C("find_one_row",
-          	 Sigma=as.double(Sigma),
-		 Sigma_diag=as.double(diag(Sigma)),
+          	 Sigma=Sigma_,
+		 Sigma_diag=Sigma_diag_,
 		 Sigma_theta=as.double(rep(0, p)),
                  ever_active=as.integer(i),
 		 nactive_ptr=as.integer(1),

From f53af23587372422cfcbe4e5d9105e2a384edbc6 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Aug 2017 18:41:19 -0700
Subject: [PATCH 191/493] success -- about 2ms now -- python still faster (as
 timed through rpy2) :)

---
 README.md                              |  9 +++++++
 selectiveInference/R/funs.fixed.R      | 35 ++++++--------------------
 selectiveInference/src/Rcpp-debias.cpp |  8 +++---
 selectiveInference/src/debias.c        | 13 +++-------
 selectiveInference/src/debias.h        |  8 +++---
 5 files changed, 29 insertions(+), 44 deletions(-)

diff --git a/README.md b/README.md
index 962151e..cf855e1 100644
--- a/README.md
+++ b/README.md
@@ -21,3 +21,12 @@ The latest release of the package can be installed through CRAN:
 install.packages("selectiveInference")
 ```
 Code in repo is under development and may be unstable.
+
+## For development
+
+You will have to run
+
+```R
+library(Rcpp)
+Rcpp::compileAttributes('selectiveInference')
+```
\ No newline at end of file
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index f4b5a26..7314511 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -295,6 +295,7 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
     incr <- 0;
     while ((mu.stop != 1)&&(try.no<10)){
       last.beta <- beta
+      useC = TRUE
       if (useC == FALSE) {
             output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
       } else {
@@ -341,37 +342,17 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
 
 InverseLinftyOneRowC <- function (Sigma, i, mu, maxiter=50) {
 
-         theta = find_one_row(Sigma, i, mu, maxiter)
-         return(theta)
-}
-#          p = nrow(Sigma)		
-#          basis_vector = rep(0, p)
-# 	 basis_vector[i] = 1.
-#          theta = rep(0, p)     
+    theta = find_one_row(Sigma, i-1, mu, maxiter)
 
-# 	 val = .C("find_one_row",
-#           	 Sigma=as.double(Sigma),
-# 		 Sigma_diag=as.double(diag(Sigma)),
-# 		 Sigma_theta=as.double(rep(0, p)),
-#                  ever_active=as.integer(i),
-# 		 nactive_ptr=as.integer(1),
-# 		 nrow=as.integer(p),
-#    		 bound=as.double(mu),
-# 		 theta=as.double(theta),
-# 		 maxiter=as.integer(50),
-# 		 row=as.integer(i-1),
-# 		 coord=as.integer(i-1),
-# 		 dup=FALSE,
-# 		 package="selectiveInference")
+   # Check feasibility
 
-# 	# Check feasibility
+  if (max(abs(Sigma %*% val$theta - basis_vector)) > 1.01 * mu) {
+     warning("Solution for row of M does not seem to be feasible")
+  } 
 
-# 	if (max(abs(Sigma %*% val$theta - basis_vector)) > 1.01 * mu) {
-# 	   warning("Solution for row of M does not seem to be feasible")
-# 	}
+  return(theta)
 
-# 	return(val$theta)
-# }
+}
 
 InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-2 ) {
   p <- nrow(sigma);
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index 384c105..d8cd729 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -37,11 +37,11 @@ Rcpp::NumericVector find_one_row(Rcpp::NumericMatrix Sigma,
 		    (double *) Sigma_theta.begin(),
 		    (int *) ever_active.begin(),
 		    (int *) nactive.begin(),
-		    (int *) &nrow,
-		    (double *) &bound,
+		    nrow,
+		    bound,
 		    (double *) theta.begin(),
-		    (int *) &maxiter,
-		    (int *) &row);
+		    maxiter,
+		    row);
 
   return theta;
 }
diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index 7932b2e..f8ff6e3 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -210,19 +210,15 @@ void find_one_row_void(double *Sigma,          /* A covariance matrix: X^TX/n */
 		       double *Sigma_theta,    /* Sigma times theta */
 		       int *ever_active,       /* Ever active set: 0-based */ 
 		       int *nactive_ptr,       /* Size of ever active set */
-		       int *nrow_ptr,          /* How many rows in Sigma */
-		       double *bound_ptr,      /* feasibility parameter */
+		       int nrow,               /* How many rows in Sigma */
+		       double bound,           /* feasibility parameter */
 		       double *theta,          /* current value */
-		       int *maxiter_ptr,       /* how many iterations */
-		       int *row_ptr)           /* which coordinate to update: 0-based */
+		       int maxiter,            /* how many iterations */
+		       int row)                /* which coordinate to update: 0-based */
 {
 
-  int maxiter = *maxiter_ptr;
   int iter = 0;
   int icoord = 0;
-  int row = *row_ptr;
-  double bound = *bound_ptr;
-  int nrow = *nrow_ptr;
 
   fprintf(stderr, "starting now\n");
 
@@ -299,6 +295,5 @@ void find_one_row_void(double *Sigma,          /* A covariance matrix: X^TX/n */
     old_value = new_value;
   }
 
-  *nrow_ptr = iter-1;
 }
 
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index 28d2a88..107d931 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -8,11 +8,11 @@ void find_one_row_void(double *Sigma,          /* A covariance matrix: X^TX/n */
 		       double *Sigma_theta,    /* Sigma times theta */
 		       int *ever_active,       /* Ever active set: 0-based */ 
 		       int *nactive_ptr,       /* Size of ever active set */
-		       int *nrow_ptr,          /* How many rows in Sigma */
-		       double *bound_ptr,      /* feasibility parameter */
+		       int nrow,          /* How many rows in Sigma */
+		       double bound,      /* feasibility parameter */
 		       double *theta,          /* current value */
-		       int *maxiter_ptr,       /* how many iterations */
-		       int *row_ptr);          /* which coordinate to update: 0-based */
+		       int maxiter,       /* how many iterations */
+		       int row);          /* which coordinate to update: 0-based */
 #ifdef __cplusplus
 }  /* extern "C" */
 #endif /* __cplusplus */

From 7920c8c5fd8564e5410104ac993303ceddb1205b Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Aug 2017 23:27:06 -0700
Subject: [PATCH 192/493] BF: column major, removing print statements

---
 selectiveInference/src/debias.c | 10 +---------
 1 file changed, 1 insertion(+), 9 deletions(-)

diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index f8ff6e3..fd9be79 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -1,4 +1,3 @@
-#include <stdio.h>
 #include <math.h> // for fabs
 
 // Find an approximate row of \hat{Sigma}^{-1}
@@ -41,10 +40,8 @@ double objective(double *Sigma,       /* A covariance matrix: X^TX/n */
       active_col_ptr = ((int *) ever_active + icol);
       active_col = *active_col_ptr;
       theta_col_ptr = ((double *) theta + active_col);
-      
-      fprintf(stderr, "%d %d \n", active_row, active_col);
 
-      Sigma_ptr = ((double *) Sigma + nrow * active_row + active_col);
+      Sigma_ptr = ((double *) Sigma + nrow * active_col + active_row); // Matrices are column-major order
 
       value += 0.5 * (*Sigma_ptr) * (*theta_row_ptr) * (*theta_col_ptr);
     }
@@ -220,8 +217,6 @@ void find_one_row_void(double *Sigma,          /* A covariance matrix: X^TX/n */
   int iter = 0;
   int icoord = 0;
 
-  fprintf(stderr, "starting now\n");
-
   double old_value = objective(Sigma,
 			       ever_active,
 			       nactive_ptr,
@@ -252,7 +247,6 @@ void find_one_row_void(double *Sigma,          /* A covariance matrix: X^TX/n */
 		  nrow,
 		  row,
 		  bound) == 1) {
-      fprintf(stderr, "ending in first KKT check\n");
       break;
     }
 					  
@@ -275,7 +269,6 @@ void find_one_row_void(double *Sigma,          /* A covariance matrix: X^TX/n */
 		  nrow,
 		  row,
 		  bound) == 1) {
-      fprintf(stderr, "ending in second KKT check\n");
       break;
     }
 					  
@@ -288,7 +281,6 @@ void find_one_row_void(double *Sigma,          /* A covariance matrix: X^TX/n */
 			  theta);
 
     if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 0)) {
-      fprintf(stderr, "ending in objective value check\n");
       break;
     }
 

From 6756ab2935c0dd93ab4bde228db31e09066b14a6 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Aug 2017 23:28:14 -0700
Subject: [PATCH 193/493] changing name of function, evaluation of feasibility
 in Rcpp

---
 Makefile                               |  4 ++++
 README.md                              |  5 ++---
 selectiveInference/R/funs.fixed.R      |  8 +++++---
 selectiveInference/src/Rcpp-debias.cpp | 25 ++++++++++++++++++++-----
 4 files changed, 31 insertions(+), 11 deletions(-)
 create mode 100644 Makefile

diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000..dfe6002
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,4 @@
+Rcpp: 
+	rm selectiveInference/src/RcppExports.cpp
+	rm selectiveInference/R/RcppExports.R
+	Rscript -e "library(Rcpp); Rcpp::compileAttributes('selectiveInference')"
\ No newline at end of file
diff --git a/README.md b/README.md
index cf855e1..0a60855 100644
--- a/README.md
+++ b/README.md
@@ -26,7 +26,6 @@ Code in repo is under development and may be unstable.
 
 You will have to run
 
-```R
-library(Rcpp)
-Rcpp::compileAttributes('selectiveInference')
+```
+make Rcpp
 ```
\ No newline at end of file
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 7314511..f2b9838 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -342,11 +342,13 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
 
 InverseLinftyOneRowC <- function (Sigma, i, mu, maxiter=50) {
 
-    theta = find_one_row(Sigma, i-1, mu, maxiter)
+  result = find_one_row_debiasingM(Sigma, i-1, mu, maxiter) # C function uses 0-based indexing
+  theta = result$theta
+  feasible_val = result$feasible_val
 
-   # Check feasibility
+  # Check feasibility
 
-  if (max(abs(Sigma %*% val$theta - basis_vector)) > 1.01 * mu) {
+  if (feasible_val > 1.01 * mu) {
      warning("Solution for row of M does not seem to be feasible")
   } 
 
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index d8cd729..48f484f 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -2,10 +2,10 @@
 #include <debias.h>    // where find_one_row_void is defined
 
 // [[Rcpp::export]]
-Rcpp::NumericVector find_one_row(Rcpp::NumericMatrix Sigma,
-				 int row, // 0-based 
-				 double bound,
-				 int maxiter) {
+Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
+				   int row, // 0-based 
+				   double bound,
+				   int maxiter) {
 
   int nrow = Sigma.nrow(); // number of features
 
@@ -42,6 +42,21 @@ Rcpp::NumericVector find_one_row(Rcpp::NumericMatrix Sigma,
 		    (double *) theta.begin(),
 		    maxiter,
 		    row);
+  
+  // Check whether feasible
+
+  double feasible_val = 0; 
+  double val;
+  for (irow=0; irow<nrow; irow++) {
+    val = Sigma_theta[irow];
+    if (irow == row) {
+      val -= 1.;
+    }
+    if (fabs(val) > feasible_val) {
+      feasible_val = fabs(val);
+    }
+  }
 
-  return theta;
+  return(Rcpp::List::create(Rcpp::Named("theta") = theta,
+			    Rcpp::Named("feasible_val") = feasible_val));
 }

From 0e584354e49c2fb68039761a0d6b040c1e37c1b9 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Aug 2017 23:37:00 -0700
Subject: [PATCH 194/493] RF: renamed C function, removing R solver for one row

---
 selectiveInference/R/funs.fixed.R      | 82 ++------------------------
 selectiveInference/src/Rcpp-debias.cpp | 25 ++++----
 selectiveInference/src/debias.c        | 22 +++----
 selectiveInference/src/debias.h        | 20 +++----
 4 files changed, 40 insertions(+), 109 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index f2b9838..d0bc861 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -159,8 +159,8 @@ sigma=NULL, alpha=0.1,
       hsigmaSinv <- solve(hsigmaS) # pinv(hsigmaS)
 
       # Approximate inverse covariance matrix for when (n < p) from lasso_Inference.R
-      useC = TRUE
-      htheta <- InverseLinfty(hsigma, n, length(S), verbose=FALSE, useC=useC)
+
+      htheta <- InverseLinfty(hsigma, n, length(S), verbose=FALSE)
       # htheta <- InverseLinfty(hsigma, n, verbose=FALSE)
       
       FS = rbind(diag(length(S)),matrix(0,pp-length(S),length(S)))
@@ -270,7 +270,7 @@ fixedLasso.poly=
 ### Functions borrowed and slightly modified from lasso_inference.R
 
 ## Approximates inverse covariance matrix theta
-InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE, useC = FALSE) {
+InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
   # InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
     isgiven <- 1;
   if (is.null(mu)){
@@ -295,12 +295,7 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
     incr <- 0;
     while ((mu.stop != 1)&&(try.no<10)){
       last.beta <- beta
-      useC = TRUE
-      if (useC == FALSE) {
-            output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
-      } else {
-            output <- InverseLinftyOneRowC(sigma, i, mu, maxiter=maxiter)
-      }
+      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter)
       beta <- output$optsol
       iter <- output$iter
       if (isgiven==1){
@@ -340,7 +335,7 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
   return(M)
 }
 
-InverseLinftyOneRowC <- function (Sigma, i, mu, maxiter=50) {
+InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50) {
 
   result = find_one_row_debiasingM(Sigma, i-1, mu, maxiter) # C function uses 0-based indexing
   theta = result$theta
@@ -352,73 +347,8 @@ InverseLinftyOneRowC <- function (Sigma, i, mu, maxiter=50) {
      warning("Solution for row of M does not seem to be feasible")
   } 
 
-  return(theta)
-
-}
+  return(result)
 
-InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-2 ) {
-  p <- nrow(sigma);
-  rho <- max(abs(sigma[i,-i])) / sigma[i,i];
-  mu0 <- rho/(1+rho);
-  beta <- rep(0,p);
-  
-  #if (mu >= mu0){
-  #  beta[i] <- (1-mu0)/sigma[i,i];
-  #  returnlist <- list("optsol" = beta, "iter" = 0);
-  #  return(returnlist);
-  #}
-  
-  diff.norm2 <- 1;
-  last.norm2 <- 1;
-  iter <- 1;
-  iter.old <- 1;
-  beta[i] <- (1-mu0)/sigma[i,i];
-  beta.old <- beta;
-  sigma.tilde <- sigma;
-  diag(sigma.tilde) <- 0;
-  vs <- -sigma.tilde%*%beta;
-  
-  while ((iter <= maxiter) && (diff.norm2 >= threshold*last.norm2)){
-    
-    for (j in 1:p){
-      oldval <- beta[j];
-      v <- vs[j];
-      if (j==i)
-        v <- v+1;
-      beta[j] <- SoftThreshold(v,mu)/sigma[j,j];
-      if (oldval != beta[j]){
-        vs <- vs + (oldval-beta[j])*sigma.tilde[,j];
-      }
-    }
-    
-    iter <- iter + 1;
-    if (iter==2*iter.old){
-      d <- beta - beta.old;
-      diff.norm2 <- sqrt(sum(d*d));
-      last.norm2 <-sqrt(sum(beta*beta));
-      iter.old <- iter;
-      beta.old <- beta;
-      if (iter>10)
-        vs <- -sigma.tilde%*%beta;
-    }
-  }
-  
-  returnlist <- list("optsol" = beta, "iter" = iter)
-  return(returnlist)
-}
-
-SoftThreshold <- function( x, lambda ) {
-  #
-  # Standard soft thresholding
-  #
-  if (x>lambda){
-    return (x-lambda);}
-  else {
-    if (x< (-lambda)){
-      return (x+lambda);}
-    else {
-      return (0); }
-  }
 }
 
 ##############################
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index 48f484f..bb8b5cd 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -32,16 +32,16 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
   
   // Now call our C function
 
-  find_one_row_void((double *) Sigma.begin(),
-		    (double *) Sigma_diag.begin(),
-		    (double *) Sigma_theta.begin(),
-		    (int *) ever_active.begin(),
-		    (int *) nactive.begin(),
-		    nrow,
-		    bound,
-		    (double *) theta.begin(),
-		    maxiter,
-		    row);
+  int iter = find_one_row_((double *) Sigma.begin(),
+			   (double *) Sigma_diag.begin(),
+			   (double *) Sigma_theta.begin(),
+			   (int *) ever_active.begin(),
+			   (int *) nactive.begin(),
+			   nrow,
+			   bound,
+			   (double *) theta.begin(),
+			   maxiter,
+			   row);
   
   // Check whether feasible
 
@@ -57,6 +57,7 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
     }
   }
 
-  return(Rcpp::List::create(Rcpp::Named("theta") = theta,
-			    Rcpp::Named("feasible_val") = feasible_val));
+  return(Rcpp::List::create(Rcpp::Named("optsol") = theta,
+			    Rcpp::Named("feasible_val") = feasible_val,
+			    Rcpp::Named("iter") = iter));
 }
diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index fd9be79..d2f671d 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -202,16 +202,16 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
 
 }
 
-void find_one_row_void(double *Sigma,          /* A covariance matrix: X^TX/n */
-		       double *Sigma_diag,     /* Diagonal entry of covariance matrix */
-		       double *Sigma_theta,    /* Sigma times theta */
-		       int *ever_active,       /* Ever active set: 0-based */ 
-		       int *nactive_ptr,       /* Size of ever active set */
-		       int nrow,               /* How many rows in Sigma */
-		       double bound,           /* feasibility parameter */
-		       double *theta,          /* current value */
-		       int maxiter,            /* how many iterations */
-		       int row)                /* which coordinate to update: 0-based */
+int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
+		  double *Sigma_diag,     /* Diagonal entry of covariance matrix */
+		  double *Sigma_theta,    /* Sigma times theta */
+		  int *ever_active,       /* Ever active set: 0-based */ 
+		  int *nactive_ptr,       /* Size of ever active set */
+		  int nrow,               /* How many rows in Sigma */
+		  double bound,           /* feasibility parameter */
+		  double *theta,          /* current value */
+		  int maxiter,            /* how many iterations */
+		  int row)                /* which coordinate to update: 0-based */
 {
 
   int iter = 0;
@@ -286,6 +286,6 @@ void find_one_row_void(double *Sigma,          /* A covariance matrix: X^TX/n */
 
     old_value = new_value;
   }
-
+  return(iter);
 }
 
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index 107d931..48f9753 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -3,16 +3,16 @@ extern "C"
 {
 #endif /* __cplusplus */
 
-void find_one_row_void(double *Sigma,          /* A covariance matrix: X^TX/n */
-		       double *Sigma_diag,     /* Diagonal entry of covariance matrix */
-		       double *Sigma_theta,    /* Sigma times theta */
-		       int *ever_active,       /* Ever active set: 0-based */ 
-		       int *nactive_ptr,       /* Size of ever active set */
-		       int nrow,          /* How many rows in Sigma */
-		       double bound,      /* feasibility parameter */
-		       double *theta,          /* current value */
-		       int maxiter,       /* how many iterations */
-		       int row);          /* which coordinate to update: 0-based */
+int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
+		  double *Sigma_diag,     /* Diagonal entry of covariance matrix */
+		  double *Sigma_theta,    /* Sigma times theta */
+		  int *ever_active,       /* Ever active set: 0-based */ 
+		  int *nactive_ptr,       /* Size of ever active set */
+		  int nrow,          /* How many rows in Sigma */
+		  double bound,      /* feasibility parameter */
+		  double *theta,          /* current value */
+		  int maxiter,       /* how many iterations */
+		  int row);          /* which coordinate to update: 0-based */
 #ifdef __cplusplus
 }  /* extern "C" */
 #endif /* __cplusplus */

From 06143059f7732e98bd30f18976775a7a5954ba9b Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Aug 2017 23:40:55 -0700
Subject: [PATCH 195/493] need to make Rcpp exports

---
 .travis.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index aa61233..a3fdd1f 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -7,8 +7,9 @@ r:
   - devel
 addons:
    apt: 
-      packages: libmpfr-dev
+      packages: libmpfr-dev 
 warnings_are_errors: true
 before_install:
   - tlmgr install index # for texlive and vignette?
+  - make Rcpp
   - cd selectiveInference

From ca60bc90d631c8723c031b6d7f8a2c748816a82a Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Aug 2017 23:44:51 -0700
Subject: [PATCH 196/493] ignore first two lines if status not OK?

---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index dfe6002..5c55bb8 100644
--- a/Makefile
+++ b/Makefile
@@ -1,4 +1,4 @@
 Rcpp: 
-	rm selectiveInference/src/RcppExports.cpp
-	rm selectiveInference/R/RcppExports.R
+	- rm -f selectiveInference/src/RcppExports.cpp
+	- rm -f selectiveInference/R/RcppExports.R
 	Rscript -e "library(Rcpp); Rcpp::compileAttributes('selectiveInference')"
\ No newline at end of file

From 60f6cdea4e355d70b305bd61062371dedc4b7e06 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 16 Aug 2017 23:50:24 -0700
Subject: [PATCH 197/493] install Rcpp earlier

---
 .travis.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.travis.yml b/.travis.yml
index a3fdd1f..21270e0 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -11,5 +11,6 @@ addons:
 warnings_are_errors: true
 before_install:
   - tlmgr install index # for texlive and vignette?
+  - R -e 'install.packages("Rcpp", repos="http://cloud.r-project.org")'
   - make Rcpp
   - cd selectiveInference

From c1aef63975841d882e0a68ec4e84382b0b4c7cd6 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 17 Aug 2017 00:26:42 -0700
Subject: [PATCH 198/493] using Rcpp for matrixcomps as well -- update1 and
 downdate1 -- examples now OK on Rcmd check

---
 selectiveInference/R/funs.common.R          |   8 +----
 selectiveInference/R/funs.lar.R             |   9 +-----
 selectiveInference/src/Rcpp-matrixcomps.cpp |  34 ++++++++++++++++++++
 selectiveInference/src/debias.h             |   1 +
 selectiveInference/src/matrixcomps.c        |  13 +++-----
 selectiveInference/src/matrixcomps.h        |  12 +++++++
 selectiveInference/src/symbols.rds          | Bin 367 -> 0 bytes
 7 files changed, 53 insertions(+), 24 deletions(-)
 create mode 100644 selectiveInference/src/Rcpp-matrixcomps.cpp
 create mode 100644 selectiveInference/src/matrixcomps.h
 delete mode 100644 selectiveInference/src/symbols.rds

diff --git a/selectiveInference/R/funs.common.R b/selectiveInference/R/funs.common.R
index 042e074..678d736 100644
--- a/selectiveInference/R/funs.common.R
+++ b/selectiveInference/R/funs.common.R
@@ -152,13 +152,7 @@ updateQR <- function(Q1,Q2,R,col) {
   n = ncol(Q1)
   k = ncol(Q2)
 
-  a = .C("update1",
-    Q2=as.double(Q2),
-    w=as.double(t(Q2)%*%col),
-    m=as.integer(m),
-    k=as.integer(k),
-    dup=FALSE,
-    package="selectiveInference")
+  a = update1_(as.matrix(Q2), t(Q2)%*%col, m, k) # Rcpp call
 
   Q2 = matrix(a$Q2,nrow=m)
   w = c(t(Q1)%*%col,a$w)
diff --git a/selectiveInference/R/funs.lar.R b/selectiveInference/R/funs.lar.R
index eb31b4e..a01b2d3 100644
--- a/selectiveInference/R/funs.lar.R
+++ b/selectiveInference/R/funs.lar.R
@@ -254,14 +254,7 @@ downdateQR <- function(Q1,Q2,R,col) {
   m = nrow(Q1)
   n = ncol(Q1)
 
-  a = .C("downdate1",
-    Q1=as.double(Q1),
-    R=as.double(R),
-    col=as.integer(col-1),
-    m=as.integer(m),
-    n=as.integer(n),
-    dup=FALSE,
-    package="selectiveInference")
+  a = downdate1_(as.matrix(Q1), R, col, m, n) # Rcpp call
 
   Q1 = matrix(a$Q1,nrow=m)
   R = matrix(a$R,nrow=n)
diff --git a/selectiveInference/src/Rcpp-matrixcomps.cpp b/selectiveInference/src/Rcpp-matrixcomps.cpp
new file mode 100644
index 0000000..045b4b7
--- /dev/null
+++ b/selectiveInference/src/Rcpp-matrixcomps.cpp
@@ -0,0 +1,34 @@
+#include <Rcpp.h>      // need to include the main Rcpp header file 
+#include <matrixcomps.h>    // where update1, downdate1 are defined
+
+// [[Rcpp::export]]
+Rcpp::List update1_(Rcpp::NumericMatrix Q2,
+		    Rcpp::NumericVector w,
+		    int m,
+		    int k) {
+
+  update1(Q2.begin(),
+	  w.begin(),
+	  m,
+	  k);
+
+  return(Rcpp::List::create(Rcpp::Named("Q2") = Q2,
+			    Rcpp::Named("w") = w));
+}
+
+// [[Rcpp::export]]
+Rcpp::List downdate1_(Rcpp::NumericMatrix Q1,
+		      Rcpp::NumericMatrix R,
+		      int j0,
+		      int m,
+		      int n) {
+
+  downdate1(Q1.begin(),
+	    R.begin(),
+	    j0,
+	    m,
+	    n);
+
+  return(Rcpp::List::create(Rcpp::Named("Q1") = Q1,
+			    Rcpp::Named("R") = R));
+}
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index 48f9753..ed14cf5 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -13,6 +13,7 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
 		  double *theta,          /* current value */
 		  int maxiter,       /* how many iterations */
 		  int row);          /* which coordinate to update: 0-based */
+
 #ifdef __cplusplus
 }  /* extern "C" */
 #endif /* __cplusplus */
diff --git a/selectiveInference/src/matrixcomps.c b/selectiveInference/src/matrixcomps.c
index 4a516a1..bec3506 100644
--- a/selectiveInference/src/matrixcomps.c
+++ b/selectiveInference/src/matrixcomps.c
@@ -53,11 +53,8 @@ void colrot(double *A, int j1, int j2, int m, int n, int i1, int i2, double c, d
 // where Q1 is m x n and R is n x n. The other part of 
 // the Q matrix, Q2 m x (m-n), isn't needed so it isn't 
 // passed for efficiency
-void downdate1(double *Q1, double *R, int *j0p, int *mp, int *np) {
-  int j0,m,n,j;
-  j0 = *j0p;
-  m = *mp;
-  n = *np;
+void downdate1(double *Q1, double *R, int j0, int m, int n) {
+  int j;
 
   double c,s;
   for (j=j0+1; j<n; j++) {
@@ -76,10 +73,8 @@ void downdate1(double *Q1, double *R, int *j0p, int *mp, int *np) {
 // For convenience, we are given w=Q2'z. Here Q2 is m x 
 // (m-n). The other part of the Q matrix, Q1 m x n, and 
 // R are not needed, so they aren't passed for efficiency 
-void update1(double *Q2, double *w, int *mp, int *kp) {
-  int m,k,j;
-  m = *mp;
-  k = *kp;
+void update1(double *Q2, double *w, int m, int k) {
+  int j;
 
   double c,s;
   for (j=k-1; j>=1; j--) {
diff --git a/selectiveInference/src/matrixcomps.h b/selectiveInference/src/matrixcomps.h
new file mode 100644
index 0000000..8632e3e
--- /dev/null
+++ b/selectiveInference/src/matrixcomps.h
@@ -0,0 +1,12 @@
+#ifdef __cplusplus
+extern "C"
+{
+#endif /* __cplusplus */
+
+void update1(double *Q2, double *w, int m, int k);
+
+void downdate1(double *Q1, double *R, int j0, int m, int n);
+
+#ifdef __cplusplus
+}  /* extern "C" */
+#endif /* __cplusplus */
diff --git a/selectiveInference/src/symbols.rds b/selectiveInference/src/symbols.rds
deleted file mode 100644
index 06b0e85e927ffbb186121204a3be110d68965ede..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 367
zcmV-#0g(P5iwFP!000001HF`8PlGTNhRes4%`92sg}43!BMkib8)hcnHDxQak<v=L
zf&O@BVYDz%Z=7B%ea_42%OU5@o+bbQ8#s;yY^TEWx(4=4ogr|+Se;||kU&CF85{~E
z)50lSvNUJ1DJO8nifn}>_6JIPWlgp?6ULIF7c6t)_l2M@%=<!UEOS=0Q+RO0yEEuo
z8(NP8So}R)_rdW4gTbxAJAc@TO{eRJ9lj4+#)j?KT#S&xcs}s`9{{dhfu3VUs5++m
zxYeiIYw=V|hA0+_n^=Mm^_$hb&G%Lfn&lhUJldjkV;%_dj`x@ATwj_{su=T(<uvLl
zuU(~%dcMg>qRk*KdBZ0#Ng2zV>P#g%js+AFbJLu%b;B1>Aj(q=rLtRA?|{WKk$T84
z()a@`zE^&?C?~o(^2)7UXU|@{XM1<Vy&eQ-q*F7i-S^^@BK22ME*a(Gis_Y^T_{Nz
NeFB_4nEVd~006gSudx6C


From f46851009fb493e9cf15c8f36b7d3986636d04de Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 17 Aug 2017 12:20:11 -0700
Subject: [PATCH 199/493] using a warm start if available

---
 selectiveInference/R/funs.fixed.R      | 28 ++++++++++++++++------
 selectiveInference/src/Rcpp-debias.cpp | 33 ++++++++++----------------
 selectiveInference/src/debias.h        | 15 ++++++++----
 3 files changed, 45 insertions(+), 31 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index d0bc861..592d61d 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -293,10 +293,13 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
     mu.stop <- 0;
     try.no <- 1;
     incr <- 0;
+
+    output = NULL
+
     while ((mu.stop != 1)&&(try.no<10)){
       last.beta <- beta
-      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter)
-      beta <- output$optsol
+      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, soln_result=output) # uses a warm start
+      beta <- output$soln
       iter <- output$iter
       if (isgiven==1){
         mu.stop <- 1
@@ -335,15 +338,26 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
   return(M)
 }
 
-InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50) {
+InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL) {
+
+  # If soln_result is not Null, it is used as a warm start.
+  # It should be a list
+  # with entries "soln" and "Sigma_soln"
+
+  if (is.null(soln_result)) {
+     soln = rep(0, nrow(Sigma))
+     Sigma_soln = rep(0, nrow(Sigma)) 
+  }
+  else {
+     soln = soln_result$soln
+     Sigma_soln = soln_result$Sigma_soln  
+  }
 
-  result = find_one_row_debiasingM(Sigma, i-1, mu, maxiter) # C function uses 0-based indexing
-  theta = result$theta
-  feasible_val = result$feasible_val
+  result = find_one_row_debiasingM(Sigma, i-1, mu, maxiter, soln, Sigma_soln) # C function uses 0-based indexing
 
   # Check feasibility
 
-  if (feasible_val > 1.01 * mu) {
+  if (!result$kkt_check) {
      warning("Solution for row of M does not seem to be feasible")
   } 
 
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index bb8b5cd..b529c4a 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -5,7 +5,9 @@
 Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
 				   int row, // 0-based 
 				   double bound,
-				   int maxiter) {
+				   int maxiter,
+				   Rcpp::NumericVector theta,
+				   Rcpp::NumericVector Sigma_theta) {
 
   int nrow = Sigma.nrow(); // number of features
 
@@ -25,11 +27,6 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
     sigma_p[irow] = Sigma(irow, irow);
   }
   
-  // The solution and its product with Sigma
-
-  Rcpp::NumericVector theta(nrow);
-  Rcpp::NumericVector Sigma_theta(nrow);
-  
   // Now call our C function
 
   int iter = find_one_row_((double *) Sigma.begin(),
@@ -45,19 +42,15 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
   
   // Check whether feasible
 
-  double feasible_val = 0; 
-  double val;
-  for (irow=0; irow<nrow; irow++) {
-    val = Sigma_theta[irow];
-    if (irow == row) {
-      val -= 1.;
-    }
-    if (fabs(val) > feasible_val) {
-      feasible_val = fabs(val);
-    }
-  }
+  int kkt_check = check_KKT(theta.begin(),
+			    Sigma_theta.begin(),
+			    nrow,
+			    row,
+			    bound);
+
+  return(Rcpp::List::create(Rcpp::Named("soln") = theta,
+			    Rcpp::Named("Sigma_soln") = Sigma_theta,
+			    Rcpp::Named("iter") = iter,
+			    Rcpp::Named("kkt_check") = kkt_check));
 
-  return(Rcpp::List::create(Rcpp::Named("optsol") = theta,
-			    Rcpp::Named("feasible_val") = feasible_val,
-			    Rcpp::Named("iter") = iter));
 }
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index ed14cf5..958df0e 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -8,11 +8,18 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
 		  double *Sigma_theta,    /* Sigma times theta */
 		  int *ever_active,       /* Ever active set: 0-based */ 
 		  int *nactive_ptr,       /* Size of ever active set */
-		  int nrow,          /* How many rows in Sigma */
-		  double bound,      /* feasibility parameter */
+		  int nrow,               /* How many rows in Sigma */
+		  double bound,           /* feasibility parameter */
 		  double *theta,          /* current value */
-		  int maxiter,       /* how many iterations */
-		  int row);          /* which coordinate to update: 0-based */
+		  int maxiter,            /* how many iterations */
+		  int row);               /* which coordinate to update: 0-based */
+
+int check_KKT(double *theta,       /* current theta */
+	      double *Sigma_theta, /* Sigma times theta */
+	      int nrow,            /* how many rows in Sigma */
+	      int row,             /* which row: 0-based */
+	      double bound);       /* Lagrange multipler for \ell_1 */
+
 
 #ifdef __cplusplus
 }  /* extern "C" */

From a8805b8e0685773ca49f44832cff6a6d1db88906 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 17 Aug 2017 17:24:20 -0700
Subject: [PATCH 200/493] trying to find segfault

---
 selectiveInference/R/funs.fixed.R | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 592d61d..4724f08 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -298,6 +298,10 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
 
     while ((mu.stop != 1)&&(try.no<10)){
       last.beta <- beta
+      if (!is.null(output)) {
+         print(c('at least second try', try.no))
+      }
+      print(output)
       output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, soln_result=output) # uses a warm start
       beta <- output$soln
       iter <- output$iter

From abf03513c56d897078132d8922b1d03c95ff43db Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 08:14:31 -0700
Subject: [PATCH 201/493] BF: order of arguments to is_active, renamed the
 function as well

---
 selectiveInference/src/debias.c | 26 ++++++++++++++++++--------
 1 file changed, 18 insertions(+), 8 deletions(-)

diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index d2f671d..5701b4c 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -1,3 +1,4 @@
+#include <stdio.h>
 #include <math.h> // for fabs
 
 // Find an approximate row of \hat{Sigma}^{-1}
@@ -41,6 +42,7 @@ double objective(double *Sigma,       /* A covariance matrix: X^TX/n */
       active_col = *active_col_ptr;
       theta_col_ptr = ((double *) theta + active_col);
 
+      fprintf(stderr, "%d %d %d\n", active_row, active_col, nactive);
       Sigma_ptr = ((double *) Sigma + nrow * active_col + active_row); // Matrices are column-major order
 
       value += 0.5 * (*Sigma_ptr) * (*theta_row_ptr) * (*theta_col_ptr);
@@ -54,9 +56,11 @@ double objective(double *Sigma,       /* A covariance matrix: X^TX/n */
   return(value);
 }
 
-int is_active(int coord,
-	      int *nactive_ptr,
-	      int *ever_active) {
+// Check if active and add it to active list if necessary
+
+int update_ever_active(int coord,
+		       int *ever_active,
+		       int *nactive_ptr) {
   int iactive;
   int active_var;
   int nactive = *nactive_ptr;
@@ -65,9 +69,17 @@ int is_active(int coord,
   for (iactive=0; iactive<nactive; iactive++) {
     active_var = (*ever_active_ptr);
     if (active_var == coord) {
+
+      fprintf(stderr, "%d %d before\n", *nactive_ptr);
+      ever_active_ptr = ((int *) ever_active + *nactive_ptr);
+      *ever_active_ptr = coord;
+      *nactive_ptr += 1;
+      fprintf(stderr, "%d %d after\n", *nactive_ptr, coord);
+
       return(1);
     }
   }
+
   return(0);
 }
 
@@ -173,11 +185,7 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
 
   // Add to active set if necessary
 
-  if ((value != 0) && (is_active(coord, ever_active, nactive_ptr) == 0)) {
-    ever_active_ptr = ((int *) ever_active + *nactive_ptr);
-    *ever_active_ptr = coord;
-    *nactive_ptr += 1;
-  }
+  update_ever_active(coord, ever_active, nactive_ptr);
 
   // Update the linear term
 
@@ -231,6 +239,7 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
 
     // Update the diagonal first
 
+    fprintf(stderr, "wtf %d %d %d\n", row, icoord, *nactive_ptr);
     update_one_coord(Sigma,
 		     Sigma_diag,
 		     Sigma_theta,
@@ -252,6 +261,7 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
 					  
     for (icoord=0; icoord<nrow; icoord++) {
 
+      fprintf(stderr, "here %d %d %d\n", row, icoord, *nactive_ptr);
       update_one_coord(Sigma,
 		       Sigma_diag,
 		       Sigma_theta,

From 27af0d501ecca06e82013fb5f87fa04d7de45059 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 08:24:49 -0700
Subject: [PATCH 202/493] removing debug statements

---
 selectiveInference/R/funs.fixed.R |  4 ---
 selectiveInference/src/debias.c   | 50 ++++++++++++++++++-------------
 2 files changed, 29 insertions(+), 25 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 4724f08..592d61d 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -298,10 +298,6 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
 
     while ((mu.stop != 1)&&(try.no<10)){
       last.beta <- beta
-      if (!is.null(output)) {
-         print(c('at least second try', try.no))
-      }
-      print(output)
       output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, soln_result=output) # uses a warm start
       beta <- output$soln
       iter <- output$iter
diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index 5701b4c..08c46ff 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -1,4 +1,3 @@
-#include <stdio.h>
 #include <math.h> // for fabs
 
 // Find an approximate row of \hat{Sigma}^{-1}
@@ -42,7 +41,6 @@ double objective(double *Sigma,       /* A covariance matrix: X^TX/n */
       active_col = *active_col_ptr;
       theta_col_ptr = ((double *) theta + active_col);
 
-      fprintf(stderr, "%d %d %d\n", active_row, active_col, nactive);
       Sigma_ptr = ((double *) Sigma + nrow * active_col + active_row); // Matrices are column-major order
 
       value += 0.5 * (*Sigma_ptr) * (*theta_row_ptr) * (*theta_col_ptr);
@@ -70,11 +68,12 @@ int update_ever_active(int coord,
     active_var = (*ever_active_ptr);
     if (active_var == coord) {
 
-      fprintf(stderr, "%d %d before\n", *nactive_ptr);
+      // Add it to the active set and increment the 
+      // number of active variables
+
       ever_active_ptr = ((int *) ever_active + *nactive_ptr);
       *ever_active_ptr = coord;
       *nactive_ptr += 1;
-      fprintf(stderr, "%d %d after\n", *nactive_ptr, coord);
 
       return(1);
     }
@@ -126,7 +125,6 @@ int check_KKT(double *theta,       /* current theta */
   return(fail == 0);
 }
 
-
 double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n */
                         double *Sigma_diag,      /* Diagonal entries of Sigma */
                         double *Sigma_theta,     /* Sigma times theta */
@@ -136,7 +134,8 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
 			double bound,            /* feasibility parameter */
 			double *theta,           /* current value */
 			int row,                 /* which row: 0-based */
-			int coord)               /* which coordinate to update: 0-based */
+			int coord,               /* which coordinate to update: 0-based */
+			int is_active)           /* Is this part of ever_active */     
 {
 
   double delta;
@@ -185,7 +184,9 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
 
   // Add to active set if necessary
 
-  update_ever_active(coord, ever_active, nactive_ptr);
+  if (!is_active) {
+    update_ever_active(coord, ever_active, nactive_ptr);
+  }
 
   // Update the linear term
 
@@ -224,6 +225,8 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
 
   int iter = 0;
   int icoord = 0;
+  int iactive = 0;
+  int *active_ptr;
 
   double old_value = objective(Sigma,
 			       ever_active,
@@ -237,19 +240,24 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
 
   for (iter=0; iter<maxiter; iter++) {
 
-    // Update the diagonal first
+    // Update the active variables first
+
+    active_ptr = (int *) ever_active;
 
-    fprintf(stderr, "wtf %d %d %d\n", row, icoord, *nactive_ptr);
-    update_one_coord(Sigma,
-		     Sigma_diag,
-		     Sigma_theta,
-		     ever_active,
-		     nactive_ptr,
-		     nrow,
-		     bound,
-		     theta,
-		     row,
-		     row);
+    for (iactive=0; iactive < *nactive_ptr; iactive++) {
+      update_one_coord(Sigma,
+		       Sigma_diag,
+		       Sigma_theta,
+		       ever_active,
+		       nactive_ptr,
+		       nrow,
+		       bound,
+		       theta,
+		       row,
+		       *active_ptr,
+		       1);
+      active_ptr++;
+    }
 
     if (check_KKT(theta,
 		  Sigma_theta,
@@ -261,7 +269,6 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
 					  
     for (icoord=0; icoord<nrow; icoord++) {
 
-      fprintf(stderr, "here %d %d %d\n", row, icoord, *nactive_ptr);
       update_one_coord(Sigma,
 		       Sigma_diag,
 		       Sigma_theta,
@@ -271,7 +278,8 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
 		       bound,
 		       theta,
 		       row,
-		       icoord);
+		       icoord,
+		       0);
     }
 
     if (check_KKT(theta,

From 3a46ad8ee787a1beaddb5e234c218a29bd3f21c2 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 08:34:01 -0700
Subject: [PATCH 203/493] not using the C code

---
 selectiveInference/R/funs.fixed.R | 1 +
 1 file changed, 1 insertion(+)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 0bbc119..3c99951 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -295,6 +295,7 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
     incr <- 0;
     while ((mu.stop != 1)&&(try.no<10)){
       last.beta <- beta
+      useC = FALSE
       if (useC == FALSE) {
             output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
       } else {

From de103f7532e3c889efc9bb7391affce454de8c58 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 08:36:20 -0700
Subject: [PATCH 204/493] removing useC

---
 selectiveInference/R/funs.fixed.R | 12 +++---------
 1 file changed, 3 insertions(+), 9 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 3c99951..fe54457 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -159,8 +159,7 @@ sigma=NULL, alpha=0.1,
       hsigmaSinv <- solve(hsigmaS) # pinv(hsigmaS)
 
       # Approximate inverse covariance matrix for when (n < p) from lasso_Inference.R
-      useC = TRUE
-      htheta <- InverseLinfty(hsigma, n, length(S), verbose=FALSE, useC=useC)
+      htheta <- InverseLinfty(hsigma, n, length(S), verbose=FALSE)
       # htheta <- InverseLinfty(hsigma, n, verbose=FALSE)
       
       FS = rbind(diag(length(S)),matrix(0,pp-length(S),length(S)))
@@ -270,7 +269,7 @@ fixedLasso.poly=
 ### Functions borrowed and slightly modified from lasso_inference.R
 
 ## Approximates inverse covariance matrix theta
-InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE, useC = FALSE) {
+InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
   # InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
     isgiven <- 1;
   if (is.null(mu)){
@@ -295,12 +294,7 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
     incr <- 0;
     while ((mu.stop != 1)&&(try.no<10)){
       last.beta <- beta
-      useC = FALSE
-      if (useC == FALSE) {
-            output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
-      } else {
-            output <- InverseLinftyOneRowC(sigma, i, mu, maxiter=maxiter)
-      }
+      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
       beta <- output$optsol
       iter <- output$iter
       if (isgiven==1){

From d4875262900a90970daf16fe6e96368c6b1a50b0 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 08:43:12 -0700
Subject: [PATCH 205/493] BF: percent signs in example

---
 selectiveInference/man/fixedLassoInf.Rd | 35 +++++++++++++++++++++----
 1 file changed, 30 insertions(+), 5 deletions(-)

diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index d675c3f..3633524 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -145,7 +145,7 @@ p = 10
 sigma = 1
 
 x = matrix(rnorm(n*p),n,p)
-x=scale(x,TRUE,TRUE)
+x = scale(x,TRUE,TRUE)
 
 beta = c(3,2,rep(0,p-2))
 y = x\%*\%beta + sigma*rnorm(n)
@@ -165,10 +165,10 @@ out
 
 ## as above, but use lar function instead to get initial 
 ## lasso fit (should get same results)
- lfit = lar(x,y,normalize=FALSE)
- beta = coef(lfit,s=lambda,mode="lambda")
- out2 = fixedLassoInf(x,y,beta,lambda,sigma=sigma)
- out2
+lfit = lar(x,y,normalize=FALSE)
+beta = coef(lfit,s=lambda,mode="lambda")
+out2 = fixedLassoInf(x,y,beta,lambda,sigma=sigma)
+out2
 
 ## mimic different penalty factors by first scaling x
  set.seed(43)
@@ -249,5 +249,30 @@ status=sample(c(0,1),size=n,replace=TRUE)
      # compute fixed lambda p-values and selection intervals
      out = fixedLassoInf(x,tim,beta_hat,lambda,status=status,family="cox")
      out
+
+# Debiased lasso or "full"
+
+n = 50
+p = 100
+sigma = 1
+
+x = matrix(rnorm(n*p),n,p)
+x = scale(x,TRUE,TRUE)
+
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# first run glmnet
+gfit = glmnet(x, y, standardize=FALSE, intercept=FALSE)
+
+# extract coef for a given lambda; note the 1/n factor!
+# (and we don't save the intercept term)
+lambda = 2.8
+beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
+
+# compute fixed lambda p-values and selection intervals
+out = fixedLassoInf(x, y, beta, lambda, sigma=sigma, type='full', intercept=FALSE)
+out
+
 }
  
\ No newline at end of file

From 7360386212cb047287211545c3eca4f8e8b477f4 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 09:02:24 -0700
Subject: [PATCH 206/493] smaller changes in mu per step in line search

---
 selectiveInference/R/funs.fixed.R | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 592d61d..db5b915 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -270,8 +270,7 @@ fixedLasso.poly=
 ### Functions borrowed and slightly modified from lasso_inference.R
 
 ## Approximates inverse covariance matrix theta
-InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
-  # InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
+InverseLinfty <- function(sigma, n, e, resol=1.2, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
     isgiven <- 1;
   if (is.null(mu)){
     isgiven <- 0;

From a9a8bbbd4208086426a29ea3cd7dbaaacb807426 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 09:40:54 -0700
Subject: [PATCH 207/493] renaming Sigma_theta to gradient, making sure we loop
 through active set

---
 selectiveInference/src/debias.c | 35 +++++++++++++++++----------------
 1 file changed, 18 insertions(+), 17 deletions(-)

diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index 08c46ff..5b1592e 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -65,6 +65,7 @@ int update_ever_active(int coord,
   int *ever_active_ptr = ever_active;
 
   for (iactive=0; iactive<nactive; iactive++) {
+    ever_active_ptr = ((int *) ever_active + iactive);
     active_var = (*ever_active_ptr);
     if (active_var == coord) {
 
@@ -83,7 +84,7 @@ int update_ever_active(int coord,
 }
 
 int check_KKT(double *theta,       /* current theta */
-	      double *Sigma_theta, /* Sigma times theta */
+	      double *gradient_ptr, /* Sigma times theta */
 	      int nrow,            /* how many rows in Sigma */
 	      int row,             /* which row: 0-based */
 	      double bound)        /* Lagrange multipler for \ell_1 */
@@ -93,16 +94,16 @@ int check_KKT(double *theta,       /* current theta */
   int irow;
   int fail = 0;
   double tol = 1.e-4;
-  double *theta_ptr, *Sigma_theta_ptr;
+  double *theta_ptr, *gradient_ptr_tmp;
   double gradient;
 
   for (irow=0; irow<nrow; irow++) {
     theta_ptr = ((double *) theta + irow);
-    Sigma_theta_ptr = ((double *) Sigma_theta + irow);
+    gradient_ptr_tmp = ((double *) gradient_ptr + irow);
 
     // Compute this coordinate of the gradient
 
-    gradient = *Sigma_theta_ptr;
+    gradient = *gradient_ptr_tmp;
     if (row == irow) {
       gradient -= 1;
     }
@@ -127,7 +128,7 @@ int check_KKT(double *theta,       /* current theta */
 
 double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n */
                         double *Sigma_diag,      /* Diagonal entries of Sigma */
-                        double *Sigma_theta,     /* Sigma times theta */
+                        double *gradient_ptr,     /* Sigma times theta */
 			int *ever_active,        /* Ever active set: 0-based */ 
 			int *nactive_ptr,        /* Size of ever active set */
 			int nrow,                /* How many rows in Sigma */
@@ -143,7 +144,7 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
   double value = 0;
   double old_value;
   double *Sigma_ptr;
-  double *Sigma_theta_ptr;
+  double *gradient_ptr_tmp;
   double *theta_ptr;
   int icol = 0;
 
@@ -152,13 +153,13 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
 
   int *ever_active_ptr;
 
-  Sigma_theta_ptr = ((double *) Sigma_theta + coord);
-  linear_term = *Sigma_theta_ptr;
+  gradient_ptr_tmp = ((double *) gradient_ptr + coord);
+  linear_term = *gradient_ptr_tmp;
 
   theta_ptr = ((double *) theta + coord);
   old_value = *theta_ptr;
 
-  // The coord entry of Sigma_theta term has a diagonal term in it:
+  // The coord entry of gradient_ptr term has a diagonal term in it:
   // Sigma[coord, coord] * theta[coord]
   // This removes it. 
   linear_term -= quadratic_term * old_value;
@@ -194,11 +195,11 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
 
     delta = value - old_value;
     Sigma_ptr = ((double *) Sigma + coord * nrow);
-    Sigma_theta_ptr = ((double *) Sigma_theta);
+    gradient_ptr_tmp = ((double *) gradient_ptr);
 
     for (icol=0; icol<nrow; icol++) {
-      *Sigma_theta_ptr = *Sigma_theta_ptr + delta * (*Sigma_ptr);
-      Sigma_theta_ptr += 1;
+      *gradient_ptr_tmp = *gradient_ptr_tmp + delta * (*Sigma_ptr);
+      gradient_ptr_tmp += 1;
       Sigma_ptr += 1;
     }
 
@@ -213,7 +214,7 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
 
 int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
 		  double *Sigma_diag,     /* Diagonal entry of covariance matrix */
-		  double *Sigma_theta,    /* Sigma times theta */
+		  double *gradient_ptr,    /* Sigma times theta */
 		  int *ever_active,       /* Ever active set: 0-based */ 
 		  int *nactive_ptr,       /* Size of ever active set */
 		  int nrow,               /* How many rows in Sigma */
@@ -247,7 +248,7 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
     for (iactive=0; iactive < *nactive_ptr; iactive++) {
       update_one_coord(Sigma,
 		       Sigma_diag,
-		       Sigma_theta,
+		       gradient_ptr,
 		       ever_active,
 		       nactive_ptr,
 		       nrow,
@@ -260,7 +261,7 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
     }
 
     if (check_KKT(theta,
-		  Sigma_theta,
+		  gradient_ptr,
 		  nrow,
 		  row,
 		  bound) == 1) {
@@ -271,7 +272,7 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
 
       update_one_coord(Sigma,
 		       Sigma_diag,
-		       Sigma_theta,
+		       gradient_ptr,
 		       ever_active,
 		       nactive_ptr,
 		       nrow,
@@ -283,7 +284,7 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
     }
 
     if (check_KKT(theta,
-		  Sigma_theta,
+		  gradient_ptr,
 		  nrow,
 		  row,
 		  bound) == 1) {

From 7f1e4a639de211eacd0eecd2a5f4224df035e7b8 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 09:50:51 -0700
Subject: [PATCH 208/493] more renaming

---
 selectiveInference/src/debias.c | 36 ++++++++++++++++-----------------
 selectiveInference/src/debias.h | 14 ++++++-------
 2 files changed, 25 insertions(+), 25 deletions(-)

diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index 5b1592e..e043934 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -11,7 +11,7 @@
 // Update one coordinate 
 
 double objective(double *Sigma,       /* A covariance matrix: X^TX/n */
-		 int *ever_active,    /* Ever active set: 0-based */ 
+		 int *ever_active_ptr,    /* Ever active set: 0-based */ 
 		 int *nactive_ptr,    /* Size of ever active set */
 		 int nrow,            /* how many rows in Sigma */
 		 int row,             /* which row: 0-based */
@@ -31,13 +31,13 @@ double objective(double *Sigma,       /* A covariance matrix: X^TX/n */
 
   for (irow=0; irow<nactive; irow++) {
 
-    active_row_ptr = ((int *) ever_active + irow);
+    active_row_ptr = ((int *) ever_active_ptr + irow);
     active_row = *active_row_ptr;
     theta_row_ptr = ((double *) theta + active_row);
 
     for (icol=0; icol<nactive; icol++) {
       
-      active_col_ptr = ((int *) ever_active + icol);
+      active_col_ptr = ((int *) ever_active_ptr + icol);
       active_col = *active_col_ptr;
       theta_col_ptr = ((double *) theta + active_col);
 
@@ -57,23 +57,23 @@ double objective(double *Sigma,       /* A covariance matrix: X^TX/n */
 // Check if active and add it to active list if necessary
 
 int update_ever_active(int coord,
-		       int *ever_active,
+		       int *ever_active_ptr,
 		       int *nactive_ptr) {
   int iactive;
   int active_var;
   int nactive = *nactive_ptr;
-  int *ever_active_ptr = ever_active;
+  int *ever_active_ptr_tmp = ever_active_ptr;
 
   for (iactive=0; iactive<nactive; iactive++) {
-    ever_active_ptr = ((int *) ever_active + iactive);
-    active_var = (*ever_active_ptr);
+    ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
+    active_var = (*ever_active_ptr_tmp);
     if (active_var == coord) {
 
       // Add it to the active set and increment the 
       // number of active variables
 
-      ever_active_ptr = ((int *) ever_active + *nactive_ptr);
-      *ever_active_ptr = coord;
+      ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
+      *ever_active_ptr_tmp = coord;
       *nactive_ptr += 1;
 
       return(1);
@@ -129,7 +129,7 @@ int check_KKT(double *theta,       /* current theta */
 double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n */
                         double *Sigma_diag,      /* Diagonal entries of Sigma */
                         double *gradient_ptr,     /* Sigma times theta */
-			int *ever_active,        /* Ever active set: 0-based */ 
+			int *ever_active_ptr,        /* Ever active set: 0-based */ 
 			int *nactive_ptr,        /* Size of ever active set */
 			int nrow,                /* How many rows in Sigma */
 			double bound,            /* feasibility parameter */
@@ -151,7 +151,7 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
   double *quadratic_ptr = ((double *) Sigma_diag + coord);
   double quadratic_term = *quadratic_ptr;
 
-  int *ever_active_ptr;
+  // int *ever_active_ptr_tmp;
 
   gradient_ptr_tmp = ((double *) gradient_ptr + coord);
   linear_term = *gradient_ptr_tmp;
@@ -186,7 +186,7 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
   // Add to active set if necessary
 
   if (!is_active) {
-    update_ever_active(coord, ever_active, nactive_ptr);
+    update_ever_active(coord, ever_active_ptr, nactive_ptr);
   }
 
   // Update the linear term
@@ -215,7 +215,7 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
 int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
 		  double *Sigma_diag,     /* Diagonal entry of covariance matrix */
 		  double *gradient_ptr,    /* Sigma times theta */
-		  int *ever_active,       /* Ever active set: 0-based */ 
+		  int *ever_active_ptr,       /* Ever active set: 0-based */ 
 		  int *nactive_ptr,       /* Size of ever active set */
 		  int nrow,               /* How many rows in Sigma */
 		  double bound,           /* feasibility parameter */
@@ -230,7 +230,7 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
   int *active_ptr;
 
   double old_value = objective(Sigma,
-			       ever_active,
+			       ever_active_ptr,
 			       nactive_ptr,
 			       nrow,
 			       row,
@@ -243,13 +243,13 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
 
     // Update the active variables first
 
-    active_ptr = (int *) ever_active;
+    active_ptr = (int *) ever_active_ptr;
 
     for (iactive=0; iactive < *nactive_ptr; iactive++) {
       update_one_coord(Sigma,
 		       Sigma_diag,
 		       gradient_ptr,
-		       ever_active,
+		       ever_active_ptr,
 		       nactive_ptr,
 		       nrow,
 		       bound,
@@ -273,7 +273,7 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
       update_one_coord(Sigma,
 		       Sigma_diag,
 		       gradient_ptr,
-		       ever_active,
+		       ever_active_ptr,
 		       nactive_ptr,
 		       nrow,
 		       bound,
@@ -292,7 +292,7 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
     }
 					  
     new_value = objective(Sigma,
-			  ever_active,
+			  ever_active_ptr,
 			  nactive_ptr,
 			  nrow,
 			  row,
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index 958df0e..f7b46c3 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -5,8 +5,8 @@ extern "C"
 
 int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
 		  double *Sigma_diag,     /* Diagonal entry of covariance matrix */
-		  double *Sigma_theta,    /* Sigma times theta */
-		  int *ever_active,       /* Ever active set: 0-based */ 
+		  double *gradient_ptr,   /* Current gradient of quadratic loss */
+		  int *ever_active_ptr,       /* Ever active set: 0-based */ 
 		  int *nactive_ptr,       /* Size of ever active set */
 		  int nrow,               /* How many rows in Sigma */
 		  double bound,           /* feasibility parameter */
@@ -14,11 +14,11 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
 		  int maxiter,            /* how many iterations */
 		  int row);               /* which coordinate to update: 0-based */
 
-int check_KKT(double *theta,       /* current theta */
-	      double *Sigma_theta, /* Sigma times theta */
-	      int nrow,            /* how many rows in Sigma */
-	      int row,             /* which row: 0-based */
-	      double bound);       /* Lagrange multipler for \ell_1 */
+int check_KKT(double *theta,           /* current theta */
+	      double *gradient_ptr,    /* Current gradient of quadratic loss */
+	      int nrow,                /* how many rows in Sigma */
+	      int row,                 /* which row: 0-based */
+	      double bound);           /* Lagrange multipler for \ell_1 */
 
 
 #ifdef __cplusplus

From a4055ce41869e3db8eb1a26fe86a6587ef3549d4 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 10:11:38 -0700
Subject: [PATCH 209/493] BF: forgot to rename in Rcpp file, passing active and
 ever_active from R

---
 selectiveInference/R/funs.fixed.R      | 14 ++++++++++----
 selectiveInference/src/Rcpp-debias.cpp | 23 ++++++++++++-----------
 2 files changed, 22 insertions(+), 15 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index db5b915..460cd0b 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -341,18 +341,24 @@ InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL) {
 
   # If soln_result is not Null, it is used as a warm start.
   # It should be a list
-  # with entries "soln" and "Sigma_soln"
+  # with entries "soln", "gradient", "ever_active", "nactive"
 
   if (is.null(soln_result)) {
      soln = rep(0, nrow(Sigma))
-     Sigma_soln = rep(0, nrow(Sigma)) 
+     gradient = rep(0, nrow(Sigma)) 
+     ever_active = rep(0, nrow(Sigma))
+     ever_active[1] = i-1             # 0-based
+     ever_active = as.integer(ever_active)
+     nactive = as.integer(1)
   }
   else {
      soln = soln_result$soln
-     Sigma_soln = soln_result$Sigma_soln  
+     gradient = soln_result$gradient  
+     ever_active = as.integer(soln_result$ever_active)
+     nactive = as.integer(soln_result$nactive)
   }
 
-  result = find_one_row_debiasingM(Sigma, i-1, mu, maxiter, soln, Sigma_soln) # C function uses 0-based indexing
+  result = find_one_row_debiasingM(Sigma, i-1, mu, maxiter, soln, gradient, ever_active, nactive) # C function uses 0-based indexing
 
   # Check feasibility
 
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index b529c4a..b6c80d2 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -7,31 +7,30 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
 				   double bound,
 				   int maxiter,
 				   Rcpp::NumericVector theta,
-				   Rcpp::NumericVector Sigma_theta) {
+				   Rcpp::NumericVector gradient,
+				   Rcpp::IntegerVector ever_active,
+				   Rcpp::IntegerVector nactive
+				   ) {
 
   int nrow = Sigma.nrow(); // number of features
 
   // Active set
 
   int irow;
-  Rcpp::IntegerVector nactive(1); // An array so we can easily modify it
-  Rcpp::IntegerVector ever_active(1);
-  int *ever_active_p = ever_active.begin();
-  *ever_active_p = row;
 
   // Extract the diagonal
   Rcpp::NumericVector Sigma_diag(nrow);
-  double *sigma_p = Sigma_diag.begin();
+  double *sigma_diag_p = Sigma_diag.begin();
 
   for (irow=0; irow<nrow; irow++) {
-    sigma_p[irow] = Sigma(irow, irow);
+    sigma_diag_p[irow] = Sigma(irow, irow);
   }
   
   // Now call our C function
 
   int iter = find_one_row_((double *) Sigma.begin(),
 			   (double *) Sigma_diag.begin(),
-			   (double *) Sigma_theta.begin(),
+			   (double *) gradient.begin(),
 			   (int *) ever_active.begin(),
 			   (int *) nactive.begin(),
 			   nrow,
@@ -43,14 +42,16 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
   // Check whether feasible
 
   int kkt_check = check_KKT(theta.begin(),
-			    Sigma_theta.begin(),
+			    gradient.begin(),
 			    nrow,
 			    row,
 			    bound);
 
   return(Rcpp::List::create(Rcpp::Named("soln") = theta,
-			    Rcpp::Named("Sigma_soln") = Sigma_theta,
+			    Rcpp::Named("gradient") = gradient,
 			    Rcpp::Named("iter") = iter,
-			    Rcpp::Named("kkt_check") = kkt_check));
+			    Rcpp::Named("kkt_check") = kkt_check,
+			    Rcpp::Named("ever_active") = ever_active,
+			    Rcpp::Named("nactive") = nactive));
 
 }

From f432b42366f33fc8991f3645ef59244467a20dfe Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 10:46:39 -0700
Subject: [PATCH 210/493] more renaming

---
 selectiveInference/src/debias.c | 44 +++++++++++++++++++--------------
 selectiveInference/src/debias.h | 18 +++++++-------
 2 files changed, 34 insertions(+), 28 deletions(-)

diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index e043934..1b841e0 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -10,7 +10,7 @@
 // Therefore we don't have to negate the answer to get theta.
 // Update one coordinate 
 
-double objective(double *Sigma,       /* A covariance matrix: X^TX/n */
+double objective(double *Sigma_ptr,       /* A covariance matrix: X^TX/n */
 		 int *ever_active_ptr,    /* Ever active set: 0-based */ 
 		 int *nactive_ptr,    /* Size of ever active set */
 		 int nrow,            /* how many rows in Sigma */
@@ -20,7 +20,7 @@ double objective(double *Sigma,       /* A covariance matrix: X^TX/n */
 {
   int irow, icol;
   double value = 0;
-  double *Sigma_ptr = Sigma;
+  double *Sigma_ptr_tmp = Sigma_ptr;
   double *theta_row_ptr, *theta_col_ptr;
   int *active_row_ptr, *active_col_ptr;
   int active_row, active_col;
@@ -41,9 +41,9 @@ double objective(double *Sigma,       /* A covariance matrix: X^TX/n */
       active_col = *active_col_ptr;
       theta_col_ptr = ((double *) theta + active_col);
 
-      Sigma_ptr = ((double *) Sigma + nrow * active_col + active_row); // Matrices are column-major order
+      Sigma_ptr_tmp = ((double *) Sigma_ptr + nrow * active_col + active_row); // Matrices are column-major order
 
-      value += 0.5 * (*Sigma_ptr) * (*theta_row_ptr) * (*theta_col_ptr);
+      value += 0.5 * (*Sigma_ptr_tmp) * (*theta_row_ptr) * (*theta_col_ptr);
     }
     value = value + bound * fabs((*theta_row_ptr)); // the \ell_1 term
   }
@@ -126,8 +126,8 @@ int check_KKT(double *theta,       /* current theta */
   return(fail == 0);
 }
 
-double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n */
-                        double *Sigma_diag,      /* Diagonal entries of Sigma */
+double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^TX/n */
+                        double *Sigma_diag_ptr,      /* Diagonal entries of Sigma */
                         double *gradient_ptr,     /* Sigma times theta */
 			int *ever_active_ptr,        /* Ever active set: 0-based */ 
 			int *nactive_ptr,        /* Size of ever active set */
@@ -143,12 +143,12 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
   double linear_term = 0;
   double value = 0;
   double old_value;
-  double *Sigma_ptr;
+  double *Sigma_ptr_tmp;
   double *gradient_ptr_tmp;
   double *theta_ptr;
   int icol = 0;
 
-  double *quadratic_ptr = ((double *) Sigma_diag + coord);
+  double *quadratic_ptr = ((double *) Sigma_diag_ptr + coord);
   double quadratic_term = *quadratic_ptr;
 
   // int *ever_active_ptr_tmp;
@@ -194,13 +194,13 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
   if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
 
     delta = value - old_value;
-    Sigma_ptr = ((double *) Sigma + coord * nrow);
+    Sigma_ptr_tmp = ((double *) Sigma_ptr + coord * nrow);
     gradient_ptr_tmp = ((double *) gradient_ptr);
 
     for (icol=0; icol<nrow; icol++) {
-      *gradient_ptr_tmp = *gradient_ptr_tmp + delta * (*Sigma_ptr);
+      *gradient_ptr_tmp = *gradient_ptr_tmp + delta * (*Sigma_ptr_tmp);
       gradient_ptr_tmp += 1;
-      Sigma_ptr += 1;
+      Sigma_ptr_tmp += 1;
     }
 
     theta_ptr = ((double *) theta + coord);
@@ -212,8 +212,8 @@ double update_one_coord(double *Sigma,           /* A covariance matrix: X^TX/n
 
 }
 
-int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
-		  double *Sigma_diag,     /* Diagonal entry of covariance matrix */
+int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
+		  double *Sigma_diag_ptr,     /* Diagonal entry of covariance matrix */
 		  double *gradient_ptr,    /* Sigma times theta */
 		  int *ever_active_ptr,       /* Ever active set: 0-based */ 
 		  int *nactive_ptr,       /* Size of ever active set */
@@ -229,7 +229,7 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
   int iactive = 0;
   int *active_ptr;
 
-  double old_value = objective(Sigma,
+  double old_value = objective(Sigma_ptr,
 			       ever_active_ptr,
 			       nactive_ptr,
 			       nrow,
@@ -246,8 +246,8 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
     active_ptr = (int *) ever_active_ptr;
 
     for (iactive=0; iactive < *nactive_ptr; iactive++) {
-      update_one_coord(Sigma,
-		       Sigma_diag,
+      update_one_coord(Sigma_ptr,
+		       Sigma_diag_ptr,
 		       gradient_ptr,
 		       ever_active_ptr,
 		       nactive_ptr,
@@ -260,6 +260,8 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
       active_ptr++;
     }
 
+    // Check KKT
+
     if (check_KKT(theta,
 		  gradient_ptr,
 		  nrow,
@@ -268,10 +270,12 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
       break;
     }
 					  
+    // Update all variables 
+
     for (icoord=0; icoord<nrow; icoord++) {
 
-      update_one_coord(Sigma,
-		       Sigma_diag,
+      update_one_coord(Sigma_ptr,
+		       Sigma_diag_ptr,
 		       gradient_ptr,
 		       ever_active_ptr,
 		       nactive_ptr,
@@ -283,6 +287,8 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
 		       0);
     }
 
+    // Check KKT
+
     if (check_KKT(theta,
 		  gradient_ptr,
 		  nrow,
@@ -291,7 +297,7 @@ int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
       break;
     }
 					  
-    new_value = objective(Sigma,
+    new_value = objective(Sigma_ptr,
 			  ever_active_ptr,
 			  nactive_ptr,
 			  nrow,
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index f7b46c3..eb8c59c 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -3,16 +3,16 @@ extern "C"
 {
 #endif /* __cplusplus */
 
-int find_one_row_(double *Sigma,          /* A covariance matrix: X^TX/n */
-		  double *Sigma_diag,     /* Diagonal entry of covariance matrix */
-		  double *gradient_ptr,   /* Current gradient of quadratic loss */
+int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
+		  double *Sigma_ptr_ptr,     /* Diagonal entry of covariance matrix */
+		  double *gradient_ptr,       /* Current gradient of quadratic loss */
 		  int *ever_active_ptr,       /* Ever active set: 0-based */ 
-		  int *nactive_ptr,       /* Size of ever active set */
-		  int nrow,               /* How many rows in Sigma */
-		  double bound,           /* feasibility parameter */
-		  double *theta,          /* current value */
-		  int maxiter,            /* how many iterations */
-		  int row);               /* which coordinate to update: 0-based */
+		  int *nactive_ptr,           /* Size of ever active set */
+		  int nrow,                   /* How many rows in Sigma */
+		  double bound,               /* feasibility parameter */
+		  double *theta,              /* current value */
+		  int maxiter,                /* how many iterations */
+		  int row);                   /* which coordinate to update: 0-based */
 
 int check_KKT(double *theta,           /* current theta */
 	      double *gradient_ptr,    /* Current gradient of quadratic loss */

From fcd8206d4c78e595350d7550271bbf1f11d9b2ff Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 11:28:31 -0700
Subject: [PATCH 211/493] trying to get arbitrary linfunc

---
 selectiveInference/R/funs.fixed.R      | 12 +++-
 selectiveInference/src/Rcpp-debias.cpp |  2 +
 selectiveInference/src/debias.c        | 87 +++++++++++++++-----------
 selectiveInference/src/debias.h        |  3 +-
 4 files changed, 65 insertions(+), 39 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 460cd0b..0b6c018 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -297,6 +297,7 @@ InverseLinfty <- function(sigma, n, e, resol=1.2, mu=NULL, maxiter=50, threshold
 
     while ((mu.stop != 1)&&(try.no<10)){
       last.beta <- beta
+      #print(c("trying ", try.no))
       output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, soln_result=output) # uses a warm start
       beta <- output$soln
       iter <- output$iter
@@ -358,7 +359,16 @@ InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL) {
      nactive = as.integer(soln_result$nactive)
   }
 
-  result = find_one_row_debiasingM(Sigma, i-1, mu, maxiter, soln, gradient, ever_active, nactive) # C function uses 0-based indexing
+  linear_func = rep(0, p)
+  linear_func[i] = -1
+  linear_func = as.numeric(linear_func)
+
+  #print(c(soln, "soln"))
+  #print(c(gradient, "grad"))
+  #print(c(ever_active, "ever_active"))
+  #print(c(nactive, "nactive"))
+  #print(c(linear_func, "linear_func"))
+  result = find_one_row_debiasingM(Sigma, i-1, mu, maxiter, soln, linear_func, gradient, ever_active, nactive) # C function uses 0-based indexing
 
   # Check feasibility
 
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index b6c80d2..83dd09c 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -7,6 +7,7 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
 				   double bound,
 				   int maxiter,
 				   Rcpp::NumericVector theta,
+				   Rcpp::NumericVector linear_func,
 				   Rcpp::NumericVector gradient,
 				   Rcpp::IntegerVector ever_active,
 				   Rcpp::IntegerVector nactive
@@ -29,6 +30,7 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
   // Now call our C function
 
   int iter = find_one_row_((double *) Sigma.begin(),
+			   (double *) linear_func.begin(),
 			   (double *) Sigma_diag.begin(),
 			   (double *) gradient.begin(),
 			   (int *) ever_active.begin(),
diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index 1b841e0..0b34436 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -1,3 +1,4 @@
+#include <stdio.h>
 #include <math.h> // for fabs
 
 // Find an approximate row of \hat{Sigma}^{-1}
@@ -11,16 +12,18 @@
 // Update one coordinate 
 
 double objective(double *Sigma_ptr,       /* A covariance matrix: X^TX/n */
+		 double *linear_func_ptr, /* Linear term in objective */
 		 int *ever_active_ptr,    /* Ever active set: 0-based */ 
-		 int *nactive_ptr,    /* Size of ever active set */
-		 int nrow,            /* how many rows in Sigma */
-		 int row,             /* which row: 0-based */
-		 double bound,        /* Lagrange multipler for \ell_1 */
-		 double *theta)       /* current value */
+		 int *nactive_ptr,        /* Size of ever active set */
+		 int nrow,                /* how many rows in Sigma */
+		 int row,                 /* which row: 0-based */
+		 double bound,            /* Lagrange multipler for \ell_1 */
+		 double *theta)           /* current value */
 {
   int irow, icol;
   double value = 0;
   double *Sigma_ptr_tmp = Sigma_ptr;
+  double *linear_func_ptr_tmp = linear_func_ptr;
   double *theta_row_ptr, *theta_col_ptr;
   int *active_row_ptr, *active_col_ptr;
   int active_row, active_col;
@@ -45,12 +48,15 @@ double objective(double *Sigma_ptr,       /* A covariance matrix: X^TX/n */
 
       value += 0.5 * (*Sigma_ptr_tmp) * (*theta_row_ptr) * (*theta_col_ptr);
     }
-    value = value + bound * fabs((*theta_row_ptr)); // the \ell_1 term
-  }
+    value += bound * fabs((*theta_row_ptr)); // the \ell_1 term
+
+    // The linear term in the objective
 
-  theta_row_ptr = ((double *) theta + row);
-  value -= (*theta_row_ptr); // the elementary basis vector term
+    value += (*linear_func_ptr_tmp) * (*theta_row_ptr); 
+    linear_func_ptr_tmp++;
 
+  }
+  
   return(value);
 }
 
@@ -66,19 +72,22 @@ int update_ever_active(int coord,
 
   for (iactive=0; iactive<nactive; iactive++) {
     ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
-    active_var = (*ever_active_ptr_tmp);
+    active_var = *ever_active_ptr_tmp;
     if (active_var == coord) {
-
-      // Add it to the active set and increment the 
-      // number of active variables
-
-      ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
-      *ever_active_ptr_tmp = coord;
-      *nactive_ptr += 1;
-
+      // fprintf(stderr, "blah %d %d %d\n", coord, active_var, *nactive_ptr);
       return(1);
     }
   }
+  
+  // If we haven't returned yet, this means the coord was not in 
+  // ever_active.
+
+  // Add it to the active set and increment the 
+  // number of active variables
+
+  ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
+  *ever_active_ptr_tmp = coord;
+  *nactive_ptr += 1;
 
   return(0);
 }
@@ -127,16 +136,17 @@ int check_KKT(double *theta,       /* current theta */
 }
 
 double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^TX/n */
+			double *linear_func_ptr,     /* Linear term in objective */
                         double *Sigma_diag_ptr,      /* Diagonal entries of Sigma */
-                        double *gradient_ptr,     /* Sigma times theta */
+                        double *gradient_ptr,        /* Sigma times theta */
 			int *ever_active_ptr,        /* Ever active set: 0-based */ 
-			int *nactive_ptr,        /* Size of ever active set */
-			int nrow,                /* How many rows in Sigma */
-			double bound,            /* feasibility parameter */
-			double *theta,           /* current value */
-			int row,                 /* which row: 0-based */
-			int coord,               /* which coordinate to update: 0-based */
-			int is_active)           /* Is this part of ever_active */     
+			int *nactive_ptr,            /* Size of ever active set */
+			int nrow,                    /* How many rows in Sigma */
+			double bound,                /* feasibility parameter */
+			double *theta,               /* current value */
+			int row,                     /* which row: 0-based */
+			int coord,                   /* which coordinate to update: 0-based */
+			int is_active)               /* Is this part of ever_active */     
 {
 
   double delta;
@@ -164,9 +174,7 @@ double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^T
   // This removes it. 
   linear_term -= quadratic_term * old_value;
 
-  if (row == coord) {
-    linear_term -= 1;
-  }
+  linear_term += *((double *) linear_func_ptr + coord);
 
   // Now soft-threshold the coord entry of theta 
 
@@ -185,7 +193,7 @@ double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^T
 
   // Add to active set if necessary
 
-  if (!is_active) {
+  if (is_active == 0) {
     update_ever_active(coord, ever_active_ptr, nactive_ptr);
   }
 
@@ -213,15 +221,16 @@ double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^T
 }
 
 int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
+ 		  double *linear_func_ptr,    /* Linear term in objective */
 		  double *Sigma_diag_ptr,     /* Diagonal entry of covariance matrix */
-		  double *gradient_ptr,    /* Sigma times theta */
+		  double *gradient_ptr,       /* Sigma times theta */
 		  int *ever_active_ptr,       /* Ever active set: 0-based */ 
-		  int *nactive_ptr,       /* Size of ever active set */
-		  int nrow,               /* How many rows in Sigma */
-		  double bound,           /* feasibility parameter */
-		  double *theta,          /* current value */
-		  int maxiter,            /* how many iterations */
-		  int row)                /* which coordinate to update: 0-based */
+		  int *nactive_ptr,           /* Size of ever active set */
+		  int nrow,                   /* How many rows in Sigma */
+		  double bound,               /* feasibility parameter */
+		  double *theta,              /* current value */
+		  int maxiter,                /* how many iterations */
+		  int row)                    /* which coordinate to update: 0-based */
 {
 
   int iter = 0;
@@ -230,6 +239,7 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
   int *active_ptr;
 
   double old_value = objective(Sigma_ptr,
+			       linear_func_ptr,
 			       ever_active_ptr,
 			       nactive_ptr,
 			       nrow,
@@ -247,6 +257,7 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 
     for (iactive=0; iactive < *nactive_ptr; iactive++) {
       update_one_coord(Sigma_ptr,
+		       linear_func_ptr,
 		       Sigma_diag_ptr,
 		       gradient_ptr,
 		       ever_active_ptr,
@@ -275,6 +286,7 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
     for (icoord=0; icoord<nrow; icoord++) {
 
       update_one_coord(Sigma_ptr,
+		       linear_func_ptr,
 		       Sigma_diag_ptr,
 		       gradient_ptr,
 		       ever_active_ptr,
@@ -298,6 +310,7 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
     }
 					  
     new_value = objective(Sigma_ptr,
+			  linear_func_ptr,
 			  ever_active_ptr,
 			  nactive_ptr,
 			  nrow,
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index eb8c59c..c20195c 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -4,7 +4,8 @@ extern "C"
 #endif /* __cplusplus */
 
 int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
-		  double *Sigma_ptr_ptr,     /* Diagonal entry of covariance matrix */
+ 		  double *linear_func_ptr,    /* Linear term in objective */
+		  double *Sigma_diag_ptr,     /* Diagonal entry of covariance matrix */
 		  double *gradient_ptr,       /* Current gradient of quadratic loss */
 		  int *ever_active_ptr,       /* Ever active set: 0-based */ 
 		  int *nactive_ptr,           /* Size of ever active set */

From a67d29fa8d131c3ff278a30a61f7afbdd64cd7cd Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 12:09:13 -0700
Subject: [PATCH 212/493] can solve arbitrary LASSO now

---
 selectiveInference/R/funs.fixed.R      | 11 +++++----
 selectiveInference/src/Rcpp-debias.cpp |  1 +
 selectiveInference/src/debias.c        | 32 +++++++-------------------
 selectiveInference/src/debias.h        |  6 ++---
 4 files changed, 18 insertions(+), 32 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 0b6c018..06c7a7d 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -346,28 +346,29 @@ InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL) {
 
   if (is.null(soln_result)) {
      soln = rep(0, nrow(Sigma))
-     gradient = rep(0, nrow(Sigma)) 
      ever_active = rep(0, nrow(Sigma))
      ever_active[1] = i-1             # 0-based
      ever_active = as.integer(ever_active)
      nactive = as.integer(1)
+     linear_func = rep(0, p)
+     linear_func[i] = -1
+     linear_func = as.numeric(linear_func)
+     gradient = 1. * linear_func
   }
   else {
      soln = soln_result$soln
      gradient = soln_result$gradient  
      ever_active = as.integer(soln_result$ever_active)
      nactive = as.integer(soln_result$nactive)
+     linear_func = soln_result$linear_func
   }
 
-  linear_func = rep(0, p)
-  linear_func[i] = -1
-  linear_func = as.numeric(linear_func)
-
   #print(c(soln, "soln"))
   #print(c(gradient, "grad"))
   #print(c(ever_active, "ever_active"))
   #print(c(nactive, "nactive"))
   #print(c(linear_func, "linear_func"))
+
   result = find_one_row_debiasingM(Sigma, i-1, mu, maxiter, soln, linear_func, gradient, ever_active, nactive) # C function uses 0-based indexing
 
   # Check feasibility
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index 83dd09c..be02abd 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -51,6 +51,7 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
 
   return(Rcpp::List::create(Rcpp::Named("soln") = theta,
 			    Rcpp::Named("gradient") = gradient,
+			    Rcpp::Named("linear_func") = linear_func,
 			    Rcpp::Named("iter") = iter,
 			    Rcpp::Named("kkt_check") = kkt_check,
 			    Rcpp::Named("ever_active") = ever_active,
diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index 0b34436..b0669f8 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -5,7 +5,8 @@
 
 // Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
 
-// Dual problem: \text{min}_{\theta} 1/2 \theta^T \Sigma \theta - e_i^T\theta + \mu \|\theta\|_1
+// Dual problem: \text{min}_{\theta} 1/2 \theta^T \Sigma \theta - l^T\theta + \mu \|\theta\|_1
+// where l is `linear_func` below
 
 // This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
 // Therefore we don't have to negate the answer to get theta.
@@ -16,7 +17,6 @@ double objective(double *Sigma_ptr,       /* A covariance matrix: X^TX/n */
 		 int *ever_active_ptr,    /* Ever active set: 0-based */ 
 		 int *nactive_ptr,        /* Size of ever active set */
 		 int nrow,                /* how many rows in Sigma */
-		 int row,                 /* which row: 0-based */
 		 double bound,            /* Lagrange multipler for \ell_1 */
 		 double *theta)           /* current value */
 {
@@ -52,8 +52,8 @@ double objective(double *Sigma_ptr,       /* A covariance matrix: X^TX/n */
 
     // The linear term in the objective
 
+    linear_func_ptr_tmp = ((double *) linear_func_ptr + active_row);
     value += (*linear_func_ptr_tmp) * (*theta_row_ptr); 
-    linear_func_ptr_tmp++;
 
   }
   
@@ -74,7 +74,6 @@ int update_ever_active(int coord,
     ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
     active_var = *ever_active_ptr_tmp;
     if (active_var == coord) {
-      // fprintf(stderr, "blah %d %d %d\n", coord, active_var, *nactive_ptr);
       return(1);
     }
   }
@@ -92,11 +91,10 @@ int update_ever_active(int coord,
   return(0);
 }
 
-int check_KKT(double *theta,       /* current theta */
+int check_KKT(double *theta,        /* current theta */
 	      double *gradient_ptr, /* Sigma times theta */
-	      int nrow,            /* how many rows in Sigma */
-	      int row,             /* which row: 0-based */
-	      double bound)        /* Lagrange multipler for \ell_1 */
+	      int nrow,             /* how many rows in Sigma */
+	      double bound)         /* Lagrange multipler for \ell_1 */
 {
   // First check inactive
 
@@ -113,9 +111,6 @@ int check_KKT(double *theta,       /* current theta */
     // Compute this coordinate of the gradient
 
     gradient = *gradient_ptr_tmp;
-    if (row == irow) {
-      gradient -= 1;
-    }
 
     if (*theta_ptr != 0) { // these coordinates of gradients should be equal to -bound
       if ((*theta_ptr > 0) &&  (fabs(gradient + bound) > (1. + tol) * bound)) {
@@ -144,7 +139,6 @@ double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^T
 			int nrow,                    /* How many rows in Sigma */
 			double bound,                /* feasibility parameter */
 			double *theta,               /* current value */
-			int row,                     /* which row: 0-based */
 			int coord,                   /* which coordinate to update: 0-based */
 			int is_active)               /* Is this part of ever_active */     
 {
@@ -161,8 +155,6 @@ double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^T
   double *quadratic_ptr = ((double *) Sigma_diag_ptr + coord);
   double quadratic_term = *quadratic_ptr;
 
-  // int *ever_active_ptr_tmp;
-
   gradient_ptr_tmp = ((double *) gradient_ptr + coord);
   linear_term = *gradient_ptr_tmp;
 
@@ -172,9 +164,8 @@ double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^T
   // The coord entry of gradient_ptr term has a diagonal term in it:
   // Sigma[coord, coord] * theta[coord]
   // This removes it. 
-  linear_term -= quadratic_term * old_value;
 
-  linear_term += *((double *) linear_func_ptr + coord);
+  linear_term -= quadratic_term * old_value;
 
   // Now soft-threshold the coord entry of theta 
 
@@ -229,8 +220,7 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 		  int nrow,                   /* How many rows in Sigma */
 		  double bound,               /* feasibility parameter */
 		  double *theta,              /* current value */
-		  int maxiter,                /* how many iterations */
-		  int row)                    /* which coordinate to update: 0-based */
+		  int maxiter)
 {
 
   int iter = 0;
@@ -243,7 +233,6 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 			       ever_active_ptr,
 			       nactive_ptr,
 			       nrow,
-			       row,
 			       bound,
 			       theta);
   double new_value; 
@@ -265,7 +254,6 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 		       nrow,
 		       bound,
 		       theta,
-		       row,
 		       *active_ptr,
 		       1);
       active_ptr++;
@@ -276,7 +264,6 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
     if (check_KKT(theta,
 		  gradient_ptr,
 		  nrow,
-		  row,
 		  bound) == 1) {
       break;
     }
@@ -294,7 +281,6 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 		       nrow,
 		       bound,
 		       theta,
-		       row,
 		       icoord,
 		       0);
     }
@@ -304,7 +290,6 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
     if (check_KKT(theta,
 		  gradient_ptr,
 		  nrow,
-		  row,
 		  bound) == 1) {
       break;
     }
@@ -314,7 +299,6 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 			  ever_active_ptr,
 			  nactive_ptr,
 			  nrow,
-			  row,
 			  bound,
 			  theta);
 
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index c20195c..bb9f6d6 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -12,13 +12,13 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 		  int nrow,                   /* How many rows in Sigma */
 		  double bound,               /* feasibility parameter */
 		  double *theta,              /* current value */
-		  int maxiter,                /* how many iterations */
-		  int row);                   /* which coordinate to update: 0-based */
+		  int maxiter) //,                /* how many iterations */
+// int row);                   /* which coordinate to update: 0-based */
 
 int check_KKT(double *theta,           /* current theta */
 	      double *gradient_ptr,    /* Current gradient of quadratic loss */
 	      int nrow,                /* how many rows in Sigma */
-	      int row,                 /* which row: 0-based */
+	      // int row,                 /* which row: 0-based */
 	      double bound);           /* Lagrange multipler for \ell_1 */
 
 

From d248a8f88c2e4e7e12a94369ae02edc95838a3f8 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 12:17:44 -0700
Subject: [PATCH 213/493] BF: updating active set

---
 selectiveInference/src/debias.c | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index 1b841e0..107ae5d 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -69,17 +69,20 @@ int update_ever_active(int coord,
     active_var = (*ever_active_ptr_tmp);
     if (active_var == coord) {
 
-      // Add it to the active set and increment the 
-      // number of active variables
-
-      ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
-      *ever_active_ptr_tmp = coord;
-      *nactive_ptr += 1;
-
       return(1);
     }
   }
 
+  // If we have not returned yet, this variable
+  // was not in ever_active
+
+  // Add it to the active set and increment the 
+  // number of active variables
+
+  ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
+  *ever_active_ptr_tmp = coord;
+  *nactive_ptr += 1;
+
   return(0);
 }
 

From aa515c972c8e1f918d6572338eae25efc28af16f Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 12:24:22 -0700
Subject: [PATCH 214/493] cosmetic change

---
 selectiveInference/src/debias.c | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index 107ae5d..532dbef 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -68,7 +68,6 @@ int update_ever_active(int coord,
     ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
     active_var = (*ever_active_ptr_tmp);
     if (active_var == coord) {
-
       return(1);
     }
   }
@@ -188,7 +187,7 @@ double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^T
 
   // Add to active set if necessary
 
-  if (!is_active) {
+  if (is_active == 0) {
     update_ever_active(coord, ever_active_ptr, nactive_ptr);
   }
 

From 5d5e8f3c4a0d1c0ed70097d3480f778a208c36f9 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 12:30:59 -0700
Subject: [PATCH 215/493] BF: fixing function signatures

---
 selectiveInference/src/debias.h | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index bb9f6d6..730b1fb 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -12,13 +12,11 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 		  int nrow,                   /* How many rows in Sigma */
 		  double bound,               /* feasibility parameter */
 		  double *theta,              /* current value */
-		  int maxiter) //,                /* how many iterations */
-// int row);                   /* which coordinate to update: 0-based */
+		  int maxiter);
 
 int check_KKT(double *theta,           /* current theta */
 	      double *gradient_ptr,    /* Current gradient of quadratic loss */
 	      int nrow,                /* how many rows in Sigma */
-	      // int row,                 /* which row: 0-based */
 	      double bound);           /* Lagrange multipler for \ell_1 */
 
 

From d1debcd5bcd7932fdff213b882d83a8b47c1a875 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 12:31:30 -0700
Subject: [PATCH 216/493] BF: fixing function signatures

---
 selectiveInference/src/Rcpp-debias.cpp | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index be02abd..db6e77e 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -38,15 +38,13 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
 			   nrow,
 			   bound,
 			   (double *) theta.begin(),
-			   maxiter,
-			   row);
+			   maxiter);
   
   // Check whether feasible
 
   int kkt_check = check_KKT(theta.begin(),
 			    gradient.begin(),
 			    nrow,
-			    row,
 			    bound);
 
   return(Rcpp::List::create(Rcpp::Named("soln") = theta,

From 15762af11f53be618e21c83af9db194db8f9a537 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 16:12:02 -0700
Subject: [PATCH 217/493] BF: KKT wrong for active conditions, still have debug
 statements

---
 selectiveInference/R/funs.fixed.R      |  8 +---
 selectiveInference/src/Rcpp-debias.cpp |  1 -
 selectiveInference/src/debias.c        | 52 ++++++++++++++++++--------
 3 files changed, 37 insertions(+), 24 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 06c7a7d..e6e0c5f 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -363,13 +363,7 @@ InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL) {
      linear_func = soln_result$linear_func
   }
 
-  #print(c(soln, "soln"))
-  #print(c(gradient, "grad"))
-  #print(c(ever_active, "ever_active"))
-  #print(c(nactive, "nactive"))
-  #print(c(linear_func, "linear_func"))
-
-  result = find_one_row_debiasingM(Sigma, i-1, mu, maxiter, soln, linear_func, gradient, ever_active, nactive) # C function uses 0-based indexing
+  result = find_one_row_debiasingM(Sigma, mu, maxiter, soln, linear_func, gradient, ever_active, nactive) # C function uses 0-based indexing
 
   # Check feasibility
 
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index db6e77e..1095c29 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -3,7 +3,6 @@
 
 // [[Rcpp::export]]
 Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
-				   int row, // 0-based 
 				   double bound,
 				   int maxiter,
 				   Rcpp::NumericVector theta,
diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index b0669f8..6af42c4 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -100,34 +100,52 @@ int check_KKT(double *theta,        /* current theta */
 
   int irow;
   int fail = 0;
-  double tol = 1.e-4;
-  double *theta_ptr, *gradient_ptr_tmp;
+  double tol = 1.e-5;
+  double *theta_ptr = theta;
+  double *gradient_ptr_tmp = gradient_ptr;
   double gradient;
 
   for (irow=0; irow<nrow; irow++) {
-    theta_ptr = ((double *) theta + irow);
-    gradient_ptr_tmp = ((double *) gradient_ptr + irow);
-
-    // Compute this coordinate of the gradient
 
     gradient = *gradient_ptr_tmp;
 
-    if (*theta_ptr != 0) { // these coordinates of gradients should be equal to -bound
-      if ((*theta_ptr > 0) &&  (fabs(gradient + bound) > (1. + tol) * bound)) {
-	fail += 1;
+    fprintf(stderr, "how does it look %d %f %f\n", irow, *theta_ptr, gradient);
+
+    // Compute this coordinate of the gradient
+
+    if (fabs(*theta_ptr) > tol) { // these coordinates of gradients should be equal to \pm bound
+      fprintf(stderr, "active %f %f\n", fabs(fabs(gradient) - bound), bound);
+      if (fabs(fabs(gradient) - bound) > tol * bound) {
+        fprintf(stderr, "here1 %d %f %f\n", irow, *theta_ptr, gradient);
+	return(0);
+	// fail += 1;
       }
-      else if ((*theta_ptr < 0) && (fabs(gradient - bound) > (1. + tol) * bound)) {
-	fail += 1;
+      else if ((*theta_ptr > 0) && (gradient > 0)) {
+        fprintf(stderr, "here2 %d %f %f\n", irow, *theta_ptr, gradient);
+	return(0);
+	// fail += 1;
+      }
+      else if ((*theta_ptr < 0) && (gradient < 0)) {
+        fprintf(stderr, "here3 %d %f %f\n", irow, *theta_ptr, gradient);
+	return(0);
+	// fail += 1;
       }
     }
     else {
+      fprintf(stderr, "before4 %d %f %f\n", irow, *theta_ptr, gradient);
       if (fabs(gradient) > (1. + tol) * bound) {
-	fail += 1;
+        fprintf(stderr, "here4 %d %f %f\n", irow, *theta_ptr, gradient);
+	return(0);
+	// fail += 1;
       }
     }
+    theta_ptr++;
+    gradient_ptr_tmp++;
   }
 
+  fprintf(stderr, "OK now\n");
   return(fail == 0);
+  
 }
 
 double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^TX/n */
@@ -236,7 +254,7 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 			       bound,
 			       theta);
   double new_value; 
-  double tol=1.e-5;
+  double tol=1.e-8;
 
   for (iter=0; iter<maxiter; iter++) {
 
@@ -265,6 +283,7 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 		  gradient_ptr,
 		  nrow,
 		  bound) == 1) {
+      fprintf(stderr, "here5 \n");
       break;
     }
 					  
@@ -291,6 +310,7 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 		  gradient_ptr,
 		  nrow,
 		  bound) == 1) {
+      fprintf(stderr, "here6 \n");
       break;
     }
 					  
@@ -302,9 +322,9 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 			  bound,
 			  theta);
 
-    if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 0)) {
-      break;
-    }
+/*     if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 0)) { */
+/*       break; */
+/*     } */
 
     old_value = new_value;
   }

From 9872b45ae05b79358c17effeaf7b7b6cca395fe7 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 16:19:21 -0700
Subject: [PATCH 218/493] removing debug statements

---
 selectiveInference/src/debias.c | 22 +++-------------------
 1 file changed, 3 insertions(+), 19 deletions(-)

diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index 6af42c4..c9809e3 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -1,4 +1,3 @@
-#include <stdio.h>
 #include <math.h> // for fabs
 
 // Find an approximate row of \hat{Sigma}^{-1}
@@ -109,41 +108,28 @@ int check_KKT(double *theta,        /* current theta */
 
     gradient = *gradient_ptr_tmp;
 
-    fprintf(stderr, "how does it look %d %f %f\n", irow, *theta_ptr, gradient);
-
     // Compute this coordinate of the gradient
 
     if (fabs(*theta_ptr) > tol) { // these coordinates of gradients should be equal to \pm bound
-      fprintf(stderr, "active %f %f\n", fabs(fabs(gradient) - bound), bound);
       if (fabs(fabs(gradient) - bound) > tol * bound) {
-        fprintf(stderr, "here1 %d %f %f\n", irow, *theta_ptr, gradient);
 	return(0);
-	// fail += 1;
       }
       else if ((*theta_ptr > 0) && (gradient > 0)) {
-        fprintf(stderr, "here2 %d %f %f\n", irow, *theta_ptr, gradient);
 	return(0);
-	// fail += 1;
       }
       else if ((*theta_ptr < 0) && (gradient < 0)) {
-        fprintf(stderr, "here3 %d %f %f\n", irow, *theta_ptr, gradient);
 	return(0);
-	// fail += 1;
       }
     }
     else {
-      fprintf(stderr, "before4 %d %f %f\n", irow, *theta_ptr, gradient);
       if (fabs(gradient) > (1. + tol) * bound) {
-        fprintf(stderr, "here4 %d %f %f\n", irow, *theta_ptr, gradient);
 	return(0);
-	// fail += 1;
       }
     }
     theta_ptr++;
     gradient_ptr_tmp++;
   }
 
-  fprintf(stderr, "OK now\n");
   return(fail == 0);
   
 }
@@ -283,7 +269,6 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 		  gradient_ptr,
 		  nrow,
 		  bound) == 1) {
-      fprintf(stderr, "here5 \n");
       break;
     }
 					  
@@ -310,7 +295,6 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 		  gradient_ptr,
 		  nrow,
 		  bound) == 1) {
-      fprintf(stderr, "here6 \n");
       break;
     }
 					  
@@ -322,9 +306,9 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 			  bound,
 			  theta);
 
-/*     if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 0)) { */
-/*       break; */
-/*     } */
+    if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 0)) {
+      break;
+    }
 
     old_value = new_value;
   }

From be84df2690dc8a62f543693d80ff44dd2a751418 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 16:23:09 -0700
Subject: [PATCH 219/493] BF: KKT condition was wrong

---
 selectiveInference/src/debias.c | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index 532dbef..0d71e49 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -95,7 +95,7 @@ int check_KKT(double *theta,       /* current theta */
 
   int irow;
   int fail = 0;
-  double tol = 1.e-4;
+  double tol = 1.e-6;
   double *theta_ptr, *gradient_ptr_tmp;
   double gradient;
 
@@ -111,10 +111,10 @@ int check_KKT(double *theta,       /* current theta */
     }
 
     if (*theta_ptr != 0) { // these coordinates of gradients should be equal to -bound
-      if ((*theta_ptr > 0) &&  (fabs(gradient + bound) > (1. + tol) * bound)) {
+      if ((*theta_ptr > 0) &&  (fabs(gradient + bound) > tol * bound)) {
 	fail += 1;
       }
-      else if ((*theta_ptr < 0) && (fabs(gradient - bound) > (1. + tol) * bound)) {
+      else if ((*theta_ptr < 0) && (fabs(gradient - bound) > tol * bound)) {
 	fail += 1;
       }
     }

From 0de59af0c8f90410ae621d981b5458e3b38e1b99 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 16:28:35 -0700
Subject: [PATCH 220/493] lenient tolerance

---
 selectiveInference/src/debias.c | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index c9809e3..52daea9 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -99,7 +99,7 @@ int check_KKT(double *theta,        /* current theta */
 
   int irow;
   int fail = 0;
-  double tol = 1.e-5;
+  double tol = 1.e-4;
   double *theta_ptr = theta;
   double *gradient_ptr_tmp = gradient_ptr;
   double gradient;

From 190c0a0c1a491a265b203c258247b288e9bbaff7 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 16:35:18 -0700
Subject: [PATCH 221/493] added a few targets to makefile

---
 Makefile | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 5c55bb8..91a89a4 100644
--- a/Makefile
+++ b/Makefile
@@ -1,4 +1,14 @@
 Rcpp: 
 	- rm -f selectiveInference/src/RcppExports.cpp
 	- rm -f selectiveInference/R/RcppExports.R
-	Rscript -e "library(Rcpp); Rcpp::compileAttributes('selectiveInference')"
\ No newline at end of file
+	Rscript -e "library(Rcpp); Rcpp::compileAttributes('selectiveInference')"
+
+install: Rcpp
+	R CMD install selectiveInference
+
+build: 
+	R CMD build selectiveInference
+
+check: Rcpp build
+	R CMD build selectiveInference
+	R CMD check selectiveInference_1.2.2.tar.gz # fix this to be a script variable
\ No newline at end of file

From 2c4cf3a19487cdcfcc7ad9912894c3238d35130b Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 16:43:41 -0700
Subject: [PATCH 222/493] not checking objective

---
 selectiveInference/src/debias.c | 71 +++++++++++++++------------------
 1 file changed, 33 insertions(+), 38 deletions(-)

diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index 52daea9..92e2fb4 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -90,34 +90,32 @@ int update_ever_active(int coord,
   return(0);
 }
 
-int check_KKT(double *theta,        /* current theta */
+int check_KKT(double *theta,       /* current theta */
 	      double *gradient_ptr, /* Sigma times theta */
-	      int nrow,             /* how many rows in Sigma */
-	      double bound)         /* Lagrange multipler for \ell_1 */
+	      int nrow,            /* how many rows in Sigma */
+	      double bound)        /* Lagrange multipler for \ell_1 */
 {
   // First check inactive
 
   int irow;
   int fail = 0;
-  double tol = 1.e-4;
-  double *theta_ptr = theta;
-  double *gradient_ptr_tmp = gradient_ptr;
+  double tol = 1.e-6;
+  double *theta_ptr, *gradient_ptr_tmp;
   double gradient;
 
   for (irow=0; irow<nrow; irow++) {
-
-    gradient = *gradient_ptr_tmp;
+    theta_ptr = ((double *) theta + irow);
+    gradient_ptr_tmp = ((double *) gradient_ptr + irow);
 
     // Compute this coordinate of the gradient
 
-    if (fabs(*theta_ptr) > tol) { // these coordinates of gradients should be equal to \pm bound
-      if (fabs(fabs(gradient) - bound) > tol * bound) {
-	return(0);
-      }
-      else if ((*theta_ptr > 0) && (gradient > 0)) {
+    gradient = *gradient_ptr_tmp;
+
+    if (*theta_ptr != 0) { // these coordinates of gradients should be equal to -bound
+      if ((*theta_ptr > 0) &&  (fabs(gradient + bound) > tol * bound)) {
 	return(0);
       }
-      else if ((*theta_ptr < 0) && (gradient < 0)) {
+      else if ((*theta_ptr < 0) && (fabs(gradient - bound) > tol * bound)) {
 	return(0);
       }
     }
@@ -126,12 +124,9 @@ int check_KKT(double *theta,        /* current theta */
 	return(0);
       }
     }
-    theta_ptr++;
-    gradient_ptr_tmp++;
   }
 
-  return(fail == 0);
-  
+  return(1);
 }
 
 double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^TX/n */
@@ -232,13 +227,13 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
   int iactive = 0;
   int *active_ptr;
 
-  double old_value = objective(Sigma_ptr,
-			       linear_func_ptr,
-			       ever_active_ptr,
-			       nactive_ptr,
-			       nrow,
-			       bound,
-			       theta);
+/*   double old_value = objective(Sigma_ptr, */
+/* 			       linear_func_ptr, */
+/* 			       ever_active_ptr, */
+/* 			       nactive_ptr, */
+/* 			       nrow, */
+/* 			       bound, */
+/* 			       theta); */
   double new_value; 
   double tol=1.e-8;
 
@@ -298,19 +293,19 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
       break;
     }
 					  
-    new_value = objective(Sigma_ptr,
-			  linear_func_ptr,
-			  ever_active_ptr,
-			  nactive_ptr,
-			  nrow,
-			  bound,
-			  theta);
-
-    if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 0)) {
-      break;
-    }
-
-    old_value = new_value;
+/*     new_value = objective(Sigma_ptr, */
+/* 			  linear_func_ptr, */
+/* 			  ever_active_ptr, */
+/* 			  nactive_ptr, */
+/* 			  nrow, */
+/* 			  bound, */
+/* 			  theta); */
+
+/*     if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 0)) { */
+/*       break; */
+/*     } */
+
+//    old_value = new_value;
   }
   return(iter);
 }

From 9ebcaabfce17a0cb7e4d48506b0695678ec495d8 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 18:00:52 -0700
Subject: [PATCH 223/493] making Rcpp in Makevars now

---
 selectiveInference/src/Makevars | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/selectiveInference/src/Makevars b/selectiveInference/src/Makevars
index c68d0bd..cde36c9 100644
--- a/selectiveInference/src/Makevars
+++ b/selectiveInference/src/Makevars
@@ -2,7 +2,10 @@ PKG_CFLAGS= -I.
 PKG_CPPFLAGS= -I.
 PKG_LIBS=-L. 
 
-$(SHLIB): Rcpp-debias.o RcppExports.o debias.o
+$(SHLIB): Rcpp Rcpp-matrixcomps.o Rcpp-debias.o RcppExports.o debias.o
 
 clean:
 	rm -f *o
+
+Rcpp:
+	Rscript -e "library(Rcpp); Rcpp::compileAttributes('..')"
\ No newline at end of file

From 735c4d6742b8782c7f1e8af328c0ab3bf3fca2e5 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 18:01:44 -0700
Subject: [PATCH 224/493] trying without making Rcpp

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 21270e0..1120a44 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -12,5 +12,5 @@ warnings_are_errors: true
 before_install:
   - tlmgr install index # for texlive and vignette?
   - R -e 'install.packages("Rcpp", repos="http://cloud.r-project.org")'
-  - make Rcpp
+  #- make Rcpp
   - cd selectiveInference

From 0865d09eebbbf1e2f8533f1badf09bb0ba04acd6 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 18:26:02 -0700
Subject: [PATCH 225/493] making new quadratic_program C file

---
 selectiveInference/src/Rcpp-debias.cpp     |  20 +-
 selectiveInference/src/debias.h            |  21 +-
 selectiveInference/src/quadratic_program.c | 319 +++++++++++++++++++++
 3 files changed, 339 insertions(+), 21 deletions(-)
 create mode 100644 selectiveInference/src/quadratic_program.c

diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index 1095c29..c1b5e91 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -28,16 +28,16 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
   
   // Now call our C function
 
-  int iter = find_one_row_((double *) Sigma.begin(),
-			   (double *) linear_func.begin(),
-			   (double *) Sigma_diag.begin(),
-			   (double *) gradient.begin(),
-			   (int *) ever_active.begin(),
-			   (int *) nactive.begin(),
-			   nrow,
-			   bound,
-			   (double *) theta.begin(),
-			   maxiter);
+  int iter = solve_qp((double *) Sigma.begin(),
+		      (double *) linear_func.begin(),
+		      (double *) Sigma_diag.begin(),
+		      (double *) gradient.begin(),
+		      (int *) ever_active.begin(),
+		      (int *) nactive.begin(),
+		      nrow,
+		      bound,
+		      (double *) theta.begin(),
+		      maxiter);
   
   // Check whether feasible
 
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index 730b1fb..1284659 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -3,23 +3,22 @@ extern "C"
 {
 #endif /* __cplusplus */
 
-int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
- 		  double *linear_func_ptr,    /* Linear term in objective */
-		  double *Sigma_diag_ptr,     /* Diagonal entry of covariance matrix */
-		  double *gradient_ptr,       /* Current gradient of quadratic loss */
-		  int *ever_active_ptr,       /* Ever active set: 0-based */ 
-		  int *nactive_ptr,           /* Size of ever active set */
-		  int nrow,                   /* How many rows in Sigma */
-		  double bound,               /* feasibility parameter */
-		  double *theta,              /* current value */
-		  int maxiter);
+int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
+	     double *linear_func_ptr,    /* Linear term in objective */
+	     double *Sigma_diag_ptr,     /* Diagonal entry of covariance matrix */
+	     double *gradient_ptr,       /* Current gradient of quadratic loss */
+	     int *ever_active_ptr,       /* Ever active set: 0-based */ 
+	     int *nactive_ptr,           /* Size of ever active set */
+	     int nrow,                   /* How many rows in Sigma */
+	     double bound,               /* feasibility parameter */
+	     double *theta,              /* current value */
+	     int maxiter);
 
 int check_KKT(double *theta,           /* current theta */
 	      double *gradient_ptr,    /* Current gradient of quadratic loss */
 	      int nrow,                /* how many rows in Sigma */
 	      double bound);           /* Lagrange multipler for \ell_1 */
 
-
 #ifdef __cplusplus
 }  /* extern "C" */
 #endif /* __cplusplus */
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
new file mode 100644
index 0000000..cf4d650
--- /dev/null
+++ b/selectiveInference/src/quadratic_program.c
@@ -0,0 +1,319 @@
+#include <math.h> // for fabs
+
+// Find an approximate row of \hat{Sigma}^{-1}
+
+// Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
+
+// Dual problem: \text{min}_{\theta} 1/2 \theta^T \Sigma \theta - l^T\theta + \mu \|\theta\|_1
+// where l is `linear_func` below
+
+// This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
+// Therefore we don't have to negate the answer to get theta.
+// Update one coordinate 
+
+double objective_qp(double *Sigma_ptr,       /* A covariance matrix: X^TX/n */
+		    double *linear_func_ptr, /* Linear term in objective */
+		    int *ever_active_ptr,    /* Ever active set: 0-based */ 
+		    int *nactive_ptr,        /* Size of ever active set */
+		    int nrow,                /* how many rows in Sigma */
+		    double bound,            /* Lagrange multipler for \ell_1 */
+		    double *theta)           /* current value */
+{
+  int irow, icol;
+  double value = 0;
+  double *Sigma_ptr_tmp = Sigma_ptr;
+  double *linear_func_ptr_tmp = linear_func_ptr;
+  double *theta_row_ptr, *theta_col_ptr;
+  int *active_row_ptr, *active_col_ptr;
+  int active_row, active_col;
+  int nactive = *nactive_ptr;
+
+  theta_row_ptr = theta;
+  theta_col_ptr = theta;
+
+  for (irow=0; irow<nactive; irow++) {
+
+    active_row_ptr = ((int *) ever_active_ptr + irow);
+    active_row = *active_row_ptr;
+    theta_row_ptr = ((double *) theta + active_row);
+
+    for (icol=0; icol<nactive; icol++) {
+      
+      active_col_ptr = ((int *) ever_active_ptr + icol);
+      active_col = *active_col_ptr;
+      theta_col_ptr = ((double *) theta + active_col);
+
+      Sigma_ptr_tmp = ((double *) Sigma_ptr + nrow * active_col + active_row); // Matrices are column-major order
+
+      value += 0.5 * (*Sigma_ptr_tmp) * (*theta_row_ptr) * (*theta_col_ptr);
+    }
+    value += bound * fabs((*theta_row_ptr)); // the \ell_1 term
+
+    // The linear term in the objective
+
+    linear_func_ptr_tmp = ((double *) linear_func_ptr + active_row);
+    value += (*linear_func_ptr_tmp) * (*theta_row_ptr); 
+
+  }
+  
+  return(value);
+}
+
+int update_ever_active_qp(int coord,
+			  int *ever_active_ptr,
+			  int *nactive_ptr) {
+  int iactive;
+  int active_var;
+  int nactive = *nactive_ptr;
+  int *ever_active_ptr_tmp = ever_active_ptr;
+
+  for (iactive=0; iactive<nactive; iactive++) {
+    ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
+    active_var = *ever_active_ptr_tmp;
+    if (active_var == coord) {
+      return(1);
+    }
+  }
+  
+  // If we haven't returned yet, this means the coord was not in 
+  // ever_active.
+
+  // Add it to the active set and increment the 
+  // number of active variables
+
+  ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
+  *ever_active_ptr_tmp = coord;
+  *nactive_ptr += 1;
+
+  return(0);
+}
+
+int check_KKT_qp(double *theta,       /* current theta */
+		 double *gradient_ptr, /* Sigma times theta */
+		 int nrow,            /* how many rows in Sigma */
+		 double bound)        /* Lagrange multipler for \ell_1 */
+{
+  // First check inactive
+
+  int irow;
+  int fail = 0;
+  double tol = 1.e-6;
+  double *theta_ptr, *gradient_ptr_tmp;
+  double gradient;
+
+  for (irow=0; irow<nrow; irow++) {
+    theta_ptr = ((double *) theta + irow);
+    gradient_ptr_tmp = ((double *) gradient_ptr + irow);
+
+    // Compute this coordinate of the gradient
+
+    gradient = *gradient_ptr_tmp;
+
+    if (*theta_ptr != 0) { // these coordinates of gradients should be equal to -bound
+      if ((*theta_ptr > 0) &&  (fabs(gradient + bound) > tol * bound)) {
+	return(0);
+      }
+      else if ((*theta_ptr < 0) && (fabs(gradient - bound) > tol * bound)) {
+	return(0);
+      }
+    }
+    else {
+      if (fabs(gradient) > (1. + tol) * bound) {
+	return(0);
+      }
+    }
+  }
+
+  return(1);
+}
+
+double update_one_coord_qp(double *Sigma_ptr,           /* A covariance matrix: X^TX/n */
+			   double *linear_func_ptr,     /* Linear term in objective */
+			   double *Sigma_diag_ptr,      /* Diagonal entries of Sigma */
+			   double *gradient_ptr,        /* Sigma times theta */
+			   int *ever_active_ptr,        /* Ever active set: 0-based */ 
+			   int *nactive_ptr,            /* Size of ever active set */
+			   int nrow,                    /* How many rows in Sigma */
+			   double bound,                /* feasibility parameter */
+			   double *theta,               /* current value */
+			   int coord,                   /* which coordinate to update: 0-based */
+			   int is_active)               /* Is this part of ever_active */     
+{
+
+  double delta;
+  double linear_term = 0;
+  double value = 0;
+  double old_value;
+  double *Sigma_ptr_tmp;
+  double *gradient_ptr_tmp;
+  double *theta_ptr;
+  int icol = 0;
+
+  double *quadratic_ptr = ((double *) Sigma_diag_ptr + coord);
+  double quadratic_term = *quadratic_ptr;
+
+  gradient_ptr_tmp = ((double *) gradient_ptr + coord);
+  linear_term = *gradient_ptr_tmp;
+
+  theta_ptr = ((double *) theta + coord);
+  old_value = *theta_ptr;
+
+  // The coord entry of gradient_ptr term has a diagonal term in it:
+  // Sigma[coord, coord] * theta[coord]
+  // This removes it. 
+
+  linear_term -= quadratic_term * old_value;
+
+  // Now soft-threshold the coord entry of theta 
+
+  // Objective is t \mapsto q/2 * t^2 + l * t + bound |t|
+  // with q=quadratic_term and l=linear_term
+
+  // With a negative linear term, solution should be
+  // positive
+
+  if (linear_term < -bound) {
+    value = (-linear_term - bound) / quadratic_term;
+  }
+  else if (linear_term > bound) {
+    value = -(linear_term - bound) / quadratic_term;
+  }
+
+  // Add to active set if necessary
+
+  if (is_active == 0) {
+    update_ever_active_qp(coord, ever_active_ptr, nactive_ptr);
+  }
+
+  // Update the linear term
+
+  if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
+
+    delta = value - old_value;
+    Sigma_ptr_tmp = ((double *) Sigma_ptr + coord * nrow);
+    gradient_ptr_tmp = ((double *) gradient_ptr);
+
+    for (icol=0; icol<nrow; icol++) {
+      *gradient_ptr_tmp = *gradient_ptr_tmp + delta * (*Sigma_ptr_tmp);
+      gradient_ptr_tmp += 1;
+      Sigma_ptr_tmp += 1;
+    }
+
+    theta_ptr = ((double *) theta + coord);
+    *theta_ptr = value;
+
+  }
+
+  return(value);
+
+}
+
+int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
+	     double *linear_func_ptr,    /* Linear term in objective */
+	     double *Sigma_diag_ptr,     /* Diagonal entry of covariance matrix */
+	     double *gradient_ptr,       /* Sigma times theta */
+	     int *ever_active_ptr,       /* Ever active set: 0-based */ 
+	     int *nactive_ptr,           /* Size of ever active set */
+	     int nrow,                   /* How many rows in Sigma */
+	     double bound,               /* feasibility parameter */
+	     double *theta,              /* current value */
+	     int maxiter)
+{
+
+  int iter = 0;
+  int icoord = 0;
+  int iactive = 0;
+  int *active_ptr;
+
+  int check_objective = 0;
+
+  double old_value, new_value; 
+  double tol=1.e-8;
+
+  if (check_objective) {
+
+    old_value = objective_qp(Sigma_ptr,
+			     linear_func_ptr,
+			     ever_active_ptr,
+			     nactive_ptr,
+			     nrow,
+			     bound,
+			     theta);
+
+  }
+
+
+  for (iter=0; iter<maxiter; iter++) {
+
+    // Update the active variables first
+
+    active_ptr = (int *) ever_active_ptr;
+
+    for (iactive=0; iactive < *nactive_ptr; iactive++) {
+      update_one_coord_qp(Sigma_ptr,
+			  linear_func_ptr,
+			  Sigma_diag_ptr,
+			  gradient_ptr,
+			  ever_active_ptr,
+			  nactive_ptr,
+			  nrow,
+			  bound,
+			  theta,
+			  *active_ptr,
+			  1);
+      active_ptr++;
+    }
+
+    // Check KKT
+
+    if (check_KKT_qp(theta,
+		     gradient_ptr,
+		     nrow,
+		     bound) == 1) {
+      break;
+    }
+					  
+    // Update all variables 
+
+    for (icoord=0; icoord<nrow; icoord++) {
+
+      update_one_coord_qp(Sigma_ptr,
+			  linear_func_ptr,
+			  Sigma_diag_ptr,
+			  gradient_ptr,
+			  ever_active_ptr,
+			  nactive_ptr,
+			  nrow,
+			  bound,
+			  theta,
+			  icoord,
+			  0);
+    }
+
+    // Check KKT
+
+    if (check_KKT_qp(theta,
+		     gradient_ptr,
+		     nrow,
+		     bound) == 1) {
+      break;
+    }
+					  
+    if (check_objective) {
+      new_value = objective_qp(Sigma_ptr,
+			       linear_func_ptr,
+			       ever_active_ptr,
+			       nactive_ptr,
+			       nrow,
+			       bound,
+			       theta);
+
+      if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 0)) {
+	break;
+      }
+      old_value = new_value;
+    }
+  }
+  return(iter);
+}
+

From bfad5057a3b91d80ae3477e74f66e59f24e8db3d Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 18:27:05 -0700
Subject: [PATCH 226/493] need to make Rcpp for travis

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 1120a44..21270e0 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -12,5 +12,5 @@ warnings_are_errors: true
 before_install:
   - tlmgr install index # for texlive and vignette?
   - R -e 'install.packages("Rcpp", repos="http://cloud.r-project.org")'
-  #- make Rcpp
+  - make Rcpp
   - cd selectiveInference

From 1237c78d74ca87edec5b9e6496288618eca6d7d5 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 18 Aug 2017 22:29:06 -0700
Subject: [PATCH 227/493] both solvers working

---
 selectiveInference/R/funs.fixed.R          | 11 ++--
 selectiveInference/src/Rcpp-debias.cpp     | 75 +++++++++++++++++++---
 selectiveInference/src/debias.c            | 62 ++++++++++--------
 selectiveInference/src/debias.h            | 18 ++++++
 selectiveInference/src/quadratic_program.c |  9 +--
 5 files changed, 123 insertions(+), 52 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index df87b5e..de609e7 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -297,7 +297,7 @@ InverseLinfty <- function(sigma, n, e, resol=1.2, mu=NULL, maxiter=50, threshold
 
     while ((mu.stop != 1)&&(try.no<10)){
       last.beta <- beta
-      print(c("#######################trying ", try.no))
+      #print(c("#######################trying ", try.no))
       output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, soln_result=output) # uses a warm start
       beta <- output$soln
       iter <- output$iter
@@ -365,11 +365,10 @@ InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL) {
      linear_func = soln_result$linear_func
   }
 
-  result = solve_QP(Sigma, mu, maxiter, soln, linear_func, gradient, ever_active, nactive) # C function uses 0-based indexing
-  result2 = find_one_row_debiasingM(Sigma, i, mu, maxiter, soln, gradient, ever_active, nactive) # C function uses 0-based indexing
-
-  print('close?')
-  print(c(sqrt(sum((result$soln-result2$soln)^2)/sum(result$soln^2)), sqrt(sum(result$soln^2)), result2$nactive))
+  result = find_one_row_debiasingM(Sigma, i, mu, maxiter, 0 * soln, gradient, ever_active, nactive) # C function uses 0-based indexing
+  #result1 = solve_QP(Sigma, mu, maxiter, soln, linear_func, gradient, ever_active, nactive) 
+  #print("close?")
+  #print(c(sqrt(sum((result1$soln-result$soln)^2)/sum(result$soln^2)), sum(result$soln^2)))
 
   # Check feasibility
 
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index c1b5e91..ce33854 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -2,15 +2,15 @@
 #include <debias.h>    // where find_one_row_void is defined
 
 // [[Rcpp::export]]
-Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
-				   double bound,
-				   int maxiter,
-				   Rcpp::NumericVector theta,
-				   Rcpp::NumericVector linear_func,
-				   Rcpp::NumericVector gradient,
-				   Rcpp::IntegerVector ever_active,
-				   Rcpp::IntegerVector nactive
-				   ) {
+Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
+		    double bound,
+		    int maxiter,
+		    Rcpp::NumericVector theta,
+		    Rcpp::NumericVector linear_func,
+		    Rcpp::NumericVector gradient,
+		    Rcpp::IntegerVector ever_active,
+		    Rcpp::IntegerVector nactive
+		    ) {
 
   int nrow = Sigma.nrow(); // number of features
 
@@ -41,14 +41,69 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
   
   // Check whether feasible
 
+  int kkt_check = check_KKT_qp(theta.begin(),
+			       gradient.begin(),
+			       nrow,
+			       bound);
+
+  return(Rcpp::List::create(Rcpp::Named("soln") = theta,
+			    Rcpp::Named("gradient") = gradient,
+			    Rcpp::Named("linear_func") = linear_func,
+			    Rcpp::Named("iter") = iter,
+			    Rcpp::Named("kkt_check") = kkt_check,
+			    Rcpp::Named("ever_active") = ever_active,
+			    Rcpp::Named("nactive") = nactive));
+
+}
+
+// [[Rcpp::export]]
+Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
+				   int row, // 0-based 
+				   double bound,
+				   int maxiter,
+				   Rcpp::NumericVector theta,
+				   Rcpp::NumericVector gradient,
+				   Rcpp::IntegerVector ever_active,
+				   Rcpp::IntegerVector nactive
+				   ) {
+
+  int nrow = Sigma.nrow(); // number of features
+
+  // Active set
+
+  int irow;
+
+  // Extract the diagonal
+  Rcpp::NumericVector Sigma_diag(nrow);
+  double *sigma_diag_p = Sigma_diag.begin();
+
+  for (irow=0; irow<nrow; irow++) {
+    sigma_diag_p[irow] = Sigma(irow, irow);
+  }
+  
+  // Now call our C function
+
+  int iter = find_one_row_((double *) Sigma.begin(),
+			   (double *) Sigma_diag.begin(),
+			   (double *) gradient.begin(),
+			   (int *) ever_active.begin(),
+			   (int *) nactive.begin(),
+			   nrow,
+			   bound,
+			   (double *) theta.begin(),
+			   maxiter,
+			   row);
+  
+  // Check whether feasible
+
   int kkt_check = check_KKT(theta.begin(),
 			    gradient.begin(),
 			    nrow,
+			    row,
 			    bound);
 
   return(Rcpp::List::create(Rcpp::Named("soln") = theta,
 			    Rcpp::Named("gradient") = gradient,
-			    Rcpp::Named("linear_func") = linear_func,
 			    Rcpp::Named("iter") = iter,
 			    Rcpp::Named("kkt_check") = kkt_check,
 			    Rcpp::Named("ever_active") = ever_active,
diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index b6bcde5..e083ccc 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -94,7 +94,6 @@ int check_KKT(double *theta,       /* current theta */
   // First check inactive
 
   int irow;
-  int fail = 0;
   double tol = 1.e-6;
   double *theta_ptr, *gradient_ptr_tmp;
   double gradient;
@@ -106,26 +105,26 @@ int check_KKT(double *theta,       /* current theta */
     // Compute this coordinate of the gradient
 
     gradient = *gradient_ptr_tmp;
-    if (row == irow) {
-      gradient -= 1;
-    }
+    // if (row == irow) {
+    //   gradient -= 1;
+    // }
 
     if (*theta_ptr != 0) { // these coordinates of gradients should be equal to -bound
       if ((*theta_ptr > 0) &&  (fabs(gradient + bound) > tol * bound)) {
-	fail += 1;
+	return(0);
       }
       else if ((*theta_ptr < 0) && (fabs(gradient - bound) > tol * bound)) {
-	fail += 1;
+	return(0);
       }
     }
     else {
       if (fabs(gradient) > (1. + tol) * bound) {
-	fail += 1;
+	return(0);
       }
     }
   }
 
-  return(fail == 0);
+  return(1);
 }
 
 double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^TX/n */
@@ -230,16 +229,21 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
   int icoord = 0;
   int iactive = 0;
   int *active_ptr;
+  double old_value, new_value, tol=1.e-5;
+
+  int check_objective = 1;
+
+  if (check_objective) {
+
+    old_value = objective(Sigma_ptr,
+			  ever_active_ptr,
+			  nactive_ptr,
+			  nrow,
+			  row,
+			  bound,
+			  theta);
+  }
 
-  double old_value = objective(Sigma_ptr,
-			       ever_active_ptr,
-			       nactive_ptr,
-			       nrow,
-			       row,
-			       bound,
-			       theta);
-  double new_value; 
-  double tol=1.e-5;
 
   for (iter=0; iter<maxiter; iter++) {
 
@@ -299,19 +303,21 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
       break;
     }
 					  
-    new_value = objective(Sigma_ptr,
-			  ever_active_ptr,
-			  nactive_ptr,
-			  nrow,
-			  row,
-			  bound,
-			  theta);
+    if (check_objective) {
+      new_value = objective(Sigma_ptr,
+			    ever_active_ptr,
+			    nactive_ptr,
+			    nrow,
+			    row,
+			    bound,
+			    theta);
+
+      if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 0)) {
+	break;
+      }
 
-    if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 0)) {
-      break;
+      old_value = new_value;
     }
-
-    old_value = new_value;
   }
   return(iter);
 }
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index 1284659..5e9d621 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -14,11 +14,29 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 	     double *theta,              /* current value */
 	     int maxiter);
 
+int check_KKT_qp(double *theta,           /* current theta */
+		 double *gradient_ptr,    /* Current gradient of quadratic loss */
+		 int nrow,                /* how many rows in Sigma */
+		 double bound);           /* Lagrange multipler for \ell_1 */
+
+int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
+		  double *Sigma_ptr_ptr,     /* Diagonal entry of covariance matrix */
+		  double *gradient_ptr,       /* Current gradient of quadratic loss */
+		  int *ever_active_ptr,       /* Ever active set: 0-based */ 
+		  int *nactive_ptr,           /* Size of ever active set */
+		  int nrow,                   /* How many rows in Sigma */
+		  double bound,               /* feasibility parameter */
+		  double *theta,              /* current value */
+		  int maxiter,                /* how many iterations */
+		  int row);                   /* which coordinate to update: 0-based */
+
 int check_KKT(double *theta,           /* current theta */
 	      double *gradient_ptr,    /* Current gradient of quadratic loss */
 	      int nrow,                /* how many rows in Sigma */
+	      int row,                 /* which row: 0-based */
 	      double bound);           /* Lagrange multipler for \ell_1 */
 
+
 #ifdef __cplusplus
 }  /* extern "C" */
 #endif /* __cplusplus */
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
index c923398..50d6514 100644
--- a/selectiveInference/src/quadratic_program.c
+++ b/selectiveInference/src/quadratic_program.c
@@ -1,5 +1,4 @@
 #include <math.h> // for fabs
-#include <stdio.h>
 
 // Find an approximate row of \hat{Sigma}^{-1}
 
@@ -82,8 +81,6 @@ int update_ever_active_qp(int coord,
   // Add it to the active set and increment the 
   // number of active variables
 
-  fprintf(stderr, "adding %d\n", coord);
-
   ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
   *ever_active_ptr_tmp = coord;
   *nactive_ptr += 1;
@@ -99,7 +96,6 @@ int check_KKT_qp(double *theta,       /* current theta */
   // First check inactive
 
   int irow;
-  int fail = 0;
   double tol = 1.e-6;
   double *theta_ptr, *gradient_ptr_tmp;
   double gradient;
@@ -228,13 +224,11 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
   int iactive = 0;
   int *active_ptr;
 
-  int check_objective = 0;
+  int check_objective = 1;
 
   double old_value, new_value; 
   double tol=1.e-8;
 
-  fprintf(stderr, "%d nactive start\n", *nactive_ptr);
-
   if (check_objective) {
 
     old_value = objective_qp(Sigma_ptr,
@@ -250,7 +244,6 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 
   for (iter=0; iter<maxiter; iter++) {
 
-    fprintf(stderr, "%d nactive loop \n", *nactive_ptr);
     // Update the active variables first
 
     active_ptr = (int *) ever_active_ptr;

From 3405f237570d1d43b9565f132d5010d4a0a37c51 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sun, 20 Aug 2017 15:16:24 -0700
Subject: [PATCH 228/493] passing tol as argument; both solvers agree

---
 selectiveInference/R/funs.fixed.R          | 18 ++++--
 selectiveInference/src/Rcpp-debias.cpp     | 22 ++++++--
 selectiveInference/src/debias.c            | 64 +++++++++++++---------
 selectiveInference/src/debias.h            | 18 ++++--
 selectiveInference/src/quadratic_program.c | 21 ++++---
 5 files changed, 90 insertions(+), 53 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index de609e7..c8f5352 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -338,7 +338,7 @@ InverseLinfty <- function(sigma, n, e, resol=1.2, mu=NULL, maxiter=50, threshold
   return(M)
 }
 
-InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL) {
+InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL, kkt_tol=1.e-6, objective_tol=1.e-6) {
 
   # If soln_result is not Null, it is used as a warm start.
   # It should be a list
@@ -365,10 +365,18 @@ InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL) {
      linear_func = soln_result$linear_func
   }
 
-  result = find_one_row_debiasingM(Sigma, i, mu, maxiter, 0 * soln, gradient, ever_active, nactive) # C function uses 0-based indexing
-  #result1 = solve_QP(Sigma, mu, maxiter, soln, linear_func, gradient, ever_active, nactive) 
-  #print("close?")
-  #print(c(sqrt(sum((result1$soln-result$soln)^2)/sum(result$soln^2)), sum(result$soln^2)))
+  soln1 = rep(0, p)
+  gradient1 = rep(0, p)
+  ever_active1 = rep(0, p)
+  ever_active1[1] = i-1
+  nactive1 = as.integer(1)
+  result1 = find_one_row_debiasingM(Sigma, i-1, mu, maxiter, soln1, gradient1, ever_active1, nactive1, kkt_tol, objective_tol) # C function uses 0-based indexing
+  result = solve_QP(Sigma, mu, maxiter, soln, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol) 
+  print("close?")
+  print(c(sqrt(sum((result1$soln-result$soln)^2)/sum(result$soln^2)), sum(result$soln^2)))
+  print(c(result1$iter, result$iter, sum(result1$soln^2)))
+
+  #result = find_one_row_debiasingM(Sigma, i-1, mu, maxiter, soln, gradient, ever_active, nactive, kkt_tol, objective_tol) # C function uses 0-based indexing
 
   # Check feasibility
 
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index ce33854..a78ae81 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -9,7 +9,9 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		    Rcpp::NumericVector linear_func,
 		    Rcpp::NumericVector gradient,
 		    Rcpp::IntegerVector ever_active,
-		    Rcpp::IntegerVector nactive
+		    Rcpp::IntegerVector nactive,
+		    double kkt_tol,
+		    double objective_tol
 		    ) {
 
   int nrow = Sigma.nrow(); // number of features
@@ -37,14 +39,17 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		      nrow,
 		      bound,
 		      (double *) theta.begin(),
-		      maxiter);
+		      maxiter,
+		      kkt_tol,
+		      objective_tol);
   
   // Check whether feasible
 
   int kkt_check = check_KKT_qp(theta.begin(),
 			       gradient.begin(),
 			       nrow,
-			       bound);
+			       bound,
+			       kkt_tol);
 
   return(Rcpp::List::create(Rcpp::Named("soln") = theta,
 			    Rcpp::Named("gradient") = gradient,
@@ -64,7 +69,9 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
 				   Rcpp::NumericVector theta,
 				   Rcpp::NumericVector gradient,
 				   Rcpp::IntegerVector ever_active,
-				   Rcpp::IntegerVector nactive
+				   Rcpp::IntegerVector nactive,
+				   double kkt_tol,
+				   double objective_tol
 				   ) {
 
   int nrow = Sigma.nrow(); // number of features
@@ -92,7 +99,9 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
 			   bound,
 			   (double *) theta.begin(),
 			   maxiter,
-			   row);
+			   row,
+			   kkt_tol,
+			   objective_tol);
   
   // Check whether feasible
 
@@ -100,7 +109,8 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
 			    gradient.begin(),
 			    nrow,
 			    row,
-			    bound);
+			    bound,
+			    kkt_tol);
 
   return(Rcpp::List::create(Rcpp::Named("soln") = theta,
 			    Rcpp::Named("gradient") = gradient,
diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index e083ccc..4d36056 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -1,3 +1,4 @@
+#include <stdio.h>
 #include <math.h> // for fabs
 
 // Find an approximate row of \hat{Sigma}^{-1}
@@ -67,6 +68,7 @@ int update_ever_active(int coord,
   for (iactive=0; iactive<nactive; iactive++) {
     ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
     active_var = (*ever_active_ptr_tmp);
+    // fprintf(stderr, "%d %d\n", iactive, active_var);
     if (active_var == coord) {
       return(1);
     }
@@ -85,16 +87,16 @@ int update_ever_active(int coord,
   return(0);
 }
 
-int check_KKT(double *theta,       /* current theta */
+int check_KKT(double *theta,        /* current theta */
 	      double *gradient_ptr, /* Sigma times theta */
-	      int nrow,            /* how many rows in Sigma */
-	      int row,             /* which row: 0-based */
-	      double bound)        /* Lagrange multipler for \ell_1 */
+	      int nrow,             /* how many rows in Sigma */
+	      int row,              /* which row: 0-based */
+	      double bound,         /* Lagrange multipler for \ell_1 */
+	      double tol)           /* precision for checking KKT conditions */
 {
   // First check inactive
 
   int irow;
-  double tol = 1.e-6;
   double *theta_ptr, *gradient_ptr_tmp;
   double gradient;
 
@@ -105,9 +107,12 @@ int check_KKT(double *theta,       /* current theta */
     // Compute this coordinate of the gradient
 
     gradient = *gradient_ptr_tmp;
-    // if (row == irow) {
-    //   gradient -= 1;
-    // }
+
+    // For the basis vector
+
+    if (row == irow) {
+       gradient -= 1;
+    }
 
     if (*theta_ptr != 0) { // these coordinates of gradients should be equal to -bound
       if ((*theta_ptr > 0) &&  (fabs(gradient + bound) > tol * bound)) {
@@ -122,6 +127,7 @@ int check_KKT(double *theta,       /* current theta */
 	return(0);
       }
     }
+
   }
 
   return(1);
@@ -129,15 +135,15 @@ int check_KKT(double *theta,       /* current theta */
 
 double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^TX/n */
                         double *Sigma_diag_ptr,      /* Diagonal entries of Sigma */
-                        double *gradient_ptr,     /* Sigma times theta */
+                        double *gradient_ptr,        /* Sigma times theta */
 			int *ever_active_ptr,        /* Ever active set: 0-based */ 
-			int *nactive_ptr,        /* Size of ever active set */
-			int nrow,                /* How many rows in Sigma */
-			double bound,            /* feasibility parameter */
-			double *theta,           /* current value */
-			int row,                 /* which row: 0-based */
-			int coord,               /* which coordinate to update: 0-based */
-			int is_active)           /* Is this part of ever_active */     
+			int *nactive_ptr,            /* Size of ever active set */
+			int nrow,                    /* How many rows in Sigma */
+			double bound,                /* feasibility parameter */
+			double *theta,               /* current value */
+			int row,                     /* which row: 0-based */
+			int coord,                   /* which coordinate to update: 0-based */
+			int is_active)               /* Is this part of ever_active */     
 {
 
   double delta;
@@ -215,21 +221,23 @@ double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^T
 
 int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 		  double *Sigma_diag_ptr,     /* Diagonal entry of covariance matrix */
-		  double *gradient_ptr,    /* Sigma times theta */
+		  double *gradient_ptr,       /* Sigma times theta */
 		  int *ever_active_ptr,       /* Ever active set: 0-based */ 
-		  int *nactive_ptr,       /* Size of ever active set */
-		  int nrow,               /* How many rows in Sigma */
-		  double bound,           /* feasibility parameter */
-		  double *theta,          /* current value */
-		  int maxiter,            /* how many iterations */
-		  int row)                /* which coordinate to update: 0-based */
+		  int *nactive_ptr,           /* Size of ever active set */
+		  int nrow,                   /* How many rows in Sigma */
+		  double bound,               /* feasibility parameter */
+		  double *theta,              /* current value */
+		  int maxiter,                /* how many iterations */
+		  int row,                    /* which coordinate to update: 0-based */
+		  double kkt_tol,             /* precision for checking KKT conditions */
+		  double objective_tol)       /* precision for checking relative decrease in objective value */
 {
 
   int iter = 0;
   int icoord = 0;
   int iactive = 0;
   int *active_ptr;
-  double old_value, new_value, tol=1.e-5;
+  double old_value, new_value;
 
   int check_objective = 1;
 
@@ -272,7 +280,8 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 		  gradient_ptr,
 		  nrow,
 		  row,
-		  bound) == 1) {
+		  bound,
+		  kkt_tol) == 1) {
       break;
     }
 					  
@@ -299,7 +308,8 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 		  gradient_ptr,
 		  nrow,
 		  row,
-		  bound) == 1) {
+		  bound,
+		  kkt_tol) == 1) {
       break;
     }
 					  
@@ -312,7 +322,7 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 			    bound,
 			    theta);
 
-      if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 0)) {
+      if (((old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
 	break;
       }
 
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index 5e9d621..5510adf 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -12,15 +12,19 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 	     int nrow,                   /* How many rows in Sigma */
 	     double bound,               /* feasibility parameter */
 	     double *theta,              /* current value */
-	     int maxiter);
+	     int maxiter,                /* how many iterations */
+	     double kkt_tol,             /* precision for checking KKT conditions */
+	     double objective_tol);      /* precision for checking relative decrease in objective value */
+
 
 int check_KKT_qp(double *theta,           /* current theta */
 		 double *gradient_ptr,    /* Current gradient of quadratic loss */
 		 int nrow,                /* how many rows in Sigma */
-		 double bound);           /* Lagrange multipler for \ell_1 */
+		 double bound,            /* Lagrange multipler for \ell_1 */ 
+		 double tol);             /* precision for checking KKT conditions */
 
 int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
-		  double *Sigma_ptr_ptr,     /* Diagonal entry of covariance matrix */
+		  double *Sigma_diag_ptr,      /* Diagonal entry of covariance matrix */
 		  double *gradient_ptr,       /* Current gradient of quadratic loss */
 		  int *ever_active_ptr,       /* Ever active set: 0-based */ 
 		  int *nactive_ptr,           /* Size of ever active set */
@@ -28,14 +32,16 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 		  double bound,               /* feasibility parameter */
 		  double *theta,              /* current value */
 		  int maxiter,                /* how many iterations */
-		  int row);                   /* which coordinate to update: 0-based */
+		  int row,                    /* which coordinate to update: 0-based */
+		  double kkt_tol,             /* precision for checking KKT conditions */
+		  double objective_tol);      /* precision for checking relative decrease in objective value */
 
 int check_KKT(double *theta,           /* current theta */
 	      double *gradient_ptr,    /* Current gradient of quadratic loss */
 	      int nrow,                /* how many rows in Sigma */
 	      int row,                 /* which row: 0-based */
-	      double bound);           /* Lagrange multipler for \ell_1 */
-
+	      double bound,            /* Lagrange multipler for \ell_1 */
+	      double kkt_tol);         /* precision for checking KKT conditions */
 
 #ifdef __cplusplus
 }  /* extern "C" */
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
index 50d6514..0cefac4 100644
--- a/selectiveInference/src/quadratic_program.c
+++ b/selectiveInference/src/quadratic_program.c
@@ -88,15 +88,15 @@ int update_ever_active_qp(int coord,
   return(0);
 }
 
-int check_KKT_qp(double *theta,       /* current theta */
+int check_KKT_qp(double *theta,        /* current theta */
 		 double *gradient_ptr, /* Sigma times theta */
-		 int nrow,            /* how many rows in Sigma */
-		 double bound)        /* Lagrange multipler for \ell_1 */
+		 int nrow,             /* how many rows in Sigma */
+		 double bound,         /* Lagrange multipler for \ell_1 */
+		 double tol)           /* precision for checking KKT conditions */        
 {
   // First check inactive
 
   int irow;
-  double tol = 1.e-6;
   double *theta_ptr, *gradient_ptr_tmp;
   double gradient;
 
@@ -216,7 +216,9 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 	     int nrow,                   /* How many rows in Sigma */
 	     double bound,               /* feasibility parameter */
 	     double *theta,              /* current value */
-	     int maxiter)
+	     int maxiter,                /* max number of iterations */
+	     double kkt_tol,             /* precision for checking KKT conditions */
+	     double objective_tol)       /* precision for checking relative decrease in objective value */
 {
 
   int iter = 0;
@@ -227,7 +229,6 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
   int check_objective = 1;
 
   double old_value, new_value; 
-  double tol=1.e-8;
 
   if (check_objective) {
 
@@ -268,7 +269,8 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
     if (check_KKT_qp(theta,
 		     gradient_ptr,
 		     nrow,
-		     bound) == 1) {
+		     bound,
+		     kkt_tol) == 1) {
       break;
     }
 					  
@@ -294,7 +296,8 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
     if (check_KKT_qp(theta,
 		     gradient_ptr,
 		     nrow,
-		     bound) == 1) {
+		     bound,
+		     kkt_tol) == 1) {
       break;
     }
 					  
@@ -307,7 +310,7 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 			       bound,
 			       theta);
 
-      if (((old_value - new_value) < tol * fabs(new_value)) && (iter > 0)) {
+      if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
 	break;
       }
       old_value = new_value;

From 8c956f84949acc42e65af548bd1c6b18ca31ddac Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sun, 20 Aug 2017 15:46:37 -0700
Subject: [PATCH 229/493] making a choice for solver

---
 selectiveInference/R/funs.fixed.R | 38 +++++++++++++++----------------
 selectiveInference/src/debias.c   |  2 --
 2 files changed, 18 insertions(+), 22 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index c8f5352..2ddc3d5 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -26,8 +26,6 @@ sigma=NULL, alpha=0.1,
   
   else{
     
-    
-    
     checkargs.xy(x,y)
     if (missing(beta) || is.null(beta)) stop("Must supply the solution beta")
     if (missing(lambda) || is.null(lambda)) stop("Must supply the tuning parameter value lambda")
@@ -338,7 +336,8 @@ InverseLinfty <- function(sigma, n, e, resol=1.2, mu=NULL, maxiter=50, threshold
   return(M)
 }
 
-InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL, kkt_tol=1.e-6, objective_tol=1.e-6) {
+InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL, kkt_tol=1.e-6, objective_tol=1.e-6,
+		                 use_QP=TRUE) {
 
   # If soln_result is not Null, it is used as a warm start.
   # It should be a list
@@ -352,31 +351,30 @@ InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL, kkt
      ever_active[1] = i-1             # 0-based
      ever_active = as.integer(ever_active)
      nactive = as.integer(1)
-     linear_func = rep(0, p)
-     linear_func[i] = -1
-     linear_func = as.numeric(linear_func)
-     gradient = 1. * linear_func
+     if (use_QP) {
+          linear_func = rep(0, p)
+	  linear_func[i] = -1
+	  linear_func = as.numeric(linear_func)
+	  gradient = 1. * linear_func 
+     } else {
+          gradient = rep(0, p)
+     }
   }
   else {
      soln = soln_result$soln
      gradient = soln_result$gradient  
      ever_active = as.integer(soln_result$ever_active)
      nactive = as.integer(soln_result$nactive)
-     linear_func = soln_result$linear_func
+     if (use_QP) { 
+         linear_func = soln_result$linear_func
+     }
   }
 
-  soln1 = rep(0, p)
-  gradient1 = rep(0, p)
-  ever_active1 = rep(0, p)
-  ever_active1[1] = i-1
-  nactive1 = as.integer(1)
-  result1 = find_one_row_debiasingM(Sigma, i-1, mu, maxiter, soln1, gradient1, ever_active1, nactive1, kkt_tol, objective_tol) # C function uses 0-based indexing
-  result = solve_QP(Sigma, mu, maxiter, soln, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol) 
-  print("close?")
-  print(c(sqrt(sum((result1$soln-result$soln)^2)/sum(result$soln^2)), sum(result$soln^2)))
-  print(c(result1$iter, result$iter, sum(result1$soln^2)))
-
-  #result = find_one_row_debiasingM(Sigma, i-1, mu, maxiter, soln, gradient, ever_active, nactive, kkt_tol, objective_tol) # C function uses 0-based indexing
+  if (use_QP) {
+      result = solve_QP(Sigma, mu, maxiter, soln, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol) 
+  } else {
+      result = find_one_row_debiasingM(Sigma, i-1, mu, maxiter, soln, gradient, ever_active, nactive, kkt_tol, objective_tol) # C function uses 0-based indexing
+  }
 
   # Check feasibility
 
diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index 4d36056..df9d0ea 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -1,4 +1,3 @@
-#include <stdio.h>
 #include <math.h> // for fabs
 
 // Find an approximate row of \hat{Sigma}^{-1}
@@ -68,7 +67,6 @@ int update_ever_active(int coord,
   for (iactive=0; iactive<nactive; iactive++) {
     ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
     active_var = (*ever_active_ptr_tmp);
-    // fprintf(stderr, "%d %d\n", iactive, active_var);
     if (active_var == coord) {
       return(1);
     }

From 93a9602753175fc713f91a174330fbc7dacc8da7 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sun, 20 Aug 2017 16:05:30 -0700
Subject: [PATCH 230/493] RF: ever_active is now 1-based indices to be more R
 like

---
 selectiveInference/R/funs.fixed.R          |  4 +--
 selectiveInference/src/debias.c            | 42 +++++++++++-----------
 selectiveInference/src/quadratic_program.c | 18 +++++-----
 3 files changed, 33 insertions(+), 31 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 2ddc3d5..ae9fdc6 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -348,7 +348,7 @@ InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL, kkt
   if (is.null(soln_result)) {
      soln = rep(0, p)
      ever_active = rep(0, p)
-     ever_active[1] = i-1             # 0-based
+     ever_active[1] = i      # 1-based
      ever_active = as.integer(ever_active)
      nactive = as.integer(1)
      if (use_QP) {
@@ -373,7 +373,7 @@ InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL, kkt
   if (use_QP) {
       result = solve_QP(Sigma, mu, maxiter, soln, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol) 
   } else {
-      result = find_one_row_debiasingM(Sigma, i-1, mu, maxiter, soln, gradient, ever_active, nactive, kkt_tol, objective_tol) # C function uses 0-based indexing
+      result = find_one_row_debiasingM(Sigma, i, mu, maxiter, soln, gradient, ever_active, nactive, kkt_tol, objective_tol) # C function uses 1-based indexing for active set
   }
 
   # Check feasibility
diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index df9d0ea..3e3efb5 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -11,12 +11,12 @@
 // Update one coordinate 
 
 double objective(double *Sigma_ptr,       /* A covariance matrix: X^TX/n */
-		 int *ever_active_ptr,    /* Ever active set: 0-based */ 
-		 int *nactive_ptr,    /* Size of ever active set */
-		 int nrow,            /* how many rows in Sigma */
-		 int row,             /* which row: 0-based */
-		 double bound,        /* Lagrange multipler for \ell_1 */
-		 double *theta)       /* current value */
+		 int *ever_active_ptr,    /* Ever active set: 1-based */ 
+		 int *nactive_ptr,        /* Size of ever active set */
+		 int nrow,                /* how many rows in Sigma */
+		 int row,                 /* which row: 1-based */
+		 double bound,            /* Lagrange multipler for \ell_1 */
+		 double *theta)           /* current value */
 {
   int irow, icol;
   double value = 0;
@@ -32,13 +32,13 @@ double objective(double *Sigma_ptr,       /* A covariance matrix: X^TX/n */
   for (irow=0; irow<nactive; irow++) {
 
     active_row_ptr = ((int *) ever_active_ptr + irow);
-    active_row = *active_row_ptr;
+    active_row = *active_row_ptr - 1;    // Ever-active set is 1-based
     theta_row_ptr = ((double *) theta + active_row);
 
     for (icol=0; icol<nactive; icol++) {
       
       active_col_ptr = ((int *) ever_active_ptr + icol);
-      active_col = *active_col_ptr;
+      active_col = *active_col_ptr - 1;    // Ever-active set is 1-based
       theta_col_ptr = ((double *) theta + active_col);
 
       Sigma_ptr_tmp = ((double *) Sigma_ptr + nrow * active_col + active_row); // Matrices are column-major order
@@ -48,14 +48,15 @@ double objective(double *Sigma_ptr,       /* A covariance matrix: X^TX/n */
     value = value + bound * fabs((*theta_row_ptr)); // the \ell_1 term
   }
 
-  theta_row_ptr = ((double *) theta + row);
+  theta_row_ptr = ((double *) theta + (row - 1));     // row is 1-based index
   value -= (*theta_row_ptr); // the elementary basis vector term
 
   return(value);
 }
 
 // Check if active and add it to active list if necessary
-
+// Ever active set is stored as 1-based indices
+// coord is 0-based
 int update_ever_active(int coord,
 		       int *ever_active_ptr,
 		       int *nactive_ptr) {
@@ -67,7 +68,7 @@ int update_ever_active(int coord,
   for (iactive=0; iactive<nactive; iactive++) {
     ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
     active_var = (*ever_active_ptr_tmp);
-    if (active_var == coord) {
+    if (active_var - 1 == coord) {       // Indices in active set are 1-based
       return(1);
     }
   }
@@ -79,7 +80,7 @@ int update_ever_active(int coord,
   // number of active variables
 
   ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
-  *ever_active_ptr_tmp = coord;
+  *ever_active_ptr_tmp = coord + 1;     // Indices are 1-based
   *nactive_ptr += 1;
 
   return(0);
@@ -88,7 +89,7 @@ int update_ever_active(int coord,
 int check_KKT(double *theta,        /* current theta */
 	      double *gradient_ptr, /* Sigma times theta */
 	      int nrow,             /* how many rows in Sigma */
-	      int row,              /* which row: 0-based */
+	      int row,              /* which row: 1-based */
 	      double bound,         /* Lagrange multipler for \ell_1 */
 	      double tol)           /* precision for checking KKT conditions */
 {
@@ -108,7 +109,7 @@ int check_KKT(double *theta,        /* current theta */
 
     // For the basis vector
 
-    if (row == irow) {
+    if (row - 1 == irow) {                // Row is a 1-based index
        gradient -= 1;
     }
 
@@ -139,7 +140,7 @@ double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^T
 			int nrow,                    /* How many rows in Sigma */
 			double bound,                /* feasibility parameter */
 			double *theta,               /* current value */
-			int row,                     /* which row: 0-based */
+			int row,                     /* which row: 1-based */
 			int coord,                   /* which coordinate to update: 0-based */
 			int is_active)               /* Is this part of ever_active */     
 {
@@ -156,8 +157,6 @@ double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^T
   double *quadratic_ptr = ((double *) Sigma_diag_ptr + coord);
   double quadratic_term = *quadratic_ptr;
 
-  // int *ever_active_ptr_tmp;
-
   gradient_ptr_tmp = ((double *) gradient_ptr + coord);
   linear_term = *gradient_ptr_tmp;
 
@@ -167,9 +166,10 @@ double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^T
   // The coord entry of gradient_ptr term has a diagonal term in it:
   // Sigma[coord, coord] * theta[coord]
   // This removes it. 
+
   linear_term -= quadratic_term * old_value;
 
-  if (row == coord) {
+  if (row - 1 == coord) {              // Row is 1-based
     linear_term -= 1;
   }
 
@@ -220,13 +220,13 @@ double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^T
 int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 		  double *Sigma_diag_ptr,     /* Diagonal entry of covariance matrix */
 		  double *gradient_ptr,       /* Sigma times theta */
-		  int *ever_active_ptr,       /* Ever active set: 0-based */ 
+		  int *ever_active_ptr,       /* Ever active set: 1-based */ 
 		  int *nactive_ptr,           /* Size of ever active set */
 		  int nrow,                   /* How many rows in Sigma */
 		  double bound,               /* feasibility parameter */
 		  double *theta,              /* current value */
 		  int maxiter,                /* how many iterations */
-		  int row,                    /* which coordinate to update: 0-based */
+		  int row,                    /* which coordinate to solve: 1-based */
 		  double kkt_tol,             /* precision for checking KKT conditions */
 		  double objective_tol)       /* precision for checking relative decrease in objective value */
 {
@@ -267,7 +267,7 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 		       bound,
 		       theta,
 		       row,
-		       *active_ptr,
+		       *active_ptr-1,  // Ever active set is 1-based
 		       1);
       active_ptr++;
     }
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
index 0cefac4..ba14d02 100644
--- a/selectiveInference/src/quadratic_program.c
+++ b/selectiveInference/src/quadratic_program.c
@@ -34,13 +34,13 @@ double objective_qp(double *Sigma_ptr,       /* A covariance matrix: X^TX/n */
   for (irow=0; irow<nactive; irow++) {
 
     active_row_ptr = ((int *) ever_active_ptr + irow);
-    active_row = *active_row_ptr;
+    active_row = *active_row_ptr - 1;          // Ever-active is 1-based
     theta_row_ptr = ((double *) theta + active_row);
 
     for (icol=0; icol<nactive; icol++) {
       
       active_col_ptr = ((int *) ever_active_ptr + icol);
-      active_col = *active_col_ptr;
+      active_col = *active_col_ptr - 1;          // Ever-active is 1-based
       theta_col_ptr = ((double *) theta + active_col);
 
       Sigma_ptr_tmp = ((double *) Sigma_ptr + nrow * active_col + active_row); // Matrices are column-major order
@@ -59,6 +59,8 @@ double objective_qp(double *Sigma_ptr,       /* A covariance matrix: X^TX/n */
   return(value);
 }
 
+// Ever-active is 1-based
+// coord is 0-based
 int update_ever_active_qp(int coord,
 			  int *ever_active_ptr,
 			  int *nactive_ptr) {
@@ -70,7 +72,7 @@ int update_ever_active_qp(int coord,
   for (iactive=0; iactive<nactive; iactive++) {
     ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
     active_var = *ever_active_ptr_tmp;
-    if (active_var == coord) {
+    if (active_var - 1 == coord) {          // Ever-active is 1-based
       return(1);
     }
   }
@@ -82,7 +84,7 @@ int update_ever_active_qp(int coord,
   // number of active variables
 
   ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
-  *ever_active_ptr_tmp = coord;
+  *ever_active_ptr_tmp = coord + 1;         // Ever-active is 1-based
   *nactive_ptr += 1;
 
   return(0);
@@ -130,13 +132,13 @@ double update_one_coord_qp(double *Sigma_ptr,           /* A covariance matrix:
 			   double *linear_func_ptr,     /* Linear term in objective */
 			   double *Sigma_diag_ptr,      /* Diagonal entries of Sigma */
 			   double *gradient_ptr,        /* Sigma times theta */
-			   int *ever_active_ptr,        /* Ever active set: 0-based */ 
+			   int *ever_active_ptr,        /* Ever active set: 1-based */ 
 			   int *nactive_ptr,            /* Size of ever active set */
 			   int nrow,                    /* How many rows in Sigma */
 			   double bound,                /* feasibility parameter */
 			   double *theta,               /* current value */
 			   int coord,                   /* which coordinate to update: 0-based */
-			   int is_active)               /* Is this part of ever_active */     
+			   int is_active)               /* Is this coord in ever_active */     
 {
 
   double delta;
@@ -211,7 +213,7 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 	     double *linear_func_ptr,    /* Linear term in objective */
 	     double *Sigma_diag_ptr,     /* Diagonal entry of covariance matrix */
 	     double *gradient_ptr,       /* Sigma times theta */
-	     int *ever_active_ptr,       /* Ever active set: 0-based */ 
+	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
 	     int *nactive_ptr,           /* Size of ever active set */
 	     int nrow,                   /* How many rows in Sigma */
 	     double bound,               /* feasibility parameter */
@@ -259,7 +261,7 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 			  nrow,
 			  bound,
 			  theta,
-			  *active_ptr,
+			  *active_ptr - 1,   // Ever-active is 1-based
 			  1);
       active_ptr++;
     }

From b1270836d1190873d120f673581617ab4792ada1 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sun, 20 Aug 2017 16:39:12 -0700
Subject: [PATCH 231/493] added an argument to limit the number of times the
 linesearch runs

---
 selectiveInference/R/funs.fixed.R       | 15 +++++++--------
 selectiveInference/man/fixedLassoInf.Rd | 10 +++++++---
 selectiveInference/src/Rcpp-debias.cpp  |  3 +++
 3 files changed, 17 insertions(+), 11 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index ae9fdc6..0ca60a3 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -3,9 +3,9 @@
 # min 1/2 || y - \beta_0 - X \beta ||_2^2 + \lambda || \beta ||_1
 
 fixedLassoInf <- function(x, y, beta, lambda, family=c("gaussian","binomial","cox"),intercept=TRUE, add.targets=NULL, status=NULL,
-sigma=NULL, alpha=0.1,
-                     type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
-                     gridrange=c(-100,100), bits=NULL, verbose=FALSE) {
+                          sigma=NULL, alpha=0.1,
+                          type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
+                          gridrange=c(-100,100), bits=NULL, verbose=FALSE, linesearch.try=5) {
 
   family = match.arg(family)
   this.call = match.call()
@@ -158,7 +158,7 @@ sigma=NULL, alpha=0.1,
 
       # Approximate inverse covariance matrix for when (n < p) from lasso_Inference.R
 
-      htheta <- InverseLinfty(hsigma, n, length(S), verbose=FALSE)
+      htheta <- InverseLinfty(hsigma, n, length(S), verbose=FALSE, max.try=linesearch.try)
       # htheta <- InverseLinfty(hsigma, n, verbose=FALSE)
       
       FS = rbind(diag(length(S)),matrix(0,pp-length(S),length(S)))
@@ -268,7 +268,7 @@ fixedLasso.poly=
 ### Functions borrowed and slightly modified from lasso_inference.R
 
 ## Approximates inverse covariance matrix theta
-InverseLinfty <- function(sigma, n, e, resol=1.2, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
+InverseLinfty <- function(sigma, n, e, resol=1.2, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE, max.try=10) {
     isgiven <- 1;
   if (is.null(mu)){
     isgiven <- 0;
@@ -293,13 +293,12 @@ InverseLinfty <- function(sigma, n, e, resol=1.2, mu=NULL, maxiter=50, threshold
 
     output = NULL
 
-    while ((mu.stop != 1)&&(try.no<10)){
+    while ((mu.stop != 1) && (try.no<max.try) ){
       last.beta <- beta
-      #print(c("#######################trying ", try.no))
       output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, soln_result=output) # uses a warm start
       beta <- output$soln
       iter <- output$iter
-      if (isgiven==1){
+      if (isgiven==1) {
         mu.stop <- 1
       }
       else{
diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index 3633524..2f8517d 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -10,9 +10,9 @@ fixed value of the tuning parameter lambda
 }
 \usage{
 fixedLassoInf(x, y, beta, lambda, family = c("gaussian", "binomial", 
-    "cox"),intercept=TRUE, add.targets=NULL, status=NULL, sigma=NULL, alpha=0.1,
+         "cox"),intercept=TRUE, add.targets=NULL, status=NULL, sigma=NULL, alpha=0.1,
          type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
-         gridrange=c(-100,100), bits=NULL, verbose=FALSE) 
+         gridrange=c(-100,100), bits=NULL, verbose=FALSE, linesearch.try=5) 
 }
 \arguments{
   \item{x}{
@@ -85,7 +85,11 @@ helpful (though computationally more costly). In particular, extra precision mig
 if the values in the output columns of \code{tailarea} differ noticeably from alpha/2.
 }
 \item{verbose}{
-Print out progress along the way? Default is FALSE}
+Print out progress along the way? Default is FALSE
+}
+\item{linesearch.try}{
+When running type="full" (i.e. debiased LASSO) how many attempts in the line search?
+}
 }
 
 \details{
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index a78ae81..85338b4 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -1,6 +1,9 @@
 #include <Rcpp.h>      // need to include the main Rcpp header file 
 #include <debias.h>    // where find_one_row_void is defined
 
+// Below, the gradient should be equal to Sigma * theta + linear_func!!
+// No check is done on this.
+
 // [[Rcpp::export]]
 Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		    double bound,

From acbf82cf120bdfbf2c55c2ff3898425f44d234c6 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sun, 20 Aug 2017 16:43:30 -0700
Subject: [PATCH 232/493] making it 10 tries as before

---
 selectiveInference/R/funs.fixed.R       | 2 +-
 selectiveInference/man/fixedLassoInf.Rd | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 0ca60a3..b03fd4d 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -5,7 +5,7 @@
 fixedLassoInf <- function(x, y, beta, lambda, family=c("gaussian","binomial","cox"),intercept=TRUE, add.targets=NULL, status=NULL,
                           sigma=NULL, alpha=0.1,
                           type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
-                          gridrange=c(-100,100), bits=NULL, verbose=FALSE, linesearch.try=5) {
+                          gridrange=c(-100,100), bits=NULL, verbose=FALSE, linesearch.try=10) {
 
   family = match.arg(family)
   this.call = match.call()
diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index 2f8517d..bd740f8 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -12,7 +12,7 @@ fixed value of the tuning parameter lambda
 fixedLassoInf(x, y, beta, lambda, family = c("gaussian", "binomial", 
          "cox"),intercept=TRUE, add.targets=NULL, status=NULL, sigma=NULL, alpha=0.1,
          type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
-         gridrange=c(-100,100), bits=NULL, verbose=FALSE, linesearch.try=5) 
+         gridrange=c(-100,100), bits=NULL, verbose=FALSE, linesearch.try=10) 
 }
 \arguments{
   \item{x}{

From 7fa51202acc293c10b2b90da726212035a02b6f5 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 28 Aug 2017 21:40:52 -0700
Subject: [PATCH 233/493] NF: stop early if active set gets too big

---
 selectiveInference/R/funs.fixed.R          | 29 ++++++++++++++++------
 selectiveInference/src/Rcpp-debias.cpp     | 22 +++++++++++-----
 selectiveInference/src/debias.c            | 11 +++++++-
 selectiveInference/src/debias.h            |  7 +++---
 selectiveInference/src/quadratic_program.c | 11 +++++++-
 5 files changed, 61 insertions(+), 19 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 455304a..082a1f6 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -271,7 +271,9 @@ fixedLasso.poly=
 ## Approximates inverse covariance matrix theta
 InverseLinfty <- function(sigma, n, e, resol=1.2, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE, max.try=10) {
 
-    isgiven <- 1;
+  max_active = max(50, 2 * sqrt(n))
+
+  isgiven <- 1;
   if (is.null(mu)){
     isgiven <- 0;
   }
@@ -295,17 +297,19 @@ InverseLinfty <- function(sigma, n, e, resol=1.2, mu=NULL, maxiter=50, threshold
 
     output = NULL
 
+    last.beta = NULL
+
     while ((mu.stop != 1) && (try.no<max.try) ){
-      last.beta <- beta
 
-      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, soln_result=output) # uses a warm start
+      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, soln_result=output, max_active=max_active) # uses a warm start
       beta <- output$soln
 
       iter <- output$iter
+
       if (isgiven==1) {
         mu.stop <- 1
       }
-      else{
+      else {
         if (try.no==1){
           if (iter == (maxiter+1)){
             incr <- 1;
@@ -325,14 +329,19 @@ InverseLinfty <- function(sigma, n, e, resol=1.2, mu=NULL, maxiter=50, threshold
           if ((incr == 0)&&(iter < (maxiter+1))){
             mu <- mu/resol;
           }
-          if ((incr == 0)&&(iter == (maxiter+1))){
+          if ((incr == 0) && (iter == (maxiter+1))){
             mu <- mu*resol;
             beta <- last.beta;
             mu.stop <- 1;
           }
         }
+	if (output$max_active_check) {
+	    mu.stop <- 1;
+	    beta <- last.beta;
+	}
       }
       try.no <- try.no+1
+      last.beta <- beta
     }
     M[i,] <- beta;
   }
@@ -341,7 +350,7 @@ InverseLinfty <- function(sigma, n, e, resol=1.2, mu=NULL, maxiter=50, threshold
 
 
 InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL, kkt_tol=1.e-6, objective_tol=1.e-6,
-		                 use_QP=TRUE) {
+		                 use_QP=TRUE, max_active=NULL) {
 
   # If soln_result is not Null, it is used as a warm start.
   # It should be a list
@@ -349,6 +358,10 @@ InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL, kkt
 
   p = nrow(Sigma)
 
+  if (is.null(max_active)) {
+      max_active = 50 # arbitrary?
+  }
+
   if (is.null(soln_result)) {
      soln = rep(0, p)
      ever_active = rep(0, p)
@@ -375,9 +388,9 @@ InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL, kkt
   }
 
   if (use_QP) {
-      result = solve_QP(Sigma, mu, maxiter, soln, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol) 
+      result = solve_QP(Sigma, mu, maxiter, soln, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) 
   } else {
-      result = find_one_row_debiasingM(Sigma, i, mu, maxiter, soln, gradient, ever_active, nactive, kkt_tol, objective_tol) # C function uses 1-based indexing for active set
+      result = find_one_row_debiasingM(Sigma, i, mu, maxiter, soln, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) # C function uses 1-based indexing for active set
   }
 
   # Check feasibility
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index 85338b4..47bbaca 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -14,7 +14,8 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		    Rcpp::IntegerVector ever_active,
 		    Rcpp::IntegerVector nactive,
 		    double kkt_tol,
-		    double objective_tol
+		    double objective_tol,
+		    int max_active
 		    ) {
 
   int nrow = Sigma.nrow(); // number of features
@@ -44,7 +45,8 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		      (double *) theta.begin(),
 		      maxiter,
 		      kkt_tol,
-		      objective_tol);
+		      objective_tol,
+		      max_active);
   
   // Check whether feasible
 
@@ -54,13 +56,16 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 			       bound,
 			       kkt_tol);
 
+  int max_active_check = (*(nactive.begin()) >= max_active);
+
   return(Rcpp::List::create(Rcpp::Named("soln") = theta,
 			    Rcpp::Named("gradient") = gradient,
 			    Rcpp::Named("linear_func") = linear_func,
 			    Rcpp::Named("iter") = iter,
 			    Rcpp::Named("kkt_check") = kkt_check,
 			    Rcpp::Named("ever_active") = ever_active,
-			    Rcpp::Named("nactive") = nactive));
+			    Rcpp::Named("nactive") = nactive,
+			    Rcpp::Named("max_active_check") = max_active_check));
 
 }
 
@@ -74,7 +79,8 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
 				   Rcpp::IntegerVector ever_active,
 				   Rcpp::IntegerVector nactive,
 				   double kkt_tol,
-				   double objective_tol
+				   double objective_tol,
+				   int max_active
 				   ) {
 
   int nrow = Sigma.nrow(); // number of features
@@ -104,7 +110,8 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
 			   maxiter,
 			   row,
 			   kkt_tol,
-			   objective_tol);
+			   objective_tol,
+			   max_active);
   
   // Check whether feasible
 
@@ -115,11 +122,14 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
 			    bound,
 			    kkt_tol);
 
+  int max_active_check = (*(nactive.begin()) >= max_active);
+
   return(Rcpp::List::create(Rcpp::Named("soln") = theta,
 			    Rcpp::Named("gradient") = gradient,
 			    Rcpp::Named("iter") = iter,
 			    Rcpp::Named("kkt_check") = kkt_check,
 			    Rcpp::Named("ever_active") = ever_active,
-			    Rcpp::Named("nactive") = nactive));
+			    Rcpp::Named("nactive") = nactive,
+			    Rcpp::Named("max_active_check") = max_active_check));
 
 }
diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index 3e3efb5..2881d17 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -228,7 +228,8 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 		  int maxiter,                /* how many iterations */
 		  int row,                    /* which coordinate to solve: 1-based */
 		  double kkt_tol,             /* precision for checking KKT conditions */
-		  double objective_tol)       /* precision for checking relative decrease in objective value */
+		  double objective_tol,       /* precision for checking relative decrease in objective value */
+		  int max_active)             /* Upper limit for size of active set -- otherwise break */ 
 {
 
   int iter = 0;
@@ -311,6 +312,14 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
       break;
     }
 					  
+    // Check size of active set
+
+    if (*nactive_ptr >= max_active) {
+      break;
+    }
+
+    // Check relative decrease of objective
+
     if (check_objective) {
       new_value = objective(Sigma_ptr,
 			    ever_active_ptr,
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index 5510adf..f76eb69 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -14,8 +14,8 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 	     double *theta,              /* current value */
 	     int maxiter,                /* how many iterations */
 	     double kkt_tol,             /* precision for checking KKT conditions */
-	     double objective_tol);      /* precision for checking relative decrease in objective value */
-
+	     double objective_tol,       /* precision for checking relative decrease in objective value */
+	     int max_active);            /* Upper limit for size of active set -- otherwise break */
 
 int check_KKT_qp(double *theta,           /* current theta */
 		 double *gradient_ptr,    /* Current gradient of quadratic loss */
@@ -34,7 +34,8 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 		  int maxiter,                /* how many iterations */
 		  int row,                    /* which coordinate to update: 0-based */
 		  double kkt_tol,             /* precision for checking KKT conditions */
-		  double objective_tol);      /* precision for checking relative decrease in objective value */
+		  double objective_tol,       /* precision for checking relative decrease in objective value */
+                  int max_active);            /* Upper limit for size of active set -- otherwise break */
 
 int check_KKT(double *theta,           /* current theta */
 	      double *gradient_ptr,    /* Current gradient of quadratic loss */
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
index ba14d02..4afbd32 100644
--- a/selectiveInference/src/quadratic_program.c
+++ b/selectiveInference/src/quadratic_program.c
@@ -220,7 +220,8 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 	     double *theta,              /* current value */
 	     int maxiter,                /* max number of iterations */
 	     double kkt_tol,             /* precision for checking KKT conditions */
-	     double objective_tol)       /* precision for checking relative decrease in objective value */
+	     double objective_tol,       /* precision for checking relative decrease in objective value */
+	     int max_active)             /* Upper limit for size of active set -- otherwise break */ 
 {
 
   int iter = 0;
@@ -303,6 +304,14 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
       break;
     }
 					  
+    // Check size of active set
+
+    if (*nactive_ptr >= max_active) {
+      break;
+    }
+
+    // Check relative decrease of objective
+
     if (check_objective) {
       new_value = objective_qp(Sigma_ptr,
 			       linear_func_ptr,

From 0c1f9ea5283d2d2d75b86eaf5f6258fdf19c7ce2 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 29 Aug 2017 17:55:44 -0700
Subject: [PATCH 234/493] renaming function, but all linesearch logic into a
 row-wise function

---
 selectiveInference/R/funs.fixed.R | 235 ++++++++++++++++--------------
 1 file changed, 129 insertions(+), 106 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 082a1f6..33f2cc3 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -158,10 +158,8 @@ fixedLassoInf <- function(x, y, beta, lambda, family=c("gaussian","binomial","co
 
       # Approximate inverse covariance matrix for when (n < p) from lasso_Inference.R
 
-      htheta <- InverseLinfty(hsigma, n, length(S), verbose=FALSE, max.try=linesearch.try)
+      htheta = debiasing_matrix(hsigma, n, 1:length(S), verbose=FALSE, max_try=linesearch.try, warn_kkt=TRUE)
 
-      # htheta <- InverseLinfty(hsigma, n, verbose=FALSE)
-      
       FS = rbind(diag(length(S)),matrix(0,pp-length(S),length(S)))
       GS = cbind(diag(length(S)),matrix(0,length(S),pp-length(S)))
       ithetasigma = (GS-(htheta%*%hsigma))
@@ -269,137 +267,162 @@ fixedLasso.poly=
 ### Functions borrowed and slightly modified from lasso_inference.R
 
 ## Approximates inverse covariance matrix theta
-InverseLinfty <- function(sigma, n, e, resol=1.2, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE, max.try=10) {
 
-  max_active = max(50, 2 * sqrt(n))
+debiasing_matrix = function(Sigma, 
+                            nsample, 
+                            rows, 
+			    verbose=FALSE, 
+			    mu=NULL,             # starting value of mu
+   			    linesearch=TRUE,     # do a linesearch?
+   		            resol=1.2,           # multiplicative factor for linesearch
+			    max_active=NULL,     # how big can active set get?
+			    max_try=10,          # how many steps in linesearch?
+			    warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
+			    max_iter=100,         # how many iterations for each optimization problem
+                            kkt_tol=1.e-4,       # tolerance for the KKT conditions
+			    objective_tol=1.e-4  # tolerance for relative decrease in objective
+                            ) {
+
+
+  if (is.null(max_active)) {
+     max_active = max(50, 0.3 * nsample)
+  } 
+
+  p = nrow(Sigma);
+  M = matrix(0, length(rows), p);
 
-  isgiven <- 1;
-  if (is.null(mu)){
-    isgiven <- 0;
+  if (is.null(mu)) {
+      mu = (1/sqrt(nsample)) * qnorm(1-(0.1/(p^2)))
   }
-  
-  p <- nrow(sigma);
-  M <- matrix(0, e, p);
+ 
   xperc = 0;
   xp = round(p/10);
-  for (i in 1:e) {
-    if ((i %% xp)==0){
+  idx = 1;
+  for (row in rows) {
+
+    if ((idx %% xp)==0){
       xperc = xperc+10;
       if (verbose) {
         print(paste(xperc,"% done",sep="")); }
     }
-    if (isgiven==0){
-      mu <- (1/sqrt(n)) * qnorm(1-(0.1/(p^2)));
-    }
-    mu.stop <- 0;
-    try.no <- 1;
-    incr <- 0;
 
-    output = NULL
+    output = debiasing_row(Sigma,
+                           row,
+                           mu,
+                           linesearch=linesearch,
+                           resol=resol,
+			   max_active=max_active,
+			   max_try=max_try,
+			   warn_kkt=FALSE,
+			   max_iter=max_iter,
+			   kkt_tol=kkt_tol,
+			   objective_tol=objective_tol)
+
+    if (warn_kkt && (!output$kkt_check)) {
+       warning("Solution for row of M does not seem to be feasible")
+    } 
+  
+    M[idx,] = output$soln;
+    idx = idx + 1;
+  }
+  return(M)
+}
 
-    last.beta = NULL
+debiasing_row = function (Sigma, 
+                          row, 
+                          mu, 
+			  linesearch=TRUE,     # do a linesearch?
+		          resol=1.2,           # multiplicative factor for linesearch
+			  max_active=NULL,     # how big can active set get?
+			  max_try=10,          # how many steps in linesearch?
+			  warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
+			  max_iter=100,         # how many iterations for each optimization problem
+                          kkt_tol=1.e-4,       # tolerance for the KKT conditions
+			  objective_tol=1.e-4  # tolerance for relative decrease in objective
+                          ) {
 
-    while ((mu.stop != 1) && (try.no<max.try) ){
+  p = nrow(Sigma)
 
-      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, soln_result=output, max_active=max_active) # uses a warm start
-      beta <- output$soln
+  if (is.null(max_active)) {
+      max_active = nrow(Sigma)
+  }
 
-      iter <- output$iter
+  # Initialize variables 
 
-      if (isgiven==1) {
-        mu.stop <- 1
-      }
-      else {
-        if (try.no==1){
-          if (iter == (maxiter+1)){
-            incr <- 1;
-            mu <- mu*resol;
-          } else {
-            incr <- 0;
-            mu <- mu/resol;
-          }
-        }
-        if (try.no > 1){
-          if ((incr == 1)&&(iter == (maxiter+1))){
-            mu <- mu*resol;
-          }
-          if ((incr == 1)&&(iter < (maxiter+1))){
-            mu.stop <- 1;
-          }
-          if ((incr == 0)&&(iter < (maxiter+1))){
-            mu <- mu/resol;
-          }
-          if ((incr == 0) && (iter == (maxiter+1))){
-            mu <- mu*resol;
-            beta <- last.beta;
-            mu.stop <- 1;
-          }
-        }
-	if (output$max_active_check) {
-	    mu.stop <- 1;
-	    beta <- last.beta;
-	}
-      }
-      try.no <- try.no+1
-      last.beta <- beta
-    }
-    M[i,] <- beta;
-  }
-  return(M)
-}
+  soln = rep(0, p)
 
+  ever_active = rep(0, p)
+  ever_active[1] = row      # 1-based
+  ever_active = as.integer(ever_active)
+  nactive = as.integer(1)
 
-InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL, kkt_tol=1.e-6, objective_tol=1.e-6,
-		                 use_QP=TRUE, max_active=NULL) {
+  linear_func = rep(0, p)
+  linear_func[row] = -1
+  linear_func = as.numeric(linear_func)
+  gradient = 1. * linear_func 
 
-  # If soln_result is not Null, it is used as a warm start.
-  # It should be a list
-  # with entries "soln", "gradient", "ever_active", "nactive"
+  counter_idx = 1;
+  incr = 0;
 
-  p = nrow(Sigma)
+  last_output = NULL
 
-  if (is.null(max_active)) {
-      max_active = 50 # arbitrary?
-  }
+  while (counter_idx < max_try) {
 
-  if (is.null(soln_result)) {
-     soln = rep(0, p)
-     ever_active = rep(0, p)
-     ever_active[1] = i      # 1-based
-     ever_active = as.integer(ever_active)
-     nactive = as.integer(1)
-     if (use_QP) {
-          linear_func = rep(0, p)
-	  linear_func[i] = -1
-	  linear_func = as.numeric(linear_func)
-	  gradient = 1. * linear_func 
-     } else {
-          gradient = rep(0, p)
-     }
-  }
-  else {
-     soln = soln_result$soln
-     gradient = soln_result$gradient  
-     ever_active = as.integer(soln_result$ever_active)
-     nactive = as.integer(soln_result$nactive)
-     if (use_QP) { 
-         linear_func = soln_result$linear_func
-     }
-  }
+      result = solve_QP(Sigma, mu, max_iter, soln, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) 
 
-  if (use_QP) {
-      result = solve_QP(Sigma, mu, maxiter, soln, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) 
-  } else {
-      result = find_one_row_debiasingM(Sigma, i, mu, maxiter, soln, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) # C function uses 1-based indexing for active set
-  }
+      iter = result$iter
+
+      # Logic for whether we should continue the line search
+
+      if (!linesearch) {
+        break
+      }
+
+      if (counter_idx == 1){
+        if (iter == (max_iter+1)){
+           incr = 1; # was the original problem feasible? 1 if not
+         } else {
+           incr = 0; # original problem was feasible
+         }
+      } 
+
+      if (incr == 1) { # trying to find a feasible point
+         if ((iter < (max_iter+1)) && (counter_idx > 1)) { 
+           break;      # we've found a feasible point and solved the problem            
+         }
+         mu = mu * resol;
+      } else {         # trying to drop the bound parameter further
+         if ((iter == (max_iter + 1)) && (counter_idx > 1)) {
+            result = last_output; # problem seems infeasible because we didn't solve it
+   	    break;                # so we revert to previously found solution
+         }
+         mu = mu / resol;
+      }
+
+      # If the active set has grown to a certain size
+      # then we stop, presuming problem has become
+      # infeasible.
+
+      # We revert to the previous solution
+	
+      if (result$max_active_check) {
+	  result = last_output;
+	  break;
+      }
+      
+      counter_idx = counter_idx + 1
+      last_output = list(soln=result$soln,
+                         kkt_check=result$kkt_check)
+    }
 
   # Check feasibility
 
-  if (!result$kkt_check) {
+  if (warn_kkt && (!result$kkt_check)) {
      warning("Solution for row of M does not seem to be feasible")
   } 
 
-  return(result)
+  return(list(soln=result$soln,
+              kkt_check=result$kkt_check))
 
 }
 

From e2924290bc5a1a595fce93f1722f18d27f7dccc8 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 29 Aug 2017 17:57:10 -0700
Subject: [PATCH 235/493] camel case function name (ew... but consistent)

---
 selectiveInference/R/funs.fixed.R | 50 +++++++++++++++----------------
 1 file changed, 25 insertions(+), 25 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 33f2cc3..3d28109 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -158,7 +158,7 @@ fixedLassoInf <- function(x, y, beta, lambda, family=c("gaussian","binomial","co
 
       # Approximate inverse covariance matrix for when (n < p) from lasso_Inference.R
 
-      htheta = debiasing_matrix(hsigma, n, 1:length(S), verbose=FALSE, max_try=linesearch.try, warn_kkt=TRUE)
+      htheta = debiasingMatrix(hsigma, n, 1:length(S), verbose=FALSE, max_try=linesearch.try, warn_kkt=TRUE)
 
       FS = rbind(diag(length(S)),matrix(0,pp-length(S),length(S)))
       GS = cbind(diag(length(S)),matrix(0,length(S),pp-length(S)))
@@ -268,7 +268,7 @@ fixedLasso.poly=
 
 ## Approximates inverse covariance matrix theta
 
-debiasing_matrix = function(Sigma, 
+debiasingMatrix = function(Sigma, 
                             nsample, 
                             rows, 
 			    verbose=FALSE, 
@@ -306,17 +306,17 @@ debiasing_matrix = function(Sigma,
         print(paste(xperc,"% done",sep="")); }
     }
 
-    output = debiasing_row(Sigma,
-                           row,
-                           mu,
-                           linesearch=linesearch,
-                           resol=resol,
-			   max_active=max_active,
-			   max_try=max_try,
-			   warn_kkt=FALSE,
-			   max_iter=max_iter,
-			   kkt_tol=kkt_tol,
-			   objective_tol=objective_tol)
+    output = debiasingRow(Sigma,
+                          row,
+                          mu,
+                          linesearch=linesearch,
+                          resol=resol,
+			  max_active=max_active,
+			  max_try=max_try,
+			  warn_kkt=FALSE,
+			  max_iter=max_iter,
+			  kkt_tol=kkt_tol,
+			  objective_tol=objective_tol)
 
     if (warn_kkt && (!output$kkt_check)) {
        warning("Solution for row of M does not seem to be feasible")
@@ -328,18 +328,18 @@ debiasing_matrix = function(Sigma,
   return(M)
 }
 
-debiasing_row = function (Sigma, 
-                          row, 
-                          mu, 
-			  linesearch=TRUE,     # do a linesearch?
-		          resol=1.2,           # multiplicative factor for linesearch
-			  max_active=NULL,     # how big can active set get?
-			  max_try=10,          # how many steps in linesearch?
-			  warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
-			  max_iter=100,         # how many iterations for each optimization problem
-                          kkt_tol=1.e-4,       # tolerance for the KKT conditions
-			  objective_tol=1.e-4  # tolerance for relative decrease in objective
-                          ) {
+debiasingRow = function (Sigma, 
+                         row, 
+                         mu, 
+		         linesearch=TRUE,     # do a linesearch?
+		         resol=1.2,           # multiplicative factor for linesearch
+			 max_active=NULL,     # how big can active set get?
+			 max_try=10,          # how many steps in linesearch?
+			 warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
+			 max_iter=100,         # how many iterations for each optimization problem
+                         kkt_tol=1.e-4,       # tolerance for the KKT conditions
+			 objective_tol=1.e-4  # tolerance for relative decrease in objective
+                         ) {
 
   p = nrow(Sigma)
 

From ec07564ccba45c7959074ea905422e3c744b86d4 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 29 Aug 2017 23:52:54 -0700
Subject: [PATCH 236/493] new export debiasingMatrix

---
 selectiveInference/NAMESPACE              |   5 +-
 selectiveInference/R/funs.fixed.R         |  57 +++++++-----
 selectiveInference/man/debiasingMatrix.Rd | 106 ++++++++++++++++++++++
 3 files changed, 144 insertions(+), 24 deletions(-)
 create mode 100644 selectiveInference/man/debiasingMatrix.Rd

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index ab2a111..d72d56a 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -6,8 +6,6 @@ export(lar,fs,
        print.larInf,print.fsInf,
        plot.lar,plot.fs,
        fixedLassoInf,print.fixedLassoInf,
-#      fixedLogitLassoInf,print.fixedLogitLassoInf,
-#       fixedCoxLassoInf,print.fixedCoxLassoInf,
        forwardStop,
        estimateSigma,
        manyMeans,print.manyMeans,
@@ -15,7 +13,8 @@ export(lar,fs,
        scaleGroups,factorDesign,
        TG.pvalue, 
        TG.limits, 
-       TG.interval
+       TG.interval,
+       debiasingMatrix
     )
 
 S3method("coef", "lar")
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 3d28109..0fca05f 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -269,19 +269,19 @@ fixedLasso.poly=
 ## Approximates inverse covariance matrix theta
 
 debiasingMatrix = function(Sigma, 
-                            nsample, 
-                            rows, 
-			    verbose=FALSE, 
-			    mu=NULL,             # starting value of mu
-   			    linesearch=TRUE,     # do a linesearch?
-   		            resol=1.2,           # multiplicative factor for linesearch
-			    max_active=NULL,     # how big can active set get?
-			    max_try=10,          # how many steps in linesearch?
-			    warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
-			    max_iter=100,         # how many iterations for each optimization problem
-                            kkt_tol=1.e-4,       # tolerance for the KKT conditions
-			    objective_tol=1.e-4  # tolerance for relative decrease in objective
-                            ) {
+                           nsample, 
+                           rows, 
+		           verbose=FALSE, 
+		           mu=NULL,             # starting value of mu
+   			   linesearch=TRUE,     # do a linesearch?
+   		           scaling_factor=1.5,  # multiplicative factor for linesearch
+			   max_active=NULL,     # how big can active set get?
+			   max_try=10,          # how many steps in linesearch?
+			   warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
+			   max_iter=100,        # how many iterations for each optimization problem
+                           kkt_tol=1.e-4,       # tolerance for the KKT conditions
+			   objective_tol=1.e-8  # tolerance for relative decrease in objective
+                           ) {
 
 
   if (is.null(max_active)) {
@@ -310,7 +310,7 @@ debiasingMatrix = function(Sigma,
                           row,
                           mu,
                           linesearch=linesearch,
-                          resol=resol,
+                          scaling_factor=scaling_factor,
 			  max_active=max_active,
 			  max_try=max_try,
 			  warn_kkt=FALSE,
@@ -322,7 +322,12 @@ debiasingMatrix = function(Sigma,
        warning("Solution for row of M does not seem to be feasible")
     } 
   
-    M[idx,] = output$soln;
+    if (!is.null(output$soln)) {
+        M[idx,] = output$soln;
+    } else {
+        stop(paste("Unable to approximate inverse row ", row));
+    }
+
     idx = idx + 1;
   }
   return(M)
@@ -332,13 +337,13 @@ debiasingRow = function (Sigma,
                          row, 
                          mu, 
 		         linesearch=TRUE,     # do a linesearch?
-		         resol=1.2,           # multiplicative factor for linesearch
+		         scaling_factor=1.2,  # multiplicative factor for linesearch
 			 max_active=NULL,     # how big can active set get?
 			 max_try=10,          # how many steps in linesearch?
 			 warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
-			 max_iter=100,         # how many iterations for each optimization problem
+			 max_iter=100,        # how many iterations for each optimization problem
                          kkt_tol=1.e-4,       # tolerance for the KKT conditions
-			 objective_tol=1.e-4  # tolerance for relative decrease in objective
+			 objective_tol=1.e-8  # tolerance for relative decrease in objective
                          ) {
 
   p = nrow(Sigma)
@@ -368,7 +373,17 @@ debiasingRow = function (Sigma,
 
   while (counter_idx < max_try) {
 
-      result = solve_QP(Sigma, mu, max_iter, soln, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) 
+      result = solve_QP(Sigma, 
+                        mu, 
+                        max_iter, 
+                        soln, 
+                        linear_func, 
+                        gradient, 
+                        ever_active, 
+                        nactive, 
+                        kkt_tol, 
+                        objective_tol, 
+                        max_active) 
 
       iter = result$iter
 
@@ -390,13 +405,13 @@ debiasingRow = function (Sigma,
          if ((iter < (max_iter+1)) && (counter_idx > 1)) { 
            break;      # we've found a feasible point and solved the problem            
          }
-         mu = mu * resol;
+         mu = mu * scaling_factor;
       } else {         # trying to drop the bound parameter further
          if ((iter == (max_iter + 1)) && (counter_idx > 1)) {
             result = last_output; # problem seems infeasible because we didn't solve it
    	    break;                # so we revert to previously found solution
          }
-         mu = mu / resol;
+         mu = mu / scaling_factor;
       }
 
       # If the active set has grown to a certain size
diff --git a/selectiveInference/man/debiasingMatrix.Rd b/selectiveInference/man/debiasingMatrix.Rd
new file mode 100644
index 0000000..973c0b4
--- /dev/null
+++ b/selectiveInference/man/debiasingMatrix.Rd
@@ -0,0 +1,106 @@
+\name{debiasingMatrix}
+\alias{debiasingMatrix}
+\title{
+Find an approximate inverse of a non-negative definite matrix.
+}
+\description{
+Find some rows of an approximate inverse of a non-negative definite 
+symmetric matrix by solving optimization problem described 
+in Javanmard and Montanari (2013).
+}
+\usage{
+debiasingMatrix(Sigma, 
+                nsample, 
+                rows, 
+		verbose=FALSE, 
+		mu=NULL,            
+   		linesearch=TRUE,    
+   		scaling_factor=1.5, 
+		max_active=NULL,    
+		max_try=10,         
+		warn_kkt=FALSE,     
+		max_iter=100,       
+                kkt_tol=1.e-4,      
+		objective_tol=1.e-8)
+}
+\arguments{
+\item{Sigma}{
+A symmetric non-negative definite matrix, often a cross-covariance matrix.
+}
+\item{nsample}{
+Number of samples used in forming the cross-covariance matrix.
+Used for default value of the bound parameter mu.
+}
+\item{rows}{
+Which rows of the approximate inverse to compute.
+}      
+\item{verbose}{
+Print out progress as rows are being computed.
+} 
+\item{mu}{
+Initial bound parameter for each row. Will be changed
+if linesearch is TRUE.
+} 
+\item{linesearch}{
+Run a line search to find as small as possible a bound parameter for each row?
+}
+\item{scaling_factor}{
+In the linesearch, the bound parameter is either multiplied or divided by this
+factor at each step.
+}
+\item{max_active}{
+How large an active set to consider in solving the problem with coordinate descent.
+Defaults to max(50, 0.3*nsample).
+}
+\item{max_try}{
+How many tries in the linesearch.
+}
+\item{warn_kkt}{
+Warn if the problem does not seem to be feasible after running the coordinate
+descent algorithm.
+}
+\item{max_iter}{
+How many full iterations to run of the coordinate descent for each
+value of the bound parameter.
+}
+\item{kkt_tol}{
+Tolerance value for assessing whether KKT conditions for solving the
+dual problem and feasibility of the original problem.
+}
+\item{objective_tol}{
+Tolerance value for assessing convergence of the problem using relative
+decrease of the objective.
+}
+}
+\details{
+This function computes an approximate inverse
+as described in Javanmard and Montanari (2013), specifically
+display (4). The problem is solved by considering a dual
+problem which has an objective similar to a LASSO problem and is solvable
+by coordinate descent. For some values of mu the original
+problem may not be feasible, in which case the dual problem has no solution. 
+An attempt to detect this is made by stopping when the active set grows quite
+large, determined by max_active.
+}
+
+\value{  
+\item{M}{Rows of approximate inverse of Sigma.}
+}
+
+\references{
+Adel Javanmard and Andrea Montanari (2013). 
+Confidence Intervals and Hypothesis Testing for High-Dimensional Regression. Arxiv: 1306.3171
+}
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\examples{
+
+set.seed(10)
+n = 50
+p = 100
+X = matrix(rnorm(n * p), n, p)
+S = t(X) %*% X / n
+M = debiasingMatrix(S, n, c(1,3,5))
+
+}
+ 
\ No newline at end of file

From 3c0ef8d50d185672c792c3be1fb307b48c394a1e Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 30 Aug 2017 00:00:16 -0700
Subject: [PATCH 237/493] minor edit

---
 selectiveInference/R/funs.fixed.R | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 0fca05f..083b3c6 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -264,9 +264,8 @@ fixedLasso.poly=
 
 ##############################
 
-### Functions borrowed and slightly modified from lasso_inference.R
-
 ## Approximates inverse covariance matrix theta
+## using coordinate descent 
 
 debiasingMatrix = function(Sigma, 
                            nsample, 
@@ -333,6 +332,8 @@ debiasingMatrix = function(Sigma,
   return(M)
 }
 
+# Find one row of the debiasing matrix
+
 debiasingRow = function (Sigma, 
                          row, 
                          mu, 

From d94d217a493e66620f84a04617a0150f94b5d038 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 30 Aug 2017 00:01:44 -0700
Subject: [PATCH 238/493] BF: latex percent

---
 selectiveInference/man/debiasingMatrix.Rd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/man/debiasingMatrix.Rd b/selectiveInference/man/debiasingMatrix.Rd
index 973c0b4..28fc47b 100644
--- a/selectiveInference/man/debiasingMatrix.Rd
+++ b/selectiveInference/man/debiasingMatrix.Rd
@@ -99,7 +99,7 @@ set.seed(10)
 n = 50
 p = 100
 X = matrix(rnorm(n * p), n, p)
-S = t(X) %*% X / n
+S = t(X) \%*\% X / n
 M = debiasingMatrix(S, n, c(1,3,5))
 
 }

From 3e9d62dd4af0721f14b887697c6b4295e2887874 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 30 Aug 2017 00:05:23 -0700
Subject: [PATCH 239/493] a little more doc

---
 selectiveInference/man/debiasingMatrix.Rd | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/selectiveInference/man/debiasingMatrix.Rd b/selectiveInference/man/debiasingMatrix.Rd
index 28fc47b..533a12c 100644
--- a/selectiveInference/man/debiasingMatrix.Rd
+++ b/selectiveInference/man/debiasingMatrix.Rd
@@ -6,7 +6,9 @@ Find an approximate inverse of a non-negative definite matrix.
 \description{
 Find some rows of an approximate inverse of a non-negative definite 
 symmetric matrix by solving optimization problem described 
-in Javanmard and Montanari (2013).
+in Javanmard and Montanari (2013). Can be used for approximate 
+Newton step from some consistent estimator (such as the LASSO)
+to find a debiased solution.
 }
 \usage{
 debiasingMatrix(Sigma, 

From 3d8b2481a99831e24c65bee34fb6291486353531 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 31 Aug 2017 14:05:21 -0700
Subject: [PATCH 240/493] exporting function to construct polyhedral
 constraints of LASSO

---
 selectiveInference/NAMESPACE             |  3 +-
 selectiveInference/R/funs.fixed.R        | 64 +++++++++----------
 selectiveInference/man/fixedLassoPoly.Rd | 80 ++++++++++++++++++++++++
 3 files changed, 111 insertions(+), 36 deletions(-)
 create mode 100644 selectiveInference/man/fixedLassoPoly.Rd

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index d72d56a..fe0237e 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -14,7 +14,8 @@ export(lar,fs,
        TG.pvalue, 
        TG.limits, 
        TG.interval,
-       debiasingMatrix
+       debiasingMatrix,
+       fixedLassoPoly
     )
 
 S3method("coef", "lar")
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 083b3c6..c65731d 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -93,14 +93,14 @@ fixedLassoInf <- function(x, y, beta, lambda, family=c("gaussian","binomial","co
                     "'thresh' parameter, for a more accurate convergence."))
     
     # Get lasso polyhedral region, of form Gy >= u
-    if (type == 'full' & p > n) out = fixedLasso.poly(x,y,beta,lambda,vars,inactive=TRUE)
-    else out = fixedLasso.poly(x,y,beta,lambda,vars)
-    G = out$G
-    u = out$u
+    if (type == 'full' & p > n) out = fixedLassoPoly(x,y,lambda,beta,vars,inactive=TRUE)
+    else out = fixedLassoPoly(x,y,lambda,beta,vars)
+    A = out$A
+    b = out$b
     
     # Check polyhedral region
     tol.poly = 0.01
-    if (min(G %*% y - u) < -tol.poly * sqrt(sum(y^2)))
+    if (max(A %*% y - b) > tol.poly * sqrt(sum(y^2)))
       stop(paste("Polyhedral constraints not satisfied; you must recompute beta",
                  "more accurately. With glmnet, make sure to use exact=TRUE in coef(),",
                  "and check whether the specified value of lambda is too small",
@@ -191,7 +191,7 @@ fixedLassoInf <- function(x, y, beta, lambda, family=c("gaussian","binomial","co
     sign[j] = sign(sum(vj*y))
     vj = sign[j] * vj
 
-    limits.info = TG.limits(y, -G, -u, vj, Sigma=diag(rep(sigma^2, n)))
+    limits.info = TG.limits(y, A, b, vj, Sigma=diag(rep(sigma^2, n)))
     a = TG.pvalue.base(limits.info, null_value=null_value[j], bits=bits)
     pv[j] = a$pv
     vlo[j] = a$vlo * mj # Unstandardize (mult by norm of vj)
@@ -221,45 +221,39 @@ fixedLassoInf <- function(x, y, beta, lambda, family=c("gaussian","binomial","co
 #############################
 
 
-fixedLasso.poly=
-  function(x, y, beta, lambda, a, inactive = FALSE) {
-    xa = x[,a,drop=F]
-    xac = x[,!a,drop=F]
-    xai = pinv(crossprod(xa))
-    xap = xai %*% t(xa)
-    za = sign(beta[a])
+fixedLassoPoly =
+  function(X, y, lambda, beta, active, inactive = FALSE) {
+    Xa = X[,active,drop=F]
+    Xac = X[,!active,drop=F]
+    Xai = pinv(crossprod(Xa))
+    Xap = Xai %*% t(Xa)
+
+    za = sign(beta[active])
     if (length(za)>1) dz = diag(za)
     if (length(za)==1) dz = matrix(za,1,1)
     
-    if (inactive) {
-      P = diag(1,nrow(xa)) - xa %*% xap
+    if (inactive) { # should we include the inactive constraints?
+      R = diag(1,nrow(Xa)) - Xa %*% Xap # R is residual forming matrix of selected model
       
-      G = -rbind(
-        1/lambda * t(xac) %*% P,
-        -1/lambda * t(xac) %*% P,
-        -dz %*% xap
+      A = rbind(
+        1/lambda * t(Xac) %*% R,
+        -1/lambda * t(Xac) %*% R,
+        -dz %*% Xap
       )
       lambda2=lambda
-      if(length(lambda)>1) lambda2=lambda[a]
-      u = -c(
-        1 - t(xac) %*% t(xap) %*% za,
-        1 + t(xac) %*% t(xap) %*% za,
-        -lambda2 * dz %*% xai %*% za)
+      if(length(lambda)>1) lambda2=lambda[active]
+      b = c(
+        1 - t(Xac) %*% t(Xap) %*% za,
+        1 + t(Xac) %*% t(Xap) %*% za,
+        -lambda2 * dz %*% Xai %*% za)
     } else {
-      G = -rbind(
-        #   1/lambda * t(xac) %*% P,
-        # -1/lambda * t(xac) %*% P,
-        -dz %*% xap
-      )
+      A = -dz %*% Xap
       lambda2=lambda
-      if(length(lambda)>1) lambda2=lambda[a]
-      u = -c(
-        #   1 - t(xac) %*% t(xap) %*% za,
-        #   1 + t(xac) %*% t(xap) %*% za,
-        -lambda2 * dz %*% xai %*% za)
+      if(length(lambda)>1) lambda2=lambda[active]
+      b = -lambda2 * dz %*% Xai %*% za
     }
     
-    return(list(G=G,u=u))
+    return(list(A=A, b=b))
   }
 
 ##############################
diff --git a/selectiveInference/man/fixedLassoPoly.Rd b/selectiveInference/man/fixedLassoPoly.Rd
new file mode 100644
index 0000000..61b1f30
--- /dev/null
+++ b/selectiveInference/man/fixedLassoPoly.Rd
@@ -0,0 +1,80 @@
+\name{fixedLassoPoly}
+\alias{fixedLassoPoly}
+
+\title{
+Compute polyhedral constraints for a LASSO problem with
+a fixed value of lambda.
+}
+\description{
+Compute polyhedral representation of the selection region of Lee et al. (2016).
+By construction, y should satisfy A %*% y elementwise less then or equal b.
+}
+\usage{
+fixedLassoPoly(X, y, lambda, beta, active, inactive = FALSE)
+}
+\arguments{
+\item{X}{
+Design matrix of LASSO problem.
+}
+\item{y}{
+Response of LASSO problem.
+}
+\item{lambda}{
+Value of regularization parameter.
+}      
+\item{beta}{
+Solution of LASSO problem with regularization parameter set to lambda.
+}      
+\item{active}{
+Active set of the LASSO problem as a boolean vector. Should correspond
+to the non-zeros of beta.
+} 
+\item{inactive}{
+Form the inactive constraints as well?
+} 
+}
+\details{
+This function computes
+the  polyhedral representation of the selection region of Lee et al. (2016).
+}
+
+\value{  
+\item{A}{Linear part of the affine inequalities.}
+\item{b}{RHS offset the affine inequalities.}
+}
+
+\references{
+Jason Lee, Dennis Sun, Yuekai Sun, and Jonathan Taylor (2016). 
+Exact post-selection inference, with application to the lasso. Annals of Statistics, 44(3), 907-927.
+
+Jonathan Taylor and Robert Tibshirani (2017) Post-selection inference for math L1-penalized likelihood models.
+Canadian Journal of Statistics, xx, 1-21. (Volume still not posted)
+}
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\examples{
+
+set.seed(43)
+n = 50
+p = 10
+sigma = 1
+
+x = matrix(rnorm(n*p),n,p)
+x = scale(x,TRUE,TRUE)
+
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# first run glmnet
+gfit = glmnet(x,y,standardize=FALSE)
+
+# extract coef for a given lambda; note the 1/n factor!
+# (and we don't save the intercept term)
+lambda = .8
+beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
+active = (beta != 0)
+
+fixedLassoPoly(x, y, lambda, beta, active)
+
+}
+ 
\ No newline at end of file

From d935d943aa29682ffc7131147e8b7e69ccb6c28c Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 31 Aug 2017 15:26:04 -0700
Subject: [PATCH 241/493] removing unused C code

---
 selectiveInference/src/Rcpp-debias.cpp     |  65 ----
 selectiveInference/src/debias.c            | 229 +++++++-------
 selectiveInference/src/debias.h            |  21 --
 selectiveInference/src/quadratic_program.c | 332 ---------------------
 4 files changed, 110 insertions(+), 537 deletions(-)
 delete mode 100644 selectiveInference/src/quadratic_program.c

diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index 47bbaca..6420e51 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -68,68 +68,3 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 			    Rcpp::Named("max_active_check") = max_active_check));
 
 }
-
-// [[Rcpp::export]]
-Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
-				   int row, // 0-based 
-				   double bound,
-				   int maxiter,
-				   Rcpp::NumericVector theta,
-				   Rcpp::NumericVector gradient,
-				   Rcpp::IntegerVector ever_active,
-				   Rcpp::IntegerVector nactive,
-				   double kkt_tol,
-				   double objective_tol,
-				   int max_active
-				   ) {
-
-  int nrow = Sigma.nrow(); // number of features
-
-  // Active set
-
-  int irow;
-
-  // Extract the diagonal
-  Rcpp::NumericVector Sigma_diag(nrow);
-  double *sigma_diag_p = Sigma_diag.begin();
-
-  for (irow=0; irow<nrow; irow++) {
-    sigma_diag_p[irow] = Sigma(irow, irow);
-  }
-  
-  // Now call our C function
-
-  int iter = find_one_row_((double *) Sigma.begin(),
-			   (double *) Sigma_diag.begin(),
-			   (double *) gradient.begin(),
-			   (int *) ever_active.begin(),
-			   (int *) nactive.begin(),
-			   nrow,
-			   bound,
-			   (double *) theta.begin(),
-			   maxiter,
-			   row,
-			   kkt_tol,
-			   objective_tol,
-			   max_active);
-  
-  // Check whether feasible
-
-  int kkt_check = check_KKT(theta.begin(),
-			    gradient.begin(),
-			    nrow,
-			    row,
-			    bound,
-			    kkt_tol);
-
-  int max_active_check = (*(nactive.begin()) >= max_active);
-
-  return(Rcpp::List::create(Rcpp::Named("soln") = theta,
-			    Rcpp::Named("gradient") = gradient,
-			    Rcpp::Named("iter") = iter,
-			    Rcpp::Named("kkt_check") = kkt_check,
-			    Rcpp::Named("ever_active") = ever_active,
-			    Rcpp::Named("nactive") = nactive,
-			    Rcpp::Named("max_active_check") = max_active_check));
-
-}
diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/debias.c
index 2881d17..4afbd32 100644
--- a/selectiveInference/src/debias.c
+++ b/selectiveInference/src/debias.c
@@ -4,23 +4,25 @@
 
 // Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
 
-// Dual problem: \text{min}_{\theta} 1/2 \theta^T \Sigma \theta - e_i^T\theta + \mu \|\theta\|_1
+// Dual problem: \text{min}_{\theta} 1/2 \theta^T \Sigma \theta - l^T\theta + \mu \|\theta\|_1
+// where l is `linear_func` below
 
 // This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
 // Therefore we don't have to negate the answer to get theta.
 // Update one coordinate 
 
-double objective(double *Sigma_ptr,       /* A covariance matrix: X^TX/n */
-		 int *ever_active_ptr,    /* Ever active set: 1-based */ 
-		 int *nactive_ptr,        /* Size of ever active set */
-		 int nrow,                /* how many rows in Sigma */
-		 int row,                 /* which row: 1-based */
-		 double bound,            /* Lagrange multipler for \ell_1 */
-		 double *theta)           /* current value */
+double objective_qp(double *Sigma_ptr,       /* A covariance matrix: X^TX/n */
+		    double *linear_func_ptr, /* Linear term in objective */
+		    int *ever_active_ptr,    /* Ever active set: 0-based */ 
+		    int *nactive_ptr,        /* Size of ever active set */
+		    int nrow,                /* how many rows in Sigma */
+		    double bound,            /* Lagrange multipler for \ell_1 */
+		    double *theta)           /* current value */
 {
   int irow, icol;
   double value = 0;
   double *Sigma_ptr_tmp = Sigma_ptr;
+  double *linear_func_ptr_tmp = linear_func_ptr;
   double *theta_row_ptr, *theta_col_ptr;
   int *active_row_ptr, *active_col_ptr;
   int active_row, active_col;
@@ -32,34 +34,36 @@ double objective(double *Sigma_ptr,       /* A covariance matrix: X^TX/n */
   for (irow=0; irow<nactive; irow++) {
 
     active_row_ptr = ((int *) ever_active_ptr + irow);
-    active_row = *active_row_ptr - 1;    // Ever-active set is 1-based
+    active_row = *active_row_ptr - 1;          // Ever-active is 1-based
     theta_row_ptr = ((double *) theta + active_row);
 
     for (icol=0; icol<nactive; icol++) {
       
       active_col_ptr = ((int *) ever_active_ptr + icol);
-      active_col = *active_col_ptr - 1;    // Ever-active set is 1-based
+      active_col = *active_col_ptr - 1;          // Ever-active is 1-based
       theta_col_ptr = ((double *) theta + active_col);
 
       Sigma_ptr_tmp = ((double *) Sigma_ptr + nrow * active_col + active_row); // Matrices are column-major order
 
       value += 0.5 * (*Sigma_ptr_tmp) * (*theta_row_ptr) * (*theta_col_ptr);
     }
-    value = value + bound * fabs((*theta_row_ptr)); // the \ell_1 term
-  }
+    value += bound * fabs((*theta_row_ptr)); // the \ell_1 term
+
+    // The linear term in the objective
 
-  theta_row_ptr = ((double *) theta + (row - 1));     // row is 1-based index
-  value -= (*theta_row_ptr); // the elementary basis vector term
+    linear_func_ptr_tmp = ((double *) linear_func_ptr + active_row);
+    value += (*linear_func_ptr_tmp) * (*theta_row_ptr); 
 
+  }
+  
   return(value);
 }
 
-// Check if active and add it to active list if necessary
-// Ever active set is stored as 1-based indices
+// Ever-active is 1-based
 // coord is 0-based
-int update_ever_active(int coord,
-		       int *ever_active_ptr,
-		       int *nactive_ptr) {
+int update_ever_active_qp(int coord,
+			  int *ever_active_ptr,
+			  int *nactive_ptr) {
   int iactive;
   int active_var;
   int nactive = *nactive_ptr;
@@ -67,31 +71,30 @@ int update_ever_active(int coord,
 
   for (iactive=0; iactive<nactive; iactive++) {
     ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
-    active_var = (*ever_active_ptr_tmp);
-    if (active_var - 1 == coord) {       // Indices in active set are 1-based
+    active_var = *ever_active_ptr_tmp;
+    if (active_var - 1 == coord) {          // Ever-active is 1-based
       return(1);
     }
   }
-
-  // If we have not returned yet, this variable
-  // was not in ever_active
+  
+  // If we haven't returned yet, this means the coord was not in 
+  // ever_active.
 
   // Add it to the active set and increment the 
   // number of active variables
 
   ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
-  *ever_active_ptr_tmp = coord + 1;     // Indices are 1-based
+  *ever_active_ptr_tmp = coord + 1;         // Ever-active is 1-based
   *nactive_ptr += 1;
 
   return(0);
 }
 
-int check_KKT(double *theta,        /* current theta */
-	      double *gradient_ptr, /* Sigma times theta */
-	      int nrow,             /* how many rows in Sigma */
-	      int row,              /* which row: 1-based */
-	      double bound,         /* Lagrange multipler for \ell_1 */
-	      double tol)           /* precision for checking KKT conditions */
+int check_KKT_qp(double *theta,        /* current theta */
+		 double *gradient_ptr, /* Sigma times theta */
+		 int nrow,             /* how many rows in Sigma */
+		 double bound,         /* Lagrange multipler for \ell_1 */
+		 double tol)           /* precision for checking KKT conditions */        
 {
   // First check inactive
 
@@ -107,12 +110,6 @@ int check_KKT(double *theta,        /* current theta */
 
     gradient = *gradient_ptr_tmp;
 
-    // For the basis vector
-
-    if (row - 1 == irow) {                // Row is a 1-based index
-       gradient -= 1;
-    }
-
     if (*theta_ptr != 0) { // these coordinates of gradients should be equal to -bound
       if ((*theta_ptr > 0) &&  (fabs(gradient + bound) > tol * bound)) {
 	return(0);
@@ -126,23 +123,22 @@ int check_KKT(double *theta,        /* current theta */
 	return(0);
       }
     }
-
   }
 
   return(1);
 }
 
-double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^TX/n */
-                        double *Sigma_diag_ptr,      /* Diagonal entries of Sigma */
-                        double *gradient_ptr,        /* Sigma times theta */
-			int *ever_active_ptr,        /* Ever active set: 0-based */ 
-			int *nactive_ptr,            /* Size of ever active set */
-			int nrow,                    /* How many rows in Sigma */
-			double bound,                /* feasibility parameter */
-			double *theta,               /* current value */
-			int row,                     /* which row: 1-based */
-			int coord,                   /* which coordinate to update: 0-based */
-			int is_active)               /* Is this part of ever_active */     
+double update_one_coord_qp(double *Sigma_ptr,           /* A covariance matrix: X^TX/n */
+			   double *linear_func_ptr,     /* Linear term in objective */
+			   double *Sigma_diag_ptr,      /* Diagonal entries of Sigma */
+			   double *gradient_ptr,        /* Sigma times theta */
+			   int *ever_active_ptr,        /* Ever active set: 1-based */ 
+			   int *nactive_ptr,            /* Size of ever active set */
+			   int nrow,                    /* How many rows in Sigma */
+			   double bound,                /* feasibility parameter */
+			   double *theta,               /* current value */
+			   int coord,                   /* which coordinate to update: 0-based */
+			   int is_active)               /* Is this coord in ever_active */     
 {
 
   double delta;
@@ -169,10 +165,6 @@ double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^T
 
   linear_term -= quadratic_term * old_value;
 
-  if (row - 1 == coord) {              // Row is 1-based
-    linear_term -= 1;
-  }
-
   // Now soft-threshold the coord entry of theta 
 
   // Objective is t \mapsto q/2 * t^2 + l * t + bound |t|
@@ -191,7 +183,7 @@ double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^T
   // Add to active set if necessary
 
   if ((is_active == 0) && (value != 0)) {
-    update_ever_active(coord, ever_active_ptr, nactive_ptr);
+    update_ever_active_qp(coord, ever_active_ptr, nactive_ptr);
   }
 
   // Update the linear term
@@ -217,38 +209,40 @@ double update_one_coord(double *Sigma_ptr,           /* A covariance matrix: X^T
 
 }
 
-int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
-		  double *Sigma_diag_ptr,     /* Diagonal entry of covariance matrix */
-		  double *gradient_ptr,       /* Sigma times theta */
-		  int *ever_active_ptr,       /* Ever active set: 1-based */ 
-		  int *nactive_ptr,           /* Size of ever active set */
-		  int nrow,                   /* How many rows in Sigma */
-		  double bound,               /* feasibility parameter */
-		  double *theta,              /* current value */
-		  int maxiter,                /* how many iterations */
-		  int row,                    /* which coordinate to solve: 1-based */
-		  double kkt_tol,             /* precision for checking KKT conditions */
-		  double objective_tol,       /* precision for checking relative decrease in objective value */
-		  int max_active)             /* Upper limit for size of active set -- otherwise break */ 
+int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
+	     double *linear_func_ptr,    /* Linear term in objective */
+	     double *Sigma_diag_ptr,     /* Diagonal entry of covariance matrix */
+	     double *gradient_ptr,       /* Sigma times theta */
+	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
+	     int *nactive_ptr,           /* Size of ever active set */
+	     int nrow,                   /* How many rows in Sigma */
+	     double bound,               /* feasibility parameter */
+	     double *theta,              /* current value */
+	     int maxiter,                /* max number of iterations */
+	     double kkt_tol,             /* precision for checking KKT conditions */
+	     double objective_tol,       /* precision for checking relative decrease in objective value */
+	     int max_active)             /* Upper limit for size of active set -- otherwise break */ 
 {
 
   int iter = 0;
   int icoord = 0;
   int iactive = 0;
   int *active_ptr;
-  double old_value, new_value;
 
   int check_objective = 1;
 
+  double old_value, new_value; 
+
   if (check_objective) {
 
-    old_value = objective(Sigma_ptr,
-			  ever_active_ptr,
-			  nactive_ptr,
-			  nrow,
-			  row,
-			  bound,
-			  theta);
+    old_value = objective_qp(Sigma_ptr,
+			     linear_func_ptr,
+			     ever_active_ptr,
+			     nactive_ptr,
+			     nrow,
+			     bound,
+			     theta);
+
   }
 
 
@@ -259,28 +253,27 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
     active_ptr = (int *) ever_active_ptr;
 
     for (iactive=0; iactive < *nactive_ptr; iactive++) {
-      update_one_coord(Sigma_ptr,
-		       Sigma_diag_ptr,
-		       gradient_ptr,
-		       ever_active_ptr,
-		       nactive_ptr,
-		       nrow,
-		       bound,
-		       theta,
-		       row,
-		       *active_ptr-1,  // Ever active set is 1-based
-		       1);
+      update_one_coord_qp(Sigma_ptr,
+			  linear_func_ptr,
+			  Sigma_diag_ptr,
+			  gradient_ptr,
+			  ever_active_ptr,
+			  nactive_ptr,
+			  nrow,
+			  bound,
+			  theta,
+			  *active_ptr - 1,   // Ever-active is 1-based
+			  1);
       active_ptr++;
     }
 
     // Check KKT
 
-    if (check_KKT(theta,
-		  gradient_ptr,
-		  nrow,
-		  row,
-		  bound,
-		  kkt_tol) == 1) {
+    if (check_KKT_qp(theta,
+		     gradient_ptr,
+		     nrow,
+		     bound,
+		     kkt_tol) == 1) {
       break;
     }
 					  
@@ -288,27 +281,26 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 
     for (icoord=0; icoord<nrow; icoord++) {
 
-      update_one_coord(Sigma_ptr,
-		       Sigma_diag_ptr,
-		       gradient_ptr,
-		       ever_active_ptr,
-		       nactive_ptr,
-		       nrow,
-		       bound,
-		       theta,
-		       row,
-		       icoord,
-		       0);
+      update_one_coord_qp(Sigma_ptr,
+			  linear_func_ptr,
+			  Sigma_diag_ptr,
+			  gradient_ptr,
+			  ever_active_ptr,
+			  nactive_ptr,
+			  nrow,
+			  bound,
+			  theta,
+			  icoord,
+			  0);
     }
 
     // Check KKT
 
-    if (check_KKT(theta,
-		  gradient_ptr,
-		  nrow,
-		  row,
-		  bound,
-		  kkt_tol) == 1) {
+    if (check_KKT_qp(theta,
+		     gradient_ptr,
+		     nrow,
+		     bound,
+		     kkt_tol) == 1) {
       break;
     }
 					  
@@ -321,18 +313,17 @@ int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
     // Check relative decrease of objective
 
     if (check_objective) {
-      new_value = objective(Sigma_ptr,
-			    ever_active_ptr,
-			    nactive_ptr,
-			    nrow,
-			    row,
-			    bound,
-			    theta);
-
-      if (((old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
+      new_value = objective_qp(Sigma_ptr,
+			       linear_func_ptr,
+			       ever_active_ptr,
+			       nactive_ptr,
+			       nrow,
+			       bound,
+			       theta);
+
+      if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
 	break;
       }
-
       old_value = new_value;
     }
   }
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index f76eb69..7cab5e3 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -23,27 +23,6 @@ int check_KKT_qp(double *theta,           /* current theta */
 		 double bound,            /* Lagrange multipler for \ell_1 */ 
 		 double tol);             /* precision for checking KKT conditions */
 
-int find_one_row_(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
-		  double *Sigma_diag_ptr,      /* Diagonal entry of covariance matrix */
-		  double *gradient_ptr,       /* Current gradient of quadratic loss */
-		  int *ever_active_ptr,       /* Ever active set: 0-based */ 
-		  int *nactive_ptr,           /* Size of ever active set */
-		  int nrow,                   /* How many rows in Sigma */
-		  double bound,               /* feasibility parameter */
-		  double *theta,              /* current value */
-		  int maxiter,                /* how many iterations */
-		  int row,                    /* which coordinate to update: 0-based */
-		  double kkt_tol,             /* precision for checking KKT conditions */
-		  double objective_tol,       /* precision for checking relative decrease in objective value */
-                  int max_active);            /* Upper limit for size of active set -- otherwise break */
-
-int check_KKT(double *theta,           /* current theta */
-	      double *gradient_ptr,    /* Current gradient of quadratic loss */
-	      int nrow,                /* how many rows in Sigma */
-	      int row,                 /* which row: 0-based */
-	      double bound,            /* Lagrange multipler for \ell_1 */
-	      double kkt_tol);         /* precision for checking KKT conditions */
-
 #ifdef __cplusplus
 }  /* extern "C" */
 #endif /* __cplusplus */
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
deleted file mode 100644
index 4afbd32..0000000
--- a/selectiveInference/src/quadratic_program.c
+++ /dev/null
@@ -1,332 +0,0 @@
-#include <math.h> // for fabs
-
-// Find an approximate row of \hat{Sigma}^{-1}
-
-// Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
-
-// Dual problem: \text{min}_{\theta} 1/2 \theta^T \Sigma \theta - l^T\theta + \mu \|\theta\|_1
-// where l is `linear_func` below
-
-// This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
-// Therefore we don't have to negate the answer to get theta.
-// Update one coordinate 
-
-double objective_qp(double *Sigma_ptr,       /* A covariance matrix: X^TX/n */
-		    double *linear_func_ptr, /* Linear term in objective */
-		    int *ever_active_ptr,    /* Ever active set: 0-based */ 
-		    int *nactive_ptr,        /* Size of ever active set */
-		    int nrow,                /* how many rows in Sigma */
-		    double bound,            /* Lagrange multipler for \ell_1 */
-		    double *theta)           /* current value */
-{
-  int irow, icol;
-  double value = 0;
-  double *Sigma_ptr_tmp = Sigma_ptr;
-  double *linear_func_ptr_tmp = linear_func_ptr;
-  double *theta_row_ptr, *theta_col_ptr;
-  int *active_row_ptr, *active_col_ptr;
-  int active_row, active_col;
-  int nactive = *nactive_ptr;
-
-  theta_row_ptr = theta;
-  theta_col_ptr = theta;
-
-  for (irow=0; irow<nactive; irow++) {
-
-    active_row_ptr = ((int *) ever_active_ptr + irow);
-    active_row = *active_row_ptr - 1;          // Ever-active is 1-based
-    theta_row_ptr = ((double *) theta + active_row);
-
-    for (icol=0; icol<nactive; icol++) {
-      
-      active_col_ptr = ((int *) ever_active_ptr + icol);
-      active_col = *active_col_ptr - 1;          // Ever-active is 1-based
-      theta_col_ptr = ((double *) theta + active_col);
-
-      Sigma_ptr_tmp = ((double *) Sigma_ptr + nrow * active_col + active_row); // Matrices are column-major order
-
-      value += 0.5 * (*Sigma_ptr_tmp) * (*theta_row_ptr) * (*theta_col_ptr);
-    }
-    value += bound * fabs((*theta_row_ptr)); // the \ell_1 term
-
-    // The linear term in the objective
-
-    linear_func_ptr_tmp = ((double *) linear_func_ptr + active_row);
-    value += (*linear_func_ptr_tmp) * (*theta_row_ptr); 
-
-  }
-  
-  return(value);
-}
-
-// Ever-active is 1-based
-// coord is 0-based
-int update_ever_active_qp(int coord,
-			  int *ever_active_ptr,
-			  int *nactive_ptr) {
-  int iactive;
-  int active_var;
-  int nactive = *nactive_ptr;
-  int *ever_active_ptr_tmp = ever_active_ptr;
-
-  for (iactive=0; iactive<nactive; iactive++) {
-    ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
-    active_var = *ever_active_ptr_tmp;
-    if (active_var - 1 == coord) {          // Ever-active is 1-based
-      return(1);
-    }
-  }
-  
-  // If we haven't returned yet, this means the coord was not in 
-  // ever_active.
-
-  // Add it to the active set and increment the 
-  // number of active variables
-
-  ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
-  *ever_active_ptr_tmp = coord + 1;         // Ever-active is 1-based
-  *nactive_ptr += 1;
-
-  return(0);
-}
-
-int check_KKT_qp(double *theta,        /* current theta */
-		 double *gradient_ptr, /* Sigma times theta */
-		 int nrow,             /* how many rows in Sigma */
-		 double bound,         /* Lagrange multipler for \ell_1 */
-		 double tol)           /* precision for checking KKT conditions */        
-{
-  // First check inactive
-
-  int irow;
-  double *theta_ptr, *gradient_ptr_tmp;
-  double gradient;
-
-  for (irow=0; irow<nrow; irow++) {
-    theta_ptr = ((double *) theta + irow);
-    gradient_ptr_tmp = ((double *) gradient_ptr + irow);
-
-    // Compute this coordinate of the gradient
-
-    gradient = *gradient_ptr_tmp;
-
-    if (*theta_ptr != 0) { // these coordinates of gradients should be equal to -bound
-      if ((*theta_ptr > 0) &&  (fabs(gradient + bound) > tol * bound)) {
-	return(0);
-      }
-      else if ((*theta_ptr < 0) && (fabs(gradient - bound) > tol * bound)) {
-	return(0);
-      }
-    }
-    else {
-      if (fabs(gradient) > (1. + tol) * bound) {
-	return(0);
-      }
-    }
-  }
-
-  return(1);
-}
-
-double update_one_coord_qp(double *Sigma_ptr,           /* A covariance matrix: X^TX/n */
-			   double *linear_func_ptr,     /* Linear term in objective */
-			   double *Sigma_diag_ptr,      /* Diagonal entries of Sigma */
-			   double *gradient_ptr,        /* Sigma times theta */
-			   int *ever_active_ptr,        /* Ever active set: 1-based */ 
-			   int *nactive_ptr,            /* Size of ever active set */
-			   int nrow,                    /* How many rows in Sigma */
-			   double bound,                /* feasibility parameter */
-			   double *theta,               /* current value */
-			   int coord,                   /* which coordinate to update: 0-based */
-			   int is_active)               /* Is this coord in ever_active */     
-{
-
-  double delta;
-  double linear_term = 0;
-  double value = 0;
-  double old_value;
-  double *Sigma_ptr_tmp;
-  double *gradient_ptr_tmp;
-  double *theta_ptr;
-  int icol = 0;
-
-  double *quadratic_ptr = ((double *) Sigma_diag_ptr + coord);
-  double quadratic_term = *quadratic_ptr;
-
-  gradient_ptr_tmp = ((double *) gradient_ptr + coord);
-  linear_term = *gradient_ptr_tmp;
-
-  theta_ptr = ((double *) theta + coord);
-  old_value = *theta_ptr;
-
-  // The coord entry of gradient_ptr term has a diagonal term in it:
-  // Sigma[coord, coord] * theta[coord]
-  // This removes it. 
-
-  linear_term -= quadratic_term * old_value;
-
-  // Now soft-threshold the coord entry of theta 
-
-  // Objective is t \mapsto q/2 * t^2 + l * t + bound |t|
-  // with q=quadratic_term and l=linear_term
-
-  // With a negative linear term, solution should be
-  // positive
-
-  if (linear_term < -bound) {
-    value = (-linear_term - bound) / quadratic_term;
-  }
-  else if (linear_term > bound) {
-    value = -(linear_term - bound) / quadratic_term;
-  }
-
-  // Add to active set if necessary
-
-  if ((is_active == 0) && (value != 0)) {
-    update_ever_active_qp(coord, ever_active_ptr, nactive_ptr);
-  }
-
-  // Update the linear term
-
-  if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
-
-    delta = value - old_value;
-    Sigma_ptr_tmp = ((double *) Sigma_ptr + coord * nrow);
-    gradient_ptr_tmp = ((double *) gradient_ptr);
-
-    for (icol=0; icol<nrow; icol++) {
-      *gradient_ptr_tmp = *gradient_ptr_tmp + delta * (*Sigma_ptr_tmp);
-      gradient_ptr_tmp += 1;
-      Sigma_ptr_tmp += 1;
-    }
-
-    theta_ptr = ((double *) theta + coord);
-    *theta_ptr = value;
-
-  }
-
-  return(value);
-
-}
-
-int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
-	     double *linear_func_ptr,    /* Linear term in objective */
-	     double *Sigma_diag_ptr,     /* Diagonal entry of covariance matrix */
-	     double *gradient_ptr,       /* Sigma times theta */
-	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
-	     int *nactive_ptr,           /* Size of ever active set */
-	     int nrow,                   /* How many rows in Sigma */
-	     double bound,               /* feasibility parameter */
-	     double *theta,              /* current value */
-	     int maxiter,                /* max number of iterations */
-	     double kkt_tol,             /* precision for checking KKT conditions */
-	     double objective_tol,       /* precision for checking relative decrease in objective value */
-	     int max_active)             /* Upper limit for size of active set -- otherwise break */ 
-{
-
-  int iter = 0;
-  int icoord = 0;
-  int iactive = 0;
-  int *active_ptr;
-
-  int check_objective = 1;
-
-  double old_value, new_value; 
-
-  if (check_objective) {
-
-    old_value = objective_qp(Sigma_ptr,
-			     linear_func_ptr,
-			     ever_active_ptr,
-			     nactive_ptr,
-			     nrow,
-			     bound,
-			     theta);
-
-  }
-
-
-  for (iter=0; iter<maxiter; iter++) {
-
-    // Update the active variables first
-
-    active_ptr = (int *) ever_active_ptr;
-
-    for (iactive=0; iactive < *nactive_ptr; iactive++) {
-      update_one_coord_qp(Sigma_ptr,
-			  linear_func_ptr,
-			  Sigma_diag_ptr,
-			  gradient_ptr,
-			  ever_active_ptr,
-			  nactive_ptr,
-			  nrow,
-			  bound,
-			  theta,
-			  *active_ptr - 1,   // Ever-active is 1-based
-			  1);
-      active_ptr++;
-    }
-
-    // Check KKT
-
-    if (check_KKT_qp(theta,
-		     gradient_ptr,
-		     nrow,
-		     bound,
-		     kkt_tol) == 1) {
-      break;
-    }
-					  
-    // Update all variables 
-
-    for (icoord=0; icoord<nrow; icoord++) {
-
-      update_one_coord_qp(Sigma_ptr,
-			  linear_func_ptr,
-			  Sigma_diag_ptr,
-			  gradient_ptr,
-			  ever_active_ptr,
-			  nactive_ptr,
-			  nrow,
-			  bound,
-			  theta,
-			  icoord,
-			  0);
-    }
-
-    // Check KKT
-
-    if (check_KKT_qp(theta,
-		     gradient_ptr,
-		     nrow,
-		     bound,
-		     kkt_tol) == 1) {
-      break;
-    }
-					  
-    // Check size of active set
-
-    if (*nactive_ptr >= max_active) {
-      break;
-    }
-
-    // Check relative decrease of objective
-
-    if (check_objective) {
-      new_value = objective_qp(Sigma_ptr,
-			       linear_func_ptr,
-			       ever_active_ptr,
-			       nactive_ptr,
-			       nrow,
-			       bound,
-			       theta);
-
-      if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
-	break;
-      }
-      old_value = new_value;
-    }
-  }
-  return(iter);
-}
-

From 7004a0b894a36cc28c879cd100ce99ba3cba118d Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 31 Aug 2017 15:31:16 -0700
Subject: [PATCH 242/493] moving file back

---
 selectiveInference/src/Makevars                          | 2 +-
 selectiveInference/src/{debias.c => quadratic_program.c} | 0
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename selectiveInference/src/{debias.c => quadratic_program.c} (100%)

diff --git a/selectiveInference/src/Makevars b/selectiveInference/src/Makevars
index cde36c9..b77ed95 100644
--- a/selectiveInference/src/Makevars
+++ b/selectiveInference/src/Makevars
@@ -2,7 +2,7 @@ PKG_CFLAGS= -I.
 PKG_CPPFLAGS= -I.
 PKG_LIBS=-L. 
 
-$(SHLIB): Rcpp Rcpp-matrixcomps.o Rcpp-debias.o RcppExports.o debias.o
+$(SHLIB): Rcpp Rcpp-matrixcomps.o Rcpp-debias.o RcppExports.o quadratic_program.o
 
 clean:
 	rm -f *o
diff --git a/selectiveInference/src/debias.c b/selectiveInference/src/quadratic_program.c
similarity index 100%
rename from selectiveInference/src/debias.c
rename to selectiveInference/src/quadratic_program.c

From 5e671a4652b5fbb1dcd0ebc1269b38d5a92a8f3b Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 31 Aug 2017 17:15:22 -0700
Subject: [PATCH 243/493] Rcpp exports

---
 selectiveInference/R/RcppExports.R     | 15 +++++++
 selectiveInference/src/RcppExports.cpp | 57 ++++++++++++++++++++++++++
 2 files changed, 72 insertions(+)
 create mode 100644 selectiveInference/R/RcppExports.R
 create mode 100644 selectiveInference/src/RcppExports.cpp

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
new file mode 100644
index 0000000..e04c446
--- /dev/null
+++ b/selectiveInference/R/RcppExports.R
@@ -0,0 +1,15 @@
+# This file was generated by Rcpp::compileAttributes
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) {
+    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
+}
+
+update1_ <- function(Q2, w, m, k) {
+    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
+}
+
+downdate1_ <- function(Q1, R, j0, m, n) {
+    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
+}
+
diff --git a/selectiveInference/src/RcppExports.cpp b/selectiveInference/src/RcppExports.cpp
new file mode 100644
index 0000000..feefe32
--- /dev/null
+++ b/selectiveInference/src/RcppExports.cpp
@@ -0,0 +1,57 @@
+// This file was generated by Rcpp::compileAttributes
+// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+#include <Rcpp.h>
+
+using namespace Rcpp;
+
+// solve_QP
+Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
+RcppExport SEXP selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject __result;
+    Rcpp::RNGScope __rngScope;
+    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Sigma(SigmaSEXP);
+    Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
+    Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
+    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type theta(thetaSEXP);
+    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type linear_func(linear_funcSEXP);
+    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type gradient(gradientSEXP);
+    Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type ever_active(ever_activeSEXP);
+    Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type nactive(nactiveSEXP);
+    Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
+    Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
+    Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
+    __result = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return __result;
+END_RCPP
+}
+// update1_
+Rcpp::List update1_(Rcpp::NumericMatrix Q2, Rcpp::NumericVector w, int m, int k);
+RcppExport SEXP selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject __result;
+    Rcpp::RNGScope __rngScope;
+    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q2(Q2SEXP);
+    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type w(wSEXP);
+    Rcpp::traits::input_parameter< int >::type m(mSEXP);
+    Rcpp::traits::input_parameter< int >::type k(kSEXP);
+    __result = Rcpp::wrap(update1_(Q2, w, m, k));
+    return __result;
+END_RCPP
+}
+// downdate1_
+Rcpp::List downdate1_(Rcpp::NumericMatrix Q1, Rcpp::NumericMatrix R, int j0, int m, int n);
+RcppExport SEXP selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject __result;
+    Rcpp::RNGScope __rngScope;
+    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q1(Q1SEXP);
+    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type R(RSEXP);
+    Rcpp::traits::input_parameter< int >::type j0(j0SEXP);
+    Rcpp::traits::input_parameter< int >::type m(mSEXP);
+    Rcpp::traits::input_parameter< int >::type n(nSEXP);
+    __result = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
+    return __result;
+END_RCPP
+}

From f4f448ec06c44ea44cee27484c16c6c42aa24d96 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 11 Sep 2017 11:08:06 -0700
Subject: [PATCH 244/493] unexporting fixedLassoPoly

---
 .../man => forLater}/fixedLassoPoly.Rd        |  1 +
 selectiveInference/NAMESPACE                  |  3 +-
 selectiveInference/R/RcppExports.R            |  8 ++--
 selectiveInference/src/RcppExports.cpp        | 44 ++++++++++++-------
 4 files changed, 34 insertions(+), 22 deletions(-)
 rename {selectiveInference/man => forLater}/fixedLassoPoly.Rd (97%)

diff --git a/selectiveInference/man/fixedLassoPoly.Rd b/forLater/fixedLassoPoly.Rd
similarity index 97%
rename from selectiveInference/man/fixedLassoPoly.Rd
rename to forLater/fixedLassoPoly.Rd
index 61b1f30..88c5446 100644
--- a/selectiveInference/man/fixedLassoPoly.Rd
+++ b/forLater/fixedLassoPoly.Rd
@@ -75,6 +75,7 @@ beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
 active = (beta != 0)
 
 fixedLassoPoly(x, y, lambda, beta, active)
+fixedLassoPoly(x, y, lambda, beta, active, inactive=TRUE)
 
 }
  
\ No newline at end of file
diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index fe0237e..d72d56a 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -14,8 +14,7 @@ export(lar,fs,
        TG.pvalue, 
        TG.limits, 
        TG.interval,
-       debiasingMatrix,
-       fixedLassoPoly
+       debiasingMatrix
     )
 
 S3method("coef", "lar")
diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
index e04c446..622d1d2 100644
--- a/selectiveInference/R/RcppExports.R
+++ b/selectiveInference/R/RcppExports.R
@@ -1,15 +1,15 @@
-# This file was generated by Rcpp::compileAttributes
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
+    .Call('_selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
 }
 
 update1_ <- function(Q2, w, m, k) {
-    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
+    .Call('_selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
 }
 
 downdate1_ <- function(Q1, R, j0, m, n) {
-    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
+    .Call('_selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
 }
 
diff --git a/selectiveInference/src/RcppExports.cpp b/selectiveInference/src/RcppExports.cpp
index feefe32..461b0e5 100644
--- a/selectiveInference/src/RcppExports.cpp
+++ b/selectiveInference/src/RcppExports.cpp
@@ -1,4 +1,4 @@
-// This file was generated by Rcpp::compileAttributes
+// Generated by using Rcpp::compileAttributes() -> do not edit by hand
 // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 #include <Rcpp.h>
@@ -7,10 +7,10 @@ using namespace Rcpp;
 
 // solve_QP
 Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+RcppExport SEXP _selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Sigma(SigmaSEXP);
     Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
     Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
@@ -22,36 +22,48 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
     Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
     Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    __result = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return rcpp_result_gen;
 END_RCPP
 }
 // update1_
 Rcpp::List update1_(Rcpp::NumericMatrix Q2, Rcpp::NumericVector w, int m, int k);
-RcppExport SEXP selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
+RcppExport SEXP _selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q2(Q2SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericVector >::type w(wSEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type k(kSEXP);
-    __result = Rcpp::wrap(update1_(Q2, w, m, k));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(update1_(Q2, w, m, k));
+    return rcpp_result_gen;
 END_RCPP
 }
 // downdate1_
 Rcpp::List downdate1_(Rcpp::NumericMatrix Q1, Rcpp::NumericMatrix R, int j0, int m, int n);
-RcppExport SEXP selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
+RcppExport SEXP _selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q1(Q1SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type R(RSEXP);
     Rcpp::traits::input_parameter< int >::type j0(j0SEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    __result = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
+    return rcpp_result_gen;
 END_RCPP
 }
+
+static const R_CallMethodDef CallEntries[] = {
+    {"_selectiveInference_solve_QP", (DL_FUNC) &_selectiveInference_solve_QP, 11},
+    {"_selectiveInference_update1_", (DL_FUNC) &_selectiveInference_update1_, 4},
+    {"_selectiveInference_downdate1_", (DL_FUNC) &_selectiveInference_downdate1_, 5},
+    {NULL, NULL, 0}
+};
+
+RcppExport void R_init_selectiveInference(DllInfo *dll) {
+    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
+    R_useDynamicSymbols(dll, FALSE);
+}

From f0010f3a1bdb1d06c361dd4b1be9c83aeb69151f Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 11 Sep 2017 11:16:57 -0700
Subject: [PATCH 245/493] changing signs in arguments to TG.limits

---
 selectiveInference/R/funs.fs.R  | 12 ++++++------
 selectiveInference/R/funs.lar.R | 12 ++++++------
 2 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/selectiveInference/R/funs.fs.R b/selectiveInference/R/funs.fs.R
index db0968b..669cf14 100644
--- a/selectiveInference/R/funs.fs.R
+++ b/selectiveInference/R/funs.fs.R
@@ -295,13 +295,13 @@ fsInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic
     for (j in 1:k) {
       if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
 
-      Gj = G[1:nconstraint[j],]
-      uj = rep(0,nconstraint[j])
+      Aj = -G[1:nconstraint[j],]
+      bj = -rep(0,nconstraint[j])
       vj = vreg[j,]
       mj = sqrt(sum(vj^2)) 
       vj = vj / mj              # Standardize (divide by norm of vj)
 
-      limits.info = TG.limits(y, -Gj, -uj, vj, Sigma=diag(rep(sigma^2, n)))
+      limits.info = TG.limits(y, Aj, bj, vj, Sigma=diag(rep(sigma^2, n)))
       a = TG.pvalue.base(limits.info, bits=bits)
 
       pv[j] = a$pv
@@ -353,10 +353,10 @@ fsInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic
       vj = vj / mj              # Standardize (divide by norm of vj)
       sign[j] = sign(sum(vj*y))
       vj = sign[j] * vj
-      Gj = rbind(G,vj)
-      uj = c(u,0)
+      Aj = -rbind(G,vj)
+      bj = -c(u,0)
 
-      limits.info = TG.limits(y, -Gj, -uj, vj, Sigma=diag(rep(sigma^2, n)))
+      limits.info = TG.limits(y, Aj, bj, vj, Sigma=diag(rep(sigma^2, n)))
       a = TG.pvalue.base(limits.info, bits=bits)
       pv[j] = a$pv
       sxj = sx[vars[j]]
diff --git a/selectiveInference/R/funs.lar.R b/selectiveInference/R/funs.lar.R
index a01b2d3..0fad04f 100644
--- a/selectiveInference/R/funs.lar.R
+++ b/selectiveInference/R/funs.lar.R
@@ -367,13 +367,13 @@ larInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","ai
     for (j in 1:k) {
       if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
 
-      Gj = G[1:nk[j],]
-      uj = rep(0,nk[j])
+      Aj = -G[1:nk[j],]
+      bj = -rep(0,nk[j])
       vj = vreg[j,]
       mj = sqrt(sum(vj^2))
       vj = vj / mj              # Standardize (divide by norm of vj)
 
-      limits.info = TG.limits(y, -Gj, -uj, vj, Sigma=diag(rep(sigma^2, n)))
+      limits.info = TG.limits(y, Aj, bj, vj, Sigma=diag(rep(sigma^2, n)))
       a = TG.pvalue.base(limits.info, bits=bits)
       pv[j] = a$pv
       sxj = sx[vars[j]]
@@ -428,10 +428,10 @@ larInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","ai
       vj = vj / mj             # Standardize (divide by norm of vj)
       sign[j] = sign(sum(vj*y))
       vj = sign[j] * vj
-      Gj = rbind(G,vj)
-      uj = c(u,0)
+      Aj = -rbind(G,vj)
+      bj = -c(u,0)
 
-      limits.info = TG.limits(y, -Gj, -uj, vj, Sigma=diag(rep(sigma^2, n)))
+      limits.info = TG.limits(y, Aj, bj, vj, Sigma=diag(rep(sigma^2, n)))
       a = TG.pvalue.base(limits.info, bits=bits)
 
       pv[j] = a$pv

From 7040ad440f72fc0784c118c00c9c705945c65001 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 22 Sep 2017 15:41:08 -0700
Subject: [PATCH 246/493] copy of CRAN version 1.2.3 in
 selectiveInference-currentCRAN

---
 selectiveInference-currentCRAN/DESCRIPTION    |  18 +
 selectiveInference-currentCRAN/MD5            |  35 +
 selectiveInference-currentCRAN/NAMESPACE      |  45 +
 .../R/funs.common.R                           | 186 ++++
 selectiveInference-currentCRAN/R/funs.fixed.R | 218 +++++
 .../R/funs.fixedCox.R                         | 120 +++
 .../R/funs.fixedLogit.R                       | 151 ++++
 selectiveInference-currentCRAN/R/funs.fs.R    | 482 +++++++++++
 .../R/funs.groupfs.R                          | 794 ++++++++++++++++++
 selectiveInference-currentCRAN/R/funs.inf.R   | 299 +++++++
 selectiveInference-currentCRAN/R/funs.lar.R   | 632 ++++++++++++++
 .../R/funs.manymeans.R                        | 196 +++++
 selectiveInference-currentCRAN/R/funs.max.R   |  84 ++
 .../R/funs.quadratic.R                        | 257 ++++++
 .../man/estimateSigma.Rd                      |  64 ++
 .../man/factorDesign.Rd                       |  30 +
 .../man/fixedLassoInf.Rd                      | 252 ++++++
 .../man/forwardStop.Rd                        |  55 ++
 selectiveInference-currentCRAN/man/fs.Rd      |  95 +++
 selectiveInference-currentCRAN/man/fsInf.Rd   | 135 +++
 selectiveInference-currentCRAN/man/groupfs.Rd |  49 ++
 .../man/groupfsInf.Rd                         |  31 +
 selectiveInference-currentCRAN/man/lar.Rd     |  95 +++
 selectiveInference-currentCRAN/man/larInf.Rd  | 140 +++
 .../man/manyMeans.Rd                          |  60 ++
 selectiveInference-currentCRAN/man/plot.fs.Rd |  42 +
 .../man/plot.lar.Rd                           |  46 +
 .../man/predict.fs.Rd                         |  49 ++
 .../man/predict.groupfs.Rd                    |  24 +
 .../man/predict.lar.Rd                        |  52 ++
 .../man/scaleGroups.Rd                        |  28 +
 .../man/selectiveInference-internal.Rd        |  24 +
 .../man/selectiveInference.Rd                 | 205 +++++
 .../src/matrixcomps.c                         | 266 ++++++
 .../src/selinf_init.c                         |  24 +
 .../src/truncnorm.c                           | 188 +++++
 36 files changed, 5471 insertions(+)
 create mode 100644 selectiveInference-currentCRAN/DESCRIPTION
 create mode 100644 selectiveInference-currentCRAN/MD5
 create mode 100644 selectiveInference-currentCRAN/NAMESPACE
 create mode 100644 selectiveInference-currentCRAN/R/funs.common.R
 create mode 100644 selectiveInference-currentCRAN/R/funs.fixed.R
 create mode 100644 selectiveInference-currentCRAN/R/funs.fixedCox.R
 create mode 100644 selectiveInference-currentCRAN/R/funs.fixedLogit.R
 create mode 100644 selectiveInference-currentCRAN/R/funs.fs.R
 create mode 100644 selectiveInference-currentCRAN/R/funs.groupfs.R
 create mode 100644 selectiveInference-currentCRAN/R/funs.inf.R
 create mode 100644 selectiveInference-currentCRAN/R/funs.lar.R
 create mode 100644 selectiveInference-currentCRAN/R/funs.manymeans.R
 create mode 100644 selectiveInference-currentCRAN/R/funs.max.R
 create mode 100644 selectiveInference-currentCRAN/R/funs.quadratic.R
 create mode 100644 selectiveInference-currentCRAN/man/estimateSigma.Rd
 create mode 100644 selectiveInference-currentCRAN/man/factorDesign.Rd
 create mode 100644 selectiveInference-currentCRAN/man/fixedLassoInf.Rd
 create mode 100644 selectiveInference-currentCRAN/man/forwardStop.Rd
 create mode 100644 selectiveInference-currentCRAN/man/fs.Rd
 create mode 100644 selectiveInference-currentCRAN/man/fsInf.Rd
 create mode 100644 selectiveInference-currentCRAN/man/groupfs.Rd
 create mode 100644 selectiveInference-currentCRAN/man/groupfsInf.Rd
 create mode 100644 selectiveInference-currentCRAN/man/lar.Rd
 create mode 100644 selectiveInference-currentCRAN/man/larInf.Rd
 create mode 100644 selectiveInference-currentCRAN/man/manyMeans.Rd
 create mode 100644 selectiveInference-currentCRAN/man/plot.fs.Rd
 create mode 100644 selectiveInference-currentCRAN/man/plot.lar.Rd
 create mode 100644 selectiveInference-currentCRAN/man/predict.fs.Rd
 create mode 100644 selectiveInference-currentCRAN/man/predict.groupfs.Rd
 create mode 100644 selectiveInference-currentCRAN/man/predict.lar.Rd
 create mode 100644 selectiveInference-currentCRAN/man/scaleGroups.Rd
 create mode 100644 selectiveInference-currentCRAN/man/selectiveInference-internal.Rd
 create mode 100644 selectiveInference-currentCRAN/man/selectiveInference.Rd
 create mode 100644 selectiveInference-currentCRAN/src/matrixcomps.c
 create mode 100644 selectiveInference-currentCRAN/src/selinf_init.c
 create mode 100644 selectiveInference-currentCRAN/src/truncnorm.c

diff --git a/selectiveInference-currentCRAN/DESCRIPTION b/selectiveInference-currentCRAN/DESCRIPTION
new file mode 100644
index 0000000..0eb38f7
--- /dev/null
+++ b/selectiveInference-currentCRAN/DESCRIPTION
@@ -0,0 +1,18 @@
+Package: selectiveInference
+Type: Package
+Title: Tools for Post-Selection Inference
+Version: 1.2.3
+Date: 2017-09-18
+Author: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor,
+    Joshua Loftus, Stephen Reid
+Maintainer: Rob Tibshirani <tibs@stanford.edu>
+Depends: glmnet, intervals, survival
+Suggests: Rmpfr
+Description: New tools for post-selection inference, for use
+    with forward stepwise regression, least angle regression, the
+    lasso, and the many means problem. The lasso function implements  Gaussian,  logistic and Cox survival models.
+License: GPL-2
+NeedsCompilation: yes
+Packaged: 2017-09-19 23:49:11 UTC; tibs
+Repository: CRAN
+Date/Publication: 2017-09-20 03:14:10 UTC
diff --git a/selectiveInference-currentCRAN/MD5 b/selectiveInference-currentCRAN/MD5
new file mode 100644
index 0000000..6675546
--- /dev/null
+++ b/selectiveInference-currentCRAN/MD5
@@ -0,0 +1,35 @@
+89792835188231f03f117ab143c2fe46 *DESCRIPTION
+5b8e448cf043849e190d2b71898eaad9 *NAMESPACE
+9c5c032cb17908e6dea15a0b89d649a9 *R/funs.common.R
+cf1d1199cf6cacb1d54fce08388d20cc *R/funs.fixed.R
+faf5eed09c13d3e80270d82305f0b348 *R/funs.fixedCox.R
+25e9f2957b4cbac8b11a283c69533f64 *R/funs.fixedLogit.R
+c7af51c32236ef56a6ed0a525f52dce4 *R/funs.fs.R
+fc41d0af77330bde0395f438c117c7d8 *R/funs.groupfs.R
+632c61c8fc3da59cde6b337f7d4341a4 *R/funs.inf.R
+dba7bfb08c9184569d97c14a0575c5a1 *R/funs.lar.R
+ed45e9aa5e6383ff9888b35af9b30e9e *R/funs.manymeans.R
+bd535e32d32e9cd0e723a5f9f00d9eef *R/funs.max.R
+6daca48218e58720c1570784706c199a *R/funs.quadratic.R
+d1db3866e82ad6e33baef9da4d994833 *man/estimateSigma.Rd
+1747e0899ef985469ae560fb828755cb *man/factorDesign.Rd
+1028942deac2fd45aaf2e49d94aa6dac *man/fixedLassoInf.Rd
+60e2065f446f1d6dc11c77a5534580bc *man/forwardStop.Rd
+2e6f87cd38e1f4b4cb60bfc8299dc1f4 *man/fs.Rd
+1483067f07f71b2d996138877e4f48ef *man/fsInf.Rd
+7d5ca8ce0ff81cf5f0e87cadffa29229 *man/groupfs.Rd
+5ccec019c69b4832438b79830649e730 *man/groupfsInf.Rd
+61bdaa3e5ac7bbe02d55f42530edf956 *man/lar.Rd
+b25bc2d93c0b266dbec45d82a5d05004 *man/larInf.Rd
+4da84515659e7a70fb7375dc2c791b4b *man/manyMeans.Rd
+c7c96850986be5e1203cca414a410a32 *man/plot.fs.Rd
+3dc4100747d7e72276a75c8e6beba37c *man/plot.lar.Rd
+192e0031a10ace23df79a314cf90c648 *man/predict.fs.Rd
+588230513bd05fd139c75d45f94a7cd6 *man/predict.groupfs.Rd
+0b477548ac30e902eca27163a947e2ca *man/predict.lar.Rd
+b275e61a2976d14595dc9dfea646675e *man/scaleGroups.Rd
+0c21e5414145f4841c3897c995dad4c2 *man/selectiveInference-internal.Rd
+b0bbe4ffe6e958215a85bb15fc43ab01 *man/selectiveInference.Rd
+4313aa781953d7f1f6e75383e938e1c7 *src/matrixcomps.c
+d7f4c478a9de5716b2da338ae6da2ea5 *src/selinf_init.c
+11b2e6c34bc1ed181b407fc658a3b0af *src/truncnorm.c
diff --git a/selectiveInference-currentCRAN/NAMESPACE b/selectiveInference-currentCRAN/NAMESPACE
new file mode 100644
index 0000000..cf2f3b8
--- /dev/null
+++ b/selectiveInference-currentCRAN/NAMESPACE
@@ -0,0 +1,45 @@
+export(lar,fs,
+       larInf,fsInf,
+       coef.lar,coef.fs,
+       predict.lar,predict.fs,
+       print.lar,print.fs,
+       print.larInf,print.fsInf,
+       plot.lar,plot.fs,
+       fixedLassoInf,print.fixedLassoInf,
+#      fixedLogitLassoInf,print.fixedLogitLassoInf,
+#       fixedCoxLassoInf,print.fixedCoxLassoInf,
+       forwardStop,
+       estimateSigma,
+       manyMeans,print.manyMeans,
+       groupfs,groupfsInf,
+       scaleGroups,factorDesign
+    )
+
+S3method("coef", "lar")
+S3method("predict", "lar")
+S3method("print", "lar")
+S3method("plot", "lar")
+S3method("print", "larInf")
+S3method("coef", "fs")
+S3method("predict", "fs")
+S3method("print", "fs")
+S3method("plot", "fs")
+S3method("print", "fsInf")
+S3method("print", "fixedLassoInf")
+S3method("print", "fixedLogitLassoInf")
+S3method("print", "fixedCoxLassoInf")
+S3method("print", "manyMeans")
+S3method("print", "groupfs")
+S3method("print", "groupfsInf")
+
+useDynLib("selectiveInference",.registration=TRUE)
+import(glmnet)
+import(intervals)
+import(survival)
+importFrom("graphics", abline, axis, matplot)
+importFrom("stats", dnorm, lsfit, pexp, pnorm, predict, 
+           qnorm, rnorm, sd, uniroot, dchisq, model.matrix, pchisq)
+importFrom("stats", "coef", "df", "lm", "pf")
+importFrom("stats", "glm", "residuals", "vcov")
+
+
diff --git a/selectiveInference-currentCRAN/R/funs.common.R b/selectiveInference-currentCRAN/R/funs.common.R
new file mode 100644
index 0000000..5945700
--- /dev/null
+++ b/selectiveInference-currentCRAN/R/funs.common.R
@@ -0,0 +1,186 @@
+# Special linear time order function, works only when x
+# is a scrambled vector of integers.
+
+Order <- function(x) {
+  n = length(x)
+  o = numeric(n)
+  o[x] = Seq(1,n)
+  return(o)
+}
+
+# Returns a sequence of integers from a to b if a <= b,
+# otherwise nothing. You have no idea how important this
+# function is...
+
+Seq <- function(a, b, ...) {
+  if (a<=b) return(seq(a,b,...))
+  else return(numeric(0))
+}
+
+# Returns the sign of x, with Sign(0)=1.
+
+Sign <- function(x) {
+  return(-1+2*(x>=0))
+}
+
+##############################
+
+# Centering and scaling convenience function
+
+standardize <- function(x, y, intercept, normalize) {
+  x = as.matrix(x)
+  y = as.numeric(y)
+  n = nrow(x)
+  p = ncol(x)
+
+  if (intercept) {
+    bx = colMeans(x)
+    by = mean(y)
+    x = scale(x,bx,FALSE)
+    y = y-mean(y)
+  } else {
+    bx = rep(0,p)
+    by = 0
+  }
+  if (normalize) {
+    sx = sqrt(colSums(x^2))
+    x = scale(x,FALSE,sx)
+  } else {
+    sx = rep(1,p)
+  }
+
+  return(list(x=x,y=y,bx=bx,by=by,sx=sx))
+}
+
+##############################
+
+# Interpolation function to get coefficients
+
+coef.interpolate <- function(betas, s, knots, dec=TRUE) {
+  # Sort the s values
+  o = order(s,dec=dec)
+  s = s[o]
+
+  k = length(s)
+  mat = matrix(rep(knots,each=k),nrow=k)
+  if (dec) b = s >= mat
+  else b = s <= mat
+  blo = max.col(b,ties.method="first")
+  bhi = pmax(blo-1,1)
+
+  i = bhi==blo
+  p = numeric(k)
+  p[i] = 0
+  p[!i] = ((s-knots[blo])/(knots[bhi]-knots[blo]))[!i]
+
+  beta = t((1-p)*t(betas[,blo,drop=FALSE]) + p*t(betas[,bhi,drop=FALSE]))
+  colnames(beta) = as.character(round(s,3))
+  rownames(beta) = NULL
+
+  # Return in original order
+  o = order(o)
+  return(beta[,o,drop=FALSE])
+}
+
+##############################
+
+checkargs.xy <- function(x, y) {
+  if (missing(x)) stop("x is missing")
+  if (is.null(x) || !is.matrix(x)) stop("x must be a matrix")
+  if (missing(y)) stop("y is missing")
+  if (is.null(y) || !is.numeric(y)) stop("y must be numeric")
+  if (ncol(x) == 0) stop("There must be at least one predictor [must have ncol(x) > 0]")
+  if (checkcols(x)) stop("x cannot have duplicate columns")
+  if (length(y) == 0) stop("There must be at least one data point [must have length(y) > 0]")
+  if (length(y)!=nrow(x)) stop("Dimensions don't match [length(y) != nrow(x)]")
+}
+
+checkargs.misc <- function(sigma=NULL, alpha=NULL, k=NULL,
+                           gridrange=NULL, gridpts=NULL, griddepth=NULL,
+                           mult=NULL, ntimes=NULL,
+                           beta=NULL, lambda=NULL, tol.beta=NULL, tol.kkt=NULL,
+                           bh.q=NULL) {
+
+  if (!is.null(sigma) && sigma <= 0) stop("sigma must be > 0")
+  if (!is.null(lambda) && lambda < 0) stop("lambda must be >= 0")
+  if (!is.null(alpha) && (alpha <= 0 || alpha >= 1)) stop("alpha must be between 0 and 1")
+  if (!is.null(k) && length(k) != 1) stop("k must be a single number")
+  if (!is.null(k) && (k < 1 || k != floor(k))) stop("k must be an integer >= 1")
+  if (!is.null(gridrange) && (length(gridrange) != 2 || gridrange[1] > gridrange[2]))
+    stop("gridrange must be an interval of the form c(a,b) with a <= b")
+  if (!is.null(gridpts) && (gridpts < 20 || gridpts != round(gridpts)))
+    stop("gridpts must be an integer >= 20")
+  if (!is.null(griddepth) && (griddepth > 10 || griddepth != round(griddepth)))
+    stop("griddepth must be an integer <= 10")
+  if (!is.null(mult) && mult < 0) stop("mult must be >= 0")
+  if (!is.null(ntimes) && (ntimes <= 0 || ntimes != round(ntimes)))
+    stop("ntimes must be an integer > 0")
+  if (!is.null(beta) && sum(beta!=0)==0) stop("Value of lambda too large, beta is zero")
+  if (!is.null(lambda) && length(lambda) != 1) stop("lambda must be a single number")
+  if (!is.null(lambda) && lambda < 0) stop("lambda must be >=0")
+  if (!is.null(tol.beta) && tol.beta <= 0) stop("tol.beta must be > 0")
+  if (!is.null(tol.kkt) && tol.kkt <= 0) stop("tol.kkt must be > 0")
+}
+
+# Make sure that no two columms of A are the same
+# (this works with probability one).
+
+checkcols <- function(A) {
+  b = rnorm(nrow(A))
+  a = sort(t(A)%*%b)
+  if (any(diff(a)==0)) return(TRUE)
+  return(FALSE)
+}
+
+estimateSigma <- function(x, y, intercept=TRUE, standardize=TRUE) {
+  checkargs.xy(x,rep(0,nrow(x)))
+  if(nrow(x)<10) stop("Number of observations must be at least 10 to run estimateSigma")
+  cvfit=cv.glmnet(x,y,intercept=intercept,standardize=standardize)
+  lamhat=cvfit$lambda.min
+  fit=glmnet(x,y,standardize=standardize)
+  yhat=predict(fit,x,s=lamhat)
+  nz=sum(predict(fit,s=lamhat, type="coef")!=0)
+  sigma=sqrt(sum((y-yhat)^2)/(length(y)-nz-1))
+  return(list(sigmahat=sigma, df=nz))
+}
+
+# Update the QR factorization, after a column has been
+# added. Here Q1 is m x n, Q2 is m x k, and R is n x n.
+
+updateQR <- function(Q1,Q2,R,col) {
+  m = nrow(Q1)
+  n = ncol(Q1)
+  k = ncol(Q2)
+
+  a = .C("update1",
+    Q2=as.double(Q2),
+    w=as.double(t(Q2)%*%col),
+    m=as.integer(m),
+    k=as.integer(k),
+    dup=FALSE,
+    package="selectiveInference")
+
+  Q2 = matrix(a$Q2,nrow=m)
+  w = c(t(Q1)%*%col,a$w)
+
+  # Re-structure: delete a column from Q2, add one to
+  # Q1, and expand R
+  Q1 = cbind(Q1,Q2[,1])
+  Q2 = Q2[,-1,drop=FALSE]
+  R = rbind(R,rep(0,n))
+  R = cbind(R,w[Seq(1,n+1)])
+
+  return(list(Q1=Q1,Q2=Q2,R=R))
+}
+
+# Moore-Penrose pseudo inverse for symmetric matrices
+
+pinv <- function(A, tol=.Machine$double.eps) {
+  e = eigen(A)
+  v = Re(e$vec)
+  d = Re(e$val)
+  d[d > tol] = 1/d[d > tol]
+  d[d < tol] = 0
+  if (length(d)==1) return(v*d*v)
+  else return(v %*% diag(d) %*% t(v))
+}
diff --git a/selectiveInference-currentCRAN/R/funs.fixed.R b/selectiveInference-currentCRAN/R/funs.fixed.R
new file mode 100644
index 0000000..b30d04c
--- /dev/null
+++ b/selectiveInference-currentCRAN/R/funs.fixed.R
@@ -0,0 +1,218 @@
+# Lasso inference function (for fixed lambda). Note: here we are providing inference
+# for the solution of
+# min 1/2 || y - \beta_0 - X \beta ||_2^2 + \lambda || \beta ||_1
+
+fixedLassoInf <- function(x, y, beta, lambda, family=c("gaussian","binomial","cox"),intercept=TRUE, status=NULL,
+sigma=NULL, alpha=0.1,
+                     type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
+                     gridrange=c(-100,100), bits=NULL, verbose=FALSE) {
+
+ family = match.arg(family)
+  this.call = match.call()
+  type = match.arg(type)
+
+  if(family=="binomial")  {
+      if(type!="partial") stop("Only type= partial allowed with binomial family")
+       out=fixedLogitLassoInf(x,y,beta,lambda,alpha=alpha, type="partial", tol.beta=tol.beta, tol.kkt=tol.kkt,
+                     gridrange=gridrange, bits=bits, verbose=verbose,this.call=this.call)
+                      return(out)
+                    }
+else if(family=="cox")  {
+    if(type!="partial") stop("Only type= partial allowed with Cox family")
+     out=fixedCoxLassoInf(x,y,status,beta,lambda,alpha=alpha, type="partial",tol.beta=tol.beta,
+          tol.kkt=tol.kkt, gridrange=gridrange, bits=bits, verbose=verbose,this.call=this.call)
+                      return(out)
+                    }
+
+else{
+
+
+
+  checkargs.xy(x,y)
+  if (missing(beta) || is.null(beta)) stop("Must supply the solution beta")
+  if (missing(lambda) || is.null(lambda)) stop("Must supply the tuning parameter value lambda")
+  checkargs.misc(beta=beta,lambda=lambda,sigma=sigma,alpha=alpha,
+                 gridrange=gridrange,tol.beta=tol.beta,tol.kkt=tol.kkt)
+  if (!is.null(bits) && !requireNamespace("Rmpfr",quietly=TRUE)) {
+    warning("Package Rmpfr is not installed, reverting to standard precision")
+    bits = NULL
+  }
+
+  n = nrow(x)
+  p = ncol(x)
+  beta = as.numeric(beta)
+  if (length(beta) != p) stop("Since family='gaussian', beta must have length equal to ncol(x)")
+
+  # If glmnet was run with an intercept term, center x and y
+  if (intercept==TRUE) {
+    obj = standardize(x,y,TRUE,FALSE)
+    x = obj$x
+    y = obj$y
+  }
+
+  # Check the KKT conditions
+  g = t(x)%*%(y-x%*%beta) / lambda
+  if (any(abs(g) > 1+tol.kkt * sqrt(sum(y^2))))
+    warning(paste("Solution beta does not satisfy the KKT conditions",
+                  "(to within specified tolerances)"))
+
+  vars = which(abs(beta) > tol.beta / sqrt(colSums(x^2)))
+  if(length(vars)==0){
+      cat("Empty model",fill=T)
+      return()
+  }
+  if (any(sign(g[vars]) != sign(beta[vars])))
+    warning(paste("Solution beta does not satisfy the KKT conditions",
+                  "(to within specified tolerances). You might try rerunning",
+                  "glmnet with a lower setting of the",
+                  "'thresh' parameter, for a more accurate convergence."))
+
+  # Get lasso polyhedral region, of form Gy >= u
+  out = fixedLasso.poly(x,y,beta,lambda,vars)
+  G = out$G
+  u = out$u
+
+  # Check polyhedral region
+  tol.poly = 0.01
+  if (min(G %*% y - u) < -tol.poly * sqrt(sum(y^2)))
+    stop(paste("Polyhedral constraints not satisfied; you must recompute beta",
+               "more accurately. With glmnet, make sure to use exact=TRUE in coef(),",
+               "and check whether the specified value of lambda is too small",
+               "(beyond the grid of values visited by glmnet).",
+               "You might also try rerunning glmnet with a lower setting of the",
+               "'thresh' parameter, for a more accurate convergence."))
+
+  # Estimate sigma
+  if (is.null(sigma)) {
+    if (n >= 2*p) {
+      oo = intercept
+      sigma = sqrt(sum(lsfit(x,y,intercept=oo)$res^2)/(n-p-oo))
+    }
+    else {
+      sigma = sd(y)
+      warning(paste(sprintf("p > n/2, and sd(y) = %0.3f used as an estimate of sigma;",sigma),
+                    "you may want to use the estimateSigma function"))
+    }
+  }
+
+  k = length(vars)
+  pv = vlo = vup = numeric(k)
+  vmat = matrix(0,k,n)
+  ci = tailarea = matrix(0,k,2)
+  sign = numeric(k)
+
+  if (type=="full" & p > n)
+      warning(paste("type='full' does not make sense when p > n;",
+                    "switching to type='partial'"))
+
+  if (type=="partial" || p > n) {
+    xa = x[,vars,drop=F]
+    M = pinv(crossprod(xa)) %*% t(xa)
+  }
+  else {
+    M = pinv(crossprod(x)) %*% t(x)
+    M = M[vars,,drop=F]
+  }
+
+  for (j in 1:k) {
+    if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
+
+    vj = M[j,]
+    mj = sqrt(sum(vj^2))
+    vj = vj / mj        # Standardize (divide by norm of vj)
+    sign[j] = sign(sum(vj*y))
+    vj = sign[j] * vj
+    a = poly.pval(y,G,u,vj,sigma,bits)
+    pv[j] = a$pv
+    vlo[j] = a$vlo * mj # Unstandardize (mult by norm of vj)
+    vup[j] = a$vup * mj # Unstandardize (mult by norm of vj)
+    vmat[j,] = vj * mj * sign[j]  # Unstandardize (mult by norm of vj)
+
+    a = poly.int(y,G,u,vj,sigma,alpha,gridrange=gridrange,
+      flip=(sign[j]==-1),bits=bits)
+    ci[j,] = a$int * mj # Unstandardize (mult by norm of vj)
+    tailarea[j,] = a$tailarea
+  }
+
+  out = list(type=type,lambda=lambda,pv=pv,ci=ci,
+    tailarea=tailarea,vlo=vlo,vup=vup,vmat=vmat,y=y,
+    vars=vars,sign=sign,sigma=sigma,alpha=alpha,
+    sd=sigma*sqrt(rowSums(vmat^2)),
+    coef0=vmat%*%y,
+    call=this.call)
+  class(out) = "fixedLassoInf"
+  return(out)
+}
+}
+
+#############################
+
+
+fixedLasso.poly=
+function(x, y, beta, lambda, a) {
+  xa = x[,a,drop=F]
+  xac = x[,!a,drop=F]
+  xai = pinv(crossprod(xa))
+  xap = xai %*% t(xa)
+  za = sign(beta[a])
+  if (length(za)>1) dz = diag(za)
+  if (length(za)==1) dz = matrix(za,1,1)
+
+#  P = diag(1,nrow(xa)) - xa %*% xap
+  #NOTE: inactive constraints not needed below!
+
+  G = -rbind(
+   #   1/lambda * t(xac) %*% P,
+   # -1/lambda * t(xac) %*% P,
+    -dz %*% xap
+      )
+     lambda2=lambda
+     if(length(lambda)>1) lambda2=lambda[a]
+  u = -c(
+   #   1 - t(xac) %*% t(xap) %*% za,
+   #   1 + t(xac) %*% t(xap) %*% za,
+    -lambda2 * dz %*% xai %*% za)
+
+  return(list(G=G,u=u))
+}
+
+##############################
+
+print.fixedLassoInf <- function(x, tailarea=TRUE, ...) {
+  cat("\nCall:\n")
+  dput(x$call)
+
+  cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n",
+              x$sigma))
+
+  cat(sprintf("\nTesting results at lambda = %0.3f, with alpha = %0.3f\n",x$lambda,x$alpha))
+  cat("",fill=T)
+  tab = cbind(x$vars,
+    round(x$coef0,3),
+    round(x$coef0 / x$sd,3),
+    round(x$pv,3),round(x$ci,3))
+  colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
+  if (tailarea) {
+    tab = cbind(tab,round(x$tailarea,3))
+    colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+  }
+  rownames(tab) = rep("",nrow(tab))
+  print(tab)
+
+  cat(sprintf("\nNote: coefficients shown are %s regression coefficients\n",
+              ifelse(x$type=="partial","partial","full")))
+  invisible()
+}
+
+#estimateLambda <- function(x, sigma, nsamp=1000){
+#  checkargs.xy(x,rep(0,nrow(x)))
+#  if(nsamp < 10) stop("More Monte Carlo samples required for estimation")
+#  if (length(sigma)!=1) stop("sigma should be a number > 0")
+ # if (sigma<=0) stop("sigma should be a number > 0")
+
+ # n = nrow(x)
+ # eps = sigma*matrix(rnorm(nsamp*n),n,nsamp)
+ # lambda = 2*mean(apply(t(x)%*%eps,2,max))
+ # return(lambda)
+#}
+
diff --git a/selectiveInference-currentCRAN/R/funs.fixedCox.R b/selectiveInference-currentCRAN/R/funs.fixedCox.R
new file mode 100644
index 0000000..ff778d9
--- /dev/null
+++ b/selectiveInference-currentCRAN/R/funs.fixedCox.R
@@ -0,0 +1,120 @@
+fixedCoxLassoInf=function(x,y,status,beta,lambda,alpha=.1, type=c("partial"),tol.beta=1e-5, tol.kkt=0.1,
+                     gridrange=c(-100,100), bits=NULL, verbose=FALSE,this.call=NULL){
+
+
+ checkargs.xy(x,y)
+ if(is.null(status)) stop("Must supply `status' argument")
+if( sum(status==0)+sum(status==1)!=length(y)) stop("status vector must have values 0 or 1")
+  if (missing(beta) || is.null(beta)) stop("Must supply the solution beta")
+  if (missing(lambda) || is.null(lambda)) stop("Must supply the tuning parameter value lambda") 
+  checkargs.misc(beta=beta,lambda=lambda,alpha=alpha,
+                 gridrange=gridrange,tol.beta=tol.beta,tol.kkt=tol.kkt)
+  if (!is.null(bits) && !requireNamespace("Rmpfr",quietly=TRUE)) {
+    warning("Package Rmpfr is not installed, reverting to standard precision")
+    bits = NULL
+  }
+
+    n=nrow(x)
+    p=ncol(x)
+     nvar=sum(beta!=0)
+    pv=vlo=vup=sd=rep(NA, nvar)
+     ci=tailarea=matrix(NA,nvar,2)
+
+
+   m=beta!=0
+vars=which(m)
+if(sum(m)>0){
+    bhat=beta[beta!=0] #penalized coefs just for active variables
+    s2=sign(bhat)
+
+ #check KKT
+    
+   aaa=coxph(Surv(y,status)~x[,m],init=bhat,iter.max=0) # this gives the Cox model at exactly bhat
+                                                        # so when we compute gradient and score 
+							# we are evaluating at the LASSO solution
+							# naming of variables could be improved...
+    res=residuals(aaa,type="score")
+if(!is.matrix(res)) res=matrix(res,ncol=1)
+scor=colSums(res)
+    g=(scor+lambda*s2)/(2*lambda)
+#    cat(c(g,lambda,tol.kkt),fill=T)
+     if (any(abs(g) > 1+tol.kkt) )
+    warning(paste("Solution beta does not satisfy the KKT conditions",
+                  "(to within specified tolerances)"))
+
+# Hessian of partial likelihood at the LASSO solution    
+MM=vcov(aaa)
+
+bbar=(bhat+lambda*MM%*%s2)
+A1=-(mydiag(s2))
+b1= -(mydiag(s2)%*%MM)%*%s2*lambda
+
+   temp=max(A1%*%bbar-b1)
+
+
+# compute p-values
+
+# JT: are we sure the signs of these are correctly handled?
+# two sided p-values numerically agree with python but
+# the one sided p-values are a bit off
+
+    for(jj in 1:length(bbar)){
+      vj=rep(0,length(bbar));vj[jj]=s2[jj]
+
+
+      junk=mypoly.pval.lee(bbar,A1,b1,vj,MM)
+
+       pv[jj] = junk$pv
+      vlo[jj]=junk$vlo
+       vup[jj]=junk$vup
+       sd[jj]=junk$sd
+
+      junk2=mypoly.int.lee(bbar,vj,vlo[jj],vup[jj],sd[jj],alpha)
+       ci[jj,]=junk2$int
+       tailarea[jj,] = junk2$tailarea
+     
+  }
+  # JT: these don't seem to be the real one-step estimators
+    fit0=coxph(Surv(y,status)~x[,m])
+      coef0=fit0$coef
+      se0=sqrt(diag(fit0$var))
+      zscore0=coef0/se0
+    
+   out = list(lambda=lambda,pv=pv,ci=ci,
+    tailarea=tailarea,vlo=vlo,vup=vup,sd=sd,
+    vars=vars,alpha=alpha,coef0=coef0,zscore0=zscore0,
+    call=this.call)
+  class(out) = "fixedCoxLassoInf" 
+}
+return(out)
+}
+
+
+
+print.fixedCoxLassoInf <- function(x, tailarea=TRUE, ...) {
+  cat("\nCall:\n")
+  dput(x$call)
+
+  cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n",
+              x$sigma))
+  
+  cat(sprintf("\nTesting results at lambda = %0.3f, with alpha = %0.3f\n",x$lambda,x$alpha))
+  cat("",fill=T)
+  tab = cbind(x$vars,
+    round(x$coef0,3),
+    round(x$zscore0,3),
+    round(x$pv,3),round(x$ci,3))
+  colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
+  if (tailarea) {
+    tab = cbind(tab,round(x$tailarea,3))
+    colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+  }
+  rownames(tab) = rep("",nrow(tab))
+  print(tab)
+ 
+  cat(sprintf("\nNote: coefficients shown are %s regression coefficients\n",
+              ifelse(x$type=="partial","partial","full")))
+  invisible()
+}
+
+
diff --git a/selectiveInference-currentCRAN/R/funs.fixedLogit.R b/selectiveInference-currentCRAN/R/funs.fixedLogit.R
new file mode 100644
index 0000000..5b67354
--- /dev/null
+++ b/selectiveInference-currentCRAN/R/funs.fixedLogit.R
@@ -0,0 +1,151 @@
+
+fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.beta=1e-5, tol.kkt=0.1,
+                     gridrange=c(-100,100), bits=NULL, verbose=FALSE,this.call=NULL){
+    
+    
+  type = match.arg(type)
+  checkargs.xy(x,y)
+  if (missing(beta) || is.null(beta)) stop("Must supply the solution beta")
+  if (missing(lambda) || is.null(lambda)) stop("Must supply the tuning parameter value lambda") 
+  checkargs.misc(beta=beta,lambda=lambda,alpha=alpha,
+                 gridrange=gridrange,tol.beta=tol.beta,tol.kkt=tol.kkt)
+  if (!is.null(bits) && !requireNamespace("Rmpfr",quietly=TRUE)) {
+    warning("Package Rmpfr is not installed, reverting to standard precision")
+    bits = NULL
+  }
+
+     
+     n=length(y)
+     p=ncol(x)
+ # I assume that intcpt was used
+     if(length(beta)!=p+1) stop("Since family='binomial', beta must be of length ncol(x)+1, that is, it should include an intercept")
+     nvar=sum(beta[-1]!=0)
+     pv=vlo=vup=sd=rep(NA, nvar)
+     ci=tailarea=matrix(NA,nvar,2)
+    
+#do we need to worry about standardization?
+
+#  obj = standardize(x,y,TRUE,FALSE)
+  #  x = obj$x
+  #  y = obj$y
+  
+ m=beta[-1]!=0  #active set
+         
+    bhat=c(beta[1],beta[-1][beta[-1]!=0]) # intcpt plus active vars
+     s2=sign(bhat)
+     lam2m=diag(c(0,rep(lambda,sum(m))))
+ 
+
+    xxm=cbind(1,x[,m])
+    
+   etahat = xxm %*% bhat
+   prhat = as.vector(exp(etahat) / (1 + exp(etahat)))
+   ww=prhat*(1-prhat)
+ #  w=diag(ww)
+      
+#check KKT
+   z=etahat+(y-prhat)/ww
+  # g=  t(x)%*%w%*%(z-etahat)/lambda # negative gradient scaled by lambda
+  g=scale(t(x),FALSE,1/ww)%*%(z-etahat)/lambda # negative gradient scaled by lambda
+     if (any(abs(g) > 1+tol.kkt) )
+    warning(paste("Solution beta does not satisfy the KKT conditions",
+                  "(to within specified tolerances)"))
+
+ vars = which(abs(beta[-1]) > tol.beta / sqrt(colSums(x^2)))
+  if(length(vars)==0){
+      cat("Empty model",fill=T)
+      return()
+  }
+  if (any(sign(g[vars]) != sign(beta[-1][vars])))
+    warning(paste("Solution beta does not satisfy the KKT conditions",
+                  "(to within specified tolerances). You might try rerunning",
+                  "glmnet with a lower setting of the",
+                  "'thresh' parameter, for a more accurate convergence."))
+  
+ #constraints for active variables             
+ # MM=solve(t(xxm)%*%w%*%xxm)
+   MM=solve(scale(t(xxm),F,1/ww)%*%xxm)
+  gm = c(0,-g[vars]*lambda) # gradient at LASSO solution, first entry is 0 because intercept is unpenalized
+                            # at exact LASSO solution it should be s2[-1]
+  dbeta = MM %*% gm
+
+  # bbar=(bhat+lam2m%*%MM%*%s2)  # JT: this is wrong, shouldn't use sign of intercept anywhere...
+  bbar = bhat - dbeta
+
+  A1=-(mydiag(s2))[-1,]
+  b1= (s2 * dbeta)[-1]
+
+  tol.poly = 0.01 
+  if (max((A1 %*% bbar) - b1) > tol.poly)
+    stop(paste("Polyhedral constraints not satisfied; you must recompute beta",
+               "more accurately. With glmnet, make sure to use exact=TRUE in coef(),",
+               "and check whether the specified value of lambda is too small",
+               "(beyond the grid of values visited by glmnet).",
+               "You might also try rerunning glmnet with a lower setting of the",
+               "'thresh' parameter, for a more accurate convergence."))
+
+
+  
+    for(jj in 1:sum(m)){
+       vj=c(rep(0,sum(m)+1));vj[jj+1]=s2[jj+1]
+      # compute p-values
+      junk=mypoly.pval.lee(bbar,A1,b1,vj,MM)
+      pv[jj] = junk$pv
+ 
+   vlo[jj]=junk$vlo
+   vup[jj]=junk$vup
+       sd[jj]=junk$sd
+  #  junk2=mypoly.int.lee(bbar[-1], A1, b1,vj,MM[-1,-1],alpha=.1)
+     junk2=mypoly.int.lee(bbar,vj,vlo[jj],vup[jj],sd[jj],alpha=.1)
+
+     ci[jj,]=junk2$int
+     tailarea[jj,] = junk2$tailarea
+   }
+
+  # JT: these are not the one step estimators but they are close
+  fit0=glm(y~x[,m],family="binomial")
+  sfit0=summary(fit0)
+      coef0=bbar[-1]        #fit0$coef[-1]
+      se0=sqrt(diag(MM)[-1]) # sfit0$cov.scaled)[-1])
+      zscore0=coef0/se0
+      
+  out = list(type=type,lambda=lambda,pv=pv,ci=ci,
+    tailarea=tailarea,vlo=vlo,vup=vup,sd=sd,
+    vars=vars,alpha=alpha,coef0=coef0,zscore0=zscore0,
+    call=this.call,
+    info.matrix=MM) # info.matrix is output just for debugging purposes at the moment
+  class(out) = "fixedLogitLassoInf"
+  return(out)
+
+ }
+
+
+
+print.fixedLogitLassoInf <- function(x, tailarea=TRUE, ...) {
+  cat("\nCall:\n")
+  dput(x$call)
+
+  cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n",
+              x$sigma))
+  
+  cat(sprintf("\nTesting results at lambda = %0.3f, with alpha = %0.3f\n",x$lambda,x$alpha))
+  cat("",fill=T)
+  tab = cbind(x$vars,
+    round(x$coef0,3),
+    round(x$zscore0,3),
+    round(x$pv,3),round(x$ci,3))
+  colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
+  if (tailarea) {
+    tab = cbind(tab,round(x$tailarea,3))
+    colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+  }
+  rownames(tab) = rep("",nrow(tab))
+  print(tab)
+ 
+  cat(sprintf("\nNote: coefficients shown are %s regression coefficients\n",
+              ifelse(x$type=="partial","partial","full")))
+  invisible()
+}
+
+
+
diff --git a/selectiveInference-currentCRAN/R/funs.fs.R b/selectiveInference-currentCRAN/R/funs.fs.R
new file mode 100644
index 0000000..b5ee511
--- /dev/null
+++ b/selectiveInference-currentCRAN/R/funs.fs.R
@@ -0,0 +1,482 @@
+# We compute the forward stepwise regression (FS) path given 
+# a response vector y and predictor matrix x.  We assume
+# that x has columns in general position.
+
+fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
+               verbose=FALSE) {
+
+  this.call = match.call()
+  checkargs.xy(x=x,y=y)
+  
+  # Center and scale, etc.
+  obj = standardize(x,y,intercept,normalize)
+  x = obj$x
+  y = obj$y
+  bx = obj$bx
+  by = obj$by
+  sx = obj$sx
+  n = nrow(x)
+  p = ncol(x)
+
+  #####
+  # To keep consistent with the lar function, we parametrize
+  # so that the first step has all zero coefficients,
+  # Also, an interesting note: the effective "lambda" (maximal
+  # correlation with the residual) may increase with stepwise!
+  # So we don't keep track of it
+
+  #####
+  # Find the first variable to enter and its sign
+  working_x = scale(x,center=F,scale=sqrt(colSums(x^2)))
+  score = t(working_x)%*%y
+  i_hit = which.max(abs(score))   # Hitting coordinate
+  sign_hit = Sign(score[i_hit])   # Sign
+  signs = sign_hit                # later signs will be appended to `signs`
+
+  if (verbose) {
+    cat(sprintf("1. Adding variable %i, |A|=%i...",i_hit,1))
+  }
+  
+  # Now iteratively find the new FS estimates
+
+  # Things to keep track of, and return at the end
+  # JT: I guess the "buf" just saves us from making huge
+  # matrices we don't need?
+
+  buf = min(maxsteps,500)
+  action = numeric(buf)      # Actions taken
+  df = numeric(buf)          # Degrees of freedom
+  beta = matrix(0,p,buf)     # FS estimates
+  
+  action[1] = i_hit
+  df[1] = 0
+  beta[,1] = 0
+
+  # Gamma matrix!
+  gbuf = max(2*p*3,2000)     # Space for 3 steps, at least
+  gi = 0                     # index into rows of Gamma matrix
+
+  Gamma = matrix(0,gbuf,n)
+  Gamma[gi+Seq(1,p-1),] = t(sign_hit*working_x[,i_hit]+working_x[,-i_hit]); gi = gi+p-1
+  Gamma[gi+Seq(1,p-1),] = t(sign_hit*working_x[,i_hit]-working_x[,-i_hit]); gi = gi+p-1
+  Gamma[gi+1,] = t(sign_hit*working_x[,i_hit]); gi = gi+1
+
+  # nconstraint
+  nconstraint = numeric(buf)
+  vreg = matrix(0,buf,n)
+  nconstraint[1] = gi
+  vreg[1,] = sign_hit*x[,i_hit] / sum(x[,i_hit]^2)
+
+  # Other things to keep track of, but not return
+  r = 1                      # Size of active set
+  A = i_hit                  # Active set -- JT: isn't this basically the same as action?
+  I = Seq(1,p)[-i_hit]       # Inactive set
+  X_active = x[,i_hit,drop=FALSE]   # Matrix X[,A]
+  X_inactive = x[,-i_hit,drop=FALSE]  # Matrix X[,I]
+  k = 2                      # What step are we at?
+                             # JT Why keep track of r and k instead of just saying k=r+1?
+
+  # Compute a skinny QR decomposition of X_active
+  # JT: obs was used as variable name above -- this is something different, no?
+  # changed it to qr_X
+
+  qr_X = qr(X_active)
+  Q = qr.Q(qr_X,complete=TRUE)
+  Q_active = Q[,1,drop=FALSE];
+  Q_inactive = Q[,-1,drop=FALSE]
+  R = qr.R(qr_X)
+  
+  # Throughout the algorithm, we will maintain
+  # the decomposition X_active = Q_active*R. Dimensions:
+  # X_active: n x r
+  # Q_active: n x r
+  # Q_inactive: n x (n-r)
+  # R:  r x r
+    
+  while (k<=maxsteps) {
+    ##########
+    # Check if we've reached the end of the buffer
+    if (k > length(action)) {
+      buf = length(action)
+      action = c(action,numeric(buf))
+      df = c(df,numeric(buf))
+      beta = cbind(beta,matrix(0,p,buf))
+      nconstraint = c(nconstraint,numeric(buf))
+      vreg = rbind(vreg,matrix(0,buf,n))
+    }
+
+    # Key quantities for the next entry
+    keepLs=backsolve(R,t(Q_active)%*%X_inactive)
+    X_inactive_resid = X_inactive - X_active %*% keepLs
+    working_x = scale(X_inactive_resid,center=F,scale=sqrt(colSums(X_inactive_resid^2)))
+    score = as.numeric(t(working_x)%*%y)
+    
+    # If the inactive set is empty, nothing will hit
+    if (r==min(n-intercept,p)) break
+
+    # Otherwise find the next hitting time
+    else {
+      sign_score = Sign(score)
+      abs_score = sign_score * score
+      i_hit = which.max(abs_score)
+      sign_hit = sign_score[i_hit]
+    }
+    
+    # Record the solution
+    # what is the difference between "action" and "A"?
+
+    action[k] = I[i_hit] 
+    df[k] = r
+    beta[A,k] = backsolve(R,t(Q_active)%*%y)
+        
+    # Gamma matrix!
+    if (gi + 2*p > nrow(Gamma)) Gamma = rbind(Gamma,matrix(0,2*p+gbuf,n))
+    working_x = t(sign_score*t(working_x))
+    Gamma[gi+Seq(1,p-r),] = t(working_x); gi = gi+p-r
+    Gamma[gi+Seq(1,p-r-1),] = t(working_x[,i_hit]-working_x[,-i_hit]); gi = gi+p-r-1
+    Gamma[gi+1,] = t(working_x[,i_hit]); gi = gi+1
+
+    # nconstraint, regression contrast
+    nconstraint[k] = gi
+    vreg[k,] = sign_hit*X_inactive_resid[,i_hit] / sum(X_inactive_resid[,i_hit]^2)
+
+    # Update all of the variables
+    r = r+1
+    A = c(A,I[i_hit])
+    I = I[-i_hit]
+    signs = c(signs,sign_hit)
+    X_active = cbind(X_active,X_inactive[,i_hit])
+    X_inactive = X_inactive[,-i_hit,drop=FALSE]
+
+    # Update the QR decomposition
+    updated_qr = updateQR(Q_active,Q_inactive,R,X_active[,r])
+    Q_active = updated_qr$Q1
+
+    # JT: why do we store Q_inactive? Doesn't seem to be used.
+    Q_inactive = updated_qr$Q2
+    R = updated_qr$R
+     
+    if (verbose) {
+      cat(sprintf("\n%i. Adding variable %i, |A|=%i...",k,A[r],r))
+    }
+            
+    # Step counter
+    k = k+1
+  }
+
+  # Trim
+  action = action[Seq(1,k-1)]
+  df = df[Seq(1,k-1),drop=FALSE]
+  beta = beta[,Seq(1,k-1),drop=FALSE]
+  Gamma = Gamma[Seq(1,gi),,drop=FALSE]
+  nconstraint = nconstraint[Seq(1,k-1)]
+  vreg = vreg[Seq(1,k-1),,drop=FALSE]
+  
+  # If we reached the maximum number of steps
+  if (k>maxsteps) {
+    if (verbose) {
+      cat(sprintf("\nReached the maximum number of steps (%i),",maxsteps))
+      cat(" skipping the rest of the path.")
+    }
+    completepath = FALSE
+    bls = NULL
+  }
+
+  # Otherwise, note that we completed the path
+  else {
+    completepath = TRUE
+    
+    # Record the least squares solution. Note that
+    # we have already computed this
+    bls = rep(0,p)
+   if(length(keepLs)>0)  bls[A] = keepLs
+  }
+
+  if (verbose) cat("\n")
+  
+  # Adjust for the effect of centering and scaling
+  if (intercept) df = df+1
+  if (normalize) beta = beta/sx
+  if (normalize && completepath) bls = bls/sx
+  
+  # Assign column names
+  colnames(beta) = as.character(Seq(1,k-1))
+
+  out = list(action=action,sign=signs,df=df,beta=beta,
+    completepath=completepath,bls=bls,
+    Gamma=Gamma,nconstraint=nconstraint,vreg=vreg,x=x,y=y,bx=bx,by=by,sx=sx,
+    intercept=intercept,normalize=normalize,call=this.call) 
+  class(out) = "fs"
+  return(out)
+}
+
+##############################
+
+# Coefficient function for fs
+
+coef.fs <- function(object, s, ...) {
+  if (object$completepath) {
+    k = length(object$action)+1
+    beta = cbind(object$beta,object$bls)
+  } else {
+    k = length(object$action)
+    beta = object$beta
+  }
+  
+  if (min(s)<0 || max(s)>k) stop(sprintf("s must be between 0 and %i",k))
+  knots = 1:k
+  dec = FALSE
+  return(coef.interpolate(beta,s,knots,dec))
+}
+
+# Prediction function for fs
+
+predict.fs <- function(object, newx, s, ...) {
+  beta = coef.fs(object,s)
+  if (missing(newx)) newx = scale(object$x,FALSE,1/object$sx)
+  else newx = scale(newx,object$bx,FALSE)
+  return(newx %*% beta + object$by)
+}
+
+##############################
+
+# FS inference function
+
+fsInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic"), 
+                  gridrange=c(-100,100), bits=NULL, mult=2, ntimes=2, verbose=FALSE) {
+  
+  this.call = match.call()
+  type = match.arg(type)
+  checkargs.misc(sigma=sigma,alpha=alpha,k=k,
+                 gridrange=gridrange,mult=mult,ntimes=ntimes)
+  if (class(obj) != "fs") stop("obj must be an object of class fs")
+  if (is.null(k) && type=="active") k = length(obj$action)
+  if (is.null(k) && type=="all") stop("k must be specified when type = all")
+  if (!is.null(bits) && !requireNamespace("Rmpfr",quietly=TRUE)) {
+    warning("Package Rmpfr is not installed, reverting to standard precision")
+    bits = NULL
+  }
+  
+  k = min(k,length(obj$action)) # Round to last step
+  x = obj$x
+  y = obj$y
+  p = ncol(x)
+  n = nrow(x)
+  G = obj$Gamma
+  nconstraint = obj$nconstraint
+  sx = obj$sx
+
+  if (is.null(sigma)) {
+    if (n >= 2*p) {
+      oo = obj$intercept
+      sigma = sqrt(sum(lsfit(x,y,intercept=oo)$res^2)/(n-p-oo))
+    }
+    else {
+      sigma = sd(y)
+      warning(paste(sprintf("p > n/2, and sd(y) = %0.3f used as an estimate of sigma;",sigma),
+                    "you may want to use the estimateSigma function"))
+    }
+  }
+
+  khat = NULL
+  
+  if (type == "active") {
+    pv = vlo = vup = numeric(k) 
+    vmat = matrix(0,k,n)
+    ci = tailarea = matrix(0,k,2)
+    vreg = obj$vreg[1:k,,drop=FALSE]
+    sign = obj$sign[1:k]
+    vars = obj$action[1:k]
+
+    for (j in 1:k) {
+      if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
+
+      Gj = G[1:nconstraint[j],]
+      uj = rep(0,nconstraint[j])
+      vj = vreg[j,]
+      mj = sqrt(sum(vj^2)) 
+      vj = vj / mj              # Standardize (divide by norm of vj)
+      a = poly.pval(y,Gj,uj,vj,sigma,bits)
+      pv[j] = a$pv
+      sxj = sx[vars[j]]
+      vlo[j] = a$vlo * mj / sxj # Unstandardize (mult by norm of vj / sxj)
+      vup[j] = a$vup * mj / sxj # Unstandardize (mult by norm of vj / sxj)
+      vmat[j,] = vj * mj / sxj  # Unstandardize (mult by norm of vj / sxj)
+  
+      a = poly.int(y,Gj,uj,vj,sigma,alpha,gridrange=gridrange,
+        flip=(sign[j]==-1),bits=bits)
+      ci[j,] = a$int * mj / sxj # Unstandardize (mult by norm of vj / sxj)
+      tailarea[j,] = a$tailarea
+    }
+
+    khat = forwardStop(pv,alpha)
+  }
+  
+  else {
+    if (type == "aic") {
+      out = aicStop(x,y,obj$action[1:k],obj$df[1:k],sigma,mult,ntimes)
+      khat = out$khat
+      m = out$stopped * ntimes
+      G = rbind(out$G,G[1:nconstraint[khat+m],])  # Take ntimes more steps past khat
+      u = c(out$u,rep(0,nconstraint[khat+m]))     # (if we need to)
+      kk = khat
+    }
+    else {
+      G = G[1:nconstraint[k],]
+      u = rep(0,nconstraint[k])
+      kk = k
+    }
+    
+    pv = vlo = vup = numeric(kk) 
+    vmat = matrix(0,kk,n)
+    ci = tailarea = matrix(0,kk,2)
+    sign = numeric(kk)
+    vars = obj$action[1:kk]
+    xa = x[,vars]
+    M = pinv(crossprod(xa)) %*% t(xa)
+    
+    for (j in 1:kk) {
+      if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
+
+      vj = M[j,]
+      mj = sqrt(sum(vj^2))
+      vj = vj / mj              # Standardize (divide by norm of vj)
+      sign[j] = sign(sum(vj*y))
+      vj = sign[j] * vj
+      Gj = rbind(G,vj)
+      uj = c(u,0)
+
+      a = poly.pval(y,Gj,uj,vj,sigma,bits)
+      pv[j] = a$pv
+      sxj = sx[vars[j]]
+      vlo[j] = a$vlo * mj / sxj # Unstandardize (mult by norm of vj / sxj)
+      vup[j] = a$vup * mj / sxj # Unstandardize (mult by norm of vj / sxj)
+      vmat[j,] = vj * mj / sxj  # Unstandardize (mult by norm of vj / sxj)
+
+      a = poly.int(y,Gj,uj,vj,sigma,alpha,gridrange=gridrange,
+        flip=(sign[j]==-1),bits=bits)
+      ci[j,] = a$int * mj / sxj # Unstandardize (mult by norm of vj / sxj)
+      tailarea[j,] = a$tailarea
+    }
+  }
+  
+  # JT: why do we output vup, vlo? Are they used somewhere else?
+
+  out = list(type=type,k=k,khat=khat,pv=pv,ci=ci,
+    tailarea=tailarea,vlo=vlo,vup=vup,vmat=vmat,y=y,
+    vars=vars,sign=sign,sigma=sigma,alpha=alpha,
+    call=this.call)
+  class(out) = "fsInf"
+  return(out)
+}
+
+##############################
+
+
+##############################
+
+print.fs <- function(x, ...) {
+  cat("\nCall:\n")
+  dput(x$call)
+  
+  cat("\nSequence of FS moves:\n")
+  nsteps = length(x$action)
+  tab = cbind(1:nsteps,x$action,x$sign)
+  colnames(tab) = c("Step","Var","Sign")
+  rownames(tab) = rep("",nrow(tab))
+  print(tab)
+  invisible()
+}
+
+print.fsInf <- function(x, tailarea=TRUE, ...) {
+  cat("\nCall:\n")
+  dput(x$call)
+
+  cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n",
+              x$sigma))
+
+  if (x$type == "active") {
+    cat(sprintf("\nSequential testing results with alpha = %0.3f\n",x$alpha))
+    tab = cbind(1:length(x$pv),x$vars,
+      round(x$sign*x$vmat%*%x$y,3),
+      round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
+      round(x$pv,3),round(x$ci,3))
+    colnames(tab) = c("Step", "Var", "Coef", "Z-score", "P-value",
+              "LowConfPt", "UpConfPt")
+    if (tailarea) {
+      tab = cbind(tab,round(x$tailarea,3))
+      colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+    }
+    rownames(tab) = rep("",nrow(tab))
+    print(tab)
+
+    cat(sprintf("\nEstimated stopping point from ForwardStop rule = %i\n",x$khat))
+  }
+
+  else if (x$type == "all") {
+    cat(sprintf("\nTesting results at step = %i, with alpha = %0.3f\n",x$k,x$alpha))
+    tab = cbind(x$vars,
+      round(x$sign*x$vmat%*%x$y,3),
+      round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
+      round(x$pv,3),round(x$ci,3))
+    colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
+    if (tailarea) {
+      tab = cbind(tab,round(x$tailarea,3))
+      colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+    }
+    rownames(tab) = rep("",nrow(tab))
+    print(tab)
+  }
+
+  else if (x$type == "aic") {
+    cat(sprintf("\nTesting results at step = %i, with alpha = %0.3f\n",x$khat,x$alpha))
+    tab = cbind(x$vars,
+      round(x$sign*x$vmat%*%x$y,3),
+      round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
+      round(x$pv,3),round(x$ci,3))
+    colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
+    if (tailarea) {
+      tab = cbind(tab,round(x$tailarea,3))
+      colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+    }
+    rownames(tab) = rep("",nrow(tab))
+    print(tab)
+    
+    cat(sprintf("\nEstimated stopping point from AIC rule = %i\n",x$khat))
+  }
+
+  invisible()
+}
+
+
+plot.fs <- function(x, breaks=TRUE, omit.zeros=TRUE, var.labels=TRUE, ...) {
+  if (x$completepath) {
+    k = length(x$action)+1
+    beta = cbind(x$beta,x$bls)
+  } else {
+    k = length(x$action)
+    beta = x$beta
+  }
+  p = nrow(beta)
+
+  xx = 1:k
+  xlab = "Step"
+
+ if (omit.zeros) {
+   good.inds = matrix(FALSE,p,k)
+   good.inds[beta!=0] = TRUE
+   changes = t(apply(beta,1,diff))!=0
+   good.inds[cbind(changes,rep(F,p))] = TRUE
+   good.inds[cbind(rep(F,p),changes)] = TRUE
+   beta[!good.inds] = NA
+  }
+
+  plot(c(),c(),xlim=range(xx,na.rm=T),ylim=range(beta,na.rm=T),
+       xlab=xlab,ylab="Coefficients",main="Forward stepwise path",...)
+  abline(h=0,lwd=2)
+  matplot(xx,t(beta),type="l",lty=1,add=TRUE)
+  if (breaks) abline(v=xx,lty=2)
+  if (var.labels) axis(4,at=beta[,k],labels=1:p,cex=0.8,adj=0)
+  invisible()
+}
+
diff --git a/selectiveInference-currentCRAN/R/funs.groupfs.R b/selectiveInference-currentCRAN/R/funs.groupfs.R
new file mode 100644
index 0000000..b2c0447
--- /dev/null
+++ b/selectiveInference-currentCRAN/R/funs.groupfs.R
@@ -0,0 +1,794 @@
+#' Select a model with forward stepwise.
+#'
+#' This function implements forward selection of linear models almost identically to \code{\link[stats]{step}} with \code{direction = "forward"}. The reason this is a separate function from \code{\link{fs}} is that groups of variables (e.g. dummies encoding levels of a categorical variable) must be handled differently in the selective inference framework.
+#'
+#' @param x Matrix of predictors (n by p).
+#' @param y Vector of outcomes (length n).
+#' @param index Group membership indicator of length p. Check that \code{sort(unique(index)) = 1:G} where \code{G} is the number of distinct groups.
+#' @param maxsteps Maximum number of steps for forward stepwise.
+#' @param sigma Estimate of error standard deviation for use in AIC criterion. This determines the relative scale between RSS and the degrees of freedom penalty. Default is NULL corresponding to unknown sigma. When NULL, \code{link{groupfsInf}} performs truncated F inference instead of truncated \eqn{\chi}. See \code{\link[stats]{extractAIC}} for details on the AIC criterion.
+#' @param k Multiplier of model size penalty, the default is \code{k = 2} for AIC. Use \code{k = log(n)} for BIC, or \code{k = 2log(p)} for RIC (best for high dimensions, when \eqn{p > n}). If \eqn{G < p} then RIC may be too restrictive and it would be better to use \code{log(G) < k < 2log(p)}.
+#' @param intercept Should an intercept be included in the model? Default is TRUE. Does not count as a step.
+#' @param center Should the columns of the design matrix be centered? Default is TRUE.
+#' @param normalize Should the design matrix be normalized? Default is TRUE.
+#' @param aicstop Early stopping if AIC increases. Default is 0 corresponding to no early stopping. Positive integer values specify the number of times the AIC is allowed to increase in a row, e.g. with \code{aicstop = 2} the algorithm will stop if the AIC criterion increases for 2 steps in a row. The default of \code{\link[stats]{step}} corresponds to \code{aicstop = 1}.
+#' @param verbose Print out progress along the way? Default is FALSE.
+#' @return An object of class "groupfs" containing information about the sequence of models in the forward stepwise algorithm. Call the function \code{\link{groupfsInf}} on this object to compute selective p-values.
+#' @examples
+#' x = matrix(rnorm(20*40), nrow=20)
+#' index = sort(rep(1:20, 2))
+#' y = rnorm(20) + 2 * x[,1] - x[,4]
+#' fit = groupfs(x, y, index, maxsteps = 5)
+#' out = groupfsInf(fit)
+#' out
+#' @seealso \code{\link{groupfsInf}}, \code{\link{factorDesign}}.
+groupfs <- function(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE, center = TRUE, normalize = TRUE, aicstop = 0, verbose = FALSE) {
+
+  if (missing(index)) stop("Missing argument: index.")
+  p <- ncol(x)
+  n <- nrow(x)
+
+  # Group labels
+  labels <- unique(index)
+  G <- length(labels)
+  inactive <- labels
+  active <- c()
+
+  if (missing(maxsteps) || maxsteps >= min(n, G)) maxsteps <- min(n-1, G)
+  checkargs.xy(x=x, y=y)
+  checkargs.groupfs(x, index, maxsteps)
+  if (maxsteps > G) stop("maxsteps is larger than number of groups")
+  gsizes <- sort(rle(sort(index))$lengths, decreasing = TRUE)
+  if (sum(gsizes[1:maxsteps]) >= nrow(x)) {
+      maxsteps <- max(which(cumsum(gsizes) < nrow(x)))
+      warning(paste("If the largest groups are included the model will be saturated/overdetermined. To prevent this maxsteps has been changed to", maxsteps))
+  }
+
+  # Initialize copies of data for loop
+  by <- mean(y)
+  y.update <- y
+  if (intercept) y.update <- y - by
+  y.last <- y.update
+
+  # Center and scale design matrix
+  xscaled <- scaleGroups(x, index, center, normalize)
+  xm <- xscaled$xm
+  xs <- xscaled$xs
+  x.update <- xscaled$x
+
+  x.begin <- x.update
+  y.begin <- y.update
+  stopped <- FALSE
+  # Store all projections computed along the path
+  terms = projections = maxprojs = aicpens = maxpens = cumprojs = vector("list", maxsteps)
+
+  # Store other information from each step
+  path.info <- data.frame(imax=integer(maxsteps), df=integer(maxsteps), AIC=numeric(maxsteps), RSS=numeric(maxsteps), RSSdrop=numeric(maxsteps), chisq=numeric(maxsteps))
+
+  modelrank <- as.numeric(intercept)
+  if (is.null(sigma)) {
+      modelrank <- modelrank + 1
+      aic.begin <- aic.last <- n*(log(2*pi) + log(mean(y.update^2))) + k * (n + modelrank)
+  } else {
+      aic.begin <- aic.last <- sum(y.update^2)/sigma^2 - n + k * modelrank
+  }
+  if (verbose) print(paste0("Start:  AIC=", round(aic.begin, 3)), quote = FALSE)
+
+  # Begin main loop
+  for (step in 1:maxsteps) {
+
+    added <- add1.groupfs(x.update, y.update, index, labels, inactive, k, sigma)
+
+    # Group to be added
+    imax <- added$imax
+    inactive <- setdiff(inactive, imax)
+    active <- union(active, imax)
+    inactive.inds <- which(!index %in% active)
+
+    # Rank of group
+    modelrank <- modelrank + added$df
+
+    # Stop without adding if model has become saturated
+    if (modelrank >= n) {
+        stop("Saturated model. Abandon ship!")
+    }
+
+    # Regress added group out of y and inactive x
+    P.imax <- added$maxproj %*% t(added$maxproj)
+    P.imax <- diag(rep(1, n)) - P.imax
+    y.update <- P.imax %*% y.update
+    x.update[, inactive.inds] <- P.imax %*% x.update[, inactive.inds]
+
+    # Compute AIC
+    if (is.null(sigma)) {
+        added$AIC <- n * log(added$maxterm/n) - k * added$df + n*log(2*pi) + k * (n + modelrank)
+    } else {
+        added$AIC <- sum(y.update^2)/sigma^2 - n + k * modelrank
+    }
+
+    projections[[step]] <- added$projections
+    maxprojs[[step]] <- added$maxproj
+    aicpens[[step]] <- added$aicpens
+    maxpens[[step]] <- added$maxpen
+    if (step == 1) cumprojs[[step]] <- P.imax
+    if (step > 1) cumprojs[[step]] <- P.imax %*% cumprojs[[step-1]]
+    terms[[step]] <- added$terms
+
+    # Compute RSS for unadjusted chisq p-values
+    added$RSS <- sum(y.update^2)
+    scale.chisq <- 1
+
+    added$RSSdrop <- sum((y.last - y.update)^2)
+    added$chisq <- pchisq(added$RSSdrop/scale.chisq, lower.tail=FALSE, df = added$df)
+    y.last <- y.update
+
+    # Projections are stored separately
+    step.info <- data.frame(added[-c(3:(length(added)-4))])
+    path.info[step, ] <- step.info
+
+    if (verbose) print(round(step.info, 3))
+
+    if (aicstop > 0 && step < maxsteps && step >= aicstop && aic.last < added$AIC) {
+        if (all(diff(c(aic.begin, path.info$AIC)[(step+1-aicstop):(step+1)]) > 0)) {
+
+            if (is.null(sigma)) {
+                added$AIC <- n * log(added$maxterm/n) - k * added$df + n + n*log(2*pi) + k * modelrank
+            } else {
+                added$AIC <- sum(y.update^2)/sigma^2 - n + k * modelrank
+            }
+
+            path.info <- path.info[1:step, ]
+            projections[(step+1):maxsteps] <- NULL
+            maxprojs[(step+1):maxsteps] <- NULL
+            aicpens[(step+1):maxsteps] <- NULL
+            maxpens[(step+1):maxsteps] <- NULL
+            cumprojs[(step+1):maxsteps] <- NULL
+            terms[(step+1):maxsteps] <- NULL
+            maxsteps <- step
+            stopped <- TRUE
+            break
+        }
+    }
+    aic.last <- added$AIC
+  }
+
+  # Is there a better way of doing this?
+  # Use some projections already computed?
+  beta <- coef(lm(y.begin ~ x.begin[,index %in% path.info$imax]-1))
+  names(beta) <- index[index %in% path.info$imax]
+
+  # Create output object
+  value <- list(action = path.info$imax, L = path.info$L, AIC = path.info$AIC, projections = projections, maxprojs = maxprojs, aicpens = aicpens, maxpens = maxpens, cumprojs = cumprojs, log = path.info, index = index, y = y.begin, x = x.begin, coefficients = beta, bx = xm, by = by, sx = xs, sigma = sigma, intercept = intercept, call = match.call(), terms = terms)
+
+  class(value) <- "groupfs"
+  attr(value, "center") <- center
+  attr(value, "normalize") <- normalize
+  attr(value, "labels") <- labels
+  attr(value, "maxsteps") <- maxsteps
+  attr(value, "sigma") <- sigma
+  attr(value, "k") <- k
+  attr(value, "aicstop") <- aicstop
+  attr(value, "stopped") <- stopped
+  if (is.null(attr(x, "varnames"))) {
+    attr(value, "varnames") <- colnames(x)
+  } else {
+    attr(value, "varnames") <- attr(x, "varnames")
+  }
+  return(value)
+}
+
+#' Add one group to the model in \code{groupfs}.
+#'
+#' For internal use by \code{\link{groupfs}}.
+#'
+#' @param xr Design matrix at current step.
+#' @param yr Response vector residual at current step.
+#' @param index Group membership indicator of length p.
+#' @param labels The unique elements of \code{index}.
+#' @param inactive Labels of inactive groups.
+#' @param k Multiplier of model size penalty, use \code{k = 2} for AIC, \code{k = log(n)} for BIC, or \code{k = log(p)} for RIC.
+#' @param sigma Estimate of error standard deviation for use in AIC criterion. This determines the relative scale between RSS and the degrees of freedom penalty. See \code{\link{extractAIC}} for details.
+#' @return Index \code{imax} of added group, value \code{L} of maximized negative AIC, lists of projection matrices defining quadratic model selection event.
+add1.groupfs <- function(xr, yr, index, labels, inactive, k, sigma = NULL) {
+
+  # Use characters to avoid issues where
+  # list() populates NULL lists in the positions
+  # of the active variables
+  ### Question for later: does this slow down lapply?
+  keys = as.character(inactive)
+  n <- nrow(xr)
+
+  # Compute sums of squares to determine which group is added
+  # penalized by rank of group if k > 0
+  projections = aicpens = terms = vector("list", length(keys))
+  names(projections) = names(terms) = names(aicpens) = keys
+  for (key in keys) {
+      inds <- which(index == key)
+      xi <- xr[,inds]
+      ui <- svdu_thresh(xi)
+      dfi <- ncol(ui)
+      projections[[key]] <- ui
+      uy <- t(ui) %*% yr
+      if (is.null(sigma)) {
+          aicpens[[key]] <- exp(k*dfi/n)
+          terms[[key]] <- (sum(yr^2) - sum(uy^2)) * aicpens[[key]]
+      } else {
+          aicpens[[key]] <- sigma^2 * k * dfi
+          terms[[key]] <- (sum(yr^2) - sum(uy^2)) + aicpens[[key]]
+      }
+  }
+
+  # Maximizer = group to be added
+  terms.optind <- which.min(terms)
+  imax <- inactive[terms.optind]
+  optkey <- which(keys == imax)
+  maxproj <- projections[[optkey]]
+  maxpen <- aicpens[[optkey]]
+  maxterm <- terms[[optkey]]
+  projections[[optkey]] <- NULL
+  aicpens[[optkey]] <- NULL
+
+  return(list(imax=imax, df = ncol(maxproj), projections = projections, maxproj = maxproj, aicpens = aicpens, maxpen = maxpen, maxterm = maxterm, terms = terms))
+}
+
+# -----------------------------------------------------------
+
+#' Compute selective p-values for a model fitted by \code{groupfs}.
+#'
+#' Computes p-values for each group of variables in a model fitted by \code{\link{groupfs}}. These p-values adjust for selection by truncating the usual \eqn{\chi^2} statistics to the regions implied by the model selection event. If the \code{sigma} to \code{\link{groupfs}} was NULL then groupfsInf uses truncated \eqn{F} statistics instead of truncated \eqn{\chi}. The \code{sigma} argument to groupfsInf allows users to override and use \eqn{\chi}, but this is not recommended unless \eqn{\sigma} can be estimated well (i.e. \eqn{n > p}).
+#'
+#' @param obj Object returned by \code{\link{groupfs}} function
+#' @param sigma Estimate of error standard deviation. Default is NULL and in this case groupfsInf uses the value of sigma specified to \code{\link{groupfs}}.
+#' @param verbose Print out progress along the way? Default is TRUE.
+#' @return An object of class "groupfsInf" containing selective p-values for the fitted model \code{obj}. For comparison with \code{\link{fsInf}}, note that the option \code{type = "active"} is not available.
+#'
+#' \describe{
+#'   \item{vars}{Labels of the active groups in the order they were included.}
+#'   \item{pv}{Selective p-values computed from appropriate truncated distributions.}
+#'   \item{sigma}{Estimate of error variance used in computing p-values.}
+#'   \item{TC or TF}{Observed value of truncated \eqn{\chi} or \eqn{F}.}
+#'   \item{df}{Rank of group of variables when it was added to the model.}
+#'   \item{support}{List of intervals defining the truncation region of the corresponding statistic.}
+#' }
+groupfsInf <- function(obj, sigma = NULL, verbose = TRUE) {
+
+  if (!is.null(obj$cvobj) && attr(obj, "stopped")) {
+      stop("Cross-validation and early stopping cannot be used simultaneously.")
+      # This shouldn't happen in the first place!
+      # (it wouldn't anyway unless someone tries to trick it)
+  }
+
+  n <- nrow(obj$x)
+  p <- ncol(obj$x)
+  maxsteps <- attr(obj, "maxsteps")
+  k <- attr(obj, "k")
+  index <- obj$index
+  x <- obj$x
+  y <- obj$y
+  Ep <- sum(index %in% obj$action)
+
+  pvals = dfs = dfs2 = Tstats = numeric(maxsteps)
+  supports <- list()
+
+  if (!is.null(sigma)) {
+      type <- "TC"
+      if (!is.null(obj$sigma)) {
+          cat(paste("Using specified value", sigma, "for sigma in place of the value", obj$sigma, "used by groupfs()\n"))
+      }
+  } else {
+      if (is.null(obj$sigma)) {
+          type <- "TF"
+          Pf <- svdu_thresh(obj$x[,which(obj$index %in% obj$action), drop = FALSE])
+          dffull <- ncol(Pf)
+          df2 <- n - dffull - obj$intercept - 1
+          Pfull <- Pf %*% t(Pf)
+      } else {
+          type <- "TC"
+          sigma <- obj$sigma
+      }
+  }
+
+  # Compute p-value for each active group
+  for (j in 1:maxsteps) {
+    i <- obj$action[j]
+    if (verbose) {
+        string <- paste0("Step ", j, "/", attr(obj, "maxsteps"), ": computing P-value for group ", i)
+        if (!is.null(obj$cvobj)) string <- paste0(string, ", including constraints from cross-validation")
+        if (attr(obj, "stopped")) string <- paste0(string, ", including constraints from AICstop")
+        cat(paste(string, "\n"))
+    }
+
+    if (type == "TC") {
+        # Form projection onto active set minus i
+        # and project x_i orthogonally
+        x_i <- obj$x[,which(obj$index == i), drop = FALSE]
+        if (length(obj$action) > 1) {
+            minus_i <- setdiff(obj$action, i)
+            x_minus_i <- svdu_thresh(obj$x[,which(obj$index %in% minus_i), drop = FALSE])
+            x_i <- x_i - x_minus_i %*% t(x_minus_i) %*% x_i
+        }
+
+        # Project y onto what remains of x_i
+        Ugtilde <- svdu_thresh(x_i)
+        R <- t(Ugtilde) %*% obj$y
+        TC <- sqrt(sum(R^2))
+        eta <- Ugtilde %*% R / TC
+        Z <- obj$y - eta * TC
+        dfi <- ncol(Ugtilde)
+        Tstats[j] <- TC
+        dfs[j] <- dfi
+
+        ydecomp <- list(Z=Z, eta=eta)
+
+    } else {
+
+        if (length(obj$action) > 1) {
+            minus_i <- setdiff(obj$action, i)
+            Psub <- svdu_thresh(obj$x[,which(obj$index %in% minus_i), drop = FALSE])
+            Z <- Psub %*% t(Psub) %*% obj$y
+            df1 <- dffull - ncol(Psub)
+        } else {
+            Z <- rep(0, n)
+            df1 <- dffull + obj$intercept + 1
+        }
+
+        C <- df1/df2
+        R1 <- obj$y - Z
+        R2 <- obj$y - Pfull %*% obj$y
+        R1sq <- sum(R1^2)
+        R2sq <- sum(R2^2)
+        R <- sqrt(R1sq)
+        delta <- R1-R2
+        Vdelta <- delta/sqrt(sum(delta^2))
+        V2 <- R2/sqrt(R2sq)
+        TF <- (R1sq-R2sq)/(C*R2sq)
+        Tstats[j] <- TF
+        dfs[j] <- df1
+
+        ydecomp <- list(R=R, Z=Z, Vd=Vdelta, V2=V2, C=C)
+
+    }
+
+    intervallist <- truncationRegion(obj, ydecomp, type)
+
+    # Additional constraints from cross-validation?
+    if (!is.null(obj$cvobj)) {
+        intervallist <- c(intervallist, do.call(c,
+                          lapply(obj$cvobj, function(cvf) {
+                              if (type == "TC") {
+                                  ydecomp <- list(R=R[-cvf$fold], eta=eta[-cvf$fold], Z=Z[-cvf$fold])
+                              } else {
+                                  ydecomp <- list(R=R, Z=Z[-cvf$fold], Vd=Vdelta[-cvf$fold], V2=V2[-cvf$fold], C=C) # C correct?
+                              }
+                              truncationRegion(cvf, ydecomp, type)
+                          })))
+        intervallist <- c(intervallist,
+                          lapply(obj$cvquad, function(cvquad) {
+                              if (type == "TC") {
+                                  etacvquad <- t(eta) %*% cvquad
+                                  A <- etacvquad %*% eta
+                                  B <- 2 * etacvquad %*% Z
+                                  C <- t(Z) %*% cvquad %*% Z
+                                  quadratic_roots(A, B, C, tol = 1e-15)
+                              } else {
+
+                                  zcvquad <- t(Z) %*% cvquad
+                                  vdcvquad <- t(Vdelta) %*% cvquad
+                                  v2cvquad <- t(V2) %*% cvquad
+                                  x0 <- zcvquad %*% Z
+                                  x1 <- 2*R*zcvquad %*% Vdelta
+                                  x2 <- 2*R*zcvquad %*% V2
+                                  x12 <- 2*R^2*vdcvquad %*% V2
+                                  x11 <- R^2*vdcvquad %*% Vdelta
+                                  x22 <- R^2*v2cvquad %*% V2
+                                  TF_roots(R, C, coeffs = list(x0=x0, x1=x1, x2=x2, x12=x12, x11=x11, x22=x22))
+                              }
+                          }))
+    }
+
+    # Additional constraints from AIC stopping
+    if (attr(obj, "stopped")) {
+        aicintervals <- vector("list", maxsteps)
+        aicstop <- attr(obj, "aicstop")
+        if (type == "TC") {
+            pen0 <- k * obj$intercept
+            aic.begin <- aic.last <- sum(obj$y^2)/sigma^2 - n + k * obj$intercept
+        } else {
+            pen0 <- exp(k * (1+obj$intercept)/n)
+            aic.begin <- n*(log(2*pi) + log(mean(obj$y^2))) + k * (1 + n + obj$intercept)
+        }
+        AICs <- c(aic.begin, obj$AIC)
+
+        ulist <- c(list(matrix(0, n, 1)), obj$maxprojs)
+        penlist <- c(pen0, obj$maxpens)
+        zlist <- vector("list", maxsteps+1)
+        zlist[[1]] <- zlist[[2]] <- Z
+        if (type == "TC") {
+            etalist <- vector("list", maxsteps+1)
+            etalist[[1]] <- etalist[[2]] <- eta
+        } else {
+            vdlist <- v2list <- vector("list", maxsteps+1)
+            vdlist[[1]] <- vdlist[[2]] <- Vdelta
+            v2list[[1]] <- v2list[[2]] <- V2
+        }
+        if (maxsteps > 1) {
+            for (step in 1:(maxsteps-1)) {
+                cproj <- obj$cumprojs[[step]]
+                zlist[[step+2]] <- cproj %*% Z
+                if (type == "TC") {
+                    etalist[[step+2]] <- cproj %*% eta
+                } else {
+                    vdlist[[step+2]] <- cproj %*% Vdelta
+                    v2list[[step+2]] <- cproj %*% V2
+                }
+            }
+        }
+
+        for (step in 1:maxsteps) {
+            # Compare AIC at s+1 to AIC at s
+            # roots() functions assume g indexes smaller AIC
+            # this is step+1 until the last step
+            peng <- penlist[[step+1]]
+            Ug <- ulist[[step+1]]
+            Uh <- ulist[[step]]
+            Zg <- zlist[[step+1]]
+            Zh <- zlist[[step]]
+
+            if (type == "TC") {
+                penh <- 0
+                etag <- etalist[[step+1]]
+                etah <- etalist[[step]]
+                coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh)
+
+                if (AICs[step] < AICs[step+1]) {
+                    coeffs <- lapply(coeffs, function(coeff) -coeff)
+                }
+
+                intstep <- quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol = 1e-15)
+
+            } else {
+                penh <- 1
+                Vdg <- vdlist[[step+1]]
+                Vdh <- vdlist[[step]]
+                V2g <- v2list[[step+1]]
+                V2h <- v2list[[step]]
+                coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h)
+
+                if (AICs[step] < AICs[step+1]) {
+                    coeffs <- lapply(coeffs, function(coeff) -coeff)
+                }
+
+                intstep <- TF_roots(R, C, coeffs)
+            }
+
+            aicintervals[[step]] <- intstep
+        }
+        intervallist <- c(intervallist, aicintervals)
+    }
+
+    # Compute intersection:
+    region <- do.call(interval_union, intervallist)
+    region <- interval_union(region, Intervals(c(-Inf,0)))
+    E <- interval_complement(region, check_valid = FALSE)
+
+    if (length(E) == 0) {
+        stop(paste("Empty support at step", j))
+    }
+    supports[[j]] <- E
+
+    # E is now potentially a union of intervals
+    if (type == "TC") {
+        pvals[j] <- TC_surv(TC, sigma, dfi, E)
+    } else {
+        # write TF_surv function first
+        pvals[j] <- TF_surv(TF, df1, df2, E)
+    }
+
+  }
+
+  if (any(is.nan(pvals))) {
+      nanp <- which(is.nan(pvals))
+      pvals[nanp] <- 0
+      warning(paste0("P-value NaNs of the form 0/0 converted to 0 for group(s) ", paste(obj$action[nanp], collapse=","), ". This typically occurs for numerical reasons in the presence of a large signal-to-noise ratio."))
+  }
+
+  names(pvals) <- obj$action
+  out <- list(vars = obj$action, pv=pvals)
+  if (type == "TC") {
+      out$TC <- Tstats
+      out$sigma <- sigma
+  } else {
+      out$TF <- Tstats
+      out$df2 <- df2
+  }
+  out$df <- dfs
+  out$support <- supports
+  class(out) <- "groupfsInf"
+  if (!is.null(attr(obj, "varnames"))) {
+      attr(out, "varnames") <- attr(obj, "varnames")
+  }
+  return(out)
+}
+
+# -----------------------------------------------------------
+
+TC_surv <- function(TC, sigma, df, E) {
+    if (length(E) == 0) {
+        stop("Empty TC support")
+    }
+
+    # Sum truncated cdf over each part of E
+    denom <- do.call(sum, lapply(1:nrow(E), function(v) {
+      tchi_interval(E[v,1], E[v,2], sigma, df)
+    }))
+
+    # Sum truncated cdf from observed value to max of
+    # truncation region
+    numer <- do.call(sum, lapply(1:nrow(E), function(v) {
+      lower <- E[v,1]
+      upper <- E[v,2]
+      if (upper > TC) {
+        # Observed value is left of this interval's right endpoint
+        if (lower < TC) {
+          # Observed value is in this interval
+          return(tchi_interval(TC, upper, sigma, df))
+        } else {
+          # Observed value is not in this interval
+          return(tchi_interval(lower, upper, sigma, df))
+        }
+      } else {
+        # Observed value is right of this entire interval
+        return(0)
+      }
+    }))
+
+    # Survival function
+    value <- numer/denom
+    # Force p-value to lie in the [0,1] interval
+    # in case of numerical issues
+    value <- max(0, min(1, value))
+    value
+}
+
+tchi_interval <- function(lower, upper, sigma, df) {
+  a <- (lower/sigma)^2
+  b <- (upper/sigma)^2
+  if (b == Inf) {
+      integral <- pchisq(a, df, lower.tail = FALSE)
+  } else {
+      integral <- pchisq(b, df) - pchisq(a, df)
+  }
+  if ((integral < .Machine$double.eps) && (b < Inf)) {
+      integral <- num_int_chi(a, b, df)
+  }
+  return(integral)
+}
+
+num_int_chi <- function(a, b, df, nsamp = 10000) {
+  grid <- seq(from=a, to=b, length.out=nsamp)
+  integrand <- dchisq(grid, df)
+  return((b-a)*mean(integrand))
+}
+
+TF_surv <- function(TF, df1, df2, E) {
+    if (length(E) == 0) {
+        stop("Empty TF support")
+    }
+
+    # Sum truncated cdf over each part of E
+    denom <- do.call(sum, lapply(1:nrow(E), function(v) {
+      TF_interval(E[v,1], E[v,2], df1, df2)
+    }))
+
+    # Sum truncated cdf from observed value to max of
+    # truncation region
+    numer <- do.call(sum, lapply(1:nrow(E), function(v) {
+      lower <- E[v,1]
+      upper <- E[v,2]
+      if (upper > TF) {
+        # Observed value is left of this interval's right endpoint
+        if (lower < TF) {
+          # Observed value is in this interval
+          return(TF_interval(TF, upper, df1, df2))
+        } else {
+          # Observed value is not in this interval
+          return(TF_interval(lower, upper, df1, df2))
+        }
+      } else {
+        # Observed value is right of this entire interval
+        return(0)
+      }
+    }))
+
+    # Survival function
+    value <- numer/denom
+    # Force p-value to lie in the [0,1] interval
+    # in case of numerical issues
+    #value <- max(0, min(1, value))
+    value
+}
+
+TF_interval <- function(lower, upper, df1, df2) {
+  a <- lower
+  b <- upper
+  if (b == Inf) {
+      integral <- pf(a, df1, df2, lower.tail = FALSE)
+  } else {
+      integral <- pf(b, df1, df2) - pf(a, df1, df2)
+  }
+  if ((integral < .Machine$double.eps) && (b < Inf)) {
+      integral <- num_int_F(a, b, df1, df2)
+  }
+  return(integral)
+}
+
+num_int_F <- function(a, b, df1, df2, nsamp = 10000) {
+  grid <- seq(from=a, to=b, length.out=nsamp)
+  integrand <- df(grid, df1, df2)
+  return((b-a)*mean(integrand))
+}
+
+#' Center and scale design matrix by groups
+#'
+#' For internal use by \code{\link{groupfs}}.
+#'
+#' @param x Design matrix.
+#' @param index Group membership indicator of length p.
+#' @param center Center groups, default is TRUE.
+#' @param normalize Scale groups by Frobenius norm, default is TRUE.
+#' @return
+#' \describe{
+#'   \item{x}{Optionally centered/scaled design matrix.}
+#'   \item{xm}{Means of groups in original design matrix.}
+#'   \item{xs}{Frobenius norms of groups in original design matrix.}
+#' }
+scaleGroups <- function(x, index, center = TRUE, normalize = TRUE) {
+  keys <- unique(index)
+  xm <- rep(0, ncol(x))
+  xs <- rep(1, ncol(x))
+
+  for (j in keys) {
+    inds <- which(index == j)
+    if (center) {
+        xmj <- mean(x[, inds])
+        xm[inds] <- xmj
+        x[, inds] <- x[, inds] - xmj
+    }
+    normsq <- sum(x[, inds]^2)
+    xsj <- sqrt(normsq)
+    xs[inds] <- xsj
+    if (xsj > 0) {
+        if (normalize) x[, inds] <- x[, inds] / xsj
+    } else {
+        stop(paste("Design matrix contains identically zero group of variables:", j))
+    }
+  }
+  return(list(x=x, xm=xm, xs=xs))
+}
+
+#' Expand a data frame with factors to form a design matrix with the full binary encoding of each factor.
+#'
+#' When using \code{\link{groupfs}} with factor variables call this function first to create a design matrix.
+#'
+#' @param df Data frame containing some columns which are \code{factors}.
+#' @return List containing
+#' \describe{
+#'   \item{x}{Design matrix, the first columns contain any numeric variables from the original date frame.}
+#'   \item{index}{Group membership indicator for expanded matrix.}
+#' }
+#' @examples
+#' \dontrun{
+#' fd = factorDesign(warpbreaks)
+#' y = rnorm(nrow(fd$x))
+#' fit = groupfs(fd$x, y, fd$index, maxsteps=2, intercept=F)
+#' pvals = groupfsInf(fit)
+#' }
+factorDesign <- function(df) {
+    factor.inds <- sapply(df[1,], is.factor)
+    factor.labels <- which(factor.inds)
+    nfacs <- sum(factor.inds)
+    nlevs <- sapply(df[1,factor.inds], function(fac) nlevels(fac))
+    totnlevs <- sum(nlevs)
+    num.num = indcounter = ncol(df) - nfacs
+    x <- matrix(nrow=nrow(df), ncol = totnlevs + num.num)
+    colnames(x) <- 1:ncol(x)
+    index <- integer(ncol(x))
+    varnames <- character(ncol(df))
+    if (num.num > 0) {
+        x[,1:num.num] <- df[, !factor.inds]
+        varnames[1:num.num] = colnames(x)[1:num.num] <- colnames(df)[1:num.num]
+        index[1:num.num] <- 1:num.num
+        indcounter <- indcounter + num.num - 1
+    }
+    for (j in 1:nfacs) {
+        submat <- model.matrix(~ df[, factor.labels[j]] - 1)
+        indcounter <- indcounter+1
+        submatinds <- indcounter:(indcounter+nlevs[j]-1)
+        indcounter <- indcounter + nlevs[j] - 1
+        colnames(x)[submatinds] <- paste0(colnames(df)[num.num + j], ":", 1:nlevs[j])
+        varnames[num.num + j] <- colnames(df)[num.num + j]
+        x[,submatinds] <- submat
+        index[submatinds] <- num.num + j
+    }
+    attr(x, "varnames") <- varnames
+    return(list(x = x, index = index))
+}
+
+svdu_thresh <- function(x) {
+    svdx <- svd(x)
+    inds <- svdx$d > svdx$d[1] * sqrt(.Machine$double.eps)
+    return(svdx$u[, inds, drop = FALSE])
+}
+
+flatten <- function(L) {
+    if (is.list(L[[1]])) return(unlist(L, recursive=FALSE))
+    return(L)
+}
+
+print.groupfs <- function(x, ...) {
+    cat("\nSequence of added groups:\n")
+    nsteps = length(x$action)
+    action <- x$action
+    vnames <- attr(x, "varnames")
+    if (length(vnames) > 0) action <- vnames[action]
+    tab = data.frame(Group = action, Rank = x$log$df, RSS = round(x$log$RSS, 3), AIC = round(x$log$AIC, 3))
+    rownames(tab) = 1:nsteps
+    print(tab)
+    cat("\nUse groupfsInf() to compute P-values\n")
+    invisible()
+}
+
+
+coef.groupfs <- function(object, ...) {
+    return(object$coefficients)
+}
+
+#' @name predict.groupfs
+#' @aliases predict.groupfs
+#' @aliases coef.groupfs
+#'
+#' @title Prediction and coefficient functions for \code{\link{groupfs}}.
+#'
+#' Make predictions or extract coefficients from a groupfs forward stepwise object.
+#'
+#' @param object Object returned by a call to \code{\link{groupfs}}.
+#' @param newx Matrix of x values at which the predictions are desired. If NULL, the x values from groupfs fitting are used.
+#' @return A vector of predictions or a vector of coefficients.
+predict.groupfs <- function(object, newx) {
+    beta <- coef.groupfs(object)
+    if (missing(newx)) {
+        newx = object$x
+    } else {
+        newx <- scaleGroups(newx, object$index, attr(object, "center"), attr(object, "normalize"))$x
+    }
+    return(newx[, object$index %in% object$action] %*% beta + ifelse(object$intercept, object$by, 0))
+}
+
+print.groupfsInf <- function(x, ...) {
+    if (!is.null(x$sigma)) {
+        isTF <- FALSE
+        Tstat <- x$TC
+        cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n", x$sigma))
+    } else {
+        isTF <- TRUE
+        Tstat <- x$TF
+    }
+    action <- x$vars
+    vnames <- attr(x, "varnames")
+    if (length(vnames) > 0) action <- vnames[action]
+    tab = data.frame(Group = action, Pvalue = round(x$pv, 3),
+        TC = round(Tstat, 3),
+        df = x$df, Size = round(unlist(lapply(lapply(x$support, size), sum)), 3),
+        Ints = unlist(lapply(x$support, nrow)), Min =round(unlist(lapply(x$support, min)), 3),
+        Max = round(unlist(lapply(x$support, max)), 3))
+    rownames(tab) = 1:length(x$vars)
+    if (isTF) names(tab)[3] <- "TF"
+    print(tab)
+    cat("\nInts is the number of intervals in the truncated chi selection region and Size is the sum of their lengths. Min and Max are the lowest and highest endpoints of the truncation region. No confidence intervals are reported by groupfsInf.\n")
+    invisible()
+}
+
+checkargs.groupfs <- function(x, index, maxsteps) {
+    if (length(index) != ncol(x)) stop("Length of index does not match number of columns of x")
+    if ((round(maxsteps) != maxsteps) || (maxsteps <= 0)) stop("maxsteps must be an integer > 0")
+}
diff --git a/selectiveInference-currentCRAN/R/funs.inf.R b/selectiveInference-currentCRAN/R/funs.inf.R
new file mode 100644
index 0000000..423b4c3
--- /dev/null
+++ b/selectiveInference-currentCRAN/R/funs.inf.R
@@ -0,0 +1,299 @@
+# Main p-value function
+
+poly.pval <- function(y, G, u, v, sigma, bits=NULL) {
+  z = sum(v*y)
+  vv = sum(v^2)
+  sd = sigma*sqrt(vv)
+  
+  rho = G %*% v / vv
+  vec = (u - G %*% y + rho*z) / rho
+  vlo = suppressWarnings(max(vec[rho>0]))
+  vup = suppressWarnings(min(vec[rho<0]))
+
+  pv = tnorm.surv(z,0,sd,vlo,vup,bits)
+  return(list(pv=pv,vlo=vlo,vup=vup))
+}
+
+# Main confidence interval function
+
+poly.int <- function(y, G, u, v, sigma, alpha, gridrange=c(-100,100),
+                     gridpts=100, griddepth=2, flip=FALSE, bits=NULL) {
+  
+  z = sum(v*y)
+  vv = sum(v^2)
+  sd = sigma*sqrt(vv)
+  
+  rho = G %*% v / vv
+  vec = (u - G %*% y + rho*z) / rho
+  vlo = suppressWarnings(max(vec[rho>0]))
+  vup = suppressWarnings(min(vec[rho<0]))
+  
+  xg = seq(gridrange[1]*sd,gridrange[2]*sd,length=gridpts)
+  fun = function(x) { tnorm.surv(z,x,sd,vlo,vup,bits) }
+
+  int = grid.search(xg,fun,alpha/2,1-alpha/2,gridpts,griddepth)
+  tailarea = c(fun(int[1]),1-fun(int[2]))
+
+  if (flip) {
+    int = -int[2:1]
+    tailarea = tailarea[2:1]
+  }
+  
+  return(list(int=int,tailarea=tailarea))
+}
+
+##############################
+
+# Assuming that grid is in sorted order from smallest to largest,
+# and vals are monotonically increasing function values over the
+# grid, returns the grid end points such that the corresponding
+# vals are approximately equal to {val1, val2}
+
+grid.search <- function(grid, fun, val1, val2, gridpts=100, griddepth=2) {
+  n = length(grid)
+  vals = fun(grid)
+    
+  ii = which(vals >= val1)
+  jj = which(vals <= val2)
+  if (length(ii)==0) return(c(grid[n],Inf))   # All vals < val1
+  if (length(jj)==0) return(c(-Inf,grid[1]))  # All vals > val2
+  # RJT: the above logic is correct ... but for simplicity, instead,
+  # we could just return c(-Inf,Inf) 
+
+  i1 = min(ii); i2 = max(jj)
+  if (i1==1) lo = -Inf
+  else lo = grid.bsearch(grid[i1-1],grid[i1],fun,val1,gridpts,
+         griddepth-1,below=TRUE)
+  if (i2==n) hi = Inf
+  else hi = grid.bsearch(grid[i2],grid[i2+1],fun,val2,gridpts,
+         griddepth-1,below=FALSE)
+  return(c(lo,hi))
+}
+
+# Repeated bin search to find the point x in the interval [left, right]
+# that satisfies f(x) approx equal to val. If below=TRUE, then we seek
+# x such that the above holds and f(x) <= val; else we seek f(x) >= val.
+
+grid.bsearch <- function(left, right, fun, val, gridpts=100, griddepth=1, below=TRUE) {
+  n = gridpts
+  depth = 1
+
+  while (depth <= griddepth) {
+    grid = seq(left,right,length=n)
+    vals = fun(grid)
+    
+    if (below) {
+      ii = which(vals >= val)
+      if (length(ii)==0) return(grid[n])   # All vals < val (shouldn't happen)
+      if ((i0=min(ii))==1) return(grid[1]) # All vals > val (shouldn't happen)
+      left = grid[i0-1]
+      right = grid[i0]
+    }
+    
+    else {
+      ii = which(vals <= val)
+      if (length(ii)==0) return(grid[1])   # All vals > val (shouldn't happen)
+      if ((i0=max(ii))==n) return(grid[n]) # All vals < val (shouldn't happen)
+      left = grid[i0]
+      right = grid[i0+1]
+    }
+
+    depth = depth+1
+  }
+
+  return(ifelse(below, left, right))
+}
+
+# Returns Prob(Z>z | Z in [a,b]), where mean can be a vector
+
+tnorm.surv <- function(z, mean, sd, a, b, bits=NULL) {
+  z = max(min(z,b),a)
+  
+  # Check silly boundary cases
+  p = numeric(length(mean))
+  p[mean==-Inf] = 0
+  p[mean==Inf] = 1
+  
+  # Try the multi precision floating point calculation first
+  o = is.finite(mean)
+  mm = mean[o]
+  pp = mpfr.tnorm.surv(z,mm,sd,a,b,bits) 
+
+  # If there are any NAs, then settle for an approximation
+  oo = is.na(pp)
+  if (any(oo)) pp[oo] = bryc.tnorm.surv(z,mm[oo],sd,a,b)
+  
+  p[o] = pp
+  return(p)
+}
+
+# Returns Prob(Z>z | Z in [a,b]), where mean cane be a vector, using
+# multi precision floating point calculations thanks to the Rmpfr package
+
+mpfr.tnorm.surv <- function(z, mean=0, sd=1, a, b, bits=NULL) {
+  # If bits is not NULL, then we are supposed to be using Rmpf
+  # (note that this was fail if Rmpfr is not installed; but
+  # by the time this function is being executed, this should
+  # have been properly checked at a higher level; and if Rmpfr
+  # is not installed, bits would have been previously set to NULL)
+  if (!is.null(bits)) {
+    z = Rmpfr::mpfr((z-mean)/sd, precBits=bits)
+    a = Rmpfr::mpfr((a-mean)/sd, precBits=bits)
+    b = Rmpfr::mpfr((b-mean)/sd, precBits=bits)
+    return(as.numeric((Rmpfr::pnorm(b)-Rmpfr::pnorm(z))/
+                      (Rmpfr::pnorm(b)-Rmpfr::pnorm(a))))
+  }
+  
+  # Else, just use standard floating point calculations
+  z = (z-mean)/sd
+  a = (a-mean)/sd
+  b = (b-mean)/sd
+  return((pnorm(b)-pnorm(z))/(pnorm(b)-pnorm(a)))
+}
+
+# Returns Prob(Z>z | Z in [a,b]), where mean can be a vector, based on
+# A UNIFORM APPROXIMATION TO THE RIGHT NORMAL TAIL INTEGRAL, W Bryc
+# Applied Mathematics and Computation
+# Volume 127, Issues 23, 15 April 2002, Pages 365--374
+# https://math.uc.edu/~brycw/preprint/z-tail/z-tail.pdf
+
+bryc.tnorm.surv <- function(z, mean=0, sd=1, a, b) {
+  z = (z-mean)/sd
+  a = (a-mean)/sd
+  b = (b-mean)/sd
+  n = length(mean)
+
+  term1 = exp(z*z)
+  o = a > -Inf
+  term1[o] = ff(a[o])*exp(-(a[o]^2-z[o]^2)/2)
+  term2 = rep(0,n)
+  oo = b < Inf
+  term2[oo] = ff(b[oo])*exp(-(b[oo]^2-z[oo]^2)/2)
+  p = (ff(z)-term2)/(term1-term2)
+
+  # Sometimes the approximation can give wacky p-values,
+  # outside of [0,1] ..
+  #p[p<0 | p>1] = NA
+  p = pmin(1,pmax(0,p))
+  return(p)
+}
+
+ff <- function(z) {
+  return((z^2+5.575192695*z+12.7743632)/
+         (z^3*sqrt(2*pi)+14.38718147*z*z+31.53531977*z+2*12.77436324))
+}
+
+# Return Prob(Z>z | Z in [a,b]), where mean can be a vector, based on
+# Riemann approximation tricks, by Max G'Sell
+
+gsell.tnorm.surv <- function(z, mean=0, sd=1, a, b) {
+  return(max.approx.frac(a/sd,b/sd,z/sd,mean/sd))
+}
+
+
+##############################
+
+forwardStop <- function(pv, alpha=.10){
+  if (alpha<0 || alpha>1) stop("alpha must be in [0,1]")
+  if (min(pv,na.rm=T)<0 || max(pv,na.rm=T)>1) stop("pvalues must be in [0,1]")
+  val=-(1/(1:length(pv)))*cumsum(log(1-pv))
+  oo = which(val <= alpha)
+  if (length(oo)==0) out=0
+  else out = oo[length(oo)]
+  return(out)
+}
+
+##############################
+
+aicStop <- function(x, y, action, df, sigma, mult=2, ntimes=2) {
+  n = length(y)
+  k = length(action)
+  aic = numeric(k)
+  G = matrix(0,nrow=0,ncol=n)
+  u = numeric(0)
+  count = 0
+  
+  for (i in 1:k) {
+    A = action[1:i]
+    aic[i] = sum(lsfit(x[,A],y,intercept=F)$res^2) + mult*sigma^2*df[i]
+
+    j = action[i]
+    if (i==1) xtil = x[,j]
+    else xtil = lsfit(x[,action[1:(i-1)]],x[,j],intercept=F)$res
+    s = sign(sum(xtil*y))
+    
+    if (i==1 || aic[i] <= aic[i-1]) {
+      G = rbind(G,s*xtil/sqrt(sum(xtil^2)))
+      u = c(u,sqrt(mult)*sigma)
+      count = 0
+    }
+
+    else {
+      G = rbind(G,-s*xtil/sqrt(sum(xtil^2)))
+      u = c(u,-sqrt(mult)*sigma)
+      count = count+1
+      if (count == ntimes) break
+    }
+  }
+
+  if (i < k) {
+    khat = i - ntimes
+    aic = aic[1:i]
+  }
+  else khat = k
+  
+  return(list(khat=khat,G=G,u=u,aic=aic,stopped=(i<k)))
+}
+
+#these next two functions are used by the binomial and Cox options of fixedLassoInf
+
+mypoly.pval.lee=
+function(y, A, b, eta, Sigma, bits=NULL) {
+    # compute pvalues from poly lemma:  full version from Lee et al for full matrix Sigma
+    nn=length(y)
+    eta=as.vector(eta)
+  temp = sum(eta*y)
+   vv=as.numeric(matrix(eta,nrow=1,ncol=nn)%*%Sigma%*%eta)
+   cc = Sigma%*%eta/vv
+   
+ z=(diag(nn)-matrix(cc,ncol=1)%*%eta)%*%y
+    rho=A%*%cc
+   
+  vec = (b- A %*% z)/rho
+  vlo = suppressWarnings(max(vec[rho<0]))
+  vup = suppressWarnings(min(vec[rho>0]))
+  sd=sqrt(vv)
+  pv = tnorm.surv(temp,0,sd,vlo,vup,bits)
+  return(list(pv=pv,vlo=vlo,vup=vup,sd=sd))
+}
+
+
+
+mypoly.int.lee=
+   function(y,eta,vlo,vup,sd, alpha, gridrange=c(-100,100),gridpts=100, griddepth=2, flip=FALSE, bits=NULL) {
+    # compute sel intervals from poly lemmma, full version from Lee et al for full matrix Sigma
+
+  temp = sum(eta*y)
+  
+  xg = seq(gridrange[1]*sd,gridrange[2]*sd,length=gridpts)
+  fun = function(x) { tnorm.surv(temp,x,sd,vlo,vup,bits) }
+
+  int = grid.search(xg,fun,alpha/2,1-alpha/2,gridpts,griddepth)
+  tailarea = c(fun(int[1]),1-fun(int[2]))
+
+  if (flip) {
+    int = -int[2:1]
+    tailarea = tailarea[2:1]
+  }
+ 
+  return(list(int=int,tailarea=tailarea))
+}
+
+
+
+mydiag=function(x){
+    if(length(x)==1) out=x
+    if(length(x)>1) out=diag(x)
+       return(out)
+   }
+
diff --git a/selectiveInference-currentCRAN/R/funs.lar.R b/selectiveInference-currentCRAN/R/funs.lar.R
new file mode 100644
index 0000000..f01ee3d
--- /dev/null
+++ b/selectiveInference-currentCRAN/R/funs.lar.R
@@ -0,0 +1,632 @@
+# We compute the least angle regression (LAR) path given
+# a response vector y and predictor matrix x.  We assume
+# that x has columns in general position.
+
+# NOTE: the df estimates at each lambda_k can be thought of as the df
+# for all solutions corresponding to lambda in (lambda_k,lambda_{k-1}),
+# the open interval to the *right* of the current lambda_k.
+
+# NOTE: x having columns in general position implies that the
+# centered x satisfies a modified version of the general position
+# condition, where we replace k < min(n,p) by k < min(n-1,p) in
+# the definition. This is still sufficient to imply the uniqueness
+# of the lasso solution, on the centered x
+
+lar <- function(x, y, maxsteps=2000, minlam=0, intercept=TRUE, normalize=TRUE,
+                verbose=FALSE) {
+
+  this.call = match.call()
+  checkargs.xy(x=x,y=y)
+
+  # Center and scale, etc.
+  obj = standardize(x,y,intercept,normalize)
+  x = obj$x
+  y = obj$y
+  bx = obj$bx
+  by = obj$by
+  sx = obj$sx
+  n = nrow(x)
+  p = ncol(x)
+
+  #####
+  # Find the first variable to enter and its sign
+  uhat = t(x)%*%y
+  ihit = which.max(abs(uhat))   # Hitting coordinate
+  hit = abs(uhat[ihit])         # Critical lambda
+  s = Sign(uhat[ihit])          # Sign
+
+  if (verbose) {
+    cat(sprintf("1. lambda=%.3f, adding variable %i, |A|=%i...",
+                hit,ihit,1))
+  }
+
+  # Now iteratively find the new LAR estimate, and
+  # the next critical lambda
+
+  # Things to keep track of, and return at the end
+  buf = min(maxsteps,500)
+  lambda = numeric(buf)      # Critical lambdas
+  action = numeric(buf)      # Actions taken
+  df = numeric(buf)          # Degrees of freedom
+  beta = matrix(0,p,buf)     # LAR estimates
+
+  lambda[1] = hit
+  action[1] = ihit
+  df[1] = 0
+  beta[,1] = 0
+
+  # Gamma matrix!
+  gbuf = max(2*p*3,2000)     # Space for 3 steps, at least
+  gi = 0
+  Gamma = matrix(0,gbuf,n)
+  Gamma[gi+Seq(1,p-1),] = t(s*x[,ihit]+x[,-ihit]); gi = gi+p-1
+  Gamma[gi+Seq(1,p-1),] = t(s*x[,ihit]-x[,-ihit]); gi = gi+p-1
+  Gamma[gi+1,] = t(s*x[,ihit]); gi = gi+1
+
+  # nk, regression contrast, M plus
+  nk = mp = numeric(buf)
+  vreg = matrix(0,buf,n)
+
+  nk[1] = gi
+  vreg[1,] = s*x[,ihit] / sum(x[,ihit]^2)
+  if (p > 1) {
+    c = t(as.numeric(Sign(t(x)%*%y)) * t(x))
+    ratio = t(c[,-ihit])%*%c[,ihit]/sum(c[,ihit]^2)
+    ip = 1-ratio > 0
+    crit = (t(c[,-ihit])%*%y - ratio*sum(c[,ihit]*y))/(1-ratio)
+    mp[1] = max(max(crit[ip]),0)
+  }
+
+  # Other things to keep track of, but not return
+  r = 1                      # Size of active set
+  A = ihit                   # Active set
+  I = Seq(1,p)[-ihit]        # Inactive set
+  X1 = x[,ihit,drop=FALSE]   # Matrix X[,A]
+  X2 = x[,-ihit,drop=FALSE]  # Matrix X[,I]
+  k = 2                      # What step are we at?
+
+  # Compute a skinny QR decomposition of X1
+  obj = qr(X1)
+  Q = qr.Q(obj,complete=TRUE)
+  Q1 = Q[,1,drop=FALSE];
+  Q2 = Q[,-1,drop=FALSE]
+  R = qr.R(obj)
+
+  # Throughout the algorithm, we will maintain
+  # the decomposition X1 = Q1*R. Dimenisons:
+  # X1: n x r
+  # Q1: n x r
+  # Q2: n x (n-r)
+  # R:  r x r
+
+  while (k<=maxsteps && lambda[k-1]>=minlam) {
+    ##########
+    # Check if we've reached the end of the buffer
+    if (k > length(lambda)) {
+      buf = length(lambda)
+      lambda = c(lambda,numeric(buf))
+      action = c(action,numeric(buf))
+      df = c(df,numeric(buf))
+      beta = cbind(beta,matrix(0,p,buf))
+      nk = c(nk,numeric(buf))
+      mp = c(mp,numeric(buf))
+      vreg = rbind(vreg,matrix(0,buf,n))
+    }
+
+    # Key quantities for the hitting times
+    a = backsolve(R,t(Q1)%*%y)
+    b = backsolve(R,backsolve(R,s,transpose=TRUE))
+    aa = as.numeric(t(X2) %*% (y - X1 %*% a))
+    bb = as.numeric(t(X2) %*% (X1 %*% b))
+
+    # If the inactive set is empty, nothing will hit
+    if (r==min(n-intercept,p)) hit = 0
+
+    # Otherwise find the next hitting time
+    else {
+      shits = Sign(aa)
+      hits = aa/(shits-bb)
+
+      # Make sure none of the hitting times are larger
+      # than the current lambda
+      hits[hits>lambda[k-1]] = 0
+
+      ihit = which.max(hits)
+      hit = hits[ihit]
+      shit = shits[ihit]
+    }
+
+    # Stop if the next critical point is negative
+    if (hit<=0) break
+
+    # Record the critical lambda and solution
+    lambda[k] = hit
+    action[k] = I[ihit]
+    df[k] = r
+    beta[A,k] = a-hit*b
+
+    # Gamma matrix!
+    if (gi + 2*p > nrow(Gamma)) Gamma = rbind(Gamma,matrix(0,2*p+gbuf,n))
+    X2perp = X2 - X1 %*% backsolve(R,t(Q1)%*%X2)
+    c = t(t(X2perp)/(shits-bb))
+    Gamma[gi+Seq(1,p-r),] = shits*t(X2perp); gi = gi+p-r
+    Gamma[gi+Seq(1,p-r-1),] = t(c[,ihit]-c[,-ihit]); gi = gi+p-r-1
+    Gamma[gi+1,] = t(c[,ihit]); gi = gi+1
+
+    # nk, regression contrast, M plus
+    nk[k] = gi
+    vreg[k,] = shit*X2perp[,ihit] / sum(X2perp[,ihit]^2)
+    if (ncol(c) > 1) {
+      ratio = t(c[,-ihit])%*%c[,ihit]/sum(c[,ihit]^2)
+      ip = 1-ratio > 0
+      crit = (t(c[,-ihit])%*%y - ratio*sum(c[,ihit]*y))/(1-ratio)
+      mp[k] = max(max(crit[ip]),0)
+    }
+
+    # Update all of the variables
+    r = r+1
+    A = c(A,I[ihit])
+    I = I[-ihit]
+    s = c(s,shit)
+    X1 = cbind(X1,X2[,ihit])
+    X2 = X2[,-ihit,drop=FALSE]
+
+    # Update the QR decomposition
+    obj = updateQR(Q1,Q2,R,X1[,r])
+    Q1 = obj$Q1
+    Q2 = obj$Q2
+    R = obj$R
+
+    if (verbose) {
+      cat(sprintf("\n%i. lambda=%.3f, adding variable %i, |A|=%i...",
+                  k,hit,A[r],r))
+    }
+
+    # Step counter
+    k = k+1
+  }
+
+  # Trim
+  lambda = lambda[Seq(1,k-1)]
+  action = action[Seq(1,k-1)]
+  df = df[Seq(1,k-1),drop=FALSE]
+  beta = beta[,Seq(1,k-1),drop=FALSE]
+  Gamma = Gamma[Seq(1,gi),,drop=FALSE]
+  nk = nk[Seq(1,k-1)]
+  mp = mp[Seq(1,k-1)]
+  vreg = vreg[Seq(1,k-1),,drop=FALSE]
+
+  # If we reached the maximum number of steps
+  if (k>maxsteps) {
+    if (verbose) {
+      cat(sprintf("\nReached the maximum number of steps (%i),",maxsteps))
+      cat(" skipping the rest of the path.")
+    }
+    completepath = FALSE
+    bls = NULL
+  }
+
+  # If we reached the minimum lambda
+  else if (lambda[k-1]<minlam) {
+    if (verbose) {
+      cat(sprintf("\nReached the minimum lambda (%.3f),",minlam))
+      cat(" skipping the rest of the path.")
+    }
+    completepath = FALSE
+    bls = NULL
+  }
+
+  # Otherwise, note that we completed the path
+  else {
+    completepath = TRUE
+
+    # Record the least squares solution. Note that
+    # we have already computed this
+    bls = rep(0,p)
+    bls[A] = a
+  }
+
+  if (verbose) cat("\n")
+
+  # Adjust for the effect of centering and scaling
+  if (intercept) df = df+1
+  if (normalize) beta = beta/sx
+  if (normalize && completepath) bls = bls/sx
+
+  # Assign column names
+  colnames(beta) = as.character(round(lambda,3))
+
+  out = list(lambda=lambda,action=action,sign=s,df=df,beta=beta,
+    completepath=completepath,bls=bls,
+    Gamma=Gamma,nk=nk,vreg=vreg,mp=mp,x=x,y=y,bx=bx,by=by,sx=sx,
+    intercept=intercept,normalize=normalize,call=this.call)
+  class(out) = "lar"
+  return(out)
+}
+
+##############################
+
+# Downdate the QR factorization, after a column has
+# been deleted. Here Q1 is m x n, Q2 is m x k, and
+# R is n x n.
+
+downdateQR <- function(Q1,Q2,R,col) {
+  m = nrow(Q1)
+  n = ncol(Q1)
+
+  a = .C("downdate1",
+    Q1=as.double(Q1),
+    R=as.double(R),
+    col=as.integer(col-1),
+    m=as.integer(m),
+    n=as.integer(n),
+    dup=FALSE,
+    package="selectiveInference")
+
+  Q1 = matrix(a$Q1,nrow=m)
+  R = matrix(a$R,nrow=n)
+
+  # Re-structure: add a column to Q2, delete one from
+  # Q1, and trim R
+  Q2 = cbind(Q2,Q1[,n])
+  Q1 = Q1[,-n,drop=FALSE]
+  R = R[-n,-col,drop=FALSE]
+
+  return(list(Q1=Q1,Q2=Q2,R=R))
+}
+
+##############################
+
+# Coefficient function for lar
+
+coef.lar <- function(object, s, mode=c("step","lambda"), ...) {
+  mode = match.arg(mode)
+
+  if (object$completepath) {
+    k = length(object$action)+1
+    lambda = c(object$lambda,0)
+    beta = cbind(object$beta,object$bls)
+  } else {
+    k = length(object$action)
+    lambda = object$lambda
+    beta = object$beta
+  }
+
+  if (mode=="step") {
+    if (min(s)<0 || max(s)>k) stop(sprintf("s must be between 0 and %i",k))
+    knots = 1:k
+    dec = FALSE
+  } else {
+    if (min(s)<min(lambda)) stop(sprintf("s must be >= %0.3f",min(lambda)))
+    knots = lambda
+    dec = TRUE
+  }
+
+  return(coef.interpolate(beta,s,knots,dec))
+}
+
+# Prediction function for lar
+
+predict.lar <- function(object, newx, s, mode=c("step","lambda"), ...) {
+  beta = coef.lar(object,s,mode)
+  if (missing(newx)) newx = scale(object$x,FALSE,1/object$sx)
+  else newx = scale(newx,object$bx,FALSE)
+  return(newx %*% beta + object$by)
+}
+
+coef.lasso <- coef.lar
+predict.lasso <- predict.lar
+
+##############################
+
+# Lar inference function
+
+larInf <- function(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic"),
+                   gridrange=c(-100,100), bits=NULL, mult=2, ntimes=2, verbose=FALSE) {
+
+  this.call = match.call()
+  type = match.arg(type)
+  checkargs.misc(sigma=sigma,alpha=alpha,k=k,
+                 gridrange=gridrange,mult=mult,ntimes=ntimes)
+  if (class(obj) != "lar") stop("obj must be an object of class lar")
+  if (is.null(k) && type=="active") k = length(obj$action)
+  if (is.null(k) && type=="all") stop("k must be specified when type = all")
+  if (!is.null(bits) && !requireNamespace("Rmpfr",quietly=TRUE)) {
+    warning("Package Rmpfr is not installed, reverting to standard precision")
+    bits = NULL
+  }
+
+  k = min(k,length(obj$action)) # Round to last step
+  x = obj$x
+  y = obj$y
+  p = ncol(x)
+  n = nrow(x)
+  G = obj$Gamma
+  nk = obj$nk
+  sx = obj$sx
+
+  if (is.null(sigma)) {
+    if (n >= 2*p) {
+      oo = obj$intercept
+      sigma = sqrt(sum(lsfit(x,y,intercept=oo)$res^2)/(n-p-oo))
+    }
+    else {
+      sigma = sd(y)
+      warning(paste(sprintf("p > n/2, and sd(y) = %0.3f used as an estimate of sigma;",sigma),
+                    "you may want to use the estimateSigma function"))
+    }
+  }
+
+  pv.spacing = pv.modspac = pv.covtest = khat = NULL
+
+  if (type == "active") {
+    pv = vlo = vup = numeric(k)
+    vmat = matrix(0,k,n)
+    ci = tailarea = matrix(0,k,2)
+    pv.spacing = pv.modspac = pv.covtest = numeric(k)
+    vreg = obj$vreg[1:k,,drop=FALSE]
+    sign = obj$sign[1:k]
+    vars = obj$action[1:k]
+
+    for (j in 1:k) {
+      if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
+
+      Gj = G[1:nk[j],]
+      uj = rep(0,nk[j])
+      vj = vreg[j,]
+      mj = sqrt(sum(vj^2))
+      vj = vj / mj              # Standardize (divide by norm of vj)
+      a = poly.pval(y,Gj,uj,vj,sigma,bits)
+      pv[j] = a$pv
+      sxj = sx[vars[j]]
+      vlo[j] = a$vlo * mj / sxj # Unstandardize (mult by norm of vj / sxj)
+      vup[j] = a$vup * mj / sxj # Unstandardize (mult by norm of vj)
+      vmat[j,] = vj * mj / sxj  # Unstandardize (mult by norm of vj / sxj)
+
+      a = poly.int(y,Gj,uj,vj,sigma,alpha,gridrange=gridrange,
+        flip=(sign[j]==-1),bits=bits)
+      ci[j,] = a$int * mj / sxj # Unstandardize (mult by norm of vj / sxj)
+      tailarea[j,] = a$tailarea
+
+      pv.spacing[j] = spacing.pval(obj,sigma,j)
+      pv.modspac[j] = modspac.pval(obj,sigma,j)
+      pv.covtest[j] = covtest.pval(obj,sigma,j)
+    }
+
+    khat = forwardStop(pv,alpha)
+  }
+
+  else {
+    if (type == "aic") {
+      out = aicStop(x,y,obj$action[1:k],obj$df[1:k],sigma,mult,ntimes)
+      khat = out$khat
+      m = out$stopped * ntimes
+      G = rbind(out$G,G[1:nk[khat+m],])  # Take ntimes more steps past khat
+      u = c(out$u,rep(0,nk[khat+m]))     # (if we need to)
+      kk = khat
+    }
+    else {
+      G = G[1:nk[k],]
+      u = rep(0,nk[k])
+      kk = k
+    }
+
+    pv = vlo = vup = numeric(kk)
+    vmat = matrix(0,kk,n)
+    ci = tailarea = matrix(0,kk,2)
+    sign = numeric(kk)
+    vars = obj$action[1:kk]
+    xa = x[,vars]
+    M = pinv(crossprod(xa)) %*% t(xa)
+
+    for (j in 1:kk) {
+      if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
+
+      vj = M[j,]
+      mj = sqrt(sum(vj^2))
+      vj = vj / mj             # Standardize (divide by norm of vj)
+      sign[j] = sign(sum(vj*y))
+      vj = sign[j] * vj
+      Gj = rbind(G,vj)
+      uj = c(u,0)
+
+      a = poly.pval(y,Gj,uj,vj,sigma,bits)
+      pv[j] = a$pv
+      sxj = sx[vars[j]]
+      vlo[j] = a$vlo * mj / sxj # Unstandardize (mult by norm of vj / sxj)
+      vup[j] = a$vup * mj / sxj # Unstandardize (mult by norm of vj / sxj)
+      vmat[j,] = vj * mj / sxj  # Unstandardize (mult by norm of vj / sxj)
+
+      a = poly.int(y,Gj,uj,vj,sigma,alpha,gridrange=gridrange,
+        flip=(sign[j]==-1),bits=bits)
+      ci[j,] = a$int * mj / sxj # Unstandardize (mult by norm of vj / sxj)
+      tailarea[j,] = a$tailarea
+    }
+  }
+
+  out = list(type=type,k=k,khat=khat,pv=pv,ci=ci,
+    tailarea=tailarea,vlo=vlo,vup=vup,vmat=vmat,y=y,
+    pv.spacing=pv.spacing,pv.modspac=pv.modspac,pv.covtest=pv.covtest,
+    vars=vars,sign=sign,sigma=sigma,alpha=alpha,
+    call=this.call)
+  class(out) = "larInf"
+  return(out)
+}
+
+##############################
+
+spacing.pval <- function(obj, sigma, k) {
+  v = obj$Gamma[obj$nk[k],]
+  sd = sigma*sqrt(sum(v^2))
+  a = obj$mp[k]
+
+  if (k==1) b = Inf
+  else b = obj$lambda[k-1]
+
+  return(tnorm.surv(obj$lambda[k],0,sd,a,b))
+}
+
+modspac.pval <- function(obj, sigma, k) {
+  v = obj$Gamma[obj$nk[k],]
+  sd = sigma*sqrt(sum(v^2))
+
+  if (k < length(obj$action)) a = obj$lambda[k+1]
+  else if (obj$completepath) a = 0
+  else {
+    warning(sprintf("Modified spacing p-values at step %i require %i steps of the lar path",k,k+1))
+    return(NA)
+  }
+
+  if (k==1) b = Inf
+  else b = obj$lambda[k-1]
+
+  return(tnorm.surv(obj$lambda[k],0,sd,a,b))
+}
+
+covtest.pval <- function(obj, sigma, k) {
+  A = which(obj$beta[,k]!=0)
+  sA = sign(obj$beta[A,k])
+  lam1 = obj$lambda[k]
+  j = obj$action[k]
+
+  if (k < length(obj$action)) {
+    lam2 = obj$lambda[k+1]
+    sj = sign(obj$beta[j,k+1])
+  } else if (obj$completepath) {
+    lam2 = 0
+    sj = sign(obj$bls[j])
+  } else {
+    warning(sprintf("Cov test p-values at step %i require %i steps of the lar path",k,k+1))
+    return(NA)
+  }
+
+  x = obj$x
+  if (length(A)==0) term1 = 0
+  else term1 = x[,A,drop=F] %*% solve(crossprod(x[,A,drop=F]),sA)
+  term2 = x[,c(A,j),drop=F] %*% solve(crossprod(x[,c(A,j),drop=F]),c(sA,sj))
+  c = sum((term2 - term1)^2)
+  t = c * lam1 * (lam1-lam2) / sigma^2
+  return(1-pexp(t))
+}
+
+##############################
+
+print.lar <- function(x, ...) {
+  cat("\nCall:\n")
+  dput(x$call)
+
+  cat("\nSequence of LAR moves:\n")
+  nsteps = length(x$action)
+  tab = cbind(1:nsteps,x$action,x$sign)
+  colnames(tab) = c("Step","Var","Sign")
+  rownames(tab) = rep("",nrow(tab))
+  print(tab)
+  invisible()
+}
+
+print.larInf <- function(x, tailarea=TRUE, ...) {
+  cat("\nCall:\n")
+  dput(x$call)
+
+  cat(sprintf("\nStandard deviation of noise (specified or estimated) sigma = %0.3f\n",
+              x$sigma))
+
+  if (x$type == "active") {
+    cat(sprintf("\nSequential testing results with alpha = %0.3f\n",x$alpha))
+    cat("",fill=T)
+    tab = cbind(1:length(x$pv),x$vars,
+      round(x$sign*x$vmat%*%x$y,3),
+      round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
+      round(x$pv,3),round(x$ci,3),round(x$pv.spacing,3),round(x$pv.cov,3))
+    colnames(tab) = c("Step", "Var", "Coef", "Z-score", "P-value",
+              "LowConfPt", "UpConfPt", "Spacing", "CovTest")
+    if (tailarea) {
+      tab = cbind(tab,round(x$tailarea,3))
+      colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+    }
+    rownames(tab) = rep("",nrow(tab))
+    print(tab)
+
+    cat(sprintf("\nEstimated stopping point from ForwardStop rule = %i\n",x$khat))
+  }
+
+  else if (x$type == "all") {
+    cat(sprintf("\nTesting results at step = %i, with alpha = %0.3f\n",x$k,x$alpha))
+    cat("",fill=T)
+    tab = cbind(x$vars,
+      round(x$sign*x$vmat%*%x$y,3),
+      round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
+      round(x$pv,3),round(x$ci,3))
+    colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
+    if (tailarea) {
+      tab = cbind(tab,round(x$tailarea,3))
+      colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+    }
+    rownames(tab) = rep("",nrow(tab))
+    print(tab)
+  }
+
+  else if (x$type == "aic") {
+    cat(sprintf("\nTesting results at step = %i, with alpha = %0.3f\n",x$khat,x$alpha))
+     cat("",fill=T)
+    tab = cbind(x$vars,
+      round(x$sign*x$vmat%*%x$y,3),
+      round(x$sign*x$vmat%*%x$y/(x$sigma*sqrt(rowSums(x$vmat^2))),3),
+      round(x$pv,3),round(x$ci,3))
+    colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
+    if (tailarea) {
+      tab = cbind(tab,round(x$tailarea,3))
+      colnames(tab)[(ncol(tab)-1):ncol(tab)] = c("LowTailArea","UpTailArea")
+    }
+    rownames(tab) = rep("",nrow(tab))
+    print(tab)
+
+    cat(sprintf("\nEstimated stopping point from AIC rule = %i\n",x$khat))
+  }
+
+  invisible()
+}
+
+plot.lar <- function(x, xvar=c("norm","step","lambda"), breaks=TRUE,
+                     omit.zeros=TRUE, var.labels=TRUE, ...) {
+
+  if (x$completepath) {
+    k = length(x$action)+1
+    lambda = c(x$lambda,0)
+    beta = cbind(x$beta,x$bls)
+  } else {
+    k = length(x$action)
+    lambda = x$lambda
+    beta = x$beta
+  }
+  p = nrow(beta)
+
+  xvar = match.arg(xvar)
+  if (xvar=="norm") {
+    xx = colSums(abs(beta))
+    xlab = "L1 norm"
+  } else if (xvar=="step") {
+    xx = 1:k
+    xlab = "Step"
+  } else {
+    xx = lambda
+    xlab = "Lambda"
+  }
+
+  if (omit.zeros) {
+    good.inds = matrix(FALSE,p,k)
+    good.inds[beta!=0] = TRUE
+    changes = t(apply(beta,1,diff))!=0
+    good.inds[cbind(changes,rep(F,p))] = TRUE
+    good.inds[cbind(rep(F,p),changes)] = TRUE
+    beta[!good.inds] = NA
+  }
+
+  plot(c(),c(),xlim=range(xx,na.rm=T),ylim=range(beta,na.rm=T),
+       xlab=xlab,ylab="Coefficients",main="Least angle regression path",...)
+  abline(h=0,lwd=2)
+  matplot(xx,t(beta),type="l",lty=1,add=TRUE)
+  if (breaks) abline(v=xx,lty=2)
+  if (var.labels) axis(4,at=beta[,k],labels=1:p,cex=0.8,adj=0)
+  invisible()
+}
diff --git a/selectiveInference-currentCRAN/R/funs.manymeans.R b/selectiveInference-currentCRAN/R/funs.manymeans.R
new file mode 100644
index 0000000..998e58d
--- /dev/null
+++ b/selectiveInference-currentCRAN/R/funs.manymeans.R
@@ -0,0 +1,196 @@
+### functions for computing the many means estimates- Stephen Reid
+### returns 
+###   i) selected indices
+###   ii) selection adjusted point estimates
+###   iii) selection adjusted interval estimates
+###   iv) selection adjusted p-value of hypothesis testing whether underlying signal is 0
+
+#########################
+##### MAIN FUNCTION #####
+#########################
+
+#### user-facing function for computing
+#### selected set
+#### point and interval estimates
+#### p-values
+#### input:
+####    - y = Vector of observations
+####    - alpha = Significance level used in CI construction
+####    - bh.q = q parameter for BH(q) procedure (default: NULL)
+####    - k = Number of largest elements to consider (default: NULL)
+####    - sigma = Estimate of standard deviation of one of the components
+#### output:
+####    * A list (of class "mm") with the following components :
+####        - mu.hat = Vector of length length(y) containing the estimated signal size. If a sample element is not selected, then its signal size estimate is 0
+####        - selected.set = Indices into the vector y of the sample elements that were selected by our procedure (either BH(q) or top-K)
+####        - CIs = Matrix with two columns and number of rows equal to number of elements in selected.set. Provides the post-selection CI bounds for the estimated signal sizes of selected elements. CIs given is rows in the same order as encountered in selected.set
+####        - p.vals = Vector of p-values for the test of nullity of the signals of the selected sample elemetns. P-values given in the same order as selected.set
+
+manyMeans <- function(y, alpha=0.1, bh.q=NULL, k=NULL, sigma=1, verbose=FALSE) {
+  this.call = match.call()
+  if (missing(y) || is.null(y)) stop("y must be specified")
+  if (!is.numeric(y)) stop("y must be numeric")
+  if (alpha <= 0 || alpha >= 1) stop("alpha must be between 0 and 1")
+  if (is.null(bh.q) && is.null(k)) stop("You must either bh.q or k; they cannot both be NULL")
+  if (!is.null(bh.q) && (bh.q <= 0 || bh.q >= 1)) stop("bh.q must be between 0 and 1")
+  if (!is.null(k) && (k < 1 || k > length(y) || k != round(k))) stop("k must be an integer between 1 and length(y)")
+  if (sigma <= 0) stop("sigma must be > 0")
+  
+  n = length(y)
+  if (!is.null(bh.q)) { # use BH selection procedure
+    
+    if (verbose && !is.null(k)) cat("(Both bh.q and k have been specified; k is being ignored)\n")
+    k = NULL
+    ci=NULL
+    ### find the selected set and threshold
+    p.vals = 2*pnorm (abs(y)/sigma, 0, 1, lower.tail=FALSE)
+    order.p.vals = order(p.vals)
+    sorted.p.vals = p.vals[order.p.vals]
+    
+    options (warn=-1) # ignore warning if max is over empty set
+    last.reject = max(which (sorted.p.vals <= bh.q*(1:n)/n))
+    options (warn=0) # reinstitute warnings
+    
+    if (last.reject == -Inf){ # none rejected
+      if (verbose) cat("No sample elements selected.\n")
+      out = list(mu.hat=rep(0,n), selected.set=NULL, pv=NULL, ci=NULL, method="BH(q)",
+        bh.q=bh.q, k=NULL, threshold=NULL, sigma=sigma, call=this.call)
+      class(out) = "manyMeans"
+      return(out)
+    }
+    
+    selected.set = order.p.vals[1:n <= last.reject]
+    threshold = sigma*qnorm (last.reject/2/n, lower.tail=FALSE)
+  }
+
+  else{ # use top-k selection procedure
+    
+    ### find the selected set and threshold
+    if (k == n) { # make no changes - return MLE
+      z.alpha = qnorm (alpha/2, lower.tail=FALSE)
+      cis = cbind(y - z.alpha*sigma, y + z.alpha*sigma)
+      p.vals = 2*pnorm (abs(y), 0, sigma, lower.tail=FALSE)
+      
+      out = list(mu.hat=y, selected.set=1:n, pv=p.vals, ci=ci, method="top-K",
+        bh.q=NULL, k=k, threshold=NULL, sigma=sigma, call=this.call)
+      class(out) = "manyMeans"
+      return(out)
+    }
+    
+    order.abs.y = order (-abs(y))
+    sorted.abs.y = y[order.abs.y]
+    
+    selected.set = order.abs.y[1:k]
+    threshold = abs(sorted.abs.y[k+1])
+  }
+  
+  ### estimate their underlying signal sizes
+  mu.hat = sapply (selected.set, function(s){
+    uniroot(f=function(mu){tn.mean(mu, -threshold, threshold, sigma=sigma) - y[s]}, lower=-10000*sigma, upper=10000*sigma)$root
+  })
+  
+  ### and CIs
+  right.ci = sapply (selected.set, function(s){
+    uniroot (f=function(mu){tn.cdf (y[s], mu, -threshold, threshold, sigma=sigma) - (alpha/2)}, lower=-10000*sigma, upper=10000*sigma)$root
+  })
+  left.ci = sapply (selected.set, function(s){
+    uniroot (f=function(mu){tn.cdf (y[s], mu, -threshold, threshold, sigma=sigma) - (1-alpha/2)}, lower=-10000*sigma, upper=10000*sigma)$root
+  })
+  
+  ### and p-values
+  p.vals = sapply (selected.set, function(s){tn.cdf (y[s], 0, -threshold, threshold, sigma=sigma)})
+  p.vals = 2*pmin(p.vals, 1-p.vals)
+  
+  ### arrange
+  order.selected.set = order (selected.set)
+  selected.set = selected.set[order.selected.set]
+  mu.hat = mu.hat[order.selected.set]
+  left.ci = left.ci[order.selected.set]
+  right.ci = right.ci[order.selected.set]
+  p.vals = p.vals[order.selected.set]
+  
+  mu.hat.final = rep(0, n)
+  mu.hat.final[selected.set] = mu.hat
+  
+  out = list(mu.hat=mu.hat.final, selected.set=selected.set, pv=p.vals, ci=cbind(left.ci,right.ci),
+    method=ifelse(is.null(bh.q), "top-K", "BH(q)"), sigma=sigma, bh.q=bh.q, k=k, threshold=threshold,
+    call=this.call)
+  class(out) = "manyMeans"
+  return(out)
+}
+
+#### prints a pretty data frame summarising the information of an object of the mm class
+#### columns for index, signal size estimate, left and right CI bounds and p values
+#### only for those sample elements selected by the selection procedure associated with the mmObj
+print.manyMeans <- function(x, ...){
+  cat("\nCall:\n")
+  dput(x$call)
+
+  cat(sprintf("\nStandard deviation of noise sigma = %0.3f\n\n",
+              x$sigma))
+  
+  tab = cbind(x$selected.set,x$mu.hat[x$selected.set],x$pv,x$ci)
+  tab = round(tab,3)
+  colnames(tab) = c("SelInd","MuHat","P-value","LowConfPt","UpConfPt")
+  rownames(tab) = rep("",nrow(tab))
+  print(tab)
+}
+
+###############################
+##### AUXILIARY FUNCTIONS #####
+###############################
+
+#### function returning the cumulative distribution function value
+#### of a truncated Gaussian RV, truncated to interval (-Inf, a) \union (b, Inf)
+#### with underlying Gaussian having mean parameter mu and standard deviation sigma
+#### at value y
+tn.cdf = function(y, mu, a, b, sigma=1){
+  ## denominator
+  d_right = pnorm (b, mu, sigma, lower.tail=FALSE, log.p=TRUE)
+  d_left = pnorm (a, mu, sigma, lower.tail=TRUE, log.p=TRUE)
+  d_max = max(d_right, d_left)
+  d_log = d_max + log(exp(d_left - d_max) + exp(d_right - d_max))
+  
+  
+  # numerator
+  if (y > a & y < b){
+    n_log = d_left
+    return (exp(n_log-d_log))
+  }else{
+    if (y > b){
+      # b and y
+      n_y_tilde = pnorm (y, mu, sigma, lower.tail=FALSE, log.p=TRUE)
+      n_b_tilde = pnorm (b, mu, sigma, lower.tail=FALSE, log.p=TRUE)
+      n_yb = n_b_tilde + log(1 - exp(n_y_tilde-n_b_tilde))
+      
+      # a
+      n_a = d_left
+      
+      # combine
+      return(exp(n_yb-d_log) + exp(n_a-d_log))
+    }else{
+      n_log = pnorm (y, mu, sigma, lower.tail=TRUE, log.p=TRUE)
+      return (exp(n_log-d_log))
+    }
+  }
+}
+
+##### function for computing the mean of an N(mu, 1) RV
+##### truncated to be on the interval (-Inf, a) \union (b, Inf)
+tn.mean = function(mu, a, b, sigma=1){
+  # denominator
+  d_left = pnorm (a, mu, sigma, lower.tail=TRUE, log.p=TRUE)
+  d_right = pnorm (b, mu, sigma, lower.tail=FALSE, log.p=TRUE)
+  d_max = max(d_left, d_right)
+  d_log = d_max + log(exp(d_left - d_max) + exp(d_right - d_max))
+  
+  # numerator
+  n_left = dnorm (b, mu, sigma, log=TRUE)
+  n_right = dnorm (a, mu, sigma, log=TRUE)
+  
+  if (n_left > n_right){
+    mu + exp(n_left + log(1 - exp(n_right-n_left)) - d_log)
+  }else{
+    mu - exp(n_right + log(1 - exp(n_left-n_right)) - d_log)
+  }
+}
diff --git a/selectiveInference-currentCRAN/R/funs.max.R b/selectiveInference-currentCRAN/R/funs.max.R
new file mode 100644
index 0000000..97f34af
--- /dev/null
+++ b/selectiveInference-currentCRAN/R/funs.max.R
@@ -0,0 +1,84 @@
+#Mills lower bound on the integral of a standard normal over an interval
+#Need to use something else near zero, where mills is bad.
+mills.lb = function(a,b){
+  t=5#threshold for switching to approximation
+  if(b<a){return(0)}
+  if(a>=t){
+    return(a*exp(-a^2/2)/(1+a^2)-exp(-b^2/2)/b) 
+  }
+  if(b <= -t){
+    return(mills.lb(-b,-a))
+  }
+  #Note, like in the rest of this program, I'm not dividing by sqrt(2*pi), so we need to rescale here
+  sqrt(2*pi)*pnorm(min(b,t))-sqrt(2*pi)*pnorm(max(a,-t))+mills.lb(a,-t)+mills.lb(t,b)
+}
+
+
+#Truncate the interval.  We chop off the ends near infinity, being careful 
+#so that the chopped tail is guaranteed to be close enough to its mills approximation
+#a,b are the left and right endpoints, z is the mid point
+#delta is the multiplicative error limit of the truncation on the final fraction (roughly)
+truncate.interval = function(a,b,z,delta=1e-16){
+  #Initialize some stuff
+  L.extra = 0#Extra probability to be added for the truncation on the left
+  R.extra = 0#Extra probability to be added for the truncation on the right
+  a.new = a#truncated interval bounds
+  b.new = b#truncated interval bounds
+  
+  #We need bounds on the integrals 
+  RL.lb = mills.lb(a,b)
+  R.lb = mills.lb(z,b)
+  
+  #Now we bound the error we can tolerate in the tail approximations
+  eps.R = min(delta*R.lb,delta*RL.lb/2)
+  eps.L = delta*RL.lb/2
+  
+  #For now, only truncate infinite end points
+  #Might want to change this one day, if we have trouble with super wide but finite intervals
+  if (b==Inf){
+    f = function(x){x^2+log(1+x^2)+log(eps.R)}#encodes error of mills approximation
+    b.new = uniroot(f,c(1.1,1000))$root
+    b.new = max(b.new,z+1)#Don't truncate past z
+    R.extra = exp(-b.new^2/2)/b.new
+  }
+  if (a==-Inf){
+    f = function(x){x^2+log(1+x^2)+log(eps.L)}#encodes error of mills approximation
+    a.new = -uniroot(f,c(1.1,1000))$root
+    a.new = min(a.new,z-1)#Don't truncate past z
+    L.extra = exp(-a.new^2/2)/a.new
+  }
+  
+  list(a=a.new,b=b.new,L.extra=L.extra,R.extra=R.extra,z=z)
+}
+
+
+#Approximates integral_a^b e^{-x^2/2+offset^2/2} dx
+# offset is used to make ratios slightly more stable
+# defaults to offset=0
+# Note that I've left out 1/sqrt(2*pi), you can add it in if you like
+approx.int = function(a,b,n=1000,offset=0){
+  delta = (b-a)/n #Step size, may want to vary in the future
+  x = seq(from=a,to=b,by=delta)
+  y = -x^2/2 + offset^2/2 # On the log scale
+  m = diff(y)/diff(x) # Line segment slopes
+  de = diff(exp(y)) # Difference on original scale
+  sum(de/m) #Sum of integrals of line segments (closed form)
+}
+
+#Uses approx.int to evaluate int_x^b phi(z)dz / int_a^b phi(z)dz
+#Right now offsets everything for a little more stability
+#Uses truncation to handle infinite endpoints
+max.approx.frac = function(a,b,x,mu=0,n=1000){
+  returns = numeric(length(mu))
+  for(i in 1:length(returns)){
+    truncation = truncate.interval(a-mu[i],b-mu[i],x-mu[i])
+    #Our offset will use the smaller of a and b in absolute value
+    offset = min(abs(truncation$a),abs(truncation$b))
+    #The truncation also shifts by the mean, so we don't need to do it again for the end points
+    #but we do need to use the center z returned by truncation, rather than x, to match
+    left = approx.int(truncation$a,truncation$z,n,offset)+truncation$L.extra
+    right = approx.int(truncation$z,truncation$b,n,offset)+truncation$R.extra
+    returns[i] = right/(left+right)
+  }
+  returns
+}
diff --git a/selectiveInference-currentCRAN/R/funs.quadratic.R b/selectiveInference-currentCRAN/R/funs.quadratic.R
new file mode 100644
index 0000000..799352b
--- /dev/null
+++ b/selectiveInference-currentCRAN/R/funs.quadratic.R
@@ -0,0 +1,257 @@
+truncationRegion <- function(obj, ydecomp, type, tol = 1e-15) {
+
+  n <- nrow(obj$x)
+  Z <- ydecomp$Z
+  if (type == "TC") {
+      eta <- ydecomp$eta
+  } else {
+      Vd <- ydecomp$Vd
+      V2 <- ydecomp$V2
+      C <- ydecomp$C
+      R <- ydecomp$R
+  }
+  L <- lapply(1:length(obj$action), function(s) {
+
+    Ug <- obj$maxprojs[[s]]
+    peng <- obj$maxpens[[s]]
+    if (s > 1) {
+        Zs <- obj$cumprojs[[s-1]] %*% Z
+        if (type == "TC") {
+            etas <- obj$cumprojs[[s-1]] %*% eta
+        } else {
+            Vds <- obj$cumprojs[[s-1]] %*% Vd
+            V2s <- obj$cumprojs[[s-1]] %*% V2
+        }
+    } else {
+        Zs <- Z
+        if (type == "TC") {
+            etas <- eta
+        } else {
+            Vds <- Vd
+            V2s <- V2
+        }
+    }
+
+    num.projs <- length(obj$projections[[s]])
+    if (num.projs == 0) {
+        return(list(Intervals(c(-Inf,0))))
+    } else {
+      lapply(1:num.projs, function(l) {
+
+          Uh <- obj$projections[[s]][[l]]
+          penh <- obj$aicpens[[s]][[l]]
+          # The quadratic form corresponding to
+          # (t*U + Z)^T %*% Q %*% (t*U + Z) \geq 0
+          # we find the roots in t, if there are any
+          # and return the interval of potential t
+          if (type == "TC") {
+              coeffs <- quadratic_coefficients(obj$sigma, Ug, Uh, peng, penh, etas, etas, Zs, Zs)
+              quadratic_roots(coeffs$A, coeffs$B, coeffs$C, tol)
+          } else {
+              coeffs <- TF_coefficients(R, Ug, Uh, peng, penh, Zs, Zs, Vds, Vds, V2s, V2s)
+              roots <- TF_roots(R, C, coeffs)
+              return(roots)
+          }
+      })
+    }
+    # LL is a list of intervals
+  })
+  # L is now a list of lists of intervals
+  return(unlist(L, recursive = FALSE, use.names = FALSE))
+}
+
+quadratic_coefficients <- function(sigma, Ug, Uh, peng, penh, etag, etah, Zg, Zh) {
+    # g indexes minimizer, h the comparison
+    Uheta <- t(Uh) %*% etah
+    Ugeta <- t(Ug) %*% etag
+    UhZ <- t(Uh) %*% Zh
+    UgZ <- t(Ug) %*% Zg
+    etaZh <- t(etah) %*% Zh
+    etaZg <- t(etag) %*% Zg
+    if (is.null(sigma)) {
+        A <- penh * (sum(etah^2) - sum(Uheta^2)) - peng * (sum(etag^2) - sum(Ugeta^2))
+        B <- 2 * penh * (etaZh - t(Uheta) %*% UhZ) - 2 * peng * (etaZg - t(Ugeta) %*% UgZ)
+        C <- penh * (sum(Zh^2) - sum(UhZ^2)) - peng * (sum(Zg^2) - sum(UgZ^2))
+    } else {
+        A <- (sum(etah^2) - sum(Uheta^2)) - (sum(etag^2) - sum(Ugeta^2))
+        B <- 2 * (etaZh - t(Uheta) %*% UhZ) - 2 * (etaZg - t(Ugeta) %*% UgZ)
+        C <- (sum(Zh^2) - sum(UhZ^2) + penh) - (sum(Zg^2) - sum(UgZ^2) + peng)
+    }
+    return(list(A = A, B = B, C = C))
+}
+
+quadratic_roots <- function(A, B, C, tol) {
+    disc <- B^2 - 4*A*C
+    b2a <- -B/(2*A)
+
+    if (disc > tol) {
+        # Real roots
+        pm <- sqrt(disc)/(2*A)
+        endpoints <- sort(c(b2a - pm, b2a + pm))
+
+    } else {
+        # No real roots
+        if (A > -tol) {
+          # Quadratic form always positive
+            return(Intervals(c(-Inf,0)))
+        } else {
+          # Quadratic form always negative
+            stop("Empty TC support is infeasible")
+        }
+    }
+
+    if (A > tol) {
+        # Parabola opens upward
+        if (min(endpoints) > 0) {
+          # Both roots positive, union of intervals
+            return(Intervals(rbind(c(-Inf,0), endpoints)))
+        } else {
+          # At least one negative root
+            return(Intervals(c(-Inf, max(0, endpoints[2]))))
+        }
+    } else {
+        if (A < -tol) {
+          # Parabola opens downward
+            if (endpoints[2] < 0) {
+            # Positive quadratic form only when t negative
+                stop("Negative TC support is infeasible")
+            } else {
+            # Part which is positive
+                if (endpoints[1] > 0) {
+                    return(Intervals(rbind(c(-Inf, endpoints[1]), c(endpoints[2], Inf))))
+                } else {
+                    return(Intervals(c(endpoints[2], Inf)))
+                }
+            }
+        } else {
+          # a is too close to 0, quadratic is actually linear
+            if (abs(B) > tol) {
+                if (B > 0) {
+                    return(Intervals(c(-Inf, max(0, -C/B))))
+                } else {
+                    if (-C/B < 0) stop("Infeasible linear equation")
+                    return(Intervals(rbind(c(-Inf, 0), c(-C/B, Inf))))
+                }
+            } else {
+                warning("Ill-conditioned quadratic")
+                return(Intervals(c(-Inf,0)))
+            }
+        }
+    }
+}
+
+# Helper functions for TF roots
+roots_to_checkpoints <- function(roots) {
+    checkpoints <- unique(sort(c(0, roots)))
+    return(c(0, (checkpoints + c(checkpoints[-1], 200 + checkpoints[length(checkpoints)]))/2))
+}
+roots_to_partition <- function(roots) {
+    checkpoints <- unique(sort(c(0, roots)))
+    return(list(endpoints = c(checkpoints, Inf), midpoints = (checkpoints + c(checkpoints[-1], 200 + checkpoints[length(checkpoints)]))/2))
+}
+
+# Efficiently compute coefficients of one-dimensional TF slice function
+TF_coefficients <- function(R, Ug, Uh, peng, penh, Zg, Zh, Vdg, Vdh, V2g, V2h) {
+
+    UhZ <- t(Uh) %*% Zh
+    UgZ <- t(Ug) %*% Zg
+    UhVd <- t(Uh) %*% Vdh
+    UgVd <- t(Ug) %*% Vdg
+    UhV2 <- t(Uh) %*% V2h
+    UgV2 <- t(Ug) %*% V2g
+    VdZh <- sum(Vdh*Zh)
+    VdZg <- sum(Vdg*Zg)
+    V2Zh <- sum(V2h*Zh)
+    V2Zg <- sum(V2g*Zg)
+
+    x0 <- penh * (sum(Zh^2) - sum(UhZ^2)) - peng * (sum(Zg^2) - sum(UgZ^2))
+    x1 <- 2*R*(penh * (VdZh - sum(UhZ*UhVd)) - peng * (VdZg - sum(UgZ*UgVd)))
+    x2 <- 2*R*(penh * (V2Zh - sum(UhZ*UhV2)) - peng * (V2Zg - sum(UgZ*UgV2)))
+    x12 <- 2*R^2*(penh * (sum(Vdh*V2h) - sum(UhVd*UhV2)) - peng * (sum(Vdg*V2g) - sum(UgVd*UgV2)))
+    x11 <- R^2*(penh * (sum(Vdh^2) - sum(UhVd^2)) - peng * (sum(Vdg^2) - sum(UgVd^2)))
+    x22 <- R^2*(penh * (sum(V2h^2) - sum(UhV2^2)) - peng * (sum(V2g^2) - sum(UgV2^2)))
+
+    return(list(x11=x11, x22=x22, x12=x12, x1=x1, x2=x2, x0=x0))
+}
+
+# Numerically solve for roots of TF slice using
+# hybrid polyroot/uniroot approach
+
+TF_roots <- function(R, C, coeffs, tol = 1e-8, tol2 = 1e-6) {
+
+    x11 <- coeffs$x11
+    x22 <- coeffs$x22
+    x12 <- coeffs$x12
+    x1 <- coeffs$x1
+    x2 <- coeffs$x2
+    x0 <- coeffs$x0
+
+    g1 <- function(t) sqrt(C*t/(1+C*t))
+    g2 <- function(t) 1/sqrt(1+C*t)
+    I <- function(t) x11*g1(t)^2 + x12*g1(t)*g2(t) + x22*g2(t)^2 + x1*g1(t) + x2*g2(t) + x0
+
+    z4 <- complex(real = -x11 + x22, imaginary = -x12)/4
+    z3 <- complex(real = x2, imaginary = -x1)/2
+    z2 <- complex(real = x11/2+x22/2+x0)
+    z1 <- Conj(z3)
+    z0 <- Conj(z4)
+
+    zcoefs <- c(z0, z1, z2, z3, z4)
+    croots <- polyroot(zcoefs)
+    thetas <- Arg(croots)
+    # Can't specify polyroot precision :(
+    modinds <- Mod(croots) <= 1 + tol2 & Mod(croots) >= 1 - tol2
+    angleinds <- thetas >=0 & thetas <= pi/2
+    roots <- unique(thetas[which(modinds & angleinds)])
+    troots <- tan(roots)^2/C
+
+    checkpoints <- c()
+    if (length(troots) > 0) checkpoints <- roots_to_checkpoints(troots)
+    checkpoints <- sort(
+        c(checkpoints, 0, tol, tol2,
+                        seq(from = sqrt(tol2), to = 1, length.out = 50),
+                        seq(from = 1.2, to=50, length.out = 20),
+                        100, 1000, 10000))
+    ## if (length(troots) == 0) {
+    ##     # Polyroot didn't catch any roots
+    ##     # ad-hoc check:
+    ##     checkpoints <- c(0, tol, tol2,
+    ##                      seq(from = sqrt(tol2), to = 1, length.out = 50),
+    ##                      seq(from = 1.2, to=50, length.out = 20),
+    ##                      100, 1000, 10000)
+    ## } else {
+    ##     checkpoints <- roots_to_checkpoints(troots)
+    ## }
+
+    signs <- sign(I(checkpoints))
+    diffs <- c(0, diff(signs))
+    changeinds <- which(diffs != 0)
+
+    if (length(changeinds) > 0) {
+
+        roots <- unlist(lapply(changeinds, function(ind) {
+            uniroot(I, lower = checkpoints[ind-1], upper = checkpoints[ind], tol = tol)$root
+        }))
+
+        partition <- roots_to_partition(roots)
+        negative <- which(I(partition$midpoints) < 0)
+
+        intervals <- matrix(NA, ncol=2)
+        for (i in 1:length(negative)) {
+            ind <- negative[i]
+            if ((i > 1) && (ind == negative[i-1] + 1)) {
+                # There was not a sign change at end of previous interval
+                intervals[nrow(intervals), 2] <- partition$endpoints[ind+1]
+            } else {
+                intervals <- rbind(intervals, c(partition$endpoints[ind], partition$endpoints[ind+1]))
+            }
+        }
+
+        return(Intervals(intervals[-1,]))
+    }
+
+    # Apparently no roots, always positive
+    if (I(0) < 0) stop("Infeasible constraint!")
+    return(Intervals(c(-Inf,0)))
+}
+
diff --git a/selectiveInference-currentCRAN/man/estimateSigma.Rd b/selectiveInference-currentCRAN/man/estimateSigma.Rd
new file mode 100644
index 0000000..c956121
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/estimateSigma.Rd
@@ -0,0 +1,64 @@
+\name{estimateSigma}
+\alias{estimateSigma}
+%- Also NEED an '\alias' for EACH other topic documented here.
+\title{
+Estimate the noise standard deviation in regression 
+}
+\description{
+Estimates the standard deviation of the noise, for use in the selectiveInference 
+package
+}
+\usage{
+estimateSigma(x, y, intercept=TRUE, standardize=TRUE)
+}
+\arguments{
+  \item{x}{
+Matrix of predictors (n by p)
+}
+  \item{y}{
+Vector of outcomes (length n)
+}
+\item{intercept}{Should glmnet be run with an intercept? Default is TRUE}
+\item{standardize}{Should glmnet be run with standardized predictors? Default is TRUE}
+}
+\details{
+This function estimates the standard deviation of the noise, in a linear regresion setting.
+A lasso regression is fit, using cross-validation to estimate the tuning parameter lambda.
+With sample size n, yhat equal to the predicted values and df being the number of nonzero 
+coefficients from the lasso fit, the estimate of sigma is \code{sqrt(sum((y-yhat)^2) / (n-df-1))}.
+Important: if you are using glmnet to compute the lasso estimate, be sure to use the settings
+for the "intercept" and "standardize" arguments in glmnet and estimateSigma. Same applies to fs 
+or lar, where the argument for standardization is called "normalize".
+}
+\value{
+\item{sigmahat}{The estimate of sigma}
+\item{df}{The degrees of freedom of lasso fit used}
+}
+\references{
+Stephen Reid, Jerome Friedman, and Rob Tibshirani (2014). 
+A study of error variance estimation in lasso regression. arXiv:1311.5274.
+}
+
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\examples{
+set.seed(33)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run forward stepwise
+fsfit = fs(x,y)
+
+# estimate sigma
+sigmahat = estimateSigma(x,y)$sigmahat
+
+# run sequential inference with estimated sigma
+out = fsInf(fsfit,sigma=sigmahat)
+out
+}
+
+
diff --git a/selectiveInference-currentCRAN/man/factorDesign.Rd b/selectiveInference-currentCRAN/man/factorDesign.Rd
new file mode 100644
index 0000000..8e061db
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/factorDesign.Rd
@@ -0,0 +1,30 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/funs.groupfs.R
+\name{factorDesign}
+\alias{factorDesign}
+\title{Expand a data frame with factors to form a design matrix with the full binary encoding of each factor.}
+\usage{
+factorDesign(df)
+}
+\arguments{
+\item{df}{Data frame containing some columns which are \code{factors}.}
+}
+\value{
+List containing
+\describe{
+  \item{x}{Design matrix, the first columns contain any numeric variables from the original date frame.}
+  \item{index}{Group membership indicator for expanded matrix.}
+}
+}
+\description{
+When using \code{\link{groupfs}} with factor variables call this function first to create a design matrix.
+}
+\examples{
+\dontrun{
+fd = factorDesign(warpbreaks)
+y = rnorm(nrow(fd$x))
+fit = groupfs(fd$x, y, fd$index, maxsteps=2, intercept=FALSE)
+pvals = groupfsInf(fit)
+}
+}
+
diff --git a/selectiveInference-currentCRAN/man/fixedLassoInf.Rd b/selectiveInference-currentCRAN/man/fixedLassoInf.Rd
new file mode 100644
index 0000000..6795ec1
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/fixedLassoInf.Rd
@@ -0,0 +1,252 @@
+\name{fixedLassoInf}
+\alias{fixedLassoInf}
+
+\title{
+Inference for the lasso, with a fixed lambda 
+}
+\description{
+Compute p-values and confidence intervals for the lasso estimate, at a 
+fixed value of the tuning parameter lambda
+}
+\usage{
+fixedLassoInf(x, y, beta, lambda, family = c("gaussian", "binomial", 
+    "cox"),intercept=TRUE, status=NULL, sigma=NULL, alpha=0.1,
+         type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
+         gridrange=c(-100,100), bits=NULL, verbose=FALSE) 
+}
+\arguments{
+  \item{x}{
+Matrix of predictors (n by p); 
+}
+  \item{y}{
+Vector of outcomes (length n)
+}
+  \item{beta}{
+Estimated lasso coefficients (e.g., from glmnet). This is of length p
+(so the intercept is not included as the first component).
+
+ Be careful! This function uses the "standard" lasso objective
+  \deqn{
+    1/2 \|y - x \beta\|_2^2 + \lambda \|\beta\|_1.
+  }
+ In contrast, glmnet multiplies the first term by a factor of 1/n.
+ So after running glmnet, to extract the beta corresponding to a value lambda, 
+ you need to use \code{beta = coef(obj, s=lambda/n)[-1]},
+ where obj is the object returned by glmnet (and [-1] removes the intercept,
+ which glmnet always puts in the first component)
+}      
+  \item{lambda}{
+Value of lambda used to compute beta. See the above warning
+} 
+
+\item{family}{Response type: "gaussian" (default), "binomial", or 
+    "cox" (for censored survival data) }
+
+\item{sigma}{
+Estimate of error standard deviation. If NULL (default), this is estimated 
+using the mean squared residual of the full least squares fit when n >= 2p, and 
+using the standard deviation of y when n < 2p. In the latter case, the user 
+should use \code{\link{estimateSigma}} function for a more accurate estimate.
+Not used for family= "binomial", or  "cox"
+}
+\item{alpha}{
+Significance level for confidence intervals (target is miscoverage alpha/2 in each tail)
+}
+\item{intercept}{
+Was the lasso problem solved (e.g., by glmnet) with an intercept in the model? 
+Default is TRUE. Must be TRUE for "binomial" family. Not used for 'cox" family, where no intercept is assumed.
+}
+\item{status}{Censoring status for Cox model; 1=failurem 0=censored}
+\item{type}{Contrast type for p-values and confidence intervals: default is
+"partial"---meaning that the contrasts tested are the partial population 
+regression coefficients, within the active set of predictors; the alternative is
+"full"---meaning that the full population regression coefficients are tested.
+The latter does not make sense when p > n.}
+\item{tol.beta}{
+Tolerance for determining if a coefficient is zero
+}
+\item{tol.kkt}{
+Tolerance for determining if an entry of the subgradient is zero 
+}
+\item{gridrange}{
+Grid range for constructing confidence intervals, on the standardized scale
+}
+\item{bits}{
+Number of bits to be used for p-value and confidence interval calculations. Default is 
+NULL, in which case standard floating point calculations are performed. When not NULL, 
+multiple precision floating point calculations are performed with the specified number 
+of bits, using the R package \code{Rmpfr} (if this package is not installed, then a 
+warning is thrown, and standard floating point calculations are pursued).
+Note: standard double precision uses 53 bits
+so, e.g., a choice of 200 bits uses about 4 times double precision. The confidence
+interval computation is sometimes numerically challenging, and the extra precision can be 
+helpful (though computationally more costly). In particular, extra precision might be tried 
+if the values in the output columns of \code{tailarea} differ noticeably from alpha/2.
+}
+\item{verbose}{
+Print out progress along the way? Default is FALSE}
+}
+
+\details{
+This function computes selective p-values and confidence intervals for the lasso,
+given a fixed value of the tuning parameter lambda. 
+Three different response types are supported:  gaussian, binomial and Cox.
+The confidence interval construction involves numerical search and can be fragile:
+if the observed statistic is too close to either end of the truncation interval
+(vlo and vup, see references), then one or possibly both endpoints of the interval of 
+desired coverage cannot be computed, and default to +/- Inf. The output \code{tailarea} 
+gives the achieved Gaussian tail areas for the reported intervals---these should be close 
+to alpha/2, and can be used for error-checking purposes.
+
+Important!: Before running glmnet (or some other lasso-solver)  x should be centered, that is x <- scale(X,TRUE,FALSE).
+In addition, if standardization of the predictors is desired, x should be scaled as well:  x <- scale(x,TRUE,TRUE).
+Then when running glmnet, set standardize=F. See example below.
+
+The penalty.factor facility in glmmet-- allowing different penalties lambda for each predictor,
+is not yet implemented in fixedLassoInf. However you can finesse this--- see the example below. One caveat- using this approach, a penalty factor of zero (forcing a predictor in)
+is not allowed.
+
+Note that the coefficients and standard errors reported are unregularized.
+Eg for the Gaussian, they are the usual least squares estimates and standard errors
+for the model fit to the actice set from the lasso.
+}
+\value{  
+\item{type}{Type of coefficients tested (partial or full)}
+\item{lambda}{Value of tuning parameter lambda used}
+\item{pv}{One-sided P-values for active variables, uses the fact we have conditioned on the sign.}
+\item{ci}{Confidence intervals}
+\item{tailarea}{Realized tail areas (lower and upper) for each confidence interval}
+\item{vlo}{Lower truncation limits for statistics}
+\item{vup}{Upper truncation limits for statistics}
+\item{vmat}{Linear contrasts that define the observed statistics}
+\item{y}{Vector of outcomes}
+\item{vars}{Variables in active set}
+\item{sign}{Signs of active coefficients}
+\item{alpha}{Desired coverage (alpha/2 in each tail)}
+\item{sigma}{Value of error standard deviation (sigma) used}
+\item{call}{The call to lassoInf}
+}
+
+\references{
+Jason Lee, Dennis Sun, Yuekai Sun, and Jonathan Taylor (2013). 
+Exact post-selection inference, with application to the lasso. arXiv:1311.6238.
+
+ Jonathan Taylor and Robert Tibshirani (2016) Post-selection inference for L1-penalized likelihood models.
+arXiv:1602.07358
+
+}
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\examples{
+set.seed(43)
+n = 50
+p = 10
+sigma = 1
+
+x = matrix(rnorm(n*p),n,p)
+x=scale(x,TRUE,TRUE)
+
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# first run glmnet
+gfit = glmnet(x,y,standardize=FALSE)
+
+# extract coef for a given lambda; note the 1/n factor!
+# (and we don't save the intercept term)
+lambda = .8
+beta = coef(gfit, s=lambda/n, exact=TRUE,x=x,y=y)[-1]
+
+# compute fixed lambda p-values and selection intervals
+out = fixedLassoInf(x,y,beta,lambda,sigma=sigma)
+out
+
+
+## as above, but use lar function instead to get initial 
+## lasso fit (should get same results)
+ lfit = lar(x,y,normalize=FALSE)
+ beta = coef(lfit,s=lambda,mode="lambda",x=x,y=y)
+ out2 = fixedLassoInf(x,y,beta,lambda,sigma=sigma)
+ out2
+
+## mimic different penalty factors by first scaling x
+ set.seed(43)
+n = 50
+p = 10
+sigma = 1
+
+x = matrix(rnorm(n*p),n,p)
+x=scale(x,TRUE,TRUE)
+
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+pf=c(rep(1,7),rep(.1,3))  #define penalty factors
+pf=p*pf/sum(pf)   # penalty factors should be rescaled so they sum to p
+xs=scale(x,FALSE,pf) #scale cols of x by penalty factors
+# first run glmnet
+gfit = glmnet(xs,y,standardize=FALSE)
+
+# extract coef for a given lambda; note the 1/n factor!
+# (and we don't save the intercept term)
+lambda = .8
+beta_hat = coef(gfit, s=lambda/n, exact=TRUE,x=x,y=y)[-1]
+
+# compute fixed lambda p-values and selection intervals
+out = fixedLassoInf(xs,y,beta_hat,lambda,sigma=sigma)
+
+#rescale conf points to undo the penalty factor
+out$ci=t(scale(t(out$ci),FALSE,pf[out$vars]))
+out
+
+#logistic model
+set.seed(43)
+     n = 50
+     p = 10
+     sigma = 1
+     
+     x = matrix(rnorm(n*p),n,p)
+     x=scale(x,TRUE,TRUE)
+     
+     beta = c(3,2,rep(0,p-2))
+     y = x\%*\%beta + sigma*rnorm(n)
+     y=1*(y>mean(y))
+     # first run glmnet
+     gfit = glmnet(x,y,standardize=FALSE,family="binomial")
+     
+     # extract coef for a given lambda; note the 1/n factor!
+     # (and here  we DO  include the intercept term)
+     lambda = .8
+     beta_hat = coef(gfit, s=lambda/n, exact=TRUE,x=x,y=y)
+     
+     # compute fixed lambda p-values and selection intervals
+     out = fixedLassoInf(x,y,beta_hat,lambda,family="binomial")
+     out
+
+#Cox model
+set.seed(43)
+     n = 50
+     p = 10
+     sigma = 1
+     
+     x = matrix(rnorm(n*p),n,p)
+     x=scale(x,TRUE,TRUE)
+     
+     beta = c(3,2,rep(0,p-2))
+     tim = as.vector(x\%*\%beta + sigma*rnorm(n))
+     tim= tim-min(tim)+1
+status=sample(c(0,1),size=n,replace=TRUE)
+     # first run glmnet
+
+
+     gfit = glmnet(x,Surv(tim,status),standardize=FALSE,family="cox")
+   
+     # extract coef for a given lambda; note the 1/n factor!
+   
+     lambda = 1.5
+     beta_hat = as.numeric(coef(gfit, s=lambda/n, exact=TRUE,x=x,y=Surv(tim,status)))
+     
+     # compute fixed lambda p-values and selection intervals
+     out = fixedLassoInf(x,tim,beta_hat,lambda,status=status,family="cox")
+     out
+}
+ 
\ No newline at end of file
diff --git a/selectiveInference-currentCRAN/man/forwardStop.Rd b/selectiveInference-currentCRAN/man/forwardStop.Rd
new file mode 100644
index 0000000..87eb7ab
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/forwardStop.Rd
@@ -0,0 +1,55 @@
+\name{forwardStop}
+\alias{forwardStop}
+%- Also NEED an '\alias' for EACH other topic documented here.
+\title{
+ForwardStop rule for sequential p-values
+}
+\description{
+Computes the ForwardStop sequential stopping rule of G'Sell et al (2014)
+}
+\usage{
+forwardStop(pv, alpha=0.1)
+}
+%- maybe also 'usage' for other objects documented here.
+\arguments{
+  \item{pv}{
+Vector of **sequential** p-values, for example from fsInf or larInf
+}
+  \item{alpha}{
+Desired type FDR level (between 0 and 1)
+}
+}
+\details{
+Computes the ForwardStop sequential stopping rule of G'Sell et al (2014).
+Guarantees FDR control at the level alpha, for independent p-values.
+}
+\value{
+Step number for sequential stop.
+}
+\references{
+Max Grazier G'Sell, Stefan Wager, Alexandra Chouldechova, and Rob Tibshirani (2014).
+Sequential selection procedures and Fflse Discovery Rate Control. arXiv:1309.5352.
+To appear in Journal of the Royal Statistical Society: Series B.
+}
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\examples{
+set.seed(33)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run forward stepwise
+fsfit = fs(x,y)
+
+# compute sequential p-values and confidence intervals
+# (sigma estimated from full model)
+out = fsInf(fsfit)
+out
+
+# estimate optimal stopping point
+forwardStop(out$pv, alpha=.10)
+}
diff --git a/selectiveInference-currentCRAN/man/fs.Rd b/selectiveInference-currentCRAN/man/fs.Rd
new file mode 100644
index 0000000..2a61c83
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/fs.Rd
@@ -0,0 +1,95 @@
+\name{fs}
+\alias{fs}
+%- Also NEED an '\alias' for EACH other topic documented here.
+\title{
+Forward stepwise regression
+}
+\description{
+This function implements forward stepwise regression, for use in the 
+selectiveInference package
+}
+\usage{
+fs(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE, verbose=FALSE)
+}
+\arguments{
+  \item{x}{
+Matrix of predictors (n by p)
+}
+  \item{y}{
+Vector of outcomes (length n)
+}
+  \item{maxsteps}{
+Maximum number of steps to take 
+}
+\item{intercept}{Should an intercept be included on the model? Default is TRUE}
+\item{normalize}{Should the predictors be normalized? Default is TRUE. (Note:
+this argument has no real effect on model selection since forward stepwise is 
+scale invariant already; however, it is included for completeness, and to match
+the interface for the \code{lar} function)
+}
+\item{verbose}{Print out progress along the way? Default is FALSE}
+}
+
+\details{
+This function implements forward stepwise regression, adding the predictor at each 
+step that maximizes the absolute correlation between the predictors---once 
+orthogonalized with respect to the current model---and the residual. This entry
+criterion is standard, and is equivalent to choosing the variable that achieves 
+the biggest drop in RSS at each step; it is used, e.g., by the \code{step} function
+in R. Note that, for example, the \code{lars} package implements a stepwise option 
+(with type="step"), but uses a (mildly) different entry criterion, based on maximal
+absolute correlation between the original (non-orthogonalized) predictors and the 
+residual. 
+}
+\value{
+\item{action}{Vector of predictors in order of entry}
+\item{sign}{Signs of coefficients of predictors, upon entry}
+\item{df}{Degrees of freedom of each active model}
+\item{beta}{Matrix of regression coefficients for each model along the path,
+one column per model}
+\item{completepath}{Was the complete stepwise path computed?}
+\item{bls}{If completepath is TRUE, the full least squares coefficients}
+\item{Gamma}{Matrix that captures the polyhedral selection at each step}
+\item{nk}{Number of polyhedral constraints at each step in path}
+\item{vreg}{Matrix of linear contrasts that gives coefficients of variables
+to enter along the path}
+\item{x}{Matrix of predictors used}
+\item{y}{Vector of outcomes used}
+\item{bx}{Vector of column means of original x}
+\item{by}{Mean of original y}
+\item{sx}{Norm of each column of original x}
+\item{intercept}{Was an intercept included?}
+\item{normalize}{Were the predictors normalized?}
+\item{call}{The call to fs}
+}
+
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\seealso{
+ \code{\link{fsInf}}, \code{\link{predict.fs}},\code{\link{coef.fs}}, \code{\link{plot.fs}}
+}
+
+\examples{
+set.seed(33)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run forward stepwise, plot results
+fsfit = fs(x,y)
+plot(fsfit)
+
+# compute sequential p-values and confidence intervals
+# (sigma estimated from full model)
+out = fsInf(fsfit)
+out
+}
+
+
+% Add one or more standard keywords, see file 'KEYWORDS' in the
+% R documentation directory.
+\keyword{ ~kwd1 }
+\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
diff --git a/selectiveInference-currentCRAN/man/fsInf.Rd b/selectiveInference-currentCRAN/man/fsInf.Rd
new file mode 100644
index 0000000..613bc5a
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/fsInf.Rd
@@ -0,0 +1,135 @@
+\name{fsInf}
+\alias{fsInf}
+\title{
+Selective inference for forward stepwise regression
+}
+\description{
+Computes p-values and confidence intervals for forward 
+stepwise regression
+}
+\usage{
+fsInf(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic"), 
+      gridrange=c(-100,100), bits=NULL, mult=2, ntimes=2, verbose=FALSE) 
+}
+\arguments{
+  \item{obj}{
+Object returned by \code{\link{fs}} function
+}
+\item{sigma}{
+Estimate of error standard deviation. If NULL (default), this is estimated 
+using the mean squared residual of the full least squares fit when n >= 2p, and 
+using the standard deviation of y when n < 2p. In the latter case, the user 
+should use \code{\link{estimateSigma}} function for a more accurate estimate
+}
+\item{alpha}{
+Significance level for confidence intervals (target is miscoverage alpha/2 in each tail)
+}
+\item{k}{
+See "type" argument below. Default is NULL, in which case k is taken to be the
+the number of steps computed in the forward stepwise path
+}
+\item{type}{
+Type of analysis desired: with "active" (default), p-values and confidence intervals are 
+computed for each predictor as it is entered into the active step, all the way through
+k steps; with "all", p-values and confidence intervals are computed for all variables in 
+the active model after k steps; with "aic", the number of steps k is first estimated using 
+a modified AIC criterion, and then the same type of analysis as in "all" is carried out for 
+this particular value of k.  
+
+Note that the AIC scheme is defined to choose a number of steps k after which the AIC criterion 
+increases \code{ntimes} in a row, where \code{ntimes} can be specified by the user (see below). 
+Under this definition, the AIC selection event is characterizable as a polyhedral set, and hence
+the extra conditioning can be taken into account exactly. Also note that an analogous BIC scheme
+can be specified through the \code{mult} argument (see below)
+}
+\item{gridrange}{
+Grid range for constructing confidence intervals, on the standardized scale
+}
+\item{bits}{
+Number of bits to be used for p-value and confidence interval calculations. Default is 
+NULL, in which case standard floating point calculations are performed. When not NULL, 
+multiple precision floating point calculations are performed with the specified number 
+of bits, using the R package \code{Rmpfr} (if this package is not installed, then a 
+warning is thrown, and standard floating point calculations are pursued).
+Note: standard double precision uses 53 bits
+so, e.g., a choice of 200 bits uses about 4 times double precision. The confidence
+interval computation is sometimes numerically challenging, and the extra precision can be 
+helpful (though computationally more costly). In particular, extra precision might be tried 
+if the values in the output columns of \code{tailarea} differ noticeably from alpha/2.
+}
+\item{mult}{Multiplier for the AIC-style penalty. Hence a value of 2 (default) 
+gives AIC, whereas a value of log(n) would give BIC}
+\item{ntimes}{Number of steps for which AIC-style criterion has to increase before 
+minimizing point is declared}
+\item{verbose}{Print out progress along the way? Default is FALSE}
+}
+
+\details{
+This function computes selective p-values and confidence intervals (selection intervals)
+for forward stepwise regression. The default is to report the results for 
+each predictor after its entry into the model. See the "type" argument for other options.
+The confidence interval construction involves numerical search and can be fragile:
+if the observed statistic is too close to either end of the truncation interval
+(vlo and vup, see references), then one or possibly both endpoints of the interval of 
+desired coverage cannot be computed, and default to +/- Inf. The output \code{tailarea} 
+gives the achieved Gaussian tail areas for the reported intervals---these should be close 
+to alpha/2, and can be used for error-checking purposes.
+}
+
+\value{  
+\item{type}{Type of analysis (active, all, or aic)}
+\item{k}{Value of k specified in call}
+\item{khat}{When type is "active", this is an estimated stopping point
+declared by \code{\link{forwardStop}}; when type is "aic", this is the
+value chosen by the modified AIC scheme}
+\item{pv}{One sided P-values for active variables, uses the sign that a variable entered the model with.}
+\item{ci}{Confidence intervals}
+\item{tailarea}{Realized tail areas (lower and upper) for each confidence interval}
+\item{vlo}{Lower truncation limits for statistics}
+\item{vup}{Upper truncation limits for statistics}
+\item{vmat}{Linear contrasts that define the observed statistics}
+\item{y}{Vector of outcomes}
+\item{vars}{Variables in active set}
+\item{sign}{Signs of active coefficients}
+\item{alpha}{Desired coverage (alpha/2 in each tail)}
+\item{sigma}{Value of error standard deviation (sigma) used}
+\item{call}{The call to fsInf}
+}
+
+\references{
+Ryan Tibshirani, Jonathan Taylor, Richard Lockhart, and Rob Tibshirani (2014).
+Exact post-selection inference for sequential regression procedures. arXiv:1401.3889.
+
+Joshua Loftus and Jonathan Taylor (2014). A significance test for forward stepwise
+model selection. arXiv:1405.3920.
+}
+
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\seealso{\code{\link{fs}}}
+
+\examples{
+set.seed(33)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run forward stepwise
+fsfit = fs(x,y)
+
+# compute sequential p-values and confidence intervals
+# (sigma estimated from full model)
+out.seq = fsInf(fsfit)
+out.seq
+
+# compute p-values and confidence intervals after AIC stopping
+out.aic = fsInf(fsfit,type="aic")
+out.aic
+
+# compute p-values and confidence intervals after 5 fixed steps
+out.fix = fsInf(fsfit,type="all",k=5)
+out.fix
+}
diff --git a/selectiveInference-currentCRAN/man/groupfs.Rd b/selectiveInference-currentCRAN/man/groupfs.Rd
new file mode 100644
index 0000000..a57c6dc
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/groupfs.Rd
@@ -0,0 +1,49 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/funs.groupfs.R
+\name{groupfs}
+\alias{groupfs}
+\title{Select a model with forward stepwise.}
+\usage{
+groupfs(x, y, index, maxsteps, sigma = NULL, k = 2, intercept = TRUE,
+  center = TRUE, normalize = TRUE, aicstop = 0, verbose = FALSE)
+}
+\arguments{
+\item{x}{Matrix of predictors (n by p).}
+
+\item{y}{Vector of outcomes (length n).}
+
+\item{index}{Group membership indicator of length p. Check that \code{sort(unique(index)) = 1:G} where \code{G} is the number of distinct groups.}
+
+\item{maxsteps}{Maximum number of steps for forward stepwise.}
+
+\item{sigma}{Estimate of error standard deviation for use in AIC criterion. This determines the relative scale between RSS and the degrees of freedom penalty. Default is NULL corresponding to unknown sigma. When NULL, \code{link{groupfsInf}} performs truncated F inference instead of truncated \eqn{\chi}. See \code{\link[stats]{extractAIC}} for details on the AIC criterion.}
+
+\item{k}{Multiplier of model size penalty, the default is \code{k = 2} for AIC. Use \code{k = log(n)} for BIC, or \code{k = 2log(p)} for RIC (best for high dimensions, when \eqn{p > n}). If \eqn{G < p} then RIC may be too restrictive and it would be better to use \code{log(G) < k < 2log(p)}.}
+
+\item{intercept}{Should an intercept be included in the model? Default is TRUE. Does not count as a step.}
+
+\item{center}{Should the columns of the design matrix be centered? Default is TRUE.}
+
+\item{normalize}{Should the design matrix be normalized? Default is TRUE.}
+
+\item{aicstop}{Early stopping if AIC increases. Default is 0 corresponding to no early stopping. Positive integer values specify the number of times the AIC is allowed to increase in a row, e.g. with \code{aicstop = 2} the algorithm will stop if the AIC criterion increases for 2 steps in a row. The default of \code{\link[stats]{step}} corresponds to \code{aicstop = 1}.}
+
+\item{verbose}{Print out progress along the way? Default is FALSE.}
+}
+\value{
+An object of class "groupfs" containing information about the sequence of models in the forward stepwise algorithm. Call the function \code{\link{groupfsInf}} on this object to compute selective p-values.
+}
+\description{
+This function implements forward selection of linear models almost identically to \code{\link[stats]{step}} with \code{direction = "forward"}. The reason this is a separate function from \code{\link{fs}} is that groups of variables (e.g. dummies encoding levels of a categorical variable) must be handled differently in the selective inference framework.
+}
+\examples{
+x = matrix(rnorm(20*40), nrow=20)
+index = sort(rep(1:20, 2))
+y = rnorm(20) + 2 * x[,1] - x[,4]
+fit = groupfs(x, y, index, maxsteps = 5)
+pvals = groupfsInf(fit)
+}
+\seealso{
+\code{\link{groupfsInf}}, \code{\link{factorDesign}}.
+}
+
diff --git a/selectiveInference-currentCRAN/man/groupfsInf.Rd b/selectiveInference-currentCRAN/man/groupfsInf.Rd
new file mode 100644
index 0000000..74b9a5e
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/groupfsInf.Rd
@@ -0,0 +1,31 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/funs.groupfs.R
+\name{groupfsInf}
+\alias{groupfsInf}
+\title{Compute selective p-values for a model fitted by \code{groupfs}.}
+\usage{
+groupfsInf(obj, sigma = NULL, verbose = TRUE)
+}
+\arguments{
+\item{obj}{Object returned by \code{\link{groupfs}} function}
+
+\item{sigma}{Estimate of error standard deviation. Default is NULL and in this case groupfsInf uses the value of sigma specified to \code{\link{groupfs}}.}
+
+\item{verbose}{Print out progress along the way? Default is TRUE.}
+}
+\value{
+An object of class "groupfsInf" containing selective p-values for the fitted model \code{obj}. For comparison with \code{\link{fsInf}}, note that the option \code{type = "active"} is not available.
+
+\describe{
+  \item{vars}{Labels of the active groups in the order they were included.}
+  \item{pv}{Selective p-values computed from appropriate truncated distributions.}
+  \item{sigma}{Estimate of error variance used in computing p-values.}
+  \item{TC or TF}{Observed value of truncated \eqn{\chi} or \eqn{F}.}
+  \item{df}{Rank of group of variables when it was added to the model.}
+  \item{support}{List of intervals defining the truncation region of the corresponding statistic.}
+}
+}
+\description{
+Computes p-values for each group of variables in a model fitted by \code{\link{groupfs}}. These p-values adjust for selection by truncating the usual \eqn{\chi^2} statistics to the regions implied by the model selection event. If the \code{sigma} to \code{\link{groupfs}} was NULL then groupfsInf uses truncated \eqn{F} statistics instead of truncated \eqn{\chi}. The \code{sigma} argument to groupfsInf allows users to override and use \eqn{\chi}, but this is not recommended unless \eqn{\sigma} can be estimated well (i.e. \eqn{n > p}).
+}
+
diff --git a/selectiveInference-currentCRAN/man/lar.Rd b/selectiveInference-currentCRAN/man/lar.Rd
new file mode 100644
index 0000000..590a663
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/lar.Rd
@@ -0,0 +1,95 @@
+\name{lar}
+\alias{lar}
+\title{
+Least angle regression
+}
+\description{
+This function implements least angle regression, for use in the 
+selectiveInference package
+}
+\usage{
+lar(x, y, maxsteps=2000, minlam=0, intercept=TRUE, normalize=TRUE,
+    verbose=FALSE) 
+}
+
+\arguments{
+  \item{x}{
+Matrix of predictors (n by p)
+}
+  \item{y}{
+Vector of outcomes (length n)
+}
+  \item{maxsteps}{
+Maximum number of steps to take 
+}
+\item{minlam}{
+Minimum value of lambda to consider
+}
+\item{intercept}{Should an intercept be included on the model? Default is TRUE}
+\item{normalize}{Should the predictors be normalized? Default is TRUE}
+\item{verbose}{Print out progress along the way? Default is FALSE}
+}
+
+\details{
+The least angle regression algorithm is described in detail by Efron et al. (2002). 
+This function should match (in terms of its output) that from the \code{lars} package,
+but returns additional information (namely, the polyhedral constraints) needed for the
+selective inference calculations.
+}
+
+\value{
+\item{lambda}{Values of lambda (knots) visited along the path}
+\item{action}{Vector of predictors in order of entry}
+\item{sign}{Signs of coefficients of predictors, upon entry}
+\item{df}{Degrees of freedom of each active model}
+\item{beta}{Matrix of regression coefficients for each model along the path,
+one model per column}
+\item{completepath}{Was the complete stepwise path computed?}
+\item{bls}{If completepath is TRUE, the full least squares coefficients}
+\item{Gamma}{Matrix that captures the polyhedral selection at each step}
+\item{nk}{Number of polyhedral constraints at each step in path}
+\item{vreg}{Matrix of linear contrasts that gives coefficients of variables
+to enter along the path}     
+\item{mp}{Value of M+ (for internal use with the spacing test)}	 
+\item{x}{Matrix of predictors used}
+\item{y}{Vector of outcomes used}
+\item{bx}{Vector of column means of original x}
+\item{by}{Mean of original y}
+\item{sx}{Norm of each column of original x}
+\item{intercept}{Was an intercept included?}
+\item{normalize}{Were the predictors normalized?}
+\item{call}{The call to lar}
+}
+
+\references{
+Brad Efron, Trevor Hastie, Iain Johnstone, and Rob Tibshirani (2002).
+Least angle regression. Annals of Statistics (with discussion).
+
+See also the descriptions in Trevor Hastie, Rob Tibshirani, and 
+Jerome Friedman (2002, 2009). Elements of Statistical Learning.
+}
+
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Max G'Sell, Joshua Loftus, Stephen Reid}
+
+\seealso{
+ \code{\link{larInf}}, \code{\link{predict.lar}}, \code{\link{coef.lar}}, \code{\link{plot.lar}}
+}
+
+\examples{
+set.seed(43)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run LAR, plot results
+larfit = lar(x,y)
+plot(larfit)
+
+# compute sequential p-values and confidence intervals
+# (sigma estimated from full model)
+out = larInf(larfit)
+out                                    
+}
diff --git a/selectiveInference-currentCRAN/man/larInf.Rd b/selectiveInference-currentCRAN/man/larInf.Rd
new file mode 100644
index 0000000..8e3b2d0
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/larInf.Rd
@@ -0,0 +1,140 @@
+\name{larInf}
+\alias{larInf}
+\title{
+Selective inference for least angle regression 
+}
+\description{
+Computes p-values and confidence intervals for least 
+angle regression
+}
+\usage{
+larInf(obj, sigma=NULL, alpha=0.1, k=NULL, type=c("active","all","aic"), 
+       gridrange=c(-100,100), bits=NULL, mult=2, ntimes=2, verbose=FALSE) 
+}
+\arguments{
+ \item{obj}{
+Object returned by \code{lar} function (not the \code{lars} function!)
+}
+\item{sigma}{
+Estimate of error standard deviation. If NULL (default), this is estimated 
+using the mean squared residual of the full least squares fit when n >= 2p, and 
+using the standard deviation of y when n < 2p. In the latter case, the user 
+should use \code{\link{estimateSigma}} function for a more accurate estimate
+}
+\item{alpha}{
+Significance level for confidence intervals (target is miscoverage alpha/2 in each tail)
+}
+\item{k}{
+See "type" argument below. Default is NULL, in which case k is taken to be the
+the number of steps computed in the least angle regression path
+}
+\item{type}{
+Type of analysis desired: with "active" (default), p-values and confidence intervals are 
+computed for each predictor as it is entered into the active step, all the way through
+k steps; with "all", p-values and confidence intervals are computed for all variables in 
+the active model after k steps; with "aic", the number of steps k is first estimated using 
+a modified AIC criterion, and then the same type of analysis as in "all" is carried out for 
+this particular value of k.  
+
+Note that the AIC scheme is defined to choose a number of steps k after which the AIC criterion 
+increases \code{ntimes} in a row, where \code{ntimes} can be specified by the user (see below). 
+Under this definition, the AIC selection event is characterizable as a polyhedral set, and hence
+the extra conditioning can be taken into account exactly. Also note that an analogous BIC scheme
+can be specified through the \code{mult} argument (see below)
+}
+\item{gridrange}{
+Grid range for constructing confidence intervals, on the standardized scale
+}
+\item{bits}{
+Number of bits to be used for p-value and confidence interval calculations. Default is 
+NULL, in which case standard floating point calculations are performed. When not NULL, 
+multiple precision floating point calculations are performed with the specified number 
+of bits, using the R package \code{Rmpfr} (if this package is not installed, then a 
+warning is thrown, and standard floating point calculations are pursued).
+Note: standard double precision uses 53 bits
+so, e.g., a choice of 200 bits uses about 4 times double precision. The confidence
+interval computation is sometimes numerically challenging, and the extra precision can be 
+helpful (though computationally more costly). In particular, extra precision might be tried 
+if the values in the output columns of \code{tailarea} differ noticeably from alpha/2.
+}
+\item{mult}{Multiplier for the AIC-style penalty. Hence a value of 2 (default) 
+gives AIC, whereas a value of log(n) would give BIC}
+\item{ntimes}{Number of steps for which AIC-style criterion has to increase before 
+minimizing point is declared}
+\item{verbose}{Print out progress along the way? Default is FALSE}
+}
+
+\details{
+This function computes selective p-values and confidence intervals (selection intervals)
+for least angle regression. The default is to report the results for 
+each predictor after its entry into the model. See the "type" argument for other options.
+The confidence interval construction involves numerical search and can be fragile:
+if the observed statistic is too close to either end of the truncation interval
+(vlo and vup, see references), then one or possibly both endpoints of the interval of 
+desired coverage cannot be computed, and default to +/- Inf. The output \code{tailarea} 
+gives the achieved Gaussian tail areas for the reported intervals---these should be close 
+to alpha/2, and can be used for error-checking purposes.
+}
+
+\value{
+
+\item{type}{Type of analysis (active, all, or aic)}
+\item{k}{Value of k specified in call}
+\item{khat}{When type is "active", this is an estimated stopping point
+declared by \code{\link{forwardStop}}; when type is "aic", this is the
+value chosen by the modified AIC scheme}
+\item{pv}{P-values for active variables}
+\item{ci}{Confidence intervals}
+\item{tailarea}{Realized tail areas (lower and upper) for each confidence interval}
+\item{vlo}{Lower truncation limits for statistics}
+\item{vup}{Upper truncation limits for statistics}
+\item{vmat}{Linear contrasts that define the observed statistics}
+\item{y}{Vector of outcomes}
+\item{pv.spacing}{P-values from the spacing test (here M+ is used)}
+\item{pv.modspac}{P-values from the modified form of the spacing test 
+(here M+ is replaced by the next knot)}
+\item{pv.covtest}{P-values from covariance test}
+\item{vars}{Variables in active set}
+\item{sign}{Signs of active coefficients}
+\item{alpha}{Desired coverage (alpha/2 in each tail)}
+\item{sigma}{Value of error standard deviation (sigma) used}
+\item{call}{The call to larInf}
+}
+
+\references{
+Ryan Tibshirani, Jonathan Taylor, Richard Lockhart, and Rob Tibshirani (2014).
+Exact post-selection inference for sequential regression procedures. arXiv:1401.3889.
+}
+
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\seealso{
+ \code{\link{lar}}
+}
+
+\examples{
+set.seed(43)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run LAR
+larfit = lar(x,y)
+
+# compute sequential p-values and confidence intervals
+# (sigma estimated from full model)
+out.seq = larInf(larfit)
+out.seq
+
+# compute p-values and confidence intervals after AIC stopping
+out.aic = larInf(larfit,type="aic")
+out.aic
+
+# compute p-values and confidence intervals after 5 fixed steps
+out.fix = larInf(larfit,type="all",k=5)
+out.fix
+}
+
diff --git a/selectiveInference-currentCRAN/man/manyMeans.Rd b/selectiveInference-currentCRAN/man/manyMeans.Rd
new file mode 100644
index 0000000..57fc429
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/manyMeans.Rd
@@ -0,0 +1,60 @@
+\name{manyMeans}
+\alias{manyMeans}
+\title{
+Selective inference for many normal means
+}
+\description{
+Computes p-values and confidence intervals for the largest k
+among many normal means
+}
+\usage{
+manyMeans(y, alpha=0.1, bh.q=NULL, k=NULL, sigma=1, verbose=FALSE)
+}
+
+\arguments{
+  \item{y}{Vector of outcomes (length n)}
+\item{alpha}{
+Significance level for confidence intervals (target is miscoverage alpha/2 in each tail)
+}
+\item{bh.q}{q parameter for BH(q) procedure}
+\item{k}{Number of means to consider}
+\item{sigma}{Estimate of error standard deviation}
+\item{verbose}{Print out progress along the way? Default is FALSE}
+}
+\details{
+This function compute p-values and confidence intervals for the largest k 
+among many normal means. One can specify a fixed number of means k to consider, 
+or choose the number to consider via the BH rule.
+}
+  
+\value{  
+\item{mu.hat}{ Vector of length n containing the estimated signal sizes.
+If a sample element is not selected, then its signal size estimate is 0}
+\item{selected.set}{Indices of the  vector y of the sample elements that 
+were selected by the procedure (either BH(q) or top-K). Labelled "Selind" in output table.}
+\item{pv}{P-values for selected signals}
+\item{ci}{Confidence intervals}
+\item{method}{Method used to choose number of means}
+\item{sigma}{Value of error standard deviation (sigma) used}
+\item{bh.q}{BH q-value used}
+\item{k}{Desired number of means}
+\item{threshold}{Computed cutoff}
+\item{call}{The call to manyMeans}
+}
+
+\references{
+Stephen Reid, Jonathan Taylor, and Rob Tibshirani (2014).
+Post-selection point and interval estimation of signal sizes in Gaussian samples.
+arXiv:1405.3340.
+}
+
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\examples{
+set.seed(12345)
+n = 100 
+mu = c(rep(3,floor(n/5)), rep(0,n-floor(n/5))) 
+y = mu + rnorm(n)
+out = manyMeans(y, bh.q=0.1)
+out
+}
diff --git a/selectiveInference-currentCRAN/man/plot.fs.Rd b/selectiveInference-currentCRAN/man/plot.fs.Rd
new file mode 100644
index 0000000..4f77013
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/plot.fs.Rd
@@ -0,0 +1,42 @@
+\name{plot.fs}
+\alias{plot.fs}
+
+\title{
+Plot function for forward stepwise regression
+}
+\description{
+Plot coefficient profiles along the forward stepwise  path
+}
+
+\usage{
+\method{plot}{fs} (x, breaks=TRUE, omit.zeros=TRUE, var.labels=TRUE, ...)
+}
+
+\arguments{
+\item{x}{
+Object returned by a call to \code{fs} function
+}
+\item{breaks}{Should vertical lines be drawn at each break point in the piecewise
+linear coefficient paths? Default is TRUE}
+\item{omit.zeros}{Should segments of the coefficients paths that are equal to 
+zero be omitted (to avoid clutter in the figure)? Default is TRUE}
+\item{var.labels}{Should paths be labelled with corresponding variable numbers? 
+Default is TRUE}
+\item{...}{Additional arguments for plotting}
+}
+
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\examples{
+set.seed(33)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run forward stepwise, plot results
+fsfit = fs(x,y)
+plot(fsfit)
+}
diff --git a/selectiveInference-currentCRAN/man/plot.lar.Rd b/selectiveInference-currentCRAN/man/plot.lar.Rd
new file mode 100644
index 0000000..baa1195
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/plot.lar.Rd
@@ -0,0 +1,46 @@
+\name{plot.lar}
+\alias{plot.lar}
+
+\title{
+Plot function for least angle regression
+}
+\description{
+Plot coefficient profiles along the LAR path
+}
+
+\usage{
+\method{plot}{lar}(x, xvar=c("norm","step","lambda"), breaks=TRUE, 
+                   omit.zeros=TRUE, var.labels=TRUE, ...)
+}
+
+\arguments{
+\item{x}{
+Object returned by a call to \code{lar} function
+(not the \code{lars} function!)
+}
+\item{xvar}{Either "norm" or "step" or "lambda", determining what is plotted 
+on the x-axis}
+\item{breaks}{Should vertical lines be drawn at each break point in the piecewise
+linear coefficient paths? Default is TRUE}
+\item{omit.zeros}{Should segments of the coefficients paths that are equal to 
+zero be omitted (to avoid clutter in the figure)? Default is TRUE}
+\item{var.labels}{Should paths be labelled with corresponding variable numbers? 
+Default is TRUE}
+\item{...}{Additional arguments for plotting}
+}
+
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\examples{
+set.seed(43)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run LAR, plot results
+larfit = lar(x,y)
+plot(larfit)
+}
diff --git a/selectiveInference-currentCRAN/man/predict.fs.Rd b/selectiveInference-currentCRAN/man/predict.fs.Rd
new file mode 100644
index 0000000..5e50482
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/predict.fs.Rd
@@ -0,0 +1,49 @@
+\name{predict.fs}
+\alias{predict.fs}
+\alias{coef.fs}
+
+\title{
+Prediction and coefficient functions for forward stepwise
+regression
+}
+\description{
+Make predictions or extract coefficients from a forward stepwise object
+}
+\usage{
+\method{predict}{fs}(object, newx, s, ...)
+\method{coef}{fs}(object, s, ...)
+}
+
+\arguments{
+\item{object}{
+Object returned by a call to \code{fs} function
+}
+\item{newx}{
+Matrix of x values at which the predictions are desired. If NULL,
+the x values from forward stepwise fitting are used
+}
+\item{s}{
+Step number(s) at which predictions or coefficients are desired
+}
+\item{\dots}{Additional arguments}
+}
+  
+\value{
+Either a vector/matrix of predictions, or a vector/matrix of coefficients.
+}
+
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\examples{
+set.seed(33)
+n = 200
+p = 20
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(rep(3,10),rep(0,p-10))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run forward stepwise and predict functions
+obj = fs(x,y)
+fit = predict(obj,x,s=3)
+}
diff --git a/selectiveInference-currentCRAN/man/predict.groupfs.Rd b/selectiveInference-currentCRAN/man/predict.groupfs.Rd
new file mode 100644
index 0000000..4a382c7
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/predict.groupfs.Rd
@@ -0,0 +1,24 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/funs.groupfs.R
+\name{predict.groupfs}
+\alias{predict.groupfs}
+\title{Prediction and coefficient functions for \code{\link{groupfs}}.
+
+Make predictions or extract coefficients from a groupfs forward stepwise object.}
+\usage{
+\method{predict}{groupfs}(object, newx)
+}
+\arguments{
+\item{object}{Object returned by a call to \code{\link{groupfs}}.}
+
+\item{newx}{Matrix of x values at which the predictions are desired. If NULL, the x values from groupfs fitting are used.}
+}
+\value{
+A vector of predictions or a vector of coefficients.
+}
+\description{
+Prediction and coefficient functions for \code{\link{groupfs}}.
+
+Make predictions or extract coefficients from a groupfs forward stepwise object.
+}
+
diff --git a/selectiveInference-currentCRAN/man/predict.lar.Rd b/selectiveInference-currentCRAN/man/predict.lar.Rd
new file mode 100644
index 0000000..c91bed3
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/predict.lar.Rd
@@ -0,0 +1,52 @@
+\name{predict.lar}
+\alias{predict.lar}
+\alias{coef.lar}
+
+\title{
+Prediction and coefficient functions for least angle regression
+}
+\description{
+Make predictions or extract coefficients from a least angle regression object
+}
+\usage{
+\method{predict}{lar}(object, newx, s, mode=c("step","lambda"), ...)
+\method{coef}{lar}(object, s, mode=c("step","lambda"), ...)
+}
+
+\arguments{
+\item{object}{
+Object returned by a call to \code{lar} function
+(not the \code{lars} function!)
+}
+\item{newx}{
+Matrix of x values at which the predictions are desired. If NULL,
+the x values from least angle regression fitting are used
+}
+\item{s}{
+Step number(s) or lambda value(s) at which predictions or coefficients 
+are desired
+}
+\item{mode}{Either "step" or "lambda", determining the role of s (above)}
+
+\item{\dots}{Additional arguments}
+}
+
+\value{
+Either a vector/matrix of predictions, or a vector/matrix of coefficients.
+}
+
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+
+\examples{
+set.seed(33)
+n = 200
+p = 20
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(rep(3,10),rep(0,p-10))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run lar and predict functions
+obj = lar(x,y)
+fit = predict(obj,x,s=3)
+}
diff --git a/selectiveInference-currentCRAN/man/scaleGroups.Rd b/selectiveInference-currentCRAN/man/scaleGroups.Rd
new file mode 100644
index 0000000..e5a93fa
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/scaleGroups.Rd
@@ -0,0 +1,28 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/funs.groupfs.R
+\name{scaleGroups}
+\alias{scaleGroups}
+\title{Center and scale design matrix by groups}
+\usage{
+scaleGroups(x, index, center = TRUE, normalize = TRUE)
+}
+\arguments{
+\item{x}{Design matrix.}
+
+\item{index}{Group membership indicator of length p.}
+
+\item{center}{Center groups, default is TRUE.}
+
+\item{normalize}{Scale groups by Frobenius norm, default is TRUE.}
+}
+\value{
+\describe{
+  \item{x}{Optionally centered/scaled design matrix.}
+  \item{xm}{Means of groups in original design matrix.}
+  \item{xs}{Frobenius norms of groups in original design matrix.}
+}
+}
+\description{
+For internal use by \code{\link{groupfs}}.
+}
+
diff --git a/selectiveInference-currentCRAN/man/selectiveInference-internal.Rd b/selectiveInference-currentCRAN/man/selectiveInference-internal.Rd
new file mode 100644
index 0000000..4808173
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/selectiveInference-internal.Rd
@@ -0,0 +1,24 @@
+\name{selectiveInference-internal}
+\title{Internal PMA functions}
+\alias{print.fixedLassoInf}
+  \alias{print.fs} 
+\alias{print.fsInf} 
+\alias{print.larInf}
+\alias{print.lar}
+  \alias{print.manyMeans}
+
+
+
+\description{Internal selectiveInference functions}
+\usage{
+\method{print}{fs}(x, ...)
+\method{print}{fsInf}(x, tailarea = TRUE, ...)
+\method{print}{lar}(x,...)
+\method{print}{larInf}(x, tailarea = TRUE, ...)
+\method{print}{fixedLassoInf}(x, tailarea = TRUE, ...)
+\method{print}{manyMeans}(x,...)
+
+}
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid}
+\keyword{internal}
+
diff --git a/selectiveInference-currentCRAN/man/selectiveInference.Rd b/selectiveInference-currentCRAN/man/selectiveInference.Rd
new file mode 100644
index 0000000..6e038d4
--- /dev/null
+++ b/selectiveInference-currentCRAN/man/selectiveInference.Rd
@@ -0,0 +1,205 @@
+\name{selectiveInference}
+\alias{selectiveInference}
+\docType{package}
+\title{
+Tools for selective inference
+}
+\description{
+Functions to perform post-selection inference for forward
+stepwise regression, least angle regression, the lasso and the 
+many normal means problem. The lasso function also supports logistic regression and the Cox model.
+}
+\details{
+\tabular{ll}{
+Package: \tab selectiveInference\cr
+Type: \tab Package\cr
+License: \tab GPL-2\cr
+}
+
+This package provides tools for inference after selection, in forward stepwise 
+regression, least angle regression, the lasso, and the many normal means problem. 
+The functions compute p-values and selection intervals that properly account for 
+the inherent selection carried out by the procedure. These have exact finite sample 
+type I error and coverage under Gaussian errors. For the logistic and Cox familes (fixedLassoInf),
+ the coverage is asymptotically valid
+
+This R package was developed as part of the selective inference software project
+in Python and R:
+
+\url{https://github.com/selective-inference}
+
+Some of the R code in this work is a modification of Python code from this
+repository. Here is the current selective inference software team:
+
+Yuval Benjamini, 
+Leonard Blier, 
+Will Fithian, 
+Jason Lee, 
+Joshua Loftus,
+Joshua Loftus, Stephen Reid,
+Dennis Sun,
+Yuekai Sun,
+Jonathan Taylor,
+Xiaoying Tian,
+Ryan Tibshirani,
+Rob Tibshirani
+
+The main functions included in the package are:
+\code{\link{fs}},
+\code{\link{fsInf}},
+\code{\link{lar}},
+\code{\link{larInf}},
+\code{\link{fixedLassoInf}},
+\code{\link{manyMeans}}
+}
+
+\author{Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid
+
+Maintainer: Rob Tibshirani <tibs@stanford.edu>
+}
+
+\references{
+Ryan Tibshirani, Jonathan Taylor, Richard Lockhart, and Rob Tibshirani (2014).
+Exact post-selection inference for sequential regression procedures. arXiv:1401.3889.
+
+Jason Lee, Dennis Sun, Yuekai Sun, and Jonathan Taylor (2013). 
+Exact post-selection inference, with application to the lasso. arXiv:1311.6238.
+
+Stephen Reid, Jonathan Taylor, and Rob Tibshirani (2014).
+Post-selection point and interval estimation of signal sizes in Gaussian samples.
+arXiv:1405.3340.
+
+
+Jonathan Taylor and Robert Tibshirani (2016) Post-selection inference for L1-penalized likelihood models.
+arXiv:1602.07358
+
+}
+
+\examples{
+set.seed(33)
+n = 50
+p = 10
+sigma = 1
+x = matrix(rnorm(n*p),n,p)
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+# run forward stepwise
+fsfit = fs(x,y)
+
+# compute sequential p-values and confidence intervals
+# (sigma estimated from full model)
+out.seq = fsInf(fsfit)
+out.seq
+
+# compute p-values and confidence intervals after AIC stopping
+out.aic = fsInf(fsfit,type="aic")
+out.aic
+
+# compute p-values and confidence intervals after 5 fixed steps
+out.fix = fsInf(fsfit,type="all",k=5)
+out.fix
+
+## NOT RUN---lasso at fixed lambda- Gaussian family
+## first run glmnet
+# gfit = glmnet(x,y)
+
+## extract coef for a given lambda; note the 1/n factor!
+## (and we don't save the intercept term)
+# lambda = .1
+# beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
+
+## compute fixed lambda p-values and selection intervals
+# out = fixedLassoInf(x,y,beta,lambda,sigma=sigma)
+# out
+
+
+#lasso at fixed lambda- logistic family
+#set.seed(43)
+  #   n = 50
+  #   p = 10
+  #   sigma = 1
+     
+ #    x = matrix(rnorm(n*p),n,p)
+     x=scale(x,TRUE,TRUE)
+  #   
+#     beta = c(3,2,rep(0,p-2))
+ #    y = x%*%beta + sigma*rnorm(n)
+ #    y=1*(y>mean(y))
+     # first run glmnet
+ #    gfit = glmnet(x,y,standardize=FALSE,family="binomial")
+     
+     # extract coef for a given lambda; note the 1/n factor!
+     # (and here  we DO  include the intercept term)
+ #    lambda = .8
+ #    beta = coef(gfit, s=lambda/n, exact=TRUE)
+     
+ #    # compute fixed lambda p-values and selection intervals
+ #    out = fixedLassoInf(x,y,beta,lambda,family="binomial")
+ #    out
+
+##lasso at fixed lambda- Cox family
+#set.seed(43)
+#     n = 50
+ #    p = 10
+ #    sigma = 1
+     
+ #    x = matrix(rnorm(n*p),n,p)
+  #   x=scale(x,TRUE,TRUE)
+     
+ #    beta = c(3,2,rep(0,p-2))
+ #    tim = as.vector(x\%*\%beta + sigma*rnorm(n))
+  #   tim= tim-min(tim)+1
+#status=sample(c(0,1),size=n,replace=T)
+     # first run glmnet
+   #  gfit = glmnet(x,Surv(tim,status),standardize=FALSE,family="cox")
+     # extract coef for a given lambda; note the 1/n factor!
+   
+  #   lambda = 1.5
+  #   beta = as.numeric(coef(gfit, s=lambda/n, exact=TRUE))
+     
+     # compute fixed lambda p-values and selection intervals
+   #  out = fixedLassoInf(x,tim,beta,lambda,status=status,family="cox")
+   #  out
+## NOT RUN---many normal means
+# set.seed(12345)
+# n = 100 
+# mu = c(rep(3,floor(n/5)), rep(0,n-floor(n/5))) 
+# y = mu + rnorm(n)
+# out = manyMeans(y, bh.q=0.1)
+# out
+
+## NOT RUN---forward stepwise with groups
+# set.seed(1)
+# n = 20
+# p = 40
+# x = matrix(rnorm(n*p), nrow=n)
+# index = sort(rep(1:(p/2), 2))
+# y = rnorm(n) + 2 * x[,1] - x[,4]
+# fit = groupfs(x, y, index, maxsteps = 5)
+# out = groupfsInf(fit)
+# out
+
+## NOT RUN---estimation of sigma for use in fsInf
+## (or larInf or fixedLassoInf)
+# set.seed(33)
+# n = 50
+# p = 10
+# sigma = 1
+# x = matrix(rnorm(n*p),n,p)
+# beta = c(3,2,rep(0,p-2))
+# y = x\%*\%beta + sigma*rnorm(n)
+
+## run forward stepwise
+# fsfit = fs(x,y)
+
+## estimate sigma
+# sigmahat = estimateSigma(x,y)$sigmahat
+
+## run sequential inference with estimated sigma
+# out = fsInf(fit,sigma=sigmahat)
+# out
+}
+\keyword{ package }
+
+
diff --git a/selectiveInference-currentCRAN/src/matrixcomps.c b/selectiveInference-currentCRAN/src/matrixcomps.c
new file mode 100644
index 0000000..4a516a1
--- /dev/null
+++ b/selectiveInference-currentCRAN/src/matrixcomps.c
@@ -0,0 +1,266 @@
+#include <R.h>
+#include <Rmath.h>
+
+// Matrices are stored as vectors, in column-major order
+
+// Givens rotation of a and b, stored in c and s
+void givens(double a, double b, double *c, double *s) {
+  if (b==0) {
+    *c = 1; 
+    *s = 0; 
+  }
+  else {
+    if (fabs(b)>fabs(a)) {
+      double t = -a/b;
+      *s = 1/sqrt(1+t*t);
+      *c = (*s)*t;
+    }
+    else {
+      double t = -b/a;
+      *c = 1/sqrt(1+t*t);
+      *s = (*c)*t;
+    }
+  }
+}
+
+// Givens rotation applied to rows i1 and i2 of the m x n
+// matrix A, on the subset of columns j1 through j2
+void rowrot(double *A, int i1, int i2, int m, int n, int j1, int j2, double c, double s) {
+  int j;
+  double t1,t2;
+  for (j=j1; j<=j2; j++) {
+    t1 = A[i1+j*m]; 
+    t2 = A[i2+j*m];
+    A[i1+j*m] = c*t1-s*t2;
+    A[i2+j*m] = s*t1+c*t2;
+  }
+}
+
+// Givens rotation applied to columns j1 and j2 of the m x n
+// matrix A, on the subset of rows i1 through i2
+void colrot(double *A, int j1, int j2, int m, int n, int i1, int i2, double c, double s) {
+  int i;
+  double t1,t2;
+  for (i=i1; i<=i2; i++) {
+    t1 = A[i+j1*m];
+    t2 = A[i+j2*m];
+    A[i+j1*m] = c*t1-s*t2;
+    A[i+j2*m] = s*t1+c*t2;
+  }
+}
+
+// Downdate the QR factorization after deleting column j0, 
+// where Q1 is m x n and R is n x n. The other part of 
+// the Q matrix, Q2 m x (m-n), isn't needed so it isn't 
+// passed for efficiency
+void downdate1(double *Q1, double *R, int *j0p, int *mp, int *np) {
+  int j0,m,n,j;
+  j0 = *j0p;
+  m = *mp;
+  n = *np;
+
+  double c,s;
+  for (j=j0+1; j<n; j++) {
+    // Compute the appropriate c and s
+    givens(R[j-1+j*n],R[j+j*n],&c,&s);
+
+    // Pre-multiply R
+    rowrot(R,j-1,j,n,n,j,n-1,c,s);
+
+    // Post-mutiply Q1
+    colrot(Q1,j-1,j,m,n,0,m-1,c,s);
+  }
+}
+
+// Update the QR factorization after adding a column z.
+// For convenience, we are given w=Q2'z. Here Q2 is m x 
+// (m-n). The other part of the Q matrix, Q1 m x n, and 
+// R are not needed, so they aren't passed for efficiency 
+void update1(double *Q2, double *w, int *mp, int *kp) {
+  int m,k,j;
+  m = *mp;
+  k = *kp;
+
+  double c,s;
+  for (j=k-1; j>=1; j--) {
+    // Compute the appropriate c and s
+    givens(w[j-1],w[j],&c,&s);
+
+    // Pre-multiply w
+    rowrot(w,j-1,j,k,1,0,0,c,s);
+
+    // Post-multiply Q2
+    colrot(Q2,j-1,j,m,k,0,m-1,c,s);
+  }
+}
+
+// Downdate the QR factorization after deleting the first row, 
+// where Q is m x m and R is m x n
+void downdate2(double *Q, double *R, int *mp, int *np) {
+  int m,n,i;
+  m = *mp;
+  n = *np;
+
+  double c,s;
+  for (i=m-1; i>=1; i--) {
+    // Compute the appropriate c and s
+    givens(Q[(i-1)*m],Q[i*m],&c,&s);
+
+    // Post-mutiply Q
+    colrot(Q,i-1,i,m,m,0,m-1,c,s);
+
+    // Pre-multiply R
+    if (i<=n) rowrot(R,i-1,i,m,n,i-1,n-1,c,s); 
+  }
+}
+
+// Update the QR factorization after adding the last row,
+// where Q is m x m and R is m x n. For efficiency, Q is not
+// passed, and only the first row of R is passed. Not counting
+// its first row, the first q columns of R are zero
+void update2(double *y, double *D, double *r, int *mp, int *np, int *qp) {
+  int m,n,q,j;
+  m = *mp;
+  n = *np;
+  q = *qp;
+
+  double c,s;
+  for (j=0; j<q-1; j++) {
+    // Compute the appropriate c and s
+    givens(r[j+1],r[j],&c,&s);
+
+    // Pre-multiply r
+    rowrot(r,j+1,j,n,1,0,0,c,s);
+
+    // Post-mutiply D
+    colrot(D,j+1,j,m,n,0,m-1,c,s);
+
+    // Pre-multiply y
+    rowrot(y,j+1,j,n,1,0,0,c,s);
+  }
+}
+
+// Make the R factor upper triangular, by Givens rotating its
+// columns. Here A is m x n, and R is n x n with rank(R) = n-k
+void maketri1(double *y, double *A, double *R, int *mp, int *np, int *kp) {
+  int m,n,k,i,j;
+  m = *mp;
+  n = *np;
+  k = *kp; 
+  
+  double c,s;
+  for (i=n-k-1; i>=0; i--) {
+    for (j=i; j<i+k; j++) {
+      // Compute the appropriate c and s
+      givens(R[i+(j+1)*n],R[i+j*n],&c,&s);
+      
+      // Post-multiply R
+      colrot(R,j+1,j,n,n,0,i,c,s);
+
+      // Post-multiply D
+      colrot(A,j+1,j,m,n,0,m-1,c,s);
+    
+      // Pre-multiply y
+      rowrot(y,j+1,j,n,1,0,0,c,s);
+    }
+  }
+}
+
+// Make the R factor upper triangular, by Givens rotating its 
+// columns. Here A is m x n, and R is m x n with rank(R) = min(m,n)-k
+void maketri2(double *y, double *A, double *R, int *mp, int *np, int *kp) {
+  int m,n,k,r,d,i,j;
+  m = *mp;
+  n = *np;
+  k = *kp;
+ 
+  r = imin2(m,n);
+  d = imax2(n-m,0);
+
+  double c,s;
+  for (i=r-k-1; i>=0; i--) {
+    for (j=i; j<i+k+d; j++) {
+      // Compute the appropriate c and s
+      givens(R[i+(j+1)*m],R[i+j*m],&c,&s);
+      
+      // Post-multiply R
+      colrot(R,j+1,j,m,n,0,i,c,s);
+
+      // Post-multiply D
+      colrot(A,j+1,j,m,n,0,m-1,c,s);
+    
+      // Pre-multiply y
+      rowrot(y,j+1,j,n,1,0,0,c,s);
+    }
+  }
+}
+
+// Make the R factor upper triangular, by Givens rotating
+// its columns. Here A is m1 x n, and R is m2 x n with 
+// rank(R) = n-q-1. The first q columns of R are zero
+void maketri3(double *y, double *A, double *R, int *m1p, int *m2p, int *np, int *qp) {
+  int m1,m2,n,q,i;
+  m1 = *m1p;
+  m2 = *m2p;
+  n = *np;
+  q = *qp;
+  
+  double c,s;
+  for (i=n-q-2; i>=0; i--) {
+    // Compute the appropriate c and s
+    givens(R[i+(i+q+1)*m2],R[i+(i+q)*m2],&c,&s);
+      
+    // Post-multiply R
+    colrot(R,i+q+1,i+q,m2,n,0,i,c,s);
+
+    // Post-multiply D
+    colrot(A,i+q+1,i+q,m1,n,0,m1-1,c,s);
+    
+    // Pre-multiply y
+    rowrot(y,i+q+1,i+q,n,1,0,0,c,s);
+  }
+}
+
+// Make the R factor upper triangular, by Givens rotating
+// its columns and rows, appropriately. Here A is m1 x n, 
+// Q is m2 x m2, and R is m2 x n with rank(R) = n-q-1. The 
+// first q columns of R are zero. The kth row of R is the 
+// last row with a zero element on the diagonal    
+void maketri4(double *y, double *A, double *Q, double *R, int *m1p, int *m2p, int *np, int *qp, int *kp) {
+  int m1,m2,n,q,k,i,j;
+  m1 = *m1p;
+  m2 = *m2p;
+  n = *np; 
+  q = *qp;
+  k = *kp;
+
+  double c,s;
+
+  // First rotate the columns
+  for (i=k-1; i>=0; i--) {
+    // Compute the appropriate c and s
+    givens(R[i+(i+q+1)*m2],R[i+(i+q)*m2],&c,&s);
+      
+    // Post-multiply R
+    colrot(R,i+q+1,i+q,m2,n,0,i,c,s);
+
+    // Post-multiply D
+    colrot(A,i+q+1,i+q,m1,n,0,m1-1,c,s);
+    
+    // Pre-multiply y
+    rowrot(y,i+q+1,i+q,n,1,0,0,c,s);
+  }
+
+  // Next rotate the rows
+  for (j=k+q+1; j<n; j++) {
+    // Compute the appropriate c and s
+    givens(R[j-q-1+j*m2],R[j-q+j*m2],&c,&s);
+
+    // Pre-multiply R
+    rowrot(R,j-q-1,j-q,m2,n,j,n-1,c,s);
+
+    // Post-multiply Q
+    colrot(Q,j-q-1,j-q,m2,m2,0,m2-1,c,s);
+  }
+}
+
diff --git a/selectiveInference-currentCRAN/src/selinf_init.c b/selectiveInference-currentCRAN/src/selinf_init.c
new file mode 100644
index 0000000..3882861
--- /dev/null
+++ b/selectiveInference-currentCRAN/src/selinf_init.c
@@ -0,0 +1,24 @@
+#include <R.h>
+#include <Rinternals.h>
+
+/* . entry points */
+extern void update1(double *Q2, double *w, int *mp, int *kp);
+static R_NativePrimitiveArgType update1_t[] = {
+  REALSXP, REALSXP, INTSXP, INTSXP
+};
+
+extern void downdate1(double *Q1, double *R, int *j0p, int *mp, int *np);
+static R_NativePrimitiveArgType downdate1_t[] = {
+  REALSXP, REALSXP, INTSXP, INTSXP, INTSXP
+};
+
+static const R_CMethodDef CEntries[] = {
+  {"update1", (DL_FUNC) &update1, 4},
+  {"downdate1", (DL_FUNC) &downdate1, 5},
+  {NULL, NULL, 0}
+};
+
+void R_init_cubature(DllInfo *dll) {
+  R_registerRoutines(dll, CEntries, NULL, NULL, NULL);
+  R_useDynamicSymbols(dll, FALSE);
+}
diff --git a/selectiveInference-currentCRAN/src/truncnorm.c b/selectiveInference-currentCRAN/src/truncnorm.c
new file mode 100644
index 0000000..cca61d9
--- /dev/null
+++ b/selectiveInference-currentCRAN/src/truncnorm.c
@@ -0,0 +1,188 @@
+#include <R.h>
+#include <Rmath.h>
+
+// Take a Gibbs hit and run step along a given direction
+
+// Assumes the covariance is identity
+
+void gibbs_step(double *state,     /* state has law N(0,I) constrained to polyhedral set \{y:Ay \leq b\}*/ 
+		double *direction, /* direction we will take Gibbs step */
+		double *U,         /* A %*% state - b */
+		double *alpha,     /* A %*% direction */
+		int nconstraint,   /* number of rows of A */
+		int nstate)        /* dimension of state */
+{
+               
+  int istate;
+  double value = 0;
+
+  /* Compute V=\eta^Ty */
+
+  for (istate = 0; istate < nstate; istate++) {
+    value += direction[istate] * state[istate];
+  }
+
+  /* Compute upper and lower bounds */
+
+  double lower_bound = -1e12;
+  double upper_bound = 1e12;
+  double bound_val = 0;
+  double tol=1.e-7;
+  int iconstraint;
+
+  for (iconstraint = 0; iconstraint < nconstraint; iconstraint++) {
+
+    bound_val = -U[iconstraint] / alpha[iconstraint] + value;
+
+    if ((alpha[iconstraint] > tol) &&
+	(bound_val < upper_bound)) {
+	upper_bound = bound_val;
+    }
+    else if ((alpha[iconstraint] < -tol) &&
+	  (bound_val > lower_bound)) {
+	lower_bound = bound_val;
+    }
+
+  }
+
+  /* Ensure constraints are satisfied */
+
+  if (lower_bound > value) {
+    lower_bound = value - tol;
+  }
+  else if (upper_bound < value) {
+    upper_bound = value + tol;
+  }
+
+  /* Check to see if constraints are satisfied */
+
+  /* if (lower_bound > upper_bound) {
+    
+     }*/
+
+  /* Now, take a step */
+
+  double tnorm; /* the 1D gaussian variable */
+  double cdfU, cdfL, unif; /* temp variables */
+
+  if (upper_bound < -10) {
+
+    /* use Exp approximation */
+    /* the approximation is that */
+    /* Z | lower_bound < Z < upper_bound */
+    /* is fabs(upper_bound) * (upper_bound - Z) = E approx Exp(1) */
+    /* so Z = upper_bound - E / fabs(upper_bound) */
+    /* and the truncation of the exponential is */
+    /* E < fabs(upper_bound - lower_bound) * fabs(upper_bound) = D */
+    
+    /* this has distribution function (1 - exp(-x)) / (1 - exp(-D)) */
+    /* so to draw from this distribution */
+    /* we set E = - log(1 - U * (1 - exp(-D))) where U is Unif(0,1) */
+    /* and Z (= tnorm below) is as stated */
+
+    unif = runif(0., 1.) * (1 - exp(-fabs((lower_bound - upper_bound) * upper_bound)));
+    tnorm = (upper_bound + log(1 - unif) / fabs(upper_bound));
+  }
+  else if (lower_bound > 10) {
+
+    /* here Z = lower_bound + E / fabs(lower_bound) (though lower_bound is positive) */
+    /* and D = fabs((upper_bound - lower_bound) * lower_bound) */
+
+    unif = runif(0., 1.) * (1 - exp(-fabs((upper_bound - lower_bound) * lower_bound)));
+    tnorm = (lower_bound - log(1 - unif) / lower_bound);
+  }
+  else if (lower_bound < 0) {
+    cdfL = pnorm(lower_bound, 0., 1., 1, 0); 
+    cdfU = pnorm(upper_bound, 0., 1., 1, 0); 
+    unif = runif(0., 1.) * (cdfU - cdfL) + cdfL; 
+    if (unif < 0.5) {
+      tnorm = qnorm(unif, 0., 1., 1, 0); 
+    }
+    else {
+      tnorm = -qnorm(1-unif, 0., 1., 1, 0); 
+    }
+  }
+  else {
+    cdfL = pnorm(-lower_bound, 0., 1., 1, 0); 
+    cdfU = pnorm(-upper_bound, 0., 1., 1, 0); 
+    unif = runif(0., 1.) * (cdfL - cdfU) + cdfU;
+    if (unif < 0.5) {
+      tnorm = -qnorm(unif, 0., 1., 1, 0); 
+    }
+    else {
+      tnorm = qnorm(1-unif, 0., 1., 1, 0);
+    }
+  }
+
+  /* Now update the state and U */
+
+  double delta = tnorm - value;
+
+  for (istate = 0; istate < nstate; istate++) {
+    state[istate] += delta * direction[istate];
+  }
+  for (iconstraint = 0; iconstraint < nconstraint; iconstraint++) {
+    U[iconstraint] += delta * alpha[iconstraint] ;
+  }
+
+  /* End of gibbs_step */
+
+}
+
+void sample_truncnorm_white(double *state,      /* state has law N(0,I) constrained to polyhedral set \{y:Ay \leq b\}*/ 
+			    double *U,          /* A %*% state - b */
+			    double *directions, /* possible steps for sampler to take */
+                                                /* assumed to be stored as list of columns of dimension nstate */
+			                        /* has shape (nstate, ndirection) */
+			    double *alphas,     /* The matrix A %*% directions */
+      			                        /* has shape (nconstraint, ndirection) */
+			    double *output,     /* array in which to store samples */
+                                                /* assumed will stored as list of vectors of dimension nstate */
+                                                /* has shape (nstate, ndraw) */  
+			    int *pnconstraint,  /* number of rows of A */
+			    int *pndirection,   /* the possible number of directions to choose from */
+                                                /* `directions` should have size nstate*ndirection */
+			    int *pnstate,       /* dimension of state */
+			    int *pburnin,       /* number of burnin steps */
+			    int *pndraw)        /* total number of samples to return */
+{
+
+  int iter_count;
+  int which_direction;
+
+  int nconstraint = *pnconstraint;
+  int ndirection = *pndirection;
+  int nstate = *pnstate;
+  int burnin = *pburnin;
+  int ndraw = *pndraw;
+
+  double *direction, *alpha;
+
+  for (iter_count = 0; iter_count < burnin + ndraw; iter_count++) {
+  
+    which_direction = (int) floor(runif(0., 1.) * ndirection); 
+    direction = ((double *) directions) + nstate * which_direction; 
+    alpha = ((double *) alphas) + nconstraint * which_direction; 
+
+    /* take a step, which implicitly updates `state` and `U` */
+
+    gibbs_step(state,
+               direction,
+	       U,
+	       alpha,
+	       nconstraint,
+	       nstate);
+
+    /* Store result if after burnin */
+
+    int istate;
+    if (iter_count >= burnin) {
+      for (istate = 0; istate < nstate; istate++) {
+	*output = state[istate];
+	output++;
+      }
+    }
+  }
+
+}
+

From 0a3bc8d0d73cb47c985b40b0b28ee018a3357c59 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 22 Sep 2017 15:51:14 -0700
Subject: [PATCH 247/493] new versions of RcppExports

---
 selectiveInference/R/RcppExports.R     |  8 ++---
 selectiveInference/src/RcppExports.cpp | 44 ++++++++++++++++----------
 2 files changed, 32 insertions(+), 20 deletions(-)

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
index e04c446..622d1d2 100644
--- a/selectiveInference/R/RcppExports.R
+++ b/selectiveInference/R/RcppExports.R
@@ -1,15 +1,15 @@
-# This file was generated by Rcpp::compileAttributes
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
+    .Call('_selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
 }
 
 update1_ <- function(Q2, w, m, k) {
-    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
+    .Call('_selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
 }
 
 downdate1_ <- function(Q1, R, j0, m, n) {
-    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
+    .Call('_selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
 }
 
diff --git a/selectiveInference/src/RcppExports.cpp b/selectiveInference/src/RcppExports.cpp
index feefe32..461b0e5 100644
--- a/selectiveInference/src/RcppExports.cpp
+++ b/selectiveInference/src/RcppExports.cpp
@@ -1,4 +1,4 @@
-// This file was generated by Rcpp::compileAttributes
+// Generated by using Rcpp::compileAttributes() -> do not edit by hand
 // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 #include <Rcpp.h>
@@ -7,10 +7,10 @@ using namespace Rcpp;
 
 // solve_QP
 Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+RcppExport SEXP _selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Sigma(SigmaSEXP);
     Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
     Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
@@ -22,36 +22,48 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
     Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
     Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    __result = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return rcpp_result_gen;
 END_RCPP
 }
 // update1_
 Rcpp::List update1_(Rcpp::NumericMatrix Q2, Rcpp::NumericVector w, int m, int k);
-RcppExport SEXP selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
+RcppExport SEXP _selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q2(Q2SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericVector >::type w(wSEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type k(kSEXP);
-    __result = Rcpp::wrap(update1_(Q2, w, m, k));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(update1_(Q2, w, m, k));
+    return rcpp_result_gen;
 END_RCPP
 }
 // downdate1_
 Rcpp::List downdate1_(Rcpp::NumericMatrix Q1, Rcpp::NumericMatrix R, int j0, int m, int n);
-RcppExport SEXP selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
+RcppExport SEXP _selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q1(Q1SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type R(RSEXP);
     Rcpp::traits::input_parameter< int >::type j0(j0SEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    __result = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
+    return rcpp_result_gen;
 END_RCPP
 }
+
+static const R_CallMethodDef CallEntries[] = {
+    {"_selectiveInference_solve_QP", (DL_FUNC) &_selectiveInference_solve_QP, 11},
+    {"_selectiveInference_update1_", (DL_FUNC) &_selectiveInference_update1_, 4},
+    {"_selectiveInference_downdate1_", (DL_FUNC) &_selectiveInference_downdate1_, 5},
+    {NULL, NULL, 0}
+};
+
+RcppExport void R_init_selectiveInference(DllInfo *dll) {
+    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
+    R_useDynamicSymbols(dll, FALSE);
+}

From 59e9c7e8050e6152c02bb638faadd43148b6231d Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 25 Sep 2017 17:33:44 -0700
Subject: [PATCH 248/493] fixing examples for new glmnet, small cleanup of
 function signature

---
 selectiveInference/R/funs.fixed.R       |  7 +++--
 selectiveInference/R/funs.fixedCox.R    | 10 ++++---
 selectiveInference/man/fixedLassoInf.Rd | 35 ++++++++++++++-----------
 3 files changed, 31 insertions(+), 21 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index c65731d..6e1ffa9 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -2,10 +2,13 @@
 # for the solution of
 # min 1/2 || y - \beta_0 - X \beta ||_2^2 + \lambda || \beta ||_1
 
-fixedLassoInf <- function(x, y, beta, lambda, family=c("gaussian","binomial","cox"),intercept=TRUE, add.targets=NULL, status=NULL,
+fixedLassoInf <- function(x, y, beta, 
+                          lambda, family=c("gaussian","binomial","cox"),
+                          intercept=TRUE, add.targets=NULL, status=NULL,
                           sigma=NULL, alpha=0.1,
                           type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
-                          gridrange=c(-100,100), bits=NULL, verbose=FALSE, linesearch.try=10) {
+                          gridrange=c(-100,100), bits=NULL, verbose=FALSE, 
+                          linesearch.try=10) {
 
   family = match.arg(family)
   this.call = match.call()
diff --git a/selectiveInference/R/funs.fixedCox.R b/selectiveInference/R/funs.fixedCox.R
index cfe764e..92567bd 100644
--- a/selectiveInference/R/funs.fixedCox.R
+++ b/selectiveInference/R/funs.fixedCox.R
@@ -1,6 +1,10 @@
-fixedCoxLassoInf=function(x,y,status,beta,lambda,alpha=.1, type=c("partial"),tol.beta=1e-5, tol.kkt=0.1,
-                     gridrange=c(-100,100), bits=NULL, verbose=FALSE,this.call=NULL){
-
+fixedCoxLassoInf=function(x, y, status,
+			  beta, lambda,
+                          alpha=.1, type=c("partial"),
+                          tol.beta=1e-5, tol.kkt=0.1,
+                          gridrange=c(-100,100), 
+                          bits=NULL, verbose=FALSE,
+                          this.call=NULL){
 
  checkargs.xy(x,y)
  if(is.null(status)) stop("Must supply `status' argument")
diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index bd740f8..5fd8c24 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -160,7 +160,7 @@ gfit = glmnet(x,y,standardize=FALSE)
 # extract coef for a given lambda; note the 1/n factor!
 # (and we don't save the intercept term)
 lambda = .8
-beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
+beta = coef(gfit, x=x, y=y, s=lambda/n, exact=TRUE)[-1]
 
 # compute fixed lambda p-values and selection intervals
 out = fixedLassoInf(x,y,beta,lambda,sigma=sigma)
@@ -170,8 +170,8 @@ out
 ## as above, but use lar function instead to get initial 
 ## lasso fit (should get same results)
 lfit = lar(x,y,normalize=FALSE)
-beta = coef(lfit,s=lambda,mode="lambda")
-out2 = fixedLassoInf(x,y,beta,lambda,sigma=sigma)
+beta = coef(lfit, s=lambda, mode="lambda")
+out2 = fixedLassoInf(x, y, beta, lambda, sigma=sigma)
 out2
 
 ## mimic different penalty factors by first scaling x
@@ -189,12 +189,12 @@ pf=c(rep(1,7),rep(.1,3))  #define penalty factors
 pf=p*pf/sum(pf)   # penalty factors should be rescaled so they sum to p
 xs=scale(x,FALSE,pf) #scale cols of x by penalty factors
 # first run glmnet
-gfit = glmnet(xs,y,standardize=FALSE)
+gfit = glmnet(xs, y, standardize=FALSE)
 
 # extract coef for a given lambda; note the 1/n factor!
 # (and we don't save the intercept term)
 lambda = .8
-beta_hat = coef(gfit, s=lambda/n, exact=TRUE)[-1]
+beta_hat = coef(gfit, x=xs, y=y, s=lambda/n, exact=TRUE)[-1]
 
 # compute fixed lambda p-values and selection intervals
 out = fixedLassoInf(xs,y,beta_hat,lambda,sigma=sigma)
@@ -221,37 +221,40 @@ set.seed(43)
      # extract coef for a given lambda; note the 1/n factor!
      # (and here  we DO  include the intercept term)
      lambda = .8
-     beta_hat = coef(gfit, s=lambda/n, exact=TRUE)
+     beta_hat = coef(gfit, x=x, y=y, s=lambda/n, exact=TRUE)
      
      # compute fixed lambda p-values and selection intervals
      out = fixedLassoInf(x,y,beta_hat,lambda,family="binomial")
      out
 
-#Cox model
-set.seed(43)
+
+     # Cox model
+     
+     set.seed(43)
      n = 50
      p = 10
      sigma = 1
      
-     x = matrix(rnorm(n*p),n,p)
-     x=scale(x,TRUE,TRUE)
+     x = matrix(rnorm(n*p), n, p)
+     x=scale(x, TRUE, TRUE)
      
      beta = c(3,2,rep(0,p-2))
      tim = as.vector(x\%*\%beta + sigma*rnorm(n))
      tim= tim-min(tim)+1
-status=sample(c(0,1),size=n,replace=TRUE)
+     status=sample(c(0,1),size=n,replace=TRUE)
      # first run glmnet
 
 
-     gfit = glmnet(x,Surv(tim,status),standardize=FALSE,family="cox")
+     y = Surv(tim,status)
+     gfit = glmnet(x, y, standardize=FALSE, family="cox")
    
      # extract coef for a given lambda; note the 1/n factor!
    
      lambda = 1.5
-     beta_hat = as.numeric(coef(gfit, s=lambda/n, exact=TRUE))
-     
+     beta_hat = as.numeric(coef(gfit, x=x, y=y, s=lambda/n, exact=TRUE))
+
      # compute fixed lambda p-values and selection intervals
-     out = fixedLassoInf(x,tim,beta_hat,lambda,status=status,family="cox")
+     out = fixedLassoInf(x, tim, beta_hat, lambda, status=status, family="cox")
      out
 
 # Debiased lasso or "full"
@@ -272,7 +275,7 @@ gfit = glmnet(x, y, standardize=FALSE, intercept=FALSE)
 # extract coef for a given lambda; note the 1/n factor!
 # (and we don't save the intercept term)
 lambda = 2.8
-beta = coef(gfit, s=lambda/n, exact=TRUE)[-1]
+beta = coef(gfit, x=x, y=y, s=lambda/n, exact=TRUE)[-1]
 
 # compute fixed lambda p-values and selection intervals
 out = fixedLassoInf(x, y, beta, lambda, sigma=sigma, type='full', intercept=FALSE)

From 41dc63aade08b6b70c074351fe1f10fea830f340 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 25 Sep 2017 17:34:31 -0700
Subject: [PATCH 249/493] removing a tab

---
 selectiveInference/R/funs.fixedCox.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.fixedCox.R b/selectiveInference/R/funs.fixedCox.R
index 92567bd..d6ebd6b 100644
--- a/selectiveInference/R/funs.fixedCox.R
+++ b/selectiveInference/R/funs.fixedCox.R
@@ -1,5 +1,5 @@
 fixedCoxLassoInf=function(x, y, status,
-			  beta, lambda,
+                          beta, lambda,
                           alpha=.1, type=c("partial"),
                           tol.beta=1e-5, tol.kkt=0.1,
                           gridrange=c(-100,100), 

From 86a92fdaa482355f148930cce04824aa3c7beb83 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 25 Sep 2017 17:56:21 -0700
Subject: [PATCH 250/493] WIP: writing a solver for wide X matrices

---
 selectiveInference/R/RcppExports.R            |   4 +
 selectiveInference/src/Rcpp-debias.cpp        |  72 ++++
 selectiveInference/src/RcppExports.cpp        |  22 ++
 selectiveInference/src/debias.h               |  21 ++
 .../src/quadratic_program_wide.c              | 341 ++++++++++++++++++
 5 files changed, 460 insertions(+)
 create mode 100644 selectiveInference/src/quadratic_program_wide.c

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
index 622d1d2..1c03053 100644
--- a/selectiveInference/R/RcppExports.R
+++ b/selectiveInference/R/RcppExports.R
@@ -5,6 +5,10 @@ solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_a
     .Call('_selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
 }
 
+solve_QP_wide <- function(X, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) {
+    .Call('_selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
+}
+
 update1_ <- function(Q2, w, m, k) {
     .Call('_selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
 }
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index 6420e51..d6e8741 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -68,3 +68,75 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 			    Rcpp::Named("max_active_check") = max_active_check));
 
 }
+
+
+// [[Rcpp::export]]
+Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
+			 double bound,
+			 int maxiter,
+			 Rcpp::NumericVector theta,
+			 Rcpp::NumericVector linear_func,
+			 Rcpp::NumericVector gradient,
+			 Rcpp::IntegerVector ever_active,
+			 Rcpp::IntegerVector nactive,
+			 double kkt_tol,
+			 double objective_tol,
+			 int max_active
+			 ) {
+
+  int nrow = X.nrow(); // number of cases
+  int ncol = X.ncol(); // number of features
+
+  // Active set
+
+  int irow, icol;
+
+  // Extract the diagonal
+  Rcpp::NumericVector X_diag(ncol);
+  double *X_diag_p = X_diag.begin();
+
+  for (icol=0; icol<ncol; icol++) {
+    X_diag_p[irow] = 0;
+    for (irow=0; irow<nrow; irow++) {
+      X_diag_p[irow] += X(irow, icol) * X(irow, icol);
+    }
+    X_diag_p[irow] = X_diag_p[irow] / nrow;
+  }
+  
+  // Now call our C function
+
+  int iter = solve_wide((double *) X.begin(),
+			(double *) linear_func.begin(),
+			(double *) X_diag.begin(),
+			(double *) gradient.begin(),
+			(int *) ever_active.begin(),
+			(int *) nactive.begin(),
+			nrow,
+			ncol,
+			bound,
+			(double *) theta.begin(),
+			maxiter,
+			kkt_tol,
+			objective_tol,
+			max_active);
+  
+  // Check whether feasible
+
+  int kkt_check = check_KKT_wide(theta.begin(), // This is the same function as check_KKT_qp at the moment!!
+				 gradient.begin(),
+				 nrow,
+				 bound,
+				 kkt_tol);
+
+  int max_active_check = (*(nactive.begin()) >= max_active);
+
+  return(Rcpp::List::create(Rcpp::Named("soln") = theta,
+			    Rcpp::Named("gradient") = gradient,
+			    Rcpp::Named("linear_func") = linear_func,
+			    Rcpp::Named("iter") = iter,
+			    Rcpp::Named("kkt_check") = kkt_check,
+			    Rcpp::Named("ever_active") = ever_active,
+			    Rcpp::Named("nactive") = nactive,
+			    Rcpp::Named("max_active_check") = max_active_check));
+
+}
diff --git a/selectiveInference/src/RcppExports.cpp b/selectiveInference/src/RcppExports.cpp
index 461b0e5..6939cf1 100644
--- a/selectiveInference/src/RcppExports.cpp
+++ b/selectiveInference/src/RcppExports.cpp
@@ -26,6 +26,27 @@ BEGIN_RCPP
     return rcpp_result_gen;
 END_RCPP
 }
+// solve_QP_wide
+Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
+RcppExport SEXP _selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type X(XSEXP);
+    Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
+    Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
+    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type theta(thetaSEXP);
+    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type linear_func(linear_funcSEXP);
+    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type gradient(gradientSEXP);
+    Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type ever_active(ever_activeSEXP);
+    Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type nactive(nactiveSEXP);
+    Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
+    Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
+    Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
+    rcpp_result_gen = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return rcpp_result_gen;
+END_RCPP
+}
 // update1_
 Rcpp::List update1_(Rcpp::NumericMatrix Q2, Rcpp::NumericVector w, int m, int k);
 RcppExport SEXP _selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
@@ -58,6 +79,7 @@ END_RCPP
 
 static const R_CallMethodDef CallEntries[] = {
     {"_selectiveInference_solve_QP", (DL_FUNC) &_selectiveInference_solve_QP, 11},
+    {"_selectiveInference_solve_QP_wide", (DL_FUNC) &_selectiveInference_solve_QP_wide, 11},
     {"_selectiveInference_update1_", (DL_FUNC) &_selectiveInference_update1_, 4},
     {"_selectiveInference_downdate1_", (DL_FUNC) &_selectiveInference_downdate1_, 5},
     {NULL, NULL, 0}
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index 7cab5e3..6de2940 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -23,6 +23,27 @@ int check_KKT_qp(double *theta,           /* current theta */
 		 double bound,            /* Lagrange multipler for \ell_1 */ 
 		 double tol);             /* precision for checking KKT conditions */
 
+int solve_wide(double *X_ptr,              /* A design matrix */
+	       double *linear_func_ptr,    /* Linear term in objective */
+	       double *X_diag_ptr,         /* Diagonal entry of covariance matrix */
+	       double *gradient_ptr,       /* Current gradient of quadratic loss */
+	       int *ever_active_ptr,       /* Ever active set: 0-based */ 
+	       int *nactive_ptr,           /* Size of ever active set */
+	       int nrow,                   /* How many rows in X */
+	       int ncol,                   /* How many rows in X */
+	       double bound,               /* feasibility parameter */
+	       double *theta,              /* current value */
+	       int maxiter,                /* how many iterations */
+	       double kkt_tol,             /* precision for checking KKT conditions */
+	       double objective_tol,       /* precision for checking relative decrease in objective value */
+	       int max_active);            /* Upper limit for size of active set -- otherwise break */
+
+int check_KKT_wide(double *theta,           /* current theta */
+		   double *gradient_ptr,    /* Current gradient of quadratic loss */
+		   int nrow,                /* how many rows in Sigma */
+		   double bound,            /* Lagrange multipler for \ell_1 */ 
+		   double tol);             /* precision for checking KKT conditions */
+  
 #ifdef __cplusplus
 }  /* extern "C" */
 #endif /* __cplusplus */
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
new file mode 100644
index 0000000..36a2723
--- /dev/null
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -0,0 +1,341 @@
+#include <math.h> // for fabs
+
+// Find an approximate row of \hat{Sigma}^{-1}
+
+// Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
+
+// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2 - l^T\theta + \mu \|\theta\|_1
+// where l is `linear_func` below
+
+// This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
+// Therefore we don't have to negate the answer to get theta.
+// Update one coordinate 
+
+// Throughout X is a design matrix
+
+double objective_wide(double *X_ptr,           /* A design matrix */
+		      double *linear_func_ptr, /* Linear term in objective */
+		      int *ever_active_ptr,    /* Ever active set: 0-based */ 
+		      int *nactive_ptr,        /* Size of ever active set */
+		      int nrow,                /* how many rows in X */
+		      int ncol,                /* how many columns in X */
+		      double bound,            /* Lagrange multipler for \ell_1 */
+		      double *theta)           /* current value */
+{
+  int irow, icol;
+  double value = 0;
+  double *X_ptr_tmp = X_ptr;
+  double *linear_func_ptr_tmp = linear_func_ptr;
+  double *theta_row_ptr, *theta_col_ptr;
+  int *active_row_ptr, *active_col_ptr;
+  int active_row, active_col;
+  int nactive = *nactive_ptr;
+
+  theta_row_ptr = theta;
+  theta_col_ptr = theta;
+
+  for (irow=0; irow<nactive; irow++) {
+
+    active_row_ptr = ((int *) ever_active_ptr + irow);
+    active_row = *active_row_ptr - 1;          // Ever-active is 1-based
+    theta_row_ptr = ((double *) theta + active_row);
+
+    for (icol=0; icol<nactive; icol++) {
+      
+      active_col_ptr = ((int *) ever_active_ptr + icol);
+      active_col = *active_col_ptr - 1;          // Ever-active is 1-based
+      theta_col_ptr = ((double *) theta + active_col);
+
+      X_ptr_tmp = ((double *) X_ptr + nrow * active_col + active_row); // Matrices are column-major order
+
+      value += 0.5 * (*X_ptr_tmp) * (*theta_row_ptr) * (*theta_col_ptr);
+    }
+    value += bound * fabs((*theta_row_ptr)); // the \ell_1 term
+
+    // The linear term in the objective
+
+    linear_func_ptr_tmp = ((double *) linear_func_ptr + active_row);
+    value += (*linear_func_ptr_tmp) * (*theta_row_ptr); 
+
+  }
+  
+  return(value);
+}
+
+// Ever-active is 1-based
+// coord is 0-based
+int update_ever_active_wide(int coord,
+			  int *ever_active_ptr,
+			  int *nactive_ptr) {
+  int iactive;
+  int active_var;
+  int nactive = *nactive_ptr;
+  int *ever_active_ptr_tmp = ever_active_ptr;
+
+  for (iactive=0; iactive<nactive; iactive++) {
+    ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
+    active_var = *ever_active_ptr_tmp;
+    if (active_var - 1 == coord) {          // Ever-active is 1-based
+      return(1);
+    }
+  }
+  
+  // If we haven't returned yet, this means the coord was not in 
+  // ever_active.
+
+  // Add it to the active set and increment the 
+  // number of active variables
+
+  ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
+  *ever_active_ptr_tmp = coord + 1;         // Ever-active is 1-based
+  *nactive_ptr += 1;
+
+  return(0);
+}
+
+int check_KKT_wide(double *theta,        /* current theta */
+		 double *gradient_ptr, /* X^TX/n times theta */
+		 int nrow,             /* how many rows in X */
+		 double bound,         /* Lagrange multipler for \ell_1 */
+		 double tol)           /* precision for checking KKT conditions */        
+{
+  // First check inactive
+
+  int irow;
+  double *theta_ptr, *gradient_ptr_tmp;
+  double gradient;
+
+  for (irow=0; irow<nrow; irow++) {
+    theta_ptr = ((double *) theta + irow);
+    gradient_ptr_tmp = ((double *) gradient_ptr + irow);
+
+    // Compute this coordinate of the gradient
+
+    gradient = *gradient_ptr_tmp;
+
+    if (*theta_ptr != 0) { // these coordinates of gradients should be equal to -bound
+      if ((*theta_ptr > 0) &&  (fabs(gradient + bound) > tol * bound)) {
+	return(0);
+      }
+      else if ((*theta_ptr < 0) && (fabs(gradient - bound) > tol * bound)) {
+	return(0);
+      }
+    }
+    else {
+      if (fabs(gradient) > (1. + tol) * bound) {
+	return(0);
+      }
+    }
+  }
+
+  return(1);
+}
+
+double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
+			     double *linear_func_ptr,     /* Linear term in objective */
+			     double *X_diag_ptr,          /* Diagonal entries of Sigma */
+			     double *gradient_ptr,        /* X^TX/n times theta */
+			     int *ever_active_ptr,        /* Ever active set: 1-based */ 
+			     int *nactive_ptr,            /* Size of ever active set */
+			     int nrow,                    /* How many rows in X */
+			     int ncol,                    /* How many rows in X */
+			     double bound,                /* feasibility parameter */
+			     double *theta,               /* current value */
+			     int coord,                   /* which coordinate to update: 0-based */
+			     int is_active)               /* Is this coord in ever_active */     
+{
+
+  double delta;
+  double linear_term = 0;
+  double value = 0;
+  double old_value;
+  double *X_ptr_tmp;
+  double *gradient_ptr_tmp;
+  double *theta_ptr;
+  int icol = 0;
+
+  double *quadratic_ptr = ((double *) X_diag_ptr + coord);
+  double quadratic_term = *quadratic_ptr;
+
+  gradient_ptr_tmp = ((double *) gradient_ptr + coord);
+  linear_term = *gradient_ptr_tmp;
+
+  theta_ptr = ((double *) theta + coord);
+  old_value = *theta_ptr;
+
+  // The coord entry of gradient_ptr term has a diagonal term in it:
+  // X[coord, coord] * theta[coord]
+  // This removes it. 
+
+  linear_term -= quadratic_term * old_value;
+
+  // Now soft-threshold the coord entry of theta 
+
+  // Objective is t \mapsto q/2 * t^2 + l * t + bound |t|
+  // with q=quadratic_term and l=linear_term
+
+  // With a negative linear term, solution should be
+  // positive
+
+  if (linear_term < -bound) {
+    value = (-linear_term - bound) / quadratic_term;
+  }
+  else if (linear_term > bound) {
+    value = -(linear_term - bound) / quadratic_term;
+  }
+
+  // Add to active set if necessary
+
+  if ((is_active == 0) && (value != 0)) {
+    update_ever_active_wide(coord, ever_active_ptr, nactive_ptr);
+  }
+
+  // Update the linear term
+
+  if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
+
+    delta = value - old_value;
+    X_ptr_tmp = ((double *) X_ptr + coord * nrow);
+    gradient_ptr_tmp = ((double *) gradient_ptr);
+
+    for (icol=0; icol<nrow; icol++) {
+      *gradient_ptr_tmp = *gradient_ptr_tmp + delta * (*X_ptr_tmp);
+      gradient_ptr_tmp += 1;
+      X_ptr_tmp += 1;
+    }
+
+    theta_ptr = ((double *) theta + coord);
+    *theta_ptr = value;
+
+  }
+
+  return(value);
+
+}
+
+int solve_wide(double *X_ptr,              /* A design matrix */
+	       double *linear_func_ptr,    /* Linear term in objective */
+	       double *X_diag_ptr,         /* Diagonal entry of covariance matrix */
+	       double *gradient_ptr,       /* X times theta */
+	       int *ever_active_ptr,       /* Ever active set: 1-based */ 
+	       int *nactive_ptr,           /* Size of ever active set */
+	       int nrow,                   /* How many rows in X */
+	       int ncol,                   /* How many columns in X */
+	       double bound,               /* feasibility parameter */
+	       double *theta,              /* current value */
+	       int maxiter,                /* max number of iterations */
+	       double kkt_tol,             /* precision for checking KKT conditions */
+	       double objective_tol,       /* precision for checking relative decrease in objective value */
+	       int max_active)             /* Upper limit for size of active set -- otherwise break */ 
+{
+
+  int iter = 0;
+  int icoord = 0;
+  int iactive = 0;
+  int *active_ptr;
+
+  int check_objective = 1;
+
+  double old_value, new_value; 
+
+  if (check_objective) {
+
+    old_value = objective_wide(X_ptr,
+			       linear_func_ptr,
+			       ever_active_ptr,
+			       nactive_ptr,
+			       nrow,
+			       ncol,
+			       bound,
+			       theta);
+
+  }
+
+
+  for (iter=0; iter<maxiter; iter++) {
+
+    // Update the active variables first
+
+    active_ptr = (int *) ever_active_ptr;
+
+    for (iactive=0; iactive < *nactive_ptr; iactive++) {
+      update_one_coord_wide(X_ptr,
+			    linear_func_ptr,
+			    X_diag_ptr,
+			    gradient_ptr,
+			    ever_active_ptr,
+			    nactive_ptr,
+			    nrow,
+			    ncol,
+			    bound,
+			    theta,
+			    *active_ptr - 1,   // Ever-active is 1-based
+			    1);
+      active_ptr++;
+    }
+
+    // Check KKT
+
+    if (check_KKT_wide(theta,
+		     gradient_ptr,
+		     nrow,
+		     bound,
+		     kkt_tol) == 1) {
+      break;
+    }
+					  
+    // Update all variables 
+
+    for (icoord=0; icoord<nrow; icoord++) {
+
+      update_one_coord_wide(X_ptr,
+			    linear_func_ptr,
+			    X_diag_ptr,
+			    gradient_ptr,
+			    ever_active_ptr,
+			    nactive_ptr,
+			    nrow,
+			    ncol,
+			    bound,
+			    theta,
+			    icoord,
+			    0);
+    }
+
+    // Check KKT
+
+    if (check_KKT_wide(theta,
+		       gradient_ptr,
+		       nrow,
+		       bound,
+		       kkt_tol) == 1) {
+      break;
+    }
+					  
+    // Check size of active set
+
+    if (*nactive_ptr >= max_active) {
+      break;
+    }
+
+    // Check relative decrease of objective
+
+    if (check_objective) {
+      new_value = objective_wide(X_ptr,
+				 linear_func_ptr,
+				 ever_active_ptr,
+				 nactive_ptr,
+				 nrow,
+				 ncol,
+				 bound,
+				 theta);
+
+      if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
+	break;
+      }
+      old_value = new_value;
+    }
+  }
+  return(iter);
+}
+

From 2f05bf60764ebd47ab04286e5f479badf31c6944 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 25 Sep 2017 17:57:01 -0700
Subject: [PATCH 251/493] for devel version -- needs intervals reinstalled

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 21270e0..541fa3a 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -11,6 +11,6 @@ addons:
 warnings_are_errors: true
 before_install:
   - tlmgr install index # for texlive and vignette?
-  - R -e 'install.packages("Rcpp", repos="http://cloud.r-project.org")'
+  - R -e 'install.packages(c("Rcpp", "intervals"), repos="http://cloud.r-project.org")'
   - make Rcpp
   - cd selectiveInference

From 6238a92c3c484edc160123fa2be73562e5d272e5 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 26 Sep 2017 11:44:27 -0700
Subject: [PATCH 252/493] WIP: working on wide X QP

---
 .../src/quadratic_program_wide.c              | 32 +++++++++++--------
 1 file changed, 18 insertions(+), 14 deletions(-)

diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
index 36a2723..21ad990 100644
--- a/selectiveInference/src/quadratic_program_wide.c
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -14,6 +14,7 @@
 // Throughout X is a design matrix
 
 double objective_wide(double *X_ptr,           /* A design matrix */
+		      double *X_theta_ptr,     /* Fitted values */
 		      double *linear_func_ptr, /* Linear term in objective */
 		      int *ever_active_ptr,    /* Ever active set: 0-based */ 
 		      int *nactive_ptr,        /* Size of ever active set */
@@ -24,7 +25,7 @@ double objective_wide(double *X_ptr,           /* A design matrix */
 {
   int irow, icol;
   double value = 0;
-  double *X_ptr_tmp = X_ptr;
+  double *X_theta_ptr_tmp = X_theta_ptr;
   double *linear_func_ptr_tmp = linear_func_ptr;
   double *theta_row_ptr, *theta_col_ptr;
   int *active_row_ptr, *active_col_ptr;
@@ -34,29 +35,32 @@ double objective_wide(double *X_ptr,           /* A design matrix */
   theta_row_ptr = theta;
   theta_col_ptr = theta;
 
-  for (irow=0; irow<nactive; irow++) {
+  double entry = 0; // An entry of X\theta
 
-    active_row_ptr = ((int *) ever_active_ptr + irow);
-    active_row = *active_row_ptr - 1;          // Ever-active is 1-based
-    theta_row_ptr = ((double *) theta + active_row);
+  // The term \|X\theta\|^2_2/nrow
 
-    for (icol=0; icol<nactive; icol++) {
-      
-      active_col_ptr = ((int *) ever_active_ptr + icol);
-      active_col = *active_col_ptr - 1;          // Ever-active is 1-based
-      theta_col_ptr = ((double *) theta + active_col);
+  for (irow=0; irow<nrow; irow++) {
 
-      X_ptr_tmp = ((double *) X_ptr + nrow * active_col + active_row); // Matrices are column-major order
+    X_theta_ptr = ((double *) X_theta_ptr + irow);
+    value += (*(X_theta_ptr)) * (*(X_theta_ptr));
 
-      value += 0.5 * (*X_ptr_tmp) * (*theta_row_ptr) * (*theta_col_ptr);
-    }
-    value += bound * fabs((*theta_row_ptr)); // the \ell_1 term
+  }
+
+  for (irow=0; irow<nactive; irow++) {
 
     // The linear term in the objective
 
+    active_row_ptr = ((int *) ever_active_ptr + irow);
+    active_row = *active_row_ptr - 1;          // Ever-active is 1-based
+    theta_row_ptr = ((double *) theta + active_row);
+
     linear_func_ptr_tmp = ((double *) linear_func_ptr + active_row);
     value += (*linear_func_ptr_tmp) * (*theta_row_ptr); 
 
+    // The \ell_1 term
+
+    value += bound * fabs((*theta_row_ptr));
+
   }
   
   return(value);

From dd30abce3911570c81f70f63d60ccc67de8acdb2 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 26 Sep 2017 14:47:15 -0700
Subject: [PATCH 253/493] WIP: builds now, but segfaulting

---
 selectiveInference/R/RcppExports.R            |  12 +-
 selectiveInference/src/Rcpp-debias.cpp        |  51 ++-
 selectiveInference/src/RcppExports.cpp        |  58 ++--
 selectiveInference/src/debias.h               |  39 ++-
 .../src/quadratic_program_wide.c              | 296 +++++++++++++-----
 5 files changed, 302 insertions(+), 154 deletions(-)

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
index 1c03053..8af4640 100644
--- a/selectiveInference/R/RcppExports.R
+++ b/selectiveInference/R/RcppExports.R
@@ -1,19 +1,19 @@
-# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# This file was generated by Rcpp::compileAttributes
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('_selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
+    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
 }
 
-solve_QP_wide <- function(X, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('_selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
+solve_QP_wide <- function(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active) {
+    .Call('selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active)
 }
 
 update1_ <- function(Q2, w, m, k) {
-    .Call('_selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
+    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
 }
 
 downdate1_ <- function(Q1, R, j0, m, n) {
-    .Call('_selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
+    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
 }
 
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index d6e8741..fcf0eb6 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -77,6 +77,7 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			 Rcpp::NumericVector theta,
 			 Rcpp::NumericVector linear_func,
 			 Rcpp::NumericVector gradient,
+			 Rcpp::NumericVector X_theta,
 			 Rcpp::IntegerVector ever_active,
 			 Rcpp::IntegerVector nactive,
 			 double kkt_tol,
@@ -84,35 +85,41 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			 int max_active
 			 ) {
 
-  int nrow = X.nrow(); // number of cases
-  int ncol = X.ncol(); // number of features
+  int ncase = X.nrow(); // number of cases
+  int nfeature = X.ncol(); // number of features
 
   // Active set
 
-  int irow, icol;
+  int icase, ifeature;
+
+  // A vector to keep track of gradient updates
+
+  Rcpp::IntegerVector need_update(nfeature);
 
   // Extract the diagonal
-  Rcpp::NumericVector X_diag(ncol);
+  Rcpp::NumericVector X_diag(nfeature);
   double *X_diag_p = X_diag.begin();
 
-  for (icol=0; icol<ncol; icol++) {
-    X_diag_p[irow] = 0;
-    for (irow=0; irow<nrow; irow++) {
-      X_diag_p[irow] += X(irow, icol) * X(irow, icol);
+  for (icase=0; icase<ncase; icase++) {
+    X_diag_p[icase] = 0;
+    for (ifeature=0; ifeature<nfeature; ifeature++) {
+      X_diag_p[icase] += X(icase, ifeature) * X(icase, ifeature);
     }
-    X_diag_p[irow] = X_diag_p[irow] / nrow;
+    X_diag_p[icase] = X_diag_p[icase] / ncase;
   }
   
   // Now call our C function
 
   int iter = solve_wide((double *) X.begin(),
+			(double *) X_theta.begin(),
 			(double *) linear_func.begin(),
 			(double *) X_diag.begin(),
 			(double *) gradient.begin(),
+			(int *) need_update.begin(),
 			(int *) ever_active.begin(),
 			(int *) nactive.begin(),
-			nrow,
-			ncol,
+			ncase,
+			nfeature,
 			bound,
 			(double *) theta.begin(),
 			maxiter,
@@ -122,16 +129,32 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
   
   // Check whether feasible
 
-  int kkt_check = check_KKT_wide(theta.begin(), // This is the same function as check_KKT_qp at the moment!!
-				 gradient.begin(),
-				 nrow,
+  int kkt_check = check_KKT_wide((double *) theta.begin(),
+				 (double *) gradient.begin(),
+				 (double *) X_theta.begin(),
+				 (double *) X.begin(),
+				 (double *) linear_func.begin(),
+				 (int *) need_update.begin(),
+				 nfeature,
+				 ncase,
 				 bound,
 				 kkt_tol);
 
   int max_active_check = (*(nactive.begin()) >= max_active);
 
+  // Make sure gradient is updated -- essentially a matrix multiply
+
+  update_gradient_wide((double *) gradient.begin(),
+		       (double *) X_theta.begin(),
+		       (double *) X.begin(),
+		       (double *) linear_func.begin(),
+		       (int *) need_update.begin(),
+		       nfeature,
+		       ncase);
+
   return(Rcpp::List::create(Rcpp::Named("soln") = theta,
 			    Rcpp::Named("gradient") = gradient,
+			    Rcpp::Named("X_theta") = X_theta,
 			    Rcpp::Named("linear_func") = linear_func,
 			    Rcpp::Named("iter") = iter,
 			    Rcpp::Named("kkt_check") = kkt_check,
diff --git a/selectiveInference/src/RcppExports.cpp b/selectiveInference/src/RcppExports.cpp
index 6939cf1..efa87d5 100644
--- a/selectiveInference/src/RcppExports.cpp
+++ b/selectiveInference/src/RcppExports.cpp
@@ -1,4 +1,4 @@
-// Generated by using Rcpp::compileAttributes() -> do not edit by hand
+// This file was generated by Rcpp::compileAttributes
 // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 #include <Rcpp.h>
@@ -7,10 +7,10 @@ using namespace Rcpp;
 
 // solve_QP
 Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP _selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+RcppExport SEXP selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::RObject __result;
+    Rcpp::RNGScope __rngScope;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Sigma(SigmaSEXP);
     Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
     Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
@@ -22,70 +22,58 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
     Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
     Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    rcpp_result_gen = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return rcpp_result_gen;
+    __result = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return __result;
 END_RCPP
 }
 // solve_QP_wide
-Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP _selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::NumericVector X_theta, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
+RcppExport SEXP selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP X_thetaSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::RObject __result;
+    Rcpp::RNGScope __rngScope;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type X(XSEXP);
     Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
     Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericVector >::type theta(thetaSEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericVector >::type linear_func(linear_funcSEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericVector >::type gradient(gradientSEXP);
+    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type X_theta(X_thetaSEXP);
     Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type ever_active(ever_activeSEXP);
     Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type nactive(nactiveSEXP);
     Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
     Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
     Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    rcpp_result_gen = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return rcpp_result_gen;
+    __result = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return __result;
 END_RCPP
 }
 // update1_
 Rcpp::List update1_(Rcpp::NumericMatrix Q2, Rcpp::NumericVector w, int m, int k);
-RcppExport SEXP _selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
+RcppExport SEXP selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::RObject __result;
+    Rcpp::RNGScope __rngScope;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q2(Q2SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericVector >::type w(wSEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type k(kSEXP);
-    rcpp_result_gen = Rcpp::wrap(update1_(Q2, w, m, k));
-    return rcpp_result_gen;
+    __result = Rcpp::wrap(update1_(Q2, w, m, k));
+    return __result;
 END_RCPP
 }
 // downdate1_
 Rcpp::List downdate1_(Rcpp::NumericMatrix Q1, Rcpp::NumericMatrix R, int j0, int m, int n);
-RcppExport SEXP _selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
+RcppExport SEXP selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::RObject __result;
+    Rcpp::RNGScope __rngScope;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q1(Q1SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type R(RSEXP);
     Rcpp::traits::input_parameter< int >::type j0(j0SEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    rcpp_result_gen = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
-    return rcpp_result_gen;
+    __result = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
+    return __result;
 END_RCPP
 }
-
-static const R_CallMethodDef CallEntries[] = {
-    {"_selectiveInference_solve_QP", (DL_FUNC) &_selectiveInference_solve_QP, 11},
-    {"_selectiveInference_solve_QP_wide", (DL_FUNC) &_selectiveInference_solve_QP_wide, 11},
-    {"_selectiveInference_update1_", (DL_FUNC) &_selectiveInference_update1_, 4},
-    {"_selectiveInference_downdate1_", (DL_FUNC) &_selectiveInference_downdate1_, 5},
-    {NULL, NULL, 0}
-};
-
-RcppExport void R_init_selectiveInference(DllInfo *dll) {
-    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
-    R_useDynamicSymbols(dll, FALSE);
-}
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index 6de2940..70ba536 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -24,26 +24,41 @@ int check_KKT_qp(double *theta,           /* current theta */
 		 double tol);             /* precision for checking KKT conditions */
 
 int solve_wide(double *X_ptr,              /* A design matrix */
+	       double *X_theta_ptr,        /* Fitted values   */
 	       double *linear_func_ptr,    /* Linear term in objective */
 	       double *X_diag_ptr,         /* Diagonal entry of covariance matrix */
-	       double *gradient_ptr,       /* Current gradient of quadratic loss */
-	       int *ever_active_ptr,       /* Ever active set: 0-based */ 
+	       double *gradient_ptr,       /* X times theta */
+	       int *need_update_ptr,       /* Keeps track of updated gradient coords */
+	       int *ever_active_ptr,       /* Ever active set: 1-based */ 
 	       int *nactive_ptr,           /* Size of ever active set */
-	       int nrow,                   /* How many rows in X */
-	       int ncol,                   /* How many rows in X */
+	       int ncase,                  /* How many rows in X */
+	       int nfeature,               /* How many columns in X */
 	       double bound,               /* feasibility parameter */
-	       double *theta,              /* current value */
-	       int maxiter,                /* how many iterations */
+	       double *theta_ptr,          /* current value */
+	       int maxiter,                /* max number of iterations */
 	       double kkt_tol,             /* precision for checking KKT conditions */
 	       double objective_tol,       /* precision for checking relative decrease in objective value */
-	       int max_active);            /* Upper limit for size of active set -- otherwise break */
+	       int max_active);            /* Upper limit for size of active set -- otherwise break */ 
 
-int check_KKT_wide(double *theta,           /* current theta */
-		   double *gradient_ptr,    /* Current gradient of quadratic loss */
-		   int nrow,                /* how many rows in Sigma */
-		   double bound,            /* Lagrange multipler for \ell_1 */ 
-		   double tol);             /* precision for checking KKT conditions */
+int check_KKT_wide(double *theta_ptr,        /* current theta */
+		   double *gradient_ptr,     /* X^TX/n times theta */
+		   double *X_theta_ptr,      /* Current fitted values */
+		   double *X_ptr,            /* A design matrix */
+		   double *linear_func_ptr,    /* Linear term in objective */
+		   int *need_update_ptr,     /* Which coordinates need to be updated? */
+		   int nfeature,             /* how many columns in X */
+		   int ncase,                /* how many rows in X */
+   		   double bound,             /* Lagrange multipler for \ell_1 */
+		   double tol);              /* precision for checking KKT conditions */        
   
+void update_gradient_wide(double *gradient_ptr,     /* X^TX/n times theta */
+			  double *X_theta_ptr,      /* Current fitted values */
+			  double *X_ptr,            /* A design matrix */
+			  double *linear_func_ptr,    /* Linear term in objective */
+			  int *need_update_ptr,     /* Which coordinates need to be updated? */
+			  int nfeature,             /* how many columns in X */
+			  int ncase);               /* how many rows in X */
+
 #ifdef __cplusplus
 }  /* extern "C" */
 #endif /* __cplusplus */
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
index 21ad990..be7411d 100644
--- a/selectiveInference/src/quadratic_program_wide.c
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -1,4 +1,5 @@
 #include <math.h> // for fabs
+#include <stdio.h>
 
 // Find an approximate row of \hat{Sigma}^{-1}
 
@@ -13,64 +14,113 @@
 
 // Throughout X is a design matrix
 
-double objective_wide(double *X_ptr,           /* A design matrix */
-		      double *X_theta_ptr,     /* Fitted values */
+double objective_wide(double *X_theta_ptr,     /* Fitted values */
 		      double *linear_func_ptr, /* Linear term in objective */
 		      int *ever_active_ptr,    /* Ever active set: 0-based */ 
 		      int *nactive_ptr,        /* Size of ever active set */
-		      int nrow,                /* how many rows in X */
-		      int ncol,                /* how many columns in X */
+		      int ncase,               /* how many rows in X */
+		      int nfeature,            /* how many columns in X */
 		      double bound,            /* Lagrange multipler for \ell_1 */
-		      double *theta)           /* current value */
+		      double *theta_ptr)       /* current value */
 {
-  int irow, icol;
+  int icase, iactive;
   double value = 0;
   double *X_theta_ptr_tmp = X_theta_ptr;
   double *linear_func_ptr_tmp = linear_func_ptr;
-  double *theta_row_ptr, *theta_col_ptr;
-  int *active_row_ptr, *active_col_ptr;
-  int active_row, active_col;
+  double *theta_ptr_tmp;
+  int *active_feature_ptr;
+  int active_feature;
   int nactive = *nactive_ptr;
 
-  theta_row_ptr = theta;
-  theta_col_ptr = theta;
+  // The term \|X\theta\|^2_2/n, with n=ncase
 
-  double entry = 0; // An entry of X\theta
+  for (icase=0; icase<ncase; icase++) {
 
-  // The term \|X\theta\|^2_2/nrow
-
-  for (irow=0; irow<nrow; irow++) {
-
-    X_theta_ptr = ((double *) X_theta_ptr + irow);
-    value += (*(X_theta_ptr)) * (*(X_theta_ptr));
+    X_theta_ptr_tmp = ((double *) X_theta_ptr + icase);
+    value += (*(X_theta_ptr_tmp)) * (*(X_theta_ptr_tmp));
 
   }
 
-  for (irow=0; irow<nactive; irow++) {
+  value *= 0.5 / ncase;
+
+  for (iactive=0; iactive<nactive; iactive++) {
 
     // The linear term in the objective
 
-    active_row_ptr = ((int *) ever_active_ptr + irow);
-    active_row = *active_row_ptr - 1;          // Ever-active is 1-based
-    theta_row_ptr = ((double *) theta + active_row);
+    active_feature_ptr = ((int *) ever_active_ptr + iactive);
+    active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
+
+    // fprintf(stderr, "active %d\n", active_feature);
 
-    linear_func_ptr_tmp = ((double *) linear_func_ptr + active_row);
-    value += (*linear_func_ptr_tmp) * (*theta_row_ptr); 
+    theta_ptr_tmp = ((double *) theta_ptr + active_feature);
+    linear_func_ptr_tmp = ((double *) linear_func_ptr + active_feature);
+    value += (*linear_func_ptr_tmp) * (*theta_ptr_tmp); 
 
     // The \ell_1 term
 
-    value += bound * fabs((*theta_row_ptr));
+    value += bound * fabs((*theta_ptr_tmp));
 
   }
   
   return(value);
 }
 
+// Compute, update and return one coordinate of the gradient of \|X\theta\|^2_2/2n
+
+double compute_gradient_coord(double *gradient_ptr,        /* Gradient -- one coordinate will be updated if needed */
+			      double *X_theta_ptr,         /* Current fitted values */
+			      double *X_ptr,               /* A design matrix */
+			      double *linear_func_ptr,     /* Linear term in objective */   
+			      int *need_update_ptr,        /* Which coordinates need to be updated? */
+			      int coord,                   /* Coordinate we are trying to update */
+			      int ncase)                   /* How many rows in X */
+{
+
+  double *gradient_ptr_tmp;
+  double *X_theta_ptr_tmp;
+  double *X_ptr_tmp;  
+  double *linear_func_ptr_tmp;  
+  int *need_update_ptr_tmp;
+  int icase;
+  double value = 0.;
+
+  need_update_ptr_tmp = ((int *) need_update_ptr + coord);
+  // Check if this coordinate needs updating
+  if (*need_update_ptr_tmp == 1) {
+
+    // fprintf(stderr, "compute grad %d %d\n", ncase, coord);
+
+    for (icase=0; icase<ncase; icase++) {
+      X_ptr_tmp = ((double *) X_ptr + coord * ncase + icase);
+      X_theta_ptr_tmp = ((double *) X_theta_ptr + icase);
+      value += (*X_ptr_tmp) * (*X_theta_ptr_tmp);
+    }
+
+    value /= ncase;
+
+    linear_func_ptr_tmp = ((double *) linear_func_ptr + coord);
+    value += *linear_func_ptr_tmp;
+
+    gradient_ptr_tmp = ((double *) gradient_ptr + coord);
+    *gradient_ptr_tmp = value;
+
+    *need_update_ptr_tmp = 0;
+  }
+  else {
+    gradient_ptr_tmp = ((double *) gradient_ptr + coord);
+    value = *gradient_ptr_tmp;
+  }
+
+  // fprintf(stderr, "grad %d, %f\n", coord, value);
+  return(value);
+}
+
+
 // Ever-active is 1-based
 // coord is 0-based
 int update_ever_active_wide(int coord,
-			  int *ever_active_ptr,
-			  int *nactive_ptr) {
+			    int *ever_active_ptr,
+			    int *nactive_ptr) {
   int iactive;
   int active_var;
   int nactive = *nactive_ptr;
@@ -79,6 +129,7 @@ int update_ever_active_wide(int coord,
   for (iactive=0; iactive<nactive; iactive++) {
     ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
     active_var = *ever_active_ptr_tmp;
+    // fprintf(stderr, "active check %d\n", active_var-1);
     if (active_var - 1 == coord) {          // Ever-active is 1-based
       return(1);
     }
@@ -97,36 +148,62 @@ int update_ever_active_wide(int coord,
   return(0);
 }
 
-int check_KKT_wide(double *theta,        /* current theta */
-		 double *gradient_ptr, /* X^TX/n times theta */
-		 int nrow,             /* how many rows in X */
-		 double bound,         /* Lagrange multipler for \ell_1 */
-		 double tol)           /* precision for checking KKT conditions */        
+void update_gradient_wide(double *gradient_ptr,     /* X^TX/n times theta */
+			  double *X_theta_ptr,      /* Current fitted values */
+			  double *X_ptr,            /* A design matrix */
+			  double *linear_func_ptr,     /* Linear term in objective */   
+			  int *need_update_ptr,     /* Which coordinates need to be updated? */
+			  int nfeature,             /* how many columns in X */
+			  int ncase)                /* how many rows in X */
+{
+  int ifeature;
+
+  for (ifeature=0; ifeature<nfeature; ifeature++) {
+    compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, ifeature, ncase);
+  }
+
+}
+
+int check_KKT_wide(double *theta_ptr,        /* current theta */
+		   double *gradient_ptr,     /* X^TX/n times theta */
+		   double *X_theta_ptr,      /* Current fitted values */
+		   double *X_ptr,            /* A design matrix */
+		   double *linear_func_ptr,     /* Linear term in objective */   
+		   int *need_update_ptr,     /* Which coordinates need to be updated? */
+		   int nfeature,             /* how many columns in X */
+		   int ncase,                /* how many rows in X */
+   		   double bound,             /* Lagrange multipler for \ell_1 */
+		   double tol)               /* precision for checking KKT conditions */        
 {
   // First check inactive
 
-  int irow;
-  double *theta_ptr, *gradient_ptr_tmp;
+  int ifeature;
+  double *theta_ptr_tmp;
   double gradient;
 
-  for (irow=0; irow<nrow; irow++) {
-    theta_ptr = ((double *) theta + irow);
-    gradient_ptr_tmp = ((double *) gradient_ptr + irow);
+  for (ifeature=0; ifeature<nfeature; ifeature++) {
+
+    theta_ptr_tmp = ((double *) theta_ptr + ifeature);
 
     // Compute this coordinate of the gradient
 
-    gradient = *gradient_ptr_tmp;
+    // fprintf(stderr, "%d \n", ifeature);
+    gradient = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, ifeature, ncase);
 
-    if (*theta_ptr != 0) { // these coordinates of gradients should be equal to -bound
-      if ((*theta_ptr > 0) &&  (fabs(gradient + bound) > tol * bound)) {
+    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
+      // fprintf(stderr, "WTF\n");
+      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
+	// fprintf(stderr, "WTF2\n");
 	return(0);
       }
-      else if ((*theta_ptr < 0) && (fabs(gradient - bound) > tol * bound)) {
+      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
+	// fprintf(stderr, "WTF3\n");
 	return(0);
       }
     }
     else {
       if (fabs(gradient) > (1. + tol) * bound) {
+	// fprintf(stderr, "inactive\n");
 	return(0);
       }
     }
@@ -141,10 +218,12 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
 			     double *gradient_ptr,        /* X^TX/n times theta */
 			     int *ever_active_ptr,        /* Ever active set: 1-based */ 
 			     int *nactive_ptr,            /* Size of ever active set */
-			     int nrow,                    /* How many rows in X */
-			     int ncol,                    /* How many rows in X */
+			     double *X_theta_ptr,         /* X\theta -- fitted values */
+			     int *need_update_ptr,        /* Whether a gradient coordinate needs update or not */
+			     int ncase,                    /* How many rows in X */
+			     int nfeature,                    /* How many rows in X */
 			     double bound,                /* feasibility parameter */
-			     double *theta,               /* current value */
+			     double *theta_ptr,               /* current value */
 			     int coord,                   /* which coordinate to update: 0-based */
 			     int is_active)               /* Is this coord in ever_active */     
 {
@@ -154,18 +233,18 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
   double value = 0;
   double old_value;
   double *X_ptr_tmp;
-  double *gradient_ptr_tmp;
-  double *theta_ptr;
-  int icol = 0;
+  double *X_theta_ptr_tmp;
+  int *need_update_ptr_tmp;
+  double *theta_ptr_tmp;
+  int ifeature, icase;
 
   double *quadratic_ptr = ((double *) X_diag_ptr + coord);
   double quadratic_term = *quadratic_ptr;
 
-  gradient_ptr_tmp = ((double *) gradient_ptr + coord);
-  linear_term = *gradient_ptr_tmp;
+  linear_term = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, coord, ncase);
 
-  theta_ptr = ((double *) theta + coord);
-  old_value = *theta_ptr;
+  theta_ptr_tmp = ((double *) theta_ptr + coord);
+  old_value = *theta_ptr_tmp;
 
   // The coord entry of gradient_ptr term has a diagonal term in it:
   // X[coord, coord] * theta[coord]
@@ -198,18 +277,26 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
 
   if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
 
+    // Set the update_gradient_ptr to 1
+
+    need_update_ptr_tmp = need_update_ptr;
+    for (ifeature=0; ifeature<nfeature; ifeature++) {
+      *need_update_ptr_tmp = 1;
+      need_update_ptr_tmp++;
+    }
+
+    // Update X\theta
+
     delta = value - old_value;
-    X_ptr_tmp = ((double *) X_ptr + coord * nrow);
-    gradient_ptr_tmp = ((double *) gradient_ptr);
 
-    for (icol=0; icol<nrow; icol++) {
-      *gradient_ptr_tmp = *gradient_ptr_tmp + delta * (*X_ptr_tmp);
-      gradient_ptr_tmp += 1;
-      X_ptr_tmp += 1;
+    for (icase=0; icase<ncase; icase++) {
+      X_theta_ptr_tmp = ((double *) X_theta_ptr + icase);
+      X_ptr_tmp = ((double *) X_ptr + coord * ncase + icase);
+      *X_theta_ptr_tmp = (*X_theta_ptr_tmp) + delta * (*X_ptr_tmp);
     }
 
-    theta_ptr = ((double *) theta + coord);
-    *theta_ptr = value;
+    theta_ptr_tmp = ((double *) theta_ptr + coord);
+    *theta_ptr_tmp = value;
 
   }
 
@@ -218,15 +305,17 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
 }
 
 int solve_wide(double *X_ptr,              /* A design matrix */
+	       double *X_theta_ptr,        /* Fitted values   */
 	       double *linear_func_ptr,    /* Linear term in objective */
 	       double *X_diag_ptr,         /* Diagonal entry of covariance matrix */
 	       double *gradient_ptr,       /* X times theta */
+	       int *need_update_ptr,       /* Keeps track of updated gradient coords */
 	       int *ever_active_ptr,       /* Ever active set: 1-based */ 
 	       int *nactive_ptr,           /* Size of ever active set */
-	       int nrow,                   /* How many rows in X */
-	       int ncol,                   /* How many columns in X */
+	       int ncase,                  /* How many rows in X */
+	       int nfeature,               /* How many columns in X */
 	       double bound,               /* feasibility parameter */
-	       double *theta,              /* current value */
+	       double *theta_ptr,          /* current value */
 	       int maxiter,                /* max number of iterations */
 	       double kkt_tol,             /* precision for checking KKT conditions */
 	       double objective_tol,       /* precision for checking relative decrease in objective value */
@@ -234,7 +323,7 @@ int solve_wide(double *X_ptr,              /* A design matrix */
 {
 
   int iter = 0;
-  int icoord = 0;
+  int ifeature = 0;
   int iactive = 0;
   int *active_ptr;
 
@@ -242,20 +331,25 @@ int solve_wide(double *X_ptr,              /* A design matrix */
 
   double old_value, new_value; 
 
+  // fprintf(stderr, "here1\n");
+
   if (check_objective) {
+    // fprintf(stderr, "here9 %d %d\n", ncase, nfeature);
 
-    old_value = objective_wide(X_ptr,
+    old_value = objective_wide(X_theta_ptr,
 			       linear_func_ptr,
 			       ever_active_ptr,
 			       nactive_ptr,
-			       nrow,
-			       ncol,
+			       ncase,
+			       nfeature,
 			       bound,
-			       theta);
+			       theta_ptr);
 
   }
 
 
+  // fprintf(stderr, "here2\n");
+
   for (iter=0; iter<maxiter; iter++) {
 
     // Update the active variables first
@@ -269,48 +363,67 @@ int solve_wide(double *X_ptr,              /* A design matrix */
 			    gradient_ptr,
 			    ever_active_ptr,
 			    nactive_ptr,
-			    nrow,
-			    ncol,
+			    X_theta_ptr,
+			    need_update_ptr,
+			    ncase,
+			    nfeature,
 			    bound,
-			    theta,
+			    theta_ptr,
 			    *active_ptr - 1,   // Ever-active is 1-based
 			    1);
       active_ptr++;
     }
 
+  // fprintf(stderr, "here3\n");
+
     // Check KKT
 
-    if (check_KKT_wide(theta,
-		     gradient_ptr,
-		     nrow,
-		     bound,
-		     kkt_tol) == 1) {
+    if (check_KKT_wide(theta_ptr,
+		       gradient_ptr,
+		       X_theta_ptr,
+		       X_ptr,
+		       linear_func_ptr,
+		       need_update_ptr,
+		       nfeature,
+		       ncase,
+		       bound,
+		       kkt_tol) == 1) {
       break;
     }
-					  
+
     // Update all variables 
 
-    for (icoord=0; icoord<nrow; icoord++) {
+    for (ifeature=0; ifeature<nfeature; ifeature++) {
 
+      // fprintf(stderr, "updating %d\n", ifeature);
       update_one_coord_wide(X_ptr,
 			    linear_func_ptr,
 			    X_diag_ptr,
 			    gradient_ptr,
 			    ever_active_ptr,
 			    nactive_ptr,
-			    nrow,
-			    ncol,
+			    X_theta_ptr,
+			    need_update_ptr,
+			    ncase,
+			    nfeature,
 			    bound,
-			    theta,
-			    icoord,
+			    theta_ptr,
+			    ifeature,
 			    0);
     }
 
+    // fprintf(stderr, "here6\n");
+
     // Check KKT
 
-    if (check_KKT_wide(theta,
+    if (check_KKT_wide(theta_ptr,
 		       gradient_ptr,
-		       nrow,
+		       X_theta_ptr,
+		       X_ptr,
+		       linear_func_ptr,
+		       need_update_ptr,
+		       nfeature,
+		       ncase,
 		       bound,
 		       kkt_tol) == 1) {
       break;
@@ -318,27 +431,36 @@ int solve_wide(double *X_ptr,              /* A design matrix */
 					  
     // Check size of active set
 
+    // fprintf(stderr, "here18 %d\n", *nactive_ptr);
+
     if (*nactive_ptr >= max_active) {
       break;
     }
 
     // Check relative decrease of objective
 
+    // fprintf(stderr, "here7\n");
+
     if (check_objective) {
-      new_value = objective_wide(X_ptr,
+      new_value = objective_wide(X_theta_ptr,
 				 linear_func_ptr,
 				 ever_active_ptr,
 				 nactive_ptr,
-				 nrow,
-				 ncol,
+				 ncase,
+				 nfeature,
 				 bound,
-				 theta);
+				 theta_ptr);
+
+      // fprintf(stderr, "here8\n");
 
       if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
 	break;
       }
       old_value = new_value;
     }
+
+      // fprintf(stderr, "here10\n");
+
   }
   return(iter);
 }

From ed19c2942f72d331a5c7231a28c40b4bd94cb202 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 26 Sep 2017 14:49:04 -0700
Subject: [PATCH 254/493] test script for wide problem

---
 test.R | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)
 create mode 100644 test.R

diff --git a/test.R b/test.R
new file mode 100644
index 0000000..0b48686
--- /dev/null
+++ b/test.R
@@ -0,0 +1,23 @@
+set.seed(43)
+
+    n = 100                                                                                                                                                                              
+    p = 200                                                                                                                                                                              
+    lam = 10                                                                                                                                                                             
+    X = matrix(rnorm(n*p), n, p)                                                                                                                                                         
+    Y = rnorm(n)                                                                                                                                                                         
+    library(selectiveInference)                                                                                                                                                          
+    p = ncol(X)                                                                                                                                                                          
+    soln_R = rep(0, p)                                                                                                                                                                   
+    grad = -t(X) %*% Y                                                                                                                                                                   
+    ever_active = c(1, rep(0, p-1))                                                                                                                                                      
+    nactive = as.integer(1)                                                                                                                                                              
+    kkt_tol = 1.e-12                                                                                                                                                                     
+    objective_tol = 1.e-12                                                                                                                                                               
+    maxiter = 500                                                                                                                                                                        
+    Xtheta = rep(0, n)                                                                                                                                                                   
+    soln_R = selectiveInference:::solve_QP(t(X) %*% X, lam, maxiter, soln_R, -t(X) %*% Y, grad, ever_active, nactive, kkt_tol, objective_tol, p)$soln                                    
+    print(soln_R)                                                                                                                                                                        
+    # test wide solver                                                                                                                                                                   
+                                                                                                                                                                                       
+    soln_R_wide = selectiveInference:::solve_QP_wide(X, lam, maxiter, soln_R, -t(X) %*% Y, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)
+    print(soln_R_wide)
\ No newline at end of file

From ce0dc0741063173f700e9e1e1237fd77c88d65ee Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 26 Sep 2017 14:55:18 -0700
Subject: [PATCH 255/493] test running now -- no segfault?

---
 test.R | 20 +++++++++++++-------
 1 file changed, 13 insertions(+), 7 deletions(-)

diff --git a/test.R b/test.R
index 0b48686..2d9349d 100644
--- a/test.R
+++ b/test.R
@@ -2,22 +2,28 @@ set.seed(43)
 
     n = 100                                                                                                                                                                              
     p = 200                                                                                                                                                                              
-    lam = 10                                                                                                                                                                             
+    lam = 0.1                                                                                                                                                                             
     X = matrix(rnorm(n*p), n, p)                                                                                                                                                         
     Y = rnorm(n)                                                                                                                                                                         
     library(selectiveInference)                                                                                                                                                          
     p = ncol(X)                                                                                                                                                                          
     soln_R = rep(0, p)                                                                                                                                                                   
-    grad = -t(X) %*% Y                                                                                                                                                                   
+    grad = -t(X) %*% Y / n                                                                                                                                                                   
     ever_active = c(1, rep(0, p-1))                                                                                                                                                      
     nactive = as.integer(1)                                                                                                                                                              
     kkt_tol = 1.e-12                                                                                                                                                                     
     objective_tol = 1.e-12                                                                                                                                                               
     maxiter = 500                                                                                                                                                                        
-    Xtheta = rep(0, n)                                                                                                                                                                   
-    soln_R = selectiveInference:::solve_QP(t(X) %*% X, lam, maxiter, soln_R, -t(X) %*% Y, grad, ever_active, nactive, kkt_tol, objective_tol, p)$soln                                    
+    soln_R = selectiveInference:::solve_QP(t(X) %*% X / n, lam, maxiter, soln_R, -t(X) %*% Y / n, grad, ever_active, nactive, kkt_tol, objective_tol, p)$soln                                    
     print(soln_R)                                                                                                                                                                        
+    Xtheta = rep(0, n)                                                                                                                                                                   
+    nactive = as.integer(1)
+    ever_active = c(1, rep(0, p-1))                                                                                                                                                      
+    soln_R = rep(0, p)
+    grad = - t(X) %*% Y / n
     # test wide solver                                                                                                                                                                   
-                                                                                                                                                                                       
-    soln_R_wide = selectiveInference:::solve_QP_wide(X, lam, maxiter, soln_R, -t(X) %*% Y, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)
-    print(soln_R_wide)
\ No newline at end of file
+    soln_R_wide = selectiveInference:::solve_QP_wide(X, lam, maxiter, soln_R, -t(X) %*% Y / n, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)
+    print(soln_R_wide)
+    print(soln_R)
+    print(soln_R_wide$soln)
+    print(soln_R_wide$soln - soln_R)
\ No newline at end of file

From c26a06b243084eb9cdffa208ffaf9418e2c49850 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 26 Sep 2017 21:29:58 -0700
Subject: [PATCH 256/493] WIP: running but not agreeing with other solver

---
 selectiveInference/R/RcppExports.R            | 10 ++--
 selectiveInference/src/RcppExports.cpp        | 55 ++++++++++++-------
 .../src/quadratic_program_wide.c              |  4 ++
 test.R                                        | 38 +++++++++----
 4 files changed, 71 insertions(+), 36 deletions(-)

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
index 8af4640..f5ebee4 100644
--- a/selectiveInference/R/RcppExports.R
+++ b/selectiveInference/R/RcppExports.R
@@ -1,19 +1,19 @@
-# This file was generated by Rcpp::compileAttributes
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
+    .Call('_selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
 }
 
 solve_QP_wide <- function(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active)
+    .Call('_selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active)
 }
 
 update1_ <- function(Q2, w, m, k) {
-    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
+    .Call('_selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
 }
 
 downdate1_ <- function(Q1, R, j0, m, n) {
-    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
+    .Call('_selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
 }
 
diff --git a/selectiveInference/src/RcppExports.cpp b/selectiveInference/src/RcppExports.cpp
index efa87d5..02a7741 100644
--- a/selectiveInference/src/RcppExports.cpp
+++ b/selectiveInference/src/RcppExports.cpp
@@ -1,4 +1,4 @@
-// This file was generated by Rcpp::compileAttributes
+// Generated by using Rcpp::compileAttributes() -> do not edit by hand
 // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 #include <Rcpp.h>
@@ -7,10 +7,10 @@ using namespace Rcpp;
 
 // solve_QP
 Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+RcppExport SEXP _selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Sigma(SigmaSEXP);
     Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
     Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
@@ -22,16 +22,16 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
     Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
     Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    __result = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return rcpp_result_gen;
 END_RCPP
 }
 // solve_QP_wide
 Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::NumericVector X_theta, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP X_thetaSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+RcppExport SEXP _selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP X_thetaSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type X(XSEXP);
     Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
     Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
@@ -44,36 +44,49 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
     Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
     Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    __result = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return rcpp_result_gen;
 END_RCPP
 }
 // update1_
 Rcpp::List update1_(Rcpp::NumericMatrix Q2, Rcpp::NumericVector w, int m, int k);
-RcppExport SEXP selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
+RcppExport SEXP _selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q2(Q2SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericVector >::type w(wSEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type k(kSEXP);
-    __result = Rcpp::wrap(update1_(Q2, w, m, k));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(update1_(Q2, w, m, k));
+    return rcpp_result_gen;
 END_RCPP
 }
 // downdate1_
 Rcpp::List downdate1_(Rcpp::NumericMatrix Q1, Rcpp::NumericMatrix R, int j0, int m, int n);
-RcppExport SEXP selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
+RcppExport SEXP _selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q1(Q1SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type R(RSEXP);
     Rcpp::traits::input_parameter< int >::type j0(j0SEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    __result = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
+    return rcpp_result_gen;
 END_RCPP
 }
+
+static const R_CallMethodDef CallEntries[] = {
+    {"_selectiveInference_solve_QP", (DL_FUNC) &_selectiveInference_solve_QP, 11},
+    {"_selectiveInference_solve_QP_wide", (DL_FUNC) &_selectiveInference_solve_QP_wide, 12},
+    {"_selectiveInference_update1_", (DL_FUNC) &_selectiveInference_update1_, 4},
+    {"_selectiveInference_downdate1_", (DL_FUNC) &_selectiveInference_downdate1_, 5},
+    {NULL, NULL, 0}
+};
+
+RcppExport void R_init_selectiveInference(DllInfo *dll) {
+    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
+    R_useDynamicSymbols(dll, FALSE);
+}
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
index be7411d..ca9301e 100644
--- a/selectiveInference/src/quadratic_program_wide.c
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -388,6 +388,7 @@ int solve_wide(double *X_ptr,              /* A design matrix */
 		       ncase,
 		       bound,
 		       kkt_tol) == 1) {
+      fprintf(stderr, "break1\n");
       break;
     }
 
@@ -426,6 +427,7 @@ int solve_wide(double *X_ptr,              /* A design matrix */
 		       ncase,
 		       bound,
 		       kkt_tol) == 1) {
+      fprintf(stderr, "break2\n");
       break;
     }
 					  
@@ -434,6 +436,7 @@ int solve_wide(double *X_ptr,              /* A design matrix */
     // fprintf(stderr, "here18 %d\n", *nactive_ptr);
 
     if (*nactive_ptr >= max_active) {
+      fprintf(stderr, "break3\n");
       break;
     }
 
@@ -454,6 +457,7 @@ int solve_wide(double *X_ptr,              /* A design matrix */
       // fprintf(stderr, "here8\n");
 
       if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
+	fprintf(stderr, "break5 %f %f %f %d\n", old_value, new_value, objective_tol, iter);
 	break;
       }
       old_value = new_value;
diff --git a/test.R b/test.R
index 2d9349d..31264be 100644
--- a/test.R
+++ b/test.R
@@ -2,28 +2,46 @@ set.seed(43)
 
     n = 100                                                                                                                                                                              
     p = 200                                                                                                                                                                              
-    lam = 0.1                                                                                                                                                                             
+    lam = 0.2                                                                                                                                                                             
     X = matrix(rnorm(n*p), n, p)                                                                                                                                                         
     Y = rnorm(n)                                                                                                                                                                         
     library(selectiveInference)                                                                                                                                                          
     p = ncol(X)                                                                                                                                                                          
     soln_R = rep(0, p)                                                                                                                                                                   
     grad = -t(X) %*% Y / n                                                                                                                                                                   
-    ever_active = c(1, rep(0, p-1))                                                                                                                                                      
+    ever_active = as.integer(c(1, rep(0, p-1)))
     nactive = as.integer(1)                                                                                                                                                              
     kkt_tol = 1.e-12                                                                                                                                                                     
-    objective_tol = 1.e-12                                                                                                                                                               
+    objective_tol = 1.e-16                                                                                                                                                               
     maxiter = 500                                                                                                                                                                        
-    soln_R = selectiveInference:::solve_QP(t(X) %*% X / n, lam, maxiter, soln_R, -t(X) %*% Y / n, grad, ever_active, nactive, kkt_tol, objective_tol, p)$soln                                    
+    soln_R = selectiveInference:::solve_QP(t(X) %*% X / n, lam, maxiter, soln_R, -t(X) %*% Y / n, grad, ever_active, nactive, kkt_tol, objective_tol, p)
+    print('active')
+    print(nactive)
+    print(ever_active)
+    print(soln_R$ever_active)
+    soln_R = soln_R$soln
+    soln_R_old = soln_R
     print(soln_R)                                                                                                                                                                        
     Xtheta = rep(0, n)                                                                                                                                                                   
     nactive = as.integer(1)
-    ever_active = c(1, rep(0, p-1))                                                                                                                                                      
+    ever_active = as.integer(c(1, rep(0, p-1)))
     soln_R = rep(0, p)
     grad = - t(X) %*% Y / n
     # test wide solver                                                                                                                                                                   
-    soln_R_wide = selectiveInference:::solve_QP_wide(X, lam, maxiter, soln_R, -t(X) %*% Y / n, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)
-    print(soln_R_wide)
-    print(soln_R)
-    print(soln_R_wide$soln)
-    print(soln_R_wide$soln - soln_R)
\ No newline at end of file
+    soln_R_wide = selectiveInference:::solve_QP_wide(X, lam, maxiter, soln_R*1., -t(X) %*% Y / n, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)
+    print(nactive)
+    print(soln_R_wide$ever_active)
+
+    print('diff')
+    print(soln_R_wide$soln - soln_R_old)
+    print(soln_R_wide$gradient[soln_R_wide$ever_active])
+    print(max(abs(soln_R_wide$gradient[-soln_R_wide$ever_active])))
+    print(soln_R_wide$kkt_check)
+    print(soln_R_wide$iter)
+#    print(Xtheta - X %*% soln_R_wide$soln)
+#     print(Xtheta)
+
+     soln_R_wide = selectiveInference:::solve_QP_wide(X, 0.7 * lam, maxiter, soln_R_wide$soln, -t(X) %*% Y / n, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)
+#     print(Xtheta - X %*% soln_R_wide$soln)
+#     print(soln_R_wide$soln)
+     soln_R_wide = selectiveInference:::solve_QP_wide(X, 0.5 * lam, maxiter, soln_R_wide$soln, -t(X) %*% Y / n, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)
\ No newline at end of file

From bf65665533fcfad41225ea64f0bb8da72c753063 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 27 Sep 2017 22:12:46 -0700
Subject: [PATCH 257/493] WIP: C code for wide version working -- need to work
 on R code

---
 selectiveInference/src/Makevars               |  2 +-
 selectiveInference/src/Rcpp-debias.cpp        | 18 ++--
 selectiveInference/src/debias.h               | 46 +++++-----
 selectiveInference/src/quadratic_program.c    | 56 ++++++------
 .../src/quadratic_program_wide.c              | 88 +++++++------------
 5 files changed, 92 insertions(+), 118 deletions(-)

diff --git a/selectiveInference/src/Makevars b/selectiveInference/src/Makevars
index b77ed95..2adf9a6 100644
--- a/selectiveInference/src/Makevars
+++ b/selectiveInference/src/Makevars
@@ -2,7 +2,7 @@ PKG_CFLAGS= -I.
 PKG_CPPFLAGS= -I.
 PKG_LIBS=-L. 
 
-$(SHLIB): Rcpp Rcpp-matrixcomps.o Rcpp-debias.o RcppExports.o quadratic_program.o
+$(SHLIB): Rcpp Rcpp-matrixcomps.o Rcpp-debias.o RcppExports.o quadratic_program.o quadratic_program_wide.o
 
 clean:
 	rm -f *o
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index fcf0eb6..ce8bb15 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -1,5 +1,5 @@
 #include <Rcpp.h>      // need to include the main Rcpp header file 
-#include <debias.h>    // where find_one_row_void is defined
+#include <debias.h>    // where solve_QP, solve_QP_wide are defined
 
 // Below, the gradient should be equal to Sigma * theta + linear_func!!
 // No check is done on this.
@@ -97,15 +97,15 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
   Rcpp::IntegerVector need_update(nfeature);
 
   // Extract the diagonal
-  Rcpp::NumericVector X_diag(nfeature);
-  double *X_diag_p = X_diag.begin();
+  Rcpp::NumericVector nndef_diag(nfeature);
+  double *nndef_diag_p = nndef_diag.begin();
 
-  for (icase=0; icase<ncase; icase++) {
-    X_diag_p[icase] = 0;
-    for (ifeature=0; ifeature<nfeature; ifeature++) {
-      X_diag_p[icase] += X(icase, ifeature) * X(icase, ifeature);
+  for (ifeature=0; ifeature<nfeature; ifeature++) {
+    nndef_diag_p[ifeature] = 0;
+    for (icase=0; icase<ncase; icase++) {
+      nndef_diag_p[ifeature] += X(icase, ifeature) * X(icase, ifeature);
     }
-    X_diag_p[icase] = X_diag_p[icase] / ncase;
+    nndef_diag_p[ifeature] = nndef_diag_p[ifeature] / ncase;
   }
   
   // Now call our C function
@@ -113,7 +113,7 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
   int iter = solve_wide((double *) X.begin(),
 			(double *) X_theta.begin(),
 			(double *) linear_func.begin(),
-			(double *) X_diag.begin(),
+			(double *) nndef_diag.begin(),
 			(double *) gradient.begin(),
 			(int *) need_update.begin(),
 			(int *) ever_active.begin(),
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index 70ba536..916bc14 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -3,31 +3,31 @@ extern "C"
 {
 #endif /* __cplusplus */
 
-int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
+int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	     double *linear_func_ptr,    /* Linear term in objective */
-	     double *Sigma_diag_ptr,     /* Diagonal entry of covariance matrix */
-	     double *gradient_ptr,       /* Current gradient of quadratic loss */
-	     int *ever_active_ptr,       /* Ever active set: 0-based */ 
+	     double *nndef_diag_ptr,     /* Diagonal of nndef */
+	     double *gradient_ptr,       /* nndef times theta */
+	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
 	     int *nactive_ptr,           /* Size of ever active set */
-	     int nrow,                   /* How many rows in Sigma */
+	     int nrow,                   /* How many rows in nndef */
 	     double bound,               /* feasibility parameter */
 	     double *theta,              /* current value */
-	     int maxiter,                /* how many iterations */
+	     int maxiter,                /* max number of iterations */
 	     double kkt_tol,             /* precision for checking KKT conditions */
 	     double objective_tol,       /* precision for checking relative decrease in objective value */
-	     int max_active);            /* Upper limit for size of active set -- otherwise break */
+	     int max_active);             /* Upper limit for size of active set -- otherwise break */ 
 
-int check_KKT_qp(double *theta,           /* current theta */
-		 double *gradient_ptr,    /* Current gradient of quadratic loss */
-		 int nrow,                /* how many rows in Sigma */
-		 double bound,            /* Lagrange multipler for \ell_1 */ 
-		 double tol);             /* precision for checking KKT conditions */
+int check_KKT_qp(double *theta,        /* current theta */
+		 double *gradient_ptr, /* nndef times theta + linear_func */
+		 int nrow,             /* how many rows in nndef */
+		 double bound,         /* Lagrange multipler for \ell_1 */
+		 double tol)           /* precision for checking KKT conditions */        
 
-int solve_wide(double *X_ptr,              /* A design matrix */
+int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
 	       double *X_theta_ptr,        /* Fitted values   */
 	       double *linear_func_ptr,    /* Linear term in objective */
-	       double *X_diag_ptr,         /* Diagonal entry of covariance matrix */
-	       double *gradient_ptr,       /* X times theta */
+	       double *nndef_diag_ptr,     /* Diagonal entries of non-neg def matrix */
+	       double *gradient_ptr,       /* X^TX/ncase times theta + linear_func*/
 	       int *need_update_ptr,       /* Keeps track of updated gradient coords */
 	       int *ever_active_ptr,       /* Ever active set: 1-based */ 
 	       int *nactive_ptr,           /* Size of ever active set */
@@ -38,23 +38,23 @@ int solve_wide(double *X_ptr,              /* A design matrix */
 	       int maxiter,                /* max number of iterations */
 	       double kkt_tol,             /* precision for checking KKT conditions */
 	       double objective_tol,       /* precision for checking relative decrease in objective value */
-	       int max_active);            /* Upper limit for size of active set -- otherwise break */ 
+	       int max_active);             /* Upper limit for size of active set -- otherwise break */ 
 
 int check_KKT_wide(double *theta_ptr,        /* current theta */
-		   double *gradient_ptr,     /* X^TX/n times theta */
+		   double *gradient_ptr,     /* X^TX/ncase times theta + linear_func*/
 		   double *X_theta_ptr,      /* Current fitted values */
-		   double *X_ptr,            /* A design matrix */
-		   double *linear_func_ptr,    /* Linear term in objective */
+		   double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
+		   double *linear_func_ptr,  /* Linear term in objective */   
 		   int *need_update_ptr,     /* Which coordinates need to be updated? */
 		   int nfeature,             /* how many columns in X */
 		   int ncase,                /* how many rows in X */
    		   double bound,             /* Lagrange multipler for \ell_1 */
-		   double tol);              /* precision for checking KKT conditions */        
+		   double tol)               /* precision for checking KKT conditions */        
   
-void update_gradient_wide(double *gradient_ptr,     /* X^TX/n times theta */
+void update_gradient_wide(double *gradient_ptr,     /* X^TX/ncase times theta + linear_func */
 			  double *X_theta_ptr,      /* Current fitted values */
-			  double *X_ptr,            /* A design matrix */
-			  double *linear_func_ptr,    /* Linear term in objective */
+			  double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
+			  double *linear_func_ptr,  /* Linear term in objective */   
 			  int *need_update_ptr,     /* Which coordinates need to be updated? */
 			  int nfeature,             /* how many columns in X */
 			  int ncase);               /* how many rows in X */
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
index 4afbd32..cee3fa4 100644
--- a/selectiveInference/src/quadratic_program.c
+++ b/selectiveInference/src/quadratic_program.c
@@ -1,6 +1,6 @@
 #include <math.h> // for fabs
 
-// Find an approximate row of \hat{Sigma}^{-1}
+// Find an approximate row of \hat{nndef}^{-1}
 
 // Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
 
@@ -11,17 +11,17 @@
 // Therefore we don't have to negate the answer to get theta.
 // Update one coordinate 
 
-double objective_qp(double *Sigma_ptr,       /* A covariance matrix: X^TX/n */
+double objective_qp(double *nndef_ptr,       /* A non-negative definite matrix */
 		    double *linear_func_ptr, /* Linear term in objective */
 		    int *ever_active_ptr,    /* Ever active set: 0-based */ 
 		    int *nactive_ptr,        /* Size of ever active set */
-		    int nrow,                /* how many rows in Sigma */
+		    int nrow,                /* how many rows in nndef */
 		    double bound,            /* Lagrange multipler for \ell_1 */
 		    double *theta)           /* current value */
 {
   int irow, icol;
   double value = 0;
-  double *Sigma_ptr_tmp = Sigma_ptr;
+  double *nndef_ptr_tmp = nndef_ptr;
   double *linear_func_ptr_tmp = linear_func_ptr;
   double *theta_row_ptr, *theta_col_ptr;
   int *active_row_ptr, *active_col_ptr;
@@ -43,9 +43,9 @@ double objective_qp(double *Sigma_ptr,       /* A covariance matrix: X^TX/n */
       active_col = *active_col_ptr - 1;          // Ever-active is 1-based
       theta_col_ptr = ((double *) theta + active_col);
 
-      Sigma_ptr_tmp = ((double *) Sigma_ptr + nrow * active_col + active_row); // Matrices are column-major order
+      nndef_ptr_tmp = ((double *) nndef_ptr + nrow * active_col + active_row); // Matrices are column-major order
 
-      value += 0.5 * (*Sigma_ptr_tmp) * (*theta_row_ptr) * (*theta_col_ptr);
+      value += 0.5 * (*nndef_ptr_tmp) * (*theta_row_ptr) * (*theta_col_ptr);
     }
     value += bound * fabs((*theta_row_ptr)); // the \ell_1 term
 
@@ -91,8 +91,8 @@ int update_ever_active_qp(int coord,
 }
 
 int check_KKT_qp(double *theta,        /* current theta */
-		 double *gradient_ptr, /* Sigma times theta */
-		 int nrow,             /* how many rows in Sigma */
+		 double *gradient_ptr, /* nndef times theta + linear_func */
+		 int nrow,             /* how many rows in nndef */
 		 double bound,         /* Lagrange multipler for \ell_1 */
 		 double tol)           /* precision for checking KKT conditions */        
 {
@@ -128,13 +128,13 @@ int check_KKT_qp(double *theta,        /* current theta */
   return(1);
 }
 
-double update_one_coord_qp(double *Sigma_ptr,           /* A covariance matrix: X^TX/n */
+double update_one_coord_qp(double *nndef_ptr,           /* A non-negative definite matrix */
 			   double *linear_func_ptr,     /* Linear term in objective */
-			   double *Sigma_diag_ptr,      /* Diagonal entries of Sigma */
-			   double *gradient_ptr,        /* Sigma times theta */
+			   double *nndef_diag_ptr,      /* Diagonal of nndef */
+			   double *gradient_ptr,        /* nndef times theta + linear_func */
 			   int *ever_active_ptr,        /* Ever active set: 1-based */ 
 			   int *nactive_ptr,            /* Size of ever active set */
-			   int nrow,                    /* How many rows in Sigma */
+			   int nrow,                    /* How many rows in nndef */
 			   double bound,                /* feasibility parameter */
 			   double *theta,               /* current value */
 			   int coord,                   /* which coordinate to update: 0-based */
@@ -145,12 +145,12 @@ double update_one_coord_qp(double *Sigma_ptr,           /* A covariance matrix:
   double linear_term = 0;
   double value = 0;
   double old_value;
-  double *Sigma_ptr_tmp;
+  double *nndef_ptr_tmp;
   double *gradient_ptr_tmp;
   double *theta_ptr;
   int icol = 0;
 
-  double *quadratic_ptr = ((double *) Sigma_diag_ptr + coord);
+  double *quadratic_ptr = ((double *) nndef_diag_ptr + coord);
   double quadratic_term = *quadratic_ptr;
 
   gradient_ptr_tmp = ((double *) gradient_ptr + coord);
@@ -160,7 +160,7 @@ double update_one_coord_qp(double *Sigma_ptr,           /* A covariance matrix:
   old_value = *theta_ptr;
 
   // The coord entry of gradient_ptr term has a diagonal term in it:
-  // Sigma[coord, coord] * theta[coord]
+  // nndef[coord, coord] * theta[coord]
   // This removes it. 
 
   linear_term -= quadratic_term * old_value;
@@ -191,13 +191,13 @@ double update_one_coord_qp(double *Sigma_ptr,           /* A covariance matrix:
   if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
 
     delta = value - old_value;
-    Sigma_ptr_tmp = ((double *) Sigma_ptr + coord * nrow);
+    nndef_ptr_tmp = ((double *) nndef_ptr + coord * nrow);
     gradient_ptr_tmp = ((double *) gradient_ptr);
 
     for (icol=0; icol<nrow; icol++) {
-      *gradient_ptr_tmp = *gradient_ptr_tmp + delta * (*Sigma_ptr_tmp);
+      *gradient_ptr_tmp = *gradient_ptr_tmp + delta * (*nndef_ptr_tmp);
       gradient_ptr_tmp += 1;
-      Sigma_ptr_tmp += 1;
+      nndef_ptr_tmp += 1;
     }
 
     theta_ptr = ((double *) theta + coord);
@@ -209,13 +209,13 @@ double update_one_coord_qp(double *Sigma_ptr,           /* A covariance matrix:
 
 }
 
-int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
+int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	     double *linear_func_ptr,    /* Linear term in objective */
-	     double *Sigma_diag_ptr,     /* Diagonal entry of covariance matrix */
-	     double *gradient_ptr,       /* Sigma times theta */
+	     double *nndef_diag_ptr,     /* Diagonal of nndef */
+	     double *gradient_ptr,       /* nndef times theta */
 	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
 	     int *nactive_ptr,           /* Size of ever active set */
-	     int nrow,                   /* How many rows in Sigma */
+	     int nrow,                   /* How many rows in nndef */
 	     double bound,               /* feasibility parameter */
 	     double *theta,              /* current value */
 	     int maxiter,                /* max number of iterations */
@@ -235,7 +235,7 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 
   if (check_objective) {
 
-    old_value = objective_qp(Sigma_ptr,
+    old_value = objective_qp(nndef_ptr,
 			     linear_func_ptr,
 			     ever_active_ptr,
 			     nactive_ptr,
@@ -253,9 +253,9 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
     active_ptr = (int *) ever_active_ptr;
 
     for (iactive=0; iactive < *nactive_ptr; iactive++) {
-      update_one_coord_qp(Sigma_ptr,
+      update_one_coord_qp(nndef_ptr,
 			  linear_func_ptr,
-			  Sigma_diag_ptr,
+			  nndef_diag_ptr,
 			  gradient_ptr,
 			  ever_active_ptr,
 			  nactive_ptr,
@@ -281,9 +281,9 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
 
     for (icoord=0; icoord<nrow; icoord++) {
 
-      update_one_coord_qp(Sigma_ptr,
+      update_one_coord_qp(nndef_ptr,
 			  linear_func_ptr,
-			  Sigma_diag_ptr,
+			  nndef_diag_ptr,
 			  gradient_ptr,
 			  ever_active_ptr,
 			  nactive_ptr,
@@ -313,7 +313,7 @@ int solve_qp(double *Sigma_ptr,          /* A covariance matrix: X^TX/n */
     // Check relative decrease of objective
 
     if (check_objective) {
-      new_value = objective_qp(Sigma_ptr,
+      new_value = objective_qp(nndef_ptr,
 			       linear_func_ptr,
 			       ever_active_ptr,
 			       nactive_ptr,
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
index ca9301e..3504492 100644
--- a/selectiveInference/src/quadratic_program_wide.c
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -1,5 +1,4 @@
 #include <math.h> // for fabs
-#include <stdio.h>
 
 // Find an approximate row of \hat{Sigma}^{-1}
 
@@ -50,8 +49,6 @@ double objective_wide(double *X_theta_ptr,     /* Fitted values */
     active_feature_ptr = ((int *) ever_active_ptr + iactive);
     active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
 
-    // fprintf(stderr, "active %d\n", active_feature);
-
     theta_ptr_tmp = ((double *) theta_ptr + active_feature);
     linear_func_ptr_tmp = ((double *) linear_func_ptr + active_feature);
     value += (*linear_func_ptr_tmp) * (*theta_ptr_tmp); 
@@ -69,7 +66,7 @@ double objective_wide(double *X_theta_ptr,     /* Fitted values */
 
 double compute_gradient_coord(double *gradient_ptr,        /* Gradient -- one coordinate will be updated if needed */
 			      double *X_theta_ptr,         /* Current fitted values */
-			      double *X_ptr,               /* A design matrix */
+			      double *X_ptr,               /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
 			      double *linear_func_ptr,     /* Linear term in objective */   
 			      int *need_update_ptr,        /* Which coordinates need to be updated? */
 			      int coord,                   /* Coordinate we are trying to update */
@@ -85,11 +82,10 @@ double compute_gradient_coord(double *gradient_ptr,        /* Gradient -- one co
   double value = 0.;
 
   need_update_ptr_tmp = ((int *) need_update_ptr + coord);
+
   // Check if this coordinate needs updating
   if (*need_update_ptr_tmp == 1) {
 
-    // fprintf(stderr, "compute grad %d %d\n", ncase, coord);
-
     for (icase=0; icase<ncase; icase++) {
       X_ptr_tmp = ((double *) X_ptr + coord * ncase + icase);
       X_theta_ptr_tmp = ((double *) X_theta_ptr + icase);
@@ -111,7 +107,6 @@ double compute_gradient_coord(double *gradient_ptr,        /* Gradient -- one co
     value = *gradient_ptr_tmp;
   }
 
-  // fprintf(stderr, "grad %d, %f\n", coord, value);
   return(value);
 }
 
@@ -129,7 +124,7 @@ int update_ever_active_wide(int coord,
   for (iactive=0; iactive<nactive; iactive++) {
     ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
     active_var = *ever_active_ptr_tmp;
-    // fprintf(stderr, "active check %d\n", active_var-1);
+
     if (active_var - 1 == coord) {          // Ever-active is 1-based
       return(1);
     }
@@ -148,10 +143,10 @@ int update_ever_active_wide(int coord,
   return(0);
 }
 
-void update_gradient_wide(double *gradient_ptr,     /* X^TX/n times theta */
+void update_gradient_wide(double *gradient_ptr,     /* X^TX/ncase times theta + linear_func */
 			  double *X_theta_ptr,      /* Current fitted values */
-			  double *X_ptr,            /* A design matrix */
-			  double *linear_func_ptr,     /* Linear term in objective */   
+			  double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
+			  double *linear_func_ptr,  /* Linear term in objective */   
 			  int *need_update_ptr,     /* Which coordinates need to be updated? */
 			  int nfeature,             /* how many columns in X */
 			  int ncase)                /* how many rows in X */
@@ -165,9 +160,9 @@ void update_gradient_wide(double *gradient_ptr,     /* X^TX/n times theta */
 }
 
 int check_KKT_wide(double *theta_ptr,        /* current theta */
-		   double *gradient_ptr,     /* X^TX/n times theta */
+		   double *gradient_ptr,     /* X^TX/ncase times theta + linear_func*/
 		   double *X_theta_ptr,      /* Current fitted values */
-		   double *X_ptr,            /* A design matrix */
+		   double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
 		   double *linear_func_ptr,     /* Linear term in objective */   
 		   int *need_update_ptr,     /* Which coordinates need to be updated? */
 		   int nfeature,             /* how many columns in X */
@@ -187,23 +182,20 @@ int check_KKT_wide(double *theta_ptr,        /* current theta */
 
     // Compute this coordinate of the gradient
 
-    // fprintf(stderr, "%d \n", ifeature);
     gradient = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, ifeature, ncase);
 
     if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
-      // fprintf(stderr, "WTF\n");
+
       if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
-	// fprintf(stderr, "WTF2\n");
 	return(0);
       }
       else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
-	// fprintf(stderr, "WTF3\n");
 	return(0);
       }
+
     }
     else {
       if (fabs(gradient) > (1. + tol) * bound) {
-	// fprintf(stderr, "inactive\n");
 	return(0);
       }
     }
@@ -214,8 +206,8 @@ int check_KKT_wide(double *theta_ptr,        /* current theta */
 
 double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
 			     double *linear_func_ptr,     /* Linear term in objective */
-			     double *X_diag_ptr,          /* Diagonal entries of Sigma */
-			     double *gradient_ptr,        /* X^TX/n times theta */
+			     double *nndef_diag_ptr,          /* Diagonal entries of Sigma */
+			     double *gradient_ptr,     /* X^TX/ncase times theta + linear_func*/
 			     int *ever_active_ptr,        /* Ever active set: 1-based */ 
 			     int *nactive_ptr,            /* Size of ever active set */
 			     double *X_theta_ptr,         /* X\theta -- fitted values */
@@ -238,8 +230,8 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
   double *theta_ptr_tmp;
   int ifeature, icase;
 
-  double *quadratic_ptr = ((double *) X_diag_ptr + coord);
-  double quadratic_term = *quadratic_ptr;
+  double *diagonal_ptr = ((double *) nndef_diag_ptr + coord);
+  double diagonal_entry = *diagonal_ptr;
 
   linear_term = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, coord, ncase);
 
@@ -247,24 +239,24 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
   old_value = *theta_ptr_tmp;
 
   // The coord entry of gradient_ptr term has a diagonal term in it:
-  // X[coord, coord] * theta[coord]
+  // (X^TX)[coord, coord] * theta[coord] / ncase
   // This removes it. 
 
-  linear_term -= quadratic_term * old_value;
+  linear_term -= diagonal_entry * old_value;
 
   // Now soft-threshold the coord entry of theta 
 
   // Objective is t \mapsto q/2 * t^2 + l * t + bound |t|
-  // with q=quadratic_term and l=linear_term
+  // with q=diagonal_entry and l=linear_term
 
   // With a negative linear term, solution should be
   // positive
 
   if (linear_term < -bound) {
-    value = (-linear_term - bound) / quadratic_term;
+    value = (-linear_term - bound) / diagonal_entry;
   }
   else if (linear_term > bound) {
-    value = -(linear_term - bound) / quadratic_term;
+    value = -(linear_term - bound) / diagonal_entry;
   }
 
   // Add to active set if necessary
@@ -273,7 +265,7 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
     update_ever_active_wide(coord, ever_active_ptr, nactive_ptr);
   }
 
-  // Update the linear term
+  // Update X\theta if changed
 
   if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
 
@@ -298,17 +290,22 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
     theta_ptr_tmp = ((double *) theta_ptr + coord);
     *theta_ptr_tmp = value;
 
+    theta_ptr_tmp = theta_ptr;
+    for (ifeature=0; ifeature<nfeature; ifeature++) {
+      theta_ptr_tmp++;
+    }
+
   }
 
   return(value);
 
 }
 
-int solve_wide(double *X_ptr,              /* A design matrix */
+int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
 	       double *X_theta_ptr,        /* Fitted values   */
 	       double *linear_func_ptr,    /* Linear term in objective */
-	       double *X_diag_ptr,         /* Diagonal entry of covariance matrix */
-	       double *gradient_ptr,       /* X times theta */
+	       double *nndef_diag_ptr,     /* Diagonal entries of non-neg def matrix */
+	       double *gradient_ptr,       /* X^TX/ncase times theta + linear_func*/
 	       int *need_update_ptr,       /* Keeps track of updated gradient coords */
 	       int *ever_active_ptr,       /* Ever active set: 1-based */ 
 	       int *nactive_ptr,           /* Size of ever active set */
@@ -331,10 +328,7 @@ int solve_wide(double *X_ptr,              /* A design matrix */
 
   double old_value, new_value; 
 
-  // fprintf(stderr, "here1\n");
-
   if (check_objective) {
-    // fprintf(stderr, "here9 %d %d\n", ncase, nfeature);
 
     old_value = objective_wide(X_theta_ptr,
 			       linear_func_ptr,
@@ -347,19 +341,16 @@ int solve_wide(double *X_ptr,              /* A design matrix */
 
   }
 
-
-  // fprintf(stderr, "here2\n");
-
   for (iter=0; iter<maxiter; iter++) {
 
     // Update the active variables first
 
     active_ptr = (int *) ever_active_ptr;
-
     for (iactive=0; iactive < *nactive_ptr; iactive++) {
+
       update_one_coord_wide(X_ptr,
 			    linear_func_ptr,
-			    X_diag_ptr,
+			    nndef_diag_ptr,
 			    gradient_ptr,
 			    ever_active_ptr,
 			    nactive_ptr,
@@ -374,8 +365,6 @@ int solve_wide(double *X_ptr,              /* A design matrix */
       active_ptr++;
     }
 
-  // fprintf(stderr, "here3\n");
-
     // Check KKT
 
     if (check_KKT_wide(theta_ptr,
@@ -388,7 +377,6 @@ int solve_wide(double *X_ptr,              /* A design matrix */
 		       ncase,
 		       bound,
 		       kkt_tol) == 1) {
-      fprintf(stderr, "break1\n");
       break;
     }
 
@@ -396,10 +384,9 @@ int solve_wide(double *X_ptr,              /* A design matrix */
 
     for (ifeature=0; ifeature<nfeature; ifeature++) {
 
-      // fprintf(stderr, "updating %d\n", ifeature);
       update_one_coord_wide(X_ptr,
 			    linear_func_ptr,
-			    X_diag_ptr,
+			    nndef_diag_ptr,
 			    gradient_ptr,
 			    ever_active_ptr,
 			    nactive_ptr,
@@ -413,8 +400,6 @@ int solve_wide(double *X_ptr,              /* A design matrix */
 			    0);
     }
 
-    // fprintf(stderr, "here6\n");
-
     // Check KKT
 
     if (check_KKT_wide(theta_ptr,
@@ -427,23 +412,17 @@ int solve_wide(double *X_ptr,              /* A design matrix */
 		       ncase,
 		       bound,
 		       kkt_tol) == 1) {
-      fprintf(stderr, "break2\n");
       break;
     }
 					  
     // Check size of active set
 
-    // fprintf(stderr, "here18 %d\n", *nactive_ptr);
-
     if (*nactive_ptr >= max_active) {
-      fprintf(stderr, "break3\n");
       break;
     }
 
     // Check relative decrease of objective
 
-    // fprintf(stderr, "here7\n");
-
     if (check_objective) {
       new_value = objective_wide(X_theta_ptr,
 				 linear_func_ptr,
@@ -454,17 +433,12 @@ int solve_wide(double *X_ptr,              /* A design matrix */
 				 bound,
 				 theta_ptr);
 
-      // fprintf(stderr, "here8\n");
-
       if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
-	fprintf(stderr, "break5 %f %f %f %d\n", old_value, new_value, objective_tol, iter);
 	break;
       }
       old_value = new_value;
     }
 
-      // fprintf(stderr, "here10\n");
-
   }
   return(iter);
 }

From f85a568facc0f6d72d424d486a56642b42ffd97d Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 27 Sep 2017 22:35:15 -0700
Subject: [PATCH 258/493] test of wide solver

---
 test.R | 75 ++++++++++++++++++++++++++++++++++++++--------------------
 1 file changed, 49 insertions(+), 26 deletions(-)

diff --git a/test.R b/test.R
index 31264be..0660840 100644
--- a/test.R
+++ b/test.R
@@ -14,34 +14,57 @@ set.seed(43)
     kkt_tol = 1.e-12                                                                                                                                                                     
     objective_tol = 1.e-16                                                                                                                                                               
     maxiter = 500                                                                                                                                                                        
-    soln_R = selectiveInference:::solve_QP(t(X) %*% X / n, lam, maxiter, soln_R, -t(X) %*% Y / n, grad, ever_active, nactive, kkt_tol, objective_tol, p)
-    print('active')
-    print(nactive)
-    print(ever_active)
-    print(soln_R$ever_active)
-    soln_R = soln_R$soln
-    soln_R_old = soln_R
-    print(soln_R)                                                                                                                                                                        
-    Xtheta = rep(0, n)                                                                                                                                                                   
+    soln_R = rep(0, p)
+    grad = -t(X) %*% Y / n
+    ever_active = as.integer(c(1, rep(0, p-1)))
+    nactive = as.integer(1)
+    kkt_tol = 1.e-12
+    objective_tol = 1.e-16
+    maxiter = 500
+    soln_R = selectiveInference:::solve_QP(t(X) %*% X / n, lam, maxiter, soln_R, -t(X) %*% Y / n, grad, ever_active, nactive, kkt_tol, objective_tol, p)$soln
+
+    # test wide solver
+    Xtheta = rep(0, n)
     nactive = as.integer(1)
     ever_active = as.integer(c(1, rep(0, p-1)))
-    soln_R = rep(0, p)
+    soln_R_wide = rep(0, p)
     grad = - t(X) %*% Y / n
-    # test wide solver                                                                                                                                                                   
-    soln_R_wide = selectiveInference:::solve_QP_wide(X, lam, maxiter, soln_R*1., -t(X) %*% Y / n, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)
-    print(nactive)
-    print(soln_R_wide$ever_active)
+    soln_R_wide = selectiveInference:::solve_QP_wide(X, lam, maxiter, soln_R_wide, -t(X) %*% Y / n, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)$soln
+
+#     soln_R = selectiveInference:::solve_QP(t(X) %*% X / n, lam, maxiter, soln_R, -t(X) %*% Y / n, grad, ever_active, nactive, kkt_tol, objective_tol, p)
+#     print('active')
+#     print(nactive)
+#     print(ever_active)
+#     print(soln_R$ever_active)
+#     soln_R = soln_R$soln
+#     soln_R_old = soln_R
+#     print(soln_R)                                                                                                                                                                        
+#     Xtheta = rep(0, n)                                                                                                                                                                   
+#     nactive = as.integer(1)
+#     ever_active = as.integer(c(1, rep(0, p-1)))
+#     soln_R = rep(0, p)
+#     grad = - t(X) %*% Y / n
+#     # test wide solver                                                                                                                                                                   
+#     soln_R_wide = selectiveInference:::solve_QP_wide(X, lam, maxiter, soln_R*1., -t(X) %*% Y / n, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)
+#     print(nactive)
+#     print(soln_R_wide$ever_active)
 
-    print('diff')
-    print(soln_R_wide$soln - soln_R_old)
-    print(soln_R_wide$gradient[soln_R_wide$ever_active])
-    print(max(abs(soln_R_wide$gradient[-soln_R_wide$ever_active])))
-    print(soln_R_wide$kkt_check)
-    print(soln_R_wide$iter)
-#    print(Xtheta - X %*% soln_R_wide$soln)
-#     print(Xtheta)
+     print('diff')
+    print(soln_R_wide - soln_R)
+#     print(soln_R_wide$gradient[soln_R_wide$ever_active])
+#     print(max(abs(soln_R_wide$gradient[-soln_R_wide$ever_active])))
+#     print(soln_R_wide$kkt_check)
+#     print(soln_R_wide$iter)
+#     print(max(abs(Xtheta - X %*% soln_R_wide$soln)))
+# #     print(Xtheta)
 
-     soln_R_wide = selectiveInference:::solve_QP_wide(X, 0.7 * lam, maxiter, soln_R_wide$soln, -t(X) %*% Y / n, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)
-#     print(Xtheta - X %*% soln_R_wide$soln)
-#     print(soln_R_wide$soln)
-     soln_R_wide = selectiveInference:::solve_QP_wide(X, 0.5 * lam, maxiter, soln_R_wide$soln, -t(X) %*% Y / n, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)
\ No newline at end of file
+#       print('R objective')
+#       print(0.5 * sum(Xtheta^2)/n - sum(Xtheta*Y)/n + lam * 0.7 * sum(abs(soln_R_wide$soln)))
+#       print(max(abs(soln_R_wide$gradient - t(X) %*% X %*% soln_R_wide$soln / n + t(X) %*% Y / n)))
+#       print(lam)
+#      print(max(abs(soln_R_wide$gradient[soln_R_wide$soln != 0])))
+#      print(which(soln_R_wide$soln != 0))
+#      soln_R_wide = selectiveInference:::solve_QP_wide(X, 0.7 * lam, maxiter, soln_R_wide$soln, -t(X) %*% Y / n, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)
+# #     print(Xtheta - X %*% soln_R_wide$soln)
+# #     print(soln_R_wide$soln)
+#      soln_R_wide = selectiveInference:::solve_QP_wide(X, 0.5 * lam, maxiter, soln_R_wide$soln, -t(X) %*% Y / n, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)
\ No newline at end of file

From 636483b6afc5a29fd740cd8bcb1473d0cd2ea5d7 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 27 Sep 2017 22:35:42 -0700
Subject: [PATCH 259/493] removing unneeded test, adding Rcpp exports

---
 selectiveInference/R/RcppExports.R     | 10 ++--
 selectiveInference/src/RcppExports.cpp | 55 ++++++++------------
 test.R                                 | 70 --------------------------
 3 files changed, 26 insertions(+), 109 deletions(-)
 delete mode 100644 test.R

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
index f5ebee4..8af4640 100644
--- a/selectiveInference/R/RcppExports.R
+++ b/selectiveInference/R/RcppExports.R
@@ -1,19 +1,19 @@
-# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# This file was generated by Rcpp::compileAttributes
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('_selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
+    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
 }
 
 solve_QP_wide <- function(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('_selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active)
+    .Call('selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active)
 }
 
 update1_ <- function(Q2, w, m, k) {
-    .Call('_selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
+    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
 }
 
 downdate1_ <- function(Q1, R, j0, m, n) {
-    .Call('_selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
+    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
 }
 
diff --git a/selectiveInference/src/RcppExports.cpp b/selectiveInference/src/RcppExports.cpp
index 02a7741..efa87d5 100644
--- a/selectiveInference/src/RcppExports.cpp
+++ b/selectiveInference/src/RcppExports.cpp
@@ -1,4 +1,4 @@
-// Generated by using Rcpp::compileAttributes() -> do not edit by hand
+// This file was generated by Rcpp::compileAttributes
 // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 #include <Rcpp.h>
@@ -7,10 +7,10 @@ using namespace Rcpp;
 
 // solve_QP
 Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP _selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+RcppExport SEXP selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::RObject __result;
+    Rcpp::RNGScope __rngScope;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Sigma(SigmaSEXP);
     Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
     Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
@@ -22,16 +22,16 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
     Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
     Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    rcpp_result_gen = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return rcpp_result_gen;
+    __result = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return __result;
 END_RCPP
 }
 // solve_QP_wide
 Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::NumericVector X_theta, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP _selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP X_thetaSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+RcppExport SEXP selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP X_thetaSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::RObject __result;
+    Rcpp::RNGScope __rngScope;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type X(XSEXP);
     Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
     Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
@@ -44,49 +44,36 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
     Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
     Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    rcpp_result_gen = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return rcpp_result_gen;
+    __result = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return __result;
 END_RCPP
 }
 // update1_
 Rcpp::List update1_(Rcpp::NumericMatrix Q2, Rcpp::NumericVector w, int m, int k);
-RcppExport SEXP _selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
+RcppExport SEXP selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::RObject __result;
+    Rcpp::RNGScope __rngScope;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q2(Q2SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericVector >::type w(wSEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type k(kSEXP);
-    rcpp_result_gen = Rcpp::wrap(update1_(Q2, w, m, k));
-    return rcpp_result_gen;
+    __result = Rcpp::wrap(update1_(Q2, w, m, k));
+    return __result;
 END_RCPP
 }
 // downdate1_
 Rcpp::List downdate1_(Rcpp::NumericMatrix Q1, Rcpp::NumericMatrix R, int j0, int m, int n);
-RcppExport SEXP _selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
+RcppExport SEXP selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::RObject __result;
+    Rcpp::RNGScope __rngScope;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q1(Q1SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type R(RSEXP);
     Rcpp::traits::input_parameter< int >::type j0(j0SEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    rcpp_result_gen = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
-    return rcpp_result_gen;
+    __result = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
+    return __result;
 END_RCPP
 }
-
-static const R_CallMethodDef CallEntries[] = {
-    {"_selectiveInference_solve_QP", (DL_FUNC) &_selectiveInference_solve_QP, 11},
-    {"_selectiveInference_solve_QP_wide", (DL_FUNC) &_selectiveInference_solve_QP_wide, 12},
-    {"_selectiveInference_update1_", (DL_FUNC) &_selectiveInference_update1_, 4},
-    {"_selectiveInference_downdate1_", (DL_FUNC) &_selectiveInference_downdate1_, 5},
-    {NULL, NULL, 0}
-};
-
-RcppExport void R_init_selectiveInference(DllInfo *dll) {
-    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
-    R_useDynamicSymbols(dll, FALSE);
-}
diff --git a/test.R b/test.R
deleted file mode 100644
index 0660840..0000000
--- a/test.R
+++ /dev/null
@@ -1,70 +0,0 @@
-set.seed(43)
-
-    n = 100                                                                                                                                                                              
-    p = 200                                                                                                                                                                              
-    lam = 0.2                                                                                                                                                                             
-    X = matrix(rnorm(n*p), n, p)                                                                                                                                                         
-    Y = rnorm(n)                                                                                                                                                                         
-    library(selectiveInference)                                                                                                                                                          
-    p = ncol(X)                                                                                                                                                                          
-    soln_R = rep(0, p)                                                                                                                                                                   
-    grad = -t(X) %*% Y / n                                                                                                                                                                   
-    ever_active = as.integer(c(1, rep(0, p-1)))
-    nactive = as.integer(1)                                                                                                                                                              
-    kkt_tol = 1.e-12                                                                                                                                                                     
-    objective_tol = 1.e-16                                                                                                                                                               
-    maxiter = 500                                                                                                                                                                        
-    soln_R = rep(0, p)
-    grad = -t(X) %*% Y / n
-    ever_active = as.integer(c(1, rep(0, p-1)))
-    nactive = as.integer(1)
-    kkt_tol = 1.e-12
-    objective_tol = 1.e-16
-    maxiter = 500
-    soln_R = selectiveInference:::solve_QP(t(X) %*% X / n, lam, maxiter, soln_R, -t(X) %*% Y / n, grad, ever_active, nactive, kkt_tol, objective_tol, p)$soln
-
-    # test wide solver
-    Xtheta = rep(0, n)
-    nactive = as.integer(1)
-    ever_active = as.integer(c(1, rep(0, p-1)))
-    soln_R_wide = rep(0, p)
-    grad = - t(X) %*% Y / n
-    soln_R_wide = selectiveInference:::solve_QP_wide(X, lam, maxiter, soln_R_wide, -t(X) %*% Y / n, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)$soln
-
-#     soln_R = selectiveInference:::solve_QP(t(X) %*% X / n, lam, maxiter, soln_R, -t(X) %*% Y / n, grad, ever_active, nactive, kkt_tol, objective_tol, p)
-#     print('active')
-#     print(nactive)
-#     print(ever_active)
-#     print(soln_R$ever_active)
-#     soln_R = soln_R$soln
-#     soln_R_old = soln_R
-#     print(soln_R)                                                                                                                                                                        
-#     Xtheta = rep(0, n)                                                                                                                                                                   
-#     nactive = as.integer(1)
-#     ever_active = as.integer(c(1, rep(0, p-1)))
-#     soln_R = rep(0, p)
-#     grad = - t(X) %*% Y / n
-#     # test wide solver                                                                                                                                                                   
-#     soln_R_wide = selectiveInference:::solve_QP_wide(X, lam, maxiter, soln_R*1., -t(X) %*% Y / n, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)
-#     print(nactive)
-#     print(soln_R_wide$ever_active)
-
-     print('diff')
-    print(soln_R_wide - soln_R)
-#     print(soln_R_wide$gradient[soln_R_wide$ever_active])
-#     print(max(abs(soln_R_wide$gradient[-soln_R_wide$ever_active])))
-#     print(soln_R_wide$kkt_check)
-#     print(soln_R_wide$iter)
-#     print(max(abs(Xtheta - X %*% soln_R_wide$soln)))
-# #     print(Xtheta)
-
-#       print('R objective')
-#       print(0.5 * sum(Xtheta^2)/n - sum(Xtheta*Y)/n + lam * 0.7 * sum(abs(soln_R_wide$soln)))
-#       print(max(abs(soln_R_wide$gradient - t(X) %*% X %*% soln_R_wide$soln / n + t(X) %*% Y / n)))
-#       print(lam)
-#      print(max(abs(soln_R_wide$gradient[soln_R_wide$soln != 0])))
-#      print(which(soln_R_wide$soln != 0))
-#      soln_R_wide = selectiveInference:::solve_QP_wide(X, 0.7 * lam, maxiter, soln_R_wide$soln, -t(X) %*% Y / n, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)
-# #     print(Xtheta - X %*% soln_R_wide$soln)
-# #     print(soln_R_wide$soln)
-#      soln_R_wide = selectiveInference:::solve_QP_wide(X, 0.5 * lam, maxiter, soln_R_wide$soln, -t(X) %*% Y / n, grad, Xtheta, ever_active, nactive, kkt_tol, objective_tol, p)
\ No newline at end of file

From 4d376c497741c3d404a20e9428cc103efcd049ec Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 27 Sep 2017 23:10:33 -0700
Subject: [PATCH 260/493] added R code decision for wide or not; R check
 passing

---
 selectiveInference/R/funs.fixed.R         | 91 +++++++++++++++--------
 selectiveInference/man/debiasingMatrix.Rd | 15 +++-
 selectiveInference/src/debias.h           |  4 +-
 3 files changed, 72 insertions(+), 38 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 6e1ffa9..f119a30 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -154,20 +154,24 @@ fixedLassoInf <- function(x, y, beta,
       
       # Reorder so that active set S is first
       Xordered = Xint[,c(S,notS,recursive=T)]
+      hsigmaS = 1/n*(t(XS)%*%XS) # hsigma[S,S]
+      hsigmaSinv = solve(hsigmaS) # pinv(hsigmaS)
       
-      hsigma <- 1/n*(t(Xordered)%*%Xordered)
-      hsigmaS <- 1/n*(t(XS)%*%XS) # hsigma[S,S]
-      hsigmaSinv <- solve(hsigmaS) # pinv(hsigmaS)
+      FS = rbind(diag(length(S)),matrix(0,pp-length(S),length(S)))
+      GS = cbind(diag(length(S)),matrix(0,length(S),pp-length(S)))
 
-      # Approximate inverse covariance matrix for when (n < p) from lasso_Inference.R
+      is_wide = n < (2 * p) # somewhat arbitrary decision -- it is really for when we don't want to form with pxp matrices
 
-      htheta = debiasingMatrix(hsigma, n, 1:length(S), verbose=FALSE, max_try=linesearch.try, warn_kkt=TRUE)
+      # Approximate inverse covariance matrix for when (n < p) from lasso_Inference.R
+      if (!is_wide) {
+           hsigma = 1/n*(t(Xordered)%*%Xordered)
+           htheta = debiasingMatrix(hsigma, is_wide, n, 1:length(S), verbose=FALSE, max_try=linesearch.try, warn_kkt=TRUE)
+           ithetasigma = (GS-(htheta%*%hsigma))
+      } else {
+           htheta = debiasingMatrix(Xordered, is_wide, n, 1:length(S), verbose=FALSE, max_try=linesearch.try, warn_kkt=TRUE)
+           ithetasigma = (GS-((htheta%*%t(Xordered)) %*% Xordered)/n)
+      }
 
-      FS = rbind(diag(length(S)),matrix(0,pp-length(S),length(S)))
-      GS = cbind(diag(length(S)),matrix(0,length(S),pp-length(S)))
-      ithetasigma = (GS-(htheta%*%hsigma))
-      # ithetasigma = (diag(pp) - (htheta%*%hsigma))
-      
       M <- (((htheta%*%t(Xordered))+ithetasigma%*%FS%*%hsigmaSinv%*%t(XS))/n)
       # vector which is offset for testing debiased beta's
       null_value <- (((ithetasigma%*%FS%*%hsigmaSinv)%*%sign(hbetaS))*lambda/n)
@@ -264,10 +268,11 @@ fixedLassoPoly =
 ## Approximates inverse covariance matrix theta
 ## using coordinate descent 
 
-debiasingMatrix = function(Sigma, 
+debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n depending on is_wide
+                           is_wide,
                            nsample, 
                            rows, 
-		           verbose=FALSE, 
+ 		           verbose=FALSE, 
 		           mu=NULL,             # starting value of mu
    			   linesearch=TRUE,     # do a linesearch?
    		           scaling_factor=1.5,  # multiplicative factor for linesearch
@@ -284,7 +289,7 @@ debiasingMatrix = function(Sigma,
      max_active = max(50, 0.3 * nsample)
   } 
 
-  p = nrow(Sigma);
+  p = ncol(Xinfo);
   M = matrix(0, length(rows), p);
 
   if (is.null(mu)) {
@@ -302,12 +307,13 @@ debiasingMatrix = function(Sigma,
         print(paste(xperc,"% done",sep="")); }
     }
 
-    output = debiasingRow(Sigma,
+    output = debiasingRow(Xinfo,               # could be X or t(X) %*% X / n depending on is_wide
+                          is_wide,
                           row,
                           mu,
                           linesearch=linesearch,
                           scaling_factor=scaling_factor,
-			  max_active=max_active,
+                          max_active=max_active,
 			  max_try=max_try,
 			  warn_kkt=FALSE,
 			  max_iter=max_iter,
@@ -329,14 +335,15 @@ debiasingMatrix = function(Sigma,
   return(M)
 }
 
-# Find one row of the debiasing matrix
+# Find one row of the debiasing matrix -- assuming X^TX/n is not too large -- i.e. X is tall
 
-debiasingRow = function (Sigma, 
+debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n depending on is_wide
+                         is_wide, 
                          row, 
                          mu, 
-		         linesearch=TRUE,     # do a linesearch?
+	                 linesearch=TRUE,     # do a linesearch?
 		         scaling_factor=1.2,  # multiplicative factor for linesearch
-			 max_active=NULL,     # how big can active set get?
+		         max_active=NULL,     # how big can active set get?
 			 max_try=10,          # how many steps in linesearch?
 			 warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
 			 max_iter=100,        # how many iterations for each optimization problem
@@ -344,10 +351,10 @@ debiasingRow = function (Sigma,
 			 objective_tol=1.e-8  # tolerance for relative decrease in objective
                          ) {
 
-  p = nrow(Sigma)
+  p = ncol(Xinfo)
 
   if (is.null(max_active)) {
-      max_active = nrow(Sigma)
+      max_active = nrow(Xinfo)
   }
 
   # Initialize variables 
@@ -371,18 +378,37 @@ debiasingRow = function (Sigma,
 
   while (counter_idx < max_try) {
 
-      result = solve_QP(Sigma, 
-                        mu, 
-                        max_iter, 
-                        soln, 
-                        linear_func, 
-                        gradient, 
-                        ever_active, 
-                        nactive, 
-                        kkt_tol, 
-                        objective_tol, 
-                        max_active) 
+      if (!is_wide) {
+          Sigma = Xinfo
+          result = solve_QP(Sigma, 
+                            mu, 
+                            max_iter, 
+                            soln, 
+                            linear_func, 
+                            gradient, 
+                            ever_active, 
+                            nactive, 
+                            kkt_tol, 
+                            objective_tol, 
+                            max_active) 
+      } else {
+          X = Xinfo
+	  n = nrow(X)
+          Xsoln = rep(0, n)
+          result = solve_QP_wide(X,
+                                 mu, 
+                                 max_iter, 
+                                 soln, 
+                                 linear_func, 
+                                 gradient, 
+                                 Xsoln,
+                                 ever_active, 
+                                 nactive, 
+                                 kkt_tol, 
+                                 objective_tol, 
+                                 max_active) 
 
+      }
       iter = result$iter
 
       # Logic for whether we should continue the line search
@@ -439,6 +465,7 @@ debiasingRow = function (Sigma,
 
 }
 
+
 ##############################
 
 print.fixedLassoInf <- function(x, tailarea=TRUE, ...) {
diff --git a/selectiveInference/man/debiasingMatrix.Rd b/selectiveInference/man/debiasingMatrix.Rd
index 533a12c..8c0b3c1 100644
--- a/selectiveInference/man/debiasingMatrix.Rd
+++ b/selectiveInference/man/debiasingMatrix.Rd
@@ -11,7 +11,8 @@ Newton step from some consistent estimator (such as the LASSO)
 to find a debiased solution.
 }
 \usage{
-debiasingMatrix(Sigma, 
+debiasingMatrix(Xinfo, 
+                is_wide,			
                 nsample, 
                 rows, 
 		verbose=FALSE, 
@@ -26,8 +27,14 @@ debiasingMatrix(Sigma,
 		objective_tol=1.e-8)
 }
 \arguments{
-\item{Sigma}{
-A symmetric non-negative definite matrix, often a cross-covariance matrix.
+\item{Xinfo}{
+Either a non-negative definite matrix S=t(X) %*% X / n or X itself. If 
+is_wide is TRUE, then Xinfo should be X, otherwise it should be S.
+}
+\item{is_wide}{
+Are we solving for rows of the debiasing matrix assuming it is 
+a wide matrix so that Xinfo=X and the non-negative definite
+matrix of interest is t(X) %*% X / nrow(X).
 }
 \item{nsample}{
 Number of samples used in forming the cross-covariance matrix.
@@ -102,7 +109,7 @@ n = 50
 p = 100
 X = matrix(rnorm(n * p), n, p)
 S = t(X) \%*\% X / n
-M = debiasingMatrix(S, n, c(1,3,5))
+M = debiasingMatrix(S, FALSE, n, c(1,3,5))
 
 }
  
\ No newline at end of file
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index 916bc14..ebcbc93 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -21,7 +21,7 @@ int check_KKT_qp(double *theta,        /* current theta */
 		 double *gradient_ptr, /* nndef times theta + linear_func */
 		 int nrow,             /* how many rows in nndef */
 		 double bound,         /* Lagrange multipler for \ell_1 */
-		 double tol)           /* precision for checking KKT conditions */        
+		 double tol);          /* precision for checking KKT conditions */        
 
 int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
 	       double *X_theta_ptr,        /* Fitted values   */
@@ -49,7 +49,7 @@ int check_KKT_wide(double *theta_ptr,        /* current theta */
 		   int nfeature,             /* how many columns in X */
 		   int ncase,                /* how many rows in X */
    		   double bound,             /* Lagrange multipler for \ell_1 */
-		   double tol)               /* precision for checking KKT conditions */        
+		   double tol);              /* precision for checking KKT conditions */        
   
 void update_gradient_wide(double *gradient_ptr,     /* X^TX/ncase times theta + linear_func */
 			  double *X_theta_ptr,      /* Current fitted values */

From fb7f85f60ebf6b2588230fa6cb6e1490c9a7dde2 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 27 Sep 2017 23:23:10 -0700
Subject: [PATCH 261/493] solving active problem more accurately with more
 iterations -- KKT check to break

---
 .../src/quadratic_program_wide.c              | 120 ++++++++++++++----
 1 file changed, 97 insertions(+), 23 deletions(-)

diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
index 3504492..c4f1231 100644
--- a/selectiveInference/src/quadratic_program_wide.c
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -163,11 +163,11 @@ int check_KKT_wide(double *theta_ptr,        /* current theta */
 		   double *gradient_ptr,     /* X^TX/ncase times theta + linear_func*/
 		   double *X_theta_ptr,      /* Current fitted values */
 		   double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-		   double *linear_func_ptr,     /* Linear term in objective */   
+		   double *linear_func_ptr,  /* Linear term in objective */   
 		   int *need_update_ptr,     /* Which coordinates need to be updated? */
 		   int nfeature,             /* how many columns in X */
 		   int ncase,                /* how many rows in X */
-   		   double bound,             /* Lagrange multipler for \ell_1 */
+		   double bound,             /* Lagrange multipler for \ell_1 */
 		   double tol)               /* precision for checking KKT conditions */        
 {
   // First check inactive
@@ -204,6 +204,59 @@ int check_KKT_wide(double *theta_ptr,        /* current theta */
   return(1);
 }
 
+int check_KKT_wide_active(int *ever_active_ptr,           /* Ever active set: 0-based */ 
+			  int *nactive_ptr,               /* Size of ever active set */
+			  double *theta_ptr,              /* current theta */
+			  double *gradient_ptr,           /* X^TX/ncase times theta + linear_func*/
+			  double *X_theta_ptr,            /* Current fitted values */
+			  double *X_ptr,                  /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
+			  double *linear_func_ptr,        /* Linear term in objective */   
+			  int *need_update_ptr,           /* Which coordinates need to be updated? */
+			  int nfeature,                   /* how many columns in X */
+			  int ncase,                      /* how many rows in X */
+			  double bound,                   /* Lagrange multipler for \ell_1 */
+			  double tol)                     /* precision for checking KKT conditions */        
+{
+  // First check inactive
+
+  int iactive;
+  double *theta_ptr_tmp;
+  double gradient;
+  int ever_active_ptr_tmp;
+  int nactive = *nactive_ptr;
+  int active_feature;
+  int *active_feature_ptr;
+
+  for (iactive=0; iactive<nactive; iactive++) {
+
+    active_feature_ptr = ((int *) ever_active_ptr + iactive);
+    active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
+    theta_ptr_tmp = ((double *) theta_ptr + active_feature);
+
+    // Compute this coordinate of the gradient
+
+    gradient = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, active_feature, ncase);
+
+    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
+
+      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
+	return(0);
+      }
+      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
+	return(0);
+      }
+
+    }
+    else {
+      if (fabs(gradient) > (1. + tol) * bound) {
+	return(0);
+      }
+    }
+  }
+
+  return(1);
+}
+
 double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
 			     double *linear_func_ptr,     /* Linear term in objective */
 			     double *nndef_diag_ptr,          /* Diagonal entries of Sigma */
@@ -323,10 +376,10 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
   int ifeature = 0;
   int iactive = 0;
   int *active_ptr;
-
   int check_objective = 1;
-
   double old_value, new_value; 
+  int niter_active = 5;
+  int iter_active;
 
   if (check_objective) {
 
@@ -343,26 +396,47 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 
   for (iter=0; iter<maxiter; iter++) {
 
-    // Update the active variables first
+    // Update the active variables first -- do this niter_active times
+
+    for (iter_active=0; iter_active<niter_active; iter_active++) { 
+
+        active_ptr = (int *) ever_active_ptr;
+        for (iactive=0; iactive < *nactive_ptr; iactive++) {
+
+          update_one_coord_wide(X_ptr,
+    			        linear_func_ptr,
+				nndef_diag_ptr,
+				gradient_ptr,
+				ever_active_ptr,
+				nactive_ptr,
+				X_theta_ptr,
+				need_update_ptr,
+				ncase,
+				nfeature,
+				bound,
+				theta_ptr,
+				*active_ptr - 1,   // Ever-active is 1-based
+				1);
+	  active_ptr++;
+	}
+
+	// Check KKT of active subproblem
+
+	if (check_KKT_wide_active(ever_active_ptr,
+				  nactive_ptr,
+				  theta_ptr,
+				  gradient_ptr,
+				  X_theta_ptr,
+				  X_ptr,
+				  linear_func_ptr,
+				  need_update_ptr,
+				  nfeature,
+				  ncase,
+				  bound,
+				  kkt_tol) == 1) {
+	  break;
+	}
 
-    active_ptr = (int *) ever_active_ptr;
-    for (iactive=0; iactive < *nactive_ptr; iactive++) {
-
-      update_one_coord_wide(X_ptr,
-			    linear_func_ptr,
-			    nndef_diag_ptr,
-			    gradient_ptr,
-			    ever_active_ptr,
-			    nactive_ptr,
-			    X_theta_ptr,
-			    need_update_ptr,
-			    ncase,
-			    nfeature,
-			    bound,
-			    theta_ptr,
-			    *active_ptr - 1,   // Ever-active is 1-based
-			    1);
-      active_ptr++;
     }
 
     // Check KKT

From 76f3e7e2d3d25e5925b5570f19af2a2fc0cfcaf3 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 28 Sep 2017 00:14:02 -0700
Subject: [PATCH 262/493] R code now produces almost identical answer when
 assuming wide or not

---
 selectiveInference/R/funs.fixed.R             |  14 +-
 selectiveInference/man/debiasingMatrix.Rd     |   5 +-
 selectiveInference/src/quadratic_program.c    | 136 +++++++++++++-----
 .../src/quadratic_program_wide.c              |  13 +-
 4 files changed, 116 insertions(+), 52 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index f119a30..1f9ac2c 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -300,7 +300,6 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
   xp = round(p/10);
   idx = 1;
   for (row in rows) {
-
     if ((idx %% xp)==0){
       xperc = xperc+10;
       if (verbose) {
@@ -354,13 +353,13 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
   p = ncol(Xinfo)
 
   if (is.null(max_active)) {
-      max_active = nrow(Xinfo)
+      max_active = min(nrow(Xinfo), ncol(Xinfo))
   }
 
   # Initialize variables 
 
   soln = rep(0, p)
-
+  Xsoln = rep(0, n)
   ever_active = rep(0, p)
   ever_active[1] = row      # 1-based
   ever_active = as.integer(ever_active)
@@ -379,8 +378,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
   while (counter_idx < max_try) {
 
       if (!is_wide) {
-          Sigma = Xinfo
-          result = solve_QP(Sigma, 
+          result = solve_QP(Xinfo, # this is non-neg-def matrix
                             mu, 
                             max_iter, 
                             soln, 
@@ -392,10 +390,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                             objective_tol, 
                             max_active) 
       } else {
-          X = Xinfo
-	  n = nrow(X)
-          Xsoln = rep(0, n)
-          result = solve_QP_wide(X,
+          result = solve_QP_wide(Xinfo, # this is a design matrix
                                  mu, 
                                  max_iter, 
                                  soln, 
@@ -409,6 +404,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                                  max_active) 
 
       }
+
       iter = result$iter
 
       # Logic for whether we should continue the line search
diff --git a/selectiveInference/man/debiasingMatrix.Rd b/selectiveInference/man/debiasingMatrix.Rd
index 8c0b3c1..d0e908d 100644
--- a/selectiveInference/man/debiasingMatrix.Rd
+++ b/selectiveInference/man/debiasingMatrix.Rd
@@ -108,8 +108,9 @@ set.seed(10)
 n = 50
 p = 100
 X = matrix(rnorm(n * p), n, p)
-S = t(X) \%*\% X / n
+S = t(X) %*% X / n
 M = debiasingMatrix(S, FALSE, n, c(1,3,5))
-
+M2 = debiasingMatrix(X, TRUE, n, c(1,3,5))
+max(M - M2)
 }
  
\ No newline at end of file
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
index cee3fa4..d9bd017 100644
--- a/selectiveInference/src/quadratic_program.c
+++ b/selectiveInference/src/quadratic_program.c
@@ -17,7 +17,7 @@ double objective_qp(double *nndef_ptr,       /* A non-negative definite matrix *
 		    int *nactive_ptr,        /* Size of ever active set */
 		    int nrow,                /* how many rows in nndef */
 		    double bound,            /* Lagrange multipler for \ell_1 */
-		    double *theta)           /* current value */
+		    double *theta_ptr)           /* current value */
 {
   int irow, icol;
   double value = 0;
@@ -28,20 +28,20 @@ double objective_qp(double *nndef_ptr,       /* A non-negative definite matrix *
   int active_row, active_col;
   int nactive = *nactive_ptr;
 
-  theta_row_ptr = theta;
-  theta_col_ptr = theta;
+  theta_row_ptr = theta_ptr;
+  theta_col_ptr = theta_ptr;
 
   for (irow=0; irow<nactive; irow++) {
 
     active_row_ptr = ((int *) ever_active_ptr + irow);
     active_row = *active_row_ptr - 1;          // Ever-active is 1-based
-    theta_row_ptr = ((double *) theta + active_row);
+    theta_row_ptr = ((double *) theta_ptr + active_row);
 
     for (icol=0; icol<nactive; icol++) {
       
       active_col_ptr = ((int *) ever_active_ptr + icol);
       active_col = *active_col_ptr - 1;          // Ever-active is 1-based
-      theta_col_ptr = ((double *) theta + active_col);
+      theta_col_ptr = ((double *) theta_ptr + active_col);
 
       nndef_ptr_tmp = ((double *) nndef_ptr + nrow * active_col + active_row); // Matrices are column-major order
 
@@ -90,7 +90,7 @@ int update_ever_active_qp(int coord,
   return(0);
 }
 
-int check_KKT_qp(double *theta,        /* current theta */
+int check_KKT_qp(double *theta_ptr,        /* current theta */
 		 double *gradient_ptr, /* nndef times theta + linear_func */
 		 int nrow,             /* how many rows in nndef */
 		 double bound,         /* Lagrange multipler for \ell_1 */
@@ -99,22 +99,22 @@ int check_KKT_qp(double *theta,        /* current theta */
   // First check inactive
 
   int irow;
-  double *theta_ptr, *gradient_ptr_tmp;
+  double *theta_ptr_tmp, *gradient_ptr_tmp;
   double gradient;
 
   for (irow=0; irow<nrow; irow++) {
-    theta_ptr = ((double *) theta + irow);
+    theta_ptr_tmp = ((double *) theta_ptr + irow);
     gradient_ptr_tmp = ((double *) gradient_ptr + irow);
 
     // Compute this coordinate of the gradient
 
     gradient = *gradient_ptr_tmp;
 
-    if (*theta_ptr != 0) { // these coordinates of gradients should be equal to -bound
-      if ((*theta_ptr > 0) &&  (fabs(gradient + bound) > tol * bound)) {
+    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
+      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
 	return(0);
       }
-      else if ((*theta_ptr < 0) && (fabs(gradient - bound) > tol * bound)) {
+      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
 	return(0);
       }
     }
@@ -128,6 +128,57 @@ int check_KKT_qp(double *theta,        /* current theta */
   return(1);
 }
 
+int check_KKT_qp_active(int *ever_active_ptr,           /* Ever active set: 0-based */ 
+		        int *nactive_ptr,               /* Size of ever active set */
+			double *theta_ptr,        /* current theta */
+			double *gradient_ptr, /* nndef times theta + linear_func */
+			int nrow,             /* how many rows in nndef */
+			double bound,         /* Lagrange multipler for \ell_1 */
+			double tol)           /* precision for checking KKT conditions */        
+{
+  // First check inactive
+
+  int iactive;
+  double *theta_ptr_tmp;
+  double gradient;
+  double *gradient_ptr_tmp;
+  int nactive = *nactive_ptr;
+  int active_feature;
+  int *active_feature_ptr;
+
+  for (iactive=0; iactive<nactive; iactive++) {
+
+    active_feature_ptr = ((int *) ever_active_ptr + iactive);
+    active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
+    theta_ptr_tmp = ((double *) theta_ptr + active_feature);
+
+    gradient_ptr_tmp = ((double *) gradient_ptr + active_feature);
+
+    // Compute this coordinate of the gradient
+
+    gradient = *gradient_ptr_tmp;
+
+    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
+
+      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
+	return(0);
+      }
+      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
+	return(0);
+      }
+
+    }
+    else {
+      if (fabs(gradient) > (1. + tol) * bound) {
+	return(0);
+      }
+    }
+  }
+
+  return(1);
+}
+
+
 double update_one_coord_qp(double *nndef_ptr,           /* A non-negative definite matrix */
 			   double *linear_func_ptr,     /* Linear term in objective */
 			   double *nndef_diag_ptr,      /* Diagonal of nndef */
@@ -136,7 +187,7 @@ double update_one_coord_qp(double *nndef_ptr,           /* A non-negative defini
 			   int *nactive_ptr,            /* Size of ever active set */
 			   int nrow,                    /* How many rows in nndef */
 			   double bound,                /* feasibility parameter */
-			   double *theta,               /* current value */
+			   double *theta_ptr,               /* current value */
 			   int coord,                   /* which coordinate to update: 0-based */
 			   int is_active)               /* Is this coord in ever_active */     
 {
@@ -147,7 +198,7 @@ double update_one_coord_qp(double *nndef_ptr,           /* A non-negative defini
   double old_value;
   double *nndef_ptr_tmp;
   double *gradient_ptr_tmp;
-  double *theta_ptr;
+  double *theta_ptr_tmp;
   int icol = 0;
 
   double *quadratic_ptr = ((double *) nndef_diag_ptr + coord);
@@ -156,8 +207,8 @@ double update_one_coord_qp(double *nndef_ptr,           /* A non-negative defini
   gradient_ptr_tmp = ((double *) gradient_ptr + coord);
   linear_term = *gradient_ptr_tmp;
 
-  theta_ptr = ((double *) theta + coord);
-  old_value = *theta_ptr;
+  theta_ptr_tmp = ((double *) theta_ptr + coord);
+  old_value = *theta_ptr_tmp;
 
   // The coord entry of gradient_ptr term has a diagonal term in it:
   // nndef[coord, coord] * theta[coord]
@@ -200,8 +251,8 @@ double update_one_coord_qp(double *nndef_ptr,           /* A non-negative defini
       nndef_ptr_tmp += 1;
     }
 
-    theta_ptr = ((double *) theta + coord);
-    *theta_ptr = value;
+    theta_ptr_tmp = ((double *) theta_ptr + coord);
+    *theta_ptr_tmp = value;
 
   }
 
@@ -230,6 +281,8 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
   int *active_ptr;
 
   int check_objective = 1;
+  int iter_active;
+  int niter_active=5;
 
   double old_value, new_value; 
 
@@ -248,23 +301,38 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 
   for (iter=0; iter<maxiter; iter++) {
 
-    // Update the active variables first
-
-    active_ptr = (int *) ever_active_ptr;
-
-    for (iactive=0; iactive < *nactive_ptr; iactive++) {
-      update_one_coord_qp(nndef_ptr,
-			  linear_func_ptr,
-			  nndef_diag_ptr,
-			  gradient_ptr,
-			  ever_active_ptr,
-			  nactive_ptr,
-			  nrow,
-			  bound,
-			  theta,
-			  *active_ptr - 1,   // Ever-active is 1-based
-			  1);
-      active_ptr++;
+    // Update the active variables first -- do this niter_active times
+
+    for (iter_active=0; iter_active<niter_active; iter_active++) { 
+
+        active_ptr = (int *) ever_active_ptr;
+        for (iactive=0; iactive < *nactive_ptr; iactive++) {
+
+	  update_one_coord_qp(nndef_ptr,
+			      linear_func_ptr,
+			      nndef_diag_ptr,
+			      gradient_ptr,
+			      ever_active_ptr,
+			      nactive_ptr,
+			      nrow,
+			      bound,
+			      theta,
+			      *active_ptr - 1,   // Ever-active is 1-based
+			      1);
+	  active_ptr++;
+	}
+
+	// Check KKT of active subproblem
+
+	if (check_KKT_qp_active(ever_active_ptr,
+				nactive_ptr,
+				theta,
+				gradient_ptr,
+				nrow,
+				bound,
+				kkt_tol) == 1) {
+	  break;
+	}
     }
 
     // Check KKT
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
index c4f1231..3beb578 100644
--- a/selectiveInference/src/quadratic_program_wide.c
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -1,6 +1,6 @@
 #include <math.h> // for fabs
 
-// Find an approximate row of \hat{Sigma}^{-1}
+// Find an approximate row of \hat{nndef}^{-1}
 
 // Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
 
@@ -222,7 +222,6 @@ int check_KKT_wide_active(int *ever_active_ptr,           /* Ever active set: 0-
   int iactive;
   double *theta_ptr_tmp;
   double gradient;
-  int ever_active_ptr_tmp;
   int nactive = *nactive_ptr;
   int active_feature;
   int *active_feature_ptr;
@@ -259,16 +258,16 @@ int check_KKT_wide_active(int *ever_active_ptr,           /* Ever active set: 0-
 
 double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
 			     double *linear_func_ptr,     /* Linear term in objective */
-			     double *nndef_diag_ptr,          /* Diagonal entries of Sigma */
-			     double *gradient_ptr,     /* X^TX/ncase times theta + linear_func*/
+			     double *nndef_diag_ptr,      /* Diagonal of nndef */
+			     double *gradient_ptr,        /* X^TX/ncase times theta + linear_func*/
 			     int *ever_active_ptr,        /* Ever active set: 1-based */ 
 			     int *nactive_ptr,            /* Size of ever active set */
 			     double *X_theta_ptr,         /* X\theta -- fitted values */
 			     int *need_update_ptr,        /* Whether a gradient coordinate needs update or not */
-			     int ncase,                    /* How many rows in X */
-			     int nfeature,                    /* How many rows in X */
+			     int ncase,                   /* How many rows in X */
+			     int nfeature,                /* How many rows in X */
 			     double bound,                /* feasibility parameter */
-			     double *theta_ptr,               /* current value */
+			     double *theta_ptr,           /* current value */
 			     int coord,                   /* which coordinate to update: 0-based */
 			     int is_active)               /* Is this coord in ever_active */     
 {

From c0dab8cf27d226cbeb0c8a7c671c4155cfb546bb Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Tue, 3 Oct 2017 18:15:48 -0700
Subject: [PATCH 263/493] rob fixed type=full option

---
 selectiveInference/R/funs.fixed.R | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 1f9ac2c..a2776e5 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -82,10 +82,11 @@ fixedLassoInf <- function(x, y, beta,
     
     tol.coef = tol.beta * sqrt(n^2 / colSums(x^2))
     # print(tol.coef)
-    vars = which(abs(beta) > tol.coef)
+   #   vars = which(abs(beta) > tol.coef)
+       vars = abs(beta) > tol.coef
     # print(beta)
     # print(vars)
-    if(length(vars)==0){
+    if(sum(vars)==0){
       cat("Empty model",fill=T)
       return()
     }
@@ -96,10 +97,17 @@ fixedLassoInf <- function(x, y, beta,
                     "'thresh' parameter, for a more accurate convergence."))
     
     # Get lasso polyhedral region, of form Gy >= u
+<<<<<<< HEAD
+    if (type == 'full') out = fixedLasso.poly(x,y,beta,lambda,vars,inactive=TRUE)
+    else out = fixedLasso.poly(x,y,beta,lambda,vars)
+    G = out$G
+    u = out$u
+=======
     if (type == 'full' & p > n) out = fixedLassoPoly(x,y,lambda,beta,vars,inactive=TRUE)
     else out = fixedLassoPoly(x,y,lambda,beta,vars)
     A = out$A
     b = out$b
+>>>>>>> 726b917649c7aaabd030b2cab062836ca774ef57
     
     # Check polyhedral region
     tol.poly = 0.01
@@ -127,7 +135,8 @@ fixedLassoInf <- function(x, y, beta,
     # add additional targets for inference if provided
     if (!is.null(add.targets)) vars = sort(unique(c(vars,add.targets,recursive=T)))
     
-    k = length(vars)
+      k = length(vars)
+      k=sum(vars)
     pv = vlo = vup = numeric(k)
     vmat = matrix(0,k,n)
     ci = tailarea = matrix(0,k,2)

From ae37fdcd5dce5d2e2870b25542e141810509d08c Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Tue, 3 Oct 2017 22:35:22 -0700
Subject: [PATCH 264/493] rob type=full

---
 selectiveInference/R/RcppExports.R     | 10 ++---
 selectiveInference/R/funs.fixed.R      | 12 +-----
 selectiveInference/R/funs.inf.R        |  2 +-
 selectiveInference/src/RcppExports.cpp | 55 ++++++++++++++++----------
 4 files changed, 42 insertions(+), 37 deletions(-)

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
index 8af4640..f5ebee4 100644
--- a/selectiveInference/R/RcppExports.R
+++ b/selectiveInference/R/RcppExports.R
@@ -1,19 +1,19 @@
-# This file was generated by Rcpp::compileAttributes
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
+    .Call('_selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
 }
 
 solve_QP_wide <- function(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active)
+    .Call('_selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active)
 }
 
 update1_ <- function(Q2, w, m, k) {
-    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
+    .Call('_selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
 }
 
 downdate1_ <- function(Q1, R, j0, m, n) {
-    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
+    .Call('_selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
 }
 
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index a2776e5..102ca46 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -97,17 +97,10 @@ fixedLassoInf <- function(x, y, beta,
                     "'thresh' parameter, for a more accurate convergence."))
     
     # Get lasso polyhedral region, of form Gy >= u
-<<<<<<< HEAD
-    if (type == 'full') out = fixedLasso.poly(x,y,beta,lambda,vars,inactive=TRUE)
-    else out = fixedLasso.poly(x,y,beta,lambda,vars)
-    G = out$G
-    u = out$u
-=======
-    if (type == 'full' & p > n) out = fixedLassoPoly(x,y,lambda,beta,vars,inactive=TRUE)
+    if (type == 'full') out = fixedLassoPoly(x,y,lambda,beta,vars,inactive=TRUE)
     else out = fixedLassoPoly(x,y,lambda,beta,vars)
     A = out$A
     b = out$b
->>>>>>> 726b917649c7aaabd030b2cab062836ca774ef57
     
     # Check polyhedral region
     tol.poly = 0.01
@@ -135,7 +128,7 @@ fixedLassoInf <- function(x, y, beta,
     # add additional targets for inference if provided
     if (!is.null(add.targets)) vars = sort(unique(c(vars,add.targets,recursive=T)))
     
-      k = length(vars)
+   #   k = length(vars)
       k=sum(vars)
     pv = vlo = vup = numeric(k)
     vmat = matrix(0,k,n)
@@ -510,4 +503,3 @@ print.fixedLassoInf <- function(x, tailarea=TRUE, ...) {
  # lambda = 2*mean(apply(t(x)%*%eps,2,max))
  # return(lambda)
 #}
-
diff --git a/selectiveInference/R/funs.inf.R b/selectiveInference/R/funs.inf.R
index 9d7e740..0688c1f 100644
--- a/selectiveInference/R/funs.inf.R
+++ b/selectiveInference/R/funs.inf.R
@@ -209,7 +209,7 @@ TG.limits = function(Z, A, b, eta, Sigma=NULL) {
     target_estimate = sum(as.numeric(eta) * as.numeric(Z))
 
     if (max(A %*% as.numeric(Z) - b) > 0) {
-        warning('Contsraint not satisfied. A %*% Z should be elementwise less than or equal to b')
+        warning('Constraint not satisfied. A %*% Z should be elementwise less than or equal to b')
     }
 
     if (is.null(Sigma)) {
diff --git a/selectiveInference/src/RcppExports.cpp b/selectiveInference/src/RcppExports.cpp
index efa87d5..02a7741 100644
--- a/selectiveInference/src/RcppExports.cpp
+++ b/selectiveInference/src/RcppExports.cpp
@@ -1,4 +1,4 @@
-// This file was generated by Rcpp::compileAttributes
+// Generated by using Rcpp::compileAttributes() -> do not edit by hand
 // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 #include <Rcpp.h>
@@ -7,10 +7,10 @@ using namespace Rcpp;
 
 // solve_QP
 Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+RcppExport SEXP _selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Sigma(SigmaSEXP);
     Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
     Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
@@ -22,16 +22,16 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
     Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
     Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    __result = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return rcpp_result_gen;
 END_RCPP
 }
 // solve_QP_wide
 Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::NumericVector X_theta, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP X_thetaSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+RcppExport SEXP _selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP X_thetaSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type X(XSEXP);
     Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
     Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
@@ -44,36 +44,49 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
     Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
     Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    __result = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return rcpp_result_gen;
 END_RCPP
 }
 // update1_
 Rcpp::List update1_(Rcpp::NumericMatrix Q2, Rcpp::NumericVector w, int m, int k);
-RcppExport SEXP selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
+RcppExport SEXP _selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q2(Q2SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericVector >::type w(wSEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type k(kSEXP);
-    __result = Rcpp::wrap(update1_(Q2, w, m, k));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(update1_(Q2, w, m, k));
+    return rcpp_result_gen;
 END_RCPP
 }
 // downdate1_
 Rcpp::List downdate1_(Rcpp::NumericMatrix Q1, Rcpp::NumericMatrix R, int j0, int m, int n);
-RcppExport SEXP selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
+RcppExport SEXP _selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q1(Q1SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type R(RSEXP);
     Rcpp::traits::input_parameter< int >::type j0(j0SEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    __result = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
+    return rcpp_result_gen;
 END_RCPP
 }
+
+static const R_CallMethodDef CallEntries[] = {
+    {"_selectiveInference_solve_QP", (DL_FUNC) &_selectiveInference_solve_QP, 11},
+    {"_selectiveInference_solve_QP_wide", (DL_FUNC) &_selectiveInference_solve_QP_wide, 12},
+    {"_selectiveInference_update1_", (DL_FUNC) &_selectiveInference_update1_, 4},
+    {"_selectiveInference_downdate1_", (DL_FUNC) &_selectiveInference_downdate1_, 5},
+    {NULL, NULL, 0}
+};
+
+RcppExport void R_init_selectiveInference(DllInfo *dll) {
+    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
+    R_useDynamicSymbols(dll, FALSE);
+}

From 8f356ff9228e0b148814761525fc65abd565c547 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Wed, 4 Oct 2017 08:46:23 -0700
Subject: [PATCH 265/493] rob fixed another type=full bug

---
 selectiveInference/R/funs.fixed.R | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 102ca46..250a9d4 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -82,8 +82,8 @@ fixedLassoInf <- function(x, y, beta,
     
     tol.coef = tol.beta * sqrt(n^2 / colSums(x^2))
     # print(tol.coef)
-   #   vars = which(abs(beta) > tol.coef)
-       vars = abs(beta) > tol.coef
+      vars = which(abs(beta) > tol.coef)
+   #    vars = abs(beta) > tol.coef
     # print(beta)
     # print(vars)
     if(sum(vars)==0){
@@ -97,8 +97,10 @@ fixedLassoInf <- function(x, y, beta,
                     "'thresh' parameter, for a more accurate convergence."))
     
     # Get lasso polyhedral region, of form Gy >= u
-    if (type == 'full') out = fixedLassoPoly(x,y,lambda,beta,vars,inactive=TRUE)
-    else out = fixedLassoPoly(x,y,lambda,beta,vars)
+logical.vars=rep(FALSE,p)
+logical.vars[vars]=TRUE
+    if (type == 'full') out = fixedLassoPoly(x,y,lambda,beta,logical.vars,inactive=TRUE)
+    else out = fixedLassoPoly(x,y,lambda,beta,logical.vars)
     A = out$A
     b = out$b
     
@@ -128,8 +130,7 @@ fixedLassoInf <- function(x, y, beta,
     # add additional targets for inference if provided
     if (!is.null(add.targets)) vars = sort(unique(c(vars,add.targets,recursive=T)))
     
-   #   k = length(vars)
-      k=sum(vars)
+      k = length(vars)
     pv = vlo = vup = numeric(k)
     vmat = matrix(0,k,n)
     ci = tailarea = matrix(0,k,2)

From 782290e86f61a8e1245f8581f5fd65059c7d855f Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Wed, 4 Oct 2017 09:35:38 -0700
Subject: [PATCH 266/493] small change to current CRAN

---
 selectiveInference-currentCRAN/DESCRIPTION | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference-currentCRAN/DESCRIPTION b/selectiveInference-currentCRAN/DESCRIPTION
index 0eb38f7..f7af810 100644
--- a/selectiveInference-currentCRAN/DESCRIPTION
+++ b/selectiveInference-currentCRAN/DESCRIPTION
@@ -1,12 +1,12 @@
 Package: selectiveInference
 Type: Package
 Title: Tools for Post-Selection Inference
-Version: 1.2.3
+Version: 1.2.4
 Date: 2017-09-18
 Author: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor,
     Joshua Loftus, Stephen Reid
 Maintainer: Rob Tibshirani <tibs@stanford.edu>
-Depends: glmnet, intervals, survival
+Depends: glmnet, intervals, survival, R (>= 3.4.0)
 Suggests: Rmpfr
 Description: New tools for post-selection inference, for use
     with forward stepwise regression, least angle regression, the

From 96e87abe19ad8e1b972000099fd2c837ef06e434 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 9 Sep 2017 14:14:56 -0700
Subject: [PATCH 267/493] travis failing with install for shell reason -- maybe
 caps works better?

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 91a89a4..e10e111 100644
--- a/Makefile
+++ b/Makefile
@@ -4,7 +4,7 @@ Rcpp:
 	Rscript -e "library(Rcpp); Rcpp::compileAttributes('selectiveInference')"
 
 install: Rcpp
-	R CMD install selectiveInference
+	R CMD INSTALL selectiveInference
 
 build: 
 	R CMD build selectiveInference

From be257f10ad4740eb16d3283432cbb2611a87cadb Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 10 Oct 2017 13:21:54 -0700
Subject: [PATCH 268/493] small cleanup of fixedLassoPoly

---
 selectiveInference/R/funs.fixed.R | 49 ++++++++++++++++++-------------
 1 file changed, 29 insertions(+), 20 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 250a9d4..6ddf513 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -80,12 +80,9 @@ fixedLassoInf <- function(x, y, beta,
       warning(paste("Solution beta does not satisfy the KKT conditions",
                     "(to within specified tolerances)"))
     
-    tol.coef = tol.beta * sqrt(n^2 / colSums(x^2))
-    # print(tol.coef)
-      vars = which(abs(beta) > tol.coef)
-   #    vars = abs(beta) > tol.coef
-    # print(beta)
-    # print(vars)
+    tol.coef = tol.beta * sqrt(n / colSums(x^2))
+    vars = which(abs(beta) > tol.coef)
+
     if(sum(vars)==0){
       cat("Empty model",fill=T)
       return()
@@ -97,10 +94,17 @@ fixedLassoInf <- function(x, y, beta,
                     "'thresh' parameter, for a more accurate convergence."))
     
     # Get lasso polyhedral region, of form Gy >= u
-logical.vars=rep(FALSE,p)
-logical.vars[vars]=TRUE
-    if (type == 'full') out = fixedLassoPoly(x,y,lambda,beta,logical.vars,inactive=TRUE)
-    else out = fixedLassoPoly(x,y,lambda,beta,logical.vars)
+
+    logical.vars=rep(FALSE,p)
+    logical.vars[vars]=TRUE
+    
+    if (type == 'full') {
+       out = fixedLassoPoly(x, y, lambda, beta, logical.vars, inactive=TRUE)
+    } 
+    else {
+       out = fixedLassoPoly(x, y, lambda, beta, logical.vars)
+    }
+    
     A = out$A
     b = out$b
     
@@ -233,8 +237,8 @@ logical.vars[vars]=TRUE
 
 fixedLassoPoly =
   function(X, y, lambda, beta, active, inactive = FALSE) {
-    Xa = X[,active,drop=F]
-    Xac = X[,!active,drop=F]
+    Xa = X[, active, drop=FALSE]
+    Xac = X[, !active, drop=FALSE]
     Xai = pinv(crossprod(Xa))
     Xap = Xai %*% t(Xa)
 
@@ -242,25 +246,30 @@ fixedLassoPoly =
     if (length(za)>1) dz = diag(za)
     if (length(za)==1) dz = matrix(za,1,1)
     
+    if(length(lambda)>1) {
+       lambdaA= lambda[active]
+       lambdaI = lambda[!active]
+    } else {
+       lambdaA = rep(lambda, sum(active))
+       lambdaI = rep(lambda, sum(!active))
+    }
     if (inactive) { # should we include the inactive constraints?
-      R = diag(1,nrow(Xa)) - Xa %*% Xap # R is residual forming matrix of selected model
+      R = diag(rep(1, nrow(Xa))) - Xa %*% Xap # R is residual forming matrix of selected model
       
       A = rbind(
-        1/lambda * t(Xac) %*% R,
-        -1/lambda * t(Xac) %*% R,
+        1/lambdaI * t(Xac) %*% R,
+        -1/lambdaI * t(Xac) %*% R,
         -dz %*% Xap
       )
       lambda2=lambda
-      if(length(lambda)>1) lambda2=lambda[active]
+
       b = c(
         1 - t(Xac) %*% t(Xap) %*% za,
         1 + t(Xac) %*% t(Xap) %*% za,
-        -lambda2 * dz %*% Xai %*% za)
+        -lambdaA * dz %*% Xai %*% za)
     } else {
       A = -dz %*% Xap
-      lambda2=lambda
-      if(length(lambda)>1) lambda2=lambda[active]
-      b = -lambda2 * dz %*% Xai %*% za
+      b = -lambdaA * dz %*% Xai %*% za
     }
     
     return(list(A=A, b=b))

From 25dce248fcd030f3ae05f0aba310a0adedd71c51 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 10 Oct 2017 14:45:03 -0700
Subject: [PATCH 269/493] rewriting fixedLassoPoly to allow for vector lambda
 (including unpenalized 0 coords)

---
 selectiveInference/R/RcppExports.R     | 10 ++---
 selectiveInference/R/funs.fixed.R      | 42 +++++++++++---------
 selectiveInference/src/RcppExports.cpp | 55 ++++++++++----------------
 3 files changed, 49 insertions(+), 58 deletions(-)

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
index f5ebee4..8af4640 100644
--- a/selectiveInference/R/RcppExports.R
+++ b/selectiveInference/R/RcppExports.R
@@ -1,19 +1,19 @@
-# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# This file was generated by Rcpp::compileAttributes
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('_selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
+    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
 }
 
 solve_QP_wide <- function(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('_selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active)
+    .Call('selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active)
 }
 
 update1_ <- function(Q2, w, m, k) {
-    .Call('_selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
+    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
 }
 
 downdate1_ <- function(Q1, R, j0, m, n) {
-    .Call('_selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
+    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
 }
 
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 6ddf513..0fa0d71 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -237,15 +237,13 @@ fixedLassoInf <- function(x, y, beta,
 
 fixedLassoPoly =
   function(X, y, lambda, beta, active, inactive = FALSE) {
-    Xa = X[, active, drop=FALSE]
-    Xac = X[, !active, drop=FALSE]
-    Xai = pinv(crossprod(Xa))
-    Xap = Xai %*% t(Xa)
-
-    za = sign(beta[active])
-    if (length(za)>1) dz = diag(za)
-    if (length(za)==1) dz = matrix(za,1,1)
-    
+
+    XA = X[, active, drop=FALSE]
+    XI = X[, !active, drop=FALSE]
+    XAi = pinv(crossprod(XA))
+    XAp = XAi %*% t(XA)
+    Ir = t(XI) %*% t(XAp)  # matrix in the "irrepresentable" condition
+
     if(length(lambda)>1) {
        lambdaA= lambda[active]
        lambdaI = lambda[!active]
@@ -253,23 +251,29 @@ fixedLassoPoly =
        lambdaA = rep(lambda, sum(active))
        lambdaI = rep(lambda, sum(!active))
     }
+
+    penalized = lambdaA != 0
+    signA = sign(beta[active])
+    active_subgrad = signA * lambdaA
+    if (length(signA)>1) sign_diag = diag(signA)
+    if (length(signA)==1) sign_diag = matrix(signA, 1, 1)
+    
     if (inactive) { # should we include the inactive constraints?
-      R = diag(rep(1, nrow(Xa))) - Xa %*% Xap # R is residual forming matrix of selected model
+      RA = diag(rep(1, nrow(XA))) - XA %*% XAp # RA is residual forming matrix of selected model
       
       A = rbind(
-        1/lambdaI * t(Xac) %*% R,
-        -1/lambdaI * t(Xac) %*% R,
-        -dz %*% Xap
+        t(XI) %*% RA,
+        -t(XI) %*% RA,
+        -(sign_diag %*% XAp)[penalized,] # no constraints for unpenalized
       )
-      lambda2=lambda
 
       b = c(
-        1 - t(Xac) %*% t(Xap) %*% za,
-        1 + t(Xac) %*% t(Xap) %*% za,
-        -lambdaA * dz %*% Xai %*% za)
+        lambdaI - Ir %*% active_subgrad,
+        lambdaI + Ir %*% active_subgrad,
+        -(sign_diag %*% XAi %*% active_subgrad)[penalized])
     } else {
-      A = -dz %*% Xap
-      b = -lambdaA * dz %*% Xai %*% za
+      A = -(sign_diag %*% XAp)[penalized,]  # no constraints for unpenalized
+      b = -(sign_diag %*% XAi %*% active_subgrad)[penalized]
     }
     
     return(list(A=A, b=b))
diff --git a/selectiveInference/src/RcppExports.cpp b/selectiveInference/src/RcppExports.cpp
index 02a7741..efa87d5 100644
--- a/selectiveInference/src/RcppExports.cpp
+++ b/selectiveInference/src/RcppExports.cpp
@@ -1,4 +1,4 @@
-// Generated by using Rcpp::compileAttributes() -> do not edit by hand
+// This file was generated by Rcpp::compileAttributes
 // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 #include <Rcpp.h>
@@ -7,10 +7,10 @@ using namespace Rcpp;
 
 // solve_QP
 Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP _selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+RcppExport SEXP selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::RObject __result;
+    Rcpp::RNGScope __rngScope;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Sigma(SigmaSEXP);
     Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
     Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
@@ -22,16 +22,16 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
     Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
     Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    rcpp_result_gen = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return rcpp_result_gen;
+    __result = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return __result;
 END_RCPP
 }
 // solve_QP_wide
 Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::NumericVector X_theta, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP _selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP X_thetaSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+RcppExport SEXP selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP X_thetaSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::RObject __result;
+    Rcpp::RNGScope __rngScope;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type X(XSEXP);
     Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
     Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
@@ -44,49 +44,36 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
     Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
     Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    rcpp_result_gen = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return rcpp_result_gen;
+    __result = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return __result;
 END_RCPP
 }
 // update1_
 Rcpp::List update1_(Rcpp::NumericMatrix Q2, Rcpp::NumericVector w, int m, int k);
-RcppExport SEXP _selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
+RcppExport SEXP selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::RObject __result;
+    Rcpp::RNGScope __rngScope;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q2(Q2SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericVector >::type w(wSEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type k(kSEXP);
-    rcpp_result_gen = Rcpp::wrap(update1_(Q2, w, m, k));
-    return rcpp_result_gen;
+    __result = Rcpp::wrap(update1_(Q2, w, m, k));
+    return __result;
 END_RCPP
 }
 // downdate1_
 Rcpp::List downdate1_(Rcpp::NumericMatrix Q1, Rcpp::NumericMatrix R, int j0, int m, int n);
-RcppExport SEXP _selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
+RcppExport SEXP selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::RObject __result;
+    Rcpp::RNGScope __rngScope;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q1(Q1SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type R(RSEXP);
     Rcpp::traits::input_parameter< int >::type j0(j0SEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    rcpp_result_gen = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
-    return rcpp_result_gen;
+    __result = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
+    return __result;
 END_RCPP
 }
-
-static const R_CallMethodDef CallEntries[] = {
-    {"_selectiveInference_solve_QP", (DL_FUNC) &_selectiveInference_solve_QP, 11},
-    {"_selectiveInference_solve_QP_wide", (DL_FUNC) &_selectiveInference_solve_QP_wide, 12},
-    {"_selectiveInference_update1_", (DL_FUNC) &_selectiveInference_update1_, 4},
-    {"_selectiveInference_downdate1_", (DL_FUNC) &_selectiveInference_downdate1_, 5},
-    {NULL, NULL, 0}
-};
-
-RcppExport void R_init_selectiveInference(DllInfo *dll) {
-    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
-    R_useDynamicSymbols(dll, FALSE);
-}

From 17eaff40d0d880745815a44428978a1b84af78e7 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 10 Oct 2017 14:48:59 -0700
Subject: [PATCH 270/493] undefined reference to n

---
 selectiveInference/R/funs.fixed.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 0fa0d71..83c0f93 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -375,7 +375,6 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
   # Initialize variables 
 
   soln = rep(0, p)
-  Xsoln = rep(0, n)
   ever_active = rep(0, p)
   ever_active[1] = row      # 1-based
   ever_active = as.integer(ever_active)
@@ -406,6 +405,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                             objective_tol, 
                             max_active) 
       } else {
+          Xsoln = rep(0, nrow(Xinfo))
           result = solve_QP_wide(Xinfo, # this is a design matrix
                                  mu, 
                                  max_iter, 

From eb2a5e4b47d270a71ddd8855a97e1c32955de0ed Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 10 Oct 2017 14:52:28 -0700
Subject: [PATCH 271/493] needed LaTeX escape chars

---
 selectiveInference/man/debiasingMatrix.Rd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/man/debiasingMatrix.Rd b/selectiveInference/man/debiasingMatrix.Rd
index d0e908d..4da925e 100644
--- a/selectiveInference/man/debiasingMatrix.Rd
+++ b/selectiveInference/man/debiasingMatrix.Rd
@@ -108,7 +108,7 @@ set.seed(10)
 n = 50
 p = 100
 X = matrix(rnorm(n * p), n, p)
-S = t(X) %*% X / n
+S = t(X) \%*\% X / n
 M = debiasingMatrix(S, FALSE, n, c(1,3,5))
 M2 = debiasingMatrix(X, TRUE, n, c(1,3,5))
 max(M - M2)

From 43fbddf9d2a18dea84326867dcf0e0ac90bfdc12 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 10 Oct 2017 15:54:14 -0700
Subject: [PATCH 272/493] added a full n>p example

---
 selectiveInference/man/fixedLassoInf.Rd | 59 ++++++++++++++++++-------
 1 file changed, 42 insertions(+), 17 deletions(-)

diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index 5fd8c24..445e72a 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -257,29 +257,54 @@ set.seed(43)
      out = fixedLassoInf(x, tim, beta_hat, lambda, status=status, family="cox")
      out
 
-# Debiased lasso or "full"
+     # Debiased lasso or "full"
 
-n = 50
-p = 100
-sigma = 1
+     n = 50
+     p = 100
+     sigma = 1
 
-x = matrix(rnorm(n*p),n,p)
-x = scale(x,TRUE,TRUE)
+     x = matrix(rnorm(n*p),n,p)
+     x = scale(x,TRUE,TRUE)
 
-beta = c(3,2,rep(0,p-2))
-y = x\%*\%beta + sigma*rnorm(n)
+     beta = c(3,2,rep(0,p-2))
+     y = x\%*\%beta + sigma*rnorm(n)
 
-# first run glmnet
-gfit = glmnet(x, y, standardize=FALSE, intercept=FALSE)
+     # first run glmnet
+     gfit = glmnet(x, y, standardize=FALSE, intercept=FALSE)
 
-# extract coef for a given lambda; note the 1/n factor!
-# (and we don't save the intercept term)
-lambda = 2.8
-beta = coef(gfit, x=x, y=y, s=lambda/n, exact=TRUE)[-1]
+     # extract coef for a given lambda; note the 1/n factor!
+     # (and we don't save the intercept term)
+     lambda = 2.8
+     beta = coef(gfit, x=x, y=y, s=lambda/n, exact=TRUE)[-1]
 
-# compute fixed lambda p-values and selection intervals
-out = fixedLassoInf(x, y, beta, lambda, sigma=sigma, type='full', intercept=FALSE)
-out
+     # compute fixed lambda p-values and selection intervals
+     out = fixedLassoInf(x, y, beta, lambda, sigma=sigma, type='full', intercept=FALSE)
+     out
+
+     # When n > p and "full" we use the full inverse
+     # instead of Javanmard and Montanari's approximate inverse
+
+     n = 200
+     p = 50
+     sigma = 1
+
+     x = matrix(rnorm(n*p),n,p)
+     x = scale(x,TRUE,TRUE)
+
+     beta = c(3,2,rep(0,p-2))
+     y = x\%*\%beta + sigma*rnorm(n)
+
+     # first run glmnet
+     gfit = glmnet(x, y, standardize=FALSE, intercept=FALSE)
+
+     # extract coef for a given lambda; note the 1/n factor!
+     # (and we don't save the intercept term)
+     lambda = 2.8
+     beta = coef(gfit, x=x, y=y, s=lambda/n, exact=TRUE)[-1]
+
+     # compute fixed lambda p-values and selection intervals
+     out = fixedLassoInf(x, y, beta, lambda, sigma=sigma, type='full', intercept=FALSE)
+     out
 
 }
  
\ No newline at end of file

From a6fcd6e283c32e3b20c7f1cd762e0ccecf728915 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 10 Oct 2017 23:00:08 -0700
Subject: [PATCH 273/493] fix of signs for full

---
 selectiveInference/R/funs.fixed.R | 41 ++++++++++++++++++++++---------
 1 file changed, 29 insertions(+), 12 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 83c0f93..be314af 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -82,17 +82,20 @@ fixedLassoInf <- function(x, y, beta,
     
     tol.coef = tol.beta * sqrt(n / colSums(x^2))
     vars = which(abs(beta) > tol.coef)
+    sign_vars = sign(beta[vars])
 
     if(sum(vars)==0){
       cat("Empty model",fill=T)
       return()
     }
-    if (any(sign(g[vars]) != sign(beta[vars])))
+
+    if (any(sign(g[vars]) != sign_vars)) {
       warning(paste("Solution beta does not satisfy the KKT conditions",
                     "(to within specified tolerances). You might try rerunning",
                     "glmnet with a lower setting of the",
                     "'thresh' parameter, for a more accurate convergence."))
-    
+    }
+
     # Get lasso polyhedral region, of form Gy >= u
 
     logical.vars=rep(FALSE,p)
@@ -132,13 +135,19 @@ fixedLassoInf <- function(x, y, beta,
     }
     
     # add additional targets for inference if provided
-    if (!is.null(add.targets)) vars = sort(unique(c(vars,add.targets,recursive=T)))
-    
-      k = length(vars)
+    if (!is.null(add.targets)) {
+       # vars is boolean...
+       old_vars = vars & TRUE
+       vars[add.targets] = TRUE
+       sign_vars = sign(beta[vars]) 
+       sign_vars[!old_vars] = NA
+       stop("`add.targets` not fully implemented yet")
+    }
+
+    k = length(vars)
     pv = vlo = vup = numeric(k)
     vmat = matrix(0,k,n)
     ci = tailarea = matrix(0,k,2)
-    sign = numeric(k)
       
     if (type=="full" & p > n) {
       if (intercept == T) {
@@ -202,20 +211,28 @@ fixedLassoInf <- function(x, y, beta,
     vj = M[j,]
     mj = sqrt(sum(vj^2))
     vj = vj / mj        # Standardize (divide by norm of vj)
-    sign[j] = sign(sum(vj*y))
-    vj = sign[j] * vj
+
+    if (!is.na(sign_vars[j])) {
+        vj = sign_vars[j] * vj
+    }
 
     limits.info = TG.limits(y, A, b, vj, Sigma=diag(rep(sigma^2, n)))
     a = TG.pvalue.base(limits.info, null_value=null_value[j], bits=bits)
     pv[j] = a$pv
+    if (is.na(sign_vars[j])) { # for variables not in the active set, report 2-sided pvalue
+       pv[j] = 2 * min(pv[j], 1 - pv[j])
+    }
     vlo[j] = a$vlo * mj # Unstandardize (mult by norm of vj)
     vup[j] = a$vup * mj # Unstandardize (mult by norm of vj)
-    vmat[j,] = vj * mj * sign[j]  # Unstandardize (mult by norm of vj)
-
+    if (!is.na(sign_vars[j])) { 
+        vmat[j,] = vj * mj * sign_vars[j]  # Unstandardize (mult by norm of vj) and fix sign
+    } else {
+        vmat[j,] = vj * mj # Unstandardize (mult by norm of vj)
+    }
     a = TG.interval.base(limits.info, 
                          alpha=alpha,
                          gridrange=gridrange,
-			 flip=(sign[j]==-1),
+			 flip=(sign_vars[j]==-1),
                          bits=bits)
     ci[j,] = (a$int-null_value[j]) * mj # Unstandardize (mult by norm of vj)
     tailarea[j,] = a$tailarea
@@ -223,7 +240,7 @@ fixedLassoInf <- function(x, y, beta,
 
   out = list(type=type,lambda=lambda,pv=pv,ci=ci,
     tailarea=tailarea,vlo=vlo,vup=vup,vmat=vmat,y=y,
-    vars=vars,sign=sign,sigma=sigma,alpha=alpha,
+    vars=vars,sign=sign_vars,sigma=sigma,alpha=alpha,
     sd=sigma*sqrt(rowSums(vmat^2)),
     coef0=vmat%*%y,
     call=this.call)

From 8ad2f63d42d04bdd5212ac9c9b10ad75d98498e3 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 11 Oct 2017 07:38:07 -0700
Subject: [PATCH 274/493] variable rename

---
 selectiveInference/R/funs.fixedCox.R   | 12 ++++++------
 selectiveInference/R/funs.fixedLogit.R | 12 ++++++------
 2 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/selectiveInference/R/funs.fixedCox.R b/selectiveInference/R/funs.fixedCox.R
index d6ebd6b..7530511 100644
--- a/selectiveInference/R/funs.fixedCox.R
+++ b/selectiveInference/R/funs.fixedCox.R
@@ -29,7 +29,7 @@ if( sum(status==0)+sum(status==1)!=length(y)) stop("status vector must have valu
 vars=which(m)
 if(sum(m)>0){
     bhat=beta[beta!=0] #penalized coefs just for active variables
-    s2=sign(bhat)
+    sign_bhat=sign(bhat)
 
  #check KKT
     
@@ -40,7 +40,7 @@ if(sum(m)>0){
     res=residuals(aaa,type="score")
 if(!is.matrix(res)) res=matrix(res,ncol=1)
 scor=colSums(res)
-    g=(scor+lambda*s2)/(2*lambda)
+    g=(scor+lambda*sign_bhat)/(2*lambda)
 #    cat(c(g,lambda,tol.kkt),fill=T)
      if (any(abs(g) > 1+tol.kkt) )
     warning(paste("Solution beta does not satisfy the KKT conditions",
@@ -49,9 +49,9 @@ scor=colSums(res)
 # Hessian of partial likelihood at the LASSO solution    
 MM=vcov(aaa)
 
-bbar=(bhat+lambda*MM%*%s2)
-A1=-(mydiag(s2))
-b1= -(mydiag(s2)%*%MM)%*%s2*lambda
+bbar=(bhat+lambda*MM%*%sign_bhat)
+A1=-(mydiag(sign_bhat))
+b1= -(mydiag(sign_bhat)%*%MM)%*%sign_bhat*lambda
 
    temp=max(A1%*%bbar-b1)
 
@@ -63,7 +63,7 @@ b1= -(mydiag(s2)%*%MM)%*%s2*lambda
 # the one sided p-values are a bit off
 
     for(jj in 1:length(bbar)){
-      vj=rep(0,length(bbar));vj[jj]=s2[jj]
+      vj=rep(0,length(bbar));vj[jj]=sign_bhat[jj]
 
 
       junk=TG.pvalue(bbar, A1, b1, vj,MM)
diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index 19936b0..16cafdc 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -32,7 +32,7 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
  m=beta[-1]!=0  #active set
          
     bhat=c(beta[1],beta[-1][beta[-1]!=0]) # intcpt plus active vars
-     s2=sign(bhat)
+     sign_bhat=sign(bhat)
      lam2m=diag(c(0,rep(lambda,sum(m))))
  
 
@@ -66,14 +66,14 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
  # MM=solve(t(xxm)%*%w%*%xxm)
    MM=solve(scale(t(xxm),F,1/ww)%*%xxm)
   gm = c(0,-g[vars]*lambda) # gradient at LASSO solution, first entry is 0 because intercept is unpenalized
-                            # at exact LASSO solution it should be s2[-1]
+                            # at exact LASSO solution it should be sign_bhat[-1]
   dbeta = MM %*% gm
 
-  # bbar=(bhat+lam2m%*%MM%*%s2)  # JT: this is wrong, shouldn't use sign of intercept anywhere...
+  # bbar=(bhat+lam2m%*%MM%*%sign_bhat)  # JT: this is wrong, shouldn't use sign of intercept anywhere...
   bbar = bhat - dbeta
 
-  A1=-(mydiag(s2))[-1,]
-  b1= (s2 * dbeta)[-1]
+  A1=-(mydiag(sign_bhat))[-1,]
+  b1= (sign_bhat * dbeta)[-1]
 
   tol.poly = 0.01 
   if (max((A1 %*% bbar) - b1) > tol.poly)
@@ -87,7 +87,7 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
 
   
     for(jj in 1:sum(m)){
-       vj=c(rep(0,sum(m)+1));vj[jj+1]=s2[jj+1]
+       vj=c(rep(0,sum(m)+1));vj[jj+1]=sign_bhat[jj+1]
       # compute p-values
       junk=TG.pvalue(bbar, A1, b1, vj, MM)
       pv[jj] = junk$pv

From 749c136eddbbb9928c0e8940b09def5d8aece550 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Wed, 11 Oct 2017 10:08:57 -0700
Subject: [PATCH 275/493] rob added unifTest.R

---
 selectiveInference/R/RcppExports.R     |  10 +--
 selectiveInference/src/RcppExports.cpp |  55 ++++++++-----
 tests/unifTest.R                       | 110 +++++++++++++++++++++++++
 3 files changed, 149 insertions(+), 26 deletions(-)
 create mode 100644 tests/unifTest.R

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
index 8af4640..f5ebee4 100644
--- a/selectiveInference/R/RcppExports.R
+++ b/selectiveInference/R/RcppExports.R
@@ -1,19 +1,19 @@
-# This file was generated by Rcpp::compileAttributes
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
+    .Call('_selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
 }
 
 solve_QP_wide <- function(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active)
+    .Call('_selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active)
 }
 
 update1_ <- function(Q2, w, m, k) {
-    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
+    .Call('_selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
 }
 
 downdate1_ <- function(Q1, R, j0, m, n) {
-    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
+    .Call('_selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
 }
 
diff --git a/selectiveInference/src/RcppExports.cpp b/selectiveInference/src/RcppExports.cpp
index efa87d5..02a7741 100644
--- a/selectiveInference/src/RcppExports.cpp
+++ b/selectiveInference/src/RcppExports.cpp
@@ -1,4 +1,4 @@
-// This file was generated by Rcpp::compileAttributes
+// Generated by using Rcpp::compileAttributes() -> do not edit by hand
 // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 #include <Rcpp.h>
@@ -7,10 +7,10 @@ using namespace Rcpp;
 
 // solve_QP
 Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+RcppExport SEXP _selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Sigma(SigmaSEXP);
     Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
     Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
@@ -22,16 +22,16 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
     Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
     Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    __result = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return rcpp_result_gen;
 END_RCPP
 }
 // solve_QP_wide
 Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::NumericVector X_theta, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP X_thetaSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+RcppExport SEXP _selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP X_thetaSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type X(XSEXP);
     Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
     Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
@@ -44,36 +44,49 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
     Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
     Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    __result = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return rcpp_result_gen;
 END_RCPP
 }
 // update1_
 Rcpp::List update1_(Rcpp::NumericMatrix Q2, Rcpp::NumericVector w, int m, int k);
-RcppExport SEXP selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
+RcppExport SEXP _selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q2(Q2SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericVector >::type w(wSEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type k(kSEXP);
-    __result = Rcpp::wrap(update1_(Q2, w, m, k));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(update1_(Q2, w, m, k));
+    return rcpp_result_gen;
 END_RCPP
 }
 // downdate1_
 Rcpp::List downdate1_(Rcpp::NumericMatrix Q1, Rcpp::NumericMatrix R, int j0, int m, int n);
-RcppExport SEXP selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
+RcppExport SEXP _selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q1(Q1SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type R(RSEXP);
     Rcpp::traits::input_parameter< int >::type j0(j0SEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    __result = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
+    return rcpp_result_gen;
 END_RCPP
 }
+
+static const R_CallMethodDef CallEntries[] = {
+    {"_selectiveInference_solve_QP", (DL_FUNC) &_selectiveInference_solve_QP, 11},
+    {"_selectiveInference_solve_QP_wide", (DL_FUNC) &_selectiveInference_solve_QP_wide, 12},
+    {"_selectiveInference_update1_", (DL_FUNC) &_selectiveInference_update1_, 4},
+    {"_selectiveInference_downdate1_", (DL_FUNC) &_selectiveInference_downdate1_, 5},
+    {NULL, NULL, 0}
+};
+
+RcppExport void R_init_selectiveInference(DllInfo *dll) {
+    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
+    R_useDynamicSymbols(dll, FALSE);
+}
diff --git a/tests/unifTest.R b/tests/unifTest.R
new file mode 100644
index 0000000..535fa68
--- /dev/null
+++ b/tests/unifTest.R
@@ -0,0 +1,110 @@
+                                      
+library(selectiveInference)
+
+library(glmnet)
+
+set.seed(424)
+
+#n=100
+#p=30
+
+n=20
+p=40
+sigma=.4
+beta=c(3,2,-1,4,-2,2,rep(0,p-6))
+#beta=rep(0,p)
+
+tr=beta!=0
+
+#type="full"
+type="part"
+
+nsim = 1000
+lambda=.3
+nzb=0
+pvals <- matrix(NA, nrow=nsim, ncol=p)
+x = matrix(rnorm(n*p),n,p)
+x = scale(x,T,T)/sqrt(n-1)
+mu = x%*%beta
+
+for (i in 1:nsim) {
+    cat(i)
+y=mu+sigma*rnorm(n)
+y=y-mean(y) 
+# first run  glmnet
+gfit=glmnet(x,y,intercept=F,standardize=F,thresh=1e-8)
+   
+#extract coef for a given lambda; Note the 1/n factor!
+    bhat = coef(gfit, s=lambda/n, exact=TRUE,x=x,y=y)[-1]
+    nzb=nzb+sum(bhat!=0)
+# compute fixed lambda p-values and selection intervals
+aa = fixedLassoInf(x,y,bhat,lambda,intercept=F,sigma=sigma,type=type)
+pvals[i, aa$vars] <- aa$pv
+}
+
+# summarize results
+
+if(type=="partial"){
+nulls=rowSums(is.na(pvals[,tr]))==0  # for type=partial, nonnull setting
+np = pvals[nulls,-(1:sum(beta!=0))]
+}
+
+if(type=="full"){
+nulls=1:nrow(pvals)   # for type=full  non null setting
+np = pvals[nulls,-(1:sum(beta!=0))]
+}
+
+
+
+#np=pvals         #for null setting
+
+o=!is.na(np)
+
+#check uniformity 
+
+plot((1:sum(o))/sum(o),sort(np[o]),xlab="Expected pvalue",ylab="Observed pvalue")
+abline(0,1)
+
+
+ # estimate and plot FDR
+
+pvadj=pvadj.by=pvadj.holm=matrix(NA,nsim,p)
+for(ii in 1:nsim){
+    o=!is.na(pvals[ii,])
+    pvadj[ii,o]=p.adjust(pvals[ii,o],method="BH")
+    pvadj.by[ii,o]=p.adjust(pvals[ii,o],method="BY")
+      pvadj.holm[ii,o]=p.adjust(pvals[ii,o],method="holm")
+    }
+qqlist=fdr=se=fdr.by=se.by=fdr.holm=se.holm=c(.05, .1,.15,.2,.25,.3)
+jj=0
+for(qq in qqlist){
+    jj=jj+1
+
+r=v=r.by=v.by=r.holm=v.holm=rep(NA,nsim)
+for(ii in 1:nsim){
+    v[ii]=sum( (pvadj[ii,]<qq & !tr), na.rm=T)
+    r[ii]=sum( (pvadj[ii,]<qq), na.rm=T)
+    v.by[ii]=sum( (pvadj.by[ii,]<qq & !tr), na.rm=T)
+    r.by[ii]=sum( (pvadj.by[ii,]<qq), na.rm=T)
+      v.holm[ii]=sum( (pvadj.holm[ii,]<qq & !tr), na.rm=T)
+    r.holm[ii]=sum( (pvadj.holm[ii,]<qq), na.rm=T)
+    
+}
+oo=r!=0
+    fdr[jj]=mean((v/r)[oo])
+    se[jj]=sqrt(var((v/r)[oo])/sum(oo))
+    oo=r.by!=0
+     fdr.by[jj]=mean((v.by/r.by)[oo])
+    se.by[jj]=sqrt(var((v.by/r.by)[oo])/sum(oo))
+     oo=r.by!=0
+     fdr.holm[jj]=mean((v.holm/r.holm)[oo])
+    se.holm[jj]=sqrt(var((v.holm/r.holm)[oo])/sum(oo))
+}
+
+
+plot(qqlist,fdr,type="b",xlab="target FDR",ylab="observed FDR",ylim=c(0,.6),xlim=c(0,.6))
+lines(qqlist,fdr.by,type="b",col=3)
+lines(qqlist,fdr.holm,type="b",col=4)
+abline(0,1,lty=2)
+title(paste("n=",as.character(n)," p=",as.character(p),"  ",as.character(type)))
+legend("bottomright",c("BH","BY","Holm"),col=c(1,3,4),lty=1)

From 9c7ee2b3a2e05feb5c4e16058f10b9ef5fe9cb67 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Wed, 11 Oct 2017 10:32:44 -0700
Subject: [PATCH 276/493] rob modified unifTest.R

---
 tests/unifTest.R | 40 +++++++++++++++++++---------------------
 1 file changed, 19 insertions(+), 21 deletions(-)

diff --git a/tests/unifTest.R b/tests/unifTest.R
index 535fa68..bc15bf7 100644
--- a/tests/unifTest.R
+++ b/tests/unifTest.R
@@ -1,15 +1,16 @@
                                       
-library(selectiveInference)
+library(selectiveInference, lib.loc="/Users/tibs/dropbox/git/R-software/mylib")
 
 library(glmnet)
 
 set.seed(424)
 
-#n=100
-#p=30
+n=100
+p=30
+
+n=100
+p=200
 
-n=20
-p=40
 sigma=.4
 beta=c(3,2,-1,4,-2,2,rep(0,p-6))
 #beta=rep(0,p)
@@ -17,7 +18,7 @@ beta=c(3,2,-1,4,-2,2,rep(0,p-6))
 tr=beta!=0
 
 #type="full"
-type="part"
+type="partial"
 
 nsim = 1000
 lambda=.3
@@ -28,7 +29,7 @@ x = scale(x,T,T)/sqrt(n-1)
 mu = x%*%beta
 
 for (i in 1:nsim) {
-    cat(i)
+    cat(i,fill=T)
 y=mu+sigma*rnorm(n)
 y=y-mean(y) 
 # first run  glmnet
@@ -68,26 +69,25 @@ abline(0,1)
 
  # estimate and plot FDR
 
-pvadj=pvadj.by=pvadj.holm=matrix(NA,nsim,p)
+pvadj=pvadj.by=matrix(NA,nsim,p)
 for(ii in 1:nsim){
-    o=!is.na(pvals[ii,])
-    pvadj[ii,o]=p.adjust(pvals[ii,o],method="BH")
-    pvadj.by[ii,o]=p.adjust(pvals[ii,o],method="BY")
-      pvadj.holm[ii,o]=p.adjust(pvals[ii,o],method="holm")
+    oo=!is.na(pvals[ii,])
+    pvadj[ii,oo]=p.adjust(pvals[ii,oo],method="BH")
+    pvadj.by[ii,oo]=p.adjust(pvals[ii,oo],method="BY")
+   
     }
-qqlist=fdr=se=fdr.by=se.by=fdr.holm=se.holm=c(.05, .1,.15,.2,.25,.3)
+qqlist=c(.05, .1,.15,.2,.25,.3)
+fdr=se=fdr.by=se.by=rep(NA,length(qqlist))
 jj=0
 for(qq in qqlist){
     jj=jj+1
 
-r=v=r.by=v.by=r.holm=v.holm=rep(NA,nsim)
+r=v=r.by=v.by=rep(NA,nsim)
 for(ii in 1:nsim){
     v[ii]=sum( (pvadj[ii,]<qq & !tr), na.rm=T)
     r[ii]=sum( (pvadj[ii,]<qq), na.rm=T)
     v.by[ii]=sum( (pvadj.by[ii,]<qq & !tr), na.rm=T)
     r.by[ii]=sum( (pvadj.by[ii,]<qq), na.rm=T)
-      v.holm[ii]=sum( (pvadj.holm[ii,]<qq & !tr), na.rm=T)
-    r.holm[ii]=sum( (pvadj.holm[ii,]<qq), na.rm=T)
     
 }
 oo=r!=0
@@ -96,15 +96,13 @@ oo=r!=0
     oo=r.by!=0
      fdr.by[jj]=mean((v.by/r.by)[oo])
     se.by[jj]=sqrt(var((v.by/r.by)[oo])/sum(oo))
-     oo=r.by!=0
-     fdr.holm[jj]=mean((v.holm/r.holm)[oo])
-    se.holm[jj]=sqrt(var((v.holm/r.holm)[oo])/sum(oo))
+   
 }
 
 
 plot(qqlist,fdr,type="b",xlab="target FDR",ylab="observed FDR",ylim=c(0,.6),xlim=c(0,.6))
 lines(qqlist,fdr.by,type="b",col=3)
-lines(qqlist,fdr.holm,type="b",col=4)
+
 abline(0,1,lty=2)
 title(paste("n=",as.character(n)," p=",as.character(p),"  ",as.character(type)))
-legend("bottomright",c("BH","BY","Holm"),col=c(1,3,4),lty=1)
+legend("bottomright",c("BH","BY"),col=c(1,3),lty=1)

From 85f7063020b99858790f0858896c8a4889f34742 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Wed, 11 Oct 2017 10:46:34 -0700
Subject: [PATCH 277/493] rob modified unifTest.R

---
 tests/unifTest.R | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/tests/unifTest.R b/tests/unifTest.R
index bc15bf7..3f251ca 100644
--- a/tests/unifTest.R
+++ b/tests/unifTest.R
@@ -106,3 +106,9 @@ lines(qqlist,fdr.by,type="b",col=3)
 abline(0,1,lty=2)
 title(paste("n=",as.character(n)," p=",as.character(p),"  ",as.character(type)))
 legend("bottomright",c("BH","BY"),col=c(1,3),lty=1)
+
+
+pv=pvals[ii,]
+pvv=sort(pv)
+oo=which(pvv<=.2*(1:length(pvv))/length(pvv))
+oo=oo[length(oo)]

From 5f971f6c36c11b4fbe04bb9263dcd4563ac22339 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Wed, 11 Oct 2017 10:08:57 -0700
Subject: [PATCH 278/493] rob added unifTest.R

---
 selectiveInference/R/RcppExports.R     |  10 +--
 selectiveInference/src/RcppExports.cpp |  55 ++++++++-----
 tests/unifTest.R                       | 110 +++++++++++++++++++++++++
 3 files changed, 149 insertions(+), 26 deletions(-)
 create mode 100644 tests/unifTest.R

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
index 8af4640..f5ebee4 100644
--- a/selectiveInference/R/RcppExports.R
+++ b/selectiveInference/R/RcppExports.R
@@ -1,19 +1,19 @@
-# This file was generated by Rcpp::compileAttributes
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
+    .Call('_selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
 }
 
 solve_QP_wide <- function(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active)
+    .Call('_selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active)
 }
 
 update1_ <- function(Q2, w, m, k) {
-    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
+    .Call('_selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
 }
 
 downdate1_ <- function(Q1, R, j0, m, n) {
-    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
+    .Call('_selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
 }
 
diff --git a/selectiveInference/src/RcppExports.cpp b/selectiveInference/src/RcppExports.cpp
index efa87d5..02a7741 100644
--- a/selectiveInference/src/RcppExports.cpp
+++ b/selectiveInference/src/RcppExports.cpp
@@ -1,4 +1,4 @@
-// This file was generated by Rcpp::compileAttributes
+// Generated by using Rcpp::compileAttributes() -> do not edit by hand
 // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 #include <Rcpp.h>
@@ -7,10 +7,10 @@ using namespace Rcpp;
 
 // solve_QP
 Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+RcppExport SEXP _selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Sigma(SigmaSEXP);
     Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
     Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
@@ -22,16 +22,16 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
     Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
     Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    __result = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return rcpp_result_gen;
 END_RCPP
 }
 // solve_QP_wide
 Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::NumericVector X_theta, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP X_thetaSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
+RcppExport SEXP _selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP X_thetaSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type X(XSEXP);
     Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
     Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
@@ -44,36 +44,49 @@ BEGIN_RCPP
     Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
     Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
     Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    __result = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active));
+    return rcpp_result_gen;
 END_RCPP
 }
 // update1_
 Rcpp::List update1_(Rcpp::NumericMatrix Q2, Rcpp::NumericVector w, int m, int k);
-RcppExport SEXP selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
+RcppExport SEXP _selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q2(Q2SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericVector >::type w(wSEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type k(kSEXP);
-    __result = Rcpp::wrap(update1_(Q2, w, m, k));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(update1_(Q2, w, m, k));
+    return rcpp_result_gen;
 END_RCPP
 }
 // downdate1_
 Rcpp::List downdate1_(Rcpp::NumericMatrix Q1, Rcpp::NumericMatrix R, int j0, int m, int n);
-RcppExport SEXP selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
+RcppExport SEXP _selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
 BEGIN_RCPP
-    Rcpp::RObject __result;
-    Rcpp::RNGScope __rngScope;
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q1(Q1SEXP);
     Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type R(RSEXP);
     Rcpp::traits::input_parameter< int >::type j0(j0SEXP);
     Rcpp::traits::input_parameter< int >::type m(mSEXP);
     Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    __result = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
-    return __result;
+    rcpp_result_gen = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
+    return rcpp_result_gen;
 END_RCPP
 }
+
+static const R_CallMethodDef CallEntries[] = {
+    {"_selectiveInference_solve_QP", (DL_FUNC) &_selectiveInference_solve_QP, 11},
+    {"_selectiveInference_solve_QP_wide", (DL_FUNC) &_selectiveInference_solve_QP_wide, 12},
+    {"_selectiveInference_update1_", (DL_FUNC) &_selectiveInference_update1_, 4},
+    {"_selectiveInference_downdate1_", (DL_FUNC) &_selectiveInference_downdate1_, 5},
+    {NULL, NULL, 0}
+};
+
+RcppExport void R_init_selectiveInference(DllInfo *dll) {
+    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
+    R_useDynamicSymbols(dll, FALSE);
+}
diff --git a/tests/unifTest.R b/tests/unifTest.R
new file mode 100644
index 0000000..535fa68
--- /dev/null
+++ b/tests/unifTest.R
@@ -0,0 +1,110 @@
+                                      
+library(selectiveInference)
+
+library(glmnet)
+
+set.seed(424)
+
+#n=100
+#p=30
+
+n=20
+p=40
+sigma=.4
+beta=c(3,2,-1,4,-2,2,rep(0,p-6))
+#beta=rep(0,p)
+
+tr=beta!=0
+
+#type="full"
+type="part"
+
+nsim = 1000
+lambda=.3
+nzb=0
+pvals <- matrix(NA, nrow=nsim, ncol=p)
+x = matrix(rnorm(n*p),n,p)
+x = scale(x,T,T)/sqrt(n-1)
+mu = x%*%beta
+
+for (i in 1:nsim) {
+    cat(i)
+y=mu+sigma*rnorm(n)
+y=y-mean(y) 
+# first run  glmnet
+gfit=glmnet(x,y,intercept=F,standardize=F,thresh=1e-8)
+   
+#extract coef for a given lambda; Note the 1/n factor!
+    bhat = coef(gfit, s=lambda/n, exact=TRUE,x=x,y=y)[-1]
+    nzb=nzb+sum(bhat!=0)
+# compute fixed lambda p-values and selection intervals
+aa = fixedLassoInf(x,y,bhat,lambda,intercept=F,sigma=sigma,type=type)
+pvals[i, aa$vars] <- aa$pv
+}
+
+# summarize results
+
+if(type=="partial"){
+nulls=rowSums(is.na(pvals[,tr]))==0  # for type=partial, nonnull setting
+np = pvals[nulls,-(1:sum(beta!=0))]
+}
+
+if(type=="full"){
+nulls=1:nrow(pvals)   # for type=full  non null setting
+np = pvals[nulls,-(1:sum(beta!=0))]
+}
+
+
+
+#np=pvals         #for null setting
+
+o=!is.na(np)
+
+#check uniformity 
+
+plot((1:sum(o))/sum(o),sort(np[o]),xlab="Expected pvalue",ylab="Observed pvalue")
+abline(0,1)
+
+
+ # estimate and plot FDR
+
+pvadj=pvadj.by=pvadj.holm=matrix(NA,nsim,p)
+for(ii in 1:nsim){
+    o=!is.na(pvals[ii,])
+    pvadj[ii,o]=p.adjust(pvals[ii,o],method="BH")
+    pvadj.by[ii,o]=p.adjust(pvals[ii,o],method="BY")
+      pvadj.holm[ii,o]=p.adjust(pvals[ii,o],method="holm")
+    }
+qqlist=fdr=se=fdr.by=se.by=fdr.holm=se.holm=c(.05, .1,.15,.2,.25,.3)
+jj=0
+for(qq in qqlist){
+    jj=jj+1
+
+r=v=r.by=v.by=r.holm=v.holm=rep(NA,nsim)
+for(ii in 1:nsim){
+    v[ii]=sum( (pvadj[ii,]<qq & !tr), na.rm=T)
+    r[ii]=sum( (pvadj[ii,]<qq), na.rm=T)
+    v.by[ii]=sum( (pvadj.by[ii,]<qq & !tr), na.rm=T)
+    r.by[ii]=sum( (pvadj.by[ii,]<qq), na.rm=T)
+      v.holm[ii]=sum( (pvadj.holm[ii,]<qq & !tr), na.rm=T)
+    r.holm[ii]=sum( (pvadj.holm[ii,]<qq), na.rm=T)
+    
+}
+oo=r!=0
+    fdr[jj]=mean((v/r)[oo])
+    se[jj]=sqrt(var((v/r)[oo])/sum(oo))
+    oo=r.by!=0
+     fdr.by[jj]=mean((v.by/r.by)[oo])
+    se.by[jj]=sqrt(var((v.by/r.by)[oo])/sum(oo))
+     oo=r.by!=0
+     fdr.holm[jj]=mean((v.holm/r.holm)[oo])
+    se.holm[jj]=sqrt(var((v.holm/r.holm)[oo])/sum(oo))
+}
+
+
+plot(qqlist,fdr,type="b",xlab="target FDR",ylab="observed FDR",ylim=c(0,.6),xlim=c(0,.6))
+lines(qqlist,fdr.by,type="b",col=3)
+lines(qqlist,fdr.holm,type="b",col=4)
+abline(0,1,lty=2)
+title(paste("n=",as.character(n)," p=",as.character(p),"  ",as.character(type)))
+legend("bottomright",c("BH","BY","Holm"),col=c(1,3,4),lty=1)

From 42fc515b5d2e040799f4dfc2cd88a288a7dbfbe2 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Wed, 11 Oct 2017 10:32:44 -0700
Subject: [PATCH 279/493] rob modified unifTest.R

---
 tests/unifTest.R | 40 +++++++++++++++++++---------------------
 1 file changed, 19 insertions(+), 21 deletions(-)

diff --git a/tests/unifTest.R b/tests/unifTest.R
index 535fa68..bc15bf7 100644
--- a/tests/unifTest.R
+++ b/tests/unifTest.R
@@ -1,15 +1,16 @@
                                       
-library(selectiveInference)
+library(selectiveInference, lib.loc="/Users/tibs/dropbox/git/R-software/mylib")
 
 library(glmnet)
 
 set.seed(424)
 
-#n=100
-#p=30
+n=100
+p=30
+
+n=100
+p=200
 
-n=20
-p=40
 sigma=.4
 beta=c(3,2,-1,4,-2,2,rep(0,p-6))
 #beta=rep(0,p)
@@ -17,7 +18,7 @@ beta=c(3,2,-1,4,-2,2,rep(0,p-6))
 tr=beta!=0
 
 #type="full"
-type="part"
+type="partial"
 
 nsim = 1000
 lambda=.3
@@ -28,7 +29,7 @@ x = scale(x,T,T)/sqrt(n-1)
 mu = x%*%beta
 
 for (i in 1:nsim) {
-    cat(i)
+    cat(i,fill=T)
 y=mu+sigma*rnorm(n)
 y=y-mean(y) 
 # first run  glmnet
@@ -68,26 +69,25 @@ abline(0,1)
 
  # estimate and plot FDR
 
-pvadj=pvadj.by=pvadj.holm=matrix(NA,nsim,p)
+pvadj=pvadj.by=matrix(NA,nsim,p)
 for(ii in 1:nsim){
-    o=!is.na(pvals[ii,])
-    pvadj[ii,o]=p.adjust(pvals[ii,o],method="BH")
-    pvadj.by[ii,o]=p.adjust(pvals[ii,o],method="BY")
-      pvadj.holm[ii,o]=p.adjust(pvals[ii,o],method="holm")
+    oo=!is.na(pvals[ii,])
+    pvadj[ii,oo]=p.adjust(pvals[ii,oo],method="BH")
+    pvadj.by[ii,oo]=p.adjust(pvals[ii,oo],method="BY")
+   
     }
-qqlist=fdr=se=fdr.by=se.by=fdr.holm=se.holm=c(.05, .1,.15,.2,.25,.3)
+qqlist=c(.05, .1,.15,.2,.25,.3)
+fdr=se=fdr.by=se.by=rep(NA,length(qqlist))
 jj=0
 for(qq in qqlist){
     jj=jj+1
 
-r=v=r.by=v.by=r.holm=v.holm=rep(NA,nsim)
+r=v=r.by=v.by=rep(NA,nsim)
 for(ii in 1:nsim){
     v[ii]=sum( (pvadj[ii,]<qq & !tr), na.rm=T)
     r[ii]=sum( (pvadj[ii,]<qq), na.rm=T)
     v.by[ii]=sum( (pvadj.by[ii,]<qq & !tr), na.rm=T)
     r.by[ii]=sum( (pvadj.by[ii,]<qq), na.rm=T)
-      v.holm[ii]=sum( (pvadj.holm[ii,]<qq & !tr), na.rm=T)
-    r.holm[ii]=sum( (pvadj.holm[ii,]<qq), na.rm=T)
     
 }
 oo=r!=0
@@ -96,15 +96,13 @@ oo=r!=0
     oo=r.by!=0
      fdr.by[jj]=mean((v.by/r.by)[oo])
     se.by[jj]=sqrt(var((v.by/r.by)[oo])/sum(oo))
-     oo=r.by!=0
-     fdr.holm[jj]=mean((v.holm/r.holm)[oo])
-    se.holm[jj]=sqrt(var((v.holm/r.holm)[oo])/sum(oo))
+   
 }
 
 
 plot(qqlist,fdr,type="b",xlab="target FDR",ylab="observed FDR",ylim=c(0,.6),xlim=c(0,.6))
 lines(qqlist,fdr.by,type="b",col=3)
-lines(qqlist,fdr.holm,type="b",col=4)
+
 abline(0,1,lty=2)
 title(paste("n=",as.character(n)," p=",as.character(p),"  ",as.character(type)))
-legend("bottomright",c("BH","BY","Holm"),col=c(1,3,4),lty=1)
+legend("bottomright",c("BH","BY"),col=c(1,3),lty=1)

From 781609d991583dae3edfe5ac19f9abddd02e6101 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Wed, 11 Oct 2017 10:46:34 -0700
Subject: [PATCH 280/493] rob modified unifTest.R

---
 tests/unifTest.R | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/tests/unifTest.R b/tests/unifTest.R
index bc15bf7..3f251ca 100644
--- a/tests/unifTest.R
+++ b/tests/unifTest.R
@@ -106,3 +106,9 @@ lines(qqlist,fdr.by,type="b",col=3)
 abline(0,1,lty=2)
 title(paste("n=",as.character(n)," p=",as.character(p),"  ",as.character(type)))
 legend("bottomright",c("BH","BY"),col=c(1,3),lty=1)
+
+
+pv=pvals[ii,]
+pvv=sort(pv)
+oo=which(pvv<=.2*(1:length(pvv))/length(pvv))
+oo=oo[length(oo)]

From 3da6c507fa366aaa8c8b4b86124092d40bea5255 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 25 Oct 2017 14:06:10 -0700
Subject: [PATCH 281/493] NF: density functions for randomized LASSO

---
 selectiveInference/src/randomized_lasso.c | 201 ++++++++++++++++++++++
 selectiveInference/src/randomized_lasso.h |  42 +++++
 2 files changed, 243 insertions(+)
 create mode 100644 selectiveInference/src/randomized_lasso.c
 create mode 100644 selectiveInference/src/randomized_lasso.h

diff --git a/selectiveInference/src/randomized_lasso.c b/selectiveInference/src/randomized_lasso.c
new file mode 100644
index 0000000..ac55cf4
--- /dev/null
+++ b/selectiveInference/src/randomized_lasso.c
@@ -0,0 +1,201 @@
+#include <math.h> // for fabs
+
+// Augmented density for randomized LASSO after
+// Gaussian randomization
+
+// Described in https://arxiv.org/abs/1609.05609
+
+// Gaussian is product of IID N(0, noise_scale^2) density
+// Evaluated at A_D D + A_O O + h
+
+// Laplace is product of IID Laplace with scale noise_scale
+// Also evaluated at A_D D + A_O O + h
+
+double log_density_gaussian(double noise_scale,             // Scale of randomization
+			    int ndim,                       // Number of features -- "p"
+			    int ninternal,                  // Dimension of internal data representation often 1
+			    int noptimization,              // Dimension of optimization variables -- "p"
+			    double *internal_linear,        // A_D -- linear part for data
+			    double *internal_state,         // D -- data state
+			    double *optimization_linear,    // A_O -- linear part for optimization variables
+			    double *optimization_state,     // O -- optimization state
+			    double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double denom = 2 * noise_scale * noise_scale;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Internal (i.e. data) contribution
+    for (icol=0; icol<ninternal; icol++) {
+      
+      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
+      internal_state_ptr = ((double *) internal_state + icol);
+
+      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
+    }
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= (reconstruction * reconstruction) / denom;
+  }
+
+  return(value);
+}
+
+double log_density_laplace(double noise_scale,             // Scale of randomization
+			   int ndim,                       // Number of features -- "p"
+			   int ninternal,                  // Dimension of internal data representation often 1
+			   int noptimization,              // Dimension of optimization variables -- "p"
+			   double *internal_linear,        // A_D -- linear part for data
+			   double *internal_state,         // D -- data state
+			   double *optimization_linear,    // A_O -- linear part for optimization variables
+			   double *optimization_state,     // O -- optimization state
+			   double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Internal (i.e. data) contribution
+    for (icol=0; icol<ninternal; icol++) {
+      
+      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
+      internal_state_ptr = ((double *) internal_state + icol);
+
+      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
+    }
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= fabs(reconstruction) / noise_scale;
+  }
+
+  return(value);
+}
+
+// Keeping internal (data) state fixed
+
+double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
+					int ndim,                       // Number of features -- "p"
+					int noptimization,              // Dimension of optimization variables -- "p"
+					double *optimization_linear,    // A_O -- linear part for optimization variables
+					double *optimization_state,     // O -- optimization state
+					double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double denom = 2 * noise_scale * noise_scale;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= reconstruction * reconstruction / denom;
+  }
+
+  return(value);
+}
+
+double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
+				       int ndim,                       // Number of features -- "p"
+				       int noptimization,              // Dimension of optimization variables -- "p"
+				       double *optimization_linear,    // A_O -- linear part for optimization variables
+				       double *optimization_state,     // O -- optimization state
+				       double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Internal (i.e. data) contribution
+    for (icol=0; icol<ninternal; icol++) {
+      
+      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
+      internal_state_ptr = ((double *) internal_state + icol);
+
+      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
+    }
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= fabs(reconstruction) / noise_scale;
+  }
+
+  return(value);
+}
diff --git a/selectiveInference/src/randomized_lasso.h b/selectiveInference/src/randomized_lasso.h
new file mode 100644
index 0000000..1301b72
--- /dev/null
+++ b/selectiveInference/src/randomized_lasso.h
@@ -0,0 +1,42 @@
+#ifdef __cplusplus
+extern "C"
+{
+#endif /* __cplusplus */
+
+double log_density_gaussian(double noise_scale,             // Scale of randomization
+			    int ndim,                       // Number of features -- "p"
+			    int ninternal,                  // Dimension of internal data representation often 1
+			    int noptimization,              // Dimension of optimization variables -- "p"
+			    double *internal_linear,        // A_D -- linear part for data
+			    double *internal_state,         // D -- data state
+			    double *optimization_linear,    // A_O -- linear part for optimization variables
+			    double *optimization_state,     // O -- optimization state
+			    double *offset);                // h -- offset in affine transform -- "p" dimensional 
+  
+double log_density_laplace(double noise_scale,             // Scale of randomization
+			   int ndim,                       // Number of features -- "p"
+			   int ninternal,                  // Dimension of internal data representation often 1
+			   int noptimization,              // Dimension of optimization variables -- "p"
+			   double *internal_linear,        // A_D -- linear part for data
+			   double *internal_state,         // D -- data state
+			   double *optimization_linear,    // A_O -- linear part for optimization variables
+			   double *optimization_state,     // O -- optimization state
+			   double *offset);                // h -- offset in affine transform -- "p" dimensional 
+
+double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
+					int ndim,                       // Number of features -- "p"
+					int noptimization,              // Dimension of optimization variables -- "p"
+					double *optimization_linear,    // A_O -- linear part for optimization variables
+					double *optimization_state,     // O -- optimization state
+					double *offset);                // h -- offset in affine transform -- "p" dimensional 
+
+double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
+				       int ndim,                       // Number of features -- "p"
+				       int noptimization,              // Dimension of optimization variables -- "p"
+				       double *optimization_linear,    // A_O -- linear part for optimization variables
+				       double *optimization_state,     // O -- optimization state
+				       double *offset).                // h -- offset in affine transform -- "p" dimensional 
+
+#ifdef __cplusplus
+}  /* extern "C" */
+#endif /* __cplusplus */

From e0eb1c6e8737eafba9c66fa53e54c8cd1f78c3b0 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 25 Oct 2017 16:35:52 -0700
Subject: [PATCH 282/493] T to TRUE

---
 selectiveInference/R/funs.fixed.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index be314af..66a3088 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -6,7 +6,7 @@ fixedLassoInf <- function(x, y, beta,
                           lambda, family=c("gaussian","binomial","cox"),
                           intercept=TRUE, add.targets=NULL, status=NULL,
                           sigma=NULL, alpha=0.1,
-                          type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
+                          type=c("partial", "full"), tol.beta=1e-5, tol.kkt=0.1,
                           gridrange=c(-100,100), bits=NULL, verbose=FALSE, 
                           linesearch.try=10) {
 
@@ -150,7 +150,7 @@ fixedLassoInf <- function(x, y, beta,
     ci = tailarea = matrix(0,k,2)
       
     if (type=="full" & p > n) {
-      if (intercept == T) {
+      if (intercept == TRUE) {
         pp=p+1
         Xint <- cbind(rep(1,n),x)
         # indices of selected predictors

From b23b189f380ae04ee7a5de0728e55110f46eee42 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 13:08:05 -0700
Subject: [PATCH 283/493] updated wide C code -- can now do ridge as well as
 variable weights -- ready to solve randomized LASSO

---
 selectiveInference/R/funs.fixed.R             |  40 +++-
 selectiveInference/src/Rcpp-debias.cpp        |  35 +++-
 selectiveInference/src/debias.h               |  22 ++-
 selectiveInference/src/quadratic_program.c    | 108 +++++++----
 .../src/quadratic_program_wide.c              | 176 ++++++++++++------
 selectiveInference/src/randomized_lasso.c     |  13 --
 6 files changed, 270 insertions(+), 124 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 66a3088..aaceaaa 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -189,8 +189,10 @@ fixedLassoInf <- function(x, y, beta,
       }
 
       M <- (((htheta%*%t(Xordered))+ithetasigma%*%FS%*%hsigmaSinv%*%t(XS))/n)
+
       # vector which is offset for testing debiased beta's
       null_value <- (((ithetasigma%*%FS%*%hsigmaSinv)%*%sign(hbetaS))*lambda/n)
+
       if (intercept == T) {
         M = M[-1,] # remove intercept row
         null_value = null_value[-1] # remove intercept element
@@ -238,12 +240,23 @@ fixedLassoInf <- function(x, y, beta,
     tailarea[j,] = a$tailarea
   }
 
-  out = list(type=type,lambda=lambda,pv=pv,ci=ci,
-    tailarea=tailarea,vlo=vlo,vup=vup,vmat=vmat,y=y,
-    vars=vars,sign=sign_vars,sigma=sigma,alpha=alpha,
-    sd=sigma*sqrt(rowSums(vmat^2)),
-    coef0=vmat%*%y,
-    call=this.call)
+  out = list(type=type,
+             lambda=lambda,
+             pv=pv,
+             ci=ci,
+             tailarea=tailarea,
+             vlo=vlo,
+             vup=vup,
+             vmat=vmat,
+             y=y,
+             vars=vars,
+             sign=sign_vars,
+             sigma=sigma,
+             alpha=alpha,
+             sd=sigma*sqrt(rowSums(vmat^2)),
+             coef0=vmat%*%y,
+             call=this.call)
+
   class(out) = "fixedLassoInf"
   return(out)
 }
@@ -374,7 +387,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                          row, 
                          mu, 
 	                 linesearch=TRUE,     # do a linesearch?
-		         scaling_factor=1.2,  # multiplicative factor for linesearch
+		         scaling_factor=1.5,  # multiplicative factor for linesearch
 		         max_active=NULL,     # how big can active set get?
 			 max_try=10,          # how many steps in linesearch?
 			 warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
@@ -420,11 +433,15 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                             nactive, 
                             kkt_tol, 
                             objective_tol, 
-                            max_active) 
+                            max_active,
+			    FALSE,        # objective_stop
+			    FALSE,        # kkt_stop
+			    TRUE)         # param_stop
       } else {
           Xsoln = rep(0, nrow(Xinfo))
           result = solve_QP_wide(Xinfo, # this is a design matrix
-                                 mu, 
+                                 rep(mu, p),  # vector of Lagrange multipliers
+				 0,           # ridge_term 
                                  max_iter, 
                                  soln, 
                                  linear_func, 
@@ -434,7 +451,10 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                                  nactive, 
                                  kkt_tol, 
                                  objective_tol, 
-                                 max_active) 
+                                 max_active,
+				 FALSE,       # objective_stop
+				 FALSE,       # kkt_stop
+				 TRUE)        # param_stop
 
       }
 
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index ce8bb15..24bbae8 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -15,7 +15,10 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		    Rcpp::IntegerVector nactive,
 		    double kkt_tol,
 		    double objective_tol,
-		    int max_active
+		    int max_active,
+		    int objective_stop,
+		    int kkt_stop,
+		    int param_stop
 		    ) {
 
   int nrow = Sigma.nrow(); // number of features
@@ -28,6 +31,8 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
   Rcpp::NumericVector Sigma_diag(nrow);
   double *sigma_diag_p = Sigma_diag.begin();
 
+  Rcpp::NumericVector theta_old(nrow);
+
   for (irow=0; irow<nrow; irow++) {
     sigma_diag_p[irow] = Sigma(irow, irow);
   }
@@ -43,10 +48,14 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		      nrow,
 		      bound,
 		      (double *) theta.begin(),
+		      (double *) theta_old.begin(),
 		      maxiter,
 		      kkt_tol,
 		      objective_tol,
-		      max_active);
+		      max_active,
+		      objective_stop,
+		      kkt_stop,
+		      param_stop);
   
   // Check whether feasible
 
@@ -72,7 +81,8 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 
 // [[Rcpp::export]]
 Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
-			 double bound,
+			 Rcpp::NumericVector bound,
+			 double ridge_term,
 			 int maxiter,
 			 Rcpp::NumericVector theta,
 			 Rcpp::NumericVector linear_func,
@@ -82,7 +92,10 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			 Rcpp::IntegerVector nactive,
 			 double kkt_tol,
 			 double objective_tol,
-			 int max_active
+			 int max_active,
+			 int objective_stop,
+			 int kkt_stop,
+			 int param_stop
 			 ) {
 
   int ncase = X.nrow(); // number of cases
@@ -100,6 +113,8 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
   Rcpp::NumericVector nndef_diag(nfeature);
   double *nndef_diag_p = nndef_diag.begin();
 
+  Rcpp::NumericVector theta_old(nfeature);
+
   for (ifeature=0; ifeature<nfeature; ifeature++) {
     nndef_diag_p[ifeature] = 0;
     for (icase=0; icase<ncase; icase++) {
@@ -120,12 +135,17 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			(int *) nactive.begin(),
 			ncase,
 			nfeature,
-			bound,
+			(double *) bound.begin(),
+			ridge_term,
 			(double *) theta.begin(),
+			(double *) theta_old.begin(),
 			maxiter,
 			kkt_tol,
 			objective_tol,
-			max_active);
+			max_active,
+			objective_stop,
+			kkt_stop,
+			param_stop);
   
   // Check whether feasible
 
@@ -137,7 +157,8 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 				 (int *) need_update.begin(),
 				 nfeature,
 				 ncase,
-				 bound,
+				 (double *) bound.begin(),
+				 ridge_term,
 				 kkt_tol);
 
   int max_active_check = (*(nactive.begin()) >= max_active);
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index ebcbc93..052af7a 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -9,13 +9,17 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	     double *gradient_ptr,       /* nndef times theta */
 	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
 	     int *nactive_ptr,           /* Size of ever active set */
-	     int nrow,                   /* How many rows in nndef */
+	     int nfeature,               /* How many features in nndef */
 	     double bound,               /* feasibility parameter */
 	     double *theta,              /* current value */
+	     double *theta_old,          /* previous value */
 	     int maxiter,                /* max number of iterations */
 	     double kkt_tol,             /* precision for checking KKT conditions */
 	     double objective_tol,       /* precision for checking relative decrease in objective value */
-	     int max_active);             /* Upper limit for size of active set -- otherwise break */ 
+	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
+	     int objective_stop,         /* Break based on convergence of objective value? */
+             int kkt_stop,               /* Break based on KKT? */
+	     int param_stop);            /* Break based on parameter convergence? */
 
 int check_KKT_qp(double *theta,        /* current theta */
 		 double *gradient_ptr, /* nndef times theta + linear_func */
@@ -33,12 +37,17 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 	       int *nactive_ptr,           /* Size of ever active set */
 	       int ncase,                  /* How many rows in X */
 	       int nfeature,               /* How many columns in X */
-	       double bound,               /* feasibility parameter */
+	       double *bound_ptr,          /* Lagrange multipliers */
+	       double ridge_term,          /* Ridge / ENet term */
 	       double *theta_ptr,          /* current value */
+	       double *theta_old_ptr,      /* previous value */
 	       int maxiter,                /* max number of iterations */
 	       double kkt_tol,             /* precision for checking KKT conditions */
 	       double objective_tol,       /* precision for checking relative decrease in objective value */
-	       int max_active);             /* Upper limit for size of active set -- otherwise break */ 
+	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
+	       int objective_stop,         /* Break based on convergence of objective value? */
+	       int kkt_stop,               /* Break based on KKT? */
+	       int param_stop);            /* Break based on parameter convergence? */
 
 int check_KKT_wide(double *theta_ptr,        /* current theta */
 		   double *gradient_ptr,     /* X^TX/ncase times theta + linear_func*/
@@ -48,8 +57,9 @@ int check_KKT_wide(double *theta_ptr,        /* current theta */
 		   int *need_update_ptr,     /* Which coordinates need to be updated? */
 		   int nfeature,             /* how many columns in X */
 		   int ncase,                /* how many rows in X */
-   		   double bound,             /* Lagrange multipler for \ell_1 */
-		   double tol);              /* precision for checking KKT conditions */        
+		   double *bound_ptr,        /* Lagrange multiplers for \ell_1 */
+		   double ridge_term,        /* Ridge / ENet term */
+		   double tol);               /* precision for checking KKT conditions */        
   
 void update_gradient_wide(double *gradient_ptr,     /* X^TX/ncase times theta + linear_func */
 			  double *X_theta_ptr,      /* Current fitted values */
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
index d9bd017..1bc7fa3 100644
--- a/selectiveInference/src/quadratic_program.c
+++ b/selectiveInference/src/quadratic_program.c
@@ -92,19 +92,19 @@ int update_ever_active_qp(int coord,
 
 int check_KKT_qp(double *theta_ptr,        /* current theta */
 		 double *gradient_ptr, /* nndef times theta + linear_func */
-		 int nrow,             /* how many rows in nndef */
+		 int nfeature,             /* how many features in nndef */
 		 double bound,         /* Lagrange multipler for \ell_1 */
 		 double tol)           /* precision for checking KKT conditions */        
 {
   // First check inactive
 
-  int irow;
+  int ifeature;
   double *theta_ptr_tmp, *gradient_ptr_tmp;
   double gradient;
 
-  for (irow=0; irow<nrow; irow++) {
-    theta_ptr_tmp = ((double *) theta_ptr + irow);
-    gradient_ptr_tmp = ((double *) gradient_ptr + irow);
+  for (ifeature=0; ifeature<nfeature; ifeature++) {
+    theta_ptr_tmp = ((double *) theta_ptr + ifeature);
+    gradient_ptr_tmp = ((double *) gradient_ptr + ifeature);
 
     // Compute this coordinate of the gradient
 
@@ -132,7 +132,7 @@ int check_KKT_qp_active(int *ever_active_ptr,           /* Ever active set: 0-ba
 		        int *nactive_ptr,               /* Size of ever active set */
 			double *theta_ptr,        /* current theta */
 			double *gradient_ptr, /* nndef times theta + linear_func */
-			int nrow,             /* how many rows in nndef */
+			int nfeature,             /* how many features in nndef */
 			double bound,         /* Lagrange multipler for \ell_1 */
 			double tol)           /* precision for checking KKT conditions */        
 {
@@ -185,7 +185,7 @@ double update_one_coord_qp(double *nndef_ptr,           /* A non-negative defini
 			   double *gradient_ptr,        /* nndef times theta + linear_func */
 			   int *ever_active_ptr,        /* Ever active set: 1-based */ 
 			   int *nactive_ptr,            /* Size of ever active set */
-			   int nrow,                    /* How many rows in nndef */
+			   int nfeature,                    /* How many features in nndef */
 			   double bound,                /* feasibility parameter */
 			   double *theta_ptr,               /* current value */
 			   int coord,                   /* which coordinate to update: 0-based */
@@ -242,10 +242,10 @@ double update_one_coord_qp(double *nndef_ptr,           /* A non-negative defini
   if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
 
     delta = value - old_value;
-    nndef_ptr_tmp = ((double *) nndef_ptr + coord * nrow);
+    nndef_ptr_tmp = ((double *) nndef_ptr + coord * nfeature);
     gradient_ptr_tmp = ((double *) gradient_ptr);
 
-    for (icol=0; icol<nrow; icol++) {
+    for (icol=0; icol<nfeature; icol++) {
       *gradient_ptr_tmp = *gradient_ptr_tmp + delta * (*nndef_ptr_tmp);
       gradient_ptr_tmp += 1;
       nndef_ptr_tmp += 1;
@@ -266,33 +266,42 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	     double *gradient_ptr,       /* nndef times theta */
 	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
 	     int *nactive_ptr,           /* Size of ever active set */
-	     int nrow,                   /* How many rows in nndef */
+	     int nfeature,               /* How many features in nndef */
 	     double bound,               /* feasibility parameter */
 	     double *theta,              /* current value */
+	     double *theta_old,          /* previous value */
 	     int maxiter,                /* max number of iterations */
 	     double kkt_tol,             /* precision for checking KKT conditions */
 	     double objective_tol,       /* precision for checking relative decrease in objective value */
-	     int max_active)             /* Upper limit for size of active set -- otherwise break */ 
+	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
+	     int objective_stop,         /* Break based on convergence of objective value? */
+             int kkt_stop,               /* Break based on KKT? */
+	     int param_stop)             /* Break based on parameter convergence? */
 {
 
   int iter = 0;
-  int icoord = 0;
+  int iter_old = 1;
+  int ifeature = 0;
   int iactive = 0;
   int *active_ptr;
 
-  int check_objective = 1;
   int iter_active;
   int niter_active=5;
 
   double old_value, new_value; 
+  double norm_diff = 1.;
+  double norm_last = 1.;
+  double delta;
+  double threshold = 1.e-2;
+  double *theta_ptr, *theta_old_ptr;
 
-  if (check_objective) {
+  if (objective_stop) {
 
     old_value = objective_qp(nndef_ptr,
 			     linear_func_ptr,
 			     ever_active_ptr,
 			     nactive_ptr,
-			     nrow,
+			     nfeature,
 			     bound,
 			     theta);
 
@@ -314,7 +323,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 			      gradient_ptr,
 			      ever_active_ptr,
 			      nactive_ptr,
-			      nrow,
+			      nfeature,
 			      bound,
 			      theta,
 			      *active_ptr - 1,   // Ever-active is 1-based
@@ -328,7 +337,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 				nactive_ptr,
 				theta,
 				gradient_ptr,
-				nrow,
+				nfeature,
 				bound,
 				kkt_tol) == 1) {
 	  break;
@@ -337,17 +346,19 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 
     // Check KKT
 
-    if (check_KKT_qp(theta,
-		     gradient_ptr,
-		     nrow,
-		     bound,
-		     kkt_tol) == 1) {
-      break;
+    if (kkt_stop) {
+      if (check_KKT_qp(theta, 
+		       gradient_ptr,
+		       nfeature,
+		       bound,
+		       kkt_tol) == 1) {
+	break;
+      }
     }
-					  
+
     // Update all variables 
 
-    for (icoord=0; icoord<nrow; icoord++) {
+    for (ifeature=0; ifeature<nfeature; ifeature++) {
 
       update_one_coord_qp(nndef_ptr,
 			  linear_func_ptr,
@@ -355,23 +366,49 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 			  gradient_ptr,
 			  ever_active_ptr,
 			  nactive_ptr,
-			  nrow,
+			  nfeature,
 			  bound,
 			  theta,
-			  icoord,
+			  ifeature,
 			  0);
     }
 
     // Check KKT
 
-    if (check_KKT_qp(theta,
-		     gradient_ptr,
-		     nrow,
-		     bound,
-		     kkt_tol) == 1) {
-      break;
+    if (kkt_stop) {
+      if (check_KKT_qp(theta, 
+		       gradient_ptr,
+		       nfeature,
+		       bound,
+		       kkt_tol) == 1) {
+	break;
+      }
     }
 					  
+    // Check based on norm -- from Adel's debiasing code
+
+    if (param_stop) { 
+      if (iter == 2 * iter_old) {
+	iter_old = iter;
+	norm_diff = 0;
+	norm_last = 0;
+	for (ifeature=0; ifeature<nfeature; ifeature++) {
+	  theta_old_ptr = ((double *) theta_old + ifeature);
+	  theta_ptr = ((double *) theta + ifeature);
+	  delta = (*theta_ptr - *theta_old_ptr);
+	  norm_diff += delta * delta;
+	  norm_last += (*theta_ptr) * (*theta_ptr);
+	  *theta_old_ptr = (*theta_ptr);
+	}
+	norm_diff = sqrt(norm_diff);
+	norm_last = sqrt(norm_last);
+	
+	if (norm_diff < threshold * norm_last) {
+	  break;
+	}
+      }
+    }
+
     // Check size of active set
 
     if (*nactive_ptr >= max_active) {
@@ -380,12 +417,12 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 
     // Check relative decrease of objective
 
-    if (check_objective) {
+    if (objective_stop) {
       new_value = objective_qp(nndef_ptr,
 			       linear_func_ptr,
 			       ever_active_ptr,
 			       nactive_ptr,
-			       nrow,
+			       nfeature,
 			       bound,
 			       theta);
 
@@ -394,6 +431,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
       }
       old_value = new_value;
     }
+
   }
   return(iter);
 }
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
index 3beb578..c6cb9f3 100644
--- a/selectiveInference/src/quadratic_program_wide.c
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -4,7 +4,7 @@
 
 // Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
 
-// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2 - l^T\theta + \mu \|\theta\|_1
+// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2 - l^T\theta + \mu \|\theta\|_1 + \frac{\epsilon}{2} \|\theta\|^2_2
 // where l is `linear_func` below
 
 // This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
@@ -19,11 +19,13 @@ double objective_wide(double *X_theta_ptr,     /* Fitted values */
 		      int *nactive_ptr,        /* Size of ever active set */
 		      int ncase,               /* how many rows in X */
 		      int nfeature,            /* how many columns in X */
-		      double bound,            /* Lagrange multipler for \ell_1 */
+		      double *bound_ptr,       /* Lagrange multiplers for \ell_1 */
+		      double ridge_term,       /* Ridge / ENet term */
 		      double *theta_ptr)       /* current value */
 {
   int icase, iactive;
   double value = 0;
+  double *bound_ptr_tmp;
   double *X_theta_ptr_tmp = X_theta_ptr;
   double *linear_func_ptr_tmp = linear_func_ptr;
   double *theta_ptr_tmp;
@@ -55,8 +57,12 @@ double objective_wide(double *X_theta_ptr,     /* Fitted values */
 
     // The \ell_1 term
 
-    value += bound * fabs((*theta_ptr_tmp));
+    bound_ptr_tmp = ((double *) bound_ptr + active_feature);
+    value += (*bound_ptr_tmp) * fabs((*theta_ptr_tmp));
 
+    // The ridge term
+
+    value += 0.5 * ridge_term * (*theta_ptr_tmp) * (*theta_ptr_tmp);
   }
   
   return(value);
@@ -167,34 +173,39 @@ int check_KKT_wide(double *theta_ptr,        /* current theta */
 		   int *need_update_ptr,     /* Which coordinates need to be updated? */
 		   int nfeature,             /* how many columns in X */
 		   int ncase,                /* how many rows in X */
-		   double bound,             /* Lagrange multipler for \ell_1 */
+		   double *bound_ptr,        /* Lagrange multiplers for \ell_1 */
+		   double ridge_term,        /* Ridge / ENet term */
 		   double tol)               /* precision for checking KKT conditions */        
 {
   // First check inactive
 
   int ifeature;
   double *theta_ptr_tmp;
+  double *bound_ptr_tmp;
+  double bound;
   double gradient;
 
   for (ifeature=0; ifeature<nfeature; ifeature++) {
 
     theta_ptr_tmp = ((double *) theta_ptr + ifeature);
+    bound_ptr_tmp = ((double *) bound_ptr + ifeature);
+    bound = *bound_ptr_tmp;
 
     // Compute this coordinate of the gradient
 
     gradient = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, ifeature, ncase);
 
-    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
-
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
+    if ((*theta_ptr_tmp != 0) && (bound != 0)) { // these coordinates of gradients should be equal to -bound
+      
+      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + ridge_term * (*theta_ptr_tmp) + bound) > tol * bound)) {
 	return(0);
       }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
+      else if ((*theta_ptr_tmp < 0) && (fabs(gradient + ridge_term * (*theta_ptr_tmp) - bound) > tol * bound)) {
 	return(0);
       }
-
+      
     }
-    else {
+    else if (bound != 0) {
       if (fabs(gradient) > (1. + tol) * bound) {
 	return(0);
       }
@@ -214,13 +225,16 @@ int check_KKT_wide_active(int *ever_active_ptr,           /* Ever active set: 0-
 			  int *need_update_ptr,           /* Which coordinates need to be updated? */
 			  int nfeature,                   /* how many columns in X */
 			  int ncase,                      /* how many rows in X */
-			  double bound,                   /* Lagrange multipler for \ell_1 */
+			  double *bound_ptr,              /* Lagrange multipliers for \ell_1 */
+			  double ridge_term,              /* Ridge / ENet term */
 			  double tol)                     /* precision for checking KKT conditions */        
 {
   // First check inactive
 
   int iactive;
   double *theta_ptr_tmp;
+  double *bound_ptr_tmp;
+  double bound;
   double gradient;
   int nactive = *nactive_ptr;
   int active_feature;
@@ -230,23 +244,26 @@ int check_KKT_wide_active(int *ever_active_ptr,           /* Ever active set: 0-
 
     active_feature_ptr = ((int *) ever_active_ptr + iactive);
     active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
+
     theta_ptr_tmp = ((double *) theta_ptr + active_feature);
+    bound_ptr_tmp = ((double *) bound_ptr + active_feature);
+    bound = *bound_ptr_tmp;
 
     // Compute this coordinate of the gradient
 
     gradient = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, active_feature, ncase);
 
-    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
+    if ((*theta_ptr_tmp != 0) && (bound != 0)) { // these coordinates of gradients should be equal to -bound
 
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
+      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + ridge_term * (*theta_ptr_tmp) + bound) > tol * bound)) {
 	return(0);
       }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
+      else if ((*theta_ptr_tmp < 0) && (fabs(gradient + ridge_term * (*theta_ptr_tmp) - bound) > tol * bound)) {
 	return(0);
       }
 
     }
-    else {
+    else if (bound != 0) {
       if (fabs(gradient) > (1. + tol) * bound) {
 	return(0);
       }
@@ -266,7 +283,8 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
 			     int *need_update_ptr,        /* Whether a gradient coordinate needs update or not */
 			     int ncase,                   /* How many rows in X */
 			     int nfeature,                /* How many rows in X */
-			     double bound,                /* feasibility parameter */
+			     double *bound_ptr,           /* Lagrange multipliers */
+			     double ridge_term,           /* Ridge / ENet term */
 			     double *theta_ptr,           /* current value */
 			     int coord,                   /* which coordinate to update: 0-based */
 			     int is_active)               /* Is this coord in ever_active */     
@@ -280,6 +298,8 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
   double *X_theta_ptr_tmp;
   int *need_update_ptr_tmp;
   double *theta_ptr_tmp;
+  double *bound_ptr_tmp;
+  double bound;
   int ifeature, icase;
 
   double *diagonal_ptr = ((double *) nndef_diag_ptr + coord);
@@ -290,6 +310,9 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
   theta_ptr_tmp = ((double *) theta_ptr + coord);
   old_value = *theta_ptr_tmp;
 
+  bound_ptr_tmp = ((double *) bound_ptr + coord);
+  bound = *bound_ptr_tmp;
+
   // The coord entry of gradient_ptr term has a diagonal term in it:
   // (X^TX)[coord, coord] * theta[coord] / ncase
   // This removes it. 
@@ -298,17 +321,17 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
 
   // Now soft-threshold the coord entry of theta 
 
-  // Objective is t \mapsto q/2 * t^2 + l * t + bound |t|
-  // with q=diagonal_entry and l=linear_term
+  // Objective is t \mapsto (q+eps)/2 * t^2 + l * t + bound |t| + 
+  // with q=diagonal_entry and l=linear_term and eps=ridge_Term
 
   // With a negative linear term, solution should be
   // positive
 
   if (linear_term < -bound) {
-    value = (-linear_term - bound) / diagonal_entry;
+    value = (-linear_term - bound) / (diagonal_entry + ridge_term);
   }
   else if (linear_term > bound) {
-    value = -(linear_term - bound) / diagonal_entry;
+    value = -(linear_term - bound) / (diagonal_entry + ridge_term);
   }
 
   // Add to active set if necessary
@@ -363,24 +386,36 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 	       int *nactive_ptr,           /* Size of ever active set */
 	       int ncase,                  /* How many rows in X */
 	       int nfeature,               /* How many columns in X */
-	       double bound,               /* feasibility parameter */
+	       double *bound_ptr,          /* Lagrange multipliers */
+	       double ridge_term,          /* Ridge / ENet term */
 	       double *theta_ptr,          /* current value */
+	       double *theta_old_ptr,      /* previous value */
 	       int maxiter,                /* max number of iterations */
 	       double kkt_tol,             /* precision for checking KKT conditions */
 	       double objective_tol,       /* precision for checking relative decrease in objective value */
-	       int max_active)             /* Upper limit for size of active set -- otherwise break */ 
+	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
+	       int objective_stop,         /* Break based on convergence of objective value? */
+	       int kkt_stop,               /* Break based on KKT? */
+	       int param_stop)             /* Break based on parameter convergence? */
 {
 
   int iter = 0;
+  int iter_old = 1;
   int ifeature = 0;
   int iactive = 0;
   int *active_ptr;
-  int check_objective = 1;
+
   double old_value, new_value; 
   int niter_active = 5;
   int iter_active;
 
-  if (check_objective) {
+  double norm_diff = 1.;
+  double norm_last = 1.;
+  double delta;
+  double threshold = 1.e-2;
+  double *theta_ptr_tmp, *theta_old_ptr_tmp;
+
+  if (objective_stop) {
 
     old_value = objective_wide(X_theta_ptr,
 			       linear_func_ptr,
@@ -388,8 +423,10 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 			       nactive_ptr,
 			       ncase,
 			       nfeature,
-			       bound,
+			       bound_ptr,
+			       ridge_term,
 			       theta_ptr);
+    new_value = old_value;
 
   }
 
@@ -412,7 +449,8 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 				need_update_ptr,
 				ncase,
 				nfeature,
-				bound,
+				bound_ptr,
+				ridge_term,
 				theta_ptr,
 				*active_ptr - 1,   // Ever-active is 1-based
 				1);
@@ -431,7 +469,8 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 				  need_update_ptr,
 				  nfeature,
 				  ncase,
-				  bound,
+				  bound_ptr,
+				  ridge_term,
 				  kkt_tol) == 1) {
 	  break;
 	}
@@ -440,17 +479,20 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 
     // Check KKT
 
-    if (check_KKT_wide(theta_ptr,
-		       gradient_ptr,
-		       X_theta_ptr,
-		       X_ptr,
-		       linear_func_ptr,
-		       need_update_ptr,
-		       nfeature,
-		       ncase,
-		       bound,
-		       kkt_tol) == 1) {
-      break;
+    if (kkt_stop) { 
+      if (check_KKT_wide(theta_ptr,
+			 gradient_ptr,
+			 X_theta_ptr,
+			 X_ptr,
+			 linear_func_ptr,
+			 need_update_ptr,
+			 nfeature,
+			 ncase,
+			 bound_ptr,
+			 ridge_term,
+			 kkt_tol) == 1) {
+	break;
+      }
     }
 
     // Update all variables 
@@ -467,7 +509,8 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 			    need_update_ptr,
 			    ncase,
 			    nfeature,
-			    bound,
+			    bound_ptr,
+			    ridge_term,
 			    theta_ptr,
 			    ifeature,
 			    0);
@@ -475,19 +518,45 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 
     // Check KKT
 
-    if (check_KKT_wide(theta_ptr,
-		       gradient_ptr,
-		       X_theta_ptr,
-		       X_ptr,
-		       linear_func_ptr,
-		       need_update_ptr,
-		       nfeature,
-		       ncase,
-		       bound,
-		       kkt_tol) == 1) {
-      break;
+    if (kkt_stop) {
+      if (check_KKT_wide(theta_ptr,
+			 gradient_ptr,
+			 X_theta_ptr,
+			 X_ptr,
+			 linear_func_ptr,
+			 need_update_ptr,
+			 nfeature,
+			 ncase,
+			 bound_ptr,
+			 ridge_term,
+			 kkt_tol) == 1) {
+	break;
+      }
+    }
+
+    // Check based on norm -- from Adel's debiasing code
+
+    if (param_stop) {
+      if (iter == 2 * iter_old) {
+	iter_old = iter;
+	norm_diff = 0;
+	norm_last = 0;
+	for (ifeature=0; ifeature<nfeature; ifeature++) {
+	  theta_old_ptr_tmp = ((double *) theta_old_ptr + ifeature);
+	  theta_ptr_tmp = ((double *) theta_ptr + ifeature);
+	  delta = (*theta_ptr_tmp - *theta_old_ptr_tmp);
+	  norm_diff += delta * delta;
+	  norm_last += (*theta_ptr_tmp) * (*theta_ptr_tmp);
+	  *theta_old_ptr = (*theta_ptr_tmp);
+	}
+	norm_diff = sqrt(norm_diff);
+	norm_last = sqrt(norm_last);
+	
+	if (norm_diff < threshold * norm_last) {
+	  break;
+	}
+      }
     }
-					  
     // Check size of active set
 
     if (*nactive_ptr >= max_active) {
@@ -496,14 +565,15 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 
     // Check relative decrease of objective
 
-    if (check_objective) {
+    if (objective_stop) {
       new_value = objective_wide(X_theta_ptr,
 				 linear_func_ptr,
 				 ever_active_ptr,
 				 nactive_ptr,
 				 ncase,
 				 nfeature,
-				 bound,
+				 bound_ptr,
+				 ridge_term,
 				 theta_ptr);
 
       if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
diff --git a/selectiveInference/src/randomized_lasso.c b/selectiveInference/src/randomized_lasso.c
index ac55cf4..123c81d 100644
--- a/selectiveInference/src/randomized_lasso.c
+++ b/selectiveInference/src/randomized_lasso.c
@@ -126,8 +126,6 @@ double log_density_gaussian_conditional(double noise_scale,             // Scale
   double denom = 2 * noise_scale * noise_scale;
   double reconstruction = 0;
   double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
   double *optimization_linear_ptr;
   double *optimization_state_ptr;
 
@@ -164,8 +162,6 @@ double log_density_laplace_conditional(double noise_scale,             // Scale
   double value = 0;
   double reconstruction = 0;
   double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
   double *optimization_linear_ptr;
   double *optimization_state_ptr;
 
@@ -176,15 +172,6 @@ double log_density_laplace_conditional(double noise_scale,             // Scale
     offset_ptr = ((double *) offset + irow);
     reconstruction = *offset_ptr;
 
-    // Internal (i.e. data) contribution
-    for (icol=0; icol<ninternal; icol++) {
-      
-      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
-      internal_state_ptr = ((double *) internal_state + icol);
-
-      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
-    }
-
     // Optimization contribution
     for (icol=0; icol<noptimization; icol++) {
       

From ffccea53276fc444b23a4f780e6aacf3c944ff2a Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 13:08:37 -0700
Subject: [PATCH 284/493] removing machine generated files

---
 selectiveInference/R/RcppExports.R     | 19 ------
 selectiveInference/src/RcppExports.cpp | 92 --------------------------
 2 files changed, 111 deletions(-)
 delete mode 100644 selectiveInference/R/RcppExports.R
 delete mode 100644 selectiveInference/src/RcppExports.cpp

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
deleted file mode 100644
index f5ebee4..0000000
--- a/selectiveInference/R/RcppExports.R
+++ /dev/null
@@ -1,19 +0,0 @@
-# Generated by using Rcpp::compileAttributes() -> do not edit by hand
-# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
-
-solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('_selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
-}
-
-solve_QP_wide <- function(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('_selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active)
-}
-
-update1_ <- function(Q2, w, m, k) {
-    .Call('_selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
-}
-
-downdate1_ <- function(Q1, R, j0, m, n) {
-    .Call('_selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
-}
-
diff --git a/selectiveInference/src/RcppExports.cpp b/selectiveInference/src/RcppExports.cpp
deleted file mode 100644
index 02a7741..0000000
--- a/selectiveInference/src/RcppExports.cpp
+++ /dev/null
@@ -1,92 +0,0 @@
-// Generated by using Rcpp::compileAttributes() -> do not edit by hand
-// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
-
-#include <Rcpp.h>
-
-using namespace Rcpp;
-
-// solve_QP
-Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP _selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Sigma(SigmaSEXP);
-    Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
-    Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type theta(thetaSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type linear_func(linear_funcSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type gradient(gradientSEXP);
-    Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type ever_active(ever_activeSEXP);
-    Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type nactive(nactiveSEXP);
-    Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
-    Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
-    Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    rcpp_result_gen = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return rcpp_result_gen;
-END_RCPP
-}
-// solve_QP_wide
-Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::NumericVector X_theta, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP _selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP X_thetaSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type X(XSEXP);
-    Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
-    Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type theta(thetaSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type linear_func(linear_funcSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type gradient(gradientSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type X_theta(X_thetaSEXP);
-    Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type ever_active(ever_activeSEXP);
-    Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type nactive(nactiveSEXP);
-    Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
-    Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
-    Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    rcpp_result_gen = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return rcpp_result_gen;
-END_RCPP
-}
-// update1_
-Rcpp::List update1_(Rcpp::NumericMatrix Q2, Rcpp::NumericVector w, int m, int k);
-RcppExport SEXP _selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q2(Q2SEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type w(wSEXP);
-    Rcpp::traits::input_parameter< int >::type m(mSEXP);
-    Rcpp::traits::input_parameter< int >::type k(kSEXP);
-    rcpp_result_gen = Rcpp::wrap(update1_(Q2, w, m, k));
-    return rcpp_result_gen;
-END_RCPP
-}
-// downdate1_
-Rcpp::List downdate1_(Rcpp::NumericMatrix Q1, Rcpp::NumericMatrix R, int j0, int m, int n);
-RcppExport SEXP _selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q1(Q1SEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type R(RSEXP);
-    Rcpp::traits::input_parameter< int >::type j0(j0SEXP);
-    Rcpp::traits::input_parameter< int >::type m(mSEXP);
-    Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    rcpp_result_gen = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
-    return rcpp_result_gen;
-END_RCPP
-}
-
-static const R_CallMethodDef CallEntries[] = {
-    {"_selectiveInference_solve_QP", (DL_FUNC) &_selectiveInference_solve_QP, 11},
-    {"_selectiveInference_solve_QP_wide", (DL_FUNC) &_selectiveInference_solve_QP_wide, 12},
-    {"_selectiveInference_update1_", (DL_FUNC) &_selectiveInference_update1_, 4},
-    {"_selectiveInference_downdate1_", (DL_FUNC) &_selectiveInference_downdate1_, 5},
-    {NULL, NULL, 0}
-};
-
-RcppExport void R_init_selectiveInference(DllInfo *dll) {
-    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
-    R_useDynamicSymbols(dll, FALSE);
-}

From 2c0e3d60e883620ee6b2ba970c719dc00d08ee6c Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 14:30:59 -0700
Subject: [PATCH 285/493] WIP: function to fit randomized lasso

---
 selectiveInference/R/funs.randomized.R | 64 ++++++++++++++++++++++++++
 tests/test_randomized.R                | 14 ++++++
 2 files changed, 78 insertions(+)
 create mode 100644 selectiveInference/R/funs.randomized.R
 create mode 100644 tests/test_randomized.R

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
new file mode 100644
index 0000000..01e6aa4
--- /dev/null
+++ b/selectiveInference/R/funs.randomized.R
@@ -0,0 +1,64 @@
+# Functions to fit and "infer" about parameters in the
+# randomized LASSO
+#
+# min 1/2 || y - \beta_0 - X \beta ||_2^2 + \lambda || \beta ||_1 - \omega^T\beta + \frac{\epsilon}{2} \|\beta\|^2_2
+
+fit_randomized_lasso = function(X, 
+                                y, 
+                                lam, 
+                                noise_scale, 
+                                ridge_term, 
+                                noise_type=c('gaussian', 'laplace'),
+                                max_iter=100,        # how many iterations for each optimization problem
+                                kkt_tol=1.e-4,       # tolerance for the KKT conditions
+                                objective_tol=1.e-8, # tolerance for relative decrease in objective
+                                objective_stop=FALSE,
+                                kkt_stop=TRUE,
+                                param_stop=TRUE)
+{
+
+    n = nrow(X); p = ncol(X)
+    			
+    noise_type = match.arg(noise_type)
+
+    if (noise_type == 'gaussian') {
+       D = Norm(mean=0, sd=noise_scale)
+    }
+    else if (noise_type == 'laplace') {
+       D = DExp(rate = 1 / noise_scale) # D is a Laplace distribution with rate = 1.
+    }
+    perturb_ = distr::r(D)(p)
+			
+    lam = as.numeric(lam)
+    if (length(lam) == 1) {
+       lam = rep(lam, p)
+    }
+    if (length(lam) != p) {
+       stop("Lagrange parameter should be single float or of length ncol(X)")
+    }    
+
+    soln = rep(0, p)
+    Xsoln = rep(0, n)
+    linear_func = (- t(X) %*% y - perturb_)
+    gradient = 1. * linear_func
+    ever_active = rep(0, p)
+    nactive = as.integer(0)
+
+    result = solve_QP_wide(X,                  # design matrix
+    	                   lam,                # vector of Lagrange multipliers
+		           ridge_term / n,     # ridge_term 
+                           max_iter, 
+                           soln, 
+                           linear_func, 
+                           gradient, 
+                           Xsoln,
+                           ever_active, 
+                           nactive, 
+                           kkt_tol, 
+                           objective_tol, 
+                           p,
+		           objective_stop,     # objective_stop
+			   kkt_stop,           # kkt_stop
+			   param_stop)         # param_stop
+    return(result)
+}
diff --git a/tests/test_randomized.R b/tests/test_randomized.R
new file mode 100644
index 0000000..e4e35a1
--- /dev/null
+++ b/tests/test_randomized.R
@@ -0,0 +1,14 @@
+library(selectiveInference)
+
+test = function() {
+
+    n = 100; p = 50
+    X = matrix(rnorm(n * p), n, p)
+    y = rnorm(n)
+    lam = 20 / sqrt(n)
+    noise_scale = 0.01 * sqrt(n)
+    ridge_term = .1 / sqrt(n)
+    fit_randomized_lasso(X, y, lam, noise_scale, ridge_term)
+}
+
+print(test())

From 2439d7318bcf9bec47be22766e1e35192abcf31f Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 21:36:18 -0700
Subject: [PATCH 286/493] check that solution is same as old code -- currently
 failing

---
 tests/test_debiasing.R | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)
 create mode 100644 tests/test_debiasing.R

diff --git a/tests/test_debiasing.R b/tests/test_debiasing.R
new file mode 100644
index 0000000..27bd621
--- /dev/null
+++ b/tests/test_debiasing.R
@@ -0,0 +1,27 @@
+library(selectiveInference)
+source('oldcode.R')
+
+n = 500; p = 50
+
+X = matrix(rnorm(n * p), n, p)
+S = t(X) %*% X / n
+
+mu = 7.791408e-02
+
+A1 = debiasingMatrix(S, FALSE, n, 1:5, mu=mu, max_iter=1000)
+A2 = debiasingMatrix(S / n, FALSE, n, 1:5, mu=mu, max_iter=1000)
+
+B1 = debiasingMatrix(X, TRUE, n, 1:5, mu=mu, max_iter=1000)
+B2 = debiasingMatrix(X / sqrt(n), TRUE, n, 1:5, mu=mu, max_iter=1000)
+
+C1 = InverseLinfty(S, n, mu=mu, maxiter=1000)[1:5,]
+C2 = InverseLinfty(S / n, n, mu=mu, maxiter=1000)[1:5,]
+
+par(mfrow=c(2,3))
+plot(A1[1,], C1[1,])
+plot(A1[1,], B1[1,])
+plot(B1[1,], C1[1,])
+
+plot(A1[1,], A2[1,])
+plot(B1[1,], B2[1,])
+plot(C1[1,], C2[1,])

From 83b04268b9f39749b37444914e227c63b87fb220 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 22:17:39 -0700
Subject: [PATCH 287/493] bug found in qp solver -- look at tests/test_QP.R

---
 selectiveInference/NAMESPACE                  |   2 +-
 selectiveInference/R/funs.fixed.R             |   5 +
 selectiveInference/R/funs.randomized.R        |   2 +
 selectiveInference/src/Rcpp-debias.cpp        |   4 +
 selectiveInference/src/debias.h               |   2 +
 selectiveInference/src/quadratic_program.c    |   4 +-
 .../src/quadratic_program_wide.c              |   6 +-
 tests/test_QP.R                               |  15 ++
 tests/test_debiasing.R                        | 172 +++++++++++++++++-
 9 files changed, 204 insertions(+), 8 deletions(-)
 create mode 100644 tests/test_QP.R

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index d72d56a..c7d08a1 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -44,4 +44,4 @@ importFrom("stats", dnorm, lsfit, pexp, pnorm, predict,
 importFrom("stats", "coef", "df", "lm", "pf")
 importFrom("stats", "glm", "residuals", "vcov")
 importFrom("Rcpp", "sourceCpp")
-
+importFrom("distr", "Norm", "DExp")
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index aaceaaa..5490316 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -327,6 +327,7 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
 			   warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
 			   max_iter=100,        # how many iterations for each optimization problem
                            kkt_tol=1.e-4,       # tolerance for the KKT conditions
+                           parameter_tol=1.e-4, # tolerance for relative convergence of parameter
 			   objective_tol=1.e-8  # tolerance for relative decrease in objective
                            ) {
 
@@ -363,6 +364,7 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
 			  warn_kkt=FALSE,
 			  max_iter=max_iter,
 			  kkt_tol=kkt_tol,
+			  parameter_tol=parameter_tol,
 			  objective_tol=objective_tol)
 
     if (warn_kkt && (!output$kkt_check)) {
@@ -393,6 +395,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
 			 warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
 			 max_iter=100,        # how many iterations for each optimization problem
                          kkt_tol=1.e-4,       # tolerance for the KKT conditions
+			 parameter_tol=1.e-4, # tolerance for relative convergence of parameter
 			 objective_tol=1.e-8  # tolerance for relative decrease in objective
                          ) {
 
@@ -433,6 +436,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                             nactive, 
                             kkt_tol, 
                             objective_tol, 
+			    parameter_tol,
                             max_active,
 			    FALSE,        # objective_stop
 			    FALSE,        # kkt_stop
@@ -451,6 +455,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                                  nactive, 
                                  kkt_tol, 
                                  objective_tol, 
+				 parameter_tol,
                                  max_active,
 				 FALSE,       # objective_stop
 				 FALSE,       # kkt_stop
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 01e6aa4..25a0b95 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -11,6 +11,7 @@ fit_randomized_lasso = function(X,
                                 noise_type=c('gaussian', 'laplace'),
                                 max_iter=100,        # how many iterations for each optimization problem
                                 kkt_tol=1.e-4,       # tolerance for the KKT conditions
+                                parameter_tol=1.e-8, # tolerance for relative convergence of parameter
                                 objective_tol=1.e-8, # tolerance for relative decrease in objective
                                 objective_stop=FALSE,
                                 kkt_stop=TRUE,
@@ -56,6 +57,7 @@ fit_randomized_lasso = function(X,
                            nactive, 
                            kkt_tol, 
                            objective_tol, 
+			   parameter_tol,
                            p,
 		           objective_stop,     # objective_stop
 			   kkt_stop,           # kkt_stop
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index 24bbae8..9cda705 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -15,6 +15,7 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		    Rcpp::IntegerVector nactive,
 		    double kkt_tol,
 		    double objective_tol,
+		    double parameter_tol,
 		    int max_active,
 		    int objective_stop,
 		    int kkt_stop,
@@ -52,6 +53,7 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		      maxiter,
 		      kkt_tol,
 		      objective_tol,
+		      parameter_tol,
 		      max_active,
 		      objective_stop,
 		      kkt_stop,
@@ -92,6 +94,7 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			 Rcpp::IntegerVector nactive,
 			 double kkt_tol,
 			 double objective_tol,
+			 double parameter_tol,
 			 int max_active,
 			 int objective_stop,
 			 int kkt_stop,
@@ -142,6 +145,7 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			maxiter,
 			kkt_tol,
 			objective_tol,
+			parameter_tol,
 			max_active,
 			objective_stop,
 			kkt_stop,
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index 052af7a..d3db26d 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -16,6 +16,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	     int maxiter,                /* max number of iterations */
 	     double kkt_tol,             /* precision for checking KKT conditions */
 	     double objective_tol,       /* precision for checking relative decrease in objective value */
+	     double parameter_tol,       /* precision for checking relative convergence of parameter */
 	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
 	     int objective_stop,         /* Break based on convergence of objective value? */
              int kkt_stop,               /* Break based on KKT? */
@@ -44,6 +45,7 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 	       int maxiter,                /* max number of iterations */
 	       double kkt_tol,             /* precision for checking KKT conditions */
 	       double objective_tol,       /* precision for checking relative decrease in objective value */
+	       double parameter_tol,       /* precision for checking relative convergence of parameter */
 	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
 	       int objective_stop,         /* Break based on convergence of objective value? */
 	       int kkt_stop,               /* Break based on KKT? */
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
index 1bc7fa3..822ddf5 100644
--- a/selectiveInference/src/quadratic_program.c
+++ b/selectiveInference/src/quadratic_program.c
@@ -273,6 +273,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	     int maxiter,                /* max number of iterations */
 	     double kkt_tol,             /* precision for checking KKT conditions */
 	     double objective_tol,       /* precision for checking relative decrease in objective value */
+	     double parameter_tol,       /* precision for checking relative convergence of parameter */
 	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
 	     int objective_stop,         /* Break based on convergence of objective value? */
              int kkt_stop,               /* Break based on KKT? */
@@ -292,7 +293,6 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
   double norm_diff = 1.;
   double norm_last = 1.;
   double delta;
-  double threshold = 1.e-2;
   double *theta_ptr, *theta_old_ptr;
 
   if (objective_stop) {
@@ -403,7 +403,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	norm_diff = sqrt(norm_diff);
 	norm_last = sqrt(norm_last);
 	
-	if (norm_diff < threshold * norm_last) {
+	if (norm_diff < parameter_tol * norm_last) {
 	  break;
 	}
       }
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
index c6cb9f3..3e4bdb0 100644
--- a/selectiveInference/src/quadratic_program_wide.c
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -4,7 +4,7 @@
 
 // Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
 
-// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2 - l^T\theta + \mu \|\theta\|_1 + \frac{\epsilon}{2} \|\theta\|^2_2
+// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2/n - l^T\theta + \mu \|\theta\|_1 + \frac{\epsilon}{2} \|\theta\|^2_2
 // where l is `linear_func` below
 
 // This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
@@ -393,6 +393,7 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 	       int maxiter,                /* max number of iterations */
 	       double kkt_tol,             /* precision for checking KKT conditions */
 	       double objective_tol,       /* precision for checking relative decrease in objective value */
+	       double parameter_tol,       /* precision for checking relative convergence of parameter */
 	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
 	       int objective_stop,         /* Break based on convergence of objective value? */
 	       int kkt_stop,               /* Break based on KKT? */
@@ -412,7 +413,6 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
   double norm_diff = 1.;
   double norm_last = 1.;
   double delta;
-  double threshold = 1.e-2;
   double *theta_ptr_tmp, *theta_old_ptr_tmp;
 
   if (objective_stop) {
@@ -552,7 +552,7 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 	norm_diff = sqrt(norm_diff);
 	norm_last = sqrt(norm_last);
 	
-	if (norm_diff < threshold * norm_last) {
+	if (norm_diff < parameter_tol * norm_last) {
 	  break;
 	}
       }
diff --git a/tests/test_QP.R b/tests/test_QP.R
new file mode 100644
index 0000000..cf5ca64
--- /dev/null
+++ b/tests/test_QP.R
@@ -0,0 +1,15 @@
+library(selectiveInference)
+### Test
+
+n = 100; p = 50
+
+X = matrix(rnorm(n * p), n, p)
+Y = rnorm(n)
+lam = 2
+
+soln1 = selectiveInference:::fit_randomized_lasso(X, Y, lam, 1.e-12, 0)$soln
+G = glmnet(X, Y, intercept=FALSE, standardize=FALSE)
+soln2 = coef(G, s=1/n, exact=TRUE, x=X, y=Y)[-1]
+
+print(soln1)
+print(soln2)
\ No newline at end of file
diff --git a/tests/test_debiasing.R b/tests/test_debiasing.R
index 27bd621..b1fdf24 100644
--- a/tests/test_debiasing.R
+++ b/tests/test_debiasing.R
@@ -1,7 +1,143 @@
 library(selectiveInference)
-source('oldcode.R')
 
-n = 500; p = 50
+
+## Approximates inverse covariance matrix theta
+InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e-10, verbose = TRUE) {
+  isgiven <- 1;
+  if (is.null(mu)){
+    isgiven <- 0;
+  }
+  
+  p <- nrow(sigma);
+  M <- matrix(0, p, p);
+  xperc = 0;
+  xp = round(p/10);
+  for (i in 1:p) {
+    if ((i %% xp)==0){
+      xperc = xperc+10;
+      if (verbose) {
+        print(paste(xperc,"% done",sep="")); }
+    }
+    if (isgiven==0){
+      mu <- (1/sqrt(n)) * qnorm(1-(0.1/(p^2)));
+    }
+    mu.stop <- 0;
+    try.no <- 1;
+    incr <- 0;
+    while ((mu.stop != 1)&&(try.no<10)){
+      last.beta <- beta
+      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
+      beta <- output$optsol
+      iter <- output$iter
+      if (isgiven==1){
+        mu.stop <- 1
+      }
+      else{
+        if (try.no==1){
+          if (iter == (maxiter+1)){
+            incr <- 1;
+            mu <- mu*resol;
+          } else {
+            incr <- 0;
+            mu <- mu/resol;
+          }
+        }
+        if (try.no > 1){
+          if ((incr == 1)&&(iter == (maxiter+1))){
+            mu <- mu*resol;
+          }
+          if ((incr == 1)&&(iter < (maxiter+1))){
+            mu.stop <- 1;
+          }
+          if ((incr == 0)&&(iter < (maxiter+1))){
+            mu <- mu/resol;
+          }
+          if ((incr == 0)&&(iter == (maxiter+1))){
+            mu <- mu*resol;
+            beta <- last.beta;
+            mu.stop <- 1;
+          }
+        }
+      }
+      try.no <- try.no+1
+    }
+    M[i,] <- beta;
+  }
+  return(M)
+}
+
+InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-10) {
+  p <- nrow(sigma);
+  rho <- max(abs(sigma[i,-i])) / sigma[i,i];
+  mu0 <- rho/(1+rho);
+  beta <- rep(0,p);
+  
+  #if (mu >= mu0){
+  #  beta[i] <- (1-mu0)/sigma[i,i];
+  #  returnlist <- list("optsol" = beta, "iter" = 0);
+  #  return(returnlist);
+  #}
+  
+  diff.norm2 <- 1;
+  last.norm2 <- 1;
+  iter <- 1;
+  iter.old <- 1;
+  beta[i] <- (1-mu0)/sigma[i,i];
+  beta.old <- beta;
+  sigma.tilde <- sigma;
+  diag(sigma.tilde) <- 0;
+  vs <- -sigma.tilde%*%beta;
+  
+  while ((iter <= maxiter) && (diff.norm2 >= threshold*last.norm2)){
+    
+    for (j in 1:p){
+      oldval <- beta[j];
+      v <- vs[j];
+      if (j==i)
+        v <- v+1;
+      beta[j] <- SoftThreshold(v,mu)/sigma[j,j];
+      if (oldval != beta[j]){
+        vs <- vs + (oldval-beta[j])*sigma.tilde[,j];
+      }
+    }
+    
+    iter <- iter + 1;
+    if (iter==2*iter.old){
+      d <- beta - beta.old;
+      diff.norm2 <- sqrt(sum(d*d));
+      last.norm2 <-sqrt(sum(beta*beta));
+      iter.old <- iter;
+      beta.old <- beta;
+      #if (iter>10)
+      #  vs <- -sigma.tilde%*%beta;
+    }
+
+    # print(c(iter, maxiter, diff.norm2, threshold * last.norm2, threshold, mu))
+
+  }
+  
+  returnlist <- list("optsol" = beta, "iter" = iter)
+  return(returnlist)
+}
+
+SoftThreshold <- function( x, lambda ) {
+  #
+  # Standard soft thresholding
+  #
+  if (x>lambda){
+    return (x-lambda);}
+  else {
+    if (x< (-lambda)){
+      return (x+lambda);}
+    else {
+      return (0); }
+  }
+}
+
+
+### Test
+
+n = 100; p = 50
 
 X = matrix(rnorm(n * p), n, p)
 S = t(X) %*% X / n
@@ -25,3 +161,35 @@ plot(B1[1,], C1[1,])
 plot(A1[1,], A2[1,])
 plot(B1[1,], B2[1,])
 plot(C1[1,], C2[1,])
+
+print(c('A', sum(A1[1,] == 0)))
+print(c('B', sum(B1[1,] == 0)))
+print(c('C', sum(C1[1,] == 0)))
+
+## Are our points feasible
+
+feasibility = function(S, soln, j, mu) {
+     p = nrow(S)
+     E = rep(0, p)
+     E[j] = 1
+     G = S %*% soln - E
+     return(c(max(abs(G)), mu))
+}
+
+print(c('feasibility A', feasibility(S, A1[1,], 1, mu)))
+print(c('feasibility B', feasibility(S, B1[1,], 1, mu)))
+print(c('feasibility C', feasibility(S, C1[1,], 1, mu)))
+
+active_KKT = function(S, soln, j, mu) {
+     p = nrow(S)
+     E = rep(0, p)
+     E[j] = 1
+     G = S %*% soln - E
+     return(c(G[soln != 0] * sign(soln)[soln != 0], mu))
+}
+
+print(c('active_KKT A', active_KKT(S, A1[1,], 1, mu)))
+print(c('active_KKT B', active_KKT(S, B1[1,], 1, mu)))
+print(c('active_KKT C', active_KKT(S, C1[1,], 1, mu)))
+
+

From f13bce45b2e05c6454adeb1dea82be5f02e7844d Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 22:19:20 -0700
Subject: [PATCH 288/493] allowing randomization to be 0 in solver

---
 selectiveInference/R/funs.randomized.R | 18 +++++++++++-------
 tests/test_QP.R                        |  2 +-
 2 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 25a0b95..b79e2fb 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -22,14 +22,18 @@ fit_randomized_lasso = function(X,
     			
     noise_type = match.arg(noise_type)
 
-    if (noise_type == 'gaussian') {
-       D = Norm(mean=0, sd=noise_scale)
+    if (noise_scale > 0) {
+        if (noise_type == 'gaussian') {
+            D = Norm(mean=0, sd=noise_scale)
+        }
+        else if (noise_type == 'laplace') {
+            D = DExp(rate = 1 / noise_scale) # D is a Laplace distribution with rate = 1.
+        }
+        perturb_ = distr::r(D)(p)
+    } else {
+        perturb_ = rep(0, p)
     }
-    else if (noise_type == 'laplace') {
-       D = DExp(rate = 1 / noise_scale) # D is a Laplace distribution with rate = 1.
-    }
-    perturb_ = distr::r(D)(p)
-			
+
     lam = as.numeric(lam)
     if (length(lam) == 1) {
        lam = rep(lam, p)
diff --git a/tests/test_QP.R b/tests/test_QP.R
index cf5ca64..4aebec3 100644
--- a/tests/test_QP.R
+++ b/tests/test_QP.R
@@ -7,7 +7,7 @@ X = matrix(rnorm(n * p), n, p)
 Y = rnorm(n)
 lam = 2
 
-soln1 = selectiveInference:::fit_randomized_lasso(X, Y, lam, 1.e-12, 0)$soln
+soln1 = selectiveInference:::fit_randomized_lasso(X, Y, lam, 0, 0)$soln
 G = glmnet(X, Y, intercept=FALSE, standardize=FALSE)
 soln2 = coef(G, s=1/n, exact=TRUE, x=X, y=Y)[-1]
 

From a6064f27241eab8d2abc0ac253b6d4b41c31f9ae Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 22:25:32 -0700
Subject: [PATCH 289/493] cosmetic edit

---
 selectiveInference/src/Rcpp-debias.cpp | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index 9cda705..5c18184 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -112,12 +112,13 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 
   Rcpp::IntegerVector need_update(nfeature);
 
-  // Extract the diagonal
+  Rcpp::NumericVector theta_old(nfeature);
+
+  // Extract the diagonal -- divide by ncase
+
   Rcpp::NumericVector nndef_diag(nfeature);
   double *nndef_diag_p = nndef_diag.begin();
 
-  Rcpp::NumericVector theta_old(nfeature);
-
   for (ifeature=0; ifeature<nfeature; ifeature++) {
     nndef_diag_p[ifeature] = 0;
     for (icase=0; icase<ncase; icase++) {

From 2d685c048505a772dc1d718fafbf10e195cca7ed Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 22:46:48 -0700
Subject: [PATCH 290/493] LASSO solver is right up to scale

---
 tests/test_QP.R | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tests/test_QP.R b/tests/test_QP.R
index 4aebec3..61c4d53 100644
--- a/tests/test_QP.R
+++ b/tests/test_QP.R
@@ -9,7 +9,8 @@ lam = 2
 
 soln1 = selectiveInference:::fit_randomized_lasso(X, Y, lam, 0, 0)$soln
 G = glmnet(X, Y, intercept=FALSE, standardize=FALSE)
-soln2 = coef(G, s=1/n, exact=TRUE, x=X, y=Y)[-1]
+soln2 = coef(G, s=lam/n, exact=TRUE, x=X, y=Y)[-1]
 
 print(soln1)
-print(soln2)
\ No newline at end of file
+print(soln2)
+plot(soln1, soln2)

From cc88b978c6719157b4a5f36762496f0fbbae7fb1 Mon Sep 17 00:00:00 2001
From: Nicolas Ballarini <nicoballarini@gmail.com>
Date: Fri, 27 Oct 2017 10:04:32 +0200
Subject: [PATCH 291/493] Confidence intervals when estimate is negative

Confidence intervals are not flipped when beta hat is negative
---
 selectiveInference/R/funs.fixedCox.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.fixedCox.R b/selectiveInference/R/funs.fixedCox.R
index d6ebd6b..2fa8c08 100644
--- a/selectiveInference/R/funs.fixedCox.R
+++ b/selectiveInference/R/funs.fixedCox.R
@@ -73,7 +73,7 @@ b1= -(mydiag(s2)%*%MM)%*%s2*lambda
        vup[jj]=junk$vup
        sd[jj]=junk$sd
 
-      junk2=TG.interval(bbar, A1, b1, vj, MM, alpha)
+      junk2=TG.interval(bbar, A1, b1, vj, MM, alpha, flip=(s2[jj]==-1))
        ci[jj,]=junk2$int
        tailarea[jj,] = junk2$tailarea
      

From 7b231508cc47fec41b074ee0352d42bf8c8cc0d7 Mon Sep 17 00:00:00 2001
From: Nicolas Ballarini <nicoballarini@gmail.com>
Date: Fri, 27 Oct 2017 10:04:40 +0200
Subject: [PATCH 292/493] Confidence intervals when estimate is negative

Confidence intervals are not flipped when beta hat is negative
---
 selectiveInference/R/funs.fixedLogit.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index 19936b0..4ab3398 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -96,7 +96,7 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
    vup[jj]=junk$vup
        sd[jj]=junk$sd
 
-     junk2=TG.interval(bbar, A1, b1, vj, MM,alpha=alpha)
+     junk2=TG.interval(bbar, A1, b1, vj, MM,alpha=alpha, flip=(s2[jj+1]==-1))
 
      ci[jj,]=junk2$int
      tailarea[jj,] = junk2$tailarea

From 928fee90f833cd0a2cee39040c075d9292d9fd21 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 11 Oct 2017 07:38:07 -0700
Subject: [PATCH 293/493] variable rename

---
 selectiveInference/R/funs.fixedCox.R   | 12 ++++++------
 selectiveInference/R/funs.fixedLogit.R | 12 ++++++------
 2 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/selectiveInference/R/funs.fixedCox.R b/selectiveInference/R/funs.fixedCox.R
index d6ebd6b..7530511 100644
--- a/selectiveInference/R/funs.fixedCox.R
+++ b/selectiveInference/R/funs.fixedCox.R
@@ -29,7 +29,7 @@ if( sum(status==0)+sum(status==1)!=length(y)) stop("status vector must have valu
 vars=which(m)
 if(sum(m)>0){
     bhat=beta[beta!=0] #penalized coefs just for active variables
-    s2=sign(bhat)
+    sign_bhat=sign(bhat)
 
  #check KKT
     
@@ -40,7 +40,7 @@ if(sum(m)>0){
     res=residuals(aaa,type="score")
 if(!is.matrix(res)) res=matrix(res,ncol=1)
 scor=colSums(res)
-    g=(scor+lambda*s2)/(2*lambda)
+    g=(scor+lambda*sign_bhat)/(2*lambda)
 #    cat(c(g,lambda,tol.kkt),fill=T)
      if (any(abs(g) > 1+tol.kkt) )
     warning(paste("Solution beta does not satisfy the KKT conditions",
@@ -49,9 +49,9 @@ scor=colSums(res)
 # Hessian of partial likelihood at the LASSO solution    
 MM=vcov(aaa)
 
-bbar=(bhat+lambda*MM%*%s2)
-A1=-(mydiag(s2))
-b1= -(mydiag(s2)%*%MM)%*%s2*lambda
+bbar=(bhat+lambda*MM%*%sign_bhat)
+A1=-(mydiag(sign_bhat))
+b1= -(mydiag(sign_bhat)%*%MM)%*%sign_bhat*lambda
 
    temp=max(A1%*%bbar-b1)
 
@@ -63,7 +63,7 @@ b1= -(mydiag(s2)%*%MM)%*%s2*lambda
 # the one sided p-values are a bit off
 
     for(jj in 1:length(bbar)){
-      vj=rep(0,length(bbar));vj[jj]=s2[jj]
+      vj=rep(0,length(bbar));vj[jj]=sign_bhat[jj]
 
 
       junk=TG.pvalue(bbar, A1, b1, vj,MM)
diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index 19936b0..16cafdc 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -32,7 +32,7 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
  m=beta[-1]!=0  #active set
          
     bhat=c(beta[1],beta[-1][beta[-1]!=0]) # intcpt plus active vars
-     s2=sign(bhat)
+     sign_bhat=sign(bhat)
      lam2m=diag(c(0,rep(lambda,sum(m))))
  
 
@@ -66,14 +66,14 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
  # MM=solve(t(xxm)%*%w%*%xxm)
    MM=solve(scale(t(xxm),F,1/ww)%*%xxm)
   gm = c(0,-g[vars]*lambda) # gradient at LASSO solution, first entry is 0 because intercept is unpenalized
-                            # at exact LASSO solution it should be s2[-1]
+                            # at exact LASSO solution it should be sign_bhat[-1]
   dbeta = MM %*% gm
 
-  # bbar=(bhat+lam2m%*%MM%*%s2)  # JT: this is wrong, shouldn't use sign of intercept anywhere...
+  # bbar=(bhat+lam2m%*%MM%*%sign_bhat)  # JT: this is wrong, shouldn't use sign of intercept anywhere...
   bbar = bhat - dbeta
 
-  A1=-(mydiag(s2))[-1,]
-  b1= (s2 * dbeta)[-1]
+  A1=-(mydiag(sign_bhat))[-1,]
+  b1= (sign_bhat * dbeta)[-1]
 
   tol.poly = 0.01 
   if (max((A1 %*% bbar) - b1) > tol.poly)
@@ -87,7 +87,7 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
 
   
     for(jj in 1:sum(m)){
-       vj=c(rep(0,sum(m)+1));vj[jj+1]=s2[jj+1]
+       vj=c(rep(0,sum(m)+1));vj[jj+1]=sign_bhat[jj+1]
       # compute p-values
       junk=TG.pvalue(bbar, A1, b1, vj, MM)
       pv[jj] = junk$pv

From 2e9b11d9ce155df3a8d201ce7af434a25ba142d0 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 25 Oct 2017 14:06:10 -0700
Subject: [PATCH 294/493] NF: density functions for randomized LASSO

---
 selectiveInference/src/randomized_lasso.c | 201 ++++++++++++++++++++++
 selectiveInference/src/randomized_lasso.h |  42 +++++
 2 files changed, 243 insertions(+)
 create mode 100644 selectiveInference/src/randomized_lasso.c
 create mode 100644 selectiveInference/src/randomized_lasso.h

diff --git a/selectiveInference/src/randomized_lasso.c b/selectiveInference/src/randomized_lasso.c
new file mode 100644
index 0000000..ac55cf4
--- /dev/null
+++ b/selectiveInference/src/randomized_lasso.c
@@ -0,0 +1,201 @@
+#include <math.h> // for fabs
+
+// Augmented density for randomized LASSO after
+// Gaussian randomization
+
+// Described in https://arxiv.org/abs/1609.05609
+
+// Gaussian is product of IID N(0, noise_scale^2) density
+// Evaluated at A_D D + A_O O + h
+
+// Laplace is product of IID Laplace with scale noise_scale
+// Also evaluated at A_D D + A_O O + h
+
+double log_density_gaussian(double noise_scale,             // Scale of randomization
+			    int ndim,                       // Number of features -- "p"
+			    int ninternal,                  // Dimension of internal data representation often 1
+			    int noptimization,              // Dimension of optimization variables -- "p"
+			    double *internal_linear,        // A_D -- linear part for data
+			    double *internal_state,         // D -- data state
+			    double *optimization_linear,    // A_O -- linear part for optimization variables
+			    double *optimization_state,     // O -- optimization state
+			    double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double denom = 2 * noise_scale * noise_scale;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Internal (i.e. data) contribution
+    for (icol=0; icol<ninternal; icol++) {
+      
+      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
+      internal_state_ptr = ((double *) internal_state + icol);
+
+      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
+    }
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= (reconstruction * reconstruction) / denom;
+  }
+
+  return(value);
+}
+
+double log_density_laplace(double noise_scale,             // Scale of randomization
+			   int ndim,                       // Number of features -- "p"
+			   int ninternal,                  // Dimension of internal data representation often 1
+			   int noptimization,              // Dimension of optimization variables -- "p"
+			   double *internal_linear,        // A_D -- linear part for data
+			   double *internal_state,         // D -- data state
+			   double *optimization_linear,    // A_O -- linear part for optimization variables
+			   double *optimization_state,     // O -- optimization state
+			   double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Internal (i.e. data) contribution
+    for (icol=0; icol<ninternal; icol++) {
+      
+      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
+      internal_state_ptr = ((double *) internal_state + icol);
+
+      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
+    }
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= fabs(reconstruction) / noise_scale;
+  }
+
+  return(value);
+}
+
+// Keeping internal (data) state fixed
+
+double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
+					int ndim,                       // Number of features -- "p"
+					int noptimization,              // Dimension of optimization variables -- "p"
+					double *optimization_linear,    // A_O -- linear part for optimization variables
+					double *optimization_state,     // O -- optimization state
+					double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double denom = 2 * noise_scale * noise_scale;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= reconstruction * reconstruction / denom;
+  }
+
+  return(value);
+}
+
+double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
+				       int ndim,                       // Number of features -- "p"
+				       int noptimization,              // Dimension of optimization variables -- "p"
+				       double *optimization_linear,    // A_O -- linear part for optimization variables
+				       double *optimization_state,     // O -- optimization state
+				       double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Internal (i.e. data) contribution
+    for (icol=0; icol<ninternal; icol++) {
+      
+      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
+      internal_state_ptr = ((double *) internal_state + icol);
+
+      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
+    }
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= fabs(reconstruction) / noise_scale;
+  }
+
+  return(value);
+}
diff --git a/selectiveInference/src/randomized_lasso.h b/selectiveInference/src/randomized_lasso.h
new file mode 100644
index 0000000..1301b72
--- /dev/null
+++ b/selectiveInference/src/randomized_lasso.h
@@ -0,0 +1,42 @@
+#ifdef __cplusplus
+extern "C"
+{
+#endif /* __cplusplus */
+
+double log_density_gaussian(double noise_scale,             // Scale of randomization
+			    int ndim,                       // Number of features -- "p"
+			    int ninternal,                  // Dimension of internal data representation often 1
+			    int noptimization,              // Dimension of optimization variables -- "p"
+			    double *internal_linear,        // A_D -- linear part for data
+			    double *internal_state,         // D -- data state
+			    double *optimization_linear,    // A_O -- linear part for optimization variables
+			    double *optimization_state,     // O -- optimization state
+			    double *offset);                // h -- offset in affine transform -- "p" dimensional 
+  
+double log_density_laplace(double noise_scale,             // Scale of randomization
+			   int ndim,                       // Number of features -- "p"
+			   int ninternal,                  // Dimension of internal data representation often 1
+			   int noptimization,              // Dimension of optimization variables -- "p"
+			   double *internal_linear,        // A_D -- linear part for data
+			   double *internal_state,         // D -- data state
+			   double *optimization_linear,    // A_O -- linear part for optimization variables
+			   double *optimization_state,     // O -- optimization state
+			   double *offset);                // h -- offset in affine transform -- "p" dimensional 
+
+double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
+					int ndim,                       // Number of features -- "p"
+					int noptimization,              // Dimension of optimization variables -- "p"
+					double *optimization_linear,    // A_O -- linear part for optimization variables
+					double *optimization_state,     // O -- optimization state
+					double *offset);                // h -- offset in affine transform -- "p" dimensional 
+
+double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
+				       int ndim,                       // Number of features -- "p"
+				       int noptimization,              // Dimension of optimization variables -- "p"
+				       double *optimization_linear,    // A_O -- linear part for optimization variables
+				       double *optimization_state,     // O -- optimization state
+				       double *offset).                // h -- offset in affine transform -- "p" dimensional 
+
+#ifdef __cplusplus
+}  /* extern "C" */
+#endif /* __cplusplus */

From c3b55f53b3a41514697c9a682668cd01afed1165 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 25 Oct 2017 16:35:52 -0700
Subject: [PATCH 295/493] T to TRUE

---
 selectiveInference/R/funs.fixed.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index be314af..66a3088 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -6,7 +6,7 @@ fixedLassoInf <- function(x, y, beta,
                           lambda, family=c("gaussian","binomial","cox"),
                           intercept=TRUE, add.targets=NULL, status=NULL,
                           sigma=NULL, alpha=0.1,
-                          type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
+                          type=c("partial", "full"), tol.beta=1e-5, tol.kkt=0.1,
                           gridrange=c(-100,100), bits=NULL, verbose=FALSE, 
                           linesearch.try=10) {
 
@@ -150,7 +150,7 @@ fixedLassoInf <- function(x, y, beta,
     ci = tailarea = matrix(0,k,2)
       
     if (type=="full" & p > n) {
-      if (intercept == T) {
+      if (intercept == TRUE) {
         pp=p+1
         Xint <- cbind(rep(1,n),x)
         # indices of selected predictors

From 3748723714d2fc2b837d3e21f8f5e0976d6be9dc Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 13:08:05 -0700
Subject: [PATCH 296/493] updated wide C code -- can now do ridge as well as
 variable weights -- ready to solve randomized LASSO

---
 selectiveInference/R/funs.fixed.R             |  40 +++-
 selectiveInference/src/Rcpp-debias.cpp        |  35 +++-
 selectiveInference/src/debias.h               |  22 ++-
 selectiveInference/src/quadratic_program.c    | 108 +++++++----
 .../src/quadratic_program_wide.c              | 176 ++++++++++++------
 selectiveInference/src/randomized_lasso.c     |  13 --
 6 files changed, 270 insertions(+), 124 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 66a3088..aaceaaa 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -189,8 +189,10 @@ fixedLassoInf <- function(x, y, beta,
       }
 
       M <- (((htheta%*%t(Xordered))+ithetasigma%*%FS%*%hsigmaSinv%*%t(XS))/n)
+
       # vector which is offset for testing debiased beta's
       null_value <- (((ithetasigma%*%FS%*%hsigmaSinv)%*%sign(hbetaS))*lambda/n)
+
       if (intercept == T) {
         M = M[-1,] # remove intercept row
         null_value = null_value[-1] # remove intercept element
@@ -238,12 +240,23 @@ fixedLassoInf <- function(x, y, beta,
     tailarea[j,] = a$tailarea
   }
 
-  out = list(type=type,lambda=lambda,pv=pv,ci=ci,
-    tailarea=tailarea,vlo=vlo,vup=vup,vmat=vmat,y=y,
-    vars=vars,sign=sign_vars,sigma=sigma,alpha=alpha,
-    sd=sigma*sqrt(rowSums(vmat^2)),
-    coef0=vmat%*%y,
-    call=this.call)
+  out = list(type=type,
+             lambda=lambda,
+             pv=pv,
+             ci=ci,
+             tailarea=tailarea,
+             vlo=vlo,
+             vup=vup,
+             vmat=vmat,
+             y=y,
+             vars=vars,
+             sign=sign_vars,
+             sigma=sigma,
+             alpha=alpha,
+             sd=sigma*sqrt(rowSums(vmat^2)),
+             coef0=vmat%*%y,
+             call=this.call)
+
   class(out) = "fixedLassoInf"
   return(out)
 }
@@ -374,7 +387,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                          row, 
                          mu, 
 	                 linesearch=TRUE,     # do a linesearch?
-		         scaling_factor=1.2,  # multiplicative factor for linesearch
+		         scaling_factor=1.5,  # multiplicative factor for linesearch
 		         max_active=NULL,     # how big can active set get?
 			 max_try=10,          # how many steps in linesearch?
 			 warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
@@ -420,11 +433,15 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                             nactive, 
                             kkt_tol, 
                             objective_tol, 
-                            max_active) 
+                            max_active,
+			    FALSE,        # objective_stop
+			    FALSE,        # kkt_stop
+			    TRUE)         # param_stop
       } else {
           Xsoln = rep(0, nrow(Xinfo))
           result = solve_QP_wide(Xinfo, # this is a design matrix
-                                 mu, 
+                                 rep(mu, p),  # vector of Lagrange multipliers
+				 0,           # ridge_term 
                                  max_iter, 
                                  soln, 
                                  linear_func, 
@@ -434,7 +451,10 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                                  nactive, 
                                  kkt_tol, 
                                  objective_tol, 
-                                 max_active) 
+                                 max_active,
+				 FALSE,       # objective_stop
+				 FALSE,       # kkt_stop
+				 TRUE)        # param_stop
 
       }
 
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index ce8bb15..24bbae8 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -15,7 +15,10 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		    Rcpp::IntegerVector nactive,
 		    double kkt_tol,
 		    double objective_tol,
-		    int max_active
+		    int max_active,
+		    int objective_stop,
+		    int kkt_stop,
+		    int param_stop
 		    ) {
 
   int nrow = Sigma.nrow(); // number of features
@@ -28,6 +31,8 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
   Rcpp::NumericVector Sigma_diag(nrow);
   double *sigma_diag_p = Sigma_diag.begin();
 
+  Rcpp::NumericVector theta_old(nrow);
+
   for (irow=0; irow<nrow; irow++) {
     sigma_diag_p[irow] = Sigma(irow, irow);
   }
@@ -43,10 +48,14 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		      nrow,
 		      bound,
 		      (double *) theta.begin(),
+		      (double *) theta_old.begin(),
 		      maxiter,
 		      kkt_tol,
 		      objective_tol,
-		      max_active);
+		      max_active,
+		      objective_stop,
+		      kkt_stop,
+		      param_stop);
   
   // Check whether feasible
 
@@ -72,7 +81,8 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 
 // [[Rcpp::export]]
 Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
-			 double bound,
+			 Rcpp::NumericVector bound,
+			 double ridge_term,
 			 int maxiter,
 			 Rcpp::NumericVector theta,
 			 Rcpp::NumericVector linear_func,
@@ -82,7 +92,10 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			 Rcpp::IntegerVector nactive,
 			 double kkt_tol,
 			 double objective_tol,
-			 int max_active
+			 int max_active,
+			 int objective_stop,
+			 int kkt_stop,
+			 int param_stop
 			 ) {
 
   int ncase = X.nrow(); // number of cases
@@ -100,6 +113,8 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
   Rcpp::NumericVector nndef_diag(nfeature);
   double *nndef_diag_p = nndef_diag.begin();
 
+  Rcpp::NumericVector theta_old(nfeature);
+
   for (ifeature=0; ifeature<nfeature; ifeature++) {
     nndef_diag_p[ifeature] = 0;
     for (icase=0; icase<ncase; icase++) {
@@ -120,12 +135,17 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			(int *) nactive.begin(),
 			ncase,
 			nfeature,
-			bound,
+			(double *) bound.begin(),
+			ridge_term,
 			(double *) theta.begin(),
+			(double *) theta_old.begin(),
 			maxiter,
 			kkt_tol,
 			objective_tol,
-			max_active);
+			max_active,
+			objective_stop,
+			kkt_stop,
+			param_stop);
   
   // Check whether feasible
 
@@ -137,7 +157,8 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 				 (int *) need_update.begin(),
 				 nfeature,
 				 ncase,
-				 bound,
+				 (double *) bound.begin(),
+				 ridge_term,
 				 kkt_tol);
 
   int max_active_check = (*(nactive.begin()) >= max_active);
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index ebcbc93..052af7a 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -9,13 +9,17 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	     double *gradient_ptr,       /* nndef times theta */
 	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
 	     int *nactive_ptr,           /* Size of ever active set */
-	     int nrow,                   /* How many rows in nndef */
+	     int nfeature,               /* How many features in nndef */
 	     double bound,               /* feasibility parameter */
 	     double *theta,              /* current value */
+	     double *theta_old,          /* previous value */
 	     int maxiter,                /* max number of iterations */
 	     double kkt_tol,             /* precision for checking KKT conditions */
 	     double objective_tol,       /* precision for checking relative decrease in objective value */
-	     int max_active);             /* Upper limit for size of active set -- otherwise break */ 
+	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
+	     int objective_stop,         /* Break based on convergence of objective value? */
+             int kkt_stop,               /* Break based on KKT? */
+	     int param_stop);            /* Break based on parameter convergence? */
 
 int check_KKT_qp(double *theta,        /* current theta */
 		 double *gradient_ptr, /* nndef times theta + linear_func */
@@ -33,12 +37,17 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 	       int *nactive_ptr,           /* Size of ever active set */
 	       int ncase,                  /* How many rows in X */
 	       int nfeature,               /* How many columns in X */
-	       double bound,               /* feasibility parameter */
+	       double *bound_ptr,          /* Lagrange multipliers */
+	       double ridge_term,          /* Ridge / ENet term */
 	       double *theta_ptr,          /* current value */
+	       double *theta_old_ptr,      /* previous value */
 	       int maxiter,                /* max number of iterations */
 	       double kkt_tol,             /* precision for checking KKT conditions */
 	       double objective_tol,       /* precision for checking relative decrease in objective value */
-	       int max_active);             /* Upper limit for size of active set -- otherwise break */ 
+	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
+	       int objective_stop,         /* Break based on convergence of objective value? */
+	       int kkt_stop,               /* Break based on KKT? */
+	       int param_stop);            /* Break based on parameter convergence? */
 
 int check_KKT_wide(double *theta_ptr,        /* current theta */
 		   double *gradient_ptr,     /* X^TX/ncase times theta + linear_func*/
@@ -48,8 +57,9 @@ int check_KKT_wide(double *theta_ptr,        /* current theta */
 		   int *need_update_ptr,     /* Which coordinates need to be updated? */
 		   int nfeature,             /* how many columns in X */
 		   int ncase,                /* how many rows in X */
-   		   double bound,             /* Lagrange multipler for \ell_1 */
-		   double tol);              /* precision for checking KKT conditions */        
+		   double *bound_ptr,        /* Lagrange multiplers for \ell_1 */
+		   double ridge_term,        /* Ridge / ENet term */
+		   double tol);               /* precision for checking KKT conditions */        
   
 void update_gradient_wide(double *gradient_ptr,     /* X^TX/ncase times theta + linear_func */
 			  double *X_theta_ptr,      /* Current fitted values */
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
index d9bd017..1bc7fa3 100644
--- a/selectiveInference/src/quadratic_program.c
+++ b/selectiveInference/src/quadratic_program.c
@@ -92,19 +92,19 @@ int update_ever_active_qp(int coord,
 
 int check_KKT_qp(double *theta_ptr,        /* current theta */
 		 double *gradient_ptr, /* nndef times theta + linear_func */
-		 int nrow,             /* how many rows in nndef */
+		 int nfeature,             /* how many features in nndef */
 		 double bound,         /* Lagrange multipler for \ell_1 */
 		 double tol)           /* precision for checking KKT conditions */        
 {
   // First check inactive
 
-  int irow;
+  int ifeature;
   double *theta_ptr_tmp, *gradient_ptr_tmp;
   double gradient;
 
-  for (irow=0; irow<nrow; irow++) {
-    theta_ptr_tmp = ((double *) theta_ptr + irow);
-    gradient_ptr_tmp = ((double *) gradient_ptr + irow);
+  for (ifeature=0; ifeature<nfeature; ifeature++) {
+    theta_ptr_tmp = ((double *) theta_ptr + ifeature);
+    gradient_ptr_tmp = ((double *) gradient_ptr + ifeature);
 
     // Compute this coordinate of the gradient
 
@@ -132,7 +132,7 @@ int check_KKT_qp_active(int *ever_active_ptr,           /* Ever active set: 0-ba
 		        int *nactive_ptr,               /* Size of ever active set */
 			double *theta_ptr,        /* current theta */
 			double *gradient_ptr, /* nndef times theta + linear_func */
-			int nrow,             /* how many rows in nndef */
+			int nfeature,             /* how many features in nndef */
 			double bound,         /* Lagrange multipler for \ell_1 */
 			double tol)           /* precision for checking KKT conditions */        
 {
@@ -185,7 +185,7 @@ double update_one_coord_qp(double *nndef_ptr,           /* A non-negative defini
 			   double *gradient_ptr,        /* nndef times theta + linear_func */
 			   int *ever_active_ptr,        /* Ever active set: 1-based */ 
 			   int *nactive_ptr,            /* Size of ever active set */
-			   int nrow,                    /* How many rows in nndef */
+			   int nfeature,                    /* How many features in nndef */
 			   double bound,                /* feasibility parameter */
 			   double *theta_ptr,               /* current value */
 			   int coord,                   /* which coordinate to update: 0-based */
@@ -242,10 +242,10 @@ double update_one_coord_qp(double *nndef_ptr,           /* A non-negative defini
   if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
 
     delta = value - old_value;
-    nndef_ptr_tmp = ((double *) nndef_ptr + coord * nrow);
+    nndef_ptr_tmp = ((double *) nndef_ptr + coord * nfeature);
     gradient_ptr_tmp = ((double *) gradient_ptr);
 
-    for (icol=0; icol<nrow; icol++) {
+    for (icol=0; icol<nfeature; icol++) {
       *gradient_ptr_tmp = *gradient_ptr_tmp + delta * (*nndef_ptr_tmp);
       gradient_ptr_tmp += 1;
       nndef_ptr_tmp += 1;
@@ -266,33 +266,42 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	     double *gradient_ptr,       /* nndef times theta */
 	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
 	     int *nactive_ptr,           /* Size of ever active set */
-	     int nrow,                   /* How many rows in nndef */
+	     int nfeature,               /* How many features in nndef */
 	     double bound,               /* feasibility parameter */
 	     double *theta,              /* current value */
+	     double *theta_old,          /* previous value */
 	     int maxiter,                /* max number of iterations */
 	     double kkt_tol,             /* precision for checking KKT conditions */
 	     double objective_tol,       /* precision for checking relative decrease in objective value */
-	     int max_active)             /* Upper limit for size of active set -- otherwise break */ 
+	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
+	     int objective_stop,         /* Break based on convergence of objective value? */
+             int kkt_stop,               /* Break based on KKT? */
+	     int param_stop)             /* Break based on parameter convergence? */
 {
 
   int iter = 0;
-  int icoord = 0;
+  int iter_old = 1;
+  int ifeature = 0;
   int iactive = 0;
   int *active_ptr;
 
-  int check_objective = 1;
   int iter_active;
   int niter_active=5;
 
   double old_value, new_value; 
+  double norm_diff = 1.;
+  double norm_last = 1.;
+  double delta;
+  double threshold = 1.e-2;
+  double *theta_ptr, *theta_old_ptr;
 
-  if (check_objective) {
+  if (objective_stop) {
 
     old_value = objective_qp(nndef_ptr,
 			     linear_func_ptr,
 			     ever_active_ptr,
 			     nactive_ptr,
-			     nrow,
+			     nfeature,
 			     bound,
 			     theta);
 
@@ -314,7 +323,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 			      gradient_ptr,
 			      ever_active_ptr,
 			      nactive_ptr,
-			      nrow,
+			      nfeature,
 			      bound,
 			      theta,
 			      *active_ptr - 1,   // Ever-active is 1-based
@@ -328,7 +337,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 				nactive_ptr,
 				theta,
 				gradient_ptr,
-				nrow,
+				nfeature,
 				bound,
 				kkt_tol) == 1) {
 	  break;
@@ -337,17 +346,19 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 
     // Check KKT
 
-    if (check_KKT_qp(theta,
-		     gradient_ptr,
-		     nrow,
-		     bound,
-		     kkt_tol) == 1) {
-      break;
+    if (kkt_stop) {
+      if (check_KKT_qp(theta, 
+		       gradient_ptr,
+		       nfeature,
+		       bound,
+		       kkt_tol) == 1) {
+	break;
+      }
     }
-					  
+
     // Update all variables 
 
-    for (icoord=0; icoord<nrow; icoord++) {
+    for (ifeature=0; ifeature<nfeature; ifeature++) {
 
       update_one_coord_qp(nndef_ptr,
 			  linear_func_ptr,
@@ -355,23 +366,49 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 			  gradient_ptr,
 			  ever_active_ptr,
 			  nactive_ptr,
-			  nrow,
+			  nfeature,
 			  bound,
 			  theta,
-			  icoord,
+			  ifeature,
 			  0);
     }
 
     // Check KKT
 
-    if (check_KKT_qp(theta,
-		     gradient_ptr,
-		     nrow,
-		     bound,
-		     kkt_tol) == 1) {
-      break;
+    if (kkt_stop) {
+      if (check_KKT_qp(theta, 
+		       gradient_ptr,
+		       nfeature,
+		       bound,
+		       kkt_tol) == 1) {
+	break;
+      }
     }
 					  
+    // Check based on norm -- from Adel's debiasing code
+
+    if (param_stop) { 
+      if (iter == 2 * iter_old) {
+	iter_old = iter;
+	norm_diff = 0;
+	norm_last = 0;
+	for (ifeature=0; ifeature<nfeature; ifeature++) {
+	  theta_old_ptr = ((double *) theta_old + ifeature);
+	  theta_ptr = ((double *) theta + ifeature);
+	  delta = (*theta_ptr - *theta_old_ptr);
+	  norm_diff += delta * delta;
+	  norm_last += (*theta_ptr) * (*theta_ptr);
+	  *theta_old_ptr = (*theta_ptr);
+	}
+	norm_diff = sqrt(norm_diff);
+	norm_last = sqrt(norm_last);
+	
+	if (norm_diff < threshold * norm_last) {
+	  break;
+	}
+      }
+    }
+
     // Check size of active set
 
     if (*nactive_ptr >= max_active) {
@@ -380,12 +417,12 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 
     // Check relative decrease of objective
 
-    if (check_objective) {
+    if (objective_stop) {
       new_value = objective_qp(nndef_ptr,
 			       linear_func_ptr,
 			       ever_active_ptr,
 			       nactive_ptr,
-			       nrow,
+			       nfeature,
 			       bound,
 			       theta);
 
@@ -394,6 +431,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
       }
       old_value = new_value;
     }
+
   }
   return(iter);
 }
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
index 3beb578..c6cb9f3 100644
--- a/selectiveInference/src/quadratic_program_wide.c
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -4,7 +4,7 @@
 
 // Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
 
-// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2 - l^T\theta + \mu \|\theta\|_1
+// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2 - l^T\theta + \mu \|\theta\|_1 + \frac{\epsilon}{2} \|\theta\|^2_2
 // where l is `linear_func` below
 
 // This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
@@ -19,11 +19,13 @@ double objective_wide(double *X_theta_ptr,     /* Fitted values */
 		      int *nactive_ptr,        /* Size of ever active set */
 		      int ncase,               /* how many rows in X */
 		      int nfeature,            /* how many columns in X */
-		      double bound,            /* Lagrange multipler for \ell_1 */
+		      double *bound_ptr,       /* Lagrange multiplers for \ell_1 */
+		      double ridge_term,       /* Ridge / ENet term */
 		      double *theta_ptr)       /* current value */
 {
   int icase, iactive;
   double value = 0;
+  double *bound_ptr_tmp;
   double *X_theta_ptr_tmp = X_theta_ptr;
   double *linear_func_ptr_tmp = linear_func_ptr;
   double *theta_ptr_tmp;
@@ -55,8 +57,12 @@ double objective_wide(double *X_theta_ptr,     /* Fitted values */
 
     // The \ell_1 term
 
-    value += bound * fabs((*theta_ptr_tmp));
+    bound_ptr_tmp = ((double *) bound_ptr + active_feature);
+    value += (*bound_ptr_tmp) * fabs((*theta_ptr_tmp));
 
+    // The ridge term
+
+    value += 0.5 * ridge_term * (*theta_ptr_tmp) * (*theta_ptr_tmp);
   }
   
   return(value);
@@ -167,34 +173,39 @@ int check_KKT_wide(double *theta_ptr,        /* current theta */
 		   int *need_update_ptr,     /* Which coordinates need to be updated? */
 		   int nfeature,             /* how many columns in X */
 		   int ncase,                /* how many rows in X */
-		   double bound,             /* Lagrange multipler for \ell_1 */
+		   double *bound_ptr,        /* Lagrange multiplers for \ell_1 */
+		   double ridge_term,        /* Ridge / ENet term */
 		   double tol)               /* precision for checking KKT conditions */        
 {
   // First check inactive
 
   int ifeature;
   double *theta_ptr_tmp;
+  double *bound_ptr_tmp;
+  double bound;
   double gradient;
 
   for (ifeature=0; ifeature<nfeature; ifeature++) {
 
     theta_ptr_tmp = ((double *) theta_ptr + ifeature);
+    bound_ptr_tmp = ((double *) bound_ptr + ifeature);
+    bound = *bound_ptr_tmp;
 
     // Compute this coordinate of the gradient
 
     gradient = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, ifeature, ncase);
 
-    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
-
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
+    if ((*theta_ptr_tmp != 0) && (bound != 0)) { // these coordinates of gradients should be equal to -bound
+      
+      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + ridge_term * (*theta_ptr_tmp) + bound) > tol * bound)) {
 	return(0);
       }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
+      else if ((*theta_ptr_tmp < 0) && (fabs(gradient + ridge_term * (*theta_ptr_tmp) - bound) > tol * bound)) {
 	return(0);
       }
-
+      
     }
-    else {
+    else if (bound != 0) {
       if (fabs(gradient) > (1. + tol) * bound) {
 	return(0);
       }
@@ -214,13 +225,16 @@ int check_KKT_wide_active(int *ever_active_ptr,           /* Ever active set: 0-
 			  int *need_update_ptr,           /* Which coordinates need to be updated? */
 			  int nfeature,                   /* how many columns in X */
 			  int ncase,                      /* how many rows in X */
-			  double bound,                   /* Lagrange multipler for \ell_1 */
+			  double *bound_ptr,              /* Lagrange multipliers for \ell_1 */
+			  double ridge_term,              /* Ridge / ENet term */
 			  double tol)                     /* precision for checking KKT conditions */        
 {
   // First check inactive
 
   int iactive;
   double *theta_ptr_tmp;
+  double *bound_ptr_tmp;
+  double bound;
   double gradient;
   int nactive = *nactive_ptr;
   int active_feature;
@@ -230,23 +244,26 @@ int check_KKT_wide_active(int *ever_active_ptr,           /* Ever active set: 0-
 
     active_feature_ptr = ((int *) ever_active_ptr + iactive);
     active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
+
     theta_ptr_tmp = ((double *) theta_ptr + active_feature);
+    bound_ptr_tmp = ((double *) bound_ptr + active_feature);
+    bound = *bound_ptr_tmp;
 
     // Compute this coordinate of the gradient
 
     gradient = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, active_feature, ncase);
 
-    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
+    if ((*theta_ptr_tmp != 0) && (bound != 0)) { // these coordinates of gradients should be equal to -bound
 
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
+      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + ridge_term * (*theta_ptr_tmp) + bound) > tol * bound)) {
 	return(0);
       }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
+      else if ((*theta_ptr_tmp < 0) && (fabs(gradient + ridge_term * (*theta_ptr_tmp) - bound) > tol * bound)) {
 	return(0);
       }
 
     }
-    else {
+    else if (bound != 0) {
       if (fabs(gradient) > (1. + tol) * bound) {
 	return(0);
       }
@@ -266,7 +283,8 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
 			     int *need_update_ptr,        /* Whether a gradient coordinate needs update or not */
 			     int ncase,                   /* How many rows in X */
 			     int nfeature,                /* How many rows in X */
-			     double bound,                /* feasibility parameter */
+			     double *bound_ptr,           /* Lagrange multipliers */
+			     double ridge_term,           /* Ridge / ENet term */
 			     double *theta_ptr,           /* current value */
 			     int coord,                   /* which coordinate to update: 0-based */
 			     int is_active)               /* Is this coord in ever_active */     
@@ -280,6 +298,8 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
   double *X_theta_ptr_tmp;
   int *need_update_ptr_tmp;
   double *theta_ptr_tmp;
+  double *bound_ptr_tmp;
+  double bound;
   int ifeature, icase;
 
   double *diagonal_ptr = ((double *) nndef_diag_ptr + coord);
@@ -290,6 +310,9 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
   theta_ptr_tmp = ((double *) theta_ptr + coord);
   old_value = *theta_ptr_tmp;
 
+  bound_ptr_tmp = ((double *) bound_ptr + coord);
+  bound = *bound_ptr_tmp;
+
   // The coord entry of gradient_ptr term has a diagonal term in it:
   // (X^TX)[coord, coord] * theta[coord] / ncase
   // This removes it. 
@@ -298,17 +321,17 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
 
   // Now soft-threshold the coord entry of theta 
 
-  // Objective is t \mapsto q/2 * t^2 + l * t + bound |t|
-  // with q=diagonal_entry and l=linear_term
+  // Objective is t \mapsto (q+eps)/2 * t^2 + l * t + bound |t| + 
+  // with q=diagonal_entry and l=linear_term and eps=ridge_Term
 
   // With a negative linear term, solution should be
   // positive
 
   if (linear_term < -bound) {
-    value = (-linear_term - bound) / diagonal_entry;
+    value = (-linear_term - bound) / (diagonal_entry + ridge_term);
   }
   else if (linear_term > bound) {
-    value = -(linear_term - bound) / diagonal_entry;
+    value = -(linear_term - bound) / (diagonal_entry + ridge_term);
   }
 
   // Add to active set if necessary
@@ -363,24 +386,36 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 	       int *nactive_ptr,           /* Size of ever active set */
 	       int ncase,                  /* How many rows in X */
 	       int nfeature,               /* How many columns in X */
-	       double bound,               /* feasibility parameter */
+	       double *bound_ptr,          /* Lagrange multipliers */
+	       double ridge_term,          /* Ridge / ENet term */
 	       double *theta_ptr,          /* current value */
+	       double *theta_old_ptr,      /* previous value */
 	       int maxiter,                /* max number of iterations */
 	       double kkt_tol,             /* precision for checking KKT conditions */
 	       double objective_tol,       /* precision for checking relative decrease in objective value */
-	       int max_active)             /* Upper limit for size of active set -- otherwise break */ 
+	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
+	       int objective_stop,         /* Break based on convergence of objective value? */
+	       int kkt_stop,               /* Break based on KKT? */
+	       int param_stop)             /* Break based on parameter convergence? */
 {
 
   int iter = 0;
+  int iter_old = 1;
   int ifeature = 0;
   int iactive = 0;
   int *active_ptr;
-  int check_objective = 1;
+
   double old_value, new_value; 
   int niter_active = 5;
   int iter_active;
 
-  if (check_objective) {
+  double norm_diff = 1.;
+  double norm_last = 1.;
+  double delta;
+  double threshold = 1.e-2;
+  double *theta_ptr_tmp, *theta_old_ptr_tmp;
+
+  if (objective_stop) {
 
     old_value = objective_wide(X_theta_ptr,
 			       linear_func_ptr,
@@ -388,8 +423,10 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 			       nactive_ptr,
 			       ncase,
 			       nfeature,
-			       bound,
+			       bound_ptr,
+			       ridge_term,
 			       theta_ptr);
+    new_value = old_value;
 
   }
 
@@ -412,7 +449,8 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 				need_update_ptr,
 				ncase,
 				nfeature,
-				bound,
+				bound_ptr,
+				ridge_term,
 				theta_ptr,
 				*active_ptr - 1,   // Ever-active is 1-based
 				1);
@@ -431,7 +469,8 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 				  need_update_ptr,
 				  nfeature,
 				  ncase,
-				  bound,
+				  bound_ptr,
+				  ridge_term,
 				  kkt_tol) == 1) {
 	  break;
 	}
@@ -440,17 +479,20 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 
     // Check KKT
 
-    if (check_KKT_wide(theta_ptr,
-		       gradient_ptr,
-		       X_theta_ptr,
-		       X_ptr,
-		       linear_func_ptr,
-		       need_update_ptr,
-		       nfeature,
-		       ncase,
-		       bound,
-		       kkt_tol) == 1) {
-      break;
+    if (kkt_stop) { 
+      if (check_KKT_wide(theta_ptr,
+			 gradient_ptr,
+			 X_theta_ptr,
+			 X_ptr,
+			 linear_func_ptr,
+			 need_update_ptr,
+			 nfeature,
+			 ncase,
+			 bound_ptr,
+			 ridge_term,
+			 kkt_tol) == 1) {
+	break;
+      }
     }
 
     // Update all variables 
@@ -467,7 +509,8 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 			    need_update_ptr,
 			    ncase,
 			    nfeature,
-			    bound,
+			    bound_ptr,
+			    ridge_term,
 			    theta_ptr,
 			    ifeature,
 			    0);
@@ -475,19 +518,45 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 
     // Check KKT
 
-    if (check_KKT_wide(theta_ptr,
-		       gradient_ptr,
-		       X_theta_ptr,
-		       X_ptr,
-		       linear_func_ptr,
-		       need_update_ptr,
-		       nfeature,
-		       ncase,
-		       bound,
-		       kkt_tol) == 1) {
-      break;
+    if (kkt_stop) {
+      if (check_KKT_wide(theta_ptr,
+			 gradient_ptr,
+			 X_theta_ptr,
+			 X_ptr,
+			 linear_func_ptr,
+			 need_update_ptr,
+			 nfeature,
+			 ncase,
+			 bound_ptr,
+			 ridge_term,
+			 kkt_tol) == 1) {
+	break;
+      }
+    }
+
+    // Check based on norm -- from Adel's debiasing code
+
+    if (param_stop) {
+      if (iter == 2 * iter_old) {
+	iter_old = iter;
+	norm_diff = 0;
+	norm_last = 0;
+	for (ifeature=0; ifeature<nfeature; ifeature++) {
+	  theta_old_ptr_tmp = ((double *) theta_old_ptr + ifeature);
+	  theta_ptr_tmp = ((double *) theta_ptr + ifeature);
+	  delta = (*theta_ptr_tmp - *theta_old_ptr_tmp);
+	  norm_diff += delta * delta;
+	  norm_last += (*theta_ptr_tmp) * (*theta_ptr_tmp);
+	  *theta_old_ptr = (*theta_ptr_tmp);
+	}
+	norm_diff = sqrt(norm_diff);
+	norm_last = sqrt(norm_last);
+	
+	if (norm_diff < threshold * norm_last) {
+	  break;
+	}
+      }
     }
-					  
     // Check size of active set
 
     if (*nactive_ptr >= max_active) {
@@ -496,14 +565,15 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 
     // Check relative decrease of objective
 
-    if (check_objective) {
+    if (objective_stop) {
       new_value = objective_wide(X_theta_ptr,
 				 linear_func_ptr,
 				 ever_active_ptr,
 				 nactive_ptr,
 				 ncase,
 				 nfeature,
-				 bound,
+				 bound_ptr,
+				 ridge_term,
 				 theta_ptr);
 
       if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
diff --git a/selectiveInference/src/randomized_lasso.c b/selectiveInference/src/randomized_lasso.c
index ac55cf4..123c81d 100644
--- a/selectiveInference/src/randomized_lasso.c
+++ b/selectiveInference/src/randomized_lasso.c
@@ -126,8 +126,6 @@ double log_density_gaussian_conditional(double noise_scale,             // Scale
   double denom = 2 * noise_scale * noise_scale;
   double reconstruction = 0;
   double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
   double *optimization_linear_ptr;
   double *optimization_state_ptr;
 
@@ -164,8 +162,6 @@ double log_density_laplace_conditional(double noise_scale,             // Scale
   double value = 0;
   double reconstruction = 0;
   double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
   double *optimization_linear_ptr;
   double *optimization_state_ptr;
 
@@ -176,15 +172,6 @@ double log_density_laplace_conditional(double noise_scale,             // Scale
     offset_ptr = ((double *) offset + irow);
     reconstruction = *offset_ptr;
 
-    // Internal (i.e. data) contribution
-    for (icol=0; icol<ninternal; icol++) {
-      
-      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
-      internal_state_ptr = ((double *) internal_state + icol);
-
-      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
-    }
-
     // Optimization contribution
     for (icol=0; icol<noptimization; icol++) {
       

From 5a072459771c7515bf9bb486289458cb68dfd5ec Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 13:08:37 -0700
Subject: [PATCH 297/493] removing machine generated files

---
 selectiveInference/R/RcppExports.R     | 19 ------
 selectiveInference/src/RcppExports.cpp | 92 --------------------------
 2 files changed, 111 deletions(-)
 delete mode 100644 selectiveInference/R/RcppExports.R
 delete mode 100644 selectiveInference/src/RcppExports.cpp

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
deleted file mode 100644
index f5ebee4..0000000
--- a/selectiveInference/R/RcppExports.R
+++ /dev/null
@@ -1,19 +0,0 @@
-# Generated by using Rcpp::compileAttributes() -> do not edit by hand
-# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
-
-solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('_selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
-}
-
-solve_QP_wide <- function(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('_selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active)
-}
-
-update1_ <- function(Q2, w, m, k) {
-    .Call('_selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
-}
-
-downdate1_ <- function(Q1, R, j0, m, n) {
-    .Call('_selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
-}
-
diff --git a/selectiveInference/src/RcppExports.cpp b/selectiveInference/src/RcppExports.cpp
deleted file mode 100644
index 02a7741..0000000
--- a/selectiveInference/src/RcppExports.cpp
+++ /dev/null
@@ -1,92 +0,0 @@
-// Generated by using Rcpp::compileAttributes() -> do not edit by hand
-// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
-
-#include <Rcpp.h>
-
-using namespace Rcpp;
-
-// solve_QP
-Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP _selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Sigma(SigmaSEXP);
-    Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
-    Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type theta(thetaSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type linear_func(linear_funcSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type gradient(gradientSEXP);
-    Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type ever_active(ever_activeSEXP);
-    Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type nactive(nactiveSEXP);
-    Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
-    Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
-    Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    rcpp_result_gen = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return rcpp_result_gen;
-END_RCPP
-}
-// solve_QP_wide
-Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::NumericVector X_theta, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP _selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP X_thetaSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type X(XSEXP);
-    Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
-    Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type theta(thetaSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type linear_func(linear_funcSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type gradient(gradientSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type X_theta(X_thetaSEXP);
-    Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type ever_active(ever_activeSEXP);
-    Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type nactive(nactiveSEXP);
-    Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
-    Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
-    Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    rcpp_result_gen = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return rcpp_result_gen;
-END_RCPP
-}
-// update1_
-Rcpp::List update1_(Rcpp::NumericMatrix Q2, Rcpp::NumericVector w, int m, int k);
-RcppExport SEXP _selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q2(Q2SEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type w(wSEXP);
-    Rcpp::traits::input_parameter< int >::type m(mSEXP);
-    Rcpp::traits::input_parameter< int >::type k(kSEXP);
-    rcpp_result_gen = Rcpp::wrap(update1_(Q2, w, m, k));
-    return rcpp_result_gen;
-END_RCPP
-}
-// downdate1_
-Rcpp::List downdate1_(Rcpp::NumericMatrix Q1, Rcpp::NumericMatrix R, int j0, int m, int n);
-RcppExport SEXP _selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q1(Q1SEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type R(RSEXP);
-    Rcpp::traits::input_parameter< int >::type j0(j0SEXP);
-    Rcpp::traits::input_parameter< int >::type m(mSEXP);
-    Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    rcpp_result_gen = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
-    return rcpp_result_gen;
-END_RCPP
-}
-
-static const R_CallMethodDef CallEntries[] = {
-    {"_selectiveInference_solve_QP", (DL_FUNC) &_selectiveInference_solve_QP, 11},
-    {"_selectiveInference_solve_QP_wide", (DL_FUNC) &_selectiveInference_solve_QP_wide, 12},
-    {"_selectiveInference_update1_", (DL_FUNC) &_selectiveInference_update1_, 4},
-    {"_selectiveInference_downdate1_", (DL_FUNC) &_selectiveInference_downdate1_, 5},
-    {NULL, NULL, 0}
-};
-
-RcppExport void R_init_selectiveInference(DllInfo *dll) {
-    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
-    R_useDynamicSymbols(dll, FALSE);
-}

From ede2816a33aed4a7bc1c8aad81585c549c1422d6 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 14:30:59 -0700
Subject: [PATCH 298/493] WIP: function to fit randomized lasso

---
 selectiveInference/R/funs.randomized.R | 64 ++++++++++++++++++++++++++
 tests/test_randomized.R                | 14 ++++++
 2 files changed, 78 insertions(+)
 create mode 100644 selectiveInference/R/funs.randomized.R
 create mode 100644 tests/test_randomized.R

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
new file mode 100644
index 0000000..01e6aa4
--- /dev/null
+++ b/selectiveInference/R/funs.randomized.R
@@ -0,0 +1,64 @@
+# Functions to fit and "infer" about parameters in the
+# randomized LASSO
+#
+# min 1/2 || y - \beta_0 - X \beta ||_2^2 + \lambda || \beta ||_1 - \omega^T\beta + \frac{\epsilon}{2} \|\beta\|^2_2
+
+fit_randomized_lasso = function(X, 
+                                y, 
+                                lam, 
+                                noise_scale, 
+                                ridge_term, 
+                                noise_type=c('gaussian', 'laplace'),
+                                max_iter=100,        # how many iterations for each optimization problem
+                                kkt_tol=1.e-4,       # tolerance for the KKT conditions
+                                objective_tol=1.e-8, # tolerance for relative decrease in objective
+                                objective_stop=FALSE,
+                                kkt_stop=TRUE,
+                                param_stop=TRUE)
+{
+
+    n = nrow(X); p = ncol(X)
+    			
+    noise_type = match.arg(noise_type)
+
+    if (noise_type == 'gaussian') {
+       D = Norm(mean=0, sd=noise_scale)
+    }
+    else if (noise_type == 'laplace') {
+       D = DExp(rate = 1 / noise_scale) # D is a Laplace distribution with rate = 1.
+    }
+    perturb_ = distr::r(D)(p)
+			
+    lam = as.numeric(lam)
+    if (length(lam) == 1) {
+       lam = rep(lam, p)
+    }
+    if (length(lam) != p) {
+       stop("Lagrange parameter should be single float or of length ncol(X)")
+    }    
+
+    soln = rep(0, p)
+    Xsoln = rep(0, n)
+    linear_func = (- t(X) %*% y - perturb_)
+    gradient = 1. * linear_func
+    ever_active = rep(0, p)
+    nactive = as.integer(0)
+
+    result = solve_QP_wide(X,                  # design matrix
+    	                   lam,                # vector of Lagrange multipliers
+		           ridge_term / n,     # ridge_term 
+                           max_iter, 
+                           soln, 
+                           linear_func, 
+                           gradient, 
+                           Xsoln,
+                           ever_active, 
+                           nactive, 
+                           kkt_tol, 
+                           objective_tol, 
+                           p,
+		           objective_stop,     # objective_stop
+			   kkt_stop,           # kkt_stop
+			   param_stop)         # param_stop
+    return(result)
+}
diff --git a/tests/test_randomized.R b/tests/test_randomized.R
new file mode 100644
index 0000000..e4e35a1
--- /dev/null
+++ b/tests/test_randomized.R
@@ -0,0 +1,14 @@
+library(selectiveInference)
+
+test = function() {
+
+    n = 100; p = 50
+    X = matrix(rnorm(n * p), n, p)
+    y = rnorm(n)
+    lam = 20 / sqrt(n)
+    noise_scale = 0.01 * sqrt(n)
+    ridge_term = .1 / sqrt(n)
+    fit_randomized_lasso(X, y, lam, noise_scale, ridge_term)
+}
+
+print(test())

From 32feb0eae830e98d5dcd28b74c327d9bb195b6d1 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 21:36:18 -0700
Subject: [PATCH 299/493] check that solution is same as old code -- currently
 failing

---
 tests/test_debiasing.R | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)
 create mode 100644 tests/test_debiasing.R

diff --git a/tests/test_debiasing.R b/tests/test_debiasing.R
new file mode 100644
index 0000000..27bd621
--- /dev/null
+++ b/tests/test_debiasing.R
@@ -0,0 +1,27 @@
+library(selectiveInference)
+source('oldcode.R')
+
+n = 500; p = 50
+
+X = matrix(rnorm(n * p), n, p)
+S = t(X) %*% X / n
+
+mu = 7.791408e-02
+
+A1 = debiasingMatrix(S, FALSE, n, 1:5, mu=mu, max_iter=1000)
+A2 = debiasingMatrix(S / n, FALSE, n, 1:5, mu=mu, max_iter=1000)
+
+B1 = debiasingMatrix(X, TRUE, n, 1:5, mu=mu, max_iter=1000)
+B2 = debiasingMatrix(X / sqrt(n), TRUE, n, 1:5, mu=mu, max_iter=1000)
+
+C1 = InverseLinfty(S, n, mu=mu, maxiter=1000)[1:5,]
+C2 = InverseLinfty(S / n, n, mu=mu, maxiter=1000)[1:5,]
+
+par(mfrow=c(2,3))
+plot(A1[1,], C1[1,])
+plot(A1[1,], B1[1,])
+plot(B1[1,], C1[1,])
+
+plot(A1[1,], A2[1,])
+plot(B1[1,], B2[1,])
+plot(C1[1,], C2[1,])

From 0a99f5fb50f48b4ab5cac1006b33c9acd8958ece Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 22:17:39 -0700
Subject: [PATCH 300/493] bug found in qp solver -- look at tests/test_QP.R

---
 selectiveInference/NAMESPACE                  |   2 +-
 selectiveInference/R/funs.fixed.R             |   5 +
 selectiveInference/R/funs.randomized.R        |   2 +
 selectiveInference/src/Rcpp-debias.cpp        |   4 +
 selectiveInference/src/debias.h               |   2 +
 selectiveInference/src/quadratic_program.c    |   4 +-
 .../src/quadratic_program_wide.c              |   6 +-
 tests/test_QP.R                               |  15 ++
 tests/test_debiasing.R                        | 172 +++++++++++++++++-
 9 files changed, 204 insertions(+), 8 deletions(-)
 create mode 100644 tests/test_QP.R

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index d72d56a..c7d08a1 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -44,4 +44,4 @@ importFrom("stats", dnorm, lsfit, pexp, pnorm, predict,
 importFrom("stats", "coef", "df", "lm", "pf")
 importFrom("stats", "glm", "residuals", "vcov")
 importFrom("Rcpp", "sourceCpp")
-
+importFrom("distr", "Norm", "DExp")
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index aaceaaa..5490316 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -327,6 +327,7 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
 			   warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
 			   max_iter=100,        # how many iterations for each optimization problem
                            kkt_tol=1.e-4,       # tolerance for the KKT conditions
+                           parameter_tol=1.e-4, # tolerance for relative convergence of parameter
 			   objective_tol=1.e-8  # tolerance for relative decrease in objective
                            ) {
 
@@ -363,6 +364,7 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
 			  warn_kkt=FALSE,
 			  max_iter=max_iter,
 			  kkt_tol=kkt_tol,
+			  parameter_tol=parameter_tol,
 			  objective_tol=objective_tol)
 
     if (warn_kkt && (!output$kkt_check)) {
@@ -393,6 +395,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
 			 warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
 			 max_iter=100,        # how many iterations for each optimization problem
                          kkt_tol=1.e-4,       # tolerance for the KKT conditions
+			 parameter_tol=1.e-4, # tolerance for relative convergence of parameter
 			 objective_tol=1.e-8  # tolerance for relative decrease in objective
                          ) {
 
@@ -433,6 +436,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                             nactive, 
                             kkt_tol, 
                             objective_tol, 
+			    parameter_tol,
                             max_active,
 			    FALSE,        # objective_stop
 			    FALSE,        # kkt_stop
@@ -451,6 +455,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                                  nactive, 
                                  kkt_tol, 
                                  objective_tol, 
+				 parameter_tol,
                                  max_active,
 				 FALSE,       # objective_stop
 				 FALSE,       # kkt_stop
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 01e6aa4..25a0b95 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -11,6 +11,7 @@ fit_randomized_lasso = function(X,
                                 noise_type=c('gaussian', 'laplace'),
                                 max_iter=100,        # how many iterations for each optimization problem
                                 kkt_tol=1.e-4,       # tolerance for the KKT conditions
+                                parameter_tol=1.e-8, # tolerance for relative convergence of parameter
                                 objective_tol=1.e-8, # tolerance for relative decrease in objective
                                 objective_stop=FALSE,
                                 kkt_stop=TRUE,
@@ -56,6 +57,7 @@ fit_randomized_lasso = function(X,
                            nactive, 
                            kkt_tol, 
                            objective_tol, 
+			   parameter_tol,
                            p,
 		           objective_stop,     # objective_stop
 			   kkt_stop,           # kkt_stop
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index 24bbae8..9cda705 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -15,6 +15,7 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		    Rcpp::IntegerVector nactive,
 		    double kkt_tol,
 		    double objective_tol,
+		    double parameter_tol,
 		    int max_active,
 		    int objective_stop,
 		    int kkt_stop,
@@ -52,6 +53,7 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		      maxiter,
 		      kkt_tol,
 		      objective_tol,
+		      parameter_tol,
 		      max_active,
 		      objective_stop,
 		      kkt_stop,
@@ -92,6 +94,7 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			 Rcpp::IntegerVector nactive,
 			 double kkt_tol,
 			 double objective_tol,
+			 double parameter_tol,
 			 int max_active,
 			 int objective_stop,
 			 int kkt_stop,
@@ -142,6 +145,7 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			maxiter,
 			kkt_tol,
 			objective_tol,
+			parameter_tol,
 			max_active,
 			objective_stop,
 			kkt_stop,
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index 052af7a..d3db26d 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -16,6 +16,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	     int maxiter,                /* max number of iterations */
 	     double kkt_tol,             /* precision for checking KKT conditions */
 	     double objective_tol,       /* precision for checking relative decrease in objective value */
+	     double parameter_tol,       /* precision for checking relative convergence of parameter */
 	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
 	     int objective_stop,         /* Break based on convergence of objective value? */
              int kkt_stop,               /* Break based on KKT? */
@@ -44,6 +45,7 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 	       int maxiter,                /* max number of iterations */
 	       double kkt_tol,             /* precision for checking KKT conditions */
 	       double objective_tol,       /* precision for checking relative decrease in objective value */
+	       double parameter_tol,       /* precision for checking relative convergence of parameter */
 	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
 	       int objective_stop,         /* Break based on convergence of objective value? */
 	       int kkt_stop,               /* Break based on KKT? */
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
index 1bc7fa3..822ddf5 100644
--- a/selectiveInference/src/quadratic_program.c
+++ b/selectiveInference/src/quadratic_program.c
@@ -273,6 +273,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	     int maxiter,                /* max number of iterations */
 	     double kkt_tol,             /* precision for checking KKT conditions */
 	     double objective_tol,       /* precision for checking relative decrease in objective value */
+	     double parameter_tol,       /* precision for checking relative convergence of parameter */
 	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
 	     int objective_stop,         /* Break based on convergence of objective value? */
              int kkt_stop,               /* Break based on KKT? */
@@ -292,7 +293,6 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
   double norm_diff = 1.;
   double norm_last = 1.;
   double delta;
-  double threshold = 1.e-2;
   double *theta_ptr, *theta_old_ptr;
 
   if (objective_stop) {
@@ -403,7 +403,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	norm_diff = sqrt(norm_diff);
 	norm_last = sqrt(norm_last);
 	
-	if (norm_diff < threshold * norm_last) {
+	if (norm_diff < parameter_tol * norm_last) {
 	  break;
 	}
       }
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
index c6cb9f3..3e4bdb0 100644
--- a/selectiveInference/src/quadratic_program_wide.c
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -4,7 +4,7 @@
 
 // Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
 
-// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2 - l^T\theta + \mu \|\theta\|_1 + \frac{\epsilon}{2} \|\theta\|^2_2
+// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2/n - l^T\theta + \mu \|\theta\|_1 + \frac{\epsilon}{2} \|\theta\|^2_2
 // where l is `linear_func` below
 
 // This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
@@ -393,6 +393,7 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 	       int maxiter,                /* max number of iterations */
 	       double kkt_tol,             /* precision for checking KKT conditions */
 	       double objective_tol,       /* precision for checking relative decrease in objective value */
+	       double parameter_tol,       /* precision for checking relative convergence of parameter */
 	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
 	       int objective_stop,         /* Break based on convergence of objective value? */
 	       int kkt_stop,               /* Break based on KKT? */
@@ -412,7 +413,6 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
   double norm_diff = 1.;
   double norm_last = 1.;
   double delta;
-  double threshold = 1.e-2;
   double *theta_ptr_tmp, *theta_old_ptr_tmp;
 
   if (objective_stop) {
@@ -552,7 +552,7 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 	norm_diff = sqrt(norm_diff);
 	norm_last = sqrt(norm_last);
 	
-	if (norm_diff < threshold * norm_last) {
+	if (norm_diff < parameter_tol * norm_last) {
 	  break;
 	}
       }
diff --git a/tests/test_QP.R b/tests/test_QP.R
new file mode 100644
index 0000000..cf5ca64
--- /dev/null
+++ b/tests/test_QP.R
@@ -0,0 +1,15 @@
+library(selectiveInference)
+### Test
+
+n = 100; p = 50
+
+X = matrix(rnorm(n * p), n, p)
+Y = rnorm(n)
+lam = 2
+
+soln1 = selectiveInference:::fit_randomized_lasso(X, Y, lam, 1.e-12, 0)$soln
+G = glmnet(X, Y, intercept=FALSE, standardize=FALSE)
+soln2 = coef(G, s=1/n, exact=TRUE, x=X, y=Y)[-1]
+
+print(soln1)
+print(soln2)
\ No newline at end of file
diff --git a/tests/test_debiasing.R b/tests/test_debiasing.R
index 27bd621..b1fdf24 100644
--- a/tests/test_debiasing.R
+++ b/tests/test_debiasing.R
@@ -1,7 +1,143 @@
 library(selectiveInference)
-source('oldcode.R')
 
-n = 500; p = 50
+
+## Approximates inverse covariance matrix theta
+InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e-10, verbose = TRUE) {
+  isgiven <- 1;
+  if (is.null(mu)){
+    isgiven <- 0;
+  }
+  
+  p <- nrow(sigma);
+  M <- matrix(0, p, p);
+  xperc = 0;
+  xp = round(p/10);
+  for (i in 1:p) {
+    if ((i %% xp)==0){
+      xperc = xperc+10;
+      if (verbose) {
+        print(paste(xperc,"% done",sep="")); }
+    }
+    if (isgiven==0){
+      mu <- (1/sqrt(n)) * qnorm(1-(0.1/(p^2)));
+    }
+    mu.stop <- 0;
+    try.no <- 1;
+    incr <- 0;
+    while ((mu.stop != 1)&&(try.no<10)){
+      last.beta <- beta
+      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
+      beta <- output$optsol
+      iter <- output$iter
+      if (isgiven==1){
+        mu.stop <- 1
+      }
+      else{
+        if (try.no==1){
+          if (iter == (maxiter+1)){
+            incr <- 1;
+            mu <- mu*resol;
+          } else {
+            incr <- 0;
+            mu <- mu/resol;
+          }
+        }
+        if (try.no > 1){
+          if ((incr == 1)&&(iter == (maxiter+1))){
+            mu <- mu*resol;
+          }
+          if ((incr == 1)&&(iter < (maxiter+1))){
+            mu.stop <- 1;
+          }
+          if ((incr == 0)&&(iter < (maxiter+1))){
+            mu <- mu/resol;
+          }
+          if ((incr == 0)&&(iter == (maxiter+1))){
+            mu <- mu*resol;
+            beta <- last.beta;
+            mu.stop <- 1;
+          }
+        }
+      }
+      try.no <- try.no+1
+    }
+    M[i,] <- beta;
+  }
+  return(M)
+}
+
+InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-10) {
+  p <- nrow(sigma);
+  rho <- max(abs(sigma[i,-i])) / sigma[i,i];
+  mu0 <- rho/(1+rho);
+  beta <- rep(0,p);
+  
+  #if (mu >= mu0){
+  #  beta[i] <- (1-mu0)/sigma[i,i];
+  #  returnlist <- list("optsol" = beta, "iter" = 0);
+  #  return(returnlist);
+  #}
+  
+  diff.norm2 <- 1;
+  last.norm2 <- 1;
+  iter <- 1;
+  iter.old <- 1;
+  beta[i] <- (1-mu0)/sigma[i,i];
+  beta.old <- beta;
+  sigma.tilde <- sigma;
+  diag(sigma.tilde) <- 0;
+  vs <- -sigma.tilde%*%beta;
+  
+  while ((iter <= maxiter) && (diff.norm2 >= threshold*last.norm2)){
+    
+    for (j in 1:p){
+      oldval <- beta[j];
+      v <- vs[j];
+      if (j==i)
+        v <- v+1;
+      beta[j] <- SoftThreshold(v,mu)/sigma[j,j];
+      if (oldval != beta[j]){
+        vs <- vs + (oldval-beta[j])*sigma.tilde[,j];
+      }
+    }
+    
+    iter <- iter + 1;
+    if (iter==2*iter.old){
+      d <- beta - beta.old;
+      diff.norm2 <- sqrt(sum(d*d));
+      last.norm2 <-sqrt(sum(beta*beta));
+      iter.old <- iter;
+      beta.old <- beta;
+      #if (iter>10)
+      #  vs <- -sigma.tilde%*%beta;
+    }
+
+    # print(c(iter, maxiter, diff.norm2, threshold * last.norm2, threshold, mu))
+
+  }
+  
+  returnlist <- list("optsol" = beta, "iter" = iter)
+  return(returnlist)
+}
+
+SoftThreshold <- function( x, lambda ) {
+  #
+  # Standard soft thresholding
+  #
+  if (x>lambda){
+    return (x-lambda);}
+  else {
+    if (x< (-lambda)){
+      return (x+lambda);}
+    else {
+      return (0); }
+  }
+}
+
+
+### Test
+
+n = 100; p = 50
 
 X = matrix(rnorm(n * p), n, p)
 S = t(X) %*% X / n
@@ -25,3 +161,35 @@ plot(B1[1,], C1[1,])
 plot(A1[1,], A2[1,])
 plot(B1[1,], B2[1,])
 plot(C1[1,], C2[1,])
+
+print(c('A', sum(A1[1,] == 0)))
+print(c('B', sum(B1[1,] == 0)))
+print(c('C', sum(C1[1,] == 0)))
+
+## Are our points feasible
+
+feasibility = function(S, soln, j, mu) {
+     p = nrow(S)
+     E = rep(0, p)
+     E[j] = 1
+     G = S %*% soln - E
+     return(c(max(abs(G)), mu))
+}
+
+print(c('feasibility A', feasibility(S, A1[1,], 1, mu)))
+print(c('feasibility B', feasibility(S, B1[1,], 1, mu)))
+print(c('feasibility C', feasibility(S, C1[1,], 1, mu)))
+
+active_KKT = function(S, soln, j, mu) {
+     p = nrow(S)
+     E = rep(0, p)
+     E[j] = 1
+     G = S %*% soln - E
+     return(c(G[soln != 0] * sign(soln)[soln != 0], mu))
+}
+
+print(c('active_KKT A', active_KKT(S, A1[1,], 1, mu)))
+print(c('active_KKT B', active_KKT(S, B1[1,], 1, mu)))
+print(c('active_KKT C', active_KKT(S, C1[1,], 1, mu)))
+
+

From 242218e6f3da9153fca4e3a1cc0fcd47b0ebad8a Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 22:19:20 -0700
Subject: [PATCH 301/493] allowing randomization to be 0 in solver

---
 selectiveInference/R/funs.randomized.R | 18 +++++++++++-------
 tests/test_QP.R                        |  2 +-
 2 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 25a0b95..b79e2fb 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -22,14 +22,18 @@ fit_randomized_lasso = function(X,
     			
     noise_type = match.arg(noise_type)
 
-    if (noise_type == 'gaussian') {
-       D = Norm(mean=0, sd=noise_scale)
+    if (noise_scale > 0) {
+        if (noise_type == 'gaussian') {
+            D = Norm(mean=0, sd=noise_scale)
+        }
+        else if (noise_type == 'laplace') {
+            D = DExp(rate = 1 / noise_scale) # D is a Laplace distribution with rate = 1.
+        }
+        perturb_ = distr::r(D)(p)
+    } else {
+        perturb_ = rep(0, p)
     }
-    else if (noise_type == 'laplace') {
-       D = DExp(rate = 1 / noise_scale) # D is a Laplace distribution with rate = 1.
-    }
-    perturb_ = distr::r(D)(p)
-			
+
     lam = as.numeric(lam)
     if (length(lam) == 1) {
        lam = rep(lam, p)
diff --git a/tests/test_QP.R b/tests/test_QP.R
index cf5ca64..4aebec3 100644
--- a/tests/test_QP.R
+++ b/tests/test_QP.R
@@ -7,7 +7,7 @@ X = matrix(rnorm(n * p), n, p)
 Y = rnorm(n)
 lam = 2
 
-soln1 = selectiveInference:::fit_randomized_lasso(X, Y, lam, 1.e-12, 0)$soln
+soln1 = selectiveInference:::fit_randomized_lasso(X, Y, lam, 0, 0)$soln
 G = glmnet(X, Y, intercept=FALSE, standardize=FALSE)
 soln2 = coef(G, s=1/n, exact=TRUE, x=X, y=Y)[-1]
 

From 360dcbd722a674f9168ae7f2c1aa230b024ad95c Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 22:25:32 -0700
Subject: [PATCH 302/493] cosmetic edit

---
 selectiveInference/src/Rcpp-debias.cpp | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index 9cda705..5c18184 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -112,12 +112,13 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 
   Rcpp::IntegerVector need_update(nfeature);
 
-  // Extract the diagonal
+  Rcpp::NumericVector theta_old(nfeature);
+
+  // Extract the diagonal -- divide by ncase
+
   Rcpp::NumericVector nndef_diag(nfeature);
   double *nndef_diag_p = nndef_diag.begin();
 
-  Rcpp::NumericVector theta_old(nfeature);
-
   for (ifeature=0; ifeature<nfeature; ifeature++) {
     nndef_diag_p[ifeature] = 0;
     for (icase=0; icase<ncase; icase++) {

From f5a3b96155e8fcd3e69f9904838189682ba13105 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 22:46:48 -0700
Subject: [PATCH 303/493] LASSO solver is right up to scale

---
 tests/test_QP.R | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tests/test_QP.R b/tests/test_QP.R
index 4aebec3..61c4d53 100644
--- a/tests/test_QP.R
+++ b/tests/test_QP.R
@@ -9,7 +9,8 @@ lam = 2
 
 soln1 = selectiveInference:::fit_randomized_lasso(X, Y, lam, 0, 0)$soln
 G = glmnet(X, Y, intercept=FALSE, standardize=FALSE)
-soln2 = coef(G, s=1/n, exact=TRUE, x=X, y=Y)[-1]
+soln2 = coef(G, s=lam/n, exact=TRUE, x=X, y=Y)[-1]
 
 print(soln1)
-print(soln2)
\ No newline at end of file
+print(soln2)
+plot(soln1, soln2)

From 506a205b316e71e2a7f2c562afc14d20a9b0ffef Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 23:55:49 -0700
Subject: [PATCH 304/493] BF: fixing Xsoln incorrectly set in wide solver

---
 selectiveInference/R/funs.fixed.R             | 24 +++++++++++++------
 selectiveInference/R/funs.randomized.R        |  4 ++--
 .../src/quadratic_program_wide.c              |  2 +-
 tests/test_QP.R                               |  2 +-
 tests/test_debiasing.R                        | 13 ++++++----
 5 files changed, 30 insertions(+), 15 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 5490316..22a31e5 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -326,6 +326,9 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
 			   max_try=10,          # how many steps in linesearch?
 			   warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
 			   max_iter=100,        # how many iterations for each optimization problem
+                           kkt_stop=TRUE,       # stop based on KKT conditions?
+                           parameter_stop=TRUE, # stop based on relative convergence of parameter?
+			   objective_stop=TRUE, # stop based on relative decrease in objective?
                            kkt_tol=1.e-4,       # tolerance for the KKT conditions
                            parameter_tol=1.e-4, # tolerance for relative convergence of parameter
 			   objective_tol=1.e-8  # tolerance for relative decrease in objective
@@ -363,6 +366,9 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
 			  max_try=max_try,
 			  warn_kkt=FALSE,
 			  max_iter=max_iter,
+			  kkt_stop=kkt_stop,
+			  parameter_stop=parameter_stop,
+			  objective_stop=objective_stop,
 			  kkt_tol=kkt_tol,
 			  parameter_tol=parameter_tol,
 			  objective_tol=objective_tol)
@@ -394,6 +400,9 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
 			 max_try=10,          # how many steps in linesearch?
 			 warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
 			 max_iter=100,        # how many iterations for each optimization problem
+                         kkt_stop=TRUE,       # stop based on KKT conditions?
+                         parameter_stop=TRUE, # stop based on relative convergence of parameter?
+                         objective_stop=TRUE, # stop based on relative decrease in objective?
                          kkt_tol=1.e-4,       # tolerance for the KKT conditions
 			 parameter_tol=1.e-4, # tolerance for relative convergence of parameter
 			 objective_tol=1.e-8  # tolerance for relative decrease in objective
@@ -423,6 +432,8 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
 
   last_output = NULL
 
+  Xsoln = rep(0, n)
+
   while (counter_idx < max_try) {
 
       if (!is_wide) {
@@ -438,11 +449,10 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                             objective_tol, 
 			    parameter_tol,
                             max_active,
-			    FALSE,        # objective_stop
-			    FALSE,        # kkt_stop
-			    TRUE)         # param_stop
+			    objective_stop,
+			    kkt_stop,
+			    parameter_stop)
       } else {
-          Xsoln = rep(0, nrow(Xinfo))
           result = solve_QP_wide(Xinfo, # this is a design matrix
                                  rep(mu, p),  # vector of Lagrange multipliers
 				 0,           # ridge_term 
@@ -457,9 +467,9 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                                  objective_tol, 
 				 parameter_tol,
                                  max_active,
-				 FALSE,       # objective_stop
-				 FALSE,       # kkt_stop
-				 TRUE)        # param_stop
+				 objective_stop,	
+				 kkt_stop,
+				 parameter_stop)
 
       }
 
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index b79e2fb..7bf6008 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -44,13 +44,13 @@ fit_randomized_lasso = function(X,
 
     soln = rep(0, p)
     Xsoln = rep(0, n)
-    linear_func = (- t(X) %*% y - perturb_)
+    linear_func = (- t(X) %*% y - perturb_) / n
     gradient = 1. * linear_func
     ever_active = rep(0, p)
     nactive = as.integer(0)
 
     result = solve_QP_wide(X,                  # design matrix
-    	                   lam,                # vector of Lagrange multipliers
+    	                   lam / n,                # vector of Lagrange multipliers
 		           ridge_term / n,     # ridge_term 
                            max_iter, 
                            soln, 
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
index 3e4bdb0..3546fcd 100644
--- a/selectiveInference/src/quadratic_program_wide.c
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -4,7 +4,7 @@
 
 // Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
 
-// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2/n - l^T\theta + \mu \|\theta\|_1 + \frac{\epsilon}{2} \|\theta\|^2_2
+// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2/n + l^T\theta + \mu \|\theta\|_1 + \frac{\epsilon}{2} \|\theta\|^2_2
 // where l is `linear_func` below
 
 // This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
diff --git a/tests/test_QP.R b/tests/test_QP.R
index 61c4d53..1764225 100644
--- a/tests/test_QP.R
+++ b/tests/test_QP.R
@@ -1,7 +1,7 @@
 library(selectiveInference)
 ### Test
 
-n = 100; p = 50
+n = 80; p = 50
 
 X = matrix(rnorm(n * p), n, p)
 Y = rnorm(n)
diff --git a/tests/test_debiasing.R b/tests/test_debiasing.R
index b1fdf24..e2743fe 100644
--- a/tests/test_debiasing.R
+++ b/tests/test_debiasing.R
@@ -144,16 +144,19 @@ S = t(X) %*% X / n
 
 mu = 7.791408e-02
 
-A1 = debiasingMatrix(S, FALSE, n, 1:5, mu=mu, max_iter=1000)
-A2 = debiasingMatrix(S / n, FALSE, n, 1:5, mu=mu, max_iter=1000)
+tol = 1.e-12
 
-B1 = debiasingMatrix(X, TRUE, n, 1:5, mu=mu, max_iter=1000)
-B2 = debiasingMatrix(X / sqrt(n), TRUE, n, 1:5, mu=mu, max_iter=1000)
+A1 = debiasingMatrix(S, FALSE, n, 1:5, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+A2 = debiasingMatrix(S / n, FALSE, n, 1:5, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+
+B1 = debiasingMatrix(X, TRUE, n, 1:5, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+B2 = debiasingMatrix(X / sqrt(n), TRUE, n, 1:5, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
 
 C1 = InverseLinfty(S, n, mu=mu, maxiter=1000)[1:5,]
 C2 = InverseLinfty(S / n, n, mu=mu, maxiter=1000)[1:5,]
 
 par(mfrow=c(2,3))
+
 plot(A1[1,], C1[1,])
 plot(A1[1,], B1[1,])
 plot(B1[1,], C1[1,])
@@ -185,6 +188,8 @@ active_KKT = function(S, soln, j, mu) {
      E = rep(0, p)
      E[j] = 1
      G = S %*% soln - E
+     print(which(soln != 0))
+     print(G[j])
      return(c(G[soln != 0] * sign(soln)[soln != 0], mu))
 }
 

From 0be39c476d946569d472a4c7e41b232a9eca67e0 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 27 Oct 2017 00:03:09 -0700
Subject: [PATCH 305/493] gradient not current?

---
 selectiveInference/R/funs.fixed.R |  3 ++-
 tests/test_debiasing.R            | 13 +++++++------
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 22a31e5..8d3f727 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -525,7 +525,8 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
   } 
 
   return(list(soln=result$soln,
-              kkt_check=result$kkt_check))
+              kkt_check=result$kkt_check,
+	      gradient=result$gradient))
 
 }
 
diff --git a/tests/test_debiasing.R b/tests/test_debiasing.R
index e2743fe..50b43d2 100644
--- a/tests/test_debiasing.R
+++ b/tests/test_debiasing.R
@@ -146,14 +146,15 @@ mu = 7.791408e-02
 
 tol = 1.e-12
 
-A1 = debiasingMatrix(S, FALSE, n, 1:5, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
-A2 = debiasingMatrix(S / n, FALSE, n, 1:5, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+rows = c(1:2)
+A1 = debiasingMatrix(S, FALSE, n, rows, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+A2 = debiasingMatrix(S / n, FALSE, n, rows, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
 
-B1 = debiasingMatrix(X, TRUE, n, 1:5, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
-B2 = debiasingMatrix(X / sqrt(n), TRUE, n, 1:5, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+B1 = debiasingMatrix(X, TRUE, n, rows, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+B2 = debiasingMatrix(X / sqrt(n), TRUE, n, rows, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
 
-C1 = InverseLinfty(S, n, mu=mu, maxiter=1000)[1:5,]
-C2 = InverseLinfty(S / n, n, mu=mu, maxiter=1000)[1:5,]
+C1 = InverseLinfty(S, n, mu=mu, maxiter=1000)[rows,]
+C2 = InverseLinfty(S / n, n, mu=mu, maxiter=1000)[rows,]
 
 par(mfrow=c(2,3))
 

From 3ee2a434ff0e8e8c138cc688931f84eb8144ab26 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 27 Oct 2017 00:07:30 -0700
Subject: [PATCH 306/493] print R2 from lm

---
 tests/test_QP.R | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/test_QP.R b/tests/test_QP.R
index 1764225..79638e0 100644
--- a/tests/test_QP.R
+++ b/tests/test_QP.R
@@ -14,3 +14,4 @@ soln2 = coef(G, s=lam/n, exact=TRUE, x=X, y=Y)[-1]
 print(soln1)
 print(soln2)
 plot(soln1, soln2)
+print(summary(lm(soln1 ~ soln2)))
\ No newline at end of file

From 92812fc08b09015b8e713dd3dd3e5f44100dd9c6 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 27 Oct 2017 09:47:31 -0700
Subject: [PATCH 307/493] without linesearch we know agree with Adel's code at
 fixed mu

---
 selectiveInference/DESCRIPTION         |  2 +-
 selectiveInference/NAMESPACE           |  2 +-
 selectiveInference/R/funs.fixed.R      | 29 ++++++----
 selectiveInference/R/funs.randomized.R | 31 +++++-----
 tests/test_debiasing.R                 | 80 +++++++++++++-------------
 5 files changed, 75 insertions(+), 69 deletions(-)

diff --git a/selectiveInference/DESCRIPTION b/selectiveInference/DESCRIPTION
index d902622..fad072d 100644
--- a/selectiveInference/DESCRIPTION
+++ b/selectiveInference/DESCRIPTION
@@ -9,7 +9,7 @@ Maintainer: Rob Tibshirani <tibs@stanford.edu>
 Depends:
     glmnet,
     intervals,
-    survival
+    survival,
 Suggests:
     Rmpfr
 Description: New tools for post-selection inference, for use with forward
diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index c7d08a1..d72d56a 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -44,4 +44,4 @@ importFrom("stats", dnorm, lsfit, pexp, pnorm, predict,
 importFrom("stats", "coef", "df", "lm", "pf")
 importFrom("stats", "glm", "residuals", "vcov")
 importFrom("Rcpp", "sourceCpp")
-importFrom("distr", "Norm", "DExp")
+
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 8d3f727..1928487 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -319,7 +319,7 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
                            nsample, 
                            rows, 
  		           verbose=FALSE, 
-		           mu=NULL,             # starting value of mu
+		           bound=NULL,             # starting value of bound
    			   linesearch=TRUE,     # do a linesearch?
    		           scaling_factor=1.5,  # multiplicative factor for linesearch
 			   max_active=NULL,     # how big can active set get?
@@ -342,8 +342,8 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
   p = ncol(Xinfo);
   M = matrix(0, length(rows), p);
 
-  if (is.null(mu)) {
-      mu = (1/sqrt(nsample)) * qnorm(1-(0.1/(p^2)))
+  if (is.null(bound)) {
+      bound = (1/sqrt(nsample)) * qnorm(1-(0.1/(p^2)))
   }
  
   xperc = 0;
@@ -359,7 +359,7 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
     output = debiasingRow(Xinfo,               # could be X or t(X) %*% X / n depending on is_wide
                           is_wide,
                           row,
-                          mu,
+                          bound,
                           linesearch=linesearch,
                           scaling_factor=scaling_factor,
                           max_active=max_active,
@@ -393,7 +393,7 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
 debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n depending on is_wide
                          is_wide, 
                          row, 
-                         mu, 
+                         bound, 
 	                 linesearch=TRUE,     # do a linesearch?
 		         scaling_factor=1.5,  # multiplicative factor for linesearch
 		         max_active=NULL,     # how big can active set get?
@@ -414,9 +414,11 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
       max_active = min(nrow(Xinfo), ncol(Xinfo))
   }
 
+   
   # Initialize variables 
 
   soln = rep(0, p)
+  soln = as.numeric(soln)
   ever_active = rep(0, p)
   ever_active[1] = row      # 1-based
   ever_active = as.integer(ever_active)
@@ -432,13 +434,16 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
 
   last_output = NULL
 
-  Xsoln = rep(0, n)
+  if (is_wide) {
+     n = nrow(Xinfo)
+     Xsoln = as.numeric(rep(0, n))
+  }
 
   while (counter_idx < max_try) {
 
       if (!is_wide) {
           result = solve_QP(Xinfo, # this is non-neg-def matrix
-                            mu, 
+                            bound, 
                             max_iter, 
                             soln, 
                             linear_func, 
@@ -453,9 +458,9 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
 			    kkt_stop,
 			    parameter_stop)
       } else {
-          result = solve_QP_wide(Xinfo, # this is a design matrix
-                                 rep(mu, p),  # vector of Lagrange multipliers
-				 0,           # ridge_term 
+          result = solve_QP_wide(Xinfo,                      # this is a design matrix
+                                 as.numeric(rep(bound, p)),  # vector of Lagrange multipliers
+				 0,                          # ridge_term 
                                  max_iter, 
                                  soln, 
                                  linear_func, 
@@ -493,13 +498,13 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
          if ((iter < (max_iter+1)) && (counter_idx > 1)) { 
            break;      # we've found a feasible point and solved the problem            
          }
-         mu = mu * scaling_factor;
+         bound = bound * scaling_factor;
       } else {         # trying to drop the bound parameter further
          if ((iter == (max_iter + 1)) && (counter_idx > 1)) {
             result = last_output; # problem seems infeasible because we didn't solve it
    	    break;                # so we revert to previously found solution
          }
-         mu = mu / scaling_factor;
+         bound = bound / scaling_factor;
       }
 
       # If the active set has grown to a certain size
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 7bf6008..3a171f7 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -3,19 +3,19 @@
 #
 # min 1/2 || y - \beta_0 - X \beta ||_2^2 + \lambda || \beta ||_1 - \omega^T\beta + \frac{\epsilon}{2} \|\beta\|^2_2
 
-fit_randomized_lasso = function(X, 
-                                y, 
-                                lam, 
-                                noise_scale, 
-                                ridge_term, 
-                                noise_type=c('gaussian', 'laplace'),
-                                max_iter=100,        # how many iterations for each optimization problem
-                                kkt_tol=1.e-4,       # tolerance for the KKT conditions
-                                parameter_tol=1.e-8, # tolerance for relative convergence of parameter
-                                objective_tol=1.e-8, # tolerance for relative decrease in objective
-                                objective_stop=FALSE,
-                                kkt_stop=TRUE,
-                                param_stop=TRUE)
+randomizedLASSO = function(X, 
+                           y, 
+                           lam, 
+                           noise_scale, 
+                           ridge_term, 
+                           noise_type=c('gaussian', 'laplace'),
+                           max_iter=100,        # how many iterations for each optimization problem
+                           kkt_tol=1.e-4,       # tolerance for the KKT conditions
+                           parameter_tol=1.e-8, # tolerance for relative convergence of parameter
+                           objective_tol=1.e-8, # tolerance for relative decrease in objective
+                           objective_stop=FALSE,
+                           kkt_stop=TRUE,
+                           param_stop=TRUE)
 {
 
     n = nrow(X); p = ncol(X)
@@ -24,12 +24,11 @@ fit_randomized_lasso = function(X,
 
     if (noise_scale > 0) {
         if (noise_type == 'gaussian') {
-            D = Norm(mean=0, sd=noise_scale)
+            perturb_ = rnorm(p) * noise_scale
         }
         else if (noise_type == 'laplace') {
-            D = DExp(rate = 1 / noise_scale) # D is a Laplace distribution with rate = 1.
+            perturb_ = rexp(p) * (2 * rbinom(p, 1, 0.5) - 1) * noise_scale
         }
-        perturb_ = distr::r(D)(p)
     } else {
         perturb_ = rep(0, p)
     }
diff --git a/tests/test_debiasing.R b/tests/test_debiasing.R
index 50b43d2..8e81b16 100644
--- a/tests/test_debiasing.R
+++ b/tests/test_debiasing.R
@@ -2,9 +2,9 @@ library(selectiveInference)
 
 
 ## Approximates inverse covariance matrix theta
-InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e-10, verbose = TRUE) {
+InverseLinfty <- function(sigma, n, resol=1.5, bound=NULL, maxiter=50, threshold=1e-10, verbose = TRUE) {
   isgiven <- 1;
-  if (is.null(mu)){
+  if (is.null(bound)){
     isgiven <- 0;
   }
   
@@ -19,43 +19,43 @@ InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e
         print(paste(xperc,"% done",sep="")); }
     }
     if (isgiven==0){
-      mu <- (1/sqrt(n)) * qnorm(1-(0.1/(p^2)));
+      bound <- (1/sqrt(n)) * qnorm(1-(0.1/(p^2)));
     }
-    mu.stop <- 0;
+    bound.stop <- 0;
     try.no <- 1;
     incr <- 0;
-    while ((mu.stop != 1)&&(try.no<10)){
+    while ((bound.stop != 1)&&(try.no<10)){
       last.beta <- beta
-      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
+      output <- InverseLinftyOneRow(sigma, i, bound, maxiter=maxiter, threshold=threshold)
       beta <- output$optsol
       iter <- output$iter
       if (isgiven==1){
-        mu.stop <- 1
+        bound.stop <- 1
       }
       else{
         if (try.no==1){
           if (iter == (maxiter+1)){
             incr <- 1;
-            mu <- mu*resol;
+            bound <- bound*resol;
           } else {
             incr <- 0;
-            mu <- mu/resol;
+            bound <- bound/resol;
           }
         }
         if (try.no > 1){
           if ((incr == 1)&&(iter == (maxiter+1))){
-            mu <- mu*resol;
+            bound <- bound*resol;
           }
           if ((incr == 1)&&(iter < (maxiter+1))){
-            mu.stop <- 1;
+            bound.stop <- 1;
           }
           if ((incr == 0)&&(iter < (maxiter+1))){
-            mu <- mu/resol;
+            bound <- bound/resol;
           }
           if ((incr == 0)&&(iter == (maxiter+1))){
-            mu <- mu*resol;
+            bound <- bound*resol;
             beta <- last.beta;
-            mu.stop <- 1;
+            bound.stop <- 1;
           }
         }
       }
@@ -66,14 +66,14 @@ InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e
   return(M)
 }
 
-InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-10) {
+InverseLinftyOneRow <- function ( sigma, i, bound, maxiter=50, threshold=1e-10) {
   p <- nrow(sigma);
   rho <- max(abs(sigma[i,-i])) / sigma[i,i];
-  mu0 <- rho/(1+rho);
+  bound0 <- rho/(1+rho);
   beta <- rep(0,p);
   
-  #if (mu >= mu0){
-  #  beta[i] <- (1-mu0)/sigma[i,i];
+  #if (bound >= bound0){
+  #  beta[i] <- (1-bound0)/sigma[i,i];
   #  returnlist <- list("optsol" = beta, "iter" = 0);
   #  return(returnlist);
   #}
@@ -82,7 +82,7 @@ InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-10) {
   last.norm2 <- 1;
   iter <- 1;
   iter.old <- 1;
-  beta[i] <- (1-mu0)/sigma[i,i];
+  beta[i] <- (1-bound0)/sigma[i,i];
   beta.old <- beta;
   sigma.tilde <- sigma;
   diag(sigma.tilde) <- 0;
@@ -95,7 +95,7 @@ InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-10) {
       v <- vs[j];
       if (j==i)
         v <- v+1;
-      beta[j] <- SoftThreshold(v,mu)/sigma[j,j];
+      beta[j] <- SoftThreshold(v,bound)/sigma[j,j];
       if (oldval != beta[j]){
         vs <- vs + (oldval-beta[j])*sigma.tilde[,j];
       }
@@ -112,7 +112,7 @@ InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-10) {
       #  vs <- -sigma.tilde%*%beta;
     }
 
-    # print(c(iter, maxiter, diff.norm2, threshold * last.norm2, threshold, mu))
+    # print(c(iter, maxiter, diff.norm2, threshold * last.norm2, threshold, bound))
 
   }
   
@@ -142,19 +142,21 @@ n = 100; p = 50
 X = matrix(rnorm(n * p), n, p)
 S = t(X) %*% X / n
 
-mu = 7.791408e-02
+debiasing_bound = 7.791408e-02
 
 tol = 1.e-12
 
-rows = c(1:2)
-A1 = debiasingMatrix(S, FALSE, n, rows, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
-A2 = debiasingMatrix(S / n, FALSE, n, rows, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+rows = as.integer(c(1:2))
+print('here')
+print(rows)
+A1 = debiasingMatrix(S, FALSE, n, rows, bound=debiasing_bound, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol, linesearch=FALSE)
 
-B1 = debiasingMatrix(X, TRUE, n, rows, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
-B2 = debiasingMatrix(X / sqrt(n), TRUE, n, rows, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+A2 = debiasingMatrix(S / n, FALSE, n, rows, bound=debiasing_bound, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol, linesearch=FALSE)
+B1 = debiasingMatrix(X, TRUE, n, rows, bound=debiasing_bound, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol, linesearch=FALSE)
+B2 = debiasingMatrix(X / sqrt(n), TRUE, n, rows, bound=debiasing_bound, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol, linesearch=FALSE)
 
-C1 = InverseLinfty(S, n, mu=mu, maxiter=1000)[rows,]
-C2 = InverseLinfty(S / n, n, mu=mu, maxiter=1000)[rows,]
+C1 = InverseLinfty(S, n, bound=debiasing_bound, maxiter=1000)[rows,]
+C2 = InverseLinfty(S / n, n, bound=debiasing_bound, maxiter=1000)[rows,]
 
 par(mfrow=c(2,3))
 
@@ -172,30 +174,30 @@ print(c('C', sum(C1[1,] == 0)))
 
 ## Are our points feasible
 
-feasibility = function(S, soln, j, mu) {
+feasibility = function(S, soln, j, debiasing_bound) {
      p = nrow(S)
      E = rep(0, p)
      E[j] = 1
      G = S %*% soln - E
-     return(c(max(abs(G)), mu))
+     return(c(max(abs(G)), debiasing_bound))
 }
 
-print(c('feasibility A', feasibility(S, A1[1,], 1, mu)))
-print(c('feasibility B', feasibility(S, B1[1,], 1, mu)))
-print(c('feasibility C', feasibility(S, C1[1,], 1, mu)))
+print(c('feasibility A', feasibility(S, A1[1,], 1, debiasing_bound)))
+print(c('feasibility B', feasibility(S, B1[1,], 1, debiasing_bound)))
+print(c('feasibility C', feasibility(S, C1[1,], 1, debiasing_bound)))
 
-active_KKT = function(S, soln, j, mu) {
+active_KKT = function(S, soln, j, debiasing_bound) {
      p = nrow(S)
      E = rep(0, p)
      E[j] = 1
      G = S %*% soln - E
      print(which(soln != 0))
      print(G[j])
-     return(c(G[soln != 0] * sign(soln)[soln != 0], mu))
+     return(c(G[soln != 0] * sign(soln)[soln != 0], debiasing_bound))
 }
 
-print(c('active_KKT A', active_KKT(S, A1[1,], 1, mu)))
-print(c('active_KKT B', active_KKT(S, B1[1,], 1, mu)))
-print(c('active_KKT C', active_KKT(S, C1[1,], 1, mu)))
+print(c('active_KKT A', active_KKT(S, A1[1,], 1, debiasing_bound)))
+print(c('active_KKT B', active_KKT(S, B1[1,], 1, debiasing_bound)))
+print(c('active_KKT C', active_KKT(S, C1[1,], 1, debiasing_bound)))
 
 

From abf0824bde4fc76ddde1b5aa66669ff7ab19a469 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 27 Oct 2017 10:03:45 -0700
Subject: [PATCH 308/493] fixing documentation

---
 selectiveInference/NAMESPACE                  |  1 +
 selectiveInference/man/debiasingMatrix.Rd     | 27 +++++++++--
 selectiveInference/src/quadratic_program.c    | 43 +++++++++--------
 .../src/quadratic_program_wide.c              | 46 ++++++++++---------
 4 files changed, 71 insertions(+), 46 deletions(-)

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index d72d56a..e7b1e80 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -43,5 +43,6 @@ importFrom("stats", dnorm, lsfit, pexp, pnorm, predict,
            qnorm, rnorm, sd, uniroot, dchisq, model.matrix, pchisq)
 importFrom("stats", "coef", "df", "lm", "pf")
 importFrom("stats", "glm", "residuals", "vcov")
+importFrom("stats", "rbinom", "rexp")
 importFrom("Rcpp", "sourceCpp")
 
diff --git a/selectiveInference/man/debiasingMatrix.Rd b/selectiveInference/man/debiasingMatrix.Rd
index 4da925e..b95c75b 100644
--- a/selectiveInference/man/debiasingMatrix.Rd
+++ b/selectiveInference/man/debiasingMatrix.Rd
@@ -16,14 +16,18 @@ debiasingMatrix(Xinfo,
                 nsample, 
                 rows, 
 		verbose=FALSE, 
-		mu=NULL,            
+		bound=NULL,            
    		linesearch=TRUE,    
    		scaling_factor=1.5, 
 		max_active=NULL,    
 		max_try=10,         
 		warn_kkt=FALSE,     
 		max_iter=100,       
+		kkt_stop=TRUE,
+		parameter_stop=TRUE,
+		objective_stop=TRUE,
                 kkt_tol=1.e-4,      
+		parameter_tol=1.e-4,
 		objective_tol=1.e-8)
 }
 \arguments{
@@ -38,7 +42,7 @@ matrix of interest is t(X) %*% X / nrow(X).
 }
 \item{nsample}{
 Number of samples used in forming the cross-covariance matrix.
-Used for default value of the bound parameter mu.
+Used for default value of the bound parameter.
 }
 \item{rows}{
 Which rows of the approximate inverse to compute.
@@ -46,7 +50,7 @@ Which rows of the approximate inverse to compute.
 \item{verbose}{
 Print out progress as rows are being computed.
 } 
-\item{mu}{
+\item{bound}{
 Initial bound parameter for each row. Will be changed
 if linesearch is TRUE.
 } 
@@ -72,10 +76,25 @@ descent algorithm.
 How many full iterations to run of the coordinate descent for each
 value of the bound parameter.
 }
+\item{kkt_stop}{
+If TRUE, check to stop coordinate descent when KKT conditions are approximately satisfied.
+}
+\item{parameter_stop}{
+If TRUE, check to stop coordinate descent based on relative convergence of parameter vector,
+checked at geometrically spaced iterations 2^k.
+}
+\item{objective_stop}{
+If TRUE, check to stop coordinate descent based on relative decrease of objective value,
+checked at geometrically spaced iterations 2^k.
+}
 \item{kkt_tol}{
 Tolerance value for assessing whether KKT conditions for solving the
 dual problem and feasibility of the original problem.
 }
+\item{parameter_tol}{
+Tolerance value for assessing convergence of the problem using relative
+convergence of the parameter.
+}
 \item{objective_tol}{
 Tolerance value for assessing convergence of the problem using relative
 decrease of the objective.
@@ -86,7 +105,7 @@ This function computes an approximate inverse
 as described in Javanmard and Montanari (2013), specifically
 display (4). The problem is solved by considering a dual
 problem which has an objective similar to a LASSO problem and is solvable
-by coordinate descent. For some values of mu the original
+by coordinate descent. For some values of bound the original
 problem may not be feasible, in which case the dual problem has no solution. 
 An attempt to detect this is made by stopping when the active set grows quite
 large, determined by max_active.
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
index 822ddf5..1f7fcb3 100644
--- a/selectiveInference/src/quadratic_program.c
+++ b/selectiveInference/src/quadratic_program.c
@@ -385,10 +385,12 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
       }
     }
 					  
-    // Check based on norm -- from Adel's debiasing code
 
-    if (param_stop) { 
-      if (iter == 2 * iter_old) {
+    if (iter == 2 * iter_old) { // Geometric iterations from Adel's code
+
+      // Check based on norm 
+
+      if (param_stop) { 
 	iter_old = iter;
 	norm_diff = 0;
 	norm_last = 0;
@@ -407,6 +409,24 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	  break;
 	}
       }
+
+      // Check relative decrease of objective
+
+      if (objective_stop) {
+	new_value = objective_qp(nndef_ptr,
+				 linear_func_ptr,
+				 ever_active_ptr,
+				 nactive_ptr,
+				 nfeature,
+				 bound,
+				 theta);
+
+	if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
+	  break;
+	}
+	old_value = new_value;
+      }
+
     }
 
     // Check size of active set
@@ -415,23 +435,6 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
       break;
     }
 
-    // Check relative decrease of objective
-
-    if (objective_stop) {
-      new_value = objective_qp(nndef_ptr,
-			       linear_func_ptr,
-			       ever_active_ptr,
-			       nactive_ptr,
-			       nfeature,
-			       bound,
-			       theta);
-
-      if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
-	break;
-      }
-      old_value = new_value;
-    }
-
   }
   return(iter);
 }
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
index 3546fcd..41e29ce 100644
--- a/selectiveInference/src/quadratic_program_wide.c
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -534,10 +534,11 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
       }
     }
 
-    // Check based on norm -- from Adel's debiasing code
+    if (iter == 2 * iter_old) { // Geometric iterations from Adel's code
 
-    if (param_stop) {
-      if (iter == 2 * iter_old) {
+      // Check based on norm 
+
+      if (param_stop) {
 	iter_old = iter;
 	norm_diff = 0;
 	norm_last = 0;
@@ -556,32 +557,33 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 	  break;
 	}
       }
+
+      // Check relative decrease of objective
+
+      if (objective_stop) {
+	new_value = objective_wide(X_theta_ptr,
+				   linear_func_ptr,
+				   ever_active_ptr,
+				   nactive_ptr,
+				   ncase,
+				   nfeature,
+				   bound_ptr,
+				   ridge_term,
+				   theta_ptr);
+
+	if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
+	  break;
+	}
+	old_value = new_value;
+      }
     }
+
     // Check size of active set
 
     if (*nactive_ptr >= max_active) {
       break;
     }
 
-    // Check relative decrease of objective
-
-    if (objective_stop) {
-      new_value = objective_wide(X_theta_ptr,
-				 linear_func_ptr,
-				 ever_active_ptr,
-				 nactive_ptr,
-				 ncase,
-				 nfeature,
-				 bound_ptr,
-				 ridge_term,
-				 theta_ptr);
-
-      if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
-	break;
-      }
-      old_value = new_value;
-    }
-
   }
   return(iter);
 }

From 1658ecbad9ab779909e3985dda9750903b39d86e Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 27 Oct 2017 10:29:46 -0700
Subject: [PATCH 309/493] checking comparison example again -- looks good

---
 selectiveInference/R/funs.fixed.R           |  13 +-
 selectiveInference/man/debiasingMatrix.Rd   |   4 +-
 tests/debiased_lasso/comparison_scaled.R    |  78 ++
 tests/debiased_lasso/comparison_unscaled.R  |  78 ++
 tests/debiased_lasso/javanmard_montanari.R  | 770 ++++++++++++++++++++
 tests/{ => debiased_lasso}/test_debiasing.R |   0
 tests/debiased_lasso/test_debiasing_wide.R  | 202 +++++
 7 files changed, 1138 insertions(+), 7 deletions(-)
 create mode 100644 tests/debiased_lasso/comparison_scaled.R
 create mode 100644 tests/debiased_lasso/comparison_unscaled.R
 create mode 100644 tests/debiased_lasso/javanmard_montanari.R
 rename tests/{ => debiased_lasso}/test_debiasing.R (100%)
 create mode 100644 tests/debiased_lasso/test_debiasing_wide.R

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 1928487..7c8318d 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -8,7 +8,7 @@ fixedLassoInf <- function(x, y, beta,
                           sigma=NULL, alpha=0.1,
                           type=c("partial", "full"), tol.beta=1e-5, tol.kkt=0.1,
                           gridrange=c(-100,100), bits=NULL, verbose=FALSE, 
-                          linesearch.try=10) {
+                          linesearch.try=10, offset_correction=TRUE) {
 
   family = match.arg(family)
   this.call = match.call()
@@ -197,6 +197,9 @@ fixedLassoInf <- function(x, y, beta,
         M = M[-1,] # remove intercept row
         null_value = null_value[-1] # remove intercept element
       }
+      if (!offset_correction) {
+        null_value = 0 * null_value
+      }
     } else if (type=="partial" || p > n) {
       xa = x[,vars,drop=F]
       M = pinv(crossprod(xa)) %*% t(xa)
@@ -325,13 +328,13 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
 			   max_active=NULL,     # how big can active set get?
 			   max_try=10,          # how many steps in linesearch?
 			   warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
-			   max_iter=100,        # how many iterations for each optimization problem
+			   max_iter=50,         # how many iterations for each optimization problem
                            kkt_stop=TRUE,       # stop based on KKT conditions?
                            parameter_stop=TRUE, # stop based on relative convergence of parameter?
 			   objective_stop=TRUE, # stop based on relative decrease in objective?
                            kkt_tol=1.e-4,       # tolerance for the KKT conditions
                            parameter_tol=1.e-4, # tolerance for relative convergence of parameter
-			   objective_tol=1.e-8  # tolerance for relative decrease in objective
+			   objective_tol=1.e-4  # tolerance for relative decrease in objective
                            ) {
 
 
@@ -399,13 +402,13 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
 		         max_active=NULL,     # how big can active set get?
 			 max_try=10,          # how many steps in linesearch?
 			 warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
-			 max_iter=100,        # how many iterations for each optimization problem
+			 max_iter=50,         # how many iterations for each optimization problem
                          kkt_stop=TRUE,       # stop based on KKT conditions?
                          parameter_stop=TRUE, # stop based on relative convergence of parameter?
                          objective_stop=TRUE, # stop based on relative decrease in objective?
                          kkt_tol=1.e-4,       # tolerance for the KKT conditions
 			 parameter_tol=1.e-4, # tolerance for relative convergence of parameter
-			 objective_tol=1.e-8  # tolerance for relative decrease in objective
+			 objective_tol=1.e-4  # tolerance for relative decrease in objective
                          ) {
 
   p = ncol(Xinfo)
diff --git a/selectiveInference/man/debiasingMatrix.Rd b/selectiveInference/man/debiasingMatrix.Rd
index b95c75b..6a34850 100644
--- a/selectiveInference/man/debiasingMatrix.Rd
+++ b/selectiveInference/man/debiasingMatrix.Rd
@@ -22,13 +22,13 @@ debiasingMatrix(Xinfo,
 		max_active=NULL,    
 		max_try=10,         
 		warn_kkt=FALSE,     
-		max_iter=100,       
+		max_iter=50,       
 		kkt_stop=TRUE,
 		parameter_stop=TRUE,
 		objective_stop=TRUE,
                 kkt_tol=1.e-4,      
 		parameter_tol=1.e-4,
-		objective_tol=1.e-8)
+		objective_tol=1.e-4)
 }
 \arguments{
 \item{Xinfo}{
diff --git a/tests/debiased_lasso/comparison_scaled.R b/tests/debiased_lasso/comparison_scaled.R
new file mode 100644
index 0000000..e0c0a6a
--- /dev/null
+++ b/tests/debiased_lasso/comparison_scaled.R
@@ -0,0 +1,78 @@
+source('javanmard_montanari.R')
+
+##############################################
+
+# Runs nsims simulations under the global null, computing p-values
+# using both the old code (slow one using Adel's code) and the new
+# code (faster using Jon's code), and produces qq-plots for both.
+# Runing 50 sims takes about 10-15 mins because old code is slow, so
+# feel free to lower nsims if you want
+
+
+library(selectiveInference)
+library(glmnet)
+
+# set.seed(424)
+
+n=100
+p=200
+
+sigma=.5
+
+theor_lambda = sigma * sqrt(2 * log(p))
+lambda=c(0.25, 0.5, 1, 0.8 * theor_lambda, theor_lambda)
+
+for (j in c(3,4,5,1,2)) {
+
+thresh = 1e-10
+
+beta=rep(0,p)
+type="full"
+
+nsim = 20
+
+scaling = sqrt(n)
+pvs_old = c()
+pvs_new <- c()
+pvs_old_0 = c()   # don't add the offset correction
+pvs_new_0 = c()   # don't add the offset correction
+for (i in 1:nsim) {
+  cat(i,fill=T)
+  x = matrix(rnorm(n*p),n,p)
+  x = scale(x,T,T) / scaling
+  mu = x%*%beta 
+  y=mu+sigma*rnorm(n)
+
+  # first run  glmnet
+  gfit=glmnet(x,y,intercept=F,standardize=F,thresh=thresh)
+
+  bhat = coef(gfit, s=lambda[j]/(sqrt(n) * scaling), exact=TRUE,x=x,y=y)[-1]
+
+  if(sum(bhat != 0) > 0) {
+
+  # compute fixed lambda p-values and selection intervals
+
+  aa = fixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type)
+  bb = oldFixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type)
+  cc = fixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type, offset_correction=FALSE)
+  dd = oldFixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type, offset_correction=FALSE)
+  pvs_new <- c(pvs_new, aa$pv, recursive=TRUE)
+  pvs_old <- c(pvs_old, bb$pv,recursive=TRUE)
+  pvs_new_0 <- c(pvs_new_0, cc$pv, recursive=TRUE)
+  pvs_old_0 <- c(pvs_old_0, dd$pv, recursive=TRUE)
+
+  cat()
+  }
+}
+
+#check uniformity 
+
+png(paste('comparison_scaled', j, '.png', sep=''))
+plot(ecdf(pvs_old), pch=23, col='green', xlim=c(0,1), ylim=c(0,1), main='ECDF of p-values')
+plot(ecdf(pvs_new), pch=24, col='purple', add=TRUE)
+plot(ecdf(pvs_old_0), pch=23, col='red', add=TRUE)
+plot(ecdf(pvs_new_0), pch=24, col='black', add=TRUE)
+abline(0,1)
+legend("bottomright", legend=c("Old","New", "Old 0", "New 0"), pch=c(23,24,23,24), pt.bg=c("green","purple","red","black"))
+dev.off()
+}
\ No newline at end of file
diff --git a/tests/debiased_lasso/comparison_unscaled.R b/tests/debiased_lasso/comparison_unscaled.R
new file mode 100644
index 0000000..3bb408e
--- /dev/null
+++ b/tests/debiased_lasso/comparison_unscaled.R
@@ -0,0 +1,78 @@
+source('javanmard_montanari.R')
+
+##############################################
+
+# Runs nsims simulations under the global null, computing p-values
+# using both the old code (slow one using Adel's code) and the new
+# code (faster using Jon's code), and produces qq-plots for both.
+# Runing 50 sims takes about 10-15 mins because old code is slow, so
+# feel free to lower nsims if you want
+
+
+library(selectiveInference)
+library(glmnet)
+
+# set.seed(424)
+
+n=100
+p=200
+
+sigma=.5
+
+theor_lambda = sigma * sqrt(2 * log(p))
+lambda=c(0.25, 0.5, 1, 0.8 * theor_lambda, theor_lambda)
+
+for (j in c(3,4,5,1,2)) {
+
+thresh = 1e-10
+
+beta=rep(0,p)
+type="full"
+
+nsim = 20
+
+scaling = sqrt(n)
+pvs_old = c()
+pvs_new <- c()
+pvs_old_0 = c()   # don't add the offset correction
+pvs_new_0 = c()   # don't add the offset correction
+for (i in 1:nsim) {
+  cat(i,fill=T)
+  x = matrix(rnorm(n*p),n,p)
+  x = scale(x,T,T) / scaling
+  mu = x%*%beta 
+  y=mu+sigma*rnorm(n)
+
+  # first run  glmnet
+  gfit=glmnet(x,y,intercept=F,standardize=F,thresh=thresh)
+
+  bhat = coef(gfit, s=lambda[j]/(sqrt(n) * scaling), exact=TRUE,x=x,y=y)[-1]
+
+  if(sum(bhat != 0) > 0) {
+
+  # compute fixed lambda p-values and selection intervals
+
+  aa = fixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type)
+  bb = oldFixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type)
+  cc = fixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type, offset_correction=FALSE)
+  dd = oldFixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type, offset_correction=FALSE)
+  pvs_new <- c(pvs_new, aa$pv, recursive=TRUE)
+  pvs_old <- c(pvs_old, bb$pv,recursive=TRUE)
+  pvs_new_0 <- c(pvs_new_0, cc$pv, recursive=TRUE)
+  pvs_old_0 <- c(pvs_old_0, dd$pv, recursive=TRUE)
+
+  cat()
+  }
+}
+
+#check uniformity 
+
+png(paste('comparison_unscaled', j, '.png', sep=''))
+plot(ecdf(pvs_old), pch=23, col='green', xlim=c(0,1), ylim=c(0,1), main='ECDF of p-values')
+plot(ecdf(pvs_new), pch=24, col='purple', add=TRUE)
+plot(ecdf(pvs_old_0), pch=23, col='red', add=TRUE)
+plot(ecdf(pvs_new_0), pch=24, col='black', add=TRUE)
+abline(0,1)
+legend("bottomright", legend=c("Old","New", "Old 0", "New 0"), pch=c(23,24,23,24), pt.bg=c("green","purple","red","black"))
+dev.off()
+}
\ No newline at end of file
diff --git a/tests/debiased_lasso/javanmard_montanari.R b/tests/debiased_lasso/javanmard_montanari.R
new file mode 100644
index 0000000..09f3355
--- /dev/null
+++ b/tests/debiased_lasso/javanmard_montanari.R
@@ -0,0 +1,770 @@
+# First part is only functions from the old code. At the bottom is
+# the bit of code that actually compares the old vs new code
+
+######################################################
+
+### Old code (using Adel's R code)
+
+## Approximates inverse covariance matrix theta
+InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e-10, verbose = TRUE) {
+  isgiven <- 1;
+  if (is.null(mu)){
+    isgiven <- 0;
+  }
+  
+  p <- nrow(sigma);
+  M <- matrix(0, p, p);
+  xperc = 0;
+  xp = round(p/10);
+  for (i in 1:p) {
+    if ((i %% xp)==0){
+      xperc = xperc+10;
+      if (verbose) {
+        print(paste(xperc,"% done",sep="")); }
+    }
+    if (isgiven==0){
+      mu <- (1/sqrt(n)) * qnorm(1-(0.1/(p^2)));
+    }
+    mu.stop <- 0;
+    try.no <- 1;
+    incr <- 0;
+    while ((mu.stop != 1)&&(try.no<10)){
+      last.beta <- beta
+      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
+      beta <- output$optsol
+      iter <- output$iter
+      if (isgiven==1){
+        mu.stop <- 1
+      }
+      else{
+        if (try.no==1){
+          if (iter == (maxiter+1)){
+            incr <- 1;
+            mu <- mu*resol;
+          } else {
+            incr <- 0;
+            mu <- mu/resol;
+          }
+        }
+        if (try.no > 1){
+          if ((incr == 1)&&(iter == (maxiter+1))){
+            mu <- mu*resol;
+          }
+          if ((incr == 1)&&(iter < (maxiter+1))){
+            mu.stop <- 1;
+          }
+          if ((incr == 0)&&(iter < (maxiter+1))){
+            mu <- mu/resol;
+          }
+          if ((incr == 0)&&(iter == (maxiter+1))){
+            mu <- mu*resol;
+            beta <- last.beta;
+            mu.stop <- 1;
+          }
+        }
+      }
+      try.no <- try.no+1
+    }
+    M[i,] <- beta;
+  }
+  return(M)
+}
+
+InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-10) {
+  p <- nrow(sigma);
+  rho <- max(abs(sigma[i,-i])) / sigma[i,i];
+  mu0 <- rho/(1+rho);
+  beta <- rep(0,p);
+  
+  #if (mu >= mu0){
+  #  beta[i] <- (1-mu0)/sigma[i,i];
+  #  returnlist <- list("optsol" = beta, "iter" = 0);
+  #  return(returnlist);
+  #}
+  
+  diff.norm2 <- 1;
+  last.norm2 <- 1;
+  iter <- 1;
+  iter.old <- 1;
+  beta[i] <- (1-mu0)/sigma[i,i];
+  beta.old <- beta;
+  sigma.tilde <- sigma;
+  diag(sigma.tilde) <- 0;
+  vs <- -sigma.tilde%*%beta;
+  
+  while ((iter <= maxiter) && (diff.norm2 >= threshold*last.norm2)){
+    
+    for (j in 1:p){
+      oldval <- beta[j];
+      v <- vs[j];
+      if (j==i)
+        v <- v+1;
+      beta[j] <- SoftThreshold(v,mu)/sigma[j,j];
+      if (oldval != beta[j]){
+        vs <- vs + (oldval-beta[j])*sigma.tilde[,j];
+      }
+    }
+    
+    iter <- iter + 1;
+    if (iter==2*iter.old){
+      d <- beta - beta.old;
+      diff.norm2 <- sqrt(sum(d*d));
+      last.norm2 <-sqrt(sum(beta*beta));
+      iter.old <- iter;
+      beta.old <- beta;
+      #if (iter>10)
+      #  vs <- -sigma.tilde%*%beta;
+    }
+
+    # print(c(iter, maxiter, diff.norm2, threshold * last.norm2, threshold, mu))
+
+  }
+  
+  returnlist <- list("optsol" = beta, "iter" = iter)
+  return(returnlist)
+}
+
+SoftThreshold <- function( x, lambda ) {
+  #
+  # Standard soft thresholding
+  #
+  if (x>lambda){
+    return (x-lambda);}
+  else {
+    if (x< (-lambda)){
+      return (x+lambda);}
+    else {
+      return (0); }
+  }
+}
+
+
+### Functions borrowed from selective Inference (only fixedLassoInf and fixedLasso.poly are modified)
+
+# Special linear time order function, works only when x
+# is a scrambled vector of integers.
+
+Order <- function(x) {
+  n = length(x)
+  o = numeric(n)
+  o[x] = Seq(1,n)
+  return(o)
+}
+
+# Returns a sequence of integers from a to b if a <= b,
+# otherwise nothing. You have no idea how important this
+# function is...
+
+Seq <- function(a, b, ...) {
+  if (a<=b) return(seq(a,b,...))
+  else return(numeric(0))
+}
+
+# Returns the sign of x, with Sign(0)=1.
+
+Sign <- function(x) {
+  return(-1+2*(x>=0))
+}
+
+##############################
+
+# Centering and scaling convenience function
+
+standardize <- function(x, y, intercept, normalize) {
+  x = as.matrix(x)
+  y = as.numeric(y)
+  n = nrow(x)
+  p = ncol(x)
+  
+  if (intercept) {
+    bx = colMeans(x)
+    by = mean(y)
+    x = scale(x,bx,FALSE)
+    y = y-mean(y)
+  } else {
+    bx = rep(0,p)
+    by = 0
+  }
+  if (normalize) {
+    sx = sqrt(colSums(x^2))
+    x = scale(x,FALSE,sx)
+  } else {
+    sx = rep(1,p)
+  }
+  
+  return(list(x=x,y=y,bx=bx,by=by,sx=sx))
+}
+
+##############################
+
+# Interpolation function to get coefficients
+
+coef.interpolate <- function(betas, s, knots, dec=TRUE) {
+  # Sort the s values
+  o = order(s,dec=dec)
+  s = s[o]
+  
+  k = length(s)
+  mat = matrix(rep(knots,each=k),nrow=k)
+  if (dec) b = s >= mat
+  else b = s <= mat
+  blo = max.col(b,ties.method="first")
+  bhi = pmax(blo-1,1)
+  
+  i = bhi==blo
+  p = numeric(k)
+  p[i] = 0
+  p[!i] = ((s-knots[blo])/(knots[bhi]-knots[blo]))[!i]
+  
+  beta = t((1-p)*t(betas[,blo,drop=FALSE]) + p*t(betas[,bhi,drop=FALSE]))
+  colnames(beta) = as.character(round(s,3))
+  rownames(beta) = NULL
+  
+  # Return in original order
+  o = order(o)
+  return(beta[,o,drop=FALSE])
+}
+
+##############################
+
+checkargs.xy <- function(x, y) {
+  if (missing(x)) stop("x is missing")
+  if (is.null(x) || !is.matrix(x)) stop("x must be a matrix")
+  if (missing(y)) stop("y is missing")
+  if (is.null(y) || !is.numeric(y)) stop("y must be numeric")
+  if (ncol(x) == 0) stop("There must be at least one predictor [must have ncol(x) > 0]")
+  if (checkcols(x)) stop("x cannot have duplicate columns")
+  if (length(y) == 0) stop("There must be at least one data point [must have length(y) > 0]")
+  if (length(y)!=nrow(x)) stop("Dimensions don't match [length(y) != nrow(x)]")
+}
+
+checkargs.misc <- function(sigma=NULL, alpha=NULL, k=NULL,
+                           gridrange=NULL, gridpts=NULL, griddepth=NULL,
+                           mult=NULL, ntimes=NULL,
+                           beta=NULL, lambda=NULL, tol.beta=NULL, tol.kkt=NULL,
+                           bh.q=NULL) {
+  
+  if (!is.null(sigma) && sigma <= 0) stop("sigma must be > 0")
+  if (!is.null(lambda) && lambda < 0) stop("lambda must be >= 0")
+  if (!is.null(alpha) && (alpha <= 0 || alpha >= 1)) stop("alpha must be between 0 and 1")
+  if (!is.null(k) && length(k) != 1) stop("k must be a single number")
+  if (!is.null(k) && (k < 1 || k != floor(k))) stop("k must be an integer >= 1")
+  if (!is.null(gridrange) && (length(gridrange) != 2 || gridrange[1] > gridrange[2]))
+    stop("gridrange must be an interval of the form c(a,b) with a <= b")
+  if (!is.null(gridpts) && (gridpts < 20 || gridpts != round(gridpts)))
+    stop("gridpts must be an integer >= 20")
+  if (!is.null(griddepth) && (griddepth > 10 || griddepth != round(griddepth)))
+    stop("griddepth must be an integer <= 10")
+  if (!is.null(mult) && mult < 0) stop("mult must be >= 0")
+  if (!is.null(ntimes) && (ntimes <= 0 || ntimes != round(ntimes)))
+    stop("ntimes must be an integer > 0")
+  if (!is.null(beta) && sum(beta!=0)==0) stop("Value of lambda too large, beta is zero")
+  # if (!is.null(lambda) && length(lambda) != 1) stop("lambda must be a single number")
+  if (!is.null(lambda) && length(lambda) != 1 && length(lambda) != length(beta)) stop("lambda must be a single number or equal to the length of beta")
+  if (!is.null(lambda) && lambda < 0) stop("lambda must be >=0")
+  if (!is.null(tol.beta) && tol.beta <= 0) stop("tol.beta must be > 0")
+  if (!is.null(tol.kkt) && tol.kkt <= 0) stop("tol.kkt must be > 0")
+}
+
+# Make sure that no two columms of A are the same
+# (this works with probability one).
+
+checkcols <- function(A) {
+  b = rnorm(nrow(A))
+  a = sort(t(A)%*%b)
+  if (any(diff(a)==0)) return(TRUE)
+  return(FALSE)
+}
+
+estimateSigma <- function(x, y, intercept=TRUE, standardize=TRUE) {
+  checkargs.xy(x,rep(0,nrow(x)))
+  if(nrow(x)<10) stop("Number of observations must be at least 10 to run estimateSigma")
+  cvfit=cv.glmnet(x,y,intercept=intercept,standardize=standardize)
+  lamhat=cvfit$lambda.min
+  fit=glmnet(x,y,standardize=standardize)
+  yhat=predict(fit,x,s=lamhat)
+  nz=sum(predict(fit,s=lamhat, type="coef")!=0)
+  sigma=sqrt(sum((y-yhat)^2)/(length(y)-nz-1))
+  return(list(sigmahat=sigma, df=nz))
+}
+
+# Update the QR factorization, after a column has been
+# added. Here Q1 is m x n, Q2 is m x k, and R is n x n.
+
+updateQR <- function(Q1,Q2,R,col) {
+  m = nrow(Q1)
+  n = ncol(Q1)
+  k = ncol(Q2)
+  
+  a = .C("update1",
+         Q2=as.double(Q2),
+         w=as.double(t(Q2)%*%col),
+         m=as.integer(m),
+         k=as.integer(k),
+         dup=FALSE,
+         package="selectiveInference")
+  
+  Q2 = matrix(a$Q2,nrow=m)
+  w = c(t(Q1)%*%col,a$w)
+  
+  # Re-structure: delete a column from Q2, add one to
+  # Q1, and expand R
+  Q1 = cbind(Q1,Q2[,1])
+  Q2 = Q2[,-1,drop=FALSE]
+  R = rbind(R,rep(0,n))
+  R = cbind(R,w[Seq(1,n+1)])
+  
+  return(list(Q1=Q1,Q2=Q2,R=R))
+}
+
+# Moore-Penrose pseudo inverse for symmetric matrices
+
+pinv <- function(A, tol=.Machine$double.eps) {
+  e = eigen(A)
+  v = Re(e$vec)
+  d = Re(e$val)
+  d[d > tol] = 1/d[d > tol]
+  d[d < tol] = 0
+  if (length(d)==1) return(v*d*v)
+  else return(v %*% diag(d) %*% t(v))
+}
+
+##############################
+
+# Assuming that grid is in sorted order from smallest to largest,
+# and vals are monotonically increasing function values over the
+# grid, returns the grid end points such that the corresponding
+# vals are approximately equal to {val1, val2}
+
+grid.search <- function(grid, fun, val1, val2, gridpts=100, griddepth=2) {
+  n = length(grid)
+  vals = fun(grid)
+  
+  ii = which(vals >= val1)
+  jj = which(vals <= val2)
+  if (length(ii)==0) return(c(grid[n],Inf))   # All vals < val1
+  if (length(jj)==0) return(c(-Inf,grid[1]))  # All vals > val2
+  # RJT: the above logic is correct ... but for simplicity, instead,
+  # we could just return c(-Inf,Inf) 
+  
+  i1 = min(ii); i2 = max(jj)
+  if (i1==1) lo = -Inf
+  else lo = grid.bsearch(grid[i1-1],grid[i1],fun,val1,gridpts,
+                         griddepth-1,below=TRUE)
+  if (i2==n) hi = Inf
+  else hi = grid.bsearch(grid[i2],grid[i2+1],fun,val2,gridpts,
+                         griddepth-1,below=FALSE)
+  return(c(lo,hi))
+}
+
+# Repeated bin search to find the point x in the interval [left, right]
+# that satisfies f(x) approx equal to val. If below=TRUE, then we seek
+# x such that the above holds and f(x) <= val; else we seek f(x) >= val.
+
+grid.bsearch <- function(left, right, fun, val, gridpts=100, griddepth=1, below=TRUE) {
+  n = gridpts
+  depth = 1
+  
+  while (depth <= griddepth) {
+    grid = seq(left,right,length=n)
+    vals = fun(grid)
+    
+    if (below) {
+      ii = which(vals >= val)
+      if (length(ii)==0) return(grid[n])   # All vals < val (shouldn't happen)
+      if ((i0=min(ii))==1) return(grid[1]) # All vals > val (shouldn't happen)
+      left = grid[i0-1]
+      right = grid[i0]
+    }
+    
+    else {
+      ii = which(vals <= val)
+      if (length(ii)==0) return(grid[1])   # All vals > val (shouldn't happen)
+      if ((i0=max(ii))==n) return(grid[n]) # All vals < val (shouldn't happen)
+      left = grid[i0]
+      right = grid[i0+1]
+    }
+    
+    depth = depth+1
+  }
+  
+  return(ifelse(below, left, right))
+}
+
+# Returns Prob(Z>z | Z in [a,b]), where mean can be a vector
+
+tnorm.surv <- function(z, mean, sd, a, b, bits=NULL) {
+  z = max(min(z,b),a)
+  
+  # Check silly boundary cases
+  p = numeric(length(mean))
+  p[mean==-Inf] = 0
+  p[mean==Inf] = 1
+  
+  # Try the multi precision floating point calculation first
+  o = is.finite(mean)
+  mm = mean[o]
+  pp = mpfr.tnorm.surv(z,mm,sd,a,b,bits) 
+  
+  # If there are any NAs, then settle for an approximation
+  oo = is.na(pp)
+  if (any(oo)) pp[oo] = bryc.tnorm.surv(z,mm[oo],sd,a,b)
+  
+  p[o] = pp
+  return(p)
+}
+
+# Returns Prob(Z>z | Z in [a,b]), where mean cane be a vector, using
+# multi precision floating point calculations thanks to the Rmpfr package
+
+mpfr.tnorm.surv <- function(z, mean=0, sd=1, a, b, bits=NULL) {
+  # If bits is not NULL, then we are supposed to be using Rmpf
+  # (note that this was fail if Rmpfr is not installed; but
+  # by the time this function is being executed, this should
+  # have been properly checked at a higher level; and if Rmpfr
+  # is not installed, bits would have been previously set to NULL)
+  if (!is.null(bits)) {
+    z = Rmpfr::mpfr((z-mean)/sd, precBits=bits)
+    a = Rmpfr::mpfr((a-mean)/sd, precBits=bits)
+    b = Rmpfr::mpfr((b-mean)/sd, precBits=bits)
+    return(as.numeric((Rmpfr::pnorm(b)-Rmpfr::pnorm(z))/
+                        (Rmpfr::pnorm(b)-Rmpfr::pnorm(a))))
+  }
+  
+  # Else, just use standard floating point calculations
+  z = (z-mean)/sd
+  a = (a-mean)/sd
+  b = (b-mean)/sd
+  return((pnorm(b)-pnorm(z))/(pnorm(b)-pnorm(a)))
+}
+
+# Returns Prob(Z>z | Z in [a,b]), where mean can be a vector, based on
+# A UNIFORM APPROXIMATION TO THE RIGHT NORMAL TAIL INTEGRAL, W Bryc
+# Applied Mathematics and Computation
+# Volume 127, Issues 23, 15 April 2002, Pages 365--374
+# https://math.uc.edu/~brycw/preprint/z-tail/z-tail.pdf
+
+bryc.tnorm.surv <- function(z, mean=0, sd=1, a, b) {
+  z = (z-mean)/sd
+  a = (a-mean)/sd
+  b = (b-mean)/sd
+  n = length(mean)
+  
+  term1 = exp(z*z)
+  o = a > -Inf
+  term1[o] = ff(a[o])*exp(-(a[o]^2-z[o]^2)/2)
+  term2 = rep(0,n)
+  oo = b < Inf
+  term2[oo] = ff(b[oo])*exp(-(b[oo]^2-z[oo]^2)/2)
+  p = (ff(z)-term2)/(term1-term2)
+  
+  # Sometimes the approximation can give wacky p-values,
+  # outside of [0,1] ..
+  #p[p<0 | p>1] = NA
+  p = pmin(1,pmax(0,p))
+  return(p)
+}
+
+ff <- function(z) {
+  return((z^2+5.575192695*z+12.7743632)/
+           (z^3*sqrt(2*pi)+14.38718147*z*z+31.53531977*z+2*12.77436324))
+}
+
+############## MODIFIED FUNCTIONS ###############
+
+# Lasso inference function (for fixed lambda). Note: here we are providing inference
+# for the solution of
+# min 1/2 || y - \beta_0 - X \beta ||_2^2 + \lambda || \beta ||_1
+
+oldFixedLassoInf <- function(x, y, beta, lambda, family=c("gaussian","binomial","cox"),intercept=TRUE, status=NULL,
+                          sigma=NULL, alpha=0.1,
+                          type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
+                          gridrange=c(-100,100), bits=NULL, verbose=FALSE, offset_correction=TRUE) {
+  
+  family = match.arg(family)
+  this.call = match.call()
+  type = match.arg(type)
+  
+  if(family=="binomial")  {
+    if(type!="partial") stop("Only type= partial allowed with binomial family")
+    out=fixedLogitLassoInf(x,y,beta,lambda,alpha=alpha, type="partial", tol.beta=tol.beta, tol.kkt=tol.kkt,
+                           gridrange=gridrange, bits=bits, verbose=verbose,this.call=this.call)
+    return(out)
+  }
+  else if(family=="cox")  {
+    if(type!="partial") stop("Only type= partial allowed with Cox family")
+    out=fixedCoxLassoInf(x,y,status,beta,lambda,alpha=alpha, type="partial",tol.beta=tol.beta,
+                         tol.kkt=tol.kkt, gridrange=gridrange, bits=bits, verbose=verbose,this.call=this.call)
+    return(out)
+  }
+  
+  else{
+    
+    
+    
+    checkargs.xy(x,y)
+    if (missing(beta) || is.null(beta)) stop("Must supply the solution beta")
+    if (missing(lambda) || is.null(lambda)) stop("Must supply the tuning parameter value lambda")
+    
+    n = nrow(x)
+    p = ncol(x)
+    beta = as.numeric(beta)
+    if (type == "full") {
+      if (p > n) {
+        # need intercept (if there is one) for debiased lasso
+        hbeta = beta
+        if (intercept == T) {
+          if (length(beta) != p + 1) {
+            stop("Since type='full', p > n, and intercept=TRUE, beta must have length equal to ncol(x)+1")
+          }
+          # remove intercept if included
+          beta = beta[-1]
+        } else if (length(beta) != p) {
+          stop("Since family='gaussian', type='full' and intercept=FALSE, beta must have length equal to ncol(x)")
+        }
+      }
+    } else if (length(beta) != p) {
+      stop("Since family='gaussian' and type='partial', beta must have length equal to ncol(x)")
+    }
+    
+    checkargs.misc(beta=beta,lambda=lambda,sigma=sigma,alpha=alpha,
+                   gridrange=gridrange,tol.beta=tol.beta,tol.kkt=tol.kkt)
+    if (!is.null(bits) && !requireNamespace("Rmpfr",quietly=TRUE)) {
+      warning("Package Rmpfr is not installed, reverting to standard precision")
+      bits = NULL
+    }
+    
+    # If glmnet was run with an intercept term, center x and y
+    if (intercept==TRUE) {
+      obj = standardize(x,y,TRUE,FALSE)
+      x = obj$x
+      y = obj$y
+    }
+    
+    # Check the KKT conditions
+    g = t(x)%*%(y-x%*%beta) / lambda
+    if (any(abs(g) > 1+tol.kkt * sqrt(sum(y^2))))
+      warning(paste("Solution beta does not satisfy the KKT conditions",
+                    "(to within specified tolerances)"))
+    
+    tol.coef = tol.beta * sqrt(n^2 / colSums(x^2))
+    # print(tol.coef)
+    vars = which(abs(beta) > tol.coef)
+    # print(beta)
+    # print(vars)
+    if(length(vars)==0){
+      cat("Empty model",fill=T)
+      return()
+    }
+    if (any(sign(g[vars]) != sign(beta[vars])))
+      warning(paste("Solution beta does not satisfy the KKT conditions",
+                    "(to within specified tolerances). You might try rerunning",
+                    "glmnet with a lower setting of the",
+                    "'thresh' parameter, for a more accurate convergence."))
+    
+    # Get lasso polyhedral region, of form Gy >= u
+    if (type == 'full' & p > n) out = fixedLasso.poly(x,y,beta,lambda,vars,inactive=TRUE)
+    else out = fixedLasso.poly(x,y,beta,lambda,vars)
+    G = out$G
+    u = out$u
+    
+    # Check polyhedral region
+    tol.poly = 0.01
+    if (min(G %*% y - u) < -tol.poly * sqrt(sum(y^2)))
+      stop(paste("Polyhedral constraints not satisfied; you must recompute beta",
+                 "more accurately. With glmnet, make sure to use exact=TRUE in coef(),",
+                 "and check whether the specified value of lambda is too small",
+                 "(beyond the grid of values visited by glmnet).",
+                 "You might also try rerunning glmnet with a lower setting of the",
+                 "'thresh' parameter, for a more accurate convergence."))
+    
+    # Estimate sigma
+    if (is.null(sigma)) {
+      if (n >= 2*p) {
+        oo = intercept
+        sigma = sqrt(sum(lsfit(x,y,intercept=oo)$res^2)/(n-p-oo))
+      }
+      else {
+        sigma = sd(y)
+        warning(paste(sprintf("p > n/2, and sd(y) = %0.3f used as an estimate of sigma;",sigma),
+                      "you may want to use the estimateSigma function"))
+      }
+    }
+    
+    k = length(vars)
+    pv = vlo = vup = numeric(k)
+    vmat = matrix(0,k,n)
+    ci = tailarea = matrix(0,k,2)
+    sign = numeric(k)
+    
+    if (type=="full" & p > n) {
+      if (intercept == T) {
+        pp=p+1
+        Xint <- cbind(rep(1,n),x)
+        # indices of selected predictors
+        S = c(1,vars + 1)
+        notS = which(abs(beta) <= tol.coef) + 1
+      } else {
+        pp=p
+        Xint <- x
+        # indices of selected predictors
+        S = vars
+        notS = which(abs(beta) <= tol.coef)
+      }
+      
+      
+      XS = Xint[,S]
+      hbetaS = hbeta[S]
+      
+      # Reorder so that active set S is first
+      Xordered = Xint[,c(S,notS,recursive=T)]
+
+      hsigma <- 1/n*(t(Xordered)%*%Xordered)
+      hsigmaS <- 1/n*(t(XS)%*%XS) # hsigma[S,S]
+      hsigmaSinv <- pinv(hsigmaS) # solve(hsigmaS)
+      
+      # Approximate inverse covariance matrix for when (n < p) from lasso_Inference.R
+      htheta <- InverseLinfty(hsigma, n, verbose=FALSE)
+      
+      # 0-padding matrix
+      FS = rbind(diag(length(S)),matrix(0,pp-length(S),length(S)))
+      ithetasigma = (diag(pp)-(htheta%*%hsigma))
+
+      M <- (((htheta%*%t(Xordered))+ithetasigma%*%FS%*%hsigmaSinv%*%t(XS))/n)
+      # vector which is offset for testing debiased beta's
+      meanoffset <- -(((ithetasigma%*%FS%*%hsigmaSinv)%*%sign(hbetaS))*lambda/n)
+      if (intercept == T) {
+        M = M[-1,] # remove intercept row
+        meanoffset = meanoffset[-1] # remove intercept element
+      }
+      if (offset_correction == FALSE) {
+        meanoffset = 0 * meanoffset
+      }
+    } else if (type=="partial" || p > n) {
+      xa = x[,vars,drop=F]
+      M = pinv(crossprod(xa)) %*% t(xa)
+      meanoffset = rep(0,k)
+    } else {
+      M = pinv(crossprod(x)) %*% t(x)
+      M = M[vars,,drop=F]
+      meanoffset = rep(0,k)
+    }
+    
+    for (j in 1:k) {
+      if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
+      
+      vj = M[j,]
+      mj = sqrt(sum(vj^2))
+      vj = vj / mj        # Standardize (divide by norm of vj)
+      sign[j] = sign(sum(vj*y))
+      vj = sign[j] * vj
+
+      a = poly.pval(y,G,u,vj,offset=meanoffset[j],sigma,bits)
+      pv[j] = a$pv
+      vlo[j] = a$vlo * mj # Unstandardize (mult by norm of vj)
+      vup[j] = a$vup * mj # Unstandardize (mult by norm of vj)
+      vmat[j,] = vj * mj * sign[j]  # Unstandardize (mult by norm of vj)
+      
+      a = poly.int(y,G,u,vj,offset=meanoffset[j],sigma,alpha,gridrange=gridrange,
+                   flip=(sign[j]==-1),bits=bits)
+      ci[j,] = a$int * mj # Unstandardize (mult by norm of vj)
+      tailarea[j,] = a$tailarea
+    }
+    
+    out = list(type=type,lambda=lambda,pv=pv,ci=ci,
+               tailarea=tailarea,vlo=vlo,vup=vup,vmat=vmat,y=y,
+               vars=vars,sign=sign,sigma=sigma,alpha=alpha,
+               sd=sigma*sqrt(rowSums(vmat^2)),
+               coef0=vmat%*%y,
+               call=this.call,M=M)
+    class(out) = "fixedLassoInf"
+    return(out)
+  }
+}
+
+
+fixedLasso.poly=
+  function(x, y, beta, lambda, a, inactive = FALSE) {
+    xa = x[,a,drop=F]
+    xac = x[,!a,drop=F]
+    xai = pinv(crossprod(xa))
+    xap = xai %*% t(xa)
+    za = sign(beta[a])
+    if (length(za)>1) dz = diag(za)
+    if (length(za)==1) dz = matrix(za,1,1)
+    
+    if (inactive) {
+      P = diag(1,nrow(xa)) - xa %*% xap
+      
+      G = -rbind(
+        1/lambda * t(xac) %*% P,
+        -1/lambda * t(xac) %*% P,
+        -dz %*% xap
+      )
+      lambda2=lambda
+      if(length(lambda)>1) lambda2=lambda[a]
+      u = -c(
+        1 - t(xac) %*% t(xap) %*% za,
+        1 + t(xac) %*% t(xap) %*% za,
+        -lambda2 * dz %*% xai %*% za)
+    } else {
+      G = -rbind(
+        #   1/lambda * t(xac) %*% P,
+        # -1/lambda * t(xac) %*% P,
+        -dz %*% xap
+      )
+      lambda2=lambda
+      if(length(lambda)>1) lambda2=lambda[a]
+      u = -c(
+        #   1 - t(xac) %*% t(xap) %*% za,
+        #   1 + t(xac) %*% t(xap) %*% za,
+        -lambda2 * dz %*% xai %*% za)
+    }
+    
+    return(list(G=G,u=u))
+  }
+
+
+# Main p-value function
+
+poly.pval <- function(y, G, u, v, sigma, offset=0, bits=NULL) {
+  z = sum(v*y)
+  vv = sum(v^2)
+  sd = sigma*sqrt(vv)
+  
+  rho = G %*% v / vv
+  vec = (u - G %*% y + rho*z) / rho
+  vlo = suppressWarnings(max(vec[rho>0]))
+  vup = suppressWarnings(min(vec[rho<0]))
+  
+  pv = tnorm.surv(z,0-offset,sd,vlo,vup,bits)
+  return(list(pv=pv,vlo=vlo,vup=vup))
+}
+
+# Main confidence interval function
+
+poly.int <- function(y, G, u, v, sigma, alpha, offset=0, gridrange=c(-100,100),
+                     gridpts=100, griddepth=2, flip=FALSE, bits=NULL) {
+  
+  z = sum(v*y)
+  vv = sum(v^2)
+  sd = sigma*sqrt(vv)
+  
+  rho = G %*% v / vv
+  vec = (u - G %*% y + rho*z) / rho
+  vlo = suppressWarnings(max(vec[rho>0]))
+  vup = suppressWarnings(min(vec[rho<0]))
+  
+  xg = seq(gridrange[1]*sd,gridrange[2]*sd,length=gridpts)
+  fun = function(x) { tnorm.surv(z,x-offset,sd,vlo,vup,bits) }
+  
+  int = grid.search(xg,fun,alpha/2,1-alpha/2,gridpts,griddepth)
+  tailarea = c(fun(int[1]),1-fun(int[2]))
+  
+  if (flip) {
+    int = -int[2:1]
+    tailarea = tailarea[2:1]
+  }
+  
+  return(list(int=int,tailarea=tailarea))
+}
diff --git a/tests/test_debiasing.R b/tests/debiased_lasso/test_debiasing.R
similarity index 100%
rename from tests/test_debiasing.R
rename to tests/debiased_lasso/test_debiasing.R
diff --git a/tests/debiased_lasso/test_debiasing_wide.R b/tests/debiased_lasso/test_debiasing_wide.R
new file mode 100644
index 0000000..62801da
--- /dev/null
+++ b/tests/debiased_lasso/test_debiasing_wide.R
@@ -0,0 +1,202 @@
+library(selectiveInference)
+
+
+## Approximates inverse covariance matrix theta
+InverseLinfty <- function(sigma, n, resol=1.5, bound=NULL, maxiter=50, threshold=1e-10, verbose = TRUE) {
+  isgiven <- 1;
+  if (is.null(bound)){
+    isgiven <- 0;
+  }
+  
+  p <- nrow(sigma);
+  M <- matrix(0, p, p);
+  xperc = 0;
+  xp = round(p/10);
+  for (i in 1:p) {
+    if ((i %% xp)==0){
+      xperc = xperc+10;
+      if (verbose) {
+        print(paste(xperc,"% done",sep="")); }
+    }
+    if (isgiven==0){
+      bound <- (1/sqrt(n)) * qnorm(1-(0.1/(p^2)));
+    }
+    bound.stop <- 0;
+    try.no <- 1;
+    incr <- 0;
+    while ((bound.stop != 1)&&(try.no<10)){
+      last.beta <- beta
+      output <- InverseLinftyOneRow(sigma, i, bound, maxiter=maxiter, threshold=threshold)
+      beta <- output$optsol
+      iter <- output$iter
+      if (isgiven==1){
+        bound.stop <- 1
+      }
+      else{
+        if (try.no==1){
+          if (iter == (maxiter+1)){
+            incr <- 1;
+            bound <- bound*resol;
+          } else {
+            incr <- 0;
+            bound <- bound/resol;
+          }
+        }
+        if (try.no > 1){
+          if ((incr == 1)&&(iter == (maxiter+1))){
+            bound <- bound*resol;
+          }
+          if ((incr == 1)&&(iter < (maxiter+1))){
+            bound.stop <- 1;
+          }
+          if ((incr == 0)&&(iter < (maxiter+1))){
+            bound <- bound/resol;
+          }
+          if ((incr == 0)&&(iter == (maxiter+1))){
+            bound <- bound*resol;
+            beta <- last.beta;
+            bound.stop <- 1;
+          }
+        }
+      }
+      try.no <- try.no+1
+    }
+    M[i,] <- beta;
+  }
+  return(M)
+}
+
+InverseLinftyOneRow <- function ( sigma, i, bound, maxiter=50, threshold=1e-10) {
+  p <- nrow(sigma);
+  rho <- max(abs(sigma[i,-i])) / sigma[i,i];
+  bound0 <- rho/(1+rho);
+  beta <- rep(0,p);
+  
+  #if (bound >= bound0){
+  #  beta[i] <- (1-bound0)/sigma[i,i];
+  #  returnlist <- list("optsol" = beta, "iter" = 0);
+  #  return(returnlist);
+  #}
+  
+  diff.norm2 <- 1;
+  last.norm2 <- 1;
+  iter <- 1;
+  iter.old <- 1;
+  beta[i] <- (1-bound0)/sigma[i,i];
+  beta.old <- beta;
+  sigma.tilde <- sigma;
+  diag(sigma.tilde) <- 0;
+  vs <- -sigma.tilde%*%beta;
+  
+  while ((iter <= maxiter) && (diff.norm2 >= threshold*last.norm2)){
+    
+    for (j in 1:p){
+      oldval <- beta[j];
+      v <- vs[j];
+      if (j==i)
+        v <- v+1;
+      beta[j] <- SoftThreshold(v,bound)/sigma[j,j];
+      if (oldval != beta[j]){
+        vs <- vs + (oldval-beta[j])*sigma.tilde[,j];
+      }
+    }
+    
+    iter <- iter + 1;
+    if (iter==2*iter.old){
+      d <- beta - beta.old;
+      diff.norm2 <- sqrt(sum(d*d));
+      last.norm2 <-sqrt(sum(beta*beta));
+      iter.old <- iter;
+      beta.old <- beta;
+      #if (iter>10)
+      #  vs <- -sigma.tilde%*%beta;
+    }
+
+    # print(c(iter, maxiter, diff.norm2, threshold * last.norm2, threshold, bound))
+
+  }
+  
+  returnlist <- list("optsol" = beta, "iter" = iter)
+  return(returnlist)
+}
+
+SoftThreshold <- function( x, lambda ) {
+  #
+  # Standard soft thresholding
+  #
+  if (x>lambda){
+    return (x-lambda);}
+  else {
+    if (x< (-lambda)){
+      return (x+lambda);}
+    else {
+      return (0); }
+  }
+}
+
+
+### Test
+
+n = 100; p = 250
+
+X = matrix(rnorm(n * p), n, p)
+S = t(X) %*% X / n
+
+debiasing_bound = 0.2
+
+tol = 1.e-12
+
+rows = as.integer(c(1:2))
+
+A1 = debiasingMatrix(S, FALSE, n, rows, bound=debiasing_bound, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol, linesearch=FALSE)
+A2 = debiasingMatrix(S / n, FALSE, n, rows, bound=debiasing_bound, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol, linesearch=FALSE)
+B1 = debiasingMatrix(X, TRUE, n, rows, bound=debiasing_bound, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol, linesearch=FALSE)
+B2 = debiasingMatrix(X / sqrt(n), TRUE, n, rows, bound=debiasing_bound, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol, linesearch=FALSE)
+
+C1 = InverseLinfty(S, n, bound=debiasing_bound, maxiter=1000)[rows,]
+C2 = InverseLinfty(S / n, n, bound=debiasing_bound, maxiter=1000)[rows,]
+
+par(mfrow=c(2,3))
+
+plot(A1[1,], C1[1,])
+plot(A1[1,], B1[1,])
+plot(B1[1,], C1[1,])
+
+plot(A1[1,], A2[1,])
+plot(B1[1,], B2[1,])
+plot(C1[1,], C2[1,])
+
+print(c('A', sum(A1[1,] == 0)))
+print(c('B', sum(B1[1,] == 0)))
+print(c('C', sum(C1[1,] == 0)))
+
+## Are our points feasible
+
+feasibility = function(S, soln, j, debiasing_bound) {
+     p = nrow(S)
+     E = rep(0, p)
+     E[j] = 1
+     G = S %*% soln - E
+     return(c(max(abs(G)), debiasing_bound))
+}
+
+print(c('feasibility A', feasibility(S, A1[1,], 1, debiasing_bound)))
+print(c('feasibility B', feasibility(S, B1[1,], 1, debiasing_bound)))
+print(c('feasibility C', feasibility(S, C1[1,], 1, debiasing_bound)))
+
+active_KKT = function(S, soln, j, debiasing_bound) {
+     p = nrow(S)
+     E = rep(0, p)
+     E[j] = 1
+     G = S %*% soln - E
+     print(which(soln != 0))
+     print(G[j])
+     return(c(G[soln != 0] * sign(soln)[soln != 0], debiasing_bound))
+}
+
+print(c('active_KKT A', active_KKT(S, A1[1,], 1, debiasing_bound)))
+print(c('active_KKT B', active_KKT(S, B1[1,], 1, debiasing_bound)))
+print(c('active_KKT C', active_KKT(S, C1[1,], 1, debiasing_bound)))
+
+
+print(summary(lm(A1[1,] ~ C1[1,])))
\ No newline at end of file

From e3049cffa9892b96e8122cbde31d9e83ebf147bd Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 27 Oct 2017 10:36:58 -0700
Subject: [PATCH 310/493] removing the offset_correction which was just for the
 comparison

---
 selectiveInference/R/funs.fixed.R          |  5 +----
 tests/debiased_lasso/comparison_scaled.R   | 13 +++----------
 tests/debiased_lasso/comparison_unscaled.R | 13 +++----------
 3 files changed, 7 insertions(+), 24 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 7c8318d..d5e3d64 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -8,7 +8,7 @@ fixedLassoInf <- function(x, y, beta,
                           sigma=NULL, alpha=0.1,
                           type=c("partial", "full"), tol.beta=1e-5, tol.kkt=0.1,
                           gridrange=c(-100,100), bits=NULL, verbose=FALSE, 
-                          linesearch.try=10, offset_correction=TRUE) {
+                          linesearch.try=10) {
 
   family = match.arg(family)
   this.call = match.call()
@@ -197,9 +197,6 @@ fixedLassoInf <- function(x, y, beta,
         M = M[-1,] # remove intercept row
         null_value = null_value[-1] # remove intercept element
       }
-      if (!offset_correction) {
-        null_value = 0 * null_value
-      }
     } else if (type=="partial" || p > n) {
       xa = x[,vars,drop=F]
       M = pinv(crossprod(xa)) %*% t(xa)
diff --git a/tests/debiased_lasso/comparison_scaled.R b/tests/debiased_lasso/comparison_scaled.R
index e0c0a6a..e296bd6 100644
--- a/tests/debiased_lasso/comparison_scaled.R
+++ b/tests/debiased_lasso/comparison_scaled.R
@@ -19,10 +19,9 @@ p=200
 
 sigma=.5
 
-theor_lambda = sigma * sqrt(2 * log(p))
-lambda=c(0.25, 0.5, 1, 0.8 * theor_lambda, theor_lambda)
+lambda=c(0.25, 0.5, 1)
 
-for (j in c(3,4,5,1,2)) {
+for (j in c(3,2,1)) {
 
 thresh = 1e-10
 
@@ -54,12 +53,8 @@ for (i in 1:nsim) {
 
   aa = fixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type)
   bb = oldFixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type)
-  cc = fixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type, offset_correction=FALSE)
-  dd = oldFixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type, offset_correction=FALSE)
   pvs_new <- c(pvs_new, aa$pv, recursive=TRUE)
   pvs_old <- c(pvs_old, bb$pv,recursive=TRUE)
-  pvs_new_0 <- c(pvs_new_0, cc$pv, recursive=TRUE)
-  pvs_old_0 <- c(pvs_old_0, dd$pv, recursive=TRUE)
 
   cat()
   }
@@ -70,9 +65,7 @@ for (i in 1:nsim) {
 png(paste('comparison_scaled', j, '.png', sep=''))
 plot(ecdf(pvs_old), pch=23, col='green', xlim=c(0,1), ylim=c(0,1), main='ECDF of p-values')
 plot(ecdf(pvs_new), pch=24, col='purple', add=TRUE)
-plot(ecdf(pvs_old_0), pch=23, col='red', add=TRUE)
-plot(ecdf(pvs_new_0), pch=24, col='black', add=TRUE)
 abline(0,1)
-legend("bottomright", legend=c("Old","New", "Old 0", "New 0"), pch=c(23,24,23,24), pt.bg=c("green","purple","red","black"))
+legend("bottomright", legend=c("Old", "New"), pch=c(23,24), pt.bg=c("green","purple"))
 dev.off()
 }
\ No newline at end of file
diff --git a/tests/debiased_lasso/comparison_unscaled.R b/tests/debiased_lasso/comparison_unscaled.R
index 3bb408e..eebda68 100644
--- a/tests/debiased_lasso/comparison_unscaled.R
+++ b/tests/debiased_lasso/comparison_unscaled.R
@@ -19,10 +19,9 @@ p=200
 
 sigma=.5
 
-theor_lambda = sigma * sqrt(2 * log(p))
-lambda=c(0.25, 0.5, 1, 0.8 * theor_lambda, theor_lambda)
+lambda=c(0.25, 0.5, 1)
 
-for (j in c(3,4,5,1,2)) {
+for (j in c(3,2,1)) {
 
 thresh = 1e-10
 
@@ -54,12 +53,8 @@ for (i in 1:nsim) {
 
   aa = fixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type)
   bb = oldFixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type)
-  cc = fixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type, offset_correction=FALSE)
-  dd = oldFixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type, offset_correction=FALSE)
   pvs_new <- c(pvs_new, aa$pv, recursive=TRUE)
   pvs_old <- c(pvs_old, bb$pv,recursive=TRUE)
-  pvs_new_0 <- c(pvs_new_0, cc$pv, recursive=TRUE)
-  pvs_old_0 <- c(pvs_old_0, dd$pv, recursive=TRUE)
 
   cat()
   }
@@ -70,9 +65,7 @@ for (i in 1:nsim) {
 png(paste('comparison_unscaled', j, '.png', sep=''))
 plot(ecdf(pvs_old), pch=23, col='green', xlim=c(0,1), ylim=c(0,1), main='ECDF of p-values')
 plot(ecdf(pvs_new), pch=24, col='purple', add=TRUE)
-plot(ecdf(pvs_old_0), pch=23, col='red', add=TRUE)
-plot(ecdf(pvs_new_0), pch=24, col='black', add=TRUE)
 abline(0,1)
-legend("bottomright", legend=c("Old","New", "Old 0", "New 0"), pch=c(23,24,23,24), pt.bg=c("green","purple","red","black"))
+legend("bottomright", legend=c("Old", "New"), pch=c(23,24), pt.bg=c("green","purple"))
 dev.off()
 }
\ No newline at end of file

From fc49dcd96d34040de96feca72174106df42d67d6 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 28 Oct 2017 11:13:25 -0700
Subject: [PATCH 311/493] created the matrices for the affine transform,
 wrapper for calling Gaussian density

---
 selectiveInference/R/funs.fixed.R          |   3 +-
 selectiveInference/R/funs.randomized.R     |  34 ++++
 selectiveInference/src/Rcpp-randomized.cpp |  40 +++++
 selectiveInference/src/randomized_lasso.c  |   2 +
 selectiveInference/src/randomized_lasso.c~ | 188 +++++++++++++++++++++
 selectiveInference/src/randomized_lasso.h  |   2 +-
 selectiveInference/src/randomized_lasso.h~ |  42 +++++
 7 files changed, 308 insertions(+), 3 deletions(-)
 create mode 100644 selectiveInference/src/Rcpp-randomized.cpp
 create mode 100644 selectiveInference/src/randomized_lasso.c~
 create mode 100644 selectiveInference/src/randomized_lasso.h~

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index d5e3d64..13d8b5e 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -435,8 +435,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
   last_output = NULL
 
   if (is_wide) {
-     n = nrow(Xinfo)
-     Xsoln = as.numeric(rep(0, n))
+     Xsoln = as.numeric(rep(0, nrow(Xinfo)))
   }
 
   while (counter_idx < max_try) {
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 3a171f7..1da73b2 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -37,6 +37,7 @@ randomizedLASSO = function(X,
     if (length(lam) == 1) {
        lam = rep(lam, p)
     }
+
     if (length(lam) != p) {
        stop("Lagrange parameter should be single float or of length ncol(X)")
     }    
@@ -65,5 +66,38 @@ randomizedLASSO = function(X,
 		           objective_stop,     # objective_stop
 			   kkt_stop,           # kkt_stop
 			   param_stop)         # param_stop
+
+    
+    sign_soln = sign(result$soln)
+
+    unpenalized = lam == 0
+    active = !unpenalized * (sign_soln != 0)
+    inactive = !unpenzlied * (sign_soln == 0)
+
+    unpenalized_set = which(unpenalized)
+    active_set = which(active)
+    inactive_set = which(inactive)
+
+    coef_term = t(X) %*% X[,c(unpenalized_set,  # the coefficients
+                              active_set)]
+    coef_term = coef_term %*% diag(c(rep(1, sum(unpenalized)), sign_soln[active]))  # coefficients are non-negative
+    coef_term[active,] = coef_term[active,] + ridge_term * diag(rep(1, sum(active)))  # ridge term
+
+    subgrad_term = cbind(matrix(0, sum(inactive), sum(active) + sum(unpenalized)),
+                         diag(rep(1, sum(inactive))))
+    linear_term = rbind(coef_term,
+                        subgrad_term)
+
+    offset_term = rep(0, p)
+    offset_term[active] = lam[active] * sign_soln[active]
+
+    
+
+    list(active_set = active_set,
+         inactive_set = inactive_set,
+         unpenalized_set = unpenalized_set,
+         sign_soln = sign_soln)
+         
+         
     return(result)
 }
diff --git a/selectiveInference/src/Rcpp-randomized.cpp b/selectiveInference/src/Rcpp-randomized.cpp
new file mode 100644
index 0000000..7887f32
--- /dev/null
+++ b/selectiveInference/src/Rcpp-randomized.cpp
@@ -0,0 +1,40 @@
+#include <Rcpp.h>                // need to include the main Rcpp header file 
+#include <randomized_lasso.h>    // where densities are defined
+
+// [[Rcpp::export]]
+Rcpp::NumericVector log_density_gaussian_(double noise_scale,                         // Scale of randomization
+					  Rcpp::NumericMatrix internal_linear,        // A_D -- linear part for data
+					  Rcpp::NumericMatrix internal_state,         // D -- data state -- matrix of shape (nopt, npts)
+					  Rcpp::NumericMatrix optimization_linear,    // A_O -- linear part for optimization variables
+					  Rcpp::NumericMatrix optimization_state,     // O -- optimization state -- matrix of shape (ninternal, npts)
+					  Rcpp::NumericMatrix offset) {               // h -- offset in affine transform -- "p" dimensional 
+
+  int npt = internal_state.ncol();         // Function is vectorized
+  if (optimization_state.ncol() != npt) {  // Assuming each column is an internal or opt state because arrays are column major
+    Rcpp::stop("Number of optimization samples should equal the number of (internally represented) data.");
+  }
+
+  int ndim = optimization_linear.nrow();  
+  if (internal_linear.nrow() != ndim) {  
+    Rcpp::stop("Dimension of optimization range should be the same as the dimension of the data range.");
+  } 
+  int ninternal = internal_linear.ncol();
+  int noptimization = optimization_linear.ncol();
+
+  Rcpp::NumericVector result(npt);
+
+  int ipt;
+  for (ipt=0; ipt<npt; ipt++) {
+    result[ipt] = log_density_gaussian(noise_scale,
+				       ndim,
+				       ninternal,
+				       noptimization,
+				       (double *) internal_linear.begin(),
+				       ((double *) internal_state.begin() + ipt * ninternal),
+				       (double *) optimization_linear.begin(),
+				       ((double *) optimization_state.begin() + ipt * noptimization),
+				       (double *) offset.begin());
+  }
+
+  return(result);
+}
diff --git a/selectiveInference/src/randomized_lasso.c b/selectiveInference/src/randomized_lasso.c
index 123c81d..1f396b1 100644
--- a/selectiveInference/src/randomized_lasso.c
+++ b/selectiveInference/src/randomized_lasso.c
@@ -11,6 +11,8 @@
 // Laplace is product of IID Laplace with scale noise_scale
 // Also evaluated at A_D D + A_O O + h
 
+// Matrices are assumed in column major order! 
+
 double log_density_gaussian(double noise_scale,             // Scale of randomization
 			    int ndim,                       // Number of features -- "p"
 			    int ninternal,                  // Dimension of internal data representation often 1
diff --git a/selectiveInference/src/randomized_lasso.c~ b/selectiveInference/src/randomized_lasso.c~
new file mode 100644
index 0000000..123c81d
--- /dev/null
+++ b/selectiveInference/src/randomized_lasso.c~
@@ -0,0 +1,188 @@
+#include <math.h> // for fabs
+
+// Augmented density for randomized LASSO after
+// Gaussian randomization
+
+// Described in https://arxiv.org/abs/1609.05609
+
+// Gaussian is product of IID N(0, noise_scale^2) density
+// Evaluated at A_D D + A_O O + h
+
+// Laplace is product of IID Laplace with scale noise_scale
+// Also evaluated at A_D D + A_O O + h
+
+double log_density_gaussian(double noise_scale,             // Scale of randomization
+			    int ndim,                       // Number of features -- "p"
+			    int ninternal,                  // Dimension of internal data representation often 1
+			    int noptimization,              // Dimension of optimization variables -- "p"
+			    double *internal_linear,        // A_D -- linear part for data
+			    double *internal_state,         // D -- data state
+			    double *optimization_linear,    // A_O -- linear part for optimization variables
+			    double *optimization_state,     // O -- optimization state
+			    double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double denom = 2 * noise_scale * noise_scale;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Internal (i.e. data) contribution
+    for (icol=0; icol<ninternal; icol++) {
+      
+      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
+      internal_state_ptr = ((double *) internal_state + icol);
+
+      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
+    }
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= (reconstruction * reconstruction) / denom;
+  }
+
+  return(value);
+}
+
+double log_density_laplace(double noise_scale,             // Scale of randomization
+			   int ndim,                       // Number of features -- "p"
+			   int ninternal,                  // Dimension of internal data representation often 1
+			   int noptimization,              // Dimension of optimization variables -- "p"
+			   double *internal_linear,        // A_D -- linear part for data
+			   double *internal_state,         // D -- data state
+			   double *optimization_linear,    // A_O -- linear part for optimization variables
+			   double *optimization_state,     // O -- optimization state
+			   double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Internal (i.e. data) contribution
+    for (icol=0; icol<ninternal; icol++) {
+      
+      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
+      internal_state_ptr = ((double *) internal_state + icol);
+
+      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
+    }
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= fabs(reconstruction) / noise_scale;
+  }
+
+  return(value);
+}
+
+// Keeping internal (data) state fixed
+
+double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
+					int ndim,                       // Number of features -- "p"
+					int noptimization,              // Dimension of optimization variables -- "p"
+					double *optimization_linear,    // A_O -- linear part for optimization variables
+					double *optimization_state,     // O -- optimization state
+					double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double denom = 2 * noise_scale * noise_scale;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= reconstruction * reconstruction / denom;
+  }
+
+  return(value);
+}
+
+double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
+				       int ndim,                       // Number of features -- "p"
+				       int noptimization,              // Dimension of optimization variables -- "p"
+				       double *optimization_linear,    // A_O -- linear part for optimization variables
+				       double *optimization_state,     // O -- optimization state
+				       double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= fabs(reconstruction) / noise_scale;
+  }
+
+  return(value);
+}
diff --git a/selectiveInference/src/randomized_lasso.h b/selectiveInference/src/randomized_lasso.h
index 1301b72..e29142d 100644
--- a/selectiveInference/src/randomized_lasso.h
+++ b/selectiveInference/src/randomized_lasso.h
@@ -35,7 +35,7 @@ double log_density_laplace_conditional(double noise_scale,             // Scale
 				       int noptimization,              // Dimension of optimization variables -- "p"
 				       double *optimization_linear,    // A_O -- linear part for optimization variables
 				       double *optimization_state,     // O -- optimization state
-				       double *offset).                // h -- offset in affine transform -- "p" dimensional 
+				       double *offset);                // h -- offset in affine transform -- "p" dimensional 
 
 #ifdef __cplusplus
 }  /* extern "C" */
diff --git a/selectiveInference/src/randomized_lasso.h~ b/selectiveInference/src/randomized_lasso.h~
new file mode 100644
index 0000000..1301b72
--- /dev/null
+++ b/selectiveInference/src/randomized_lasso.h~
@@ -0,0 +1,42 @@
+#ifdef __cplusplus
+extern "C"
+{
+#endif /* __cplusplus */
+
+double log_density_gaussian(double noise_scale,             // Scale of randomization
+			    int ndim,                       // Number of features -- "p"
+			    int ninternal,                  // Dimension of internal data representation often 1
+			    int noptimization,              // Dimension of optimization variables -- "p"
+			    double *internal_linear,        // A_D -- linear part for data
+			    double *internal_state,         // D -- data state
+			    double *optimization_linear,    // A_O -- linear part for optimization variables
+			    double *optimization_state,     // O -- optimization state
+			    double *offset);                // h -- offset in affine transform -- "p" dimensional 
+  
+double log_density_laplace(double noise_scale,             // Scale of randomization
+			   int ndim,                       // Number of features -- "p"
+			   int ninternal,                  // Dimension of internal data representation often 1
+			   int noptimization,              // Dimension of optimization variables -- "p"
+			   double *internal_linear,        // A_D -- linear part for data
+			   double *internal_state,         // D -- data state
+			   double *optimization_linear,    // A_O -- linear part for optimization variables
+			   double *optimization_state,     // O -- optimization state
+			   double *offset);                // h -- offset in affine transform -- "p" dimensional 
+
+double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
+					int ndim,                       // Number of features -- "p"
+					int noptimization,              // Dimension of optimization variables -- "p"
+					double *optimization_linear,    // A_O -- linear part for optimization variables
+					double *optimization_state,     // O -- optimization state
+					double *offset);                // h -- offset in affine transform -- "p" dimensional 
+
+double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
+				       int ndim,                       // Number of features -- "p"
+				       int noptimization,              // Dimension of optimization variables -- "p"
+				       double *optimization_linear,    // A_O -- linear part for optimization variables
+				       double *optimization_state,     // O -- optimization state
+				       double *offset).                // h -- offset in affine transform -- "p" dimensional 
+
+#ifdef __cplusplus
+}  /* extern "C" */
+#endif /* __cplusplus */

From d1dc82db763ed9986facbebf41033056561546dc Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 28 Oct 2017 11:13:48 -0700
Subject: [PATCH 312/493] unwanted files

---
 selectiveInference/src/randomized_lasso.c~ | 188 ---------------------
 selectiveInference/src/randomized_lasso.h~ |  42 -----
 2 files changed, 230 deletions(-)
 delete mode 100644 selectiveInference/src/randomized_lasso.c~
 delete mode 100644 selectiveInference/src/randomized_lasso.h~

diff --git a/selectiveInference/src/randomized_lasso.c~ b/selectiveInference/src/randomized_lasso.c~
deleted file mode 100644
index 123c81d..0000000
--- a/selectiveInference/src/randomized_lasso.c~
+++ /dev/null
@@ -1,188 +0,0 @@
-#include <math.h> // for fabs
-
-// Augmented density for randomized LASSO after
-// Gaussian randomization
-
-// Described in https://arxiv.org/abs/1609.05609
-
-// Gaussian is product of IID N(0, noise_scale^2) density
-// Evaluated at A_D D + A_O O + h
-
-// Laplace is product of IID Laplace with scale noise_scale
-// Also evaluated at A_D D + A_O O + h
-
-double log_density_gaussian(double noise_scale,             // Scale of randomization
-			    int ndim,                       // Number of features -- "p"
-			    int ninternal,                  // Dimension of internal data representation often 1
-			    int noptimization,              // Dimension of optimization variables -- "p"
-			    double *internal_linear,        // A_D -- linear part for data
-			    double *internal_state,         // D -- data state
-			    double *optimization_linear,    // A_O -- linear part for optimization variables
-			    double *optimization_state,     // O -- optimization state
-			    double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double denom = 2 * noise_scale * noise_scale;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Internal (i.e. data) contribution
-    for (icol=0; icol<ninternal; icol++) {
-      
-      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
-      internal_state_ptr = ((double *) internal_state + icol);
-
-      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
-    }
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= (reconstruction * reconstruction) / denom;
-  }
-
-  return(value);
-}
-
-double log_density_laplace(double noise_scale,             // Scale of randomization
-			   int ndim,                       // Number of features -- "p"
-			   int ninternal,                  // Dimension of internal data representation often 1
-			   int noptimization,              // Dimension of optimization variables -- "p"
-			   double *internal_linear,        // A_D -- linear part for data
-			   double *internal_state,         // D -- data state
-			   double *optimization_linear,    // A_O -- linear part for optimization variables
-			   double *optimization_state,     // O -- optimization state
-			   double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Internal (i.e. data) contribution
-    for (icol=0; icol<ninternal; icol++) {
-      
-      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
-      internal_state_ptr = ((double *) internal_state + icol);
-
-      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
-    }
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= fabs(reconstruction) / noise_scale;
-  }
-
-  return(value);
-}
-
-// Keeping internal (data) state fixed
-
-double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
-					int ndim,                       // Number of features -- "p"
-					int noptimization,              // Dimension of optimization variables -- "p"
-					double *optimization_linear,    // A_O -- linear part for optimization variables
-					double *optimization_state,     // O -- optimization state
-					double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double denom = 2 * noise_scale * noise_scale;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= reconstruction * reconstruction / denom;
-  }
-
-  return(value);
-}
-
-double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
-				       int ndim,                       // Number of features -- "p"
-				       int noptimization,              // Dimension of optimization variables -- "p"
-				       double *optimization_linear,    // A_O -- linear part for optimization variables
-				       double *optimization_state,     // O -- optimization state
-				       double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= fabs(reconstruction) / noise_scale;
-  }
-
-  return(value);
-}
diff --git a/selectiveInference/src/randomized_lasso.h~ b/selectiveInference/src/randomized_lasso.h~
deleted file mode 100644
index 1301b72..0000000
--- a/selectiveInference/src/randomized_lasso.h~
+++ /dev/null
@@ -1,42 +0,0 @@
-#ifdef __cplusplus
-extern "C"
-{
-#endif /* __cplusplus */
-
-double log_density_gaussian(double noise_scale,             // Scale of randomization
-			    int ndim,                       // Number of features -- "p"
-			    int ninternal,                  // Dimension of internal data representation often 1
-			    int noptimization,              // Dimension of optimization variables -- "p"
-			    double *internal_linear,        // A_D -- linear part for data
-			    double *internal_state,         // D -- data state
-			    double *optimization_linear,    // A_O -- linear part for optimization variables
-			    double *optimization_state,     // O -- optimization state
-			    double *offset);                // h -- offset in affine transform -- "p" dimensional 
-  
-double log_density_laplace(double noise_scale,             // Scale of randomization
-			   int ndim,                       // Number of features -- "p"
-			   int ninternal,                  // Dimension of internal data representation often 1
-			   int noptimization,              // Dimension of optimization variables -- "p"
-			   double *internal_linear,        // A_D -- linear part for data
-			   double *internal_state,         // D -- data state
-			   double *optimization_linear,    // A_O -- linear part for optimization variables
-			   double *optimization_state,     // O -- optimization state
-			   double *offset);                // h -- offset in affine transform -- "p" dimensional 
-
-double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
-					int ndim,                       // Number of features -- "p"
-					int noptimization,              // Dimension of optimization variables -- "p"
-					double *optimization_linear,    // A_O -- linear part for optimization variables
-					double *optimization_state,     // O -- optimization state
-					double *offset);                // h -- offset in affine transform -- "p" dimensional 
-
-double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
-				       int ndim,                       // Number of features -- "p"
-				       int noptimization,              // Dimension of optimization variables -- "p"
-				       double *optimization_linear,    // A_O -- linear part for optimization variables
-				       double *optimization_state,     // O -- optimization state
-				       double *offset).                // h -- offset in affine transform -- "p" dimensional 
-
-#ifdef __cplusplus
-}  /* extern "C" */
-#endif /* __cplusplus */

From 8ca202826ff51e6ed71710f41e2c6928f90a9dcf Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 28 Oct 2017 11:16:12 -0700
Subject: [PATCH 313/493] wrapper for conditional density

---
 selectiveInference/src/Rcpp-randomized.cpp | 25 ++++++++++++++++++++++
 tests/{ => randomized}/test_randomized.R   |  0
 2 files changed, 25 insertions(+)
 rename tests/{ => randomized}/test_randomized.R (100%)

diff --git a/selectiveInference/src/Rcpp-randomized.cpp b/selectiveInference/src/Rcpp-randomized.cpp
index 7887f32..23f0797 100644
--- a/selectiveInference/src/Rcpp-randomized.cpp
+++ b/selectiveInference/src/Rcpp-randomized.cpp
@@ -38,3 +38,28 @@ Rcpp::NumericVector log_density_gaussian_(double noise_scale,
 
   return(result);
 }
+
+// [[Rcpp::export]]
+Rcpp::NumericVector log_density_gaussian_conditional_(double noise_scale,                         // Scale of randomization
+						      Rcpp::NumericMatrix optimization_linear,    // A_O -- linear part for optimization variables
+						      Rcpp::NumericMatrix optimization_state,     // O -- optimization state -- matrix of shape (ninternal, npts)
+						      Rcpp::NumericMatrix offset) {               // h -- offset in affine transform -- "p" dimensional 
+
+  int npt = optimization_state.ncol();         // Function is vectorized
+  int ndim = optimization_linear.nrow();  
+  int noptimization = optimization_linear.ncol();
+
+  Rcpp::NumericVector result(npt);
+
+  int ipt;
+  for (ipt=0; ipt<npt; ipt++) {
+    result[ipt] = log_density_gaussian_conditional(noise_scale,
+						   ndim,
+						   noptimization,
+						   (double *) optimization_linear.begin(),
+						   ((double *) optimization_state.begin() + ipt * noptimization),
+						   (double *) offset.begin());
+  }
+
+  return(result);
+}
diff --git a/tests/test_randomized.R b/tests/randomized/test_randomized.R
similarity index 100%
rename from tests/test_randomized.R
rename to tests/randomized/test_randomized.R

From 07d7c6c4e01a807d30cc89b503ff94f1a2acbea3 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 28 Oct 2017 11:49:40 -0700
Subject: [PATCH 314/493] forming internal affine transform

---
 selectiveInference/R/funs.randomized.R | 58 +++++++++++++++++++-------
 tests/randomized/test_randomized.R     |  5 ++-
 2 files changed, 46 insertions(+), 17 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 1da73b2..3b10423 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -71,33 +71,61 @@ randomizedLASSO = function(X,
     sign_soln = sign(result$soln)
 
     unpenalized = lam == 0
-    active = !unpenalized * (sign_soln != 0)
-    inactive = !unpenzlied * (sign_soln == 0)
+    active = (!unpenalized) & (sign_soln != 0)
+    inactive = (!unpenalized) & (sign_soln == 0)
 
     unpenalized_set = which(unpenalized)
     active_set = which(active)
     inactive_set = which(inactive)
 
-    coef_term = t(X) %*% X[,c(unpenalized_set,  # the coefficients
-                              active_set)]
+    # affine transform for optimization variables
+
+    E = c(unpenalized_set, active_set)
+    I = inactive_set
+    X_E = X[,E]
+    X_I = X[,I]
+    L_E = t(X) %*% X[,E]
+
+    coef_term = L_E
     coef_term = coef_term %*% diag(c(rep(1, sum(unpenalized)), sign_soln[active]))  # coefficients are non-negative
     coef_term[active,] = coef_term[active,] + ridge_term * diag(rep(1, sum(active)))  # ridge term
 
-    subgrad_term = cbind(matrix(0, sum(inactive), sum(active) + sum(unpenalized)),
-                         diag(rep(1, sum(inactive))))
-    linear_term = rbind(coef_term,
+    subgrad_term = matrix(0, p, sum(inactive)) # for subgrad
+    for (i in 1:sum(inactive)) {
+        subgrad_term[inactive_set[i], i] = 1
+    }
+
+    linear_term = cbind(coef_term,
                         subgrad_term)
 
     offset_term = rep(0, p)
     offset_term[active] = lam[active] * sign_soln[active]
 
-    
+    opt_transform = list(linear_term=linear_term,
+                         offset_term=offset_term)
+
+    # affine transform for internal (data) variables
+    # for now just use parametric in terms of
+    # (\bar{\beta}_E, X_{-E}^T(y-X_E\bar{\beta}_E)
+    # 
+    # we have to reconstruct -X^TY from this pair
+    #
+
+    active_term = -L_E                           # for \bar{\beta}_E
+
+    inactive_term = -subgrad_term
+    linear_term = cbind(active_term,
+                        inactive_term)
+    offset_term = rep(0, p)
+    internal_transform = list(linear_term = linear_term,
+                              offset_term = offset_term)
+
+    return(list(active_set = active_set,
+                inactive_set = inactive_set,
+                unpenalized_set = unpenalized_set,
+                sign_soln = sign_soln,
+                opt_transform = opt_transform,
+                internal_transform = internal_transform
+                ))
 
-    list(active_set = active_set,
-         inactive_set = inactive_set,
-         unpenalized_set = unpenalized_set,
-         sign_soln = sign_soln)
-         
-         
-    return(result)
 }
diff --git a/tests/randomized/test_randomized.R b/tests/randomized/test_randomized.R
index e4e35a1..305139b 100644
--- a/tests/randomized/test_randomized.R
+++ b/tests/randomized/test_randomized.R
@@ -8,7 +8,8 @@ test = function() {
     lam = 20 / sqrt(n)
     noise_scale = 0.01 * sqrt(n)
     ridge_term = .1 / sqrt(n)
-    fit_randomized_lasso(X, y, lam, noise_scale, ridge_term)
+    selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
 }
 
-print(test())
+A=test()
+#print(test())

From 9d1c79b17d1f69dbad9c8d017ef01c968da0185d Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 28 Oct 2017 12:03:00 -0700
Subject: [PATCH 315/493] vectorized densities evaluate OK -- need to check
 results

---
 selectiveInference/R/funs.randomized.R     |  2 +-
 selectiveInference/src/Rcpp-randomized.cpp |  4 ++--
 tests/randomized/test_randomized.R         | 27 +++++++++++++++++++---
 3 files changed, 27 insertions(+), 6 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 3b10423..92b871e 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -124,7 +124,7 @@ randomizedLASSO = function(X,
                 inactive_set = inactive_set,
                 unpenalized_set = unpenalized_set,
                 sign_soln = sign_soln,
-                opt_transform = opt_transform,
+                optimization_transform = opt_transform,
                 internal_transform = internal_transform
                 ))
 
diff --git a/selectiveInference/src/Rcpp-randomized.cpp b/selectiveInference/src/Rcpp-randomized.cpp
index 23f0797..d6e8517 100644
--- a/selectiveInference/src/Rcpp-randomized.cpp
+++ b/selectiveInference/src/Rcpp-randomized.cpp
@@ -7,7 +7,7 @@ Rcpp::NumericVector log_density_gaussian_(double noise_scale,
 					  Rcpp::NumericMatrix internal_state,         // D -- data state -- matrix of shape (nopt, npts)
 					  Rcpp::NumericMatrix optimization_linear,    // A_O -- linear part for optimization variables
 					  Rcpp::NumericMatrix optimization_state,     // O -- optimization state -- matrix of shape (ninternal, npts)
-					  Rcpp::NumericMatrix offset) {               // h -- offset in affine transform -- "p" dimensional 
+					  Rcpp::NumericVector offset) {               // h -- offset in affine transform -- "p" dimensional 
 
   int npt = internal_state.ncol();         // Function is vectorized
   if (optimization_state.ncol() != npt) {  // Assuming each column is an internal or opt state because arrays are column major
@@ -43,7 +43,7 @@ Rcpp::NumericVector log_density_gaussian_(double noise_scale,
 Rcpp::NumericVector log_density_gaussian_conditional_(double noise_scale,                         // Scale of randomization
 						      Rcpp::NumericMatrix optimization_linear,    // A_O -- linear part for optimization variables
 						      Rcpp::NumericMatrix optimization_state,     // O -- optimization state -- matrix of shape (ninternal, npts)
-						      Rcpp::NumericMatrix offset) {               // h -- offset in affine transform -- "p" dimensional 
+						      Rcpp::NumericVector offset) {               // h -- offset in affine transform -- "p" dimensional 
 
   int npt = optimization_state.ncol();         // Function is vectorized
   int ndim = optimization_linear.nrow();  
diff --git a/tests/randomized/test_randomized.R b/tests/randomized/test_randomized.R
index 305139b..87a3043 100644
--- a/tests/randomized/test_randomized.R
+++ b/tests/randomized/test_randomized.R
@@ -1,6 +1,6 @@
 library(selectiveInference)
 
-test = function() {
+smoke_test = function() {
 
     n = 100; p = 50
     X = matrix(rnorm(n * p), n, p)
@@ -10,6 +10,27 @@ test = function() {
     ridge_term = .1 / sqrt(n)
     selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
 }
+A = smoke_test()
 
-A=test()
-#print(test())
+density_test = function() {
+
+    random_lasso = smoke_test()
+    p = nrow(random_lasso$internal_transform$linear_term)
+    internal_state = matrix(rnorm(p * 20), p, 20)
+    optimization_state = matrix(rnorm(p * 20), p, 20)
+    offset = rnorm(p)
+
+    selectiveInference:::log_density_gaussian_(10.,
+                                               random_lasso$internal_transform$linear_term,
+                                               internal_state,
+                                               random_lasso$optimization_transform$linear_term,
+					       optimization_state,
+                                               offset)
+
+    selectiveInference:::log_density_gaussian_conditional_(10.,
+                                                           random_lasso$optimization_transform$linear_term,
+                    				           optimization_state,
+                                                           offset)
+}
+
+density_test()

From 5393dcd64b711aee524929a0c2eb55083b208d2c Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 28 Oct 2017 12:16:27 -0700
Subject: [PATCH 316/493] added laplace densities

---
 selectiveInference/src/Rcpp-randomized.cpp | 63 ++++++++++++++
 tests/randomized/test_randomized.R         | 95 +++++++++++++++++++---
 2 files changed, 145 insertions(+), 13 deletions(-)

diff --git a/selectiveInference/src/Rcpp-randomized.cpp b/selectiveInference/src/Rcpp-randomized.cpp
index d6e8517..b2b2cd2 100644
--- a/selectiveInference/src/Rcpp-randomized.cpp
+++ b/selectiveInference/src/Rcpp-randomized.cpp
@@ -63,3 +63,66 @@ Rcpp::NumericVector log_density_gaussian_conditional_(double noise_scale,
 
   return(result);
 }
+
+// [[Rcpp::export]]
+Rcpp::NumericVector log_density_laplace_(double noise_scale,                         // Scale of randomization
+					 Rcpp::NumericMatrix internal_linear,        // A_D -- linear part for data
+					 Rcpp::NumericMatrix internal_state,         // D -- data state -- matrix of shape (nopt, npts)
+					 Rcpp::NumericMatrix optimization_linear,    // A_O -- linear part for optimization variables
+					 Rcpp::NumericMatrix optimization_state,     // O -- optimization state -- matrix of shape (ninternal, npts)
+					 Rcpp::NumericVector offset) {               // h -- offset in affine transform -- "p" dimensional 
+
+  int npt = internal_state.ncol();         // Function is vectorized
+  if (optimization_state.ncol() != npt) {  // Assuming each column is an internal or opt state because arrays are column major
+    Rcpp::stop("Number of optimization samples should equal the number of (internally represented) data.");
+  }
+
+  int ndim = optimization_linear.nrow();  
+  if (internal_linear.nrow() != ndim) {  
+    Rcpp::stop("Dimension of optimization range should be the same as the dimension of the data range.");
+  } 
+  int ninternal = internal_linear.ncol();
+  int noptimization = optimization_linear.ncol();
+
+  Rcpp::NumericVector result(npt);
+
+  int ipt;
+  for (ipt=0; ipt<npt; ipt++) {
+    result[ipt] = log_density_laplace(noise_scale,
+				      ndim,
+				      ninternal,
+				      noptimization,
+				      (double *) internal_linear.begin(),
+				      ((double *) internal_state.begin() + ipt * ninternal),
+				      (double *) optimization_linear.begin(),
+				      ((double *) optimization_state.begin() + ipt * noptimization),
+				      (double *) offset.begin());
+  }
+
+  return(result);
+}
+
+// [[Rcpp::export]]
+Rcpp::NumericVector log_density_laplace_conditional_(double noise_scale,                         // Scale of randomization
+						     Rcpp::NumericMatrix optimization_linear,    // A_O -- linear part for optimization variables
+						     Rcpp::NumericMatrix optimization_state,     // O -- optimization state -- matrix of shape (ninternal, npts)
+						     Rcpp::NumericVector offset) {               // h -- offset in affine transform -- "p" dimensional 
+
+  int npt = optimization_state.ncol();         // Function is vectorized
+  int ndim = optimization_linear.nrow();  
+  int noptimization = optimization_linear.ncol();
+
+  Rcpp::NumericVector result(npt);
+
+  int ipt;
+  for (ipt=0; ipt<npt; ipt++) {
+    result[ipt] = log_density_laplace_conditional(noise_scale,
+						  ndim,
+						  noptimization,
+						  (double *) optimization_linear.begin(),
+						  ((double *) optimization_state.begin() + ipt * noptimization),
+						  (double *) offset.begin());
+  }
+
+  return(result);
+}
diff --git a/tests/randomized/test_randomized.R b/tests/randomized/test_randomized.R
index 87a3043..b9b1750 100644
--- a/tests/randomized/test_randomized.R
+++ b/tests/randomized/test_randomized.R
@@ -12,25 +12,94 @@ smoke_test = function() {
 }
 A = smoke_test()
 
-density_test = function() {
+gaussian_density_test = function() {
 
+    noise_scale = 10.
     random_lasso = smoke_test()
     p = nrow(random_lasso$internal_transform$linear_term)
     internal_state = matrix(rnorm(p * 20), p, 20)
     optimization_state = matrix(rnorm(p * 20), p, 20)
     offset = rnorm(p)
 
-    selectiveInference:::log_density_gaussian_(10.,
-                                               random_lasso$internal_transform$linear_term,
-                                               internal_state,
-                                               random_lasso$optimization_transform$linear_term,
-					       optimization_state,
-                                               offset)
-
-    selectiveInference:::log_density_gaussian_conditional_(10.,
-                                                           random_lasso$optimization_transform$linear_term,
-                    				           optimization_state,
-                                                           offset)
+    V1 = selectiveInference:::log_density_gaussian_(noise_scale,
+                                                    random_lasso$internal_transform$linear_term,
+                                                    internal_state,
+                                                    random_lasso$optimization_transform$linear_term,
+                                                    optimization_state,
+                                                    offset)
+    A1 = random_lasso$internal_transform$linear_term
+    A2 = random_lasso$optimization_transform$linear_term
+    arg = A1 %*% internal_state + A2 %*% optimization_state + offset
+    V2 = -apply(arg^2, 2, sum) / (2 * noise_scale^2)
+    print(sqrt(sum((V1-V2)^2) / sum(V1^2)))
+
+    U1 = selectiveInference:::log_density_gaussian_conditional_(noise_scale,
+                                                                random_lasso$optimization_transform$linear_term,
+                                                                optimization_state,
+                                                                offset)
+    arg = A2 %*% optimization_state + offset
+    U2 = -apply(arg^2, 2, sum) / (2 * noise_scale^2)
+    print(sqrt(sum((U1-U2)^2) / sum(U1^2)))
+
+    # test that a single column matrix works -- numeric should not
+
+    print(selectiveInference:::log_density_gaussian_conditional_(noise_scale,
+                                                                 random_lasso$optimization_transform$linear_term,
+                                                                 optimization_state[,1,drop=FALSE],
+                                                                 offset))
+    print(selectiveInference:::log_density_gaussian_(noise_scale,
+                                                     random_lasso$internal_transform$linear_term,
+                                                     internal_state[,1,drop=FALSE],
+                                                     random_lasso$optimization_transform$linear_term,
+                                                     optimization_state[,1,drop=FALSE],
+                                                     offset))
+
+}
+
+gaussian_density_test()
+
+laplace_density_test = function() {
+
+    noise_scale = 10.
+    random_lasso = smoke_test()
+    p = nrow(random_lasso$internal_transform$linear_term)
+    internal_state = matrix(rnorm(p * 20), p, 20)
+    optimization_state = matrix(rnorm(p * 20), p, 20)
+    offset = rnorm(p)
+
+    V1 = selectiveInference:::log_density_laplace_(noise_scale,
+                                                    random_lasso$internal_transform$linear_term,
+                                                    internal_state,
+                                                    random_lasso$optimization_transform$linear_term,
+                                                    optimization_state,
+                                                    offset)
+    A1 = random_lasso$internal_transform$linear_term
+    A2 = random_lasso$optimization_transform$linear_term
+    arg = A1 %*% internal_state + A2 %*% optimization_state + offset
+    V2 = -apply(abs(arg), 2, sum) / noise_scale
+    print(sqrt(sum((V1-V2)^2) / sum(V1^2)))
+
+    U1 = selectiveInference:::log_density_laplace_conditional_(noise_scale,
+                                                                random_lasso$optimization_transform$linear_term,
+                                                                optimization_state,
+                                                                offset)
+    arg = A2 %*% optimization_state + offset
+    U2 = -apply(abs(arg), 2, sum) / noise_scale
+    print(sqrt(sum((U1-U2)^2) / sum(U1^2)))
+
+    # test that a single column matrix works -- numeric should not
+
+    print(selectiveInference:::log_density_laplace_conditional_(noise_scale,
+                                                                 random_lasso$optimization_transform$linear_term,
+                                                                 optimization_state[,1,drop=FALSE],
+                                                                 offset))
+    print(selectiveInference:::log_density_laplace_(noise_scale,
+                                                     random_lasso$internal_transform$linear_term,
+                                                     internal_state[,1,drop=FALSE],
+                                                     random_lasso$optimization_transform$linear_term,
+                                                     optimization_state[,1,drop=FALSE],
+                                                     offset))
+
 }
 
-density_test()
+laplace_density_test()

From b63744821c1a2420dc0d9c533d91af0155d3a4d1 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 30 Oct 2017 21:53:04 -0700
Subject: [PATCH 317/493] using C-software submodule for selectiveInference/src

---
 .gitmodules                                   |   3 +
 C-software                                    |   1 +
 Makefile                                      |   4 +-
 selectiveInference/src/debias.h               |  76 ---
 selectiveInference/src/matrixcomps.c          | 261 --------
 selectiveInference/src/matrixcomps.h          |  12 -
 selectiveInference/src/quadratic_program.c    | 441 -------------
 .../src/quadratic_program_wide.c              | 590 ------------------
 selectiveInference/src/randomized_lasso.c     | 190 ------
 selectiveInference/src/randomized_lasso.h     |  42 --
 selectiveInference/src/truncnorm.c            | 188 ------
 11 files changed, 7 insertions(+), 1801 deletions(-)
 create mode 100644 .gitmodules
 create mode 160000 C-software
 delete mode 100644 selectiveInference/src/debias.h
 delete mode 100644 selectiveInference/src/matrixcomps.c
 delete mode 100644 selectiveInference/src/matrixcomps.h
 delete mode 100644 selectiveInference/src/quadratic_program.c
 delete mode 100644 selectiveInference/src/quadratic_program_wide.c
 delete mode 100644 selectiveInference/src/randomized_lasso.c
 delete mode 100644 selectiveInference/src/randomized_lasso.h
 delete mode 100644 selectiveInference/src/truncnorm.c

diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 0000000..f8073fb
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "C-software"]
+	path = C-software
+	url = https://github.com/selective-inference/C-software.git
diff --git a/C-software b/C-software
new file mode 160000
index 0000000..a3d9a17
--- /dev/null
+++ b/C-software
@@ -0,0 +1 @@
+Subproject commit a3d9a1723ce94cb430b5dfd3e058fd708a6bae7f
diff --git a/Makefile b/Makefile
index e10e111..5580814 100644
--- a/Makefile
+++ b/Makefile
@@ -4,11 +4,13 @@ Rcpp:
 	Rscript -e "library(Rcpp); Rcpp::compileAttributes('selectiveInference')"
 
 install: Rcpp
+	cp C-software/src/* selectiveInference/src
 	R CMD INSTALL selectiveInference
 
 build: 
+	cp C-software/src/* selectiveInference/src
 	R CMD build selectiveInference
 
-check: Rcpp build
+check: Rcpp build 
 	R CMD build selectiveInference
 	R CMD check selectiveInference_1.2.2.tar.gz # fix this to be a script variable
\ No newline at end of file
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
deleted file mode 100644
index d3db26d..0000000
--- a/selectiveInference/src/debias.h
+++ /dev/null
@@ -1,76 +0,0 @@
-#ifdef __cplusplus
-extern "C"
-{
-#endif /* __cplusplus */
-
-int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
-	     double *linear_func_ptr,    /* Linear term in objective */
-	     double *nndef_diag_ptr,     /* Diagonal of nndef */
-	     double *gradient_ptr,       /* nndef times theta */
-	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
-	     int *nactive_ptr,           /* Size of ever active set */
-	     int nfeature,               /* How many features in nndef */
-	     double bound,               /* feasibility parameter */
-	     double *theta,              /* current value */
-	     double *theta_old,          /* previous value */
-	     int maxiter,                /* max number of iterations */
-	     double kkt_tol,             /* precision for checking KKT conditions */
-	     double objective_tol,       /* precision for checking relative decrease in objective value */
-	     double parameter_tol,       /* precision for checking relative convergence of parameter */
-	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
-	     int objective_stop,         /* Break based on convergence of objective value? */
-             int kkt_stop,               /* Break based on KKT? */
-	     int param_stop);            /* Break based on parameter convergence? */
-
-int check_KKT_qp(double *theta,        /* current theta */
-		 double *gradient_ptr, /* nndef times theta + linear_func */
-		 int nrow,             /* how many rows in nndef */
-		 double bound,         /* Lagrange multipler for \ell_1 */
-		 double tol);          /* precision for checking KKT conditions */        
-
-int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-	       double *X_theta_ptr,        /* Fitted values   */
-	       double *linear_func_ptr,    /* Linear term in objective */
-	       double *nndef_diag_ptr,     /* Diagonal entries of non-neg def matrix */
-	       double *gradient_ptr,       /* X^TX/ncase times theta + linear_func*/
-	       int *need_update_ptr,       /* Keeps track of updated gradient coords */
-	       int *ever_active_ptr,       /* Ever active set: 1-based */ 
-	       int *nactive_ptr,           /* Size of ever active set */
-	       int ncase,                  /* How many rows in X */
-	       int nfeature,               /* How many columns in X */
-	       double *bound_ptr,          /* Lagrange multipliers */
-	       double ridge_term,          /* Ridge / ENet term */
-	       double *theta_ptr,          /* current value */
-	       double *theta_old_ptr,      /* previous value */
-	       int maxiter,                /* max number of iterations */
-	       double kkt_tol,             /* precision for checking KKT conditions */
-	       double objective_tol,       /* precision for checking relative decrease in objective value */
-	       double parameter_tol,       /* precision for checking relative convergence of parameter */
-	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
-	       int objective_stop,         /* Break based on convergence of objective value? */
-	       int kkt_stop,               /* Break based on KKT? */
-	       int param_stop);            /* Break based on parameter convergence? */
-
-int check_KKT_wide(double *theta_ptr,        /* current theta */
-		   double *gradient_ptr,     /* X^TX/ncase times theta + linear_func*/
-		   double *X_theta_ptr,      /* Current fitted values */
-		   double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-		   double *linear_func_ptr,  /* Linear term in objective */   
-		   int *need_update_ptr,     /* Which coordinates need to be updated? */
-		   int nfeature,             /* how many columns in X */
-		   int ncase,                /* how many rows in X */
-		   double *bound_ptr,        /* Lagrange multiplers for \ell_1 */
-		   double ridge_term,        /* Ridge / ENet term */
-		   double tol);               /* precision for checking KKT conditions */        
-  
-void update_gradient_wide(double *gradient_ptr,     /* X^TX/ncase times theta + linear_func */
-			  double *X_theta_ptr,      /* Current fitted values */
-			  double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-			  double *linear_func_ptr,  /* Linear term in objective */   
-			  int *need_update_ptr,     /* Which coordinates need to be updated? */
-			  int nfeature,             /* how many columns in X */
-			  int ncase);               /* how many rows in X */
-
-#ifdef __cplusplus
-}  /* extern "C" */
-#endif /* __cplusplus */
diff --git a/selectiveInference/src/matrixcomps.c b/selectiveInference/src/matrixcomps.c
deleted file mode 100644
index bec3506..0000000
--- a/selectiveInference/src/matrixcomps.c
+++ /dev/null
@@ -1,261 +0,0 @@
-#include <R.h>
-#include <Rmath.h>
-
-// Matrices are stored as vectors, in column-major order
-
-// Givens rotation of a and b, stored in c and s
-void givens(double a, double b, double *c, double *s) {
-  if (b==0) {
-    *c = 1; 
-    *s = 0; 
-  }
-  else {
-    if (fabs(b)>fabs(a)) {
-      double t = -a/b;
-      *s = 1/sqrt(1+t*t);
-      *c = (*s)*t;
-    }
-    else {
-      double t = -b/a;
-      *c = 1/sqrt(1+t*t);
-      *s = (*c)*t;
-    }
-  }
-}
-
-// Givens rotation applied to rows i1 and i2 of the m x n
-// matrix A, on the subset of columns j1 through j2
-void rowrot(double *A, int i1, int i2, int m, int n, int j1, int j2, double c, double s) {
-  int j;
-  double t1,t2;
-  for (j=j1; j<=j2; j++) {
-    t1 = A[i1+j*m]; 
-    t2 = A[i2+j*m];
-    A[i1+j*m] = c*t1-s*t2;
-    A[i2+j*m] = s*t1+c*t2;
-  }
-}
-
-// Givens rotation applied to columns j1 and j2 of the m x n
-// matrix A, on the subset of rows i1 through i2
-void colrot(double *A, int j1, int j2, int m, int n, int i1, int i2, double c, double s) {
-  int i;
-  double t1,t2;
-  for (i=i1; i<=i2; i++) {
-    t1 = A[i+j1*m];
-    t2 = A[i+j2*m];
-    A[i+j1*m] = c*t1-s*t2;
-    A[i+j2*m] = s*t1+c*t2;
-  }
-}
-
-// Downdate the QR factorization after deleting column j0, 
-// where Q1 is m x n and R is n x n. The other part of 
-// the Q matrix, Q2 m x (m-n), isn't needed so it isn't 
-// passed for efficiency
-void downdate1(double *Q1, double *R, int j0, int m, int n) {
-  int j;
-
-  double c,s;
-  for (j=j0+1; j<n; j++) {
-    // Compute the appropriate c and s
-    givens(R[j-1+j*n],R[j+j*n],&c,&s);
-
-    // Pre-multiply R
-    rowrot(R,j-1,j,n,n,j,n-1,c,s);
-
-    // Post-mutiply Q1
-    colrot(Q1,j-1,j,m,n,0,m-1,c,s);
-  }
-}
-
-// Update the QR factorization after adding a column z.
-// For convenience, we are given w=Q2'z. Here Q2 is m x 
-// (m-n). The other part of the Q matrix, Q1 m x n, and 
-// R are not needed, so they aren't passed for efficiency 
-void update1(double *Q2, double *w, int m, int k) {
-  int j;
-
-  double c,s;
-  for (j=k-1; j>=1; j--) {
-    // Compute the appropriate c and s
-    givens(w[j-1],w[j],&c,&s);
-
-    // Pre-multiply w
-    rowrot(w,j-1,j,k,1,0,0,c,s);
-
-    // Post-multiply Q2
-    colrot(Q2,j-1,j,m,k,0,m-1,c,s);
-  }
-}
-
-// Downdate the QR factorization after deleting the first row, 
-// where Q is m x m and R is m x n
-void downdate2(double *Q, double *R, int *mp, int *np) {
-  int m,n,i;
-  m = *mp;
-  n = *np;
-
-  double c,s;
-  for (i=m-1; i>=1; i--) {
-    // Compute the appropriate c and s
-    givens(Q[(i-1)*m],Q[i*m],&c,&s);
-
-    // Post-mutiply Q
-    colrot(Q,i-1,i,m,m,0,m-1,c,s);
-
-    // Pre-multiply R
-    if (i<=n) rowrot(R,i-1,i,m,n,i-1,n-1,c,s); 
-  }
-}
-
-// Update the QR factorization after adding the last row,
-// where Q is m x m and R is m x n. For efficiency, Q is not
-// passed, and only the first row of R is passed. Not counting
-// its first row, the first q columns of R are zero
-void update2(double *y, double *D, double *r, int *mp, int *np, int *qp) {
-  int m,n,q,j;
-  m = *mp;
-  n = *np;
-  q = *qp;
-
-  double c,s;
-  for (j=0; j<q-1; j++) {
-    // Compute the appropriate c and s
-    givens(r[j+1],r[j],&c,&s);
-
-    // Pre-multiply r
-    rowrot(r,j+1,j,n,1,0,0,c,s);
-
-    // Post-mutiply D
-    colrot(D,j+1,j,m,n,0,m-1,c,s);
-
-    // Pre-multiply y
-    rowrot(y,j+1,j,n,1,0,0,c,s);
-  }
-}
-
-// Make the R factor upper triangular, by Givens rotating its
-// columns. Here A is m x n, and R is n x n with rank(R) = n-k
-void maketri1(double *y, double *A, double *R, int *mp, int *np, int *kp) {
-  int m,n,k,i,j;
-  m = *mp;
-  n = *np;
-  k = *kp; 
-  
-  double c,s;
-  for (i=n-k-1; i>=0; i--) {
-    for (j=i; j<i+k; j++) {
-      // Compute the appropriate c and s
-      givens(R[i+(j+1)*n],R[i+j*n],&c,&s);
-      
-      // Post-multiply R
-      colrot(R,j+1,j,n,n,0,i,c,s);
-
-      // Post-multiply D
-      colrot(A,j+1,j,m,n,0,m-1,c,s);
-    
-      // Pre-multiply y
-      rowrot(y,j+1,j,n,1,0,0,c,s);
-    }
-  }
-}
-
-// Make the R factor upper triangular, by Givens rotating its 
-// columns. Here A is m x n, and R is m x n with rank(R) = min(m,n)-k
-void maketri2(double *y, double *A, double *R, int *mp, int *np, int *kp) {
-  int m,n,k,r,d,i,j;
-  m = *mp;
-  n = *np;
-  k = *kp;
- 
-  r = imin2(m,n);
-  d = imax2(n-m,0);
-
-  double c,s;
-  for (i=r-k-1; i>=0; i--) {
-    for (j=i; j<i+k+d; j++) {
-      // Compute the appropriate c and s
-      givens(R[i+(j+1)*m],R[i+j*m],&c,&s);
-      
-      // Post-multiply R
-      colrot(R,j+1,j,m,n,0,i,c,s);
-
-      // Post-multiply D
-      colrot(A,j+1,j,m,n,0,m-1,c,s);
-    
-      // Pre-multiply y
-      rowrot(y,j+1,j,n,1,0,0,c,s);
-    }
-  }
-}
-
-// Make the R factor upper triangular, by Givens rotating
-// its columns. Here A is m1 x n, and R is m2 x n with 
-// rank(R) = n-q-1. The first q columns of R are zero
-void maketri3(double *y, double *A, double *R, int *m1p, int *m2p, int *np, int *qp) {
-  int m1,m2,n,q,i;
-  m1 = *m1p;
-  m2 = *m2p;
-  n = *np;
-  q = *qp;
-  
-  double c,s;
-  for (i=n-q-2; i>=0; i--) {
-    // Compute the appropriate c and s
-    givens(R[i+(i+q+1)*m2],R[i+(i+q)*m2],&c,&s);
-      
-    // Post-multiply R
-    colrot(R,i+q+1,i+q,m2,n,0,i,c,s);
-
-    // Post-multiply D
-    colrot(A,i+q+1,i+q,m1,n,0,m1-1,c,s);
-    
-    // Pre-multiply y
-    rowrot(y,i+q+1,i+q,n,1,0,0,c,s);
-  }
-}
-
-// Make the R factor upper triangular, by Givens rotating
-// its columns and rows, appropriately. Here A is m1 x n, 
-// Q is m2 x m2, and R is m2 x n with rank(R) = n-q-1. The 
-// first q columns of R are zero. The kth row of R is the 
-// last row with a zero element on the diagonal    
-void maketri4(double *y, double *A, double *Q, double *R, int *m1p, int *m2p, int *np, int *qp, int *kp) {
-  int m1,m2,n,q,k,i,j;
-  m1 = *m1p;
-  m2 = *m2p;
-  n = *np; 
-  q = *qp;
-  k = *kp;
-
-  double c,s;
-
-  // First rotate the columns
-  for (i=k-1; i>=0; i--) {
-    // Compute the appropriate c and s
-    givens(R[i+(i+q+1)*m2],R[i+(i+q)*m2],&c,&s);
-      
-    // Post-multiply R
-    colrot(R,i+q+1,i+q,m2,n,0,i,c,s);
-
-    // Post-multiply D
-    colrot(A,i+q+1,i+q,m1,n,0,m1-1,c,s);
-    
-    // Pre-multiply y
-    rowrot(y,i+q+1,i+q,n,1,0,0,c,s);
-  }
-
-  // Next rotate the rows
-  for (j=k+q+1; j<n; j++) {
-    // Compute the appropriate c and s
-    givens(R[j-q-1+j*m2],R[j-q+j*m2],&c,&s);
-
-    // Pre-multiply R
-    rowrot(R,j-q-1,j-q,m2,n,j,n-1,c,s);
-
-    // Post-multiply Q
-    colrot(Q,j-q-1,j-q,m2,m2,0,m2-1,c,s);
-  }
-}
-
diff --git a/selectiveInference/src/matrixcomps.h b/selectiveInference/src/matrixcomps.h
deleted file mode 100644
index 8632e3e..0000000
--- a/selectiveInference/src/matrixcomps.h
+++ /dev/null
@@ -1,12 +0,0 @@
-#ifdef __cplusplus
-extern "C"
-{
-#endif /* __cplusplus */
-
-void update1(double *Q2, double *w, int m, int k);
-
-void downdate1(double *Q1, double *R, int j0, int m, int n);
-
-#ifdef __cplusplus
-}  /* extern "C" */
-#endif /* __cplusplus */
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
deleted file mode 100644
index 1f7fcb3..0000000
--- a/selectiveInference/src/quadratic_program.c
+++ /dev/null
@@ -1,441 +0,0 @@
-#include <math.h> // for fabs
-
-// Find an approximate row of \hat{nndef}^{-1}
-
-// Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
-
-// Dual problem: \text{min}_{\theta} 1/2 \theta^T \Sigma \theta - l^T\theta + \mu \|\theta\|_1
-// where l is `linear_func` below
-
-// This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
-// Therefore we don't have to negate the answer to get theta.
-// Update one coordinate 
-
-double objective_qp(double *nndef_ptr,       /* A non-negative definite matrix */
-		    double *linear_func_ptr, /* Linear term in objective */
-		    int *ever_active_ptr,    /* Ever active set: 0-based */ 
-		    int *nactive_ptr,        /* Size of ever active set */
-		    int nrow,                /* how many rows in nndef */
-		    double bound,            /* Lagrange multipler for \ell_1 */
-		    double *theta_ptr)           /* current value */
-{
-  int irow, icol;
-  double value = 0;
-  double *nndef_ptr_tmp = nndef_ptr;
-  double *linear_func_ptr_tmp = linear_func_ptr;
-  double *theta_row_ptr, *theta_col_ptr;
-  int *active_row_ptr, *active_col_ptr;
-  int active_row, active_col;
-  int nactive = *nactive_ptr;
-
-  theta_row_ptr = theta_ptr;
-  theta_col_ptr = theta_ptr;
-
-  for (irow=0; irow<nactive; irow++) {
-
-    active_row_ptr = ((int *) ever_active_ptr + irow);
-    active_row = *active_row_ptr - 1;          // Ever-active is 1-based
-    theta_row_ptr = ((double *) theta_ptr + active_row);
-
-    for (icol=0; icol<nactive; icol++) {
-      
-      active_col_ptr = ((int *) ever_active_ptr + icol);
-      active_col = *active_col_ptr - 1;          // Ever-active is 1-based
-      theta_col_ptr = ((double *) theta_ptr + active_col);
-
-      nndef_ptr_tmp = ((double *) nndef_ptr + nrow * active_col + active_row); // Matrices are column-major order
-
-      value += 0.5 * (*nndef_ptr_tmp) * (*theta_row_ptr) * (*theta_col_ptr);
-    }
-    value += bound * fabs((*theta_row_ptr)); // the \ell_1 term
-
-    // The linear term in the objective
-
-    linear_func_ptr_tmp = ((double *) linear_func_ptr + active_row);
-    value += (*linear_func_ptr_tmp) * (*theta_row_ptr); 
-
-  }
-  
-  return(value);
-}
-
-// Ever-active is 1-based
-// coord is 0-based
-int update_ever_active_qp(int coord,
-			  int *ever_active_ptr,
-			  int *nactive_ptr) {
-  int iactive;
-  int active_var;
-  int nactive = *nactive_ptr;
-  int *ever_active_ptr_tmp = ever_active_ptr;
-
-  for (iactive=0; iactive<nactive; iactive++) {
-    ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
-    active_var = *ever_active_ptr_tmp;
-    if (active_var - 1 == coord) {          // Ever-active is 1-based
-      return(1);
-    }
-  }
-  
-  // If we haven't returned yet, this means the coord was not in 
-  // ever_active.
-
-  // Add it to the active set and increment the 
-  // number of active variables
-
-  ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
-  *ever_active_ptr_tmp = coord + 1;         // Ever-active is 1-based
-  *nactive_ptr += 1;
-
-  return(0);
-}
-
-int check_KKT_qp(double *theta_ptr,        /* current theta */
-		 double *gradient_ptr, /* nndef times theta + linear_func */
-		 int nfeature,             /* how many features in nndef */
-		 double bound,         /* Lagrange multipler for \ell_1 */
-		 double tol)           /* precision for checking KKT conditions */        
-{
-  // First check inactive
-
-  int ifeature;
-  double *theta_ptr_tmp, *gradient_ptr_tmp;
-  double gradient;
-
-  for (ifeature=0; ifeature<nfeature; ifeature++) {
-    theta_ptr_tmp = ((double *) theta_ptr + ifeature);
-    gradient_ptr_tmp = ((double *) gradient_ptr + ifeature);
-
-    // Compute this coordinate of the gradient
-
-    gradient = *gradient_ptr_tmp;
-
-    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
-	return(0);
-      }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
-	return(0);
-      }
-    }
-    else {
-      if (fabs(gradient) > (1. + tol) * bound) {
-	return(0);
-      }
-    }
-  }
-
-  return(1);
-}
-
-int check_KKT_qp_active(int *ever_active_ptr,           /* Ever active set: 0-based */ 
-		        int *nactive_ptr,               /* Size of ever active set */
-			double *theta_ptr,        /* current theta */
-			double *gradient_ptr, /* nndef times theta + linear_func */
-			int nfeature,             /* how many features in nndef */
-			double bound,         /* Lagrange multipler for \ell_1 */
-			double tol)           /* precision for checking KKT conditions */        
-{
-  // First check inactive
-
-  int iactive;
-  double *theta_ptr_tmp;
-  double gradient;
-  double *gradient_ptr_tmp;
-  int nactive = *nactive_ptr;
-  int active_feature;
-  int *active_feature_ptr;
-
-  for (iactive=0; iactive<nactive; iactive++) {
-
-    active_feature_ptr = ((int *) ever_active_ptr + iactive);
-    active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
-    theta_ptr_tmp = ((double *) theta_ptr + active_feature);
-
-    gradient_ptr_tmp = ((double *) gradient_ptr + active_feature);
-
-    // Compute this coordinate of the gradient
-
-    gradient = *gradient_ptr_tmp;
-
-    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
-
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
-	return(0);
-      }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
-	return(0);
-      }
-
-    }
-    else {
-      if (fabs(gradient) > (1. + tol) * bound) {
-	return(0);
-      }
-    }
-  }
-
-  return(1);
-}
-
-
-double update_one_coord_qp(double *nndef_ptr,           /* A non-negative definite matrix */
-			   double *linear_func_ptr,     /* Linear term in objective */
-			   double *nndef_diag_ptr,      /* Diagonal of nndef */
-			   double *gradient_ptr,        /* nndef times theta + linear_func */
-			   int *ever_active_ptr,        /* Ever active set: 1-based */ 
-			   int *nactive_ptr,            /* Size of ever active set */
-			   int nfeature,                    /* How many features in nndef */
-			   double bound,                /* feasibility parameter */
-			   double *theta_ptr,               /* current value */
-			   int coord,                   /* which coordinate to update: 0-based */
-			   int is_active)               /* Is this coord in ever_active */     
-{
-
-  double delta;
-  double linear_term = 0;
-  double value = 0;
-  double old_value;
-  double *nndef_ptr_tmp;
-  double *gradient_ptr_tmp;
-  double *theta_ptr_tmp;
-  int icol = 0;
-
-  double *quadratic_ptr = ((double *) nndef_diag_ptr + coord);
-  double quadratic_term = *quadratic_ptr;
-
-  gradient_ptr_tmp = ((double *) gradient_ptr + coord);
-  linear_term = *gradient_ptr_tmp;
-
-  theta_ptr_tmp = ((double *) theta_ptr + coord);
-  old_value = *theta_ptr_tmp;
-
-  // The coord entry of gradient_ptr term has a diagonal term in it:
-  // nndef[coord, coord] * theta[coord]
-  // This removes it. 
-
-  linear_term -= quadratic_term * old_value;
-
-  // Now soft-threshold the coord entry of theta 
-
-  // Objective is t \mapsto q/2 * t^2 + l * t + bound |t|
-  // with q=quadratic_term and l=linear_term
-
-  // With a negative linear term, solution should be
-  // positive
-
-  if (linear_term < -bound) {
-    value = (-linear_term - bound) / quadratic_term;
-  }
-  else if (linear_term > bound) {
-    value = -(linear_term - bound) / quadratic_term;
-  }
-
-  // Add to active set if necessary
-
-  if ((is_active == 0) && (value != 0)) {
-    update_ever_active_qp(coord, ever_active_ptr, nactive_ptr);
-  }
-
-  // Update the linear term
-
-  if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
-
-    delta = value - old_value;
-    nndef_ptr_tmp = ((double *) nndef_ptr + coord * nfeature);
-    gradient_ptr_tmp = ((double *) gradient_ptr);
-
-    for (icol=0; icol<nfeature; icol++) {
-      *gradient_ptr_tmp = *gradient_ptr_tmp + delta * (*nndef_ptr_tmp);
-      gradient_ptr_tmp += 1;
-      nndef_ptr_tmp += 1;
-    }
-
-    theta_ptr_tmp = ((double *) theta_ptr + coord);
-    *theta_ptr_tmp = value;
-
-  }
-
-  return(value);
-
-}
-
-int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
-	     double *linear_func_ptr,    /* Linear term in objective */
-	     double *nndef_diag_ptr,     /* Diagonal of nndef */
-	     double *gradient_ptr,       /* nndef times theta */
-	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
-	     int *nactive_ptr,           /* Size of ever active set */
-	     int nfeature,               /* How many features in nndef */
-	     double bound,               /* feasibility parameter */
-	     double *theta,              /* current value */
-	     double *theta_old,          /* previous value */
-	     int maxiter,                /* max number of iterations */
-	     double kkt_tol,             /* precision for checking KKT conditions */
-	     double objective_tol,       /* precision for checking relative decrease in objective value */
-	     double parameter_tol,       /* precision for checking relative convergence of parameter */
-	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
-	     int objective_stop,         /* Break based on convergence of objective value? */
-             int kkt_stop,               /* Break based on KKT? */
-	     int param_stop)             /* Break based on parameter convergence? */
-{
-
-  int iter = 0;
-  int iter_old = 1;
-  int ifeature = 0;
-  int iactive = 0;
-  int *active_ptr;
-
-  int iter_active;
-  int niter_active=5;
-
-  double old_value, new_value; 
-  double norm_diff = 1.;
-  double norm_last = 1.;
-  double delta;
-  double *theta_ptr, *theta_old_ptr;
-
-  if (objective_stop) {
-
-    old_value = objective_qp(nndef_ptr,
-			     linear_func_ptr,
-			     ever_active_ptr,
-			     nactive_ptr,
-			     nfeature,
-			     bound,
-			     theta);
-
-  }
-
-
-  for (iter=0; iter<maxiter; iter++) {
-
-    // Update the active variables first -- do this niter_active times
-
-    for (iter_active=0; iter_active<niter_active; iter_active++) { 
-
-        active_ptr = (int *) ever_active_ptr;
-        for (iactive=0; iactive < *nactive_ptr; iactive++) {
-
-	  update_one_coord_qp(nndef_ptr,
-			      linear_func_ptr,
-			      nndef_diag_ptr,
-			      gradient_ptr,
-			      ever_active_ptr,
-			      nactive_ptr,
-			      nfeature,
-			      bound,
-			      theta,
-			      *active_ptr - 1,   // Ever-active is 1-based
-			      1);
-	  active_ptr++;
-	}
-
-	// Check KKT of active subproblem
-
-	if (check_KKT_qp_active(ever_active_ptr,
-				nactive_ptr,
-				theta,
-				gradient_ptr,
-				nfeature,
-				bound,
-				kkt_tol) == 1) {
-	  break;
-	}
-    }
-
-    // Check KKT
-
-    if (kkt_stop) {
-      if (check_KKT_qp(theta, 
-		       gradient_ptr,
-		       nfeature,
-		       bound,
-		       kkt_tol) == 1) {
-	break;
-      }
-    }
-
-    // Update all variables 
-
-    for (ifeature=0; ifeature<nfeature; ifeature++) {
-
-      update_one_coord_qp(nndef_ptr,
-			  linear_func_ptr,
-			  nndef_diag_ptr,
-			  gradient_ptr,
-			  ever_active_ptr,
-			  nactive_ptr,
-			  nfeature,
-			  bound,
-			  theta,
-			  ifeature,
-			  0);
-    }
-
-    // Check KKT
-
-    if (kkt_stop) {
-      if (check_KKT_qp(theta, 
-		       gradient_ptr,
-		       nfeature,
-		       bound,
-		       kkt_tol) == 1) {
-	break;
-      }
-    }
-					  
-
-    if (iter == 2 * iter_old) { // Geometric iterations from Adel's code
-
-      // Check based on norm 
-
-      if (param_stop) { 
-	iter_old = iter;
-	norm_diff = 0;
-	norm_last = 0;
-	for (ifeature=0; ifeature<nfeature; ifeature++) {
-	  theta_old_ptr = ((double *) theta_old + ifeature);
-	  theta_ptr = ((double *) theta + ifeature);
-	  delta = (*theta_ptr - *theta_old_ptr);
-	  norm_diff += delta * delta;
-	  norm_last += (*theta_ptr) * (*theta_ptr);
-	  *theta_old_ptr = (*theta_ptr);
-	}
-	norm_diff = sqrt(norm_diff);
-	norm_last = sqrt(norm_last);
-	
-	if (norm_diff < parameter_tol * norm_last) {
-	  break;
-	}
-      }
-
-      // Check relative decrease of objective
-
-      if (objective_stop) {
-	new_value = objective_qp(nndef_ptr,
-				 linear_func_ptr,
-				 ever_active_ptr,
-				 nactive_ptr,
-				 nfeature,
-				 bound,
-				 theta);
-
-	if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
-	  break;
-	}
-	old_value = new_value;
-      }
-
-    }
-
-    // Check size of active set
-
-    if (*nactive_ptr >= max_active) {
-      break;
-    }
-
-  }
-  return(iter);
-}
-
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
deleted file mode 100644
index 41e29ce..0000000
--- a/selectiveInference/src/quadratic_program_wide.c
+++ /dev/null
@@ -1,590 +0,0 @@
-#include <math.h> // for fabs
-
-// Find an approximate row of \hat{nndef}^{-1}
-
-// Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
-
-// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2/n + l^T\theta + \mu \|\theta\|_1 + \frac{\epsilon}{2} \|\theta\|^2_2
-// where l is `linear_func` below
-
-// This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
-// Therefore we don't have to negate the answer to get theta.
-// Update one coordinate 
-
-// Throughout X is a design matrix
-
-double objective_wide(double *X_theta_ptr,     /* Fitted values */
-		      double *linear_func_ptr, /* Linear term in objective */
-		      int *ever_active_ptr,    /* Ever active set: 0-based */ 
-		      int *nactive_ptr,        /* Size of ever active set */
-		      int ncase,               /* how many rows in X */
-		      int nfeature,            /* how many columns in X */
-		      double *bound_ptr,       /* Lagrange multiplers for \ell_1 */
-		      double ridge_term,       /* Ridge / ENet term */
-		      double *theta_ptr)       /* current value */
-{
-  int icase, iactive;
-  double value = 0;
-  double *bound_ptr_tmp;
-  double *X_theta_ptr_tmp = X_theta_ptr;
-  double *linear_func_ptr_tmp = linear_func_ptr;
-  double *theta_ptr_tmp;
-  int *active_feature_ptr;
-  int active_feature;
-  int nactive = *nactive_ptr;
-
-  // The term \|X\theta\|^2_2/n, with n=ncase
-
-  for (icase=0; icase<ncase; icase++) {
-
-    X_theta_ptr_tmp = ((double *) X_theta_ptr + icase);
-    value += (*(X_theta_ptr_tmp)) * (*(X_theta_ptr_tmp));
-
-  }
-
-  value *= 0.5 / ncase;
-
-  for (iactive=0; iactive<nactive; iactive++) {
-
-    // The linear term in the objective
-
-    active_feature_ptr = ((int *) ever_active_ptr + iactive);
-    active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
-
-    theta_ptr_tmp = ((double *) theta_ptr + active_feature);
-    linear_func_ptr_tmp = ((double *) linear_func_ptr + active_feature);
-    value += (*linear_func_ptr_tmp) * (*theta_ptr_tmp); 
-
-    // The \ell_1 term
-
-    bound_ptr_tmp = ((double *) bound_ptr + active_feature);
-    value += (*bound_ptr_tmp) * fabs((*theta_ptr_tmp));
-
-    // The ridge term
-
-    value += 0.5 * ridge_term * (*theta_ptr_tmp) * (*theta_ptr_tmp);
-  }
-  
-  return(value);
-}
-
-// Compute, update and return one coordinate of the gradient of \|X\theta\|^2_2/2n
-
-double compute_gradient_coord(double *gradient_ptr,        /* Gradient -- one coordinate will be updated if needed */
-			      double *X_theta_ptr,         /* Current fitted values */
-			      double *X_ptr,               /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-			      double *linear_func_ptr,     /* Linear term in objective */   
-			      int *need_update_ptr,        /* Which coordinates need to be updated? */
-			      int coord,                   /* Coordinate we are trying to update */
-			      int ncase)                   /* How many rows in X */
-{
-
-  double *gradient_ptr_tmp;
-  double *X_theta_ptr_tmp;
-  double *X_ptr_tmp;  
-  double *linear_func_ptr_tmp;  
-  int *need_update_ptr_tmp;
-  int icase;
-  double value = 0.;
-
-  need_update_ptr_tmp = ((int *) need_update_ptr + coord);
-
-  // Check if this coordinate needs updating
-  if (*need_update_ptr_tmp == 1) {
-
-    for (icase=0; icase<ncase; icase++) {
-      X_ptr_tmp = ((double *) X_ptr + coord * ncase + icase);
-      X_theta_ptr_tmp = ((double *) X_theta_ptr + icase);
-      value += (*X_ptr_tmp) * (*X_theta_ptr_tmp);
-    }
-
-    value /= ncase;
-
-    linear_func_ptr_tmp = ((double *) linear_func_ptr + coord);
-    value += *linear_func_ptr_tmp;
-
-    gradient_ptr_tmp = ((double *) gradient_ptr + coord);
-    *gradient_ptr_tmp = value;
-
-    *need_update_ptr_tmp = 0;
-  }
-  else {
-    gradient_ptr_tmp = ((double *) gradient_ptr + coord);
-    value = *gradient_ptr_tmp;
-  }
-
-  return(value);
-}
-
-
-// Ever-active is 1-based
-// coord is 0-based
-int update_ever_active_wide(int coord,
-			    int *ever_active_ptr,
-			    int *nactive_ptr) {
-  int iactive;
-  int active_var;
-  int nactive = *nactive_ptr;
-  int *ever_active_ptr_tmp = ever_active_ptr;
-
-  for (iactive=0; iactive<nactive; iactive++) {
-    ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
-    active_var = *ever_active_ptr_tmp;
-
-    if (active_var - 1 == coord) {          // Ever-active is 1-based
-      return(1);
-    }
-  }
-  
-  // If we haven't returned yet, this means the coord was not in 
-  // ever_active.
-
-  // Add it to the active set and increment the 
-  // number of active variables
-
-  ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
-  *ever_active_ptr_tmp = coord + 1;         // Ever-active is 1-based
-  *nactive_ptr += 1;
-
-  return(0);
-}
-
-void update_gradient_wide(double *gradient_ptr,     /* X^TX/ncase times theta + linear_func */
-			  double *X_theta_ptr,      /* Current fitted values */
-			  double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-			  double *linear_func_ptr,  /* Linear term in objective */   
-			  int *need_update_ptr,     /* Which coordinates need to be updated? */
-			  int nfeature,             /* how many columns in X */
-			  int ncase)                /* how many rows in X */
-{
-  int ifeature;
-
-  for (ifeature=0; ifeature<nfeature; ifeature++) {
-    compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, ifeature, ncase);
-  }
-
-}
-
-int check_KKT_wide(double *theta_ptr,        /* current theta */
-		   double *gradient_ptr,     /* X^TX/ncase times theta + linear_func*/
-		   double *X_theta_ptr,      /* Current fitted values */
-		   double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-		   double *linear_func_ptr,  /* Linear term in objective */   
-		   int *need_update_ptr,     /* Which coordinates need to be updated? */
-		   int nfeature,             /* how many columns in X */
-		   int ncase,                /* how many rows in X */
-		   double *bound_ptr,        /* Lagrange multiplers for \ell_1 */
-		   double ridge_term,        /* Ridge / ENet term */
-		   double tol)               /* precision for checking KKT conditions */        
-{
-  // First check inactive
-
-  int ifeature;
-  double *theta_ptr_tmp;
-  double *bound_ptr_tmp;
-  double bound;
-  double gradient;
-
-  for (ifeature=0; ifeature<nfeature; ifeature++) {
-
-    theta_ptr_tmp = ((double *) theta_ptr + ifeature);
-    bound_ptr_tmp = ((double *) bound_ptr + ifeature);
-    bound = *bound_ptr_tmp;
-
-    // Compute this coordinate of the gradient
-
-    gradient = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, ifeature, ncase);
-
-    if ((*theta_ptr_tmp != 0) && (bound != 0)) { // these coordinates of gradients should be equal to -bound
-      
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + ridge_term * (*theta_ptr_tmp) + bound) > tol * bound)) {
-	return(0);
-      }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient + ridge_term * (*theta_ptr_tmp) - bound) > tol * bound)) {
-	return(0);
-      }
-      
-    }
-    else if (bound != 0) {
-      if (fabs(gradient) > (1. + tol) * bound) {
-	return(0);
-      }
-    }
-  }
-
-  return(1);
-}
-
-int check_KKT_wide_active(int *ever_active_ptr,           /* Ever active set: 0-based */ 
-			  int *nactive_ptr,               /* Size of ever active set */
-			  double *theta_ptr,              /* current theta */
-			  double *gradient_ptr,           /* X^TX/ncase times theta + linear_func*/
-			  double *X_theta_ptr,            /* Current fitted values */
-			  double *X_ptr,                  /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-			  double *linear_func_ptr,        /* Linear term in objective */   
-			  int *need_update_ptr,           /* Which coordinates need to be updated? */
-			  int nfeature,                   /* how many columns in X */
-			  int ncase,                      /* how many rows in X */
-			  double *bound_ptr,              /* Lagrange multipliers for \ell_1 */
-			  double ridge_term,              /* Ridge / ENet term */
-			  double tol)                     /* precision for checking KKT conditions */        
-{
-  // First check inactive
-
-  int iactive;
-  double *theta_ptr_tmp;
-  double *bound_ptr_tmp;
-  double bound;
-  double gradient;
-  int nactive = *nactive_ptr;
-  int active_feature;
-  int *active_feature_ptr;
-
-  for (iactive=0; iactive<nactive; iactive++) {
-
-    active_feature_ptr = ((int *) ever_active_ptr + iactive);
-    active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
-
-    theta_ptr_tmp = ((double *) theta_ptr + active_feature);
-    bound_ptr_tmp = ((double *) bound_ptr + active_feature);
-    bound = *bound_ptr_tmp;
-
-    // Compute this coordinate of the gradient
-
-    gradient = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, active_feature, ncase);
-
-    if ((*theta_ptr_tmp != 0) && (bound != 0)) { // these coordinates of gradients should be equal to -bound
-
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + ridge_term * (*theta_ptr_tmp) + bound) > tol * bound)) {
-	return(0);
-      }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient + ridge_term * (*theta_ptr_tmp) - bound) > tol * bound)) {
-	return(0);
-      }
-
-    }
-    else if (bound != 0) {
-      if (fabs(gradient) > (1. + tol) * bound) {
-	return(0);
-      }
-    }
-  }
-
-  return(1);
-}
-
-double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
-			     double *linear_func_ptr,     /* Linear term in objective */
-			     double *nndef_diag_ptr,      /* Diagonal of nndef */
-			     double *gradient_ptr,        /* X^TX/ncase times theta + linear_func*/
-			     int *ever_active_ptr,        /* Ever active set: 1-based */ 
-			     int *nactive_ptr,            /* Size of ever active set */
-			     double *X_theta_ptr,         /* X\theta -- fitted values */
-			     int *need_update_ptr,        /* Whether a gradient coordinate needs update or not */
-			     int ncase,                   /* How many rows in X */
-			     int nfeature,                /* How many rows in X */
-			     double *bound_ptr,           /* Lagrange multipliers */
-			     double ridge_term,           /* Ridge / ENet term */
-			     double *theta_ptr,           /* current value */
-			     int coord,                   /* which coordinate to update: 0-based */
-			     int is_active)               /* Is this coord in ever_active */     
-{
-
-  double delta;
-  double linear_term = 0;
-  double value = 0;
-  double old_value;
-  double *X_ptr_tmp;
-  double *X_theta_ptr_tmp;
-  int *need_update_ptr_tmp;
-  double *theta_ptr_tmp;
-  double *bound_ptr_tmp;
-  double bound;
-  int ifeature, icase;
-
-  double *diagonal_ptr = ((double *) nndef_diag_ptr + coord);
-  double diagonal_entry = *diagonal_ptr;
-
-  linear_term = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, coord, ncase);
-
-  theta_ptr_tmp = ((double *) theta_ptr + coord);
-  old_value = *theta_ptr_tmp;
-
-  bound_ptr_tmp = ((double *) bound_ptr + coord);
-  bound = *bound_ptr_tmp;
-
-  // The coord entry of gradient_ptr term has a diagonal term in it:
-  // (X^TX)[coord, coord] * theta[coord] / ncase
-  // This removes it. 
-
-  linear_term -= diagonal_entry * old_value;
-
-  // Now soft-threshold the coord entry of theta 
-
-  // Objective is t \mapsto (q+eps)/2 * t^2 + l * t + bound |t| + 
-  // with q=diagonal_entry and l=linear_term and eps=ridge_Term
-
-  // With a negative linear term, solution should be
-  // positive
-
-  if (linear_term < -bound) {
-    value = (-linear_term - bound) / (diagonal_entry + ridge_term);
-  }
-  else if (linear_term > bound) {
-    value = -(linear_term - bound) / (diagonal_entry + ridge_term);
-  }
-
-  // Add to active set if necessary
-
-  if ((is_active == 0) && (value != 0)) {
-    update_ever_active_wide(coord, ever_active_ptr, nactive_ptr);
-  }
-
-  // Update X\theta if changed
-
-  if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
-
-    // Set the update_gradient_ptr to 1
-
-    need_update_ptr_tmp = need_update_ptr;
-    for (ifeature=0; ifeature<nfeature; ifeature++) {
-      *need_update_ptr_tmp = 1;
-      need_update_ptr_tmp++;
-    }
-
-    // Update X\theta
-
-    delta = value - old_value;
-
-    for (icase=0; icase<ncase; icase++) {
-      X_theta_ptr_tmp = ((double *) X_theta_ptr + icase);
-      X_ptr_tmp = ((double *) X_ptr + coord * ncase + icase);
-      *X_theta_ptr_tmp = (*X_theta_ptr_tmp) + delta * (*X_ptr_tmp);
-    }
-
-    theta_ptr_tmp = ((double *) theta_ptr + coord);
-    *theta_ptr_tmp = value;
-
-    theta_ptr_tmp = theta_ptr;
-    for (ifeature=0; ifeature<nfeature; ifeature++) {
-      theta_ptr_tmp++;
-    }
-
-  }
-
-  return(value);
-
-}
-
-int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-	       double *X_theta_ptr,        /* Fitted values   */
-	       double *linear_func_ptr,    /* Linear term in objective */
-	       double *nndef_diag_ptr,     /* Diagonal entries of non-neg def matrix */
-	       double *gradient_ptr,       /* X^TX/ncase times theta + linear_func*/
-	       int *need_update_ptr,       /* Keeps track of updated gradient coords */
-	       int *ever_active_ptr,       /* Ever active set: 1-based */ 
-	       int *nactive_ptr,           /* Size of ever active set */
-	       int ncase,                  /* How many rows in X */
-	       int nfeature,               /* How many columns in X */
-	       double *bound_ptr,          /* Lagrange multipliers */
-	       double ridge_term,          /* Ridge / ENet term */
-	       double *theta_ptr,          /* current value */
-	       double *theta_old_ptr,      /* previous value */
-	       int maxiter,                /* max number of iterations */
-	       double kkt_tol,             /* precision for checking KKT conditions */
-	       double objective_tol,       /* precision for checking relative decrease in objective value */
-	       double parameter_tol,       /* precision for checking relative convergence of parameter */
-	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
-	       int objective_stop,         /* Break based on convergence of objective value? */
-	       int kkt_stop,               /* Break based on KKT? */
-	       int param_stop)             /* Break based on parameter convergence? */
-{
-
-  int iter = 0;
-  int iter_old = 1;
-  int ifeature = 0;
-  int iactive = 0;
-  int *active_ptr;
-
-  double old_value, new_value; 
-  int niter_active = 5;
-  int iter_active;
-
-  double norm_diff = 1.;
-  double norm_last = 1.;
-  double delta;
-  double *theta_ptr_tmp, *theta_old_ptr_tmp;
-
-  if (objective_stop) {
-
-    old_value = objective_wide(X_theta_ptr,
-			       linear_func_ptr,
-			       ever_active_ptr,
-			       nactive_ptr,
-			       ncase,
-			       nfeature,
-			       bound_ptr,
-			       ridge_term,
-			       theta_ptr);
-    new_value = old_value;
-
-  }
-
-  for (iter=0; iter<maxiter; iter++) {
-
-    // Update the active variables first -- do this niter_active times
-
-    for (iter_active=0; iter_active<niter_active; iter_active++) { 
-
-        active_ptr = (int *) ever_active_ptr;
-        for (iactive=0; iactive < *nactive_ptr; iactive++) {
-
-          update_one_coord_wide(X_ptr,
-    			        linear_func_ptr,
-				nndef_diag_ptr,
-				gradient_ptr,
-				ever_active_ptr,
-				nactive_ptr,
-				X_theta_ptr,
-				need_update_ptr,
-				ncase,
-				nfeature,
-				bound_ptr,
-				ridge_term,
-				theta_ptr,
-				*active_ptr - 1,   // Ever-active is 1-based
-				1);
-	  active_ptr++;
-	}
-
-	// Check KKT of active subproblem
-
-	if (check_KKT_wide_active(ever_active_ptr,
-				  nactive_ptr,
-				  theta_ptr,
-				  gradient_ptr,
-				  X_theta_ptr,
-				  X_ptr,
-				  linear_func_ptr,
-				  need_update_ptr,
-				  nfeature,
-				  ncase,
-				  bound_ptr,
-				  ridge_term,
-				  kkt_tol) == 1) {
-	  break;
-	}
-
-    }
-
-    // Check KKT
-
-    if (kkt_stop) { 
-      if (check_KKT_wide(theta_ptr,
-			 gradient_ptr,
-			 X_theta_ptr,
-			 X_ptr,
-			 linear_func_ptr,
-			 need_update_ptr,
-			 nfeature,
-			 ncase,
-			 bound_ptr,
-			 ridge_term,
-			 kkt_tol) == 1) {
-	break;
-      }
-    }
-
-    // Update all variables 
-
-    for (ifeature=0; ifeature<nfeature; ifeature++) {
-
-      update_one_coord_wide(X_ptr,
-			    linear_func_ptr,
-			    nndef_diag_ptr,
-			    gradient_ptr,
-			    ever_active_ptr,
-			    nactive_ptr,
-			    X_theta_ptr,
-			    need_update_ptr,
-			    ncase,
-			    nfeature,
-			    bound_ptr,
-			    ridge_term,
-			    theta_ptr,
-			    ifeature,
-			    0);
-    }
-
-    // Check KKT
-
-    if (kkt_stop) {
-      if (check_KKT_wide(theta_ptr,
-			 gradient_ptr,
-			 X_theta_ptr,
-			 X_ptr,
-			 linear_func_ptr,
-			 need_update_ptr,
-			 nfeature,
-			 ncase,
-			 bound_ptr,
-			 ridge_term,
-			 kkt_tol) == 1) {
-	break;
-      }
-    }
-
-    if (iter == 2 * iter_old) { // Geometric iterations from Adel's code
-
-      // Check based on norm 
-
-      if (param_stop) {
-	iter_old = iter;
-	norm_diff = 0;
-	norm_last = 0;
-	for (ifeature=0; ifeature<nfeature; ifeature++) {
-	  theta_old_ptr_tmp = ((double *) theta_old_ptr + ifeature);
-	  theta_ptr_tmp = ((double *) theta_ptr + ifeature);
-	  delta = (*theta_ptr_tmp - *theta_old_ptr_tmp);
-	  norm_diff += delta * delta;
-	  norm_last += (*theta_ptr_tmp) * (*theta_ptr_tmp);
-	  *theta_old_ptr = (*theta_ptr_tmp);
-	}
-	norm_diff = sqrt(norm_diff);
-	norm_last = sqrt(norm_last);
-	
-	if (norm_diff < parameter_tol * norm_last) {
-	  break;
-	}
-      }
-
-      // Check relative decrease of objective
-
-      if (objective_stop) {
-	new_value = objective_wide(X_theta_ptr,
-				   linear_func_ptr,
-				   ever_active_ptr,
-				   nactive_ptr,
-				   ncase,
-				   nfeature,
-				   bound_ptr,
-				   ridge_term,
-				   theta_ptr);
-
-	if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
-	  break;
-	}
-	old_value = new_value;
-      }
-    }
-
-    // Check size of active set
-
-    if (*nactive_ptr >= max_active) {
-      break;
-    }
-
-  }
-  return(iter);
-}
-
diff --git a/selectiveInference/src/randomized_lasso.c b/selectiveInference/src/randomized_lasso.c
deleted file mode 100644
index 1f396b1..0000000
--- a/selectiveInference/src/randomized_lasso.c
+++ /dev/null
@@ -1,190 +0,0 @@
-#include <math.h> // for fabs
-
-// Augmented density for randomized LASSO after
-// Gaussian randomization
-
-// Described in https://arxiv.org/abs/1609.05609
-
-// Gaussian is product of IID N(0, noise_scale^2) density
-// Evaluated at A_D D + A_O O + h
-
-// Laplace is product of IID Laplace with scale noise_scale
-// Also evaluated at A_D D + A_O O + h
-
-// Matrices are assumed in column major order! 
-
-double log_density_gaussian(double noise_scale,             // Scale of randomization
-			    int ndim,                       // Number of features -- "p"
-			    int ninternal,                  // Dimension of internal data representation often 1
-			    int noptimization,              // Dimension of optimization variables -- "p"
-			    double *internal_linear,        // A_D -- linear part for data
-			    double *internal_state,         // D -- data state
-			    double *optimization_linear,    // A_O -- linear part for optimization variables
-			    double *optimization_state,     // O -- optimization state
-			    double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double denom = 2 * noise_scale * noise_scale;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Internal (i.e. data) contribution
-    for (icol=0; icol<ninternal; icol++) {
-      
-      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
-      internal_state_ptr = ((double *) internal_state + icol);
-
-      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
-    }
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= (reconstruction * reconstruction) / denom;
-  }
-
-  return(value);
-}
-
-double log_density_laplace(double noise_scale,             // Scale of randomization
-			   int ndim,                       // Number of features -- "p"
-			   int ninternal,                  // Dimension of internal data representation often 1
-			   int noptimization,              // Dimension of optimization variables -- "p"
-			   double *internal_linear,        // A_D -- linear part for data
-			   double *internal_state,         // D -- data state
-			   double *optimization_linear,    // A_O -- linear part for optimization variables
-			   double *optimization_state,     // O -- optimization state
-			   double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Internal (i.e. data) contribution
-    for (icol=0; icol<ninternal; icol++) {
-      
-      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
-      internal_state_ptr = ((double *) internal_state + icol);
-
-      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
-    }
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= fabs(reconstruction) / noise_scale;
-  }
-
-  return(value);
-}
-
-// Keeping internal (data) state fixed
-
-double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
-					int ndim,                       // Number of features -- "p"
-					int noptimization,              // Dimension of optimization variables -- "p"
-					double *optimization_linear,    // A_O -- linear part for optimization variables
-					double *optimization_state,     // O -- optimization state
-					double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double denom = 2 * noise_scale * noise_scale;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= reconstruction * reconstruction / denom;
-  }
-
-  return(value);
-}
-
-double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
-				       int ndim,                       // Number of features -- "p"
-				       int noptimization,              // Dimension of optimization variables -- "p"
-				       double *optimization_linear,    // A_O -- linear part for optimization variables
-				       double *optimization_state,     // O -- optimization state
-				       double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= fabs(reconstruction) / noise_scale;
-  }
-
-  return(value);
-}
diff --git a/selectiveInference/src/randomized_lasso.h b/selectiveInference/src/randomized_lasso.h
deleted file mode 100644
index e29142d..0000000
--- a/selectiveInference/src/randomized_lasso.h
+++ /dev/null
@@ -1,42 +0,0 @@
-#ifdef __cplusplus
-extern "C"
-{
-#endif /* __cplusplus */
-
-double log_density_gaussian(double noise_scale,             // Scale of randomization
-			    int ndim,                       // Number of features -- "p"
-			    int ninternal,                  // Dimension of internal data representation often 1
-			    int noptimization,              // Dimension of optimization variables -- "p"
-			    double *internal_linear,        // A_D -- linear part for data
-			    double *internal_state,         // D -- data state
-			    double *optimization_linear,    // A_O -- linear part for optimization variables
-			    double *optimization_state,     // O -- optimization state
-			    double *offset);                // h -- offset in affine transform -- "p" dimensional 
-  
-double log_density_laplace(double noise_scale,             // Scale of randomization
-			   int ndim,                       // Number of features -- "p"
-			   int ninternal,                  // Dimension of internal data representation often 1
-			   int noptimization,              // Dimension of optimization variables -- "p"
-			   double *internal_linear,        // A_D -- linear part for data
-			   double *internal_state,         // D -- data state
-			   double *optimization_linear,    // A_O -- linear part for optimization variables
-			   double *optimization_state,     // O -- optimization state
-			   double *offset);                // h -- offset in affine transform -- "p" dimensional 
-
-double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
-					int ndim,                       // Number of features -- "p"
-					int noptimization,              // Dimension of optimization variables -- "p"
-					double *optimization_linear,    // A_O -- linear part for optimization variables
-					double *optimization_state,     // O -- optimization state
-					double *offset);                // h -- offset in affine transform -- "p" dimensional 
-
-double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
-				       int ndim,                       // Number of features -- "p"
-				       int noptimization,              // Dimension of optimization variables -- "p"
-				       double *optimization_linear,    // A_O -- linear part for optimization variables
-				       double *optimization_state,     // O -- optimization state
-				       double *offset);                // h -- offset in affine transform -- "p" dimensional 
-
-#ifdef __cplusplus
-}  /* extern "C" */
-#endif /* __cplusplus */
diff --git a/selectiveInference/src/truncnorm.c b/selectiveInference/src/truncnorm.c
deleted file mode 100644
index cca61d9..0000000
--- a/selectiveInference/src/truncnorm.c
+++ /dev/null
@@ -1,188 +0,0 @@
-#include <R.h>
-#include <Rmath.h>
-
-// Take a Gibbs hit and run step along a given direction
-
-// Assumes the covariance is identity
-
-void gibbs_step(double *state,     /* state has law N(0,I) constrained to polyhedral set \{y:Ay \leq b\}*/ 
-		double *direction, /* direction we will take Gibbs step */
-		double *U,         /* A %*% state - b */
-		double *alpha,     /* A %*% direction */
-		int nconstraint,   /* number of rows of A */
-		int nstate)        /* dimension of state */
-{
-               
-  int istate;
-  double value = 0;
-
-  /* Compute V=\eta^Ty */
-
-  for (istate = 0; istate < nstate; istate++) {
-    value += direction[istate] * state[istate];
-  }
-
-  /* Compute upper and lower bounds */
-
-  double lower_bound = -1e12;
-  double upper_bound = 1e12;
-  double bound_val = 0;
-  double tol=1.e-7;
-  int iconstraint;
-
-  for (iconstraint = 0; iconstraint < nconstraint; iconstraint++) {
-
-    bound_val = -U[iconstraint] / alpha[iconstraint] + value;
-
-    if ((alpha[iconstraint] > tol) &&
-	(bound_val < upper_bound)) {
-	upper_bound = bound_val;
-    }
-    else if ((alpha[iconstraint] < -tol) &&
-	  (bound_val > lower_bound)) {
-	lower_bound = bound_val;
-    }
-
-  }
-
-  /* Ensure constraints are satisfied */
-
-  if (lower_bound > value) {
-    lower_bound = value - tol;
-  }
-  else if (upper_bound < value) {
-    upper_bound = value + tol;
-  }
-
-  /* Check to see if constraints are satisfied */
-
-  /* if (lower_bound > upper_bound) {
-    
-     }*/
-
-  /* Now, take a step */
-
-  double tnorm; /* the 1D gaussian variable */
-  double cdfU, cdfL, unif; /* temp variables */
-
-  if (upper_bound < -10) {
-
-    /* use Exp approximation */
-    /* the approximation is that */
-    /* Z | lower_bound < Z < upper_bound */
-    /* is fabs(upper_bound) * (upper_bound - Z) = E approx Exp(1) */
-    /* so Z = upper_bound - E / fabs(upper_bound) */
-    /* and the truncation of the exponential is */
-    /* E < fabs(upper_bound - lower_bound) * fabs(upper_bound) = D */
-    
-    /* this has distribution function (1 - exp(-x)) / (1 - exp(-D)) */
-    /* so to draw from this distribution */
-    /* we set E = - log(1 - U * (1 - exp(-D))) where U is Unif(0,1) */
-    /* and Z (= tnorm below) is as stated */
-
-    unif = runif(0., 1.) * (1 - exp(-fabs((lower_bound - upper_bound) * upper_bound)));
-    tnorm = (upper_bound + log(1 - unif) / fabs(upper_bound));
-  }
-  else if (lower_bound > 10) {
-
-    /* here Z = lower_bound + E / fabs(lower_bound) (though lower_bound is positive) */
-    /* and D = fabs((upper_bound - lower_bound) * lower_bound) */
-
-    unif = runif(0., 1.) * (1 - exp(-fabs((upper_bound - lower_bound) * lower_bound)));
-    tnorm = (lower_bound - log(1 - unif) / lower_bound);
-  }
-  else if (lower_bound < 0) {
-    cdfL = pnorm(lower_bound, 0., 1., 1, 0); 
-    cdfU = pnorm(upper_bound, 0., 1., 1, 0); 
-    unif = runif(0., 1.) * (cdfU - cdfL) + cdfL; 
-    if (unif < 0.5) {
-      tnorm = qnorm(unif, 0., 1., 1, 0); 
-    }
-    else {
-      tnorm = -qnorm(1-unif, 0., 1., 1, 0); 
-    }
-  }
-  else {
-    cdfL = pnorm(-lower_bound, 0., 1., 1, 0); 
-    cdfU = pnorm(-upper_bound, 0., 1., 1, 0); 
-    unif = runif(0., 1.) * (cdfL - cdfU) + cdfU;
-    if (unif < 0.5) {
-      tnorm = -qnorm(unif, 0., 1., 1, 0); 
-    }
-    else {
-      tnorm = qnorm(1-unif, 0., 1., 1, 0);
-    }
-  }
-
-  /* Now update the state and U */
-
-  double delta = tnorm - value;
-
-  for (istate = 0; istate < nstate; istate++) {
-    state[istate] += delta * direction[istate];
-  }
-  for (iconstraint = 0; iconstraint < nconstraint; iconstraint++) {
-    U[iconstraint] += delta * alpha[iconstraint] ;
-  }
-
-  /* End of gibbs_step */
-
-}
-
-void sample_truncnorm_white(double *state,      /* state has law N(0,I) constrained to polyhedral set \{y:Ay \leq b\}*/ 
-			    double *U,          /* A %*% state - b */
-			    double *directions, /* possible steps for sampler to take */
-                                                /* assumed to be stored as list of columns of dimension nstate */
-			                        /* has shape (nstate, ndirection) */
-			    double *alphas,     /* The matrix A %*% directions */
-      			                        /* has shape (nconstraint, ndirection) */
-			    double *output,     /* array in which to store samples */
-                                                /* assumed will stored as list of vectors of dimension nstate */
-                                                /* has shape (nstate, ndraw) */  
-			    int *pnconstraint,  /* number of rows of A */
-			    int *pndirection,   /* the possible number of directions to choose from */
-                                                /* `directions` should have size nstate*ndirection */
-			    int *pnstate,       /* dimension of state */
-			    int *pburnin,       /* number of burnin steps */
-			    int *pndraw)        /* total number of samples to return */
-{
-
-  int iter_count;
-  int which_direction;
-
-  int nconstraint = *pnconstraint;
-  int ndirection = *pndirection;
-  int nstate = *pnstate;
-  int burnin = *pburnin;
-  int ndraw = *pndraw;
-
-  double *direction, *alpha;
-
-  for (iter_count = 0; iter_count < burnin + ndraw; iter_count++) {
-  
-    which_direction = (int) floor(runif(0., 1.) * ndirection); 
-    direction = ((double *) directions) + nstate * which_direction; 
-    alpha = ((double *) alphas) + nconstraint * which_direction; 
-
-    /* take a step, which implicitly updates `state` and `U` */
-
-    gibbs_step(state,
-               direction,
-	       U,
-	       alpha,
-	       nconstraint,
-	       nstate);
-
-    /* Store result if after burnin */
-
-    int istate;
-    if (iter_count >= burnin) {
-      for (istate = 0; istate < nstate; istate++) {
-	*output = state[istate];
-	output++;
-      }
-    }
-  }
-
-}
-

From 39b967577e8e642f8da2c1d36277c0a6f47f0b6b Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 30 Oct 2017 21:54:41 -0700
Subject: [PATCH 318/493] updating travis script

---
 .travis.yml | 4 ++++
 Makefile    | 9 +++++----
 2 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 541fa3a..c324f88 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -12,5 +12,9 @@ warnings_are_errors: true
 before_install:
   - tlmgr install index # for texlive and vignette?
   - R -e 'install.packages(c("Rcpp", "intervals"), repos="http://cloud.r-project.org")'
+  - cd C-software
+  - git submodule init
+  - git submodule update
+  - make src
   - make Rcpp
   - cd selectiveInference
diff --git a/Makefile b/Makefile
index 5580814..671099f 100644
--- a/Makefile
+++ b/Makefile
@@ -3,14 +3,15 @@ Rcpp:
 	- rm -f selectiveInference/R/RcppExports.R
 	Rscript -e "library(Rcpp); Rcpp::compileAttributes('selectiveInference')"
 
-install: Rcpp
-	cp C-software/src/* selectiveInference/src
+install: Rcpp src
 	R CMD INSTALL selectiveInference
 
-build: 
-	cp C-software/src/* selectiveInference/src
+build: src 
 	R CMD build selectiveInference
 
+src:
+	cp C-software/src/* selectiveInference/src
+
 check: Rcpp build 
 	R CMD build selectiveInference
 	R CMD check selectiveInference_1.2.2.tar.gz # fix this to be a script variable
\ No newline at end of file

From cd918b1fa35e4721dafcc20e97ffc882a1edf21a Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 11 Oct 2017 07:38:07 -0700
Subject: [PATCH 319/493] variable rename

---
 selectiveInference/R/funs.fixedCox.R   | 12 ++++++------
 selectiveInference/R/funs.fixedLogit.R | 12 ++++++------
 2 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/selectiveInference/R/funs.fixedCox.R b/selectiveInference/R/funs.fixedCox.R
index 2fa8c08..9f35c95 100644
--- a/selectiveInference/R/funs.fixedCox.R
+++ b/selectiveInference/R/funs.fixedCox.R
@@ -29,7 +29,7 @@ if( sum(status==0)+sum(status==1)!=length(y)) stop("status vector must have valu
 vars=which(m)
 if(sum(m)>0){
     bhat=beta[beta!=0] #penalized coefs just for active variables
-    s2=sign(bhat)
+    sign_bhat=sign(bhat)
 
  #check KKT
     
@@ -40,7 +40,7 @@ if(sum(m)>0){
     res=residuals(aaa,type="score")
 if(!is.matrix(res)) res=matrix(res,ncol=1)
 scor=colSums(res)
-    g=(scor+lambda*s2)/(2*lambda)
+    g=(scor+lambda*sign_bhat)/(2*lambda)
 #    cat(c(g,lambda,tol.kkt),fill=T)
      if (any(abs(g) > 1+tol.kkt) )
     warning(paste("Solution beta does not satisfy the KKT conditions",
@@ -49,9 +49,9 @@ scor=colSums(res)
 # Hessian of partial likelihood at the LASSO solution    
 MM=vcov(aaa)
 
-bbar=(bhat+lambda*MM%*%s2)
-A1=-(mydiag(s2))
-b1= -(mydiag(s2)%*%MM)%*%s2*lambda
+bbar=(bhat+lambda*MM%*%sign_bhat)
+A1=-(mydiag(sign_bhat))
+b1= -(mydiag(sign_bhat)%*%MM)%*%sign_bhat*lambda
 
    temp=max(A1%*%bbar-b1)
 
@@ -63,7 +63,7 @@ b1= -(mydiag(s2)%*%MM)%*%s2*lambda
 # the one sided p-values are a bit off
 
     for(jj in 1:length(bbar)){
-      vj=rep(0,length(bbar));vj[jj]=s2[jj]
+      vj=rep(0,length(bbar));vj[jj]=sign_bhat[jj]
 
 
       junk=TG.pvalue(bbar, A1, b1, vj,MM)
diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index 4ab3398..75a31e0 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -32,7 +32,7 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
  m=beta[-1]!=0  #active set
          
     bhat=c(beta[1],beta[-1][beta[-1]!=0]) # intcpt plus active vars
-     s2=sign(bhat)
+     sign_bhat=sign(bhat)
      lam2m=diag(c(0,rep(lambda,sum(m))))
  
 
@@ -66,14 +66,14 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
  # MM=solve(t(xxm)%*%w%*%xxm)
    MM=solve(scale(t(xxm),F,1/ww)%*%xxm)
   gm = c(0,-g[vars]*lambda) # gradient at LASSO solution, first entry is 0 because intercept is unpenalized
-                            # at exact LASSO solution it should be s2[-1]
+                            # at exact LASSO solution it should be sign_bhat[-1]
   dbeta = MM %*% gm
 
-  # bbar=(bhat+lam2m%*%MM%*%s2)  # JT: this is wrong, shouldn't use sign of intercept anywhere...
+  # bbar=(bhat+lam2m%*%MM%*%sign_bhat)  # JT: this is wrong, shouldn't use sign of intercept anywhere...
   bbar = bhat - dbeta
 
-  A1=-(mydiag(s2))[-1,]
-  b1= (s2 * dbeta)[-1]
+  A1=-(mydiag(sign_bhat))[-1,]
+  b1= (sign_bhat * dbeta)[-1]
 
   tol.poly = 0.01 
   if (max((A1 %*% bbar) - b1) > tol.poly)
@@ -87,7 +87,7 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
 
   
     for(jj in 1:sum(m)){
-       vj=c(rep(0,sum(m)+1));vj[jj+1]=s2[jj+1]
+       vj=c(rep(0,sum(m)+1));vj[jj+1]=sign_bhat[jj+1]
       # compute p-values
       junk=TG.pvalue(bbar, A1, b1, vj, MM)
       pv[jj] = junk$pv

From ac2bbc82abd7a7e530f44a1be54d01f5ca940c22 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 25 Oct 2017 14:06:10 -0700
Subject: [PATCH 320/493] NF: density functions for randomized LASSO

---
 selectiveInference/src/randomized_lasso.c | 201 ++++++++++++++++++++++
 selectiveInference/src/randomized_lasso.h |  42 +++++
 2 files changed, 243 insertions(+)
 create mode 100644 selectiveInference/src/randomized_lasso.c
 create mode 100644 selectiveInference/src/randomized_lasso.h

diff --git a/selectiveInference/src/randomized_lasso.c b/selectiveInference/src/randomized_lasso.c
new file mode 100644
index 0000000..ac55cf4
--- /dev/null
+++ b/selectiveInference/src/randomized_lasso.c
@@ -0,0 +1,201 @@
+#include <math.h> // for fabs
+
+// Augmented density for randomized LASSO after
+// Gaussian randomization
+
+// Described in https://arxiv.org/abs/1609.05609
+
+// Gaussian is product of IID N(0, noise_scale^2) density
+// Evaluated at A_D D + A_O O + h
+
+// Laplace is product of IID Laplace with scale noise_scale
+// Also evaluated at A_D D + A_O O + h
+
+double log_density_gaussian(double noise_scale,             // Scale of randomization
+			    int ndim,                       // Number of features -- "p"
+			    int ninternal,                  // Dimension of internal data representation often 1
+			    int noptimization,              // Dimension of optimization variables -- "p"
+			    double *internal_linear,        // A_D -- linear part for data
+			    double *internal_state,         // D -- data state
+			    double *optimization_linear,    // A_O -- linear part for optimization variables
+			    double *optimization_state,     // O -- optimization state
+			    double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double denom = 2 * noise_scale * noise_scale;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Internal (i.e. data) contribution
+    for (icol=0; icol<ninternal; icol++) {
+      
+      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
+      internal_state_ptr = ((double *) internal_state + icol);
+
+      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
+    }
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= (reconstruction * reconstruction) / denom;
+  }
+
+  return(value);
+}
+
+double log_density_laplace(double noise_scale,             // Scale of randomization
+			   int ndim,                       // Number of features -- "p"
+			   int ninternal,                  // Dimension of internal data representation often 1
+			   int noptimization,              // Dimension of optimization variables -- "p"
+			   double *internal_linear,        // A_D -- linear part for data
+			   double *internal_state,         // D -- data state
+			   double *optimization_linear,    // A_O -- linear part for optimization variables
+			   double *optimization_state,     // O -- optimization state
+			   double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Internal (i.e. data) contribution
+    for (icol=0; icol<ninternal; icol++) {
+      
+      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
+      internal_state_ptr = ((double *) internal_state + icol);
+
+      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
+    }
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= fabs(reconstruction) / noise_scale;
+  }
+
+  return(value);
+}
+
+// Keeping internal (data) state fixed
+
+double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
+					int ndim,                       // Number of features -- "p"
+					int noptimization,              // Dimension of optimization variables -- "p"
+					double *optimization_linear,    // A_O -- linear part for optimization variables
+					double *optimization_state,     // O -- optimization state
+					double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double denom = 2 * noise_scale * noise_scale;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= reconstruction * reconstruction / denom;
+  }
+
+  return(value);
+}
+
+double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
+				       int ndim,                       // Number of features -- "p"
+				       int noptimization,              // Dimension of optimization variables -- "p"
+				       double *optimization_linear,    // A_O -- linear part for optimization variables
+				       double *optimization_state,     // O -- optimization state
+				       double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Internal (i.e. data) contribution
+    for (icol=0; icol<ninternal; icol++) {
+      
+      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
+      internal_state_ptr = ((double *) internal_state + icol);
+
+      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
+    }
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= fabs(reconstruction) / noise_scale;
+  }
+
+  return(value);
+}
diff --git a/selectiveInference/src/randomized_lasso.h b/selectiveInference/src/randomized_lasso.h
new file mode 100644
index 0000000..1301b72
--- /dev/null
+++ b/selectiveInference/src/randomized_lasso.h
@@ -0,0 +1,42 @@
+#ifdef __cplusplus
+extern "C"
+{
+#endif /* __cplusplus */
+
+double log_density_gaussian(double noise_scale,             // Scale of randomization
+			    int ndim,                       // Number of features -- "p"
+			    int ninternal,                  // Dimension of internal data representation often 1
+			    int noptimization,              // Dimension of optimization variables -- "p"
+			    double *internal_linear,        // A_D -- linear part for data
+			    double *internal_state,         // D -- data state
+			    double *optimization_linear,    // A_O -- linear part for optimization variables
+			    double *optimization_state,     // O -- optimization state
+			    double *offset);                // h -- offset in affine transform -- "p" dimensional 
+  
+double log_density_laplace(double noise_scale,             // Scale of randomization
+			   int ndim,                       // Number of features -- "p"
+			   int ninternal,                  // Dimension of internal data representation often 1
+			   int noptimization,              // Dimension of optimization variables -- "p"
+			   double *internal_linear,        // A_D -- linear part for data
+			   double *internal_state,         // D -- data state
+			   double *optimization_linear,    // A_O -- linear part for optimization variables
+			   double *optimization_state,     // O -- optimization state
+			   double *offset);                // h -- offset in affine transform -- "p" dimensional 
+
+double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
+					int ndim,                       // Number of features -- "p"
+					int noptimization,              // Dimension of optimization variables -- "p"
+					double *optimization_linear,    // A_O -- linear part for optimization variables
+					double *optimization_state,     // O -- optimization state
+					double *offset);                // h -- offset in affine transform -- "p" dimensional 
+
+double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
+				       int ndim,                       // Number of features -- "p"
+				       int noptimization,              // Dimension of optimization variables -- "p"
+				       double *optimization_linear,    // A_O -- linear part for optimization variables
+				       double *optimization_state,     // O -- optimization state
+				       double *offset).                // h -- offset in affine transform -- "p" dimensional 
+
+#ifdef __cplusplus
+}  /* extern "C" */
+#endif /* __cplusplus */

From 75c71dcd93e0b4508ac94b0940ee85b58b870c71 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 25 Oct 2017 16:35:52 -0700
Subject: [PATCH 321/493] T to TRUE

---
 selectiveInference/R/funs.fixed.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index be314af..66a3088 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -6,7 +6,7 @@ fixedLassoInf <- function(x, y, beta,
                           lambda, family=c("gaussian","binomial","cox"),
                           intercept=TRUE, add.targets=NULL, status=NULL,
                           sigma=NULL, alpha=0.1,
-                          type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
+                          type=c("partial", "full"), tol.beta=1e-5, tol.kkt=0.1,
                           gridrange=c(-100,100), bits=NULL, verbose=FALSE, 
                           linesearch.try=10) {
 
@@ -150,7 +150,7 @@ fixedLassoInf <- function(x, y, beta,
     ci = tailarea = matrix(0,k,2)
       
     if (type=="full" & p > n) {
-      if (intercept == T) {
+      if (intercept == TRUE) {
         pp=p+1
         Xint <- cbind(rep(1,n),x)
         # indices of selected predictors

From 49d0b67884454543899680b91db1fed6c67e29a0 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 13:08:05 -0700
Subject: [PATCH 322/493] updated wide C code -- can now do ridge as well as
 variable weights -- ready to solve randomized LASSO

---
 selectiveInference/R/funs.fixed.R             |  40 +++-
 selectiveInference/src/Rcpp-debias.cpp        |  35 +++-
 selectiveInference/src/debias.h               |  22 ++-
 selectiveInference/src/quadratic_program.c    | 108 +++++++----
 .../src/quadratic_program_wide.c              | 176 ++++++++++++------
 selectiveInference/src/randomized_lasso.c     |  13 --
 6 files changed, 270 insertions(+), 124 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 66a3088..aaceaaa 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -189,8 +189,10 @@ fixedLassoInf <- function(x, y, beta,
       }
 
       M <- (((htheta%*%t(Xordered))+ithetasigma%*%FS%*%hsigmaSinv%*%t(XS))/n)
+
       # vector which is offset for testing debiased beta's
       null_value <- (((ithetasigma%*%FS%*%hsigmaSinv)%*%sign(hbetaS))*lambda/n)
+
       if (intercept == T) {
         M = M[-1,] # remove intercept row
         null_value = null_value[-1] # remove intercept element
@@ -238,12 +240,23 @@ fixedLassoInf <- function(x, y, beta,
     tailarea[j,] = a$tailarea
   }
 
-  out = list(type=type,lambda=lambda,pv=pv,ci=ci,
-    tailarea=tailarea,vlo=vlo,vup=vup,vmat=vmat,y=y,
-    vars=vars,sign=sign_vars,sigma=sigma,alpha=alpha,
-    sd=sigma*sqrt(rowSums(vmat^2)),
-    coef0=vmat%*%y,
-    call=this.call)
+  out = list(type=type,
+             lambda=lambda,
+             pv=pv,
+             ci=ci,
+             tailarea=tailarea,
+             vlo=vlo,
+             vup=vup,
+             vmat=vmat,
+             y=y,
+             vars=vars,
+             sign=sign_vars,
+             sigma=sigma,
+             alpha=alpha,
+             sd=sigma*sqrt(rowSums(vmat^2)),
+             coef0=vmat%*%y,
+             call=this.call)
+
   class(out) = "fixedLassoInf"
   return(out)
 }
@@ -374,7 +387,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                          row, 
                          mu, 
 	                 linesearch=TRUE,     # do a linesearch?
-		         scaling_factor=1.2,  # multiplicative factor for linesearch
+		         scaling_factor=1.5,  # multiplicative factor for linesearch
 		         max_active=NULL,     # how big can active set get?
 			 max_try=10,          # how many steps in linesearch?
 			 warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
@@ -420,11 +433,15 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                             nactive, 
                             kkt_tol, 
                             objective_tol, 
-                            max_active) 
+                            max_active,
+			    FALSE,        # objective_stop
+			    FALSE,        # kkt_stop
+			    TRUE)         # param_stop
       } else {
           Xsoln = rep(0, nrow(Xinfo))
           result = solve_QP_wide(Xinfo, # this is a design matrix
-                                 mu, 
+                                 rep(mu, p),  # vector of Lagrange multipliers
+				 0,           # ridge_term 
                                  max_iter, 
                                  soln, 
                                  linear_func, 
@@ -434,7 +451,10 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                                  nactive, 
                                  kkt_tol, 
                                  objective_tol, 
-                                 max_active) 
+                                 max_active,
+				 FALSE,       # objective_stop
+				 FALSE,       # kkt_stop
+				 TRUE)        # param_stop
 
       }
 
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index ce8bb15..24bbae8 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -15,7 +15,10 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		    Rcpp::IntegerVector nactive,
 		    double kkt_tol,
 		    double objective_tol,
-		    int max_active
+		    int max_active,
+		    int objective_stop,
+		    int kkt_stop,
+		    int param_stop
 		    ) {
 
   int nrow = Sigma.nrow(); // number of features
@@ -28,6 +31,8 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
   Rcpp::NumericVector Sigma_diag(nrow);
   double *sigma_diag_p = Sigma_diag.begin();
 
+  Rcpp::NumericVector theta_old(nrow);
+
   for (irow=0; irow<nrow; irow++) {
     sigma_diag_p[irow] = Sigma(irow, irow);
   }
@@ -43,10 +48,14 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		      nrow,
 		      bound,
 		      (double *) theta.begin(),
+		      (double *) theta_old.begin(),
 		      maxiter,
 		      kkt_tol,
 		      objective_tol,
-		      max_active);
+		      max_active,
+		      objective_stop,
+		      kkt_stop,
+		      param_stop);
   
   // Check whether feasible
 
@@ -72,7 +81,8 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 
 // [[Rcpp::export]]
 Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
-			 double bound,
+			 Rcpp::NumericVector bound,
+			 double ridge_term,
 			 int maxiter,
 			 Rcpp::NumericVector theta,
 			 Rcpp::NumericVector linear_func,
@@ -82,7 +92,10 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			 Rcpp::IntegerVector nactive,
 			 double kkt_tol,
 			 double objective_tol,
-			 int max_active
+			 int max_active,
+			 int objective_stop,
+			 int kkt_stop,
+			 int param_stop
 			 ) {
 
   int ncase = X.nrow(); // number of cases
@@ -100,6 +113,8 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
   Rcpp::NumericVector nndef_diag(nfeature);
   double *nndef_diag_p = nndef_diag.begin();
 
+  Rcpp::NumericVector theta_old(nfeature);
+
   for (ifeature=0; ifeature<nfeature; ifeature++) {
     nndef_diag_p[ifeature] = 0;
     for (icase=0; icase<ncase; icase++) {
@@ -120,12 +135,17 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			(int *) nactive.begin(),
 			ncase,
 			nfeature,
-			bound,
+			(double *) bound.begin(),
+			ridge_term,
 			(double *) theta.begin(),
+			(double *) theta_old.begin(),
 			maxiter,
 			kkt_tol,
 			objective_tol,
-			max_active);
+			max_active,
+			objective_stop,
+			kkt_stop,
+			param_stop);
   
   // Check whether feasible
 
@@ -137,7 +157,8 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 				 (int *) need_update.begin(),
 				 nfeature,
 				 ncase,
-				 bound,
+				 (double *) bound.begin(),
+				 ridge_term,
 				 kkt_tol);
 
   int max_active_check = (*(nactive.begin()) >= max_active);
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index ebcbc93..052af7a 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -9,13 +9,17 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	     double *gradient_ptr,       /* nndef times theta */
 	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
 	     int *nactive_ptr,           /* Size of ever active set */
-	     int nrow,                   /* How many rows in nndef */
+	     int nfeature,               /* How many features in nndef */
 	     double bound,               /* feasibility parameter */
 	     double *theta,              /* current value */
+	     double *theta_old,          /* previous value */
 	     int maxiter,                /* max number of iterations */
 	     double kkt_tol,             /* precision for checking KKT conditions */
 	     double objective_tol,       /* precision for checking relative decrease in objective value */
-	     int max_active);             /* Upper limit for size of active set -- otherwise break */ 
+	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
+	     int objective_stop,         /* Break based on convergence of objective value? */
+             int kkt_stop,               /* Break based on KKT? */
+	     int param_stop);            /* Break based on parameter convergence? */
 
 int check_KKT_qp(double *theta,        /* current theta */
 		 double *gradient_ptr, /* nndef times theta + linear_func */
@@ -33,12 +37,17 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 	       int *nactive_ptr,           /* Size of ever active set */
 	       int ncase,                  /* How many rows in X */
 	       int nfeature,               /* How many columns in X */
-	       double bound,               /* feasibility parameter */
+	       double *bound_ptr,          /* Lagrange multipliers */
+	       double ridge_term,          /* Ridge / ENet term */
 	       double *theta_ptr,          /* current value */
+	       double *theta_old_ptr,      /* previous value */
 	       int maxiter,                /* max number of iterations */
 	       double kkt_tol,             /* precision for checking KKT conditions */
 	       double objective_tol,       /* precision for checking relative decrease in objective value */
-	       int max_active);             /* Upper limit for size of active set -- otherwise break */ 
+	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
+	       int objective_stop,         /* Break based on convergence of objective value? */
+	       int kkt_stop,               /* Break based on KKT? */
+	       int param_stop);            /* Break based on parameter convergence? */
 
 int check_KKT_wide(double *theta_ptr,        /* current theta */
 		   double *gradient_ptr,     /* X^TX/ncase times theta + linear_func*/
@@ -48,8 +57,9 @@ int check_KKT_wide(double *theta_ptr,        /* current theta */
 		   int *need_update_ptr,     /* Which coordinates need to be updated? */
 		   int nfeature,             /* how many columns in X */
 		   int ncase,                /* how many rows in X */
-   		   double bound,             /* Lagrange multipler for \ell_1 */
-		   double tol);              /* precision for checking KKT conditions */        
+		   double *bound_ptr,        /* Lagrange multiplers for \ell_1 */
+		   double ridge_term,        /* Ridge / ENet term */
+		   double tol);               /* precision for checking KKT conditions */        
   
 void update_gradient_wide(double *gradient_ptr,     /* X^TX/ncase times theta + linear_func */
 			  double *X_theta_ptr,      /* Current fitted values */
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
index d9bd017..1bc7fa3 100644
--- a/selectiveInference/src/quadratic_program.c
+++ b/selectiveInference/src/quadratic_program.c
@@ -92,19 +92,19 @@ int update_ever_active_qp(int coord,
 
 int check_KKT_qp(double *theta_ptr,        /* current theta */
 		 double *gradient_ptr, /* nndef times theta + linear_func */
-		 int nrow,             /* how many rows in nndef */
+		 int nfeature,             /* how many features in nndef */
 		 double bound,         /* Lagrange multipler for \ell_1 */
 		 double tol)           /* precision for checking KKT conditions */        
 {
   // First check inactive
 
-  int irow;
+  int ifeature;
   double *theta_ptr_tmp, *gradient_ptr_tmp;
   double gradient;
 
-  for (irow=0; irow<nrow; irow++) {
-    theta_ptr_tmp = ((double *) theta_ptr + irow);
-    gradient_ptr_tmp = ((double *) gradient_ptr + irow);
+  for (ifeature=0; ifeature<nfeature; ifeature++) {
+    theta_ptr_tmp = ((double *) theta_ptr + ifeature);
+    gradient_ptr_tmp = ((double *) gradient_ptr + ifeature);
 
     // Compute this coordinate of the gradient
 
@@ -132,7 +132,7 @@ int check_KKT_qp_active(int *ever_active_ptr,           /* Ever active set: 0-ba
 		        int *nactive_ptr,               /* Size of ever active set */
 			double *theta_ptr,        /* current theta */
 			double *gradient_ptr, /* nndef times theta + linear_func */
-			int nrow,             /* how many rows in nndef */
+			int nfeature,             /* how many features in nndef */
 			double bound,         /* Lagrange multipler for \ell_1 */
 			double tol)           /* precision for checking KKT conditions */        
 {
@@ -185,7 +185,7 @@ double update_one_coord_qp(double *nndef_ptr,           /* A non-negative defini
 			   double *gradient_ptr,        /* nndef times theta + linear_func */
 			   int *ever_active_ptr,        /* Ever active set: 1-based */ 
 			   int *nactive_ptr,            /* Size of ever active set */
-			   int nrow,                    /* How many rows in nndef */
+			   int nfeature,                    /* How many features in nndef */
 			   double bound,                /* feasibility parameter */
 			   double *theta_ptr,               /* current value */
 			   int coord,                   /* which coordinate to update: 0-based */
@@ -242,10 +242,10 @@ double update_one_coord_qp(double *nndef_ptr,           /* A non-negative defini
   if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
 
     delta = value - old_value;
-    nndef_ptr_tmp = ((double *) nndef_ptr + coord * nrow);
+    nndef_ptr_tmp = ((double *) nndef_ptr + coord * nfeature);
     gradient_ptr_tmp = ((double *) gradient_ptr);
 
-    for (icol=0; icol<nrow; icol++) {
+    for (icol=0; icol<nfeature; icol++) {
       *gradient_ptr_tmp = *gradient_ptr_tmp + delta * (*nndef_ptr_tmp);
       gradient_ptr_tmp += 1;
       nndef_ptr_tmp += 1;
@@ -266,33 +266,42 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	     double *gradient_ptr,       /* nndef times theta */
 	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
 	     int *nactive_ptr,           /* Size of ever active set */
-	     int nrow,                   /* How many rows in nndef */
+	     int nfeature,               /* How many features in nndef */
 	     double bound,               /* feasibility parameter */
 	     double *theta,              /* current value */
+	     double *theta_old,          /* previous value */
 	     int maxiter,                /* max number of iterations */
 	     double kkt_tol,             /* precision for checking KKT conditions */
 	     double objective_tol,       /* precision for checking relative decrease in objective value */
-	     int max_active)             /* Upper limit for size of active set -- otherwise break */ 
+	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
+	     int objective_stop,         /* Break based on convergence of objective value? */
+             int kkt_stop,               /* Break based on KKT? */
+	     int param_stop)             /* Break based on parameter convergence? */
 {
 
   int iter = 0;
-  int icoord = 0;
+  int iter_old = 1;
+  int ifeature = 0;
   int iactive = 0;
   int *active_ptr;
 
-  int check_objective = 1;
   int iter_active;
   int niter_active=5;
 
   double old_value, new_value; 
+  double norm_diff = 1.;
+  double norm_last = 1.;
+  double delta;
+  double threshold = 1.e-2;
+  double *theta_ptr, *theta_old_ptr;
 
-  if (check_objective) {
+  if (objective_stop) {
 
     old_value = objective_qp(nndef_ptr,
 			     linear_func_ptr,
 			     ever_active_ptr,
 			     nactive_ptr,
-			     nrow,
+			     nfeature,
 			     bound,
 			     theta);
 
@@ -314,7 +323,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 			      gradient_ptr,
 			      ever_active_ptr,
 			      nactive_ptr,
-			      nrow,
+			      nfeature,
 			      bound,
 			      theta,
 			      *active_ptr - 1,   // Ever-active is 1-based
@@ -328,7 +337,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 				nactive_ptr,
 				theta,
 				gradient_ptr,
-				nrow,
+				nfeature,
 				bound,
 				kkt_tol) == 1) {
 	  break;
@@ -337,17 +346,19 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 
     // Check KKT
 
-    if (check_KKT_qp(theta,
-		     gradient_ptr,
-		     nrow,
-		     bound,
-		     kkt_tol) == 1) {
-      break;
+    if (kkt_stop) {
+      if (check_KKT_qp(theta, 
+		       gradient_ptr,
+		       nfeature,
+		       bound,
+		       kkt_tol) == 1) {
+	break;
+      }
     }
-					  
+
     // Update all variables 
 
-    for (icoord=0; icoord<nrow; icoord++) {
+    for (ifeature=0; ifeature<nfeature; ifeature++) {
 
       update_one_coord_qp(nndef_ptr,
 			  linear_func_ptr,
@@ -355,23 +366,49 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 			  gradient_ptr,
 			  ever_active_ptr,
 			  nactive_ptr,
-			  nrow,
+			  nfeature,
 			  bound,
 			  theta,
-			  icoord,
+			  ifeature,
 			  0);
     }
 
     // Check KKT
 
-    if (check_KKT_qp(theta,
-		     gradient_ptr,
-		     nrow,
-		     bound,
-		     kkt_tol) == 1) {
-      break;
+    if (kkt_stop) {
+      if (check_KKT_qp(theta, 
+		       gradient_ptr,
+		       nfeature,
+		       bound,
+		       kkt_tol) == 1) {
+	break;
+      }
     }
 					  
+    // Check based on norm -- from Adel's debiasing code
+
+    if (param_stop) { 
+      if (iter == 2 * iter_old) {
+	iter_old = iter;
+	norm_diff = 0;
+	norm_last = 0;
+	for (ifeature=0; ifeature<nfeature; ifeature++) {
+	  theta_old_ptr = ((double *) theta_old + ifeature);
+	  theta_ptr = ((double *) theta + ifeature);
+	  delta = (*theta_ptr - *theta_old_ptr);
+	  norm_diff += delta * delta;
+	  norm_last += (*theta_ptr) * (*theta_ptr);
+	  *theta_old_ptr = (*theta_ptr);
+	}
+	norm_diff = sqrt(norm_diff);
+	norm_last = sqrt(norm_last);
+	
+	if (norm_diff < threshold * norm_last) {
+	  break;
+	}
+      }
+    }
+
     // Check size of active set
 
     if (*nactive_ptr >= max_active) {
@@ -380,12 +417,12 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 
     // Check relative decrease of objective
 
-    if (check_objective) {
+    if (objective_stop) {
       new_value = objective_qp(nndef_ptr,
 			       linear_func_ptr,
 			       ever_active_ptr,
 			       nactive_ptr,
-			       nrow,
+			       nfeature,
 			       bound,
 			       theta);
 
@@ -394,6 +431,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
       }
       old_value = new_value;
     }
+
   }
   return(iter);
 }
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
index 3beb578..c6cb9f3 100644
--- a/selectiveInference/src/quadratic_program_wide.c
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -4,7 +4,7 @@
 
 // Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
 
-// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2 - l^T\theta + \mu \|\theta\|_1
+// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2 - l^T\theta + \mu \|\theta\|_1 + \frac{\epsilon}{2} \|\theta\|^2_2
 // where l is `linear_func` below
 
 // This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
@@ -19,11 +19,13 @@ double objective_wide(double *X_theta_ptr,     /* Fitted values */
 		      int *nactive_ptr,        /* Size of ever active set */
 		      int ncase,               /* how many rows in X */
 		      int nfeature,            /* how many columns in X */
-		      double bound,            /* Lagrange multipler for \ell_1 */
+		      double *bound_ptr,       /* Lagrange multiplers for \ell_1 */
+		      double ridge_term,       /* Ridge / ENet term */
 		      double *theta_ptr)       /* current value */
 {
   int icase, iactive;
   double value = 0;
+  double *bound_ptr_tmp;
   double *X_theta_ptr_tmp = X_theta_ptr;
   double *linear_func_ptr_tmp = linear_func_ptr;
   double *theta_ptr_tmp;
@@ -55,8 +57,12 @@ double objective_wide(double *X_theta_ptr,     /* Fitted values */
 
     // The \ell_1 term
 
-    value += bound * fabs((*theta_ptr_tmp));
+    bound_ptr_tmp = ((double *) bound_ptr + active_feature);
+    value += (*bound_ptr_tmp) * fabs((*theta_ptr_tmp));
 
+    // The ridge term
+
+    value += 0.5 * ridge_term * (*theta_ptr_tmp) * (*theta_ptr_tmp);
   }
   
   return(value);
@@ -167,34 +173,39 @@ int check_KKT_wide(double *theta_ptr,        /* current theta */
 		   int *need_update_ptr,     /* Which coordinates need to be updated? */
 		   int nfeature,             /* how many columns in X */
 		   int ncase,                /* how many rows in X */
-		   double bound,             /* Lagrange multipler for \ell_1 */
+		   double *bound_ptr,        /* Lagrange multiplers for \ell_1 */
+		   double ridge_term,        /* Ridge / ENet term */
 		   double tol)               /* precision for checking KKT conditions */        
 {
   // First check inactive
 
   int ifeature;
   double *theta_ptr_tmp;
+  double *bound_ptr_tmp;
+  double bound;
   double gradient;
 
   for (ifeature=0; ifeature<nfeature; ifeature++) {
 
     theta_ptr_tmp = ((double *) theta_ptr + ifeature);
+    bound_ptr_tmp = ((double *) bound_ptr + ifeature);
+    bound = *bound_ptr_tmp;
 
     // Compute this coordinate of the gradient
 
     gradient = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, ifeature, ncase);
 
-    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
-
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
+    if ((*theta_ptr_tmp != 0) && (bound != 0)) { // these coordinates of gradients should be equal to -bound
+      
+      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + ridge_term * (*theta_ptr_tmp) + bound) > tol * bound)) {
 	return(0);
       }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
+      else if ((*theta_ptr_tmp < 0) && (fabs(gradient + ridge_term * (*theta_ptr_tmp) - bound) > tol * bound)) {
 	return(0);
       }
-
+      
     }
-    else {
+    else if (bound != 0) {
       if (fabs(gradient) > (1. + tol) * bound) {
 	return(0);
       }
@@ -214,13 +225,16 @@ int check_KKT_wide_active(int *ever_active_ptr,           /* Ever active set: 0-
 			  int *need_update_ptr,           /* Which coordinates need to be updated? */
 			  int nfeature,                   /* how many columns in X */
 			  int ncase,                      /* how many rows in X */
-			  double bound,                   /* Lagrange multipler for \ell_1 */
+			  double *bound_ptr,              /* Lagrange multipliers for \ell_1 */
+			  double ridge_term,              /* Ridge / ENet term */
 			  double tol)                     /* precision for checking KKT conditions */        
 {
   // First check inactive
 
   int iactive;
   double *theta_ptr_tmp;
+  double *bound_ptr_tmp;
+  double bound;
   double gradient;
   int nactive = *nactive_ptr;
   int active_feature;
@@ -230,23 +244,26 @@ int check_KKT_wide_active(int *ever_active_ptr,           /* Ever active set: 0-
 
     active_feature_ptr = ((int *) ever_active_ptr + iactive);
     active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
+
     theta_ptr_tmp = ((double *) theta_ptr + active_feature);
+    bound_ptr_tmp = ((double *) bound_ptr + active_feature);
+    bound = *bound_ptr_tmp;
 
     // Compute this coordinate of the gradient
 
     gradient = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, active_feature, ncase);
 
-    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
+    if ((*theta_ptr_tmp != 0) && (bound != 0)) { // these coordinates of gradients should be equal to -bound
 
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
+      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + ridge_term * (*theta_ptr_tmp) + bound) > tol * bound)) {
 	return(0);
       }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
+      else if ((*theta_ptr_tmp < 0) && (fabs(gradient + ridge_term * (*theta_ptr_tmp) - bound) > tol * bound)) {
 	return(0);
       }
 
     }
-    else {
+    else if (bound != 0) {
       if (fabs(gradient) > (1. + tol) * bound) {
 	return(0);
       }
@@ -266,7 +283,8 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
 			     int *need_update_ptr,        /* Whether a gradient coordinate needs update or not */
 			     int ncase,                   /* How many rows in X */
 			     int nfeature,                /* How many rows in X */
-			     double bound,                /* feasibility parameter */
+			     double *bound_ptr,           /* Lagrange multipliers */
+			     double ridge_term,           /* Ridge / ENet term */
 			     double *theta_ptr,           /* current value */
 			     int coord,                   /* which coordinate to update: 0-based */
 			     int is_active)               /* Is this coord in ever_active */     
@@ -280,6 +298,8 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
   double *X_theta_ptr_tmp;
   int *need_update_ptr_tmp;
   double *theta_ptr_tmp;
+  double *bound_ptr_tmp;
+  double bound;
   int ifeature, icase;
 
   double *diagonal_ptr = ((double *) nndef_diag_ptr + coord);
@@ -290,6 +310,9 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
   theta_ptr_tmp = ((double *) theta_ptr + coord);
   old_value = *theta_ptr_tmp;
 
+  bound_ptr_tmp = ((double *) bound_ptr + coord);
+  bound = *bound_ptr_tmp;
+
   // The coord entry of gradient_ptr term has a diagonal term in it:
   // (X^TX)[coord, coord] * theta[coord] / ncase
   // This removes it. 
@@ -298,17 +321,17 @@ double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
 
   // Now soft-threshold the coord entry of theta 
 
-  // Objective is t \mapsto q/2 * t^2 + l * t + bound |t|
-  // with q=diagonal_entry and l=linear_term
+  // Objective is t \mapsto (q+eps)/2 * t^2 + l * t + bound |t| + 
+  // with q=diagonal_entry and l=linear_term and eps=ridge_Term
 
   // With a negative linear term, solution should be
   // positive
 
   if (linear_term < -bound) {
-    value = (-linear_term - bound) / diagonal_entry;
+    value = (-linear_term - bound) / (diagonal_entry + ridge_term);
   }
   else if (linear_term > bound) {
-    value = -(linear_term - bound) / diagonal_entry;
+    value = -(linear_term - bound) / (diagonal_entry + ridge_term);
   }
 
   // Add to active set if necessary
@@ -363,24 +386,36 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 	       int *nactive_ptr,           /* Size of ever active set */
 	       int ncase,                  /* How many rows in X */
 	       int nfeature,               /* How many columns in X */
-	       double bound,               /* feasibility parameter */
+	       double *bound_ptr,          /* Lagrange multipliers */
+	       double ridge_term,          /* Ridge / ENet term */
 	       double *theta_ptr,          /* current value */
+	       double *theta_old_ptr,      /* previous value */
 	       int maxiter,                /* max number of iterations */
 	       double kkt_tol,             /* precision for checking KKT conditions */
 	       double objective_tol,       /* precision for checking relative decrease in objective value */
-	       int max_active)             /* Upper limit for size of active set -- otherwise break */ 
+	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
+	       int objective_stop,         /* Break based on convergence of objective value? */
+	       int kkt_stop,               /* Break based on KKT? */
+	       int param_stop)             /* Break based on parameter convergence? */
 {
 
   int iter = 0;
+  int iter_old = 1;
   int ifeature = 0;
   int iactive = 0;
   int *active_ptr;
-  int check_objective = 1;
+
   double old_value, new_value; 
   int niter_active = 5;
   int iter_active;
 
-  if (check_objective) {
+  double norm_diff = 1.;
+  double norm_last = 1.;
+  double delta;
+  double threshold = 1.e-2;
+  double *theta_ptr_tmp, *theta_old_ptr_tmp;
+
+  if (objective_stop) {
 
     old_value = objective_wide(X_theta_ptr,
 			       linear_func_ptr,
@@ -388,8 +423,10 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 			       nactive_ptr,
 			       ncase,
 			       nfeature,
-			       bound,
+			       bound_ptr,
+			       ridge_term,
 			       theta_ptr);
+    new_value = old_value;
 
   }
 
@@ -412,7 +449,8 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 				need_update_ptr,
 				ncase,
 				nfeature,
-				bound,
+				bound_ptr,
+				ridge_term,
 				theta_ptr,
 				*active_ptr - 1,   // Ever-active is 1-based
 				1);
@@ -431,7 +469,8 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 				  need_update_ptr,
 				  nfeature,
 				  ncase,
-				  bound,
+				  bound_ptr,
+				  ridge_term,
 				  kkt_tol) == 1) {
 	  break;
 	}
@@ -440,17 +479,20 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 
     // Check KKT
 
-    if (check_KKT_wide(theta_ptr,
-		       gradient_ptr,
-		       X_theta_ptr,
-		       X_ptr,
-		       linear_func_ptr,
-		       need_update_ptr,
-		       nfeature,
-		       ncase,
-		       bound,
-		       kkt_tol) == 1) {
-      break;
+    if (kkt_stop) { 
+      if (check_KKT_wide(theta_ptr,
+			 gradient_ptr,
+			 X_theta_ptr,
+			 X_ptr,
+			 linear_func_ptr,
+			 need_update_ptr,
+			 nfeature,
+			 ncase,
+			 bound_ptr,
+			 ridge_term,
+			 kkt_tol) == 1) {
+	break;
+      }
     }
 
     // Update all variables 
@@ -467,7 +509,8 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 			    need_update_ptr,
 			    ncase,
 			    nfeature,
-			    bound,
+			    bound_ptr,
+			    ridge_term,
 			    theta_ptr,
 			    ifeature,
 			    0);
@@ -475,19 +518,45 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 
     // Check KKT
 
-    if (check_KKT_wide(theta_ptr,
-		       gradient_ptr,
-		       X_theta_ptr,
-		       X_ptr,
-		       linear_func_ptr,
-		       need_update_ptr,
-		       nfeature,
-		       ncase,
-		       bound,
-		       kkt_tol) == 1) {
-      break;
+    if (kkt_stop) {
+      if (check_KKT_wide(theta_ptr,
+			 gradient_ptr,
+			 X_theta_ptr,
+			 X_ptr,
+			 linear_func_ptr,
+			 need_update_ptr,
+			 nfeature,
+			 ncase,
+			 bound_ptr,
+			 ridge_term,
+			 kkt_tol) == 1) {
+	break;
+      }
+    }
+
+    // Check based on norm -- from Adel's debiasing code
+
+    if (param_stop) {
+      if (iter == 2 * iter_old) {
+	iter_old = iter;
+	norm_diff = 0;
+	norm_last = 0;
+	for (ifeature=0; ifeature<nfeature; ifeature++) {
+	  theta_old_ptr_tmp = ((double *) theta_old_ptr + ifeature);
+	  theta_ptr_tmp = ((double *) theta_ptr + ifeature);
+	  delta = (*theta_ptr_tmp - *theta_old_ptr_tmp);
+	  norm_diff += delta * delta;
+	  norm_last += (*theta_ptr_tmp) * (*theta_ptr_tmp);
+	  *theta_old_ptr = (*theta_ptr_tmp);
+	}
+	norm_diff = sqrt(norm_diff);
+	norm_last = sqrt(norm_last);
+	
+	if (norm_diff < threshold * norm_last) {
+	  break;
+	}
+      }
     }
-					  
     // Check size of active set
 
     if (*nactive_ptr >= max_active) {
@@ -496,14 +565,15 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 
     // Check relative decrease of objective
 
-    if (check_objective) {
+    if (objective_stop) {
       new_value = objective_wide(X_theta_ptr,
 				 linear_func_ptr,
 				 ever_active_ptr,
 				 nactive_ptr,
 				 ncase,
 				 nfeature,
-				 bound,
+				 bound_ptr,
+				 ridge_term,
 				 theta_ptr);
 
       if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
diff --git a/selectiveInference/src/randomized_lasso.c b/selectiveInference/src/randomized_lasso.c
index ac55cf4..123c81d 100644
--- a/selectiveInference/src/randomized_lasso.c
+++ b/selectiveInference/src/randomized_lasso.c
@@ -126,8 +126,6 @@ double log_density_gaussian_conditional(double noise_scale,             // Scale
   double denom = 2 * noise_scale * noise_scale;
   double reconstruction = 0;
   double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
   double *optimization_linear_ptr;
   double *optimization_state_ptr;
 
@@ -164,8 +162,6 @@ double log_density_laplace_conditional(double noise_scale,             // Scale
   double value = 0;
   double reconstruction = 0;
   double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
   double *optimization_linear_ptr;
   double *optimization_state_ptr;
 
@@ -176,15 +172,6 @@ double log_density_laplace_conditional(double noise_scale,             // Scale
     offset_ptr = ((double *) offset + irow);
     reconstruction = *offset_ptr;
 
-    // Internal (i.e. data) contribution
-    for (icol=0; icol<ninternal; icol++) {
-      
-      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
-      internal_state_ptr = ((double *) internal_state + icol);
-
-      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
-    }
-
     // Optimization contribution
     for (icol=0; icol<noptimization; icol++) {
       

From 0dbc31084070593ecd6c6147345678dcb0b36f3e Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 13:08:37 -0700
Subject: [PATCH 323/493] removing machine generated files

---
 selectiveInference/R/RcppExports.R     | 19 ------
 selectiveInference/src/RcppExports.cpp | 92 --------------------------
 2 files changed, 111 deletions(-)
 delete mode 100644 selectiveInference/R/RcppExports.R
 delete mode 100644 selectiveInference/src/RcppExports.cpp

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
deleted file mode 100644
index f5ebee4..0000000
--- a/selectiveInference/R/RcppExports.R
+++ /dev/null
@@ -1,19 +0,0 @@
-# Generated by using Rcpp::compileAttributes() -> do not edit by hand
-# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
-
-solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('_selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active)
-}
-
-solve_QP_wide <- function(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active) {
-    .Call('_selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active)
-}
-
-update1_ <- function(Q2, w, m, k) {
-    .Call('_selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
-}
-
-downdate1_ <- function(Q1, R, j0, m, n) {
-    .Call('_selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
-}
-
diff --git a/selectiveInference/src/RcppExports.cpp b/selectiveInference/src/RcppExports.cpp
deleted file mode 100644
index 02a7741..0000000
--- a/selectiveInference/src/RcppExports.cpp
+++ /dev/null
@@ -1,92 +0,0 @@
-// Generated by using Rcpp::compileAttributes() -> do not edit by hand
-// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
-
-#include <Rcpp.h>
-
-using namespace Rcpp;
-
-// solve_QP
-Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP _selectiveInference_solve_QP(SEXP SigmaSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Sigma(SigmaSEXP);
-    Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
-    Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type theta(thetaSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type linear_func(linear_funcSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type gradient(gradientSEXP);
-    Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type ever_active(ever_activeSEXP);
-    Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type nactive(nactiveSEXP);
-    Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
-    Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
-    Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    rcpp_result_gen = Rcpp::wrap(solve_QP(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return rcpp_result_gen;
-END_RCPP
-}
-// solve_QP_wide
-Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X, double bound, int maxiter, Rcpp::NumericVector theta, Rcpp::NumericVector linear_func, Rcpp::NumericVector gradient, Rcpp::NumericVector X_theta, Rcpp::IntegerVector ever_active, Rcpp::IntegerVector nactive, double kkt_tol, double objective_tol, int max_active);
-RcppExport SEXP _selectiveInference_solve_QP_wide(SEXP XSEXP, SEXP boundSEXP, SEXP maxiterSEXP, SEXP thetaSEXP, SEXP linear_funcSEXP, SEXP gradientSEXP, SEXP X_thetaSEXP, SEXP ever_activeSEXP, SEXP nactiveSEXP, SEXP kkt_tolSEXP, SEXP objective_tolSEXP, SEXP max_activeSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type X(XSEXP);
-    Rcpp::traits::input_parameter< double >::type bound(boundSEXP);
-    Rcpp::traits::input_parameter< int >::type maxiter(maxiterSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type theta(thetaSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type linear_func(linear_funcSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type gradient(gradientSEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type X_theta(X_thetaSEXP);
-    Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type ever_active(ever_activeSEXP);
-    Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type nactive(nactiveSEXP);
-    Rcpp::traits::input_parameter< double >::type kkt_tol(kkt_tolSEXP);
-    Rcpp::traits::input_parameter< double >::type objective_tol(objective_tolSEXP);
-    Rcpp::traits::input_parameter< int >::type max_active(max_activeSEXP);
-    rcpp_result_gen = Rcpp::wrap(solve_QP_wide(X, bound, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, max_active));
-    return rcpp_result_gen;
-END_RCPP
-}
-// update1_
-Rcpp::List update1_(Rcpp::NumericMatrix Q2, Rcpp::NumericVector w, int m, int k);
-RcppExport SEXP _selectiveInference_update1_(SEXP Q2SEXP, SEXP wSEXP, SEXP mSEXP, SEXP kSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q2(Q2SEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericVector >::type w(wSEXP);
-    Rcpp::traits::input_parameter< int >::type m(mSEXP);
-    Rcpp::traits::input_parameter< int >::type k(kSEXP);
-    rcpp_result_gen = Rcpp::wrap(update1_(Q2, w, m, k));
-    return rcpp_result_gen;
-END_RCPP
-}
-// downdate1_
-Rcpp::List downdate1_(Rcpp::NumericMatrix Q1, Rcpp::NumericMatrix R, int j0, int m, int n);
-RcppExport SEXP _selectiveInference_downdate1_(SEXP Q1SEXP, SEXP RSEXP, SEXP j0SEXP, SEXP mSEXP, SEXP nSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type Q1(Q1SEXP);
-    Rcpp::traits::input_parameter< Rcpp::NumericMatrix >::type R(RSEXP);
-    Rcpp::traits::input_parameter< int >::type j0(j0SEXP);
-    Rcpp::traits::input_parameter< int >::type m(mSEXP);
-    Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    rcpp_result_gen = Rcpp::wrap(downdate1_(Q1, R, j0, m, n));
-    return rcpp_result_gen;
-END_RCPP
-}
-
-static const R_CallMethodDef CallEntries[] = {
-    {"_selectiveInference_solve_QP", (DL_FUNC) &_selectiveInference_solve_QP, 11},
-    {"_selectiveInference_solve_QP_wide", (DL_FUNC) &_selectiveInference_solve_QP_wide, 12},
-    {"_selectiveInference_update1_", (DL_FUNC) &_selectiveInference_update1_, 4},
-    {"_selectiveInference_downdate1_", (DL_FUNC) &_selectiveInference_downdate1_, 5},
-    {NULL, NULL, 0}
-};
-
-RcppExport void R_init_selectiveInference(DllInfo *dll) {
-    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
-    R_useDynamicSymbols(dll, FALSE);
-}

From 74803104aef4efbbc0126a1a4d8033c61f608a82 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 14:30:59 -0700
Subject: [PATCH 324/493] WIP: function to fit randomized lasso

---
 selectiveInference/R/funs.randomized.R | 64 ++++++++++++++++++++++++++
 tests/test_randomized.R                | 14 ++++++
 2 files changed, 78 insertions(+)
 create mode 100644 selectiveInference/R/funs.randomized.R
 create mode 100644 tests/test_randomized.R

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
new file mode 100644
index 0000000..01e6aa4
--- /dev/null
+++ b/selectiveInference/R/funs.randomized.R
@@ -0,0 +1,64 @@
+# Functions to fit and "infer" about parameters in the
+# randomized LASSO
+#
+# min 1/2 || y - \beta_0 - X \beta ||_2^2 + \lambda || \beta ||_1 - \omega^T\beta + \frac{\epsilon}{2} \|\beta\|^2_2
+
+fit_randomized_lasso = function(X, 
+                                y, 
+                                lam, 
+                                noise_scale, 
+                                ridge_term, 
+                                noise_type=c('gaussian', 'laplace'),
+                                max_iter=100,        # how many iterations for each optimization problem
+                                kkt_tol=1.e-4,       # tolerance for the KKT conditions
+                                objective_tol=1.e-8, # tolerance for relative decrease in objective
+                                objective_stop=FALSE,
+                                kkt_stop=TRUE,
+                                param_stop=TRUE)
+{
+
+    n = nrow(X); p = ncol(X)
+    			
+    noise_type = match.arg(noise_type)
+
+    if (noise_type == 'gaussian') {
+       D = Norm(mean=0, sd=noise_scale)
+    }
+    else if (noise_type == 'laplace') {
+       D = DExp(rate = 1 / noise_scale) # D is a Laplace distribution with rate = 1.
+    }
+    perturb_ = distr::r(D)(p)
+			
+    lam = as.numeric(lam)
+    if (length(lam) == 1) {
+       lam = rep(lam, p)
+    }
+    if (length(lam) != p) {
+       stop("Lagrange parameter should be single float or of length ncol(X)")
+    }    
+
+    soln = rep(0, p)
+    Xsoln = rep(0, n)
+    linear_func = (- t(X) %*% y - perturb_)
+    gradient = 1. * linear_func
+    ever_active = rep(0, p)
+    nactive = as.integer(0)
+
+    result = solve_QP_wide(X,                  # design matrix
+    	                   lam,                # vector of Lagrange multipliers
+		           ridge_term / n,     # ridge_term 
+                           max_iter, 
+                           soln, 
+                           linear_func, 
+                           gradient, 
+                           Xsoln,
+                           ever_active, 
+                           nactive, 
+                           kkt_tol, 
+                           objective_tol, 
+                           p,
+		           objective_stop,     # objective_stop
+			   kkt_stop,           # kkt_stop
+			   param_stop)         # param_stop
+    return(result)
+}
diff --git a/tests/test_randomized.R b/tests/test_randomized.R
new file mode 100644
index 0000000..e4e35a1
--- /dev/null
+++ b/tests/test_randomized.R
@@ -0,0 +1,14 @@
+library(selectiveInference)
+
+test = function() {
+
+    n = 100; p = 50
+    X = matrix(rnorm(n * p), n, p)
+    y = rnorm(n)
+    lam = 20 / sqrt(n)
+    noise_scale = 0.01 * sqrt(n)
+    ridge_term = .1 / sqrt(n)
+    fit_randomized_lasso(X, y, lam, noise_scale, ridge_term)
+}
+
+print(test())

From 38b2020b36ef81da2fe0fec8f265f3effc23610f Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 21:36:18 -0700
Subject: [PATCH 325/493] check that solution is same as old code -- currently
 failing

---
 tests/test_debiasing.R | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)
 create mode 100644 tests/test_debiasing.R

diff --git a/tests/test_debiasing.R b/tests/test_debiasing.R
new file mode 100644
index 0000000..27bd621
--- /dev/null
+++ b/tests/test_debiasing.R
@@ -0,0 +1,27 @@
+library(selectiveInference)
+source('oldcode.R')
+
+n = 500; p = 50
+
+X = matrix(rnorm(n * p), n, p)
+S = t(X) %*% X / n
+
+mu = 7.791408e-02
+
+A1 = debiasingMatrix(S, FALSE, n, 1:5, mu=mu, max_iter=1000)
+A2 = debiasingMatrix(S / n, FALSE, n, 1:5, mu=mu, max_iter=1000)
+
+B1 = debiasingMatrix(X, TRUE, n, 1:5, mu=mu, max_iter=1000)
+B2 = debiasingMatrix(X / sqrt(n), TRUE, n, 1:5, mu=mu, max_iter=1000)
+
+C1 = InverseLinfty(S, n, mu=mu, maxiter=1000)[1:5,]
+C2 = InverseLinfty(S / n, n, mu=mu, maxiter=1000)[1:5,]
+
+par(mfrow=c(2,3))
+plot(A1[1,], C1[1,])
+plot(A1[1,], B1[1,])
+plot(B1[1,], C1[1,])
+
+plot(A1[1,], A2[1,])
+plot(B1[1,], B2[1,])
+plot(C1[1,], C2[1,])

From dc423de5cf6094e18a986b1305b269aa159c422d Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 22:17:39 -0700
Subject: [PATCH 326/493] bug found in qp solver -- look at tests/test_QP.R

---
 selectiveInference/NAMESPACE                  |   2 +-
 selectiveInference/R/funs.fixed.R             |   5 +
 selectiveInference/R/funs.randomized.R        |   2 +
 selectiveInference/src/Rcpp-debias.cpp        |   4 +
 selectiveInference/src/debias.h               |   2 +
 selectiveInference/src/quadratic_program.c    |   4 +-
 .../src/quadratic_program_wide.c              |   6 +-
 tests/test_QP.R                               |  15 ++
 tests/test_debiasing.R                        | 172 +++++++++++++++++-
 9 files changed, 204 insertions(+), 8 deletions(-)
 create mode 100644 tests/test_QP.R

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index d72d56a..c7d08a1 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -44,4 +44,4 @@ importFrom("stats", dnorm, lsfit, pexp, pnorm, predict,
 importFrom("stats", "coef", "df", "lm", "pf")
 importFrom("stats", "glm", "residuals", "vcov")
 importFrom("Rcpp", "sourceCpp")
-
+importFrom("distr", "Norm", "DExp")
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index aaceaaa..5490316 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -327,6 +327,7 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
 			   warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
 			   max_iter=100,        # how many iterations for each optimization problem
                            kkt_tol=1.e-4,       # tolerance for the KKT conditions
+                           parameter_tol=1.e-4, # tolerance for relative convergence of parameter
 			   objective_tol=1.e-8  # tolerance for relative decrease in objective
                            ) {
 
@@ -363,6 +364,7 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
 			  warn_kkt=FALSE,
 			  max_iter=max_iter,
 			  kkt_tol=kkt_tol,
+			  parameter_tol=parameter_tol,
 			  objective_tol=objective_tol)
 
     if (warn_kkt && (!output$kkt_check)) {
@@ -393,6 +395,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
 			 warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
 			 max_iter=100,        # how many iterations for each optimization problem
                          kkt_tol=1.e-4,       # tolerance for the KKT conditions
+			 parameter_tol=1.e-4, # tolerance for relative convergence of parameter
 			 objective_tol=1.e-8  # tolerance for relative decrease in objective
                          ) {
 
@@ -433,6 +436,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                             nactive, 
                             kkt_tol, 
                             objective_tol, 
+			    parameter_tol,
                             max_active,
 			    FALSE,        # objective_stop
 			    FALSE,        # kkt_stop
@@ -451,6 +455,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                                  nactive, 
                                  kkt_tol, 
                                  objective_tol, 
+				 parameter_tol,
                                  max_active,
 				 FALSE,       # objective_stop
 				 FALSE,       # kkt_stop
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 01e6aa4..25a0b95 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -11,6 +11,7 @@ fit_randomized_lasso = function(X,
                                 noise_type=c('gaussian', 'laplace'),
                                 max_iter=100,        # how many iterations for each optimization problem
                                 kkt_tol=1.e-4,       # tolerance for the KKT conditions
+                                parameter_tol=1.e-8, # tolerance for relative convergence of parameter
                                 objective_tol=1.e-8, # tolerance for relative decrease in objective
                                 objective_stop=FALSE,
                                 kkt_stop=TRUE,
@@ -56,6 +57,7 @@ fit_randomized_lasso = function(X,
                            nactive, 
                            kkt_tol, 
                            objective_tol, 
+			   parameter_tol,
                            p,
 		           objective_stop,     # objective_stop
 			   kkt_stop,           # kkt_stop
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index 24bbae8..9cda705 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -15,6 +15,7 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		    Rcpp::IntegerVector nactive,
 		    double kkt_tol,
 		    double objective_tol,
+		    double parameter_tol,
 		    int max_active,
 		    int objective_stop,
 		    int kkt_stop,
@@ -52,6 +53,7 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		      maxiter,
 		      kkt_tol,
 		      objective_tol,
+		      parameter_tol,
 		      max_active,
 		      objective_stop,
 		      kkt_stop,
@@ -92,6 +94,7 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			 Rcpp::IntegerVector nactive,
 			 double kkt_tol,
 			 double objective_tol,
+			 double parameter_tol,
 			 int max_active,
 			 int objective_stop,
 			 int kkt_stop,
@@ -142,6 +145,7 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			maxiter,
 			kkt_tol,
 			objective_tol,
+			parameter_tol,
 			max_active,
 			objective_stop,
 			kkt_stop,
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
index 052af7a..d3db26d 100644
--- a/selectiveInference/src/debias.h
+++ b/selectiveInference/src/debias.h
@@ -16,6 +16,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	     int maxiter,                /* max number of iterations */
 	     double kkt_tol,             /* precision for checking KKT conditions */
 	     double objective_tol,       /* precision for checking relative decrease in objective value */
+	     double parameter_tol,       /* precision for checking relative convergence of parameter */
 	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
 	     int objective_stop,         /* Break based on convergence of objective value? */
              int kkt_stop,               /* Break based on KKT? */
@@ -44,6 +45,7 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 	       int maxiter,                /* max number of iterations */
 	       double kkt_tol,             /* precision for checking KKT conditions */
 	       double objective_tol,       /* precision for checking relative decrease in objective value */
+	       double parameter_tol,       /* precision for checking relative convergence of parameter */
 	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
 	       int objective_stop,         /* Break based on convergence of objective value? */
 	       int kkt_stop,               /* Break based on KKT? */
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
index 1bc7fa3..822ddf5 100644
--- a/selectiveInference/src/quadratic_program.c
+++ b/selectiveInference/src/quadratic_program.c
@@ -273,6 +273,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	     int maxiter,                /* max number of iterations */
 	     double kkt_tol,             /* precision for checking KKT conditions */
 	     double objective_tol,       /* precision for checking relative decrease in objective value */
+	     double parameter_tol,       /* precision for checking relative convergence of parameter */
 	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
 	     int objective_stop,         /* Break based on convergence of objective value? */
              int kkt_stop,               /* Break based on KKT? */
@@ -292,7 +293,6 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
   double norm_diff = 1.;
   double norm_last = 1.;
   double delta;
-  double threshold = 1.e-2;
   double *theta_ptr, *theta_old_ptr;
 
   if (objective_stop) {
@@ -403,7 +403,7 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	norm_diff = sqrt(norm_diff);
 	norm_last = sqrt(norm_last);
 	
-	if (norm_diff < threshold * norm_last) {
+	if (norm_diff < parameter_tol * norm_last) {
 	  break;
 	}
       }
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
index c6cb9f3..3e4bdb0 100644
--- a/selectiveInference/src/quadratic_program_wide.c
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -4,7 +4,7 @@
 
 // Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
 
-// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2 - l^T\theta + \mu \|\theta\|_1 + \frac{\epsilon}{2} \|\theta\|^2_2
+// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2/n - l^T\theta + \mu \|\theta\|_1 + \frac{\epsilon}{2} \|\theta\|^2_2
 // where l is `linear_func` below
 
 // This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
@@ -393,6 +393,7 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 	       int maxiter,                /* max number of iterations */
 	       double kkt_tol,             /* precision for checking KKT conditions */
 	       double objective_tol,       /* precision for checking relative decrease in objective value */
+	       double parameter_tol,       /* precision for checking relative convergence of parameter */
 	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
 	       int objective_stop,         /* Break based on convergence of objective value? */
 	       int kkt_stop,               /* Break based on KKT? */
@@ -412,7 +413,6 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
   double norm_diff = 1.;
   double norm_last = 1.;
   double delta;
-  double threshold = 1.e-2;
   double *theta_ptr_tmp, *theta_old_ptr_tmp;
 
   if (objective_stop) {
@@ -552,7 +552,7 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 	norm_diff = sqrt(norm_diff);
 	norm_last = sqrt(norm_last);
 	
-	if (norm_diff < threshold * norm_last) {
+	if (norm_diff < parameter_tol * norm_last) {
 	  break;
 	}
       }
diff --git a/tests/test_QP.R b/tests/test_QP.R
new file mode 100644
index 0000000..cf5ca64
--- /dev/null
+++ b/tests/test_QP.R
@@ -0,0 +1,15 @@
+library(selectiveInference)
+### Test
+
+n = 100; p = 50
+
+X = matrix(rnorm(n * p), n, p)
+Y = rnorm(n)
+lam = 2
+
+soln1 = selectiveInference:::fit_randomized_lasso(X, Y, lam, 1.e-12, 0)$soln
+G = glmnet(X, Y, intercept=FALSE, standardize=FALSE)
+soln2 = coef(G, s=1/n, exact=TRUE, x=X, y=Y)[-1]
+
+print(soln1)
+print(soln2)
\ No newline at end of file
diff --git a/tests/test_debiasing.R b/tests/test_debiasing.R
index 27bd621..b1fdf24 100644
--- a/tests/test_debiasing.R
+++ b/tests/test_debiasing.R
@@ -1,7 +1,143 @@
 library(selectiveInference)
-source('oldcode.R')
 
-n = 500; p = 50
+
+## Approximates inverse covariance matrix theta
+InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e-10, verbose = TRUE) {
+  isgiven <- 1;
+  if (is.null(mu)){
+    isgiven <- 0;
+  }
+  
+  p <- nrow(sigma);
+  M <- matrix(0, p, p);
+  xperc = 0;
+  xp = round(p/10);
+  for (i in 1:p) {
+    if ((i %% xp)==0){
+      xperc = xperc+10;
+      if (verbose) {
+        print(paste(xperc,"% done",sep="")); }
+    }
+    if (isgiven==0){
+      mu <- (1/sqrt(n)) * qnorm(1-(0.1/(p^2)));
+    }
+    mu.stop <- 0;
+    try.no <- 1;
+    incr <- 0;
+    while ((mu.stop != 1)&&(try.no<10)){
+      last.beta <- beta
+      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
+      beta <- output$optsol
+      iter <- output$iter
+      if (isgiven==1){
+        mu.stop <- 1
+      }
+      else{
+        if (try.no==1){
+          if (iter == (maxiter+1)){
+            incr <- 1;
+            mu <- mu*resol;
+          } else {
+            incr <- 0;
+            mu <- mu/resol;
+          }
+        }
+        if (try.no > 1){
+          if ((incr == 1)&&(iter == (maxiter+1))){
+            mu <- mu*resol;
+          }
+          if ((incr == 1)&&(iter < (maxiter+1))){
+            mu.stop <- 1;
+          }
+          if ((incr == 0)&&(iter < (maxiter+1))){
+            mu <- mu/resol;
+          }
+          if ((incr == 0)&&(iter == (maxiter+1))){
+            mu <- mu*resol;
+            beta <- last.beta;
+            mu.stop <- 1;
+          }
+        }
+      }
+      try.no <- try.no+1
+    }
+    M[i,] <- beta;
+  }
+  return(M)
+}
+
+InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-10) {
+  p <- nrow(sigma);
+  rho <- max(abs(sigma[i,-i])) / sigma[i,i];
+  mu0 <- rho/(1+rho);
+  beta <- rep(0,p);
+  
+  #if (mu >= mu0){
+  #  beta[i] <- (1-mu0)/sigma[i,i];
+  #  returnlist <- list("optsol" = beta, "iter" = 0);
+  #  return(returnlist);
+  #}
+  
+  diff.norm2 <- 1;
+  last.norm2 <- 1;
+  iter <- 1;
+  iter.old <- 1;
+  beta[i] <- (1-mu0)/sigma[i,i];
+  beta.old <- beta;
+  sigma.tilde <- sigma;
+  diag(sigma.tilde) <- 0;
+  vs <- -sigma.tilde%*%beta;
+  
+  while ((iter <= maxiter) && (diff.norm2 >= threshold*last.norm2)){
+    
+    for (j in 1:p){
+      oldval <- beta[j];
+      v <- vs[j];
+      if (j==i)
+        v <- v+1;
+      beta[j] <- SoftThreshold(v,mu)/sigma[j,j];
+      if (oldval != beta[j]){
+        vs <- vs + (oldval-beta[j])*sigma.tilde[,j];
+      }
+    }
+    
+    iter <- iter + 1;
+    if (iter==2*iter.old){
+      d <- beta - beta.old;
+      diff.norm2 <- sqrt(sum(d*d));
+      last.norm2 <-sqrt(sum(beta*beta));
+      iter.old <- iter;
+      beta.old <- beta;
+      #if (iter>10)
+      #  vs <- -sigma.tilde%*%beta;
+    }
+
+    # print(c(iter, maxiter, diff.norm2, threshold * last.norm2, threshold, mu))
+
+  }
+  
+  returnlist <- list("optsol" = beta, "iter" = iter)
+  return(returnlist)
+}
+
+SoftThreshold <- function( x, lambda ) {
+  #
+  # Standard soft thresholding
+  #
+  if (x>lambda){
+    return (x-lambda);}
+  else {
+    if (x< (-lambda)){
+      return (x+lambda);}
+    else {
+      return (0); }
+  }
+}
+
+
+### Test
+
+n = 100; p = 50
 
 X = matrix(rnorm(n * p), n, p)
 S = t(X) %*% X / n
@@ -25,3 +161,35 @@ plot(B1[1,], C1[1,])
 plot(A1[1,], A2[1,])
 plot(B1[1,], B2[1,])
 plot(C1[1,], C2[1,])
+
+print(c('A', sum(A1[1,] == 0)))
+print(c('B', sum(B1[1,] == 0)))
+print(c('C', sum(C1[1,] == 0)))
+
+## Are our points feasible
+
+feasibility = function(S, soln, j, mu) {
+     p = nrow(S)
+     E = rep(0, p)
+     E[j] = 1
+     G = S %*% soln - E
+     return(c(max(abs(G)), mu))
+}
+
+print(c('feasibility A', feasibility(S, A1[1,], 1, mu)))
+print(c('feasibility B', feasibility(S, B1[1,], 1, mu)))
+print(c('feasibility C', feasibility(S, C1[1,], 1, mu)))
+
+active_KKT = function(S, soln, j, mu) {
+     p = nrow(S)
+     E = rep(0, p)
+     E[j] = 1
+     G = S %*% soln - E
+     return(c(G[soln != 0] * sign(soln)[soln != 0], mu))
+}
+
+print(c('active_KKT A', active_KKT(S, A1[1,], 1, mu)))
+print(c('active_KKT B', active_KKT(S, B1[1,], 1, mu)))
+print(c('active_KKT C', active_KKT(S, C1[1,], 1, mu)))
+
+

From 71ff6e3080c6c93390fb6d074ef22085fbd036b0 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 22:19:20 -0700
Subject: [PATCH 327/493] allowing randomization to be 0 in solver

---
 selectiveInference/R/funs.randomized.R | 18 +++++++++++-------
 tests/test_QP.R                        |  2 +-
 2 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 25a0b95..b79e2fb 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -22,14 +22,18 @@ fit_randomized_lasso = function(X,
     			
     noise_type = match.arg(noise_type)
 
-    if (noise_type == 'gaussian') {
-       D = Norm(mean=0, sd=noise_scale)
+    if (noise_scale > 0) {
+        if (noise_type == 'gaussian') {
+            D = Norm(mean=0, sd=noise_scale)
+        }
+        else if (noise_type == 'laplace') {
+            D = DExp(rate = 1 / noise_scale) # D is a Laplace distribution with rate = 1.
+        }
+        perturb_ = distr::r(D)(p)
+    } else {
+        perturb_ = rep(0, p)
     }
-    else if (noise_type == 'laplace') {
-       D = DExp(rate = 1 / noise_scale) # D is a Laplace distribution with rate = 1.
-    }
-    perturb_ = distr::r(D)(p)
-			
+
     lam = as.numeric(lam)
     if (length(lam) == 1) {
        lam = rep(lam, p)
diff --git a/tests/test_QP.R b/tests/test_QP.R
index cf5ca64..4aebec3 100644
--- a/tests/test_QP.R
+++ b/tests/test_QP.R
@@ -7,7 +7,7 @@ X = matrix(rnorm(n * p), n, p)
 Y = rnorm(n)
 lam = 2
 
-soln1 = selectiveInference:::fit_randomized_lasso(X, Y, lam, 1.e-12, 0)$soln
+soln1 = selectiveInference:::fit_randomized_lasso(X, Y, lam, 0, 0)$soln
 G = glmnet(X, Y, intercept=FALSE, standardize=FALSE)
 soln2 = coef(G, s=1/n, exact=TRUE, x=X, y=Y)[-1]
 

From de8874bc22db314e2efeb99a34ed226aeea2745e Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 22:25:32 -0700
Subject: [PATCH 328/493] cosmetic edit

---
 selectiveInference/src/Rcpp-debias.cpp | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index 9cda705..5c18184 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -112,12 +112,13 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 
   Rcpp::IntegerVector need_update(nfeature);
 
-  // Extract the diagonal
+  Rcpp::NumericVector theta_old(nfeature);
+
+  // Extract the diagonal -- divide by ncase
+
   Rcpp::NumericVector nndef_diag(nfeature);
   double *nndef_diag_p = nndef_diag.begin();
 
-  Rcpp::NumericVector theta_old(nfeature);
-
   for (ifeature=0; ifeature<nfeature; ifeature++) {
     nndef_diag_p[ifeature] = 0;
     for (icase=0; icase<ncase; icase++) {

From 07f738cbf0720860b14969e4b5e5ea764ea9ee5d Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 22:46:48 -0700
Subject: [PATCH 329/493] LASSO solver is right up to scale

---
 tests/test_QP.R | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tests/test_QP.R b/tests/test_QP.R
index 4aebec3..61c4d53 100644
--- a/tests/test_QP.R
+++ b/tests/test_QP.R
@@ -9,7 +9,8 @@ lam = 2
 
 soln1 = selectiveInference:::fit_randomized_lasso(X, Y, lam, 0, 0)$soln
 G = glmnet(X, Y, intercept=FALSE, standardize=FALSE)
-soln2 = coef(G, s=1/n, exact=TRUE, x=X, y=Y)[-1]
+soln2 = coef(G, s=lam/n, exact=TRUE, x=X, y=Y)[-1]
 
 print(soln1)
-print(soln2)
\ No newline at end of file
+print(soln2)
+plot(soln1, soln2)

From 2f1686ee2c627061765f1aca1f0b533202eaf0a2 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 26 Oct 2017 23:55:49 -0700
Subject: [PATCH 330/493] BF: fixing Xsoln incorrectly set in wide solver

---
 selectiveInference/R/funs.fixed.R             | 24 +++++++++++++------
 selectiveInference/R/funs.randomized.R        |  4 ++--
 .../src/quadratic_program_wide.c              |  2 +-
 tests/test_QP.R                               |  2 +-
 tests/test_debiasing.R                        | 13 ++++++----
 5 files changed, 30 insertions(+), 15 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 5490316..22a31e5 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -326,6 +326,9 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
 			   max_try=10,          # how many steps in linesearch?
 			   warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
 			   max_iter=100,        # how many iterations for each optimization problem
+                           kkt_stop=TRUE,       # stop based on KKT conditions?
+                           parameter_stop=TRUE, # stop based on relative convergence of parameter?
+			   objective_stop=TRUE, # stop based on relative decrease in objective?
                            kkt_tol=1.e-4,       # tolerance for the KKT conditions
                            parameter_tol=1.e-4, # tolerance for relative convergence of parameter
 			   objective_tol=1.e-8  # tolerance for relative decrease in objective
@@ -363,6 +366,9 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
 			  max_try=max_try,
 			  warn_kkt=FALSE,
 			  max_iter=max_iter,
+			  kkt_stop=kkt_stop,
+			  parameter_stop=parameter_stop,
+			  objective_stop=objective_stop,
 			  kkt_tol=kkt_tol,
 			  parameter_tol=parameter_tol,
 			  objective_tol=objective_tol)
@@ -394,6 +400,9 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
 			 max_try=10,          # how many steps in linesearch?
 			 warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
 			 max_iter=100,        # how many iterations for each optimization problem
+                         kkt_stop=TRUE,       # stop based on KKT conditions?
+                         parameter_stop=TRUE, # stop based on relative convergence of parameter?
+                         objective_stop=TRUE, # stop based on relative decrease in objective?
                          kkt_tol=1.e-4,       # tolerance for the KKT conditions
 			 parameter_tol=1.e-4, # tolerance for relative convergence of parameter
 			 objective_tol=1.e-8  # tolerance for relative decrease in objective
@@ -423,6 +432,8 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
 
   last_output = NULL
 
+  Xsoln = rep(0, n)
+
   while (counter_idx < max_try) {
 
       if (!is_wide) {
@@ -438,11 +449,10 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                             objective_tol, 
 			    parameter_tol,
                             max_active,
-			    FALSE,        # objective_stop
-			    FALSE,        # kkt_stop
-			    TRUE)         # param_stop
+			    objective_stop,
+			    kkt_stop,
+			    parameter_stop)
       } else {
-          Xsoln = rep(0, nrow(Xinfo))
           result = solve_QP_wide(Xinfo, # this is a design matrix
                                  rep(mu, p),  # vector of Lagrange multipliers
 				 0,           # ridge_term 
@@ -457,9 +467,9 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                                  objective_tol, 
 				 parameter_tol,
                                  max_active,
-				 FALSE,       # objective_stop
-				 FALSE,       # kkt_stop
-				 TRUE)        # param_stop
+				 objective_stop,	
+				 kkt_stop,
+				 parameter_stop)
 
       }
 
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index b79e2fb..7bf6008 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -44,13 +44,13 @@ fit_randomized_lasso = function(X,
 
     soln = rep(0, p)
     Xsoln = rep(0, n)
-    linear_func = (- t(X) %*% y - perturb_)
+    linear_func = (- t(X) %*% y - perturb_) / n
     gradient = 1. * linear_func
     ever_active = rep(0, p)
     nactive = as.integer(0)
 
     result = solve_QP_wide(X,                  # design matrix
-    	                   lam,                # vector of Lagrange multipliers
+    	                   lam / n,                # vector of Lagrange multipliers
 		           ridge_term / n,     # ridge_term 
                            max_iter, 
                            soln, 
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
index 3e4bdb0..3546fcd 100644
--- a/selectiveInference/src/quadratic_program_wide.c
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -4,7 +4,7 @@
 
 // Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
 
-// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2/n - l^T\theta + \mu \|\theta\|_1 + \frac{\epsilon}{2} \|\theta\|^2_2
+// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2/n + l^T\theta + \mu \|\theta\|_1 + \frac{\epsilon}{2} \|\theta\|^2_2
 // where l is `linear_func` below
 
 // This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
diff --git a/tests/test_QP.R b/tests/test_QP.R
index 61c4d53..1764225 100644
--- a/tests/test_QP.R
+++ b/tests/test_QP.R
@@ -1,7 +1,7 @@
 library(selectiveInference)
 ### Test
 
-n = 100; p = 50
+n = 80; p = 50
 
 X = matrix(rnorm(n * p), n, p)
 Y = rnorm(n)
diff --git a/tests/test_debiasing.R b/tests/test_debiasing.R
index b1fdf24..e2743fe 100644
--- a/tests/test_debiasing.R
+++ b/tests/test_debiasing.R
@@ -144,16 +144,19 @@ S = t(X) %*% X / n
 
 mu = 7.791408e-02
 
-A1 = debiasingMatrix(S, FALSE, n, 1:5, mu=mu, max_iter=1000)
-A2 = debiasingMatrix(S / n, FALSE, n, 1:5, mu=mu, max_iter=1000)
+tol = 1.e-12
 
-B1 = debiasingMatrix(X, TRUE, n, 1:5, mu=mu, max_iter=1000)
-B2 = debiasingMatrix(X / sqrt(n), TRUE, n, 1:5, mu=mu, max_iter=1000)
+A1 = debiasingMatrix(S, FALSE, n, 1:5, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+A2 = debiasingMatrix(S / n, FALSE, n, 1:5, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+
+B1 = debiasingMatrix(X, TRUE, n, 1:5, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+B2 = debiasingMatrix(X / sqrt(n), TRUE, n, 1:5, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
 
 C1 = InverseLinfty(S, n, mu=mu, maxiter=1000)[1:5,]
 C2 = InverseLinfty(S / n, n, mu=mu, maxiter=1000)[1:5,]
 
 par(mfrow=c(2,3))
+
 plot(A1[1,], C1[1,])
 plot(A1[1,], B1[1,])
 plot(B1[1,], C1[1,])
@@ -185,6 +188,8 @@ active_KKT = function(S, soln, j, mu) {
      E = rep(0, p)
      E[j] = 1
      G = S %*% soln - E
+     print(which(soln != 0))
+     print(G[j])
      return(c(G[soln != 0] * sign(soln)[soln != 0], mu))
 }
 

From 6444472cd41456ce627f05986ec4222d39f1f049 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 27 Oct 2017 00:03:09 -0700
Subject: [PATCH 331/493] gradient not current?

---
 selectiveInference/R/funs.fixed.R |  3 ++-
 tests/test_debiasing.R            | 13 +++++++------
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 22a31e5..8d3f727 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -525,7 +525,8 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
   } 
 
   return(list(soln=result$soln,
-              kkt_check=result$kkt_check))
+              kkt_check=result$kkt_check,
+	      gradient=result$gradient))
 
 }
 
diff --git a/tests/test_debiasing.R b/tests/test_debiasing.R
index e2743fe..50b43d2 100644
--- a/tests/test_debiasing.R
+++ b/tests/test_debiasing.R
@@ -146,14 +146,15 @@ mu = 7.791408e-02
 
 tol = 1.e-12
 
-A1 = debiasingMatrix(S, FALSE, n, 1:5, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
-A2 = debiasingMatrix(S / n, FALSE, n, 1:5, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+rows = c(1:2)
+A1 = debiasingMatrix(S, FALSE, n, rows, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+A2 = debiasingMatrix(S / n, FALSE, n, rows, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
 
-B1 = debiasingMatrix(X, TRUE, n, 1:5, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
-B2 = debiasingMatrix(X / sqrt(n), TRUE, n, 1:5, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+B1 = debiasingMatrix(X, TRUE, n, rows, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+B2 = debiasingMatrix(X / sqrt(n), TRUE, n, rows, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
 
-C1 = InverseLinfty(S, n, mu=mu, maxiter=1000)[1:5,]
-C2 = InverseLinfty(S / n, n, mu=mu, maxiter=1000)[1:5,]
+C1 = InverseLinfty(S, n, mu=mu, maxiter=1000)[rows,]
+C2 = InverseLinfty(S / n, n, mu=mu, maxiter=1000)[rows,]
 
 par(mfrow=c(2,3))
 

From 9f2684462758fdafd5871fd748ce032a873209cd Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 27 Oct 2017 00:07:30 -0700
Subject: [PATCH 332/493] print R2 from lm

---
 tests/test_QP.R | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/test_QP.R b/tests/test_QP.R
index 1764225..79638e0 100644
--- a/tests/test_QP.R
+++ b/tests/test_QP.R
@@ -14,3 +14,4 @@ soln2 = coef(G, s=lam/n, exact=TRUE, x=X, y=Y)[-1]
 print(soln1)
 print(soln2)
 plot(soln1, soln2)
+print(summary(lm(soln1 ~ soln2)))
\ No newline at end of file

From 9c7bfcce31fbcf0bf87d381bceb6fe3d42029e04 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 27 Oct 2017 09:47:31 -0700
Subject: [PATCH 333/493] without linesearch we know agree with Adel's code at
 fixed mu

---
 selectiveInference/DESCRIPTION         |  2 +-
 selectiveInference/NAMESPACE           |  2 +-
 selectiveInference/R/funs.fixed.R      | 29 ++++++----
 selectiveInference/R/funs.randomized.R | 31 +++++-----
 tests/test_debiasing.R                 | 80 +++++++++++++-------------
 5 files changed, 75 insertions(+), 69 deletions(-)

diff --git a/selectiveInference/DESCRIPTION b/selectiveInference/DESCRIPTION
index d902622..fad072d 100644
--- a/selectiveInference/DESCRIPTION
+++ b/selectiveInference/DESCRIPTION
@@ -9,7 +9,7 @@ Maintainer: Rob Tibshirani <tibs@stanford.edu>
 Depends:
     glmnet,
     intervals,
-    survival
+    survival,
 Suggests:
     Rmpfr
 Description: New tools for post-selection inference, for use with forward
diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index c7d08a1..d72d56a 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -44,4 +44,4 @@ importFrom("stats", dnorm, lsfit, pexp, pnorm, predict,
 importFrom("stats", "coef", "df", "lm", "pf")
 importFrom("stats", "glm", "residuals", "vcov")
 importFrom("Rcpp", "sourceCpp")
-importFrom("distr", "Norm", "DExp")
+
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 8d3f727..1928487 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -319,7 +319,7 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
                            nsample, 
                            rows, 
  		           verbose=FALSE, 
-		           mu=NULL,             # starting value of mu
+		           bound=NULL,             # starting value of bound
    			   linesearch=TRUE,     # do a linesearch?
    		           scaling_factor=1.5,  # multiplicative factor for linesearch
 			   max_active=NULL,     # how big can active set get?
@@ -342,8 +342,8 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
   p = ncol(Xinfo);
   M = matrix(0, length(rows), p);
 
-  if (is.null(mu)) {
-      mu = (1/sqrt(nsample)) * qnorm(1-(0.1/(p^2)))
+  if (is.null(bound)) {
+      bound = (1/sqrt(nsample)) * qnorm(1-(0.1/(p^2)))
   }
  
   xperc = 0;
@@ -359,7 +359,7 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
     output = debiasingRow(Xinfo,               # could be X or t(X) %*% X / n depending on is_wide
                           is_wide,
                           row,
-                          mu,
+                          bound,
                           linesearch=linesearch,
                           scaling_factor=scaling_factor,
                           max_active=max_active,
@@ -393,7 +393,7 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
 debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n depending on is_wide
                          is_wide, 
                          row, 
-                         mu, 
+                         bound, 
 	                 linesearch=TRUE,     # do a linesearch?
 		         scaling_factor=1.5,  # multiplicative factor for linesearch
 		         max_active=NULL,     # how big can active set get?
@@ -414,9 +414,11 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
       max_active = min(nrow(Xinfo), ncol(Xinfo))
   }
 
+   
   # Initialize variables 
 
   soln = rep(0, p)
+  soln = as.numeric(soln)
   ever_active = rep(0, p)
   ever_active[1] = row      # 1-based
   ever_active = as.integer(ever_active)
@@ -432,13 +434,16 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
 
   last_output = NULL
 
-  Xsoln = rep(0, n)
+  if (is_wide) {
+     n = nrow(Xinfo)
+     Xsoln = as.numeric(rep(0, n))
+  }
 
   while (counter_idx < max_try) {
 
       if (!is_wide) {
           result = solve_QP(Xinfo, # this is non-neg-def matrix
-                            mu, 
+                            bound, 
                             max_iter, 
                             soln, 
                             linear_func, 
@@ -453,9 +458,9 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
 			    kkt_stop,
 			    parameter_stop)
       } else {
-          result = solve_QP_wide(Xinfo, # this is a design matrix
-                                 rep(mu, p),  # vector of Lagrange multipliers
-				 0,           # ridge_term 
+          result = solve_QP_wide(Xinfo,                      # this is a design matrix
+                                 as.numeric(rep(bound, p)),  # vector of Lagrange multipliers
+				 0,                          # ridge_term 
                                  max_iter, 
                                  soln, 
                                  linear_func, 
@@ -493,13 +498,13 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
          if ((iter < (max_iter+1)) && (counter_idx > 1)) { 
            break;      # we've found a feasible point and solved the problem            
          }
-         mu = mu * scaling_factor;
+         bound = bound * scaling_factor;
       } else {         # trying to drop the bound parameter further
          if ((iter == (max_iter + 1)) && (counter_idx > 1)) {
             result = last_output; # problem seems infeasible because we didn't solve it
    	    break;                # so we revert to previously found solution
          }
-         mu = mu / scaling_factor;
+         bound = bound / scaling_factor;
       }
 
       # If the active set has grown to a certain size
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 7bf6008..3a171f7 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -3,19 +3,19 @@
 #
 # min 1/2 || y - \beta_0 - X \beta ||_2^2 + \lambda || \beta ||_1 - \omega^T\beta + \frac{\epsilon}{2} \|\beta\|^2_2
 
-fit_randomized_lasso = function(X, 
-                                y, 
-                                lam, 
-                                noise_scale, 
-                                ridge_term, 
-                                noise_type=c('gaussian', 'laplace'),
-                                max_iter=100,        # how many iterations for each optimization problem
-                                kkt_tol=1.e-4,       # tolerance for the KKT conditions
-                                parameter_tol=1.e-8, # tolerance for relative convergence of parameter
-                                objective_tol=1.e-8, # tolerance for relative decrease in objective
-                                objective_stop=FALSE,
-                                kkt_stop=TRUE,
-                                param_stop=TRUE)
+randomizedLASSO = function(X, 
+                           y, 
+                           lam, 
+                           noise_scale, 
+                           ridge_term, 
+                           noise_type=c('gaussian', 'laplace'),
+                           max_iter=100,        # how many iterations for each optimization problem
+                           kkt_tol=1.e-4,       # tolerance for the KKT conditions
+                           parameter_tol=1.e-8, # tolerance for relative convergence of parameter
+                           objective_tol=1.e-8, # tolerance for relative decrease in objective
+                           objective_stop=FALSE,
+                           kkt_stop=TRUE,
+                           param_stop=TRUE)
 {
 
     n = nrow(X); p = ncol(X)
@@ -24,12 +24,11 @@ fit_randomized_lasso = function(X,
 
     if (noise_scale > 0) {
         if (noise_type == 'gaussian') {
-            D = Norm(mean=0, sd=noise_scale)
+            perturb_ = rnorm(p) * noise_scale
         }
         else if (noise_type == 'laplace') {
-            D = DExp(rate = 1 / noise_scale) # D is a Laplace distribution with rate = 1.
+            perturb_ = rexp(p) * (2 * rbinom(p, 1, 0.5) - 1) * noise_scale
         }
-        perturb_ = distr::r(D)(p)
     } else {
         perturb_ = rep(0, p)
     }
diff --git a/tests/test_debiasing.R b/tests/test_debiasing.R
index 50b43d2..8e81b16 100644
--- a/tests/test_debiasing.R
+++ b/tests/test_debiasing.R
@@ -2,9 +2,9 @@ library(selectiveInference)
 
 
 ## Approximates inverse covariance matrix theta
-InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e-10, verbose = TRUE) {
+InverseLinfty <- function(sigma, n, resol=1.5, bound=NULL, maxiter=50, threshold=1e-10, verbose = TRUE) {
   isgiven <- 1;
-  if (is.null(mu)){
+  if (is.null(bound)){
     isgiven <- 0;
   }
   
@@ -19,43 +19,43 @@ InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e
         print(paste(xperc,"% done",sep="")); }
     }
     if (isgiven==0){
-      mu <- (1/sqrt(n)) * qnorm(1-(0.1/(p^2)));
+      bound <- (1/sqrt(n)) * qnorm(1-(0.1/(p^2)));
     }
-    mu.stop <- 0;
+    bound.stop <- 0;
     try.no <- 1;
     incr <- 0;
-    while ((mu.stop != 1)&&(try.no<10)){
+    while ((bound.stop != 1)&&(try.no<10)){
       last.beta <- beta
-      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
+      output <- InverseLinftyOneRow(sigma, i, bound, maxiter=maxiter, threshold=threshold)
       beta <- output$optsol
       iter <- output$iter
       if (isgiven==1){
-        mu.stop <- 1
+        bound.stop <- 1
       }
       else{
         if (try.no==1){
           if (iter == (maxiter+1)){
             incr <- 1;
-            mu <- mu*resol;
+            bound <- bound*resol;
           } else {
             incr <- 0;
-            mu <- mu/resol;
+            bound <- bound/resol;
           }
         }
         if (try.no > 1){
           if ((incr == 1)&&(iter == (maxiter+1))){
-            mu <- mu*resol;
+            bound <- bound*resol;
           }
           if ((incr == 1)&&(iter < (maxiter+1))){
-            mu.stop <- 1;
+            bound.stop <- 1;
           }
           if ((incr == 0)&&(iter < (maxiter+1))){
-            mu <- mu/resol;
+            bound <- bound/resol;
           }
           if ((incr == 0)&&(iter == (maxiter+1))){
-            mu <- mu*resol;
+            bound <- bound*resol;
             beta <- last.beta;
-            mu.stop <- 1;
+            bound.stop <- 1;
           }
         }
       }
@@ -66,14 +66,14 @@ InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e
   return(M)
 }
 
-InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-10) {
+InverseLinftyOneRow <- function ( sigma, i, bound, maxiter=50, threshold=1e-10) {
   p <- nrow(sigma);
   rho <- max(abs(sigma[i,-i])) / sigma[i,i];
-  mu0 <- rho/(1+rho);
+  bound0 <- rho/(1+rho);
   beta <- rep(0,p);
   
-  #if (mu >= mu0){
-  #  beta[i] <- (1-mu0)/sigma[i,i];
+  #if (bound >= bound0){
+  #  beta[i] <- (1-bound0)/sigma[i,i];
   #  returnlist <- list("optsol" = beta, "iter" = 0);
   #  return(returnlist);
   #}
@@ -82,7 +82,7 @@ InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-10) {
   last.norm2 <- 1;
   iter <- 1;
   iter.old <- 1;
-  beta[i] <- (1-mu0)/sigma[i,i];
+  beta[i] <- (1-bound0)/sigma[i,i];
   beta.old <- beta;
   sigma.tilde <- sigma;
   diag(sigma.tilde) <- 0;
@@ -95,7 +95,7 @@ InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-10) {
       v <- vs[j];
       if (j==i)
         v <- v+1;
-      beta[j] <- SoftThreshold(v,mu)/sigma[j,j];
+      beta[j] <- SoftThreshold(v,bound)/sigma[j,j];
       if (oldval != beta[j]){
         vs <- vs + (oldval-beta[j])*sigma.tilde[,j];
       }
@@ -112,7 +112,7 @@ InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-10) {
       #  vs <- -sigma.tilde%*%beta;
     }
 
-    # print(c(iter, maxiter, diff.norm2, threshold * last.norm2, threshold, mu))
+    # print(c(iter, maxiter, diff.norm2, threshold * last.norm2, threshold, bound))
 
   }
   
@@ -142,19 +142,21 @@ n = 100; p = 50
 X = matrix(rnorm(n * p), n, p)
 S = t(X) %*% X / n
 
-mu = 7.791408e-02
+debiasing_bound = 7.791408e-02
 
 tol = 1.e-12
 
-rows = c(1:2)
-A1 = debiasingMatrix(S, FALSE, n, rows, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
-A2 = debiasingMatrix(S / n, FALSE, n, rows, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+rows = as.integer(c(1:2))
+print('here')
+print(rows)
+A1 = debiasingMatrix(S, FALSE, n, rows, bound=debiasing_bound, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol, linesearch=FALSE)
 
-B1 = debiasingMatrix(X, TRUE, n, rows, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
-B2 = debiasingMatrix(X / sqrt(n), TRUE, n, rows, mu=mu, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol)
+A2 = debiasingMatrix(S / n, FALSE, n, rows, bound=debiasing_bound, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol, linesearch=FALSE)
+B1 = debiasingMatrix(X, TRUE, n, rows, bound=debiasing_bound, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol, linesearch=FALSE)
+B2 = debiasingMatrix(X / sqrt(n), TRUE, n, rows, bound=debiasing_bound, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol, linesearch=FALSE)
 
-C1 = InverseLinfty(S, n, mu=mu, maxiter=1000)[rows,]
-C2 = InverseLinfty(S / n, n, mu=mu, maxiter=1000)[rows,]
+C1 = InverseLinfty(S, n, bound=debiasing_bound, maxiter=1000)[rows,]
+C2 = InverseLinfty(S / n, n, bound=debiasing_bound, maxiter=1000)[rows,]
 
 par(mfrow=c(2,3))
 
@@ -172,30 +174,30 @@ print(c('C', sum(C1[1,] == 0)))
 
 ## Are our points feasible
 
-feasibility = function(S, soln, j, mu) {
+feasibility = function(S, soln, j, debiasing_bound) {
      p = nrow(S)
      E = rep(0, p)
      E[j] = 1
      G = S %*% soln - E
-     return(c(max(abs(G)), mu))
+     return(c(max(abs(G)), debiasing_bound))
 }
 
-print(c('feasibility A', feasibility(S, A1[1,], 1, mu)))
-print(c('feasibility B', feasibility(S, B1[1,], 1, mu)))
-print(c('feasibility C', feasibility(S, C1[1,], 1, mu)))
+print(c('feasibility A', feasibility(S, A1[1,], 1, debiasing_bound)))
+print(c('feasibility B', feasibility(S, B1[1,], 1, debiasing_bound)))
+print(c('feasibility C', feasibility(S, C1[1,], 1, debiasing_bound)))
 
-active_KKT = function(S, soln, j, mu) {
+active_KKT = function(S, soln, j, debiasing_bound) {
      p = nrow(S)
      E = rep(0, p)
      E[j] = 1
      G = S %*% soln - E
      print(which(soln != 0))
      print(G[j])
-     return(c(G[soln != 0] * sign(soln)[soln != 0], mu))
+     return(c(G[soln != 0] * sign(soln)[soln != 0], debiasing_bound))
 }
 
-print(c('active_KKT A', active_KKT(S, A1[1,], 1, mu)))
-print(c('active_KKT B', active_KKT(S, B1[1,], 1, mu)))
-print(c('active_KKT C', active_KKT(S, C1[1,], 1, mu)))
+print(c('active_KKT A', active_KKT(S, A1[1,], 1, debiasing_bound)))
+print(c('active_KKT B', active_KKT(S, B1[1,], 1, debiasing_bound)))
+print(c('active_KKT C', active_KKT(S, C1[1,], 1, debiasing_bound)))
 
 

From c2460a162cab1db17958f02c71cfcdab6234da8a Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 27 Oct 2017 10:03:45 -0700
Subject: [PATCH 334/493] fixing documentation

---
 selectiveInference/NAMESPACE                  |  1 +
 selectiveInference/man/debiasingMatrix.Rd     | 27 +++++++++--
 selectiveInference/src/quadratic_program.c    | 43 +++++++++--------
 .../src/quadratic_program_wide.c              | 46 ++++++++++---------
 4 files changed, 71 insertions(+), 46 deletions(-)

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index d72d56a..e7b1e80 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -43,5 +43,6 @@ importFrom("stats", dnorm, lsfit, pexp, pnorm, predict,
            qnorm, rnorm, sd, uniroot, dchisq, model.matrix, pchisq)
 importFrom("stats", "coef", "df", "lm", "pf")
 importFrom("stats", "glm", "residuals", "vcov")
+importFrom("stats", "rbinom", "rexp")
 importFrom("Rcpp", "sourceCpp")
 
diff --git a/selectiveInference/man/debiasingMatrix.Rd b/selectiveInference/man/debiasingMatrix.Rd
index 4da925e..b95c75b 100644
--- a/selectiveInference/man/debiasingMatrix.Rd
+++ b/selectiveInference/man/debiasingMatrix.Rd
@@ -16,14 +16,18 @@ debiasingMatrix(Xinfo,
                 nsample, 
                 rows, 
 		verbose=FALSE, 
-		mu=NULL,            
+		bound=NULL,            
    		linesearch=TRUE,    
    		scaling_factor=1.5, 
 		max_active=NULL,    
 		max_try=10,         
 		warn_kkt=FALSE,     
 		max_iter=100,       
+		kkt_stop=TRUE,
+		parameter_stop=TRUE,
+		objective_stop=TRUE,
                 kkt_tol=1.e-4,      
+		parameter_tol=1.e-4,
 		objective_tol=1.e-8)
 }
 \arguments{
@@ -38,7 +42,7 @@ matrix of interest is t(X) %*% X / nrow(X).
 }
 \item{nsample}{
 Number of samples used in forming the cross-covariance matrix.
-Used for default value of the bound parameter mu.
+Used for default value of the bound parameter.
 }
 \item{rows}{
 Which rows of the approximate inverse to compute.
@@ -46,7 +50,7 @@ Which rows of the approximate inverse to compute.
 \item{verbose}{
 Print out progress as rows are being computed.
 } 
-\item{mu}{
+\item{bound}{
 Initial bound parameter for each row. Will be changed
 if linesearch is TRUE.
 } 
@@ -72,10 +76,25 @@ descent algorithm.
 How many full iterations to run of the coordinate descent for each
 value of the bound parameter.
 }
+\item{kkt_stop}{
+If TRUE, check to stop coordinate descent when KKT conditions are approximately satisfied.
+}
+\item{parameter_stop}{
+If TRUE, check to stop coordinate descent based on relative convergence of parameter vector,
+checked at geometrically spaced iterations 2^k.
+}
+\item{objective_stop}{
+If TRUE, check to stop coordinate descent based on relative decrease of objective value,
+checked at geometrically spaced iterations 2^k.
+}
 \item{kkt_tol}{
 Tolerance value for assessing whether KKT conditions for solving the
 dual problem and feasibility of the original problem.
 }
+\item{parameter_tol}{
+Tolerance value for assessing convergence of the problem using relative
+convergence of the parameter.
+}
 \item{objective_tol}{
 Tolerance value for assessing convergence of the problem using relative
 decrease of the objective.
@@ -86,7 +105,7 @@ This function computes an approximate inverse
 as described in Javanmard and Montanari (2013), specifically
 display (4). The problem is solved by considering a dual
 problem which has an objective similar to a LASSO problem and is solvable
-by coordinate descent. For some values of mu the original
+by coordinate descent. For some values of bound the original
 problem may not be feasible, in which case the dual problem has no solution. 
 An attempt to detect this is made by stopping when the active set grows quite
 large, determined by max_active.
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
index 822ddf5..1f7fcb3 100644
--- a/selectiveInference/src/quadratic_program.c
+++ b/selectiveInference/src/quadratic_program.c
@@ -385,10 +385,12 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
       }
     }
 					  
-    // Check based on norm -- from Adel's debiasing code
 
-    if (param_stop) { 
-      if (iter == 2 * iter_old) {
+    if (iter == 2 * iter_old) { // Geometric iterations from Adel's code
+
+      // Check based on norm 
+
+      if (param_stop) { 
 	iter_old = iter;
 	norm_diff = 0;
 	norm_last = 0;
@@ -407,6 +409,24 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
 	  break;
 	}
       }
+
+      // Check relative decrease of objective
+
+      if (objective_stop) {
+	new_value = objective_qp(nndef_ptr,
+				 linear_func_ptr,
+				 ever_active_ptr,
+				 nactive_ptr,
+				 nfeature,
+				 bound,
+				 theta);
+
+	if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
+	  break;
+	}
+	old_value = new_value;
+      }
+
     }
 
     // Check size of active set
@@ -415,23 +435,6 @@ int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
       break;
     }
 
-    // Check relative decrease of objective
-
-    if (objective_stop) {
-      new_value = objective_qp(nndef_ptr,
-			       linear_func_ptr,
-			       ever_active_ptr,
-			       nactive_ptr,
-			       nfeature,
-			       bound,
-			       theta);
-
-      if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
-	break;
-      }
-      old_value = new_value;
-    }
-
   }
   return(iter);
 }
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
index 3546fcd..41e29ce 100644
--- a/selectiveInference/src/quadratic_program_wide.c
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -534,10 +534,11 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
       }
     }
 
-    // Check based on norm -- from Adel's debiasing code
+    if (iter == 2 * iter_old) { // Geometric iterations from Adel's code
 
-    if (param_stop) {
-      if (iter == 2 * iter_old) {
+      // Check based on norm 
+
+      if (param_stop) {
 	iter_old = iter;
 	norm_diff = 0;
 	norm_last = 0;
@@ -556,32 +557,33 @@ int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX
 	  break;
 	}
       }
+
+      // Check relative decrease of objective
+
+      if (objective_stop) {
+	new_value = objective_wide(X_theta_ptr,
+				   linear_func_ptr,
+				   ever_active_ptr,
+				   nactive_ptr,
+				   ncase,
+				   nfeature,
+				   bound_ptr,
+				   ridge_term,
+				   theta_ptr);
+
+	if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
+	  break;
+	}
+	old_value = new_value;
+      }
     }
+
     // Check size of active set
 
     if (*nactive_ptr >= max_active) {
       break;
     }
 
-    // Check relative decrease of objective
-
-    if (objective_stop) {
-      new_value = objective_wide(X_theta_ptr,
-				 linear_func_ptr,
-				 ever_active_ptr,
-				 nactive_ptr,
-				 ncase,
-				 nfeature,
-				 bound_ptr,
-				 ridge_term,
-				 theta_ptr);
-
-      if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
-	break;
-      }
-      old_value = new_value;
-    }
-
   }
   return(iter);
 }

From ce15b68f64132e2929530d92f3a84326d7d59a5d Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 27 Oct 2017 10:29:46 -0700
Subject: [PATCH 335/493] checking comparison example again -- looks good

---
 selectiveInference/R/funs.fixed.R           |  13 +-
 selectiveInference/man/debiasingMatrix.Rd   |   4 +-
 tests/debiased_lasso/comparison_scaled.R    |  78 ++
 tests/debiased_lasso/comparison_unscaled.R  |  78 ++
 tests/debiased_lasso/javanmard_montanari.R  | 770 ++++++++++++++++++++
 tests/{ => debiased_lasso}/test_debiasing.R |   0
 tests/debiased_lasso/test_debiasing_wide.R  | 202 +++++
 7 files changed, 1138 insertions(+), 7 deletions(-)
 create mode 100644 tests/debiased_lasso/comparison_scaled.R
 create mode 100644 tests/debiased_lasso/comparison_unscaled.R
 create mode 100644 tests/debiased_lasso/javanmard_montanari.R
 rename tests/{ => debiased_lasso}/test_debiasing.R (100%)
 create mode 100644 tests/debiased_lasso/test_debiasing_wide.R

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 1928487..7c8318d 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -8,7 +8,7 @@ fixedLassoInf <- function(x, y, beta,
                           sigma=NULL, alpha=0.1,
                           type=c("partial", "full"), tol.beta=1e-5, tol.kkt=0.1,
                           gridrange=c(-100,100), bits=NULL, verbose=FALSE, 
-                          linesearch.try=10) {
+                          linesearch.try=10, offset_correction=TRUE) {
 
   family = match.arg(family)
   this.call = match.call()
@@ -197,6 +197,9 @@ fixedLassoInf <- function(x, y, beta,
         M = M[-1,] # remove intercept row
         null_value = null_value[-1] # remove intercept element
       }
+      if (!offset_correction) {
+        null_value = 0 * null_value
+      }
     } else if (type=="partial" || p > n) {
       xa = x[,vars,drop=F]
       M = pinv(crossprod(xa)) %*% t(xa)
@@ -325,13 +328,13 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
 			   max_active=NULL,     # how big can active set get?
 			   max_try=10,          # how many steps in linesearch?
 			   warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
-			   max_iter=100,        # how many iterations for each optimization problem
+			   max_iter=50,         # how many iterations for each optimization problem
                            kkt_stop=TRUE,       # stop based on KKT conditions?
                            parameter_stop=TRUE, # stop based on relative convergence of parameter?
 			   objective_stop=TRUE, # stop based on relative decrease in objective?
                            kkt_tol=1.e-4,       # tolerance for the KKT conditions
                            parameter_tol=1.e-4, # tolerance for relative convergence of parameter
-			   objective_tol=1.e-8  # tolerance for relative decrease in objective
+			   objective_tol=1.e-4  # tolerance for relative decrease in objective
                            ) {
 
 
@@ -399,13 +402,13 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
 		         max_active=NULL,     # how big can active set get?
 			 max_try=10,          # how many steps in linesearch?
 			 warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
-			 max_iter=100,        # how many iterations for each optimization problem
+			 max_iter=50,         # how many iterations for each optimization problem
                          kkt_stop=TRUE,       # stop based on KKT conditions?
                          parameter_stop=TRUE, # stop based on relative convergence of parameter?
                          objective_stop=TRUE, # stop based on relative decrease in objective?
                          kkt_tol=1.e-4,       # tolerance for the KKT conditions
 			 parameter_tol=1.e-4, # tolerance for relative convergence of parameter
-			 objective_tol=1.e-8  # tolerance for relative decrease in objective
+			 objective_tol=1.e-4  # tolerance for relative decrease in objective
                          ) {
 
   p = ncol(Xinfo)
diff --git a/selectiveInference/man/debiasingMatrix.Rd b/selectiveInference/man/debiasingMatrix.Rd
index b95c75b..6a34850 100644
--- a/selectiveInference/man/debiasingMatrix.Rd
+++ b/selectiveInference/man/debiasingMatrix.Rd
@@ -22,13 +22,13 @@ debiasingMatrix(Xinfo,
 		max_active=NULL,    
 		max_try=10,         
 		warn_kkt=FALSE,     
-		max_iter=100,       
+		max_iter=50,       
 		kkt_stop=TRUE,
 		parameter_stop=TRUE,
 		objective_stop=TRUE,
                 kkt_tol=1.e-4,      
 		parameter_tol=1.e-4,
-		objective_tol=1.e-8)
+		objective_tol=1.e-4)
 }
 \arguments{
 \item{Xinfo}{
diff --git a/tests/debiased_lasso/comparison_scaled.R b/tests/debiased_lasso/comparison_scaled.R
new file mode 100644
index 0000000..e0c0a6a
--- /dev/null
+++ b/tests/debiased_lasso/comparison_scaled.R
@@ -0,0 +1,78 @@
+source('javanmard_montanari.R')
+
+##############################################
+
+# Runs nsims simulations under the global null, computing p-values
+# using both the old code (slow one using Adel's code) and the new
+# code (faster using Jon's code), and produces qq-plots for both.
+# Runing 50 sims takes about 10-15 mins because old code is slow, so
+# feel free to lower nsims if you want
+
+
+library(selectiveInference)
+library(glmnet)
+
+# set.seed(424)
+
+n=100
+p=200
+
+sigma=.5
+
+theor_lambda = sigma * sqrt(2 * log(p))
+lambda=c(0.25, 0.5, 1, 0.8 * theor_lambda, theor_lambda)
+
+for (j in c(3,4,5,1,2)) {
+
+thresh = 1e-10
+
+beta=rep(0,p)
+type="full"
+
+nsim = 20
+
+scaling = sqrt(n)
+pvs_old = c()
+pvs_new <- c()
+pvs_old_0 = c()   # don't add the offset correction
+pvs_new_0 = c()   # don't add the offset correction
+for (i in 1:nsim) {
+  cat(i,fill=T)
+  x = matrix(rnorm(n*p),n,p)
+  x = scale(x,T,T) / scaling
+  mu = x%*%beta 
+  y=mu+sigma*rnorm(n)
+
+  # first run  glmnet
+  gfit=glmnet(x,y,intercept=F,standardize=F,thresh=thresh)
+
+  bhat = coef(gfit, s=lambda[j]/(sqrt(n) * scaling), exact=TRUE,x=x,y=y)[-1]
+
+  if(sum(bhat != 0) > 0) {
+
+  # compute fixed lambda p-values and selection intervals
+
+  aa = fixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type)
+  bb = oldFixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type)
+  cc = fixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type, offset_correction=FALSE)
+  dd = oldFixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type, offset_correction=FALSE)
+  pvs_new <- c(pvs_new, aa$pv, recursive=TRUE)
+  pvs_old <- c(pvs_old, bb$pv,recursive=TRUE)
+  pvs_new_0 <- c(pvs_new_0, cc$pv, recursive=TRUE)
+  pvs_old_0 <- c(pvs_old_0, dd$pv, recursive=TRUE)
+
+  cat()
+  }
+}
+
+#check uniformity 
+
+png(paste('comparison_scaled', j, '.png', sep=''))
+plot(ecdf(pvs_old), pch=23, col='green', xlim=c(0,1), ylim=c(0,1), main='ECDF of p-values')
+plot(ecdf(pvs_new), pch=24, col='purple', add=TRUE)
+plot(ecdf(pvs_old_0), pch=23, col='red', add=TRUE)
+plot(ecdf(pvs_new_0), pch=24, col='black', add=TRUE)
+abline(0,1)
+legend("bottomright", legend=c("Old","New", "Old 0", "New 0"), pch=c(23,24,23,24), pt.bg=c("green","purple","red","black"))
+dev.off()
+}
\ No newline at end of file
diff --git a/tests/debiased_lasso/comparison_unscaled.R b/tests/debiased_lasso/comparison_unscaled.R
new file mode 100644
index 0000000..3bb408e
--- /dev/null
+++ b/tests/debiased_lasso/comparison_unscaled.R
@@ -0,0 +1,78 @@
+source('javanmard_montanari.R')
+
+##############################################
+
+# Runs nsims simulations under the global null, computing p-values
+# using both the old code (slow one using Adel's code) and the new
+# code (faster using Jon's code), and produces qq-plots for both.
+# Runing 50 sims takes about 10-15 mins because old code is slow, so
+# feel free to lower nsims if you want
+
+
+library(selectiveInference)
+library(glmnet)
+
+# set.seed(424)
+
+n=100
+p=200
+
+sigma=.5
+
+theor_lambda = sigma * sqrt(2 * log(p))
+lambda=c(0.25, 0.5, 1, 0.8 * theor_lambda, theor_lambda)
+
+for (j in c(3,4,5,1,2)) {
+
+thresh = 1e-10
+
+beta=rep(0,p)
+type="full"
+
+nsim = 20
+
+scaling = sqrt(n)
+pvs_old = c()
+pvs_new <- c()
+pvs_old_0 = c()   # don't add the offset correction
+pvs_new_0 = c()   # don't add the offset correction
+for (i in 1:nsim) {
+  cat(i,fill=T)
+  x = matrix(rnorm(n*p),n,p)
+  x = scale(x,T,T) / scaling
+  mu = x%*%beta 
+  y=mu+sigma*rnorm(n)
+
+  # first run  glmnet
+  gfit=glmnet(x,y,intercept=F,standardize=F,thresh=thresh)
+
+  bhat = coef(gfit, s=lambda[j]/(sqrt(n) * scaling), exact=TRUE,x=x,y=y)[-1]
+
+  if(sum(bhat != 0) > 0) {
+
+  # compute fixed lambda p-values and selection intervals
+
+  aa = fixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type)
+  bb = oldFixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type)
+  cc = fixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type, offset_correction=FALSE)
+  dd = oldFixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type, offset_correction=FALSE)
+  pvs_new <- c(pvs_new, aa$pv, recursive=TRUE)
+  pvs_old <- c(pvs_old, bb$pv,recursive=TRUE)
+  pvs_new_0 <- c(pvs_new_0, cc$pv, recursive=TRUE)
+  pvs_old_0 <- c(pvs_old_0, dd$pv, recursive=TRUE)
+
+  cat()
+  }
+}
+
+#check uniformity 
+
+png(paste('comparison_unscaled', j, '.png', sep=''))
+plot(ecdf(pvs_old), pch=23, col='green', xlim=c(0,1), ylim=c(0,1), main='ECDF of p-values')
+plot(ecdf(pvs_new), pch=24, col='purple', add=TRUE)
+plot(ecdf(pvs_old_0), pch=23, col='red', add=TRUE)
+plot(ecdf(pvs_new_0), pch=24, col='black', add=TRUE)
+abline(0,1)
+legend("bottomright", legend=c("Old","New", "Old 0", "New 0"), pch=c(23,24,23,24), pt.bg=c("green","purple","red","black"))
+dev.off()
+}
\ No newline at end of file
diff --git a/tests/debiased_lasso/javanmard_montanari.R b/tests/debiased_lasso/javanmard_montanari.R
new file mode 100644
index 0000000..09f3355
--- /dev/null
+++ b/tests/debiased_lasso/javanmard_montanari.R
@@ -0,0 +1,770 @@
+# First part is only functions from the old code. At the bottom is
+# the bit of code that actually compares the old vs new code
+
+######################################################
+
+### Old code (using Adel's R code)
+
+## Approximates inverse covariance matrix theta
+InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e-10, verbose = TRUE) {
+  isgiven <- 1;
+  if (is.null(mu)){
+    isgiven <- 0;
+  }
+  
+  p <- nrow(sigma);
+  M <- matrix(0, p, p);
+  xperc = 0;
+  xp = round(p/10);
+  for (i in 1:p) {
+    if ((i %% xp)==0){
+      xperc = xperc+10;
+      if (verbose) {
+        print(paste(xperc,"% done",sep="")); }
+    }
+    if (isgiven==0){
+      mu <- (1/sqrt(n)) * qnorm(1-(0.1/(p^2)));
+    }
+    mu.stop <- 0;
+    try.no <- 1;
+    incr <- 0;
+    while ((mu.stop != 1)&&(try.no<10)){
+      last.beta <- beta
+      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
+      beta <- output$optsol
+      iter <- output$iter
+      if (isgiven==1){
+        mu.stop <- 1
+      }
+      else{
+        if (try.no==1){
+          if (iter == (maxiter+1)){
+            incr <- 1;
+            mu <- mu*resol;
+          } else {
+            incr <- 0;
+            mu <- mu/resol;
+          }
+        }
+        if (try.no > 1){
+          if ((incr == 1)&&(iter == (maxiter+1))){
+            mu <- mu*resol;
+          }
+          if ((incr == 1)&&(iter < (maxiter+1))){
+            mu.stop <- 1;
+          }
+          if ((incr == 0)&&(iter < (maxiter+1))){
+            mu <- mu/resol;
+          }
+          if ((incr == 0)&&(iter == (maxiter+1))){
+            mu <- mu*resol;
+            beta <- last.beta;
+            mu.stop <- 1;
+          }
+        }
+      }
+      try.no <- try.no+1
+    }
+    M[i,] <- beta;
+  }
+  return(M)
+}
+
+InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-10) {
+  p <- nrow(sigma);
+  rho <- max(abs(sigma[i,-i])) / sigma[i,i];
+  mu0 <- rho/(1+rho);
+  beta <- rep(0,p);
+  
+  #if (mu >= mu0){
+  #  beta[i] <- (1-mu0)/sigma[i,i];
+  #  returnlist <- list("optsol" = beta, "iter" = 0);
+  #  return(returnlist);
+  #}
+  
+  diff.norm2 <- 1;
+  last.norm2 <- 1;
+  iter <- 1;
+  iter.old <- 1;
+  beta[i] <- (1-mu0)/sigma[i,i];
+  beta.old <- beta;
+  sigma.tilde <- sigma;
+  diag(sigma.tilde) <- 0;
+  vs <- -sigma.tilde%*%beta;
+  
+  while ((iter <= maxiter) && (diff.norm2 >= threshold*last.norm2)){
+    
+    for (j in 1:p){
+      oldval <- beta[j];
+      v <- vs[j];
+      if (j==i)
+        v <- v+1;
+      beta[j] <- SoftThreshold(v,mu)/sigma[j,j];
+      if (oldval != beta[j]){
+        vs <- vs + (oldval-beta[j])*sigma.tilde[,j];
+      }
+    }
+    
+    iter <- iter + 1;
+    if (iter==2*iter.old){
+      d <- beta - beta.old;
+      diff.norm2 <- sqrt(sum(d*d));
+      last.norm2 <-sqrt(sum(beta*beta));
+      iter.old <- iter;
+      beta.old <- beta;
+      #if (iter>10)
+      #  vs <- -sigma.tilde%*%beta;
+    }
+
+    # print(c(iter, maxiter, diff.norm2, threshold * last.norm2, threshold, mu))
+
+  }
+  
+  returnlist <- list("optsol" = beta, "iter" = iter)
+  return(returnlist)
+}
+
+SoftThreshold <- function( x, lambda ) {
+  #
+  # Standard soft thresholding
+  #
+  if (x>lambda){
+    return (x-lambda);}
+  else {
+    if (x< (-lambda)){
+      return (x+lambda);}
+    else {
+      return (0); }
+  }
+}
+
+
+### Functions borrowed from selective Inference (only fixedLassoInf and fixedLasso.poly are modified)
+
+# Special linear time order function, works only when x
+# is a scrambled vector of integers.
+
+Order <- function(x) {
+  n = length(x)
+  o = numeric(n)
+  o[x] = Seq(1,n)
+  return(o)
+}
+
+# Returns a sequence of integers from a to b if a <= b,
+# otherwise nothing. You have no idea how important this
+# function is...
+
+Seq <- function(a, b, ...) {
+  if (a<=b) return(seq(a,b,...))
+  else return(numeric(0))
+}
+
+# Returns the sign of x, with Sign(0)=1.
+
+Sign <- function(x) {
+  return(-1+2*(x>=0))
+}
+
+##############################
+
+# Centering and scaling convenience function
+
+standardize <- function(x, y, intercept, normalize) {
+  x = as.matrix(x)
+  y = as.numeric(y)
+  n = nrow(x)
+  p = ncol(x)
+  
+  if (intercept) {
+    bx = colMeans(x)
+    by = mean(y)
+    x = scale(x,bx,FALSE)
+    y = y-mean(y)
+  } else {
+    bx = rep(0,p)
+    by = 0
+  }
+  if (normalize) {
+    sx = sqrt(colSums(x^2))
+    x = scale(x,FALSE,sx)
+  } else {
+    sx = rep(1,p)
+  }
+  
+  return(list(x=x,y=y,bx=bx,by=by,sx=sx))
+}
+
+##############################
+
+# Interpolation function to get coefficients
+
+coef.interpolate <- function(betas, s, knots, dec=TRUE) {
+  # Sort the s values
+  o = order(s,dec=dec)
+  s = s[o]
+  
+  k = length(s)
+  mat = matrix(rep(knots,each=k),nrow=k)
+  if (dec) b = s >= mat
+  else b = s <= mat
+  blo = max.col(b,ties.method="first")
+  bhi = pmax(blo-1,1)
+  
+  i = bhi==blo
+  p = numeric(k)
+  p[i] = 0
+  p[!i] = ((s-knots[blo])/(knots[bhi]-knots[blo]))[!i]
+  
+  beta = t((1-p)*t(betas[,blo,drop=FALSE]) + p*t(betas[,bhi,drop=FALSE]))
+  colnames(beta) = as.character(round(s,3))
+  rownames(beta) = NULL
+  
+  # Return in original order
+  o = order(o)
+  return(beta[,o,drop=FALSE])
+}
+
+##############################
+
+checkargs.xy <- function(x, y) {
+  if (missing(x)) stop("x is missing")
+  if (is.null(x) || !is.matrix(x)) stop("x must be a matrix")
+  if (missing(y)) stop("y is missing")
+  if (is.null(y) || !is.numeric(y)) stop("y must be numeric")
+  if (ncol(x) == 0) stop("There must be at least one predictor [must have ncol(x) > 0]")
+  if (checkcols(x)) stop("x cannot have duplicate columns")
+  if (length(y) == 0) stop("There must be at least one data point [must have length(y) > 0]")
+  if (length(y)!=nrow(x)) stop("Dimensions don't match [length(y) != nrow(x)]")
+}
+
+checkargs.misc <- function(sigma=NULL, alpha=NULL, k=NULL,
+                           gridrange=NULL, gridpts=NULL, griddepth=NULL,
+                           mult=NULL, ntimes=NULL,
+                           beta=NULL, lambda=NULL, tol.beta=NULL, tol.kkt=NULL,
+                           bh.q=NULL) {
+  
+  if (!is.null(sigma) && sigma <= 0) stop("sigma must be > 0")
+  if (!is.null(lambda) && lambda < 0) stop("lambda must be >= 0")
+  if (!is.null(alpha) && (alpha <= 0 || alpha >= 1)) stop("alpha must be between 0 and 1")
+  if (!is.null(k) && length(k) != 1) stop("k must be a single number")
+  if (!is.null(k) && (k < 1 || k != floor(k))) stop("k must be an integer >= 1")
+  if (!is.null(gridrange) && (length(gridrange) != 2 || gridrange[1] > gridrange[2]))
+    stop("gridrange must be an interval of the form c(a,b) with a <= b")
+  if (!is.null(gridpts) && (gridpts < 20 || gridpts != round(gridpts)))
+    stop("gridpts must be an integer >= 20")
+  if (!is.null(griddepth) && (griddepth > 10 || griddepth != round(griddepth)))
+    stop("griddepth must be an integer <= 10")
+  if (!is.null(mult) && mult < 0) stop("mult must be >= 0")
+  if (!is.null(ntimes) && (ntimes <= 0 || ntimes != round(ntimes)))
+    stop("ntimes must be an integer > 0")
+  if (!is.null(beta) && sum(beta!=0)==0) stop("Value of lambda too large, beta is zero")
+  # if (!is.null(lambda) && length(lambda) != 1) stop("lambda must be a single number")
+  if (!is.null(lambda) && length(lambda) != 1 && length(lambda) != length(beta)) stop("lambda must be a single number or equal to the length of beta")
+  if (!is.null(lambda) && lambda < 0) stop("lambda must be >=0")
+  if (!is.null(tol.beta) && tol.beta <= 0) stop("tol.beta must be > 0")
+  if (!is.null(tol.kkt) && tol.kkt <= 0) stop("tol.kkt must be > 0")
+}
+
+# Make sure that no two columms of A are the same
+# (this works with probability one).
+
+checkcols <- function(A) {
+  b = rnorm(nrow(A))
+  a = sort(t(A)%*%b)
+  if (any(diff(a)==0)) return(TRUE)
+  return(FALSE)
+}
+
+estimateSigma <- function(x, y, intercept=TRUE, standardize=TRUE) {
+  checkargs.xy(x,rep(0,nrow(x)))
+  if(nrow(x)<10) stop("Number of observations must be at least 10 to run estimateSigma")
+  cvfit=cv.glmnet(x,y,intercept=intercept,standardize=standardize)
+  lamhat=cvfit$lambda.min
+  fit=glmnet(x,y,standardize=standardize)
+  yhat=predict(fit,x,s=lamhat)
+  nz=sum(predict(fit,s=lamhat, type="coef")!=0)
+  sigma=sqrt(sum((y-yhat)^2)/(length(y)-nz-1))
+  return(list(sigmahat=sigma, df=nz))
+}
+
+# Update the QR factorization, after a column has been
+# added. Here Q1 is m x n, Q2 is m x k, and R is n x n.
+
+updateQR <- function(Q1,Q2,R,col) {
+  m = nrow(Q1)
+  n = ncol(Q1)
+  k = ncol(Q2)
+  
+  a = .C("update1",
+         Q2=as.double(Q2),
+         w=as.double(t(Q2)%*%col),
+         m=as.integer(m),
+         k=as.integer(k),
+         dup=FALSE,
+         package="selectiveInference")
+  
+  Q2 = matrix(a$Q2,nrow=m)
+  w = c(t(Q1)%*%col,a$w)
+  
+  # Re-structure: delete a column from Q2, add one to
+  # Q1, and expand R
+  Q1 = cbind(Q1,Q2[,1])
+  Q2 = Q2[,-1,drop=FALSE]
+  R = rbind(R,rep(0,n))
+  R = cbind(R,w[Seq(1,n+1)])
+  
+  return(list(Q1=Q1,Q2=Q2,R=R))
+}
+
+# Moore-Penrose pseudo inverse for symmetric matrices
+
+pinv <- function(A, tol=.Machine$double.eps) {
+  e = eigen(A)
+  v = Re(e$vec)
+  d = Re(e$val)
+  d[d > tol] = 1/d[d > tol]
+  d[d < tol] = 0
+  if (length(d)==1) return(v*d*v)
+  else return(v %*% diag(d) %*% t(v))
+}
+
+##############################
+
+# Assuming that grid is in sorted order from smallest to largest,
+# and vals are monotonically increasing function values over the
+# grid, returns the grid end points such that the corresponding
+# vals are approximately equal to {val1, val2}
+
+grid.search <- function(grid, fun, val1, val2, gridpts=100, griddepth=2) {
+  n = length(grid)
+  vals = fun(grid)
+  
+  ii = which(vals >= val1)
+  jj = which(vals <= val2)
+  if (length(ii)==0) return(c(grid[n],Inf))   # All vals < val1
+  if (length(jj)==0) return(c(-Inf,grid[1]))  # All vals > val2
+  # RJT: the above logic is correct ... but for simplicity, instead,
+  # we could just return c(-Inf,Inf) 
+  
+  i1 = min(ii); i2 = max(jj)
+  if (i1==1) lo = -Inf
+  else lo = grid.bsearch(grid[i1-1],grid[i1],fun,val1,gridpts,
+                         griddepth-1,below=TRUE)
+  if (i2==n) hi = Inf
+  else hi = grid.bsearch(grid[i2],grid[i2+1],fun,val2,gridpts,
+                         griddepth-1,below=FALSE)
+  return(c(lo,hi))
+}
+
+# Repeated bin search to find the point x in the interval [left, right]
+# that satisfies f(x) approx equal to val. If below=TRUE, then we seek
+# x such that the above holds and f(x) <= val; else we seek f(x) >= val.
+
+grid.bsearch <- function(left, right, fun, val, gridpts=100, griddepth=1, below=TRUE) {
+  n = gridpts
+  depth = 1
+  
+  while (depth <= griddepth) {
+    grid = seq(left,right,length=n)
+    vals = fun(grid)
+    
+    if (below) {
+      ii = which(vals >= val)
+      if (length(ii)==0) return(grid[n])   # All vals < val (shouldn't happen)
+      if ((i0=min(ii))==1) return(grid[1]) # All vals > val (shouldn't happen)
+      left = grid[i0-1]
+      right = grid[i0]
+    }
+    
+    else {
+      ii = which(vals <= val)
+      if (length(ii)==0) return(grid[1])   # All vals > val (shouldn't happen)
+      if ((i0=max(ii))==n) return(grid[n]) # All vals < val (shouldn't happen)
+      left = grid[i0]
+      right = grid[i0+1]
+    }
+    
+    depth = depth+1
+  }
+  
+  return(ifelse(below, left, right))
+}
+
+# Returns Prob(Z>z | Z in [a,b]), where mean can be a vector
+
+tnorm.surv <- function(z, mean, sd, a, b, bits=NULL) {
+  z = max(min(z,b),a)
+  
+  # Check silly boundary cases
+  p = numeric(length(mean))
+  p[mean==-Inf] = 0
+  p[mean==Inf] = 1
+  
+  # Try the multi precision floating point calculation first
+  o = is.finite(mean)
+  mm = mean[o]
+  pp = mpfr.tnorm.surv(z,mm,sd,a,b,bits) 
+  
+  # If there are any NAs, then settle for an approximation
+  oo = is.na(pp)
+  if (any(oo)) pp[oo] = bryc.tnorm.surv(z,mm[oo],sd,a,b)
+  
+  p[o] = pp
+  return(p)
+}
+
+# Returns Prob(Z>z | Z in [a,b]), where mean cane be a vector, using
+# multi precision floating point calculations thanks to the Rmpfr package
+
+mpfr.tnorm.surv <- function(z, mean=0, sd=1, a, b, bits=NULL) {
+  # If bits is not NULL, then we are supposed to be using Rmpf
+  # (note that this was fail if Rmpfr is not installed; but
+  # by the time this function is being executed, this should
+  # have been properly checked at a higher level; and if Rmpfr
+  # is not installed, bits would have been previously set to NULL)
+  if (!is.null(bits)) {
+    z = Rmpfr::mpfr((z-mean)/sd, precBits=bits)
+    a = Rmpfr::mpfr((a-mean)/sd, precBits=bits)
+    b = Rmpfr::mpfr((b-mean)/sd, precBits=bits)
+    return(as.numeric((Rmpfr::pnorm(b)-Rmpfr::pnorm(z))/
+                        (Rmpfr::pnorm(b)-Rmpfr::pnorm(a))))
+  }
+  
+  # Else, just use standard floating point calculations
+  z = (z-mean)/sd
+  a = (a-mean)/sd
+  b = (b-mean)/sd
+  return((pnorm(b)-pnorm(z))/(pnorm(b)-pnorm(a)))
+}
+
+# Returns Prob(Z>z | Z in [a,b]), where mean can be a vector, based on
+# A UNIFORM APPROXIMATION TO THE RIGHT NORMAL TAIL INTEGRAL, W Bryc
+# Applied Mathematics and Computation
+# Volume 127, Issues 23, 15 April 2002, Pages 365--374
+# https://math.uc.edu/~brycw/preprint/z-tail/z-tail.pdf
+
+bryc.tnorm.surv <- function(z, mean=0, sd=1, a, b) {
+  z = (z-mean)/sd
+  a = (a-mean)/sd
+  b = (b-mean)/sd
+  n = length(mean)
+  
+  term1 = exp(z*z)
+  o = a > -Inf
+  term1[o] = ff(a[o])*exp(-(a[o]^2-z[o]^2)/2)
+  term2 = rep(0,n)
+  oo = b < Inf
+  term2[oo] = ff(b[oo])*exp(-(b[oo]^2-z[oo]^2)/2)
+  p = (ff(z)-term2)/(term1-term2)
+  
+  # Sometimes the approximation can give wacky p-values,
+  # outside of [0,1] ..
+  #p[p<0 | p>1] = NA
+  p = pmin(1,pmax(0,p))
+  return(p)
+}
+
+ff <- function(z) {
+  return((z^2+5.575192695*z+12.7743632)/
+           (z^3*sqrt(2*pi)+14.38718147*z*z+31.53531977*z+2*12.77436324))
+}
+
+############## MODIFIED FUNCTIONS ###############
+
+# Lasso inference function (for fixed lambda). Note: here we are providing inference
+# for the solution of
+# min 1/2 || y - \beta_0 - X \beta ||_2^2 + \lambda || \beta ||_1
+
+oldFixedLassoInf <- function(x, y, beta, lambda, family=c("gaussian","binomial","cox"),intercept=TRUE, status=NULL,
+                          sigma=NULL, alpha=0.1,
+                          type=c("partial","full"), tol.beta=1e-5, tol.kkt=0.1,
+                          gridrange=c(-100,100), bits=NULL, verbose=FALSE, offset_correction=TRUE) {
+  
+  family = match.arg(family)
+  this.call = match.call()
+  type = match.arg(type)
+  
+  if(family=="binomial")  {
+    if(type!="partial") stop("Only type= partial allowed with binomial family")
+    out=fixedLogitLassoInf(x,y,beta,lambda,alpha=alpha, type="partial", tol.beta=tol.beta, tol.kkt=tol.kkt,
+                           gridrange=gridrange, bits=bits, verbose=verbose,this.call=this.call)
+    return(out)
+  }
+  else if(family=="cox")  {
+    if(type!="partial") stop("Only type= partial allowed with Cox family")
+    out=fixedCoxLassoInf(x,y,status,beta,lambda,alpha=alpha, type="partial",tol.beta=tol.beta,
+                         tol.kkt=tol.kkt, gridrange=gridrange, bits=bits, verbose=verbose,this.call=this.call)
+    return(out)
+  }
+  
+  else{
+    
+    
+    
+    checkargs.xy(x,y)
+    if (missing(beta) || is.null(beta)) stop("Must supply the solution beta")
+    if (missing(lambda) || is.null(lambda)) stop("Must supply the tuning parameter value lambda")
+    
+    n = nrow(x)
+    p = ncol(x)
+    beta = as.numeric(beta)
+    if (type == "full") {
+      if (p > n) {
+        # need intercept (if there is one) for debiased lasso
+        hbeta = beta
+        if (intercept == T) {
+          if (length(beta) != p + 1) {
+            stop("Since type='full', p > n, and intercept=TRUE, beta must have length equal to ncol(x)+1")
+          }
+          # remove intercept if included
+          beta = beta[-1]
+        } else if (length(beta) != p) {
+          stop("Since family='gaussian', type='full' and intercept=FALSE, beta must have length equal to ncol(x)")
+        }
+      }
+    } else if (length(beta) != p) {
+      stop("Since family='gaussian' and type='partial', beta must have length equal to ncol(x)")
+    }
+    
+    checkargs.misc(beta=beta,lambda=lambda,sigma=sigma,alpha=alpha,
+                   gridrange=gridrange,tol.beta=tol.beta,tol.kkt=tol.kkt)
+    if (!is.null(bits) && !requireNamespace("Rmpfr",quietly=TRUE)) {
+      warning("Package Rmpfr is not installed, reverting to standard precision")
+      bits = NULL
+    }
+    
+    # If glmnet was run with an intercept term, center x and y
+    if (intercept==TRUE) {
+      obj = standardize(x,y,TRUE,FALSE)
+      x = obj$x
+      y = obj$y
+    }
+    
+    # Check the KKT conditions
+    g = t(x)%*%(y-x%*%beta) / lambda
+    if (any(abs(g) > 1+tol.kkt * sqrt(sum(y^2))))
+      warning(paste("Solution beta does not satisfy the KKT conditions",
+                    "(to within specified tolerances)"))
+    
+    tol.coef = tol.beta * sqrt(n^2 / colSums(x^2))
+    # print(tol.coef)
+    vars = which(abs(beta) > tol.coef)
+    # print(beta)
+    # print(vars)
+    if(length(vars)==0){
+      cat("Empty model",fill=T)
+      return()
+    }
+    if (any(sign(g[vars]) != sign(beta[vars])))
+      warning(paste("Solution beta does not satisfy the KKT conditions",
+                    "(to within specified tolerances). You might try rerunning",
+                    "glmnet with a lower setting of the",
+                    "'thresh' parameter, for a more accurate convergence."))
+    
+    # Get lasso polyhedral region, of form Gy >= u
+    if (type == 'full' & p > n) out = fixedLasso.poly(x,y,beta,lambda,vars,inactive=TRUE)
+    else out = fixedLasso.poly(x,y,beta,lambda,vars)
+    G = out$G
+    u = out$u
+    
+    # Check polyhedral region
+    tol.poly = 0.01
+    if (min(G %*% y - u) < -tol.poly * sqrt(sum(y^2)))
+      stop(paste("Polyhedral constraints not satisfied; you must recompute beta",
+                 "more accurately. With glmnet, make sure to use exact=TRUE in coef(),",
+                 "and check whether the specified value of lambda is too small",
+                 "(beyond the grid of values visited by glmnet).",
+                 "You might also try rerunning glmnet with a lower setting of the",
+                 "'thresh' parameter, for a more accurate convergence."))
+    
+    # Estimate sigma
+    if (is.null(sigma)) {
+      if (n >= 2*p) {
+        oo = intercept
+        sigma = sqrt(sum(lsfit(x,y,intercept=oo)$res^2)/(n-p-oo))
+      }
+      else {
+        sigma = sd(y)
+        warning(paste(sprintf("p > n/2, and sd(y) = %0.3f used as an estimate of sigma;",sigma),
+                      "you may want to use the estimateSigma function"))
+      }
+    }
+    
+    k = length(vars)
+    pv = vlo = vup = numeric(k)
+    vmat = matrix(0,k,n)
+    ci = tailarea = matrix(0,k,2)
+    sign = numeric(k)
+    
+    if (type=="full" & p > n) {
+      if (intercept == T) {
+        pp=p+1
+        Xint <- cbind(rep(1,n),x)
+        # indices of selected predictors
+        S = c(1,vars + 1)
+        notS = which(abs(beta) <= tol.coef) + 1
+      } else {
+        pp=p
+        Xint <- x
+        # indices of selected predictors
+        S = vars
+        notS = which(abs(beta) <= tol.coef)
+      }
+      
+      
+      XS = Xint[,S]
+      hbetaS = hbeta[S]
+      
+      # Reorder so that active set S is first
+      Xordered = Xint[,c(S,notS,recursive=T)]
+
+      hsigma <- 1/n*(t(Xordered)%*%Xordered)
+      hsigmaS <- 1/n*(t(XS)%*%XS) # hsigma[S,S]
+      hsigmaSinv <- pinv(hsigmaS) # solve(hsigmaS)
+      
+      # Approximate inverse covariance matrix for when (n < p) from lasso_Inference.R
+      htheta <- InverseLinfty(hsigma, n, verbose=FALSE)
+      
+      # 0-padding matrix
+      FS = rbind(diag(length(S)),matrix(0,pp-length(S),length(S)))
+      ithetasigma = (diag(pp)-(htheta%*%hsigma))
+
+      M <- (((htheta%*%t(Xordered))+ithetasigma%*%FS%*%hsigmaSinv%*%t(XS))/n)
+      # vector which is offset for testing debiased beta's
+      meanoffset <- -(((ithetasigma%*%FS%*%hsigmaSinv)%*%sign(hbetaS))*lambda/n)
+      if (intercept == T) {
+        M = M[-1,] # remove intercept row
+        meanoffset = meanoffset[-1] # remove intercept element
+      }
+      if (offset_correction == FALSE) {
+        meanoffset = 0 * meanoffset
+      }
+    } else if (type=="partial" || p > n) {
+      xa = x[,vars,drop=F]
+      M = pinv(crossprod(xa)) %*% t(xa)
+      meanoffset = rep(0,k)
+    } else {
+      M = pinv(crossprod(x)) %*% t(x)
+      M = M[vars,,drop=F]
+      meanoffset = rep(0,k)
+    }
+    
+    for (j in 1:k) {
+      if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
+      
+      vj = M[j,]
+      mj = sqrt(sum(vj^2))
+      vj = vj / mj        # Standardize (divide by norm of vj)
+      sign[j] = sign(sum(vj*y))
+      vj = sign[j] * vj
+
+      a = poly.pval(y,G,u,vj,offset=meanoffset[j],sigma,bits)
+      pv[j] = a$pv
+      vlo[j] = a$vlo * mj # Unstandardize (mult by norm of vj)
+      vup[j] = a$vup * mj # Unstandardize (mult by norm of vj)
+      vmat[j,] = vj * mj * sign[j]  # Unstandardize (mult by norm of vj)
+      
+      a = poly.int(y,G,u,vj,offset=meanoffset[j],sigma,alpha,gridrange=gridrange,
+                   flip=(sign[j]==-1),bits=bits)
+      ci[j,] = a$int * mj # Unstandardize (mult by norm of vj)
+      tailarea[j,] = a$tailarea
+    }
+    
+    out = list(type=type,lambda=lambda,pv=pv,ci=ci,
+               tailarea=tailarea,vlo=vlo,vup=vup,vmat=vmat,y=y,
+               vars=vars,sign=sign,sigma=sigma,alpha=alpha,
+               sd=sigma*sqrt(rowSums(vmat^2)),
+               coef0=vmat%*%y,
+               call=this.call,M=M)
+    class(out) = "fixedLassoInf"
+    return(out)
+  }
+}
+
+
+fixedLasso.poly=
+  function(x, y, beta, lambda, a, inactive = FALSE) {
+    xa = x[,a,drop=F]
+    xac = x[,!a,drop=F]
+    xai = pinv(crossprod(xa))
+    xap = xai %*% t(xa)
+    za = sign(beta[a])
+    if (length(za)>1) dz = diag(za)
+    if (length(za)==1) dz = matrix(za,1,1)
+    
+    if (inactive) {
+      P = diag(1,nrow(xa)) - xa %*% xap
+      
+      G = -rbind(
+        1/lambda * t(xac) %*% P,
+        -1/lambda * t(xac) %*% P,
+        -dz %*% xap
+      )
+      lambda2=lambda
+      if(length(lambda)>1) lambda2=lambda[a]
+      u = -c(
+        1 - t(xac) %*% t(xap) %*% za,
+        1 + t(xac) %*% t(xap) %*% za,
+        -lambda2 * dz %*% xai %*% za)
+    } else {
+      G = -rbind(
+        #   1/lambda * t(xac) %*% P,
+        # -1/lambda * t(xac) %*% P,
+        -dz %*% xap
+      )
+      lambda2=lambda
+      if(length(lambda)>1) lambda2=lambda[a]
+      u = -c(
+        #   1 - t(xac) %*% t(xap) %*% za,
+        #   1 + t(xac) %*% t(xap) %*% za,
+        -lambda2 * dz %*% xai %*% za)
+    }
+    
+    return(list(G=G,u=u))
+  }
+
+
+# Main p-value function
+
+poly.pval <- function(y, G, u, v, sigma, offset=0, bits=NULL) {
+  z = sum(v*y)
+  vv = sum(v^2)
+  sd = sigma*sqrt(vv)
+  
+  rho = G %*% v / vv
+  vec = (u - G %*% y + rho*z) / rho
+  vlo = suppressWarnings(max(vec[rho>0]))
+  vup = suppressWarnings(min(vec[rho<0]))
+  
+  pv = tnorm.surv(z,0-offset,sd,vlo,vup,bits)
+  return(list(pv=pv,vlo=vlo,vup=vup))
+}
+
+# Main confidence interval function
+
+poly.int <- function(y, G, u, v, sigma, alpha, offset=0, gridrange=c(-100,100),
+                     gridpts=100, griddepth=2, flip=FALSE, bits=NULL) {
+  
+  z = sum(v*y)
+  vv = sum(v^2)
+  sd = sigma*sqrt(vv)
+  
+  rho = G %*% v / vv
+  vec = (u - G %*% y + rho*z) / rho
+  vlo = suppressWarnings(max(vec[rho>0]))
+  vup = suppressWarnings(min(vec[rho<0]))
+  
+  xg = seq(gridrange[1]*sd,gridrange[2]*sd,length=gridpts)
+  fun = function(x) { tnorm.surv(z,x-offset,sd,vlo,vup,bits) }
+  
+  int = grid.search(xg,fun,alpha/2,1-alpha/2,gridpts,griddepth)
+  tailarea = c(fun(int[1]),1-fun(int[2]))
+  
+  if (flip) {
+    int = -int[2:1]
+    tailarea = tailarea[2:1]
+  }
+  
+  return(list(int=int,tailarea=tailarea))
+}
diff --git a/tests/test_debiasing.R b/tests/debiased_lasso/test_debiasing.R
similarity index 100%
rename from tests/test_debiasing.R
rename to tests/debiased_lasso/test_debiasing.R
diff --git a/tests/debiased_lasso/test_debiasing_wide.R b/tests/debiased_lasso/test_debiasing_wide.R
new file mode 100644
index 0000000..62801da
--- /dev/null
+++ b/tests/debiased_lasso/test_debiasing_wide.R
@@ -0,0 +1,202 @@
+library(selectiveInference)
+
+
+## Approximates inverse covariance matrix theta
+InverseLinfty <- function(sigma, n, resol=1.5, bound=NULL, maxiter=50, threshold=1e-10, verbose = TRUE) {
+  isgiven <- 1;
+  if (is.null(bound)){
+    isgiven <- 0;
+  }
+  
+  p <- nrow(sigma);
+  M <- matrix(0, p, p);
+  xperc = 0;
+  xp = round(p/10);
+  for (i in 1:p) {
+    if ((i %% xp)==0){
+      xperc = xperc+10;
+      if (verbose) {
+        print(paste(xperc,"% done",sep="")); }
+    }
+    if (isgiven==0){
+      bound <- (1/sqrt(n)) * qnorm(1-(0.1/(p^2)));
+    }
+    bound.stop <- 0;
+    try.no <- 1;
+    incr <- 0;
+    while ((bound.stop != 1)&&(try.no<10)){
+      last.beta <- beta
+      output <- InverseLinftyOneRow(sigma, i, bound, maxiter=maxiter, threshold=threshold)
+      beta <- output$optsol
+      iter <- output$iter
+      if (isgiven==1){
+        bound.stop <- 1
+      }
+      else{
+        if (try.no==1){
+          if (iter == (maxiter+1)){
+            incr <- 1;
+            bound <- bound*resol;
+          } else {
+            incr <- 0;
+            bound <- bound/resol;
+          }
+        }
+        if (try.no > 1){
+          if ((incr == 1)&&(iter == (maxiter+1))){
+            bound <- bound*resol;
+          }
+          if ((incr == 1)&&(iter < (maxiter+1))){
+            bound.stop <- 1;
+          }
+          if ((incr == 0)&&(iter < (maxiter+1))){
+            bound <- bound/resol;
+          }
+          if ((incr == 0)&&(iter == (maxiter+1))){
+            bound <- bound*resol;
+            beta <- last.beta;
+            bound.stop <- 1;
+          }
+        }
+      }
+      try.no <- try.no+1
+    }
+    M[i,] <- beta;
+  }
+  return(M)
+}
+
+InverseLinftyOneRow <- function ( sigma, i, bound, maxiter=50, threshold=1e-10) {
+  p <- nrow(sigma);
+  rho <- max(abs(sigma[i,-i])) / sigma[i,i];
+  bound0 <- rho/(1+rho);
+  beta <- rep(0,p);
+  
+  #if (bound >= bound0){
+  #  beta[i] <- (1-bound0)/sigma[i,i];
+  #  returnlist <- list("optsol" = beta, "iter" = 0);
+  #  return(returnlist);
+  #}
+  
+  diff.norm2 <- 1;
+  last.norm2 <- 1;
+  iter <- 1;
+  iter.old <- 1;
+  beta[i] <- (1-bound0)/sigma[i,i];
+  beta.old <- beta;
+  sigma.tilde <- sigma;
+  diag(sigma.tilde) <- 0;
+  vs <- -sigma.tilde%*%beta;
+  
+  while ((iter <= maxiter) && (diff.norm2 >= threshold*last.norm2)){
+    
+    for (j in 1:p){
+      oldval <- beta[j];
+      v <- vs[j];
+      if (j==i)
+        v <- v+1;
+      beta[j] <- SoftThreshold(v,bound)/sigma[j,j];
+      if (oldval != beta[j]){
+        vs <- vs + (oldval-beta[j])*sigma.tilde[,j];
+      }
+    }
+    
+    iter <- iter + 1;
+    if (iter==2*iter.old){
+      d <- beta - beta.old;
+      diff.norm2 <- sqrt(sum(d*d));
+      last.norm2 <-sqrt(sum(beta*beta));
+      iter.old <- iter;
+      beta.old <- beta;
+      #if (iter>10)
+      #  vs <- -sigma.tilde%*%beta;
+    }
+
+    # print(c(iter, maxiter, diff.norm2, threshold * last.norm2, threshold, bound))
+
+  }
+  
+  returnlist <- list("optsol" = beta, "iter" = iter)
+  return(returnlist)
+}
+
+SoftThreshold <- function( x, lambda ) {
+  #
+  # Standard soft thresholding
+  #
+  if (x>lambda){
+    return (x-lambda);}
+  else {
+    if (x< (-lambda)){
+      return (x+lambda);}
+    else {
+      return (0); }
+  }
+}
+
+
+### Test
+
+n = 100; p = 250
+
+X = matrix(rnorm(n * p), n, p)
+S = t(X) %*% X / n
+
+debiasing_bound = 0.2
+
+tol = 1.e-12
+
+rows = as.integer(c(1:2))
+
+A1 = debiasingMatrix(S, FALSE, n, rows, bound=debiasing_bound, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol, linesearch=FALSE)
+A2 = debiasingMatrix(S / n, FALSE, n, rows, bound=debiasing_bound, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol, linesearch=FALSE)
+B1 = debiasingMatrix(X, TRUE, n, rows, bound=debiasing_bound, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol, linesearch=FALSE)
+B2 = debiasingMatrix(X / sqrt(n), TRUE, n, rows, bound=debiasing_bound, max_iter=1000, kkt_tol=tol, objective_tol=tol, parameter_tol=tol, linesearch=FALSE)
+
+C1 = InverseLinfty(S, n, bound=debiasing_bound, maxiter=1000)[rows,]
+C2 = InverseLinfty(S / n, n, bound=debiasing_bound, maxiter=1000)[rows,]
+
+par(mfrow=c(2,3))
+
+plot(A1[1,], C1[1,])
+plot(A1[1,], B1[1,])
+plot(B1[1,], C1[1,])
+
+plot(A1[1,], A2[1,])
+plot(B1[1,], B2[1,])
+plot(C1[1,], C2[1,])
+
+print(c('A', sum(A1[1,] == 0)))
+print(c('B', sum(B1[1,] == 0)))
+print(c('C', sum(C1[1,] == 0)))
+
+## Are our points feasible
+
+feasibility = function(S, soln, j, debiasing_bound) {
+     p = nrow(S)
+     E = rep(0, p)
+     E[j] = 1
+     G = S %*% soln - E
+     return(c(max(abs(G)), debiasing_bound))
+}
+
+print(c('feasibility A', feasibility(S, A1[1,], 1, debiasing_bound)))
+print(c('feasibility B', feasibility(S, B1[1,], 1, debiasing_bound)))
+print(c('feasibility C', feasibility(S, C1[1,], 1, debiasing_bound)))
+
+active_KKT = function(S, soln, j, debiasing_bound) {
+     p = nrow(S)
+     E = rep(0, p)
+     E[j] = 1
+     G = S %*% soln - E
+     print(which(soln != 0))
+     print(G[j])
+     return(c(G[soln != 0] * sign(soln)[soln != 0], debiasing_bound))
+}
+
+print(c('active_KKT A', active_KKT(S, A1[1,], 1, debiasing_bound)))
+print(c('active_KKT B', active_KKT(S, B1[1,], 1, debiasing_bound)))
+print(c('active_KKT C', active_KKT(S, C1[1,], 1, debiasing_bound)))
+
+
+print(summary(lm(A1[1,] ~ C1[1,])))
\ No newline at end of file

From e01d8f19052271141cfb10ffa4e9c52ea98a5e0b Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 27 Oct 2017 10:36:58 -0700
Subject: [PATCH 336/493] removing the offset_correction which was just for the
 comparison

---
 selectiveInference/R/funs.fixed.R          |  5 +----
 tests/debiased_lasso/comparison_scaled.R   | 13 +++----------
 tests/debiased_lasso/comparison_unscaled.R | 13 +++----------
 3 files changed, 7 insertions(+), 24 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 7c8318d..d5e3d64 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -8,7 +8,7 @@ fixedLassoInf <- function(x, y, beta,
                           sigma=NULL, alpha=0.1,
                           type=c("partial", "full"), tol.beta=1e-5, tol.kkt=0.1,
                           gridrange=c(-100,100), bits=NULL, verbose=FALSE, 
-                          linesearch.try=10, offset_correction=TRUE) {
+                          linesearch.try=10) {
 
   family = match.arg(family)
   this.call = match.call()
@@ -197,9 +197,6 @@ fixedLassoInf <- function(x, y, beta,
         M = M[-1,] # remove intercept row
         null_value = null_value[-1] # remove intercept element
       }
-      if (!offset_correction) {
-        null_value = 0 * null_value
-      }
     } else if (type=="partial" || p > n) {
       xa = x[,vars,drop=F]
       M = pinv(crossprod(xa)) %*% t(xa)
diff --git a/tests/debiased_lasso/comparison_scaled.R b/tests/debiased_lasso/comparison_scaled.R
index e0c0a6a..e296bd6 100644
--- a/tests/debiased_lasso/comparison_scaled.R
+++ b/tests/debiased_lasso/comparison_scaled.R
@@ -19,10 +19,9 @@ p=200
 
 sigma=.5
 
-theor_lambda = sigma * sqrt(2 * log(p))
-lambda=c(0.25, 0.5, 1, 0.8 * theor_lambda, theor_lambda)
+lambda=c(0.25, 0.5, 1)
 
-for (j in c(3,4,5,1,2)) {
+for (j in c(3,2,1)) {
 
 thresh = 1e-10
 
@@ -54,12 +53,8 @@ for (i in 1:nsim) {
 
   aa = fixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type)
   bb = oldFixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type)
-  cc = fixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type, offset_correction=FALSE)
-  dd = oldFixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type, offset_correction=FALSE)
   pvs_new <- c(pvs_new, aa$pv, recursive=TRUE)
   pvs_old <- c(pvs_old, bb$pv,recursive=TRUE)
-  pvs_new_0 <- c(pvs_new_0, cc$pv, recursive=TRUE)
-  pvs_old_0 <- c(pvs_old_0, dd$pv, recursive=TRUE)
 
   cat()
   }
@@ -70,9 +65,7 @@ for (i in 1:nsim) {
 png(paste('comparison_scaled', j, '.png', sep=''))
 plot(ecdf(pvs_old), pch=23, col='green', xlim=c(0,1), ylim=c(0,1), main='ECDF of p-values')
 plot(ecdf(pvs_new), pch=24, col='purple', add=TRUE)
-plot(ecdf(pvs_old_0), pch=23, col='red', add=TRUE)
-plot(ecdf(pvs_new_0), pch=24, col='black', add=TRUE)
 abline(0,1)
-legend("bottomright", legend=c("Old","New", "Old 0", "New 0"), pch=c(23,24,23,24), pt.bg=c("green","purple","red","black"))
+legend("bottomright", legend=c("Old", "New"), pch=c(23,24), pt.bg=c("green","purple"))
 dev.off()
 }
\ No newline at end of file
diff --git a/tests/debiased_lasso/comparison_unscaled.R b/tests/debiased_lasso/comparison_unscaled.R
index 3bb408e..eebda68 100644
--- a/tests/debiased_lasso/comparison_unscaled.R
+++ b/tests/debiased_lasso/comparison_unscaled.R
@@ -19,10 +19,9 @@ p=200
 
 sigma=.5
 
-theor_lambda = sigma * sqrt(2 * log(p))
-lambda=c(0.25, 0.5, 1, 0.8 * theor_lambda, theor_lambda)
+lambda=c(0.25, 0.5, 1)
 
-for (j in c(3,4,5,1,2)) {
+for (j in c(3,2,1)) {
 
 thresh = 1e-10
 
@@ -54,12 +53,8 @@ for (i in 1:nsim) {
 
   aa = fixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type)
   bb = oldFixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type)
-  cc = fixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type, offset_correction=FALSE)
-  dd = oldFixedLassoInf(x,y,bhat,lambda[j]*sqrt(n) / scaling,intercept=F,sigma=sigma,type=type, offset_correction=FALSE)
   pvs_new <- c(pvs_new, aa$pv, recursive=TRUE)
   pvs_old <- c(pvs_old, bb$pv,recursive=TRUE)
-  pvs_new_0 <- c(pvs_new_0, cc$pv, recursive=TRUE)
-  pvs_old_0 <- c(pvs_old_0, dd$pv, recursive=TRUE)
 
   cat()
   }
@@ -70,9 +65,7 @@ for (i in 1:nsim) {
 png(paste('comparison_unscaled', j, '.png', sep=''))
 plot(ecdf(pvs_old), pch=23, col='green', xlim=c(0,1), ylim=c(0,1), main='ECDF of p-values')
 plot(ecdf(pvs_new), pch=24, col='purple', add=TRUE)
-plot(ecdf(pvs_old_0), pch=23, col='red', add=TRUE)
-plot(ecdf(pvs_new_0), pch=24, col='black', add=TRUE)
 abline(0,1)
-legend("bottomright", legend=c("Old","New", "Old 0", "New 0"), pch=c(23,24,23,24), pt.bg=c("green","purple","red","black"))
+legend("bottomright", legend=c("Old", "New"), pch=c(23,24), pt.bg=c("green","purple"))
 dev.off()
 }
\ No newline at end of file

From eedf0fde5bf16648e5dde7f3703f5eacf59928e8 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 28 Oct 2017 11:13:25 -0700
Subject: [PATCH 337/493] created the matrices for the affine transform,
 wrapper for calling Gaussian density

---
 selectiveInference/R/funs.fixed.R          |   3 +-
 selectiveInference/R/funs.randomized.R     |  34 ++++
 selectiveInference/src/Rcpp-randomized.cpp |  40 +++++
 selectiveInference/src/randomized_lasso.c  |   2 +
 selectiveInference/src/randomized_lasso.c~ | 188 +++++++++++++++++++++
 selectiveInference/src/randomized_lasso.h  |   2 +-
 selectiveInference/src/randomized_lasso.h~ |  42 +++++
 7 files changed, 308 insertions(+), 3 deletions(-)
 create mode 100644 selectiveInference/src/Rcpp-randomized.cpp
 create mode 100644 selectiveInference/src/randomized_lasso.c~
 create mode 100644 selectiveInference/src/randomized_lasso.h~

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index d5e3d64..13d8b5e 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -435,8 +435,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
   last_output = NULL
 
   if (is_wide) {
-     n = nrow(Xinfo)
-     Xsoln = as.numeric(rep(0, n))
+     Xsoln = as.numeric(rep(0, nrow(Xinfo)))
   }
 
   while (counter_idx < max_try) {
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 3a171f7..1da73b2 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -37,6 +37,7 @@ randomizedLASSO = function(X,
     if (length(lam) == 1) {
        lam = rep(lam, p)
     }
+
     if (length(lam) != p) {
        stop("Lagrange parameter should be single float or of length ncol(X)")
     }    
@@ -65,5 +66,38 @@ randomizedLASSO = function(X,
 		           objective_stop,     # objective_stop
 			   kkt_stop,           # kkt_stop
 			   param_stop)         # param_stop
+
+    
+    sign_soln = sign(result$soln)
+
+    unpenalized = lam == 0
+    active = !unpenalized * (sign_soln != 0)
+    inactive = !unpenzlied * (sign_soln == 0)
+
+    unpenalized_set = which(unpenalized)
+    active_set = which(active)
+    inactive_set = which(inactive)
+
+    coef_term = t(X) %*% X[,c(unpenalized_set,  # the coefficients
+                              active_set)]
+    coef_term = coef_term %*% diag(c(rep(1, sum(unpenalized)), sign_soln[active]))  # coefficients are non-negative
+    coef_term[active,] = coef_term[active,] + ridge_term * diag(rep(1, sum(active)))  # ridge term
+
+    subgrad_term = cbind(matrix(0, sum(inactive), sum(active) + sum(unpenalized)),
+                         diag(rep(1, sum(inactive))))
+    linear_term = rbind(coef_term,
+                        subgrad_term)
+
+    offset_term = rep(0, p)
+    offset_term[active] = lam[active] * sign_soln[active]
+
+    
+
+    list(active_set = active_set,
+         inactive_set = inactive_set,
+         unpenalized_set = unpenalized_set,
+         sign_soln = sign_soln)
+         
+         
     return(result)
 }
diff --git a/selectiveInference/src/Rcpp-randomized.cpp b/selectiveInference/src/Rcpp-randomized.cpp
new file mode 100644
index 0000000..7887f32
--- /dev/null
+++ b/selectiveInference/src/Rcpp-randomized.cpp
@@ -0,0 +1,40 @@
+#include <Rcpp.h>                // need to include the main Rcpp header file 
+#include <randomized_lasso.h>    // where densities are defined
+
+// [[Rcpp::export]]
+Rcpp::NumericVector log_density_gaussian_(double noise_scale,                         // Scale of randomization
+					  Rcpp::NumericMatrix internal_linear,        // A_D -- linear part for data
+					  Rcpp::NumericMatrix internal_state,         // D -- data state -- matrix of shape (nopt, npts)
+					  Rcpp::NumericMatrix optimization_linear,    // A_O -- linear part for optimization variables
+					  Rcpp::NumericMatrix optimization_state,     // O -- optimization state -- matrix of shape (ninternal, npts)
+					  Rcpp::NumericMatrix offset) {               // h -- offset in affine transform -- "p" dimensional 
+
+  int npt = internal_state.ncol();         // Function is vectorized
+  if (optimization_state.ncol() != npt) {  // Assuming each column is an internal or opt state because arrays are column major
+    Rcpp::stop("Number of optimization samples should equal the number of (internally represented) data.");
+  }
+
+  int ndim = optimization_linear.nrow();  
+  if (internal_linear.nrow() != ndim) {  
+    Rcpp::stop("Dimension of optimization range should be the same as the dimension of the data range.");
+  } 
+  int ninternal = internal_linear.ncol();
+  int noptimization = optimization_linear.ncol();
+
+  Rcpp::NumericVector result(npt);
+
+  int ipt;
+  for (ipt=0; ipt<npt; ipt++) {
+    result[ipt] = log_density_gaussian(noise_scale,
+				       ndim,
+				       ninternal,
+				       noptimization,
+				       (double *) internal_linear.begin(),
+				       ((double *) internal_state.begin() + ipt * ninternal),
+				       (double *) optimization_linear.begin(),
+				       ((double *) optimization_state.begin() + ipt * noptimization),
+				       (double *) offset.begin());
+  }
+
+  return(result);
+}
diff --git a/selectiveInference/src/randomized_lasso.c b/selectiveInference/src/randomized_lasso.c
index 123c81d..1f396b1 100644
--- a/selectiveInference/src/randomized_lasso.c
+++ b/selectiveInference/src/randomized_lasso.c
@@ -11,6 +11,8 @@
 // Laplace is product of IID Laplace with scale noise_scale
 // Also evaluated at A_D D + A_O O + h
 
+// Matrices are assumed in column major order! 
+
 double log_density_gaussian(double noise_scale,             // Scale of randomization
 			    int ndim,                       // Number of features -- "p"
 			    int ninternal,                  // Dimension of internal data representation often 1
diff --git a/selectiveInference/src/randomized_lasso.c~ b/selectiveInference/src/randomized_lasso.c~
new file mode 100644
index 0000000..123c81d
--- /dev/null
+++ b/selectiveInference/src/randomized_lasso.c~
@@ -0,0 +1,188 @@
+#include <math.h> // for fabs
+
+// Augmented density for randomized LASSO after
+// Gaussian randomization
+
+// Described in https://arxiv.org/abs/1609.05609
+
+// Gaussian is product of IID N(0, noise_scale^2) density
+// Evaluated at A_D D + A_O O + h
+
+// Laplace is product of IID Laplace with scale noise_scale
+// Also evaluated at A_D D + A_O O + h
+
+double log_density_gaussian(double noise_scale,             // Scale of randomization
+			    int ndim,                       // Number of features -- "p"
+			    int ninternal,                  // Dimension of internal data representation often 1
+			    int noptimization,              // Dimension of optimization variables -- "p"
+			    double *internal_linear,        // A_D -- linear part for data
+			    double *internal_state,         // D -- data state
+			    double *optimization_linear,    // A_O -- linear part for optimization variables
+			    double *optimization_state,     // O -- optimization state
+			    double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double denom = 2 * noise_scale * noise_scale;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Internal (i.e. data) contribution
+    for (icol=0; icol<ninternal; icol++) {
+      
+      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
+      internal_state_ptr = ((double *) internal_state + icol);
+
+      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
+    }
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= (reconstruction * reconstruction) / denom;
+  }
+
+  return(value);
+}
+
+double log_density_laplace(double noise_scale,             // Scale of randomization
+			   int ndim,                       // Number of features -- "p"
+			   int ninternal,                  // Dimension of internal data representation often 1
+			   int noptimization,              // Dimension of optimization variables -- "p"
+			   double *internal_linear,        // A_D -- linear part for data
+			   double *internal_state,         // D -- data state
+			   double *optimization_linear,    // A_O -- linear part for optimization variables
+			   double *optimization_state,     // O -- optimization state
+			   double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Internal (i.e. data) contribution
+    for (icol=0; icol<ninternal; icol++) {
+      
+      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
+      internal_state_ptr = ((double *) internal_state + icol);
+
+      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
+    }
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= fabs(reconstruction) / noise_scale;
+  }
+
+  return(value);
+}
+
+// Keeping internal (data) state fixed
+
+double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
+					int ndim,                       // Number of features -- "p"
+					int noptimization,              // Dimension of optimization variables -- "p"
+					double *optimization_linear,    // A_O -- linear part for optimization variables
+					double *optimization_state,     // O -- optimization state
+					double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double denom = 2 * noise_scale * noise_scale;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= reconstruction * reconstruction / denom;
+  }
+
+  return(value);
+}
+
+double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
+				       int ndim,                       // Number of features -- "p"
+				       int noptimization,              // Dimension of optimization variables -- "p"
+				       double *optimization_linear,    // A_O -- linear part for optimization variables
+				       double *optimization_state,     // O -- optimization state
+				       double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= fabs(reconstruction) / noise_scale;
+  }
+
+  return(value);
+}
diff --git a/selectiveInference/src/randomized_lasso.h b/selectiveInference/src/randomized_lasso.h
index 1301b72..e29142d 100644
--- a/selectiveInference/src/randomized_lasso.h
+++ b/selectiveInference/src/randomized_lasso.h
@@ -35,7 +35,7 @@ double log_density_laplace_conditional(double noise_scale,             // Scale
 				       int noptimization,              // Dimension of optimization variables -- "p"
 				       double *optimization_linear,    // A_O -- linear part for optimization variables
 				       double *optimization_state,     // O -- optimization state
-				       double *offset).                // h -- offset in affine transform -- "p" dimensional 
+				       double *offset);                // h -- offset in affine transform -- "p" dimensional 
 
 #ifdef __cplusplus
 }  /* extern "C" */
diff --git a/selectiveInference/src/randomized_lasso.h~ b/selectiveInference/src/randomized_lasso.h~
new file mode 100644
index 0000000..1301b72
--- /dev/null
+++ b/selectiveInference/src/randomized_lasso.h~
@@ -0,0 +1,42 @@
+#ifdef __cplusplus
+extern "C"
+{
+#endif /* __cplusplus */
+
+double log_density_gaussian(double noise_scale,             // Scale of randomization
+			    int ndim,                       // Number of features -- "p"
+			    int ninternal,                  // Dimension of internal data representation often 1
+			    int noptimization,              // Dimension of optimization variables -- "p"
+			    double *internal_linear,        // A_D -- linear part for data
+			    double *internal_state,         // D -- data state
+			    double *optimization_linear,    // A_O -- linear part for optimization variables
+			    double *optimization_state,     // O -- optimization state
+			    double *offset);                // h -- offset in affine transform -- "p" dimensional 
+  
+double log_density_laplace(double noise_scale,             // Scale of randomization
+			   int ndim,                       // Number of features -- "p"
+			   int ninternal,                  // Dimension of internal data representation often 1
+			   int noptimization,              // Dimension of optimization variables -- "p"
+			   double *internal_linear,        // A_D -- linear part for data
+			   double *internal_state,         // D -- data state
+			   double *optimization_linear,    // A_O -- linear part for optimization variables
+			   double *optimization_state,     // O -- optimization state
+			   double *offset);                // h -- offset in affine transform -- "p" dimensional 
+
+double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
+					int ndim,                       // Number of features -- "p"
+					int noptimization,              // Dimension of optimization variables -- "p"
+					double *optimization_linear,    // A_O -- linear part for optimization variables
+					double *optimization_state,     // O -- optimization state
+					double *offset);                // h -- offset in affine transform -- "p" dimensional 
+
+double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
+				       int ndim,                       // Number of features -- "p"
+				       int noptimization,              // Dimension of optimization variables -- "p"
+				       double *optimization_linear,    // A_O -- linear part for optimization variables
+				       double *optimization_state,     // O -- optimization state
+				       double *offset).                // h -- offset in affine transform -- "p" dimensional 
+
+#ifdef __cplusplus
+}  /* extern "C" */
+#endif /* __cplusplus */

From e8a94ef8ba8e1c15d36f9ff667b193dfe000167c Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 28 Oct 2017 11:13:48 -0700
Subject: [PATCH 338/493] unwanted files

---
 selectiveInference/src/randomized_lasso.c~ | 188 ---------------------
 selectiveInference/src/randomized_lasso.h~ |  42 -----
 2 files changed, 230 deletions(-)
 delete mode 100644 selectiveInference/src/randomized_lasso.c~
 delete mode 100644 selectiveInference/src/randomized_lasso.h~

diff --git a/selectiveInference/src/randomized_lasso.c~ b/selectiveInference/src/randomized_lasso.c~
deleted file mode 100644
index 123c81d..0000000
--- a/selectiveInference/src/randomized_lasso.c~
+++ /dev/null
@@ -1,188 +0,0 @@
-#include <math.h> // for fabs
-
-// Augmented density for randomized LASSO after
-// Gaussian randomization
-
-// Described in https://arxiv.org/abs/1609.05609
-
-// Gaussian is product of IID N(0, noise_scale^2) density
-// Evaluated at A_D D + A_O O + h
-
-// Laplace is product of IID Laplace with scale noise_scale
-// Also evaluated at A_D D + A_O O + h
-
-double log_density_gaussian(double noise_scale,             // Scale of randomization
-			    int ndim,                       // Number of features -- "p"
-			    int ninternal,                  // Dimension of internal data representation often 1
-			    int noptimization,              // Dimension of optimization variables -- "p"
-			    double *internal_linear,        // A_D -- linear part for data
-			    double *internal_state,         // D -- data state
-			    double *optimization_linear,    // A_O -- linear part for optimization variables
-			    double *optimization_state,     // O -- optimization state
-			    double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double denom = 2 * noise_scale * noise_scale;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Internal (i.e. data) contribution
-    for (icol=0; icol<ninternal; icol++) {
-      
-      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
-      internal_state_ptr = ((double *) internal_state + icol);
-
-      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
-    }
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= (reconstruction * reconstruction) / denom;
-  }
-
-  return(value);
-}
-
-double log_density_laplace(double noise_scale,             // Scale of randomization
-			   int ndim,                       // Number of features -- "p"
-			   int ninternal,                  // Dimension of internal data representation often 1
-			   int noptimization,              // Dimension of optimization variables -- "p"
-			   double *internal_linear,        // A_D -- linear part for data
-			   double *internal_state,         // D -- data state
-			   double *optimization_linear,    // A_O -- linear part for optimization variables
-			   double *optimization_state,     // O -- optimization state
-			   double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Internal (i.e. data) contribution
-    for (icol=0; icol<ninternal; icol++) {
-      
-      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
-      internal_state_ptr = ((double *) internal_state + icol);
-
-      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
-    }
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= fabs(reconstruction) / noise_scale;
-  }
-
-  return(value);
-}
-
-// Keeping internal (data) state fixed
-
-double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
-					int ndim,                       // Number of features -- "p"
-					int noptimization,              // Dimension of optimization variables -- "p"
-					double *optimization_linear,    // A_O -- linear part for optimization variables
-					double *optimization_state,     // O -- optimization state
-					double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double denom = 2 * noise_scale * noise_scale;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= reconstruction * reconstruction / denom;
-  }
-
-  return(value);
-}
-
-double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
-				       int ndim,                       // Number of features -- "p"
-				       int noptimization,              // Dimension of optimization variables -- "p"
-				       double *optimization_linear,    // A_O -- linear part for optimization variables
-				       double *optimization_state,     // O -- optimization state
-				       double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= fabs(reconstruction) / noise_scale;
-  }
-
-  return(value);
-}
diff --git a/selectiveInference/src/randomized_lasso.h~ b/selectiveInference/src/randomized_lasso.h~
deleted file mode 100644
index 1301b72..0000000
--- a/selectiveInference/src/randomized_lasso.h~
+++ /dev/null
@@ -1,42 +0,0 @@
-#ifdef __cplusplus
-extern "C"
-{
-#endif /* __cplusplus */
-
-double log_density_gaussian(double noise_scale,             // Scale of randomization
-			    int ndim,                       // Number of features -- "p"
-			    int ninternal,                  // Dimension of internal data representation often 1
-			    int noptimization,              // Dimension of optimization variables -- "p"
-			    double *internal_linear,        // A_D -- linear part for data
-			    double *internal_state,         // D -- data state
-			    double *optimization_linear,    // A_O -- linear part for optimization variables
-			    double *optimization_state,     // O -- optimization state
-			    double *offset);                // h -- offset in affine transform -- "p" dimensional 
-  
-double log_density_laplace(double noise_scale,             // Scale of randomization
-			   int ndim,                       // Number of features -- "p"
-			   int ninternal,                  // Dimension of internal data representation often 1
-			   int noptimization,              // Dimension of optimization variables -- "p"
-			   double *internal_linear,        // A_D -- linear part for data
-			   double *internal_state,         // D -- data state
-			   double *optimization_linear,    // A_O -- linear part for optimization variables
-			   double *optimization_state,     // O -- optimization state
-			   double *offset);                // h -- offset in affine transform -- "p" dimensional 
-
-double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
-					int ndim,                       // Number of features -- "p"
-					int noptimization,              // Dimension of optimization variables -- "p"
-					double *optimization_linear,    // A_O -- linear part for optimization variables
-					double *optimization_state,     // O -- optimization state
-					double *offset);                // h -- offset in affine transform -- "p" dimensional 
-
-double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
-				       int ndim,                       // Number of features -- "p"
-				       int noptimization,              // Dimension of optimization variables -- "p"
-				       double *optimization_linear,    // A_O -- linear part for optimization variables
-				       double *optimization_state,     // O -- optimization state
-				       double *offset).                // h -- offset in affine transform -- "p" dimensional 
-
-#ifdef __cplusplus
-}  /* extern "C" */
-#endif /* __cplusplus */

From b2de9d928f2cc4899ef9291dab982f04cbce3dc9 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 28 Oct 2017 11:16:12 -0700
Subject: [PATCH 339/493] wrapper for conditional density

---
 selectiveInference/src/Rcpp-randomized.cpp | 25 ++++++++++++++++++++++
 tests/{ => randomized}/test_randomized.R   |  0
 2 files changed, 25 insertions(+)
 rename tests/{ => randomized}/test_randomized.R (100%)

diff --git a/selectiveInference/src/Rcpp-randomized.cpp b/selectiveInference/src/Rcpp-randomized.cpp
index 7887f32..23f0797 100644
--- a/selectiveInference/src/Rcpp-randomized.cpp
+++ b/selectiveInference/src/Rcpp-randomized.cpp
@@ -38,3 +38,28 @@ Rcpp::NumericVector log_density_gaussian_(double noise_scale,
 
   return(result);
 }
+
+// [[Rcpp::export]]
+Rcpp::NumericVector log_density_gaussian_conditional_(double noise_scale,                         // Scale of randomization
+						      Rcpp::NumericMatrix optimization_linear,    // A_O -- linear part for optimization variables
+						      Rcpp::NumericMatrix optimization_state,     // O -- optimization state -- matrix of shape (ninternal, npts)
+						      Rcpp::NumericMatrix offset) {               // h -- offset in affine transform -- "p" dimensional 
+
+  int npt = optimization_state.ncol();         // Function is vectorized
+  int ndim = optimization_linear.nrow();  
+  int noptimization = optimization_linear.ncol();
+
+  Rcpp::NumericVector result(npt);
+
+  int ipt;
+  for (ipt=0; ipt<npt; ipt++) {
+    result[ipt] = log_density_gaussian_conditional(noise_scale,
+						   ndim,
+						   noptimization,
+						   (double *) optimization_linear.begin(),
+						   ((double *) optimization_state.begin() + ipt * noptimization),
+						   (double *) offset.begin());
+  }
+
+  return(result);
+}
diff --git a/tests/test_randomized.R b/tests/randomized/test_randomized.R
similarity index 100%
rename from tests/test_randomized.R
rename to tests/randomized/test_randomized.R

From 6dfdd0d61a4a15710803b3fedc239cf6ef63806a Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 28 Oct 2017 11:49:40 -0700
Subject: [PATCH 340/493] forming internal affine transform

---
 selectiveInference/R/funs.randomized.R | 58 +++++++++++++++++++-------
 tests/randomized/test_randomized.R     |  5 ++-
 2 files changed, 46 insertions(+), 17 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 1da73b2..3b10423 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -71,33 +71,61 @@ randomizedLASSO = function(X,
     sign_soln = sign(result$soln)
 
     unpenalized = lam == 0
-    active = !unpenalized * (sign_soln != 0)
-    inactive = !unpenzlied * (sign_soln == 0)
+    active = (!unpenalized) & (sign_soln != 0)
+    inactive = (!unpenalized) & (sign_soln == 0)
 
     unpenalized_set = which(unpenalized)
     active_set = which(active)
     inactive_set = which(inactive)
 
-    coef_term = t(X) %*% X[,c(unpenalized_set,  # the coefficients
-                              active_set)]
+    # affine transform for optimization variables
+
+    E = c(unpenalized_set, active_set)
+    I = inactive_set
+    X_E = X[,E]
+    X_I = X[,I]
+    L_E = t(X) %*% X[,E]
+
+    coef_term = L_E
     coef_term = coef_term %*% diag(c(rep(1, sum(unpenalized)), sign_soln[active]))  # coefficients are non-negative
     coef_term[active,] = coef_term[active,] + ridge_term * diag(rep(1, sum(active)))  # ridge term
 
-    subgrad_term = cbind(matrix(0, sum(inactive), sum(active) + sum(unpenalized)),
-                         diag(rep(1, sum(inactive))))
-    linear_term = rbind(coef_term,
+    subgrad_term = matrix(0, p, sum(inactive)) # for subgrad
+    for (i in 1:sum(inactive)) {
+        subgrad_term[inactive_set[i], i] = 1
+    }
+
+    linear_term = cbind(coef_term,
                         subgrad_term)
 
     offset_term = rep(0, p)
     offset_term[active] = lam[active] * sign_soln[active]
 
-    
+    opt_transform = list(linear_term=linear_term,
+                         offset_term=offset_term)
+
+    # affine transform for internal (data) variables
+    # for now just use parametric in terms of
+    # (\bar{\beta}_E, X_{-E}^T(y-X_E\bar{\beta}_E)
+    # 
+    # we have to reconstruct -X^TY from this pair
+    #
+
+    active_term = -L_E                           # for \bar{\beta}_E
+
+    inactive_term = -subgrad_term
+    linear_term = cbind(active_term,
+                        inactive_term)
+    offset_term = rep(0, p)
+    internal_transform = list(linear_term = linear_term,
+                              offset_term = offset_term)
+
+    return(list(active_set = active_set,
+                inactive_set = inactive_set,
+                unpenalized_set = unpenalized_set,
+                sign_soln = sign_soln,
+                opt_transform = opt_transform,
+                internal_transform = internal_transform
+                ))
 
-    list(active_set = active_set,
-         inactive_set = inactive_set,
-         unpenalized_set = unpenalized_set,
-         sign_soln = sign_soln)
-         
-         
-    return(result)
 }
diff --git a/tests/randomized/test_randomized.R b/tests/randomized/test_randomized.R
index e4e35a1..305139b 100644
--- a/tests/randomized/test_randomized.R
+++ b/tests/randomized/test_randomized.R
@@ -8,7 +8,8 @@ test = function() {
     lam = 20 / sqrt(n)
     noise_scale = 0.01 * sqrt(n)
     ridge_term = .1 / sqrt(n)
-    fit_randomized_lasso(X, y, lam, noise_scale, ridge_term)
+    selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
 }
 
-print(test())
+A=test()
+#print(test())

From c175a8b192b56a1ea1ad9fabe688461f459f770f Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 28 Oct 2017 12:03:00 -0700
Subject: [PATCH 341/493] vectorized densities evaluate OK -- need to check
 results

---
 selectiveInference/R/funs.randomized.R     |  2 +-
 selectiveInference/src/Rcpp-randomized.cpp |  4 ++--
 tests/randomized/test_randomized.R         | 27 +++++++++++++++++++---
 3 files changed, 27 insertions(+), 6 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 3b10423..92b871e 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -124,7 +124,7 @@ randomizedLASSO = function(X,
                 inactive_set = inactive_set,
                 unpenalized_set = unpenalized_set,
                 sign_soln = sign_soln,
-                opt_transform = opt_transform,
+                optimization_transform = opt_transform,
                 internal_transform = internal_transform
                 ))
 
diff --git a/selectiveInference/src/Rcpp-randomized.cpp b/selectiveInference/src/Rcpp-randomized.cpp
index 23f0797..d6e8517 100644
--- a/selectiveInference/src/Rcpp-randomized.cpp
+++ b/selectiveInference/src/Rcpp-randomized.cpp
@@ -7,7 +7,7 @@ Rcpp::NumericVector log_density_gaussian_(double noise_scale,
 					  Rcpp::NumericMatrix internal_state,         // D -- data state -- matrix of shape (nopt, npts)
 					  Rcpp::NumericMatrix optimization_linear,    // A_O -- linear part for optimization variables
 					  Rcpp::NumericMatrix optimization_state,     // O -- optimization state -- matrix of shape (ninternal, npts)
-					  Rcpp::NumericMatrix offset) {               // h -- offset in affine transform -- "p" dimensional 
+					  Rcpp::NumericVector offset) {               // h -- offset in affine transform -- "p" dimensional 
 
   int npt = internal_state.ncol();         // Function is vectorized
   if (optimization_state.ncol() != npt) {  // Assuming each column is an internal or opt state because arrays are column major
@@ -43,7 +43,7 @@ Rcpp::NumericVector log_density_gaussian_(double noise_scale,
 Rcpp::NumericVector log_density_gaussian_conditional_(double noise_scale,                         // Scale of randomization
 						      Rcpp::NumericMatrix optimization_linear,    // A_O -- linear part for optimization variables
 						      Rcpp::NumericMatrix optimization_state,     // O -- optimization state -- matrix of shape (ninternal, npts)
-						      Rcpp::NumericMatrix offset) {               // h -- offset in affine transform -- "p" dimensional 
+						      Rcpp::NumericVector offset) {               // h -- offset in affine transform -- "p" dimensional 
 
   int npt = optimization_state.ncol();         // Function is vectorized
   int ndim = optimization_linear.nrow();  
diff --git a/tests/randomized/test_randomized.R b/tests/randomized/test_randomized.R
index 305139b..87a3043 100644
--- a/tests/randomized/test_randomized.R
+++ b/tests/randomized/test_randomized.R
@@ -1,6 +1,6 @@
 library(selectiveInference)
 
-test = function() {
+smoke_test = function() {
 
     n = 100; p = 50
     X = matrix(rnorm(n * p), n, p)
@@ -10,6 +10,27 @@ test = function() {
     ridge_term = .1 / sqrt(n)
     selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
 }
+A = smoke_test()
 
-A=test()
-#print(test())
+density_test = function() {
+
+    random_lasso = smoke_test()
+    p = nrow(random_lasso$internal_transform$linear_term)
+    internal_state = matrix(rnorm(p * 20), p, 20)
+    optimization_state = matrix(rnorm(p * 20), p, 20)
+    offset = rnorm(p)
+
+    selectiveInference:::log_density_gaussian_(10.,
+                                               random_lasso$internal_transform$linear_term,
+                                               internal_state,
+                                               random_lasso$optimization_transform$linear_term,
+					       optimization_state,
+                                               offset)
+
+    selectiveInference:::log_density_gaussian_conditional_(10.,
+                                                           random_lasso$optimization_transform$linear_term,
+                    				           optimization_state,
+                                                           offset)
+}
+
+density_test()

From c940537211e217b3dbeaa103699475e0ed67e7f1 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 28 Oct 2017 12:16:27 -0700
Subject: [PATCH 342/493] added laplace densities

---
 selectiveInference/src/Rcpp-randomized.cpp | 63 ++++++++++++++
 tests/randomized/test_randomized.R         | 95 +++++++++++++++++++---
 2 files changed, 145 insertions(+), 13 deletions(-)

diff --git a/selectiveInference/src/Rcpp-randomized.cpp b/selectiveInference/src/Rcpp-randomized.cpp
index d6e8517..b2b2cd2 100644
--- a/selectiveInference/src/Rcpp-randomized.cpp
+++ b/selectiveInference/src/Rcpp-randomized.cpp
@@ -63,3 +63,66 @@ Rcpp::NumericVector log_density_gaussian_conditional_(double noise_scale,
 
   return(result);
 }
+
+// [[Rcpp::export]]
+Rcpp::NumericVector log_density_laplace_(double noise_scale,                         // Scale of randomization
+					 Rcpp::NumericMatrix internal_linear,        // A_D -- linear part for data
+					 Rcpp::NumericMatrix internal_state,         // D -- data state -- matrix of shape (nopt, npts)
+					 Rcpp::NumericMatrix optimization_linear,    // A_O -- linear part for optimization variables
+					 Rcpp::NumericMatrix optimization_state,     // O -- optimization state -- matrix of shape (ninternal, npts)
+					 Rcpp::NumericVector offset) {               // h -- offset in affine transform -- "p" dimensional 
+
+  int npt = internal_state.ncol();         // Function is vectorized
+  if (optimization_state.ncol() != npt) {  // Assuming each column is an internal or opt state because arrays are column major
+    Rcpp::stop("Number of optimization samples should equal the number of (internally represented) data.");
+  }
+
+  int ndim = optimization_linear.nrow();  
+  if (internal_linear.nrow() != ndim) {  
+    Rcpp::stop("Dimension of optimization range should be the same as the dimension of the data range.");
+  } 
+  int ninternal = internal_linear.ncol();
+  int noptimization = optimization_linear.ncol();
+
+  Rcpp::NumericVector result(npt);
+
+  int ipt;
+  for (ipt=0; ipt<npt; ipt++) {
+    result[ipt] = log_density_laplace(noise_scale,
+				      ndim,
+				      ninternal,
+				      noptimization,
+				      (double *) internal_linear.begin(),
+				      ((double *) internal_state.begin() + ipt * ninternal),
+				      (double *) optimization_linear.begin(),
+				      ((double *) optimization_state.begin() + ipt * noptimization),
+				      (double *) offset.begin());
+  }
+
+  return(result);
+}
+
+// [[Rcpp::export]]
+Rcpp::NumericVector log_density_laplace_conditional_(double noise_scale,                         // Scale of randomization
+						     Rcpp::NumericMatrix optimization_linear,    // A_O -- linear part for optimization variables
+						     Rcpp::NumericMatrix optimization_state,     // O -- optimization state -- matrix of shape (ninternal, npts)
+						     Rcpp::NumericVector offset) {               // h -- offset in affine transform -- "p" dimensional 
+
+  int npt = optimization_state.ncol();         // Function is vectorized
+  int ndim = optimization_linear.nrow();  
+  int noptimization = optimization_linear.ncol();
+
+  Rcpp::NumericVector result(npt);
+
+  int ipt;
+  for (ipt=0; ipt<npt; ipt++) {
+    result[ipt] = log_density_laplace_conditional(noise_scale,
+						  ndim,
+						  noptimization,
+						  (double *) optimization_linear.begin(),
+						  ((double *) optimization_state.begin() + ipt * noptimization),
+						  (double *) offset.begin());
+  }
+
+  return(result);
+}
diff --git a/tests/randomized/test_randomized.R b/tests/randomized/test_randomized.R
index 87a3043..b9b1750 100644
--- a/tests/randomized/test_randomized.R
+++ b/tests/randomized/test_randomized.R
@@ -12,25 +12,94 @@ smoke_test = function() {
 }
 A = smoke_test()
 
-density_test = function() {
+gaussian_density_test = function() {
 
+    noise_scale = 10.
     random_lasso = smoke_test()
     p = nrow(random_lasso$internal_transform$linear_term)
     internal_state = matrix(rnorm(p * 20), p, 20)
     optimization_state = matrix(rnorm(p * 20), p, 20)
     offset = rnorm(p)
 
-    selectiveInference:::log_density_gaussian_(10.,
-                                               random_lasso$internal_transform$linear_term,
-                                               internal_state,
-                                               random_lasso$optimization_transform$linear_term,
-					       optimization_state,
-                                               offset)
-
-    selectiveInference:::log_density_gaussian_conditional_(10.,
-                                                           random_lasso$optimization_transform$linear_term,
-                    				           optimization_state,
-                                                           offset)
+    V1 = selectiveInference:::log_density_gaussian_(noise_scale,
+                                                    random_lasso$internal_transform$linear_term,
+                                                    internal_state,
+                                                    random_lasso$optimization_transform$linear_term,
+                                                    optimization_state,
+                                                    offset)
+    A1 = random_lasso$internal_transform$linear_term
+    A2 = random_lasso$optimization_transform$linear_term
+    arg = A1 %*% internal_state + A2 %*% optimization_state + offset
+    V2 = -apply(arg^2, 2, sum) / (2 * noise_scale^2)
+    print(sqrt(sum((V1-V2)^2) / sum(V1^2)))
+
+    U1 = selectiveInference:::log_density_gaussian_conditional_(noise_scale,
+                                                                random_lasso$optimization_transform$linear_term,
+                                                                optimization_state,
+                                                                offset)
+    arg = A2 %*% optimization_state + offset
+    U2 = -apply(arg^2, 2, sum) / (2 * noise_scale^2)
+    print(sqrt(sum((U1-U2)^2) / sum(U1^2)))
+
+    # test that a single column matrix works -- numeric should not
+
+    print(selectiveInference:::log_density_gaussian_conditional_(noise_scale,
+                                                                 random_lasso$optimization_transform$linear_term,
+                                                                 optimization_state[,1,drop=FALSE],
+                                                                 offset))
+    print(selectiveInference:::log_density_gaussian_(noise_scale,
+                                                     random_lasso$internal_transform$linear_term,
+                                                     internal_state[,1,drop=FALSE],
+                                                     random_lasso$optimization_transform$linear_term,
+                                                     optimization_state[,1,drop=FALSE],
+                                                     offset))
+
+}
+
+gaussian_density_test()
+
+laplace_density_test = function() {
+
+    noise_scale = 10.
+    random_lasso = smoke_test()
+    p = nrow(random_lasso$internal_transform$linear_term)
+    internal_state = matrix(rnorm(p * 20), p, 20)
+    optimization_state = matrix(rnorm(p * 20), p, 20)
+    offset = rnorm(p)
+
+    V1 = selectiveInference:::log_density_laplace_(noise_scale,
+                                                    random_lasso$internal_transform$linear_term,
+                                                    internal_state,
+                                                    random_lasso$optimization_transform$linear_term,
+                                                    optimization_state,
+                                                    offset)
+    A1 = random_lasso$internal_transform$linear_term
+    A2 = random_lasso$optimization_transform$linear_term
+    arg = A1 %*% internal_state + A2 %*% optimization_state + offset
+    V2 = -apply(abs(arg), 2, sum) / noise_scale
+    print(sqrt(sum((V1-V2)^2) / sum(V1^2)))
+
+    U1 = selectiveInference:::log_density_laplace_conditional_(noise_scale,
+                                                                random_lasso$optimization_transform$linear_term,
+                                                                optimization_state,
+                                                                offset)
+    arg = A2 %*% optimization_state + offset
+    U2 = -apply(abs(arg), 2, sum) / noise_scale
+    print(sqrt(sum((U1-U2)^2) / sum(U1^2)))
+
+    # test that a single column matrix works -- numeric should not
+
+    print(selectiveInference:::log_density_laplace_conditional_(noise_scale,
+                                                                 random_lasso$optimization_transform$linear_term,
+                                                                 optimization_state[,1,drop=FALSE],
+                                                                 offset))
+    print(selectiveInference:::log_density_laplace_(noise_scale,
+                                                     random_lasso$internal_transform$linear_term,
+                                                     internal_state[,1,drop=FALSE],
+                                                     random_lasso$optimization_transform$linear_term,
+                                                     optimization_state[,1,drop=FALSE],
+                                                     offset))
+
 }
 
-density_test()
+laplace_density_test()

From 206e2362e773d9fff65a0029b10da9197cb42849 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 30 Oct 2017 22:05:21 -0700
Subject: [PATCH 343/493] BF: variable rename

---
 selectiveInference/R/funs.fixedCox.R   | 2 +-
 selectiveInference/R/funs.fixedLogit.R | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.fixedCox.R b/selectiveInference/R/funs.fixedCox.R
index 9f35c95..5cbe330 100644
--- a/selectiveInference/R/funs.fixedCox.R
+++ b/selectiveInference/R/funs.fixedCox.R
@@ -73,7 +73,7 @@ b1= -(mydiag(sign_bhat)%*%MM)%*%sign_bhat*lambda
        vup[jj]=junk$vup
        sd[jj]=junk$sd
 
-      junk2=TG.interval(bbar, A1, b1, vj, MM, alpha, flip=(s2[jj]==-1))
+      junk2=TG.interval(bbar, A1, b1, vj, MM, alpha, flip=(sign_bhat[jj]==-1))
        ci[jj,]=junk2$int
        tailarea[jj,] = junk2$tailarea
      
diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index 75a31e0..60b6451 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -96,7 +96,7 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
    vup[jj]=junk$vup
        sd[jj]=junk$sd
 
-     junk2=TG.interval(bbar, A1, b1, vj, MM,alpha=alpha, flip=(s2[jj+1]==-1))
+     junk2=TG.interval(bbar, A1, b1, vj, MM,alpha=alpha, flip=(sign_bhat[jj+1]==-1))
 
      ci[jj,]=junk2$int
      tailarea[jj,] = junk2$tailarea

From 2917d6eb71f156c7c50e8cc1d05204ef4a7ee74e Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 30 Oct 2017 22:22:47 -0700
Subject: [PATCH 344/493] BF: forgot to cd up directory

---
 .travis.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.travis.yml b/.travis.yml
index c324f88..18769f7 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -15,6 +15,7 @@ before_install:
   - cd C-software
   - git submodule init
   - git submodule update
+  - cd ..
   - make src
   - make Rcpp
   - cd selectiveInference

From 63e60df3b3a886fd3337707a6f09a4c1c52237be Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 30 Oct 2017 22:50:04 -0700
Subject: [PATCH 345/493] changing order of stop arguments

---
 selectiveInference/R/funs.fixed.R      | 4 ++--
 selectiveInference/src/Rcpp-debias.cpp | 8 ++++----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 13d8b5e..5df84d3 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -453,8 +453,8 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                             objective_tol, 
 			    parameter_tol,
                             max_active,
-			    objective_stop,
 			    kkt_stop,
+			    objective_stop,
 			    parameter_stop)
       } else {
           result = solve_QP_wide(Xinfo,                      # this is a design matrix
@@ -471,8 +471,8 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                                  objective_tol, 
 				 parameter_tol,
                                  max_active,
-				 objective_stop,	
 				 kkt_stop,
+				 objective_stop,	
 				 parameter_stop)
 
       }
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index 5c18184..112a6a8 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -17,8 +17,8 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		    double objective_tol,
 		    double parameter_tol,
 		    int max_active,
-		    int objective_stop,
 		    int kkt_stop,
+		    int objective_stop,
 		    int param_stop
 		    ) {
 
@@ -55,8 +55,8 @@ Rcpp::List solve_QP(Rcpp::NumericMatrix Sigma,
 		      objective_tol,
 		      parameter_tol,
 		      max_active,
-		      objective_stop,
 		      kkt_stop,
+		      objective_stop,
 		      param_stop);
   
   // Check whether feasible
@@ -96,8 +96,8 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			 double objective_tol,
 			 double parameter_tol,
 			 int max_active,
-			 int objective_stop,
 			 int kkt_stop,
+			 int objective_stop,
 			 int param_stop
 			 ) {
 
@@ -148,8 +148,8 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			objective_tol,
 			parameter_tol,
 			max_active,
-			objective_stop,
 			kkt_stop,
+			objective_stop,
 			param_stop);
   
   // Check whether feasible

From f7744835149e57c4656740794e32e8c6670e28fe Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 31 Oct 2017 20:46:13 -0700
Subject: [PATCH 346/493] unused import

---
 selectiveInference/NAMESPACE | 1 -
 1 file changed, 1 deletion(-)

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index 7a56d45..0de5d3b 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -45,4 +45,3 @@ importFrom("stats", "coef", "df", "lm", "pf")
 importFrom("stats", "glm", "residuals", "vcov")
 importFrom("stats", "rbinom", "rexp")
 importFrom("Rcpp", "sourceCpp")
-importFrom("distr", "Norm", "DExp")

From da1b9b559f04a2a1f700f8209662a5dec8443587 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 31 Oct 2017 20:46:50 -0700
Subject: [PATCH 347/493] column major ordering in C library

---
 C-software                                |   2 +-
 selectiveInference/R/funs.fixed.R         |  21 ---
 selectiveInference/src/Rcpp-debias.cpp    |   6 +-
 selectiveInference/src/randomized_lasso.c | 188 ----------------------
 selectiveInference/src/randomized_lasso.h |  42 -----
 5 files changed, 6 insertions(+), 253 deletions(-)
 delete mode 100644 selectiveInference/src/randomized_lasso.c
 delete mode 100644 selectiveInference/src/randomized_lasso.h

diff --git a/C-software b/C-software
index a3d9a17..963ef1f 160000
--- a/C-software
+++ b/C-software
@@ -1 +1 @@
-Subproject commit a3d9a1723ce94cb430b5dfd3e058fd708a6bae7f
+Subproject commit 963ef1fd4fa3b9599a0d24c6cc4882dff2204725
diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index c679922..5df84d3 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -405,11 +405,7 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                          objective_stop=TRUE, # stop based on relative decrease in objective?
                          kkt_tol=1.e-4,       # tolerance for the KKT conditions
 			 parameter_tol=1.e-4, # tolerance for relative convergence of parameter
-<<<<<<< HEAD
-			 objective_tol=1.e-8  # tolerance for relative decrease in objective
-=======
 			 objective_tol=1.e-4  # tolerance for relative decrease in objective
->>>>>>> 232760d6aef5182e040b82e30555f4af5ad6803c
                          ) {
 
   p = ncol(Xinfo)
@@ -457,16 +453,6 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                             objective_tol, 
 			    parameter_tol,
                             max_active,
-<<<<<<< HEAD
-			    FALSE,        # objective_stop
-			    FALSE,        # kkt_stop
-			    TRUE)         # param_stop
-      } else {
-          Xsoln = rep(0, nrow(Xinfo))
-          result = solve_QP_wide(Xinfo, # this is a design matrix
-                                 rep(mu, p),  # vector of Lagrange multipliers
-				 0,           # ridge_term 
-=======
 			    kkt_stop,
 			    objective_stop,
 			    parameter_stop)
@@ -474,7 +460,6 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
           result = solve_QP_wide(Xinfo,                      # this is a design matrix
                                  as.numeric(rep(bound, p)),  # vector of Lagrange multipliers
 				 0,                          # ridge_term 
->>>>>>> 232760d6aef5182e040b82e30555f4af5ad6803c
                                  max_iter, 
                                  soln, 
                                  linear_func, 
@@ -486,15 +471,9 @@ debiasingRow = function (Xinfo,               # could be X or t(X) %*% X / n dep
                                  objective_tol, 
 				 parameter_tol,
                                  max_active,
-<<<<<<< HEAD
-				 FALSE,       # objective_stop
-				 FALSE,       # kkt_stop
-				 TRUE)        # param_stop
-=======
 				 kkt_stop,
 				 objective_stop,	
 				 parameter_stop)
->>>>>>> 232760d6aef5182e040b82e30555f4af5ad6803c
 
       }
 
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index 112a6a8..75ff1af 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -101,6 +101,7 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			 int param_stop
 			 ) {
 
+  int column_major = 1; // R has matrices in column major order
   int ncase = X.nrow(); // number of cases
   int nfeature = X.ncol(); // number of features
 
@@ -139,6 +140,7 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			(int *) nactive.begin(),
 			ncase,
 			nfeature,
+			column_major,
 			(double *) bound.begin(),
 			ridge_term,
 			(double *) theta.begin(),
@@ -162,6 +164,7 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 				 (int *) need_update.begin(),
 				 nfeature,
 				 ncase,
+				 column_major,
 				 (double *) bound.begin(),
 				 ridge_term,
 				 kkt_tol);
@@ -176,7 +179,8 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 		       (double *) linear_func.begin(),
 		       (int *) need_update.begin(),
 		       nfeature,
-		       ncase);
+		       ncase,
+		       column_major);
 
   return(Rcpp::List::create(Rcpp::Named("soln") = theta,
 			    Rcpp::Named("gradient") = gradient,
diff --git a/selectiveInference/src/randomized_lasso.c b/selectiveInference/src/randomized_lasso.c
deleted file mode 100644
index 123c81d..0000000
--- a/selectiveInference/src/randomized_lasso.c
+++ /dev/null
@@ -1,188 +0,0 @@
-#include <math.h> // for fabs
-
-// Augmented density for randomized LASSO after
-// Gaussian randomization
-
-// Described in https://arxiv.org/abs/1609.05609
-
-// Gaussian is product of IID N(0, noise_scale^2) density
-// Evaluated at A_D D + A_O O + h
-
-// Laplace is product of IID Laplace with scale noise_scale
-// Also evaluated at A_D D + A_O O + h
-
-double log_density_gaussian(double noise_scale,             // Scale of randomization
-			    int ndim,                       // Number of features -- "p"
-			    int ninternal,                  // Dimension of internal data representation often 1
-			    int noptimization,              // Dimension of optimization variables -- "p"
-			    double *internal_linear,        // A_D -- linear part for data
-			    double *internal_state,         // D -- data state
-			    double *optimization_linear,    // A_O -- linear part for optimization variables
-			    double *optimization_state,     // O -- optimization state
-			    double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double denom = 2 * noise_scale * noise_scale;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Internal (i.e. data) contribution
-    for (icol=0; icol<ninternal; icol++) {
-      
-      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
-      internal_state_ptr = ((double *) internal_state + icol);
-
-      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
-    }
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= (reconstruction * reconstruction) / denom;
-  }
-
-  return(value);
-}
-
-double log_density_laplace(double noise_scale,             // Scale of randomization
-			   int ndim,                       // Number of features -- "p"
-			   int ninternal,                  // Dimension of internal data representation often 1
-			   int noptimization,              // Dimension of optimization variables -- "p"
-			   double *internal_linear,        // A_D -- linear part for data
-			   double *internal_state,         // D -- data state
-			   double *optimization_linear,    // A_O -- linear part for optimization variables
-			   double *optimization_state,     // O -- optimization state
-			   double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Internal (i.e. data) contribution
-    for (icol=0; icol<ninternal; icol++) {
-      
-      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
-      internal_state_ptr = ((double *) internal_state + icol);
-
-      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
-    }
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= fabs(reconstruction) / noise_scale;
-  }
-
-  return(value);
-}
-
-// Keeping internal (data) state fixed
-
-double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
-					int ndim,                       // Number of features -- "p"
-					int noptimization,              // Dimension of optimization variables -- "p"
-					double *optimization_linear,    // A_O -- linear part for optimization variables
-					double *optimization_state,     // O -- optimization state
-					double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double denom = 2 * noise_scale * noise_scale;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= reconstruction * reconstruction / denom;
-  }
-
-  return(value);
-}
-
-double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
-				       int ndim,                       // Number of features -- "p"
-				       int noptimization,              // Dimension of optimization variables -- "p"
-				       double *optimization_linear,    // A_O -- linear part for optimization variables
-				       double *optimization_state,     // O -- optimization state
-				       double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= fabs(reconstruction) / noise_scale;
-  }
-
-  return(value);
-}
diff --git a/selectiveInference/src/randomized_lasso.h b/selectiveInference/src/randomized_lasso.h
deleted file mode 100644
index 1301b72..0000000
--- a/selectiveInference/src/randomized_lasso.h
+++ /dev/null
@@ -1,42 +0,0 @@
-#ifdef __cplusplus
-extern "C"
-{
-#endif /* __cplusplus */
-
-double log_density_gaussian(double noise_scale,             // Scale of randomization
-			    int ndim,                       // Number of features -- "p"
-			    int ninternal,                  // Dimension of internal data representation often 1
-			    int noptimization,              // Dimension of optimization variables -- "p"
-			    double *internal_linear,        // A_D -- linear part for data
-			    double *internal_state,         // D -- data state
-			    double *optimization_linear,    // A_O -- linear part for optimization variables
-			    double *optimization_state,     // O -- optimization state
-			    double *offset);                // h -- offset in affine transform -- "p" dimensional 
-  
-double log_density_laplace(double noise_scale,             // Scale of randomization
-			   int ndim,                       // Number of features -- "p"
-			   int ninternal,                  // Dimension of internal data representation often 1
-			   int noptimization,              // Dimension of optimization variables -- "p"
-			   double *internal_linear,        // A_D -- linear part for data
-			   double *internal_state,         // D -- data state
-			   double *optimization_linear,    // A_O -- linear part for optimization variables
-			   double *optimization_state,     // O -- optimization state
-			   double *offset);                // h -- offset in affine transform -- "p" dimensional 
-
-double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
-					int ndim,                       // Number of features -- "p"
-					int noptimization,              // Dimension of optimization variables -- "p"
-					double *optimization_linear,    // A_O -- linear part for optimization variables
-					double *optimization_state,     // O -- optimization state
-					double *offset);                // h -- offset in affine transform -- "p" dimensional 
-
-double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
-				       int ndim,                       // Number of features -- "p"
-				       int noptimization,              // Dimension of optimization variables -- "p"
-				       double *optimization_linear,    // A_O -- linear part for optimization variables
-				       double *optimization_state,     // O -- optimization state
-				       double *offset).                // h -- offset in affine transform -- "p" dimensional 
-
-#ifdef __cplusplus
-}  /* extern "C" */
-#endif /* __cplusplus */

From 8bd3c12f1e7174d137f7c559c6d810b330de659a Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 31 Oct 2017 20:49:18 -0700
Subject: [PATCH 348/493] tests are in different directory

---
 tests/test_debiasing.R  | 195 ----------------------------------------
 tests/test_randomized.R |  14 ---
 2 files changed, 209 deletions(-)
 delete mode 100644 tests/test_debiasing.R
 delete mode 100644 tests/test_randomized.R

diff --git a/tests/test_debiasing.R b/tests/test_debiasing.R
deleted file mode 100644
index b1fdf24..0000000
--- a/tests/test_debiasing.R
+++ /dev/null
@@ -1,195 +0,0 @@
-library(selectiveInference)
-
-
-## Approximates inverse covariance matrix theta
-InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e-10, verbose = TRUE) {
-  isgiven <- 1;
-  if (is.null(mu)){
-    isgiven <- 0;
-  }
-  
-  p <- nrow(sigma);
-  M <- matrix(0, p, p);
-  xperc = 0;
-  xp = round(p/10);
-  for (i in 1:p) {
-    if ((i %% xp)==0){
-      xperc = xperc+10;
-      if (verbose) {
-        print(paste(xperc,"% done",sep="")); }
-    }
-    if (isgiven==0){
-      mu <- (1/sqrt(n)) * qnorm(1-(0.1/(p^2)));
-    }
-    mu.stop <- 0;
-    try.no <- 1;
-    incr <- 0;
-    while ((mu.stop != 1)&&(try.no<10)){
-      last.beta <- beta
-      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
-      beta <- output$optsol
-      iter <- output$iter
-      if (isgiven==1){
-        mu.stop <- 1
-      }
-      else{
-        if (try.no==1){
-          if (iter == (maxiter+1)){
-            incr <- 1;
-            mu <- mu*resol;
-          } else {
-            incr <- 0;
-            mu <- mu/resol;
-          }
-        }
-        if (try.no > 1){
-          if ((incr == 1)&&(iter == (maxiter+1))){
-            mu <- mu*resol;
-          }
-          if ((incr == 1)&&(iter < (maxiter+1))){
-            mu.stop <- 1;
-          }
-          if ((incr == 0)&&(iter < (maxiter+1))){
-            mu <- mu/resol;
-          }
-          if ((incr == 0)&&(iter == (maxiter+1))){
-            mu <- mu*resol;
-            beta <- last.beta;
-            mu.stop <- 1;
-          }
-        }
-      }
-      try.no <- try.no+1
-    }
-    M[i,] <- beta;
-  }
-  return(M)
-}
-
-InverseLinftyOneRow <- function ( sigma, i, mu, maxiter=50, threshold=1e-10) {
-  p <- nrow(sigma);
-  rho <- max(abs(sigma[i,-i])) / sigma[i,i];
-  mu0 <- rho/(1+rho);
-  beta <- rep(0,p);
-  
-  #if (mu >= mu0){
-  #  beta[i] <- (1-mu0)/sigma[i,i];
-  #  returnlist <- list("optsol" = beta, "iter" = 0);
-  #  return(returnlist);
-  #}
-  
-  diff.norm2 <- 1;
-  last.norm2 <- 1;
-  iter <- 1;
-  iter.old <- 1;
-  beta[i] <- (1-mu0)/sigma[i,i];
-  beta.old <- beta;
-  sigma.tilde <- sigma;
-  diag(sigma.tilde) <- 0;
-  vs <- -sigma.tilde%*%beta;
-  
-  while ((iter <= maxiter) && (diff.norm2 >= threshold*last.norm2)){
-    
-    for (j in 1:p){
-      oldval <- beta[j];
-      v <- vs[j];
-      if (j==i)
-        v <- v+1;
-      beta[j] <- SoftThreshold(v,mu)/sigma[j,j];
-      if (oldval != beta[j]){
-        vs <- vs + (oldval-beta[j])*sigma.tilde[,j];
-      }
-    }
-    
-    iter <- iter + 1;
-    if (iter==2*iter.old){
-      d <- beta - beta.old;
-      diff.norm2 <- sqrt(sum(d*d));
-      last.norm2 <-sqrt(sum(beta*beta));
-      iter.old <- iter;
-      beta.old <- beta;
-      #if (iter>10)
-      #  vs <- -sigma.tilde%*%beta;
-    }
-
-    # print(c(iter, maxiter, diff.norm2, threshold * last.norm2, threshold, mu))
-
-  }
-  
-  returnlist <- list("optsol" = beta, "iter" = iter)
-  return(returnlist)
-}
-
-SoftThreshold <- function( x, lambda ) {
-  #
-  # Standard soft thresholding
-  #
-  if (x>lambda){
-    return (x-lambda);}
-  else {
-    if (x< (-lambda)){
-      return (x+lambda);}
-    else {
-      return (0); }
-  }
-}
-
-
-### Test
-
-n = 100; p = 50
-
-X = matrix(rnorm(n * p), n, p)
-S = t(X) %*% X / n
-
-mu = 7.791408e-02
-
-A1 = debiasingMatrix(S, FALSE, n, 1:5, mu=mu, max_iter=1000)
-A2 = debiasingMatrix(S / n, FALSE, n, 1:5, mu=mu, max_iter=1000)
-
-B1 = debiasingMatrix(X, TRUE, n, 1:5, mu=mu, max_iter=1000)
-B2 = debiasingMatrix(X / sqrt(n), TRUE, n, 1:5, mu=mu, max_iter=1000)
-
-C1 = InverseLinfty(S, n, mu=mu, maxiter=1000)[1:5,]
-C2 = InverseLinfty(S / n, n, mu=mu, maxiter=1000)[1:5,]
-
-par(mfrow=c(2,3))
-plot(A1[1,], C1[1,])
-plot(A1[1,], B1[1,])
-plot(B1[1,], C1[1,])
-
-plot(A1[1,], A2[1,])
-plot(B1[1,], B2[1,])
-plot(C1[1,], C2[1,])
-
-print(c('A', sum(A1[1,] == 0)))
-print(c('B', sum(B1[1,] == 0)))
-print(c('C', sum(C1[1,] == 0)))
-
-## Are our points feasible
-
-feasibility = function(S, soln, j, mu) {
-     p = nrow(S)
-     E = rep(0, p)
-     E[j] = 1
-     G = S %*% soln - E
-     return(c(max(abs(G)), mu))
-}
-
-print(c('feasibility A', feasibility(S, A1[1,], 1, mu)))
-print(c('feasibility B', feasibility(S, B1[1,], 1, mu)))
-print(c('feasibility C', feasibility(S, C1[1,], 1, mu)))
-
-active_KKT = function(S, soln, j, mu) {
-     p = nrow(S)
-     E = rep(0, p)
-     E[j] = 1
-     G = S %*% soln - E
-     return(c(G[soln != 0] * sign(soln)[soln != 0], mu))
-}
-
-print(c('active_KKT A', active_KKT(S, A1[1,], 1, mu)))
-print(c('active_KKT B', active_KKT(S, B1[1,], 1, mu)))
-print(c('active_KKT C', active_KKT(S, C1[1,], 1, mu)))
-
-
diff --git a/tests/test_randomized.R b/tests/test_randomized.R
deleted file mode 100644
index e4e35a1..0000000
--- a/tests/test_randomized.R
+++ /dev/null
@@ -1,14 +0,0 @@
-library(selectiveInference)
-
-test = function() {
-
-    n = 100; p = 50
-    X = matrix(rnorm(n * p), n, p)
-    y = rnorm(n)
-    lam = 20 / sqrt(n)
-    noise_scale = 0.01 * sqrt(n)
-    ridge_term = .1 / sqrt(n)
-    fit_randomized_lasso(X, y, lam, noise_scale, ridge_term)
-}
-
-print(test())

From ae9aec856a4d6fdf77847dfa13d7139949dbff57 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 2 Nov 2017 14:53:58 -0700
Subject: [PATCH 349/493] WIP: have sample from optimization density,
 importance weight -- need to write pivot function

---
 .travis.yml                            |  2 +-
 selectiveInference/DESCRIPTION         |  1 +
 selectiveInference/NAMESPACE           |  1 +
 selectiveInference/R/funs.randomized.R | 97 +++++++++++++++++++++++++-
 tests/randomized/test_randomized.R     | 14 ++++
 5 files changed, 112 insertions(+), 3 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 18769f7..76ab515 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -11,7 +11,7 @@ addons:
 warnings_are_errors: true
 before_install:
   - tlmgr install index # for texlive and vignette?
-  - R -e 'install.packages(c("Rcpp", "intervals"), repos="http://cloud.r-project.org")'
+  - R -e 'install.packages(c("Rcpp", "intervals", "adaptMCMC", "glmnet"), repos="http://cloud.r-project.org")'
   - cd C-software
   - git submodule init
   - git submodule update
diff --git a/selectiveInference/DESCRIPTION b/selectiveInference/DESCRIPTION
index fad072d..c765663 100644
--- a/selectiveInference/DESCRIPTION
+++ b/selectiveInference/DESCRIPTION
@@ -10,6 +10,7 @@ Depends:
     glmnet,
     intervals,
     survival,
+    adaptMCMC,
 Suggests:
     Rmpfr
 Description: New tools for post-selection inference, for use with forward
diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index 0de5d3b..e8f36e0 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -45,3 +45,4 @@ importFrom("stats", "coef", "df", "lm", "pf")
 importFrom("stats", "glm", "residuals", "vcov")
 importFrom("stats", "rbinom", "rexp")
 importFrom("Rcpp", "sourceCpp")
+importFrom("adaptMCMC", "MCMC")
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 3ca22b4..07af468 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -67,7 +67,7 @@ randomizedLASSO = function(X,
 		           objective_stop,     # objective_stop
 			   kkt_stop,           # kkt_stop
 			   param_stop)         # param_stop
-    
+
     sign_soln = sign(result$soln)
 
     unpenalized = lam == 0
@@ -78,6 +78,14 @@ randomizedLASSO = function(X,
     active_set = which(active)
     inactive_set = which(inactive)
 
+    # observed opt state
+
+    observed_scalings = abs(result$soln)[active]
+    observed_unpen = result$soln[unpenalized]
+    observed_subgrad = result$gradient[inactive]
+
+    observed_opt_state = c(observed_unpen, observed_scalings, observed_subgrad)
+
     # affine transform for optimization variables
 
     E = c(unpenalized_set, active_set)
@@ -120,12 +128,97 @@ randomizedLASSO = function(X,
     internal_transform = list(linear_term = linear_term,
                               offset_term = offset_term)
 
+    # density for sampling optimization variables
+
+    observed_raw = -t(X) %*% Y
+    inactive_lam = lam[inactive_set]
+    inactive_start = sum(unpenalized) + sum(active)
+    active_start = sum(unpenalized)
+
+    # XXX only for Gaussian so far
+
+    log_optimization_density = function(opt_state 
+                                        ) {
+
+
+        if ((sum(abs(opt_state[(inactive_start + 1):p]) > inactive_lam) > 0) ||
+            (sum(opt_state[(active_start+1):inactive_start] < 0) > 0)) {
+            return(-Inf)
+        }
+
+        D = log_density_gaussian_conditional_(noise_scale,
+                                              opt_transform$linear_term,
+                                              as.matrix(opt_state),
+                                              observed_raw)
+        return(D)
+    }
+
     return(list(active_set = active_set,
                 inactive_set = inactive_set,
                 unpenalized_set = unpenalized_set,
                 sign_soln = sign_soln,
                 optimization_transform = opt_transform,
-                internal_transform = internal_transform
+                internal_transform = internal_transform,
+                log_optimization_density = log_optimization_density,
+		observed_opt_state = observed_opt_state,
+                observed_raw = observed_raw
                 ))
 
 }
+
+sample_opt_variables = function(randomizedLASSO_obj, jump_scale, nsample=10000) {
+      return(MCMC(randomizedLASSO_obj$log_optimization_density, 
+                  nsample,
+		  randomizedLASSO_obj$observed_opt_state,
+                  acc.rate=0.2,
+                  scale=jump_scale))
+}
+
+# Carry out a linear decompositon of an internal
+# representation with respect to a target
+
+# Returns an affine transform into raw coordinates (i.e. \omega or randomization coordinates)
+
+linear_decomposition = function(observed_target,
+                                observed_internal,
+                                var_target,
+                                cov_target_internal,
+                                internal_transform) {
+    var_target = as.matrix(var_target) 
+    if (nrow(var_target) == 1) {
+        nuisance = observed_internal - cov_target_internal * observed_target / var_target
+        target_linear = internal_transform$linear_part %*% cov_target_internal / var_target
+    } else {
+        nuisance = observed_internal - cov_target_internal %*% solve(var_target) %*% observed_target 
+        target_linear = internal_transform$linear_part %*% cov_target_internal %*% solve(var_target)
+    }
+    target_offset = internal_transform$linear_part %*% nuisance + internal_transform$offset
+    return(list(linear_term=target_linear,
+                offset_term=target_offset))
+}
+
+# XXX only for Gaussian so far
+
+importance_weight = function(noise_scale,
+                             target_sample,
+                             opt_sample,
+                             opt_transform,
+                             target_transform,
+                             observed_raw) {
+
+    log_num = log_density_gaussian_(noise_scale,
+                                    target_transform$linear_term,
+                                    as.matrix(target_sample),
+                                    optimization_transform$linear_term,
+                                    as.matrix(opt_state),
+                                    target_transform$offset_term + optimization_transform$offset_term)
+
+    log_den = log_density_gaussian_conditional_(noise_scale,
+                                                opt_transform$linear_term,
+                                                as.matrix(opt_sample),
+                                                observed_raw)
+    W = log_num - log_den
+    W = W - max(W)
+    return(exp(W))
+}
+                             
diff --git a/tests/randomized/test_randomized.R b/tests/randomized/test_randomized.R
index b9b1750..eef0eaa 100644
--- a/tests/randomized/test_randomized.R
+++ b/tests/randomized/test_randomized.R
@@ -12,6 +12,20 @@ smoke_test = function() {
 }
 A = smoke_test()
 
+sampler_test = function() {
+
+    n = 100; p = 50
+    X = matrix(rnorm(n * p), n, p)
+    y = rnorm(n)
+    lam = 20 / sqrt(n)
+    noise_scale = 0.01 * sqrt(n)
+    ridge_term = .1 / sqrt(n)
+    obj = selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
+    S = selectiveInference:::sample_opt_variables(obj, jump_scale=rep(1/sqrt(n), p), nsample=10000)
+    return(S$samples[2001:10000,])
+}
+B = sampler_test()
+
 gaussian_density_test = function() {
 
     noise_scale = 10.

From 177ab78e2af2060083b327c8b82854b0c937651d Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 2 Nov 2017 17:13:12 -0700
Subject: [PATCH 350/493] update to C-software, using a compiler define for
 column major

---
 C-software | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/C-software b/C-software
index 963ef1f..158c64d 160000
--- a/C-software
+++ b/C-software
@@ -1 +1 @@
-Subproject commit 963ef1fd4fa3b9599a0d24c6cc4882dff2204725
+Subproject commit 158c64d8d81fbcf434869c0c68f5bb7a4a9cdf5a

From 1d60dfaf95a8263c6ec25e41d54b693a51eb6043 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 2 Nov 2017 17:13:30 -0700
Subject: [PATCH 351/493] update to C-software, using a compiler define for
 column major

---
 selectiveInference/src/Makevars        | 4 ++--
 selectiveInference/src/Rcpp-debias.cpp | 6 +-----
 2 files changed, 3 insertions(+), 7 deletions(-)

diff --git a/selectiveInference/src/Makevars b/selectiveInference/src/Makevars
index 2adf9a6..22c4c7d 100644
--- a/selectiveInference/src/Makevars
+++ b/selectiveInference/src/Makevars
@@ -1,5 +1,5 @@
-PKG_CFLAGS= -I.
-PKG_CPPFLAGS= -I.
+PKG_CFLAGS= -I. -DCOLUMN_MAJOR_ORDER
+PKG_CPPFLAGS= -I. -DCOLUMN_MAJOR_ORDER
 PKG_LIBS=-L. 
 
 $(SHLIB): Rcpp Rcpp-matrixcomps.o Rcpp-debias.o RcppExports.o quadratic_program.o quadratic_program_wide.o
diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index 75ff1af..112a6a8 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -101,7 +101,6 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			 int param_stop
 			 ) {
 
-  int column_major = 1; // R has matrices in column major order
   int ncase = X.nrow(); // number of cases
   int nfeature = X.ncol(); // number of features
 
@@ -140,7 +139,6 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 			(int *) nactive.begin(),
 			ncase,
 			nfeature,
-			column_major,
 			(double *) bound.begin(),
 			ridge_term,
 			(double *) theta.begin(),
@@ -164,7 +162,6 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 				 (int *) need_update.begin(),
 				 nfeature,
 				 ncase,
-				 column_major,
 				 (double *) bound.begin(),
 				 ridge_term,
 				 kkt_tol);
@@ -179,8 +176,7 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 		       (double *) linear_func.begin(),
 		       (int *) need_update.begin(),
 		       nfeature,
-		       ncase,
-		       column_major);
+		       ncase);
 
   return(Rcpp::List::create(Rcpp::Named("soln") = theta,
 			    Rcpp::Named("gradient") = gradient,

From 3ce4ea2044095a34be9203de7ed9c2cd43964dee Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Sun, 5 Nov 2017 14:59:24 -0800
Subject: [PATCH 352/493] pivots running but weights all zero

---
 selectiveInference/R/RcppExports.R     |  35 +++++++
 selectiveInference/R/funs.randomized.R |  14 +--
 tests/randomized/test_instances.R      | 121 +++++++++++++++++++++++++
 tests/randomized/test_randomized.R     |   1 +
 4 files changed, 164 insertions(+), 7 deletions(-)
 create mode 100644 selectiveInference/R/RcppExports.R
 create mode 100644 tests/randomized/test_instances.R

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
new file mode 100644
index 0000000..e927a3a
--- /dev/null
+++ b/selectiveInference/R/RcppExports.R
@@ -0,0 +1,35 @@
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
+    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
+}
+
+solve_QP_wide <- function(X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
+    .Call('selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
+}
+
+update1_ <- function(Q2, w, m, k) {
+    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
+}
+
+downdate1_ <- function(Q1, R, j0, m, n) {
+    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
+}
+
+log_density_gaussian_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_gaussian_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
+}
+
+log_density_gaussian_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_gaussian_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
+}
+
+log_density_laplace_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_laplace_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
+}
+
+log_density_laplace_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_laplace_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
+}
+
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 07af468..bf5f89d 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -130,7 +130,7 @@ randomizedLASSO = function(X,
 
     # density for sampling optimization variables
 
-    observed_raw = -t(X) %*% Y
+    observed_raw = -t(X) %*% y
     inactive_lam = lam[inactive_set]
     inactive_start = sum(unpenalized) + sum(active)
     active_start = sum(unpenalized)
@@ -187,12 +187,12 @@ linear_decomposition = function(observed_target,
     var_target = as.matrix(var_target) 
     if (nrow(var_target) == 1) {
         nuisance = observed_internal - cov_target_internal * observed_target / var_target
-        target_linear = internal_transform$linear_part %*% cov_target_internal / var_target
+        target_linear = internal_transform$linear_term %*% cov_target_internal / var_target[1,1]
     } else {
         nuisance = observed_internal - cov_target_internal %*% solve(var_target) %*% observed_target 
-        target_linear = internal_transform$linear_part %*% cov_target_internal %*% solve(var_target)
+        target_linear = internal_transform$linear_term %*% cov_target_internal %*% solve(var_target)
     }
-    target_offset = internal_transform$linear_part %*% nuisance + internal_transform$offset
+    target_offset = internal_transform$linear_term %*% nuisance + internal_transform$offset_term
     return(list(linear_term=target_linear,
                 offset_term=target_offset))
 }
@@ -209,9 +209,9 @@ importance_weight = function(noise_scale,
     log_num = log_density_gaussian_(noise_scale,
                                     target_transform$linear_term,
                                     as.matrix(target_sample),
-                                    optimization_transform$linear_term,
-                                    as.matrix(opt_state),
-                                    target_transform$offset_term + optimization_transform$offset_term)
+                                    opt_transform$linear_term,
+                                    as.matrix(opt_sample),
+                                    target_transform$offset_term + opt_transform$offset_term)
 
     log_den = log_density_gaussian_conditional_(noise_scale,
                                                 opt_transform$linear_term,
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
new file mode 100644
index 0000000..3119e0a
--- /dev/null
+++ b/tests/randomized/test_instances.R
@@ -0,0 +1,121 @@
+#library(devtools)
+#devtools::install_github('jonathan-taylor/R-selective/selectiveInference')
+library(selectiveInference, lib.loc='/Users/Jelena/anaconda/lib/R/library')
+
+
+gaussian_instance = function(n, p, s, sigma=1, rho=0, signal=6, X=NA,
+                             random_signs=TRUE, scale=TRUE, center=TRUE, seed=NA){
+  if (!is.na(seed)){
+    set.seed(seed)
+  }
+  
+  if (is.na(X)){
+    X = sqrt(1-rho)*matrix(rnorm(n*p),n) + sqrt(rho)*matrix(rep(rnorm(n), p), nrow = n)
+    X = scale(X)/sqrt(n)
+  }
+  beta = rep(0, p)
+  if (s>0){
+    beta[1:s] = seq(3, 6, length.out=s)
+  }
+  beta = sample(beta)
+  if (random_signs==TRUE & s>0){
+    signs = sample(c(-1,1), s, replace = TRUE)
+    beta = beta * signs
+  }
+  y = X %*% beta + rnorm(n)*sigma
+  result = list(X=X,y=y,beta=beta)
+  return(result)
+}
+
+
+run_instance = function(n, p, s){
+  rho=0.3
+  lam=1.3
+  sigma=1
+  data = gaussian_instance(n=n,p=p,s=s, rho=rho, sigma=sigma)
+  X=data$X
+  print(dim(X))
+  y=data$y
+  ridge_term=sd(y)/sqrt(n)
+  noise_scale = sd(y)/2
+  lasso_soln=selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
+  
+  active_set = lasso_soln$active_set
+  inactive_set = lasso_soln$inactive_set
+  observed_raw = lasso_soln$observed_raw
+  opt_linear = lasso_soln$optimization_transform$linear_term
+  opt_offset =  lasso_soln$optimization_transform$offset_term
+  observed_opt_state = lasso_soln$observed_opt_state
+  
+  nactive = length(active_set)
+  print(paste("nactive",nactive))
+  B = opt_linear[,1:nactive]
+  beta_offset = observed_raw+opt_offset
+  if (nactive<p){
+    U=opt_linear[,(nactive+1):p]
+    beta_offset =+U %*% observed_opt_state[(nactive+1):p]
+  }
+  opt_transform = list(linear_term=B, offset_term = beta_offset)
+  reduced_B = chol(t(B) %*% B)
+  reduced_beta_offset = solve(t(reduced_B)) %*% (t(B) %*% beta_offset)
+    
+  log_condl_optimization_density = function(opt_state) {
+      
+      if  (sum(opt_state < 0) > 0) {
+        return(-Inf)
+      }
+      D = selectiveInference:::log_density_gaussian_conditional_(noise_scale,
+                                            reduced_B,
+                                            as.matrix(observed_opt_state[1:nactive]),
+                                            reduced_beta_offset)
+      return(D)
+    }
+  lasso_soln$log_optimization_density = log_condl_optimization_density
+  lasso_soln$observed_opt_state = observed_opt_state[1:nactive]
+  S = selectiveInference:::sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), nactive), nsample=10000)
+  beta_samples = S$samples[2001:10000,]
+  print(paste("dim beta samples", dim(beta_samples)))
+  
+  X_E=X[, active_set]
+  X_minusE=X[, inactive_set]
+  target_cov = solve(t(X_E)%*%X_E)*sigma^2
+  cov_target_internal = rbind(target_cov, matrix(0, nrow=p-nactive, ncol=nactive)) * sigma^2
+  observed_target = solve(t(X_E) %*% X_E) %*% t(X_E) %*% y
+  observed_internal = c(observed_target, t(X_minusE) %*% (y-X_E%*% observed_target))
+  internal_transform = lasso_soln$internal_transform
+  
+  pivots = rep(0, nactive)
+  for (i in 1:nactive){
+    target_transform = selectiveInference:::linear_decomposition(observed_target[i], 
+                                                  observed_internal, 
+                                                  target_cov[i,i], 
+                                                  cov_target_internal[,i],
+                                                  internal_transform)
+    target_sample = rnorm(nrow(beta_samples)) * sqrt(target_cov[i,i])
+    
+    weights = selectiveInference:::importance_weight(noise_scale,
+                                                     t(as.matrix(target_sample)),
+                                                     t(beta_samples),
+                                                     opt_transform,
+                                                     target_transform,
+                                                     observed_raw)
+
+    pivots[i] = mean((target_sample<observed_target[i])*weights)/mean(weights)
+    print(pivots[i])
+  }
+  
+  return(pivots)
+}
+
+collect_instances = function(n,p,s, nsim=1){
+  
+  for (i in 1:nsim){
+    result = run_instance(n,p,s)
+  }
+}
+
+
+collect_instances(n=100, p=20, s=0)
+
+
+
diff --git a/tests/randomized/test_randomized.R b/tests/randomized/test_randomized.R
index eef0eaa..ea9eb5c 100644
--- a/tests/randomized/test_randomized.R
+++ b/tests/randomized/test_randomized.R
@@ -10,6 +10,7 @@ smoke_test = function() {
     ridge_term = .1 / sqrt(n)
     selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
 }
+
 A = smoke_test()
 
 sampler_test = function() {

From 555f38c2c5b375af9b3a4093ffb59c7f95e12c79 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Sun, 5 Nov 2017 15:46:10 -0800
Subject: [PATCH 353/493] reorg

---
 tests/randomized/test_instances.R | 94 ++++++++++++++++++++-----------
 1 file changed, 61 insertions(+), 33 deletions(-)

diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 3119e0a..1c125f2 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -27,18 +27,7 @@ gaussian_instance = function(n, p, s, sigma=1, rho=0, signal=6, X=NA,
   return(result)
 }
 
-
-run_instance = function(n, p, s){
-  rho=0.3
-  lam=1.3
-  sigma=1
-  data = gaussian_instance(n=n,p=p,s=s, rho=rho, sigma=sigma)
-  X=data$X
-  print(dim(X))
-  y=data$y
-  ridge_term=sd(y)/sqrt(n)
-  noise_scale = sd(y)/2
-  lasso_soln=selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
+conditional_density = function(noise_scale, lasso_soln){
   
   active_set = lasso_soln$active_set
   inactive_set = lasso_soln$inactive_set
@@ -48,9 +37,9 @@ run_instance = function(n, p, s){
   observed_opt_state = lasso_soln$observed_opt_state
   
   nactive = length(active_set)
-  print(paste("nactive",nactive))
   B = opt_linear[,1:nactive]
   beta_offset = observed_raw+opt_offset
+  p=length(observed_opt_state)
   if (nactive<p){
     U=opt_linear[,(nactive+1):p]
     beta_offset =+U %*% observed_opt_state[(nactive+1):p]
@@ -58,28 +47,58 @@ run_instance = function(n, p, s){
   opt_transform = list(linear_term=B, offset_term = beta_offset)
   reduced_B = chol(t(B) %*% B)
   reduced_beta_offset = solve(t(reduced_B)) %*% (t(B) %*% beta_offset)
-    
+  
   log_condl_optimization_density = function(opt_state) {
-      
-      if  (sum(opt_state < 0) > 0) {
-        return(-Inf)
-      }
-      D = selectiveInference:::log_density_gaussian_conditional_(noise_scale,
-                                            reduced_B,
-                                            as.matrix(observed_opt_state[1:nactive]),
-                                            reduced_beta_offset)
-      return(D)
+    
+    if  (sum(opt_state < 0) > 0) {
+      return(-Inf)
     }
+    D = selectiveInference:::log_density_gaussian_conditional_(noise_scale,
+                                                               reduced_B,
+                                                               as.matrix(observed_opt_state[1:nactive]),
+                                                               reduced_beta_offset)
+    return(D)
+  }
   lasso_soln$log_optimization_density = log_condl_optimization_density
   lasso_soln$observed_opt_state = observed_opt_state[1:nactive]
+  lasso_soln$optimization_transform = opt_transform
+  return(lasso_soln)
+}
+
+
+
+run_instance = function(n, p, s){
+  rho=0.3
+  lam=1.
+  sigma=1
+  data = gaussian_instance(n=n,p=p,s=s, rho=rho, sigma=sigma)
+  X=data$X
+  print(dim(X))
+  y=data$y
+  ridge_term=sd(y)/sqrt(n)
+  noise_scale = sd(y)/2
+  lasso_soln=selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
+  active_set = lasso_soln$active_set
+  inactive_set = lasso_soln$inactive_set
+  nactive = length(active_set)
+  print(paste("nactive", nactive))
+  
+  lasso_soln = conditional_density(noise_scale, lasso_soln)
+  
   S = selectiveInference:::sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), nactive), nsample=10000)
-  beta_samples = S$samples[2001:10000,]
-  print(paste("dim beta samples", dim(beta_samples)))
+  opt_samples = S$samples[2001:10000,]
+  print(paste("dim opt samples", toString(dim(opt_samples))))
+  
+  observed_raw = lasso_soln$observed_raw
+  opt_linear = lasso_soln$optimization_transform$linear_term
+  opt_offset =  lasso_soln$optimization_transform$offset_term
+  observed_opt_state = lasso_soln$observed_opt_state
+  opt_transform = lasso_soln$optimization_transform
   
   X_E=X[, active_set]
   X_minusE=X[, inactive_set]
   target_cov = solve(t(X_E)%*%X_E)*sigma^2
-  cov_target_internal = rbind(target_cov, matrix(0, nrow=p-nactive, ncol=nactive)) * sigma^2
+  cov_target_internal = rbind(target_cov, matrix(0, nrow=p-nactive, ncol=nactive))
   observed_target = solve(t(X_E) %*% X_E) %*% t(X_E) %*% y
   observed_internal = c(observed_target, t(X_minusE) %*% (y-X_E%*% observed_target))
   internal_transform = lasso_soln$internal_transform
@@ -91,16 +110,20 @@ run_instance = function(n, p, s){
                                                   target_cov[i,i], 
                                                   cov_target_internal[,i],
                                                   internal_transform)
-    target_sample = rnorm(nrow(beta_samples)) * sqrt(target_cov[i,i])
     
-    weights = selectiveInference:::importance_weight(noise_scale,
-                                                     t(as.matrix(target_sample)),
-                                                     t(beta_samples),
+    target_sample = rnorm(nrow(opt_samples)) * sqrt(target_cov[i,i])
+    
+    pivot = function(candidate){
+      weights = selectiveInference:::importance_weight(noise_scale,
+                                                     t(as.matrix(target_sample))+candidate,
+                                                     t(opt_samples),
                                                      opt_transform,
                                                      target_transform,
                                                      observed_raw)
-
-    pivots[i] = mean((target_sample<observed_target[i])*weights)/mean(weights)
+      return(mean((target_sample<observed_target[i])*weights)/mean(weights))
+    }
+    
+    pivots[i] = pivot(0)
     print(pivots[i])
   }
   
@@ -108,10 +131,15 @@ run_instance = function(n, p, s){
 }
 
 collect_instances = function(n,p,s, nsim=1){
-  
+  sample_pivots = NULL
   for (i in 1:nsim){
     result = run_instance(n,p,s)
+    sample_pivots = c(sample_pivots, result)
   }
+  jpeg('pivots.jpg')
+  plot(ecdf(sample_pivots), xlim=c(0,1),  main="Empirical CDF of null p-values", xlab="p-values", ylab="ecdf")
+  abline(0, 1, lty=2)
+  dev.off()
 }
 
 

From fdff629374a78c58448a772d539e96dd878f3f37 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Sun, 5 Nov 2017 18:17:50 -0800
Subject: [PATCH 354/493] segfault error

---
 tests/randomized/test_instances.R | 46 ++++++++++++++++---------------
 1 file changed, 24 insertions(+), 22 deletions(-)

diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 1c125f2..55807fd 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -66,28 +66,16 @@ conditional_density = function(noise_scale, lasso_soln){
 }
 
 
-
-run_instance = function(n, p, s){
-  rho=0.3
-  lam=1.
-  sigma=1
-  data = gaussian_instance(n=n,p=p,s=s, rho=rho, sigma=sigma)
-  X=data$X
-  print(dim(X))
-  y=data$y
-  ridge_term=sd(y)/sqrt(n)
-  noise_scale = sd(y)/2
+run_instance = function(X,y,sigma, lam, noise_scale, ridge_term){
+  n=nrow(X)
+  p=ncol(X)
   lasso_soln=selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
   active_set = lasso_soln$active_set
   inactive_set = lasso_soln$inactive_set
   nactive = length(active_set)
   print(paste("nactive", nactive))
   
-  lasso_soln = conditional_density(noise_scale, lasso_soln)
-  
-  S = selectiveInference:::sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), nactive), nsample=10000)
-  opt_samples = S$samples[2001:10000,]
-  print(paste("dim opt samples", toString(dim(opt_samples))))
+  #lasso_soln = conditional_density(noise_scale, lasso_soln)
   
   observed_raw = lasso_soln$observed_raw
   opt_linear = lasso_soln$optimization_transform$linear_term
@@ -95,6 +83,11 @@ run_instance = function(n, p, s){
   observed_opt_state = lasso_soln$observed_opt_state
   opt_transform = lasso_soln$optimization_transform
   
+  dim=length(observed_opt_state)
+  S = selectiveInference:::sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), dim), nsample=10000)
+  opt_samples = S$samples[2001:10000,]
+  print(paste("dim opt samples", toString(dim(opt_samples))))
+  
   X_E=X[, active_set]
   X_minusE=X[, inactive_set]
   target_cov = solve(t(X_E)%*%X_E)*sigma^2
@@ -124,26 +117,35 @@ run_instance = function(n, p, s){
     }
     
     pivots[i] = pivot(0)
-    print(pivots[i])
   }
-  
+  print(paste("pivots", toString(pivots)))
   return(pivots)
 }
 
-collect_instances = function(n,p,s, nsim=1){
+collect_results = function(n,p,s, nsim=2){
+  rho=0.3
+  lam=1.
+  sigma=1
   sample_pivots = NULL
   for (i in 1:nsim){
-    result = run_instance(n,p,s)
+    data = gaussian_instance(n=n,p=p,s=s, rho=rho, sigma=sigma)
+    X=data$X
+    print(dim(X))
+    y=data$y
+    ridge_term=sd(y)/sqrt(n)
+    noise_scale = sd(y)/2
+    result = run_instance(X,y,sigma, lam, noise_scale, ridge_term)
     sample_pivots = c(sample_pivots, result)
   }
+  
   jpeg('pivots.jpg')
   plot(ecdf(sample_pivots), xlim=c(0,1),  main="Empirical CDF of null p-values", xlab="p-values", ylab="ecdf")
   abline(0, 1, lty=2)
   dev.off()
 }
 
-
-collect_instances(n=100, p=20, s=0)
+set.seed(1)
+collect_results(n=100, p=20, s=0)
 
 
 

From 68e27f4895a84f59118cdd9f29e2c788261d1659 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Sun, 5 Nov 2017 18:56:06 -0800
Subject: [PATCH 355/493] bug in weights

---
 selectiveInference/R/funs.randomized.R |  2 +-
 tests/randomized/test_instances.R      | 25 +++++++++++--------------
 2 files changed, 12 insertions(+), 15 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index bf5f89d..4fc357c 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -216,7 +216,7 @@ importance_weight = function(noise_scale,
     log_den = log_density_gaussian_conditional_(noise_scale,
                                                 opt_transform$linear_term,
                                                 as.matrix(opt_sample),
-                                                observed_raw)
+                                                observed_raw+opt_transform$offset_term)
     W = log_num - log_den
     W = W - max(W)
     return(exp(W))
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 55807fd..91673da 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -30,7 +30,6 @@ gaussian_instance = function(n, p, s, sigma=1, rho=0, signal=6, X=NA,
 conditional_density = function(noise_scale, lasso_soln){
   
   active_set = lasso_soln$active_set
-  inactive_set = lasso_soln$inactive_set
   observed_raw = lasso_soln$observed_raw
   opt_linear = lasso_soln$optimization_transform$linear_term
   opt_offset =  lasso_soln$optimization_transform$offset_term
@@ -41,15 +40,14 @@ conditional_density = function(noise_scale, lasso_soln){
   beta_offset = observed_raw+opt_offset
   p=length(observed_opt_state)
   if (nactive<p){
-    U=opt_linear[,(nactive+1):p]
-    beta_offset =+U %*% observed_opt_state[(nactive+1):p]
+    beta_offset = beta_offset+(opt_linear[,(nactive+1):p] %*% observed_opt_state[(nactive+1):p])
   }
-  opt_transform = list(linear_term=B, offset_term = beta_offset)
+  opt_transform = list(linear_term=B, 
+                       offset_term = beta_offset)
   reduced_B = chol(t(B) %*% B)
   reduced_beta_offset = solve(t(reduced_B)) %*% (t(B) %*% beta_offset)
   
   log_condl_optimization_density = function(opt_state) {
-    
     if  (sum(opt_state < 0) > 0) {
       return(-Inf)
     }
@@ -77,25 +75,24 @@ run_instance = function(X,y,sigma, lam, noise_scale, ridge_term){
   
   #lasso_soln = conditional_density(noise_scale, lasso_soln)
   
-  observed_raw = lasso_soln$observed_raw
-  opt_linear = lasso_soln$optimization_transform$linear_term
-  opt_offset =  lasso_soln$optimization_transform$offset_term
-  observed_opt_state = lasso_soln$observed_opt_state
-  opt_transform = lasso_soln$optimization_transform
-  
-  dim=length(observed_opt_state)
+  dim=length(lasso_soln$observed_opt_state)
+  print(paste("chain dim", dim))
   S = selectiveInference:::sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), dim), nsample=10000)
   opt_samples = S$samples[2001:10000,]
   print(paste("dim opt samples", toString(dim(opt_samples))))
   
   X_E=X[, active_set]
   X_minusE=X[, inactive_set]
-  target_cov = solve(t(X_E)%*%X_E)*sigma^2
+  target_cov = solve(t(X_E) %*% X_E)*sigma^2
   cov_target_internal = rbind(target_cov, matrix(0, nrow=p-nactive, ncol=nactive))
   observed_target = solve(t(X_E) %*% X_E) %*% t(X_E) %*% y
   observed_internal = c(observed_target, t(X_minusE) %*% (y-X_E%*% observed_target))
   internal_transform = lasso_soln$internal_transform
   
+  observed_opt_state = lasso_soln$observed_opt_state
+  opt_transform = lasso_soln$optimization_transform
+  observed_raw = lasso_soln$observed_raw
+  
   pivots = rep(0, nactive)
   for (i in 1:nactive){
     target_transform = selectiveInference:::linear_decomposition(observed_target[i], 
@@ -122,7 +119,7 @@ run_instance = function(X,y,sigma, lam, noise_scale, ridge_term){
   return(pivots)
 }
 
-collect_results = function(n,p,s, nsim=2){
+collect_results = function(n,p,s, nsim=1){
   rho=0.3
   lam=1.
   sigma=1

From 0fa7c4e17bfdb2af1a436f2b11a2d0a2824a355d Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Sun, 5 Nov 2017 19:24:41 -0800
Subject: [PATCH 356/493] condl bug fixed

---
 selectiveInference/R/funs.randomized.R |  3 +--
 tests/randomized/test_instances.R      | 10 ++++------
 2 files changed, 5 insertions(+), 8 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 4fc357c..0d1ae81 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -145,11 +145,10 @@ randomizedLASSO = function(X,
             (sum(opt_state[(active_start+1):inactive_start] < 0) > 0)) {
             return(-Inf)
         }
-
         D = log_density_gaussian_conditional_(noise_scale,
                                               opt_transform$linear_term,
                                               as.matrix(opt_state),
-                                              observed_raw)
+                                              observed_raw+opt_transform$offset_term)
         return(D)
     }
 
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 91673da..3d4bc31 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -37,7 +37,7 @@ conditional_density = function(noise_scale, lasso_soln){
   
   nactive = length(active_set)
   B = opt_linear[,1:nactive]
-  beta_offset = observed_raw+opt_offset
+  beta_offset = opt_offset
   p=length(observed_opt_state)
   if (nactive<p){
     beta_offset = beta_offset+(opt_linear[,(nactive+1):p] %*% observed_opt_state[(nactive+1):p])
@@ -45,6 +45,7 @@ conditional_density = function(noise_scale, lasso_soln){
   opt_transform = list(linear_term=B, 
                        offset_term = beta_offset)
   reduced_B = chol(t(B) %*% B)
+  beta_offset = beta_offset+observed_raw
   reduced_beta_offset = solve(t(reduced_B)) %*% (t(B) %*% beta_offset)
   
   log_condl_optimization_density = function(opt_state) {
@@ -53,7 +54,7 @@ conditional_density = function(noise_scale, lasso_soln){
     }
     D = selectiveInference:::log_density_gaussian_conditional_(noise_scale,
                                                                reduced_B,
-                                                               as.matrix(observed_opt_state[1:nactive]),
+                                                               as.matrix(opt_state),
                                                                reduced_beta_offset)
     return(D)
   }
@@ -73,7 +74,7 @@ run_instance = function(X,y,sigma, lam, noise_scale, ridge_term){
   nactive = length(active_set)
   print(paste("nactive", nactive))
   
-  #lasso_soln = conditional_density(noise_scale, lasso_soln)
+  lasso_soln = conditional_density(noise_scale, lasso_soln)
   
   dim=length(lasso_soln$observed_opt_state)
   print(paste("chain dim", dim))
@@ -88,8 +89,6 @@ run_instance = function(X,y,sigma, lam, noise_scale, ridge_term){
   observed_target = solve(t(X_E) %*% X_E) %*% t(X_E) %*% y
   observed_internal = c(observed_target, t(X_minusE) %*% (y-X_E%*% observed_target))
   internal_transform = lasso_soln$internal_transform
-  
-  observed_opt_state = lasso_soln$observed_opt_state
   opt_transform = lasso_soln$optimization_transform
   observed_raw = lasso_soln$observed_raw
   
@@ -100,7 +99,6 @@ run_instance = function(X,y,sigma, lam, noise_scale, ridge_term){
                                                   target_cov[i,i], 
                                                   cov_target_internal[,i],
                                                   internal_transform)
-    
     target_sample = rnorm(nrow(opt_samples)) * sqrt(target_cov[i,i])
     
     pivot = function(candidate){

From 8f99570dc05c4fafef6bad0061bae65f309db9cd Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Mon, 6 Nov 2017 11:31:28 -0800
Subject: [PATCH 357/493] added ci

---
 tests/randomized/test_instances.R  | 31 ++++++++++++++++++++++++------
 tests/randomized/test_randomized.R |  1 -
 2 files changed, 25 insertions(+), 7 deletions(-)

diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 3d4bc31..54bbd59 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -65,7 +65,7 @@ conditional_density = function(noise_scale, lasso_soln){
 }
 
 
-run_instance = function(X,y,sigma, lam, noise_scale, ridge_term){
+randomized_inference = function(X,y,sigma, lam, noise_scale, ridge_term){
   n=nrow(X)
   p=ncol(X)
   lasso_soln=selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
@@ -74,7 +74,7 @@ run_instance = function(X,y,sigma, lam, noise_scale, ridge_term){
   nactive = length(active_set)
   print(paste("nactive", nactive))
   
-  lasso_soln = conditional_density(noise_scale, lasso_soln)
+  #lasso_soln = conditional_density(noise_scale, lasso_soln)
   
   dim=length(lasso_soln$observed_opt_state)
   print(paste("chain dim", dim))
@@ -93,6 +93,7 @@ run_instance = function(X,y,sigma, lam, noise_scale, ridge_term){
   observed_raw = lasso_soln$observed_raw
   
   pivots = rep(0, nactive)
+  ci = matrix(0, nactive, 2)
   for (i in 1:nactive){
     target_transform = selectiveInference:::linear_decomposition(observed_target[i], 
                                                   observed_internal, 
@@ -110,11 +111,24 @@ run_instance = function(X,y,sigma, lam, noise_scale, ridge_term){
                                                      observed_raw)
       return(mean((target_sample<observed_target[i])*weights)/mean(weights))
     }
-    
+    level = 0.9
+    rootU = function(candidate){
+      return (pivot(observed_target[i]+candidate)-(1-level)/2)
+    }
+    rootL = function(candidate){
+      return (pivot(observed_target[i]+candidate)-(1+level)/2)
+    }
     pivots[i] = pivot(0)
+    line_min = -10*sd(target_sample)
+    line_max = 10*sd(target_sample)
+    ci[i,1] = uniroot(rootU, c(line_min, line_max))$root+observed_target[i]
+    ci[i,2] = uniroot(rootL,c(line_min, line_max))$root+observed_target[i]
   }
   print(paste("pivots", toString(pivots)))
-  return(pivots)
+  for (i in 1:nactive){
+    print(paste("CIs", toString(ci[i,])))
+  }
+  return(list(pivots=pivots, ci=ci))
 }
 
 collect_results = function(n,p,s, nsim=1){
@@ -129,8 +143,13 @@ collect_results = function(n,p,s, nsim=1){
     y=data$y
     ridge_term=sd(y)/sqrt(n)
     noise_scale = sd(y)/2
-    result = run_instance(X,y,sigma, lam, noise_scale, ridge_term)
-    sample_pivots = c(sample_pivots, result)
+    #X = matrix(rnorm(n * p), n, p)
+    #y = rnorm(n)
+    #lam = 20 / sqrt(n)
+    #noise_scale = 0.01 * sqrt(n)
+    #ridge_term = .1 / sqrt(n)
+    result = randomized_inference(X,y,sigma,lam,noise_scale,ridge_term)
+    sample_pivots = c(sample_pivots, result$pivots)
   }
   
   jpeg('pivots.jpg')
diff --git a/tests/randomized/test_randomized.R b/tests/randomized/test_randomized.R
index ea9eb5c..b9e185a 100644
--- a/tests/randomized/test_randomized.R
+++ b/tests/randomized/test_randomized.R
@@ -1,7 +1,6 @@
 library(selectiveInference)
 
 smoke_test = function() {
-
     n = 100; p = 50
     X = matrix(rnorm(n * p), n, p)
     y = rnorm(n)

From 95974974240156cbb7510e1a17e10f1f5ffdefb8 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 6 Nov 2017 11:37:10 -0800
Subject: [PATCH 358/493] removed local import

---
 selectiveInference/R/RcppExports.R | 35 ------------------------------
 tests/randomized/test_instances.R  |  5 +----
 2 files changed, 1 insertion(+), 39 deletions(-)
 delete mode 100644 selectiveInference/R/RcppExports.R

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
deleted file mode 100644
index e927a3a..0000000
--- a/selectiveInference/R/RcppExports.R
+++ /dev/null
@@ -1,35 +0,0 @@
-# Generated by using Rcpp::compileAttributes() -> do not edit by hand
-# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
-
-solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
-    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
-}
-
-solve_QP_wide <- function(X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
-    .Call('selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
-}
-
-update1_ <- function(Q2, w, m, k) {
-    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
-}
-
-downdate1_ <- function(Q1, R, j0, m, n) {
-    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
-}
-
-log_density_gaussian_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
-    .Call('selectiveInference_log_density_gaussian_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
-}
-
-log_density_gaussian_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
-    .Call('selectiveInference_log_density_gaussian_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
-}
-
-log_density_laplace_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
-    .Call('selectiveInference_log_density_laplace_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
-}
-
-log_density_laplace_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
-    .Call('selectiveInference_log_density_laplace_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
-}
-
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 54bbd59..9ae0afd 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -1,7 +1,4 @@
-#library(devtools)
-#devtools::install_github('jonathan-taylor/R-selective/selectiveInference')
-library(selectiveInference, lib.loc='/Users/Jelena/anaconda/lib/R/library')
-
+library(selectiveInference)
 
 gaussian_instance = function(n, p, s, sigma=1, rho=0, signal=6, X=NA,
                              random_signs=TRUE, scale=TRUE, center=TRUE, seed=NA){

From 71b6586287a1cb980d439492548dcec7fbee18d1 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 6 Nov 2017 17:26:54 -0800
Subject: [PATCH 359/493] BF: segfault seems fixed -- ensured all functions
 have some order of ncase, nfeature

---
 selectiveInference/src/Rcpp-debias.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/selectiveInference/src/Rcpp-debias.cpp b/selectiveInference/src/Rcpp-debias.cpp
index 112a6a8..f5160ba 100644
--- a/selectiveInference/src/Rcpp-debias.cpp
+++ b/selectiveInference/src/Rcpp-debias.cpp
@@ -160,8 +160,8 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 				 (double *) X.begin(),
 				 (double *) linear_func.begin(),
 				 (int *) need_update.begin(),
-				 nfeature,
 				 ncase,
+				 nfeature,
 				 (double *) bound.begin(),
 				 ridge_term,
 				 kkt_tol);
@@ -175,8 +175,8 @@ Rcpp::List solve_QP_wide(Rcpp::NumericMatrix X,
 		       (double *) X.begin(),
 		       (double *) linear_func.begin(),
 		       (int *) need_update.begin(),
-		       nfeature,
-		       ncase);
+		       ncase,
+		       nfeature);
 
   return(Rcpp::List::create(Rcpp::Named("soln") = theta,
 			    Rcpp::Named("gradient") = gradient,

From e0ee2a7f583fa0c1b785ba7180a89098c5887cbe Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 6 Nov 2017 17:27:42 -0800
Subject: [PATCH 360/493] jelena's inference functions

---
 selectiveInference/R/funs.randomized.R | 105 +++++++++++++++++++++++++
 1 file changed, 105 insertions(+)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 0d1ae81..93a8dbb 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -221,3 +221,108 @@ importance_weight = function(noise_scale,
     return(exp(W))
 }
                              
+### Jelena's functions
+
+conditional_density = function(noise_scale, lasso_soln){
+  
+  active_set = lasso_soln$active_set
+  observed_raw = lasso_soln$observed_raw
+  opt_linear = lasso_soln$optimization_transform$linear_term
+  opt_offset =  lasso_soln$optimization_transform$offset_term
+  observed_opt_state = lasso_soln$observed_opt_state
+  
+  nactive = length(active_set)
+  B = opt_linear[,1:nactive]
+  beta_offset = opt_offset
+  p=length(observed_opt_state)
+  if (nactive<p){
+    beta_offset = beta_offset+(opt_linear[,(nactive+1):p] %*% observed_opt_state[(nactive+1):p])
+  }
+  opt_transform = list(linear_term=B, 
+                       offset_term = beta_offset)
+  reduced_B = chol(t(B) %*% B)
+  beta_offset = beta_offset+observed_raw
+  reduced_beta_offset = solve(t(reduced_B)) %*% (t(B) %*% beta_offset)
+  
+  log_condl_optimization_density = function(opt_state) {
+    if  (sum(opt_state < 0) > 0) {
+      return(-Inf)
+    }
+    D = selectiveInference:::log_density_gaussian_conditional_(noise_scale,
+                                                               reduced_B,
+                                                               as.matrix(opt_state),
+                                                               reduced_beta_offset)
+    return(D)
+  }
+  lasso_soln$log_optimization_density = log_condl_optimization_density
+  lasso_soln$observed_opt_state = observed_opt_state[1:nactive]
+  lasso_soln$optimization_transform = opt_transform
+  return(lasso_soln)
+}
+
+
+randomized_inference = function(X,y,sigma, lam, noise_scale, ridge_term){
+  n=nrow(X)
+  p=ncol(X)
+  lasso_soln=selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
+  active_set = lasso_soln$active_set
+  inactive_set = lasso_soln$inactive_set
+  nactive = length(active_set)
+  print(paste("nactive", nactive))
+  
+  #lasso_soln = conditional_density(noise_scale, lasso_soln)
+  
+  dim=length(lasso_soln$observed_opt_state)
+  print(paste("chain dim", dim))
+  S = selectiveInference:::sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), dim), nsample=10000)
+  opt_samples = S$samples[2001:10000,]
+  print(paste("dim opt samples", toString(dim(opt_samples))))
+  
+  X_E=X[, active_set]
+  X_minusE=X[, inactive_set]
+  target_cov = solve(t(X_E) %*% X_E)*sigma^2
+  cov_target_internal = rbind(target_cov, matrix(0, nrow=p-nactive, ncol=nactive))
+  observed_target = solve(t(X_E) %*% X_E) %*% t(X_E) %*% y
+  observed_internal = c(observed_target, t(X_minusE) %*% (y-X_E%*% observed_target))
+  internal_transform = lasso_soln$internal_transform
+  opt_transform = lasso_soln$optimization_transform
+  observed_raw = lasso_soln$observed_raw
+  
+  pivots = rep(0, nactive)
+  ci = matrix(0, nactive, 2)
+  for (i in 1:nactive){
+    target_transform = selectiveInference:::linear_decomposition(observed_target[i], 
+                                                  observed_internal, 
+                                                  target_cov[i,i], 
+                                                  cov_target_internal[,i],
+                                                  internal_transform)
+    target_sample = rnorm(nrow(opt_samples)) * sqrt(target_cov[i,i])
+    
+    pivot = function(candidate){
+      weights = selectiveInference:::importance_weight(noise_scale,
+                                                     t(as.matrix(target_sample))+candidate,
+                                                     t(opt_samples),
+                                                     opt_transform,
+                                                     target_transform,
+                                                     observed_raw)
+      return(mean((target_sample<observed_target[i])*weights)/mean(weights))
+    }
+    level = 0.9
+    rootU = function(candidate){
+      return (pivot(observed_target[i]+candidate)-(1-level)/2)
+    }
+    rootL = function(candidate){
+      return (pivot(observed_target[i]+candidate)-(1+level)/2)
+    }
+    pivots[i] = pivot(0)
+    line_min = -10*sd(target_sample)
+    line_max = 10*sd(target_sample)
+    ci[i,1] = uniroot(rootU, c(line_min, line_max))$root+observed_target[i]
+    ci[i,2] = uniroot(rootL,c(line_min, line_max))$root+observed_target[i]
+  }
+  print(paste("pivots", toString(pivots)))
+  for (i in 1:nactive){
+    print(paste("CIs", toString(ci[i,])))
+  }
+  return(list(pivots=pivots, ci=ci))
+}

From 09db28a0c97dd1cb158d560fb91d69568437596d Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 6 Nov 2017 18:03:36 -0800
Subject: [PATCH 361/493] BF: seems ncase and nfeature were swapped in some
 places in C code

---
 C-software                        |   2 +-
 tests/randomized/test_instances.R | 112 ++----------------------------
 2 files changed, 5 insertions(+), 109 deletions(-)

diff --git a/C-software b/C-software
index 158c64d..ec6a954 160000
--- a/C-software
+++ b/C-software
@@ -1 +1 @@
-Subproject commit 158c64d8d81fbcf434869c0c68f5bb7a4a9cdf5a
+Subproject commit ec6a954d6b335439115e961abde91fa5a07a3669
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 9ae0afd..3ee447f 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -7,7 +7,7 @@ gaussian_instance = function(n, p, s, sigma=1, rho=0, signal=6, X=NA,
   }
   
   if (is.na(X)){
-    X = sqrt(1-rho)*matrix(rnorm(n*p),n) + sqrt(rho)*matrix(rep(rnorm(n), p), nrow = n)
+    X = sqrt(1-rho)*matrix(rnorm(n*p),n, p) + sqrt(rho)*matrix(rep(rnorm(n), p), nrow = n)
     X = scale(X)/sqrt(n)
   }
   beta = rep(0, p)
@@ -24,119 +24,15 @@ gaussian_instance = function(n, p, s, sigma=1, rho=0, signal=6, X=NA,
   return(result)
 }
 
-conditional_density = function(noise_scale, lasso_soln){
-  
-  active_set = lasso_soln$active_set
-  observed_raw = lasso_soln$observed_raw
-  opt_linear = lasso_soln$optimization_transform$linear_term
-  opt_offset =  lasso_soln$optimization_transform$offset_term
-  observed_opt_state = lasso_soln$observed_opt_state
-  
-  nactive = length(active_set)
-  B = opt_linear[,1:nactive]
-  beta_offset = opt_offset
-  p=length(observed_opt_state)
-  if (nactive<p){
-    beta_offset = beta_offset+(opt_linear[,(nactive+1):p] %*% observed_opt_state[(nactive+1):p])
-  }
-  opt_transform = list(linear_term=B, 
-                       offset_term = beta_offset)
-  reduced_B = chol(t(B) %*% B)
-  beta_offset = beta_offset+observed_raw
-  reduced_beta_offset = solve(t(reduced_B)) %*% (t(B) %*% beta_offset)
-  
-  log_condl_optimization_density = function(opt_state) {
-    if  (sum(opt_state < 0) > 0) {
-      return(-Inf)
-    }
-    D = selectiveInference:::log_density_gaussian_conditional_(noise_scale,
-                                                               reduced_B,
-                                                               as.matrix(opt_state),
-                                                               reduced_beta_offset)
-    return(D)
-  }
-  lasso_soln$log_optimization_density = log_condl_optimization_density
-  lasso_soln$observed_opt_state = observed_opt_state[1:nactive]
-  lasso_soln$optimization_transform = opt_transform
-  return(lasso_soln)
-}
-
-
-randomized_inference = function(X,y,sigma, lam, noise_scale, ridge_term){
-  n=nrow(X)
-  p=ncol(X)
-  lasso_soln=selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
-  active_set = lasso_soln$active_set
-  inactive_set = lasso_soln$inactive_set
-  nactive = length(active_set)
-  print(paste("nactive", nactive))
-  
-  #lasso_soln = conditional_density(noise_scale, lasso_soln)
-  
-  dim=length(lasso_soln$observed_opt_state)
-  print(paste("chain dim", dim))
-  S = selectiveInference:::sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), dim), nsample=10000)
-  opt_samples = S$samples[2001:10000,]
-  print(paste("dim opt samples", toString(dim(opt_samples))))
-  
-  X_E=X[, active_set]
-  X_minusE=X[, inactive_set]
-  target_cov = solve(t(X_E) %*% X_E)*sigma^2
-  cov_target_internal = rbind(target_cov, matrix(0, nrow=p-nactive, ncol=nactive))
-  observed_target = solve(t(X_E) %*% X_E) %*% t(X_E) %*% y
-  observed_internal = c(observed_target, t(X_minusE) %*% (y-X_E%*% observed_target))
-  internal_transform = lasso_soln$internal_transform
-  opt_transform = lasso_soln$optimization_transform
-  observed_raw = lasso_soln$observed_raw
-  
-  pivots = rep(0, nactive)
-  ci = matrix(0, nactive, 2)
-  for (i in 1:nactive){
-    target_transform = selectiveInference:::linear_decomposition(observed_target[i], 
-                                                  observed_internal, 
-                                                  target_cov[i,i], 
-                                                  cov_target_internal[,i],
-                                                  internal_transform)
-    target_sample = rnorm(nrow(opt_samples)) * sqrt(target_cov[i,i])
-    
-    pivot = function(candidate){
-      weights = selectiveInference:::importance_weight(noise_scale,
-                                                     t(as.matrix(target_sample))+candidate,
-                                                     t(opt_samples),
-                                                     opt_transform,
-                                                     target_transform,
-                                                     observed_raw)
-      return(mean((target_sample<observed_target[i])*weights)/mean(weights))
-    }
-    level = 0.9
-    rootU = function(candidate){
-      return (pivot(observed_target[i]+candidate)-(1-level)/2)
-    }
-    rootL = function(candidate){
-      return (pivot(observed_target[i]+candidate)-(1+level)/2)
-    }
-    pivots[i] = pivot(0)
-    line_min = -10*sd(target_sample)
-    line_max = 10*sd(target_sample)
-    ci[i,1] = uniroot(rootU, c(line_min, line_max))$root+observed_target[i]
-    ci[i,2] = uniroot(rootL,c(line_min, line_max))$root+observed_target[i]
-  }
-  print(paste("pivots", toString(pivots)))
-  for (i in 1:nactive){
-    print(paste("CIs", toString(ci[i,])))
-  }
-  return(list(pivots=pivots, ci=ci))
-}
 
-collect_results = function(n,p,s, nsim=1){
+collect_results = function(n,p,s, nsim=10){
   rho=0.3
   lam=1.
   sigma=1
-  sample_pivots = NULL
+  sample_pivots = c()
   for (i in 1:nsim){
     data = gaussian_instance(n=n,p=p,s=s, rho=rho, sigma=sigma)
     X=data$X
-    print(dim(X))
     y=data$y
     ridge_term=sd(y)/sqrt(n)
     noise_scale = sd(y)/2
@@ -145,7 +41,7 @@ collect_results = function(n,p,s, nsim=1){
     #lam = 20 / sqrt(n)
     #noise_scale = 0.01 * sqrt(n)
     #ridge_term = .1 / sqrt(n)
-    result = randomized_inference(X,y,sigma,lam,noise_scale,ridge_term)
+    result = selectiveInference:::randomized_inference(X,y,sigma,lam,noise_scale,ridge_term)
     sample_pivots = c(sample_pivots, result$pivots)
   }
   

From 95b98c29d66f89f4673e709ab5164ffaff367409 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 6 Nov 2017 18:14:48 -0800
Subject: [PATCH 362/493] minor cleanup -- doesn't seem to use
 conditional_density?

---
 selectiveInference/R/funs.randomized.R | 39 ++++++++++----------------
 1 file changed, 15 insertions(+), 24 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 93a8dbb..e0a1eec 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -221,9 +221,7 @@ importance_weight = function(noise_scale,
     return(exp(W))
 }
                              
-### Jelena's functions
-
-conditional_density = function(noise_scale, lasso_soln){
+conditional_density = function(noise_scale, lasso_soln) {
   
   active_set = lasso_soln$active_set
   observed_raw = lasso_soln$observed_raw
@@ -241,7 +239,7 @@ conditional_density = function(noise_scale, lasso_soln){
   opt_transform = list(linear_term=B, 
                        offset_term = beta_offset)
   reduced_B = chol(t(B) %*% B)
-  beta_offset = beta_offset+observed_raw
+  beta_offset = beta_offset + observed_raw
   reduced_beta_offset = solve(t(reduced_B)) %*% (t(B) %*% beta_offset)
   
   log_condl_optimization_density = function(opt_state) {
@@ -260,26 +258,23 @@ conditional_density = function(noise_scale, lasso_soln){
   return(lasso_soln)
 }
 
+randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term){
 
-randomized_inference = function(X,y,sigma, lam, noise_scale, ridge_term){
-  n=nrow(X)
-  p=ncol(X)
-  lasso_soln=selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
+  n = nrow(X)
+  p = ncol(X)
+  lasso_soln = selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
   active_set = lasso_soln$active_set
   inactive_set = lasso_soln$inactive_set
   nactive = length(active_set)
-  print(paste("nactive", nactive))
-  
-  #lasso_soln = conditional_density(noise_scale, lasso_soln)
-  
-  dim=length(lasso_soln$observed_opt_state)
+
+  dim = length(lasso_soln$observed_opt_state)
   print(paste("chain dim", dim))
   S = selectiveInference:::sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), dim), nsample=10000)
   opt_samples = S$samples[2001:10000,]
   print(paste("dim opt samples", toString(dim(opt_samples))))
   
-  X_E=X[, active_set]
-  X_minusE=X[, inactive_set]
+  X_E = X[, active_set]
+  X_minusE = X[, inactive_set]
   target_cov = solve(t(X_E) %*% X_E)*sigma^2
   cov_target_internal = rbind(target_cov, matrix(0, nrow=p-nactive, ncol=nactive))
   observed_target = solve(t(X_E) %*% X_E) %*% t(X_E) %*% y
@@ -288,7 +283,7 @@ randomized_inference = function(X,y,sigma, lam, noise_scale, ridge_term){
   opt_transform = lasso_soln$optimization_transform
   observed_raw = lasso_soln$observed_raw
   
-  pivots = rep(0, nactive)
+  pvalus = rep(0, nactive)
   ci = matrix(0, nactive, 2)
   for (i in 1:nactive){
     target_transform = selectiveInference:::linear_decomposition(observed_target[i], 
@@ -300,7 +295,7 @@ randomized_inference = function(X,y,sigma, lam, noise_scale, ridge_term){
     
     pivot = function(candidate){
       weights = selectiveInference:::importance_weight(noise_scale,
-                                                     t(as.matrix(target_sample))+candidate,
+                                                     t(as.matrix(target_sample)) + candidate,
                                                      t(opt_samples),
                                                      opt_transform,
                                                      target_transform,
@@ -314,15 +309,11 @@ randomized_inference = function(X,y,sigma, lam, noise_scale, ridge_term){
     rootL = function(candidate){
       return (pivot(observed_target[i]+candidate)-(1+level)/2)
     }
-    pivots[i] = pivot(0)
+    pvalues[i] = pivot(0)
     line_min = -10*sd(target_sample)
     line_max = 10*sd(target_sample)
     ci[i,1] = uniroot(rootU, c(line_min, line_max))$root+observed_target[i]
-    ci[i,2] = uniroot(rootL,c(line_min, line_max))$root+observed_target[i]
-  }
-  print(paste("pivots", toString(pivots)))
-  for (i in 1:nactive){
-    print(paste("CIs", toString(ci[i,])))
+    ci[i,2] = uniroot(rootL, c(line_min, line_max))$root+observed_target[i]
   }
-  return(list(pivots=pivots, ci=ci))
+  return(list(pvalues=pvalues, ci=ci))
 }

From 07b6bb705d291f88e9ff9baaa01fc73969af1b6a Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Mon, 6 Nov 2017 22:50:13 -0800
Subject: [PATCH 363/493] coverages

---
 selectiveInference/R/RcppExports.R     | 35 ++++++++++++++++++++++++++
 selectiveInference/R/funs.randomized.R | 27 +++++++++++++-------
 tests/randomized/test_instances.R      | 30 +++++++++++++---------
 3 files changed, 71 insertions(+), 21 deletions(-)
 create mode 100644 selectiveInference/R/RcppExports.R

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
new file mode 100644
index 0000000..e927a3a
--- /dev/null
+++ b/selectiveInference/R/RcppExports.R
@@ -0,0 +1,35 @@
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
+    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
+}
+
+solve_QP_wide <- function(X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
+    .Call('selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
+}
+
+update1_ <- function(Q2, w, m, k) {
+    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
+}
+
+downdate1_ <- function(Q1, R, j0, m, n) {
+    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
+}
+
+log_density_gaussian_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_gaussian_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
+}
+
+log_density_gaussian_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_gaussian_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
+}
+
+log_density_laplace_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_laplace_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
+}
+
+log_density_laplace_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_laplace_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
+}
+
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index e0a1eec..78c3a87 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -258,7 +258,7 @@ conditional_density = function(noise_scale, lasso_soln) {
   return(lasso_soln)
 }
 
-randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term){
+randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term, level=0.9){
 
   n = nrow(X)
   p = ncol(X)
@@ -283,7 +283,7 @@ randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term){
   opt_transform = lasso_soln$optimization_transform
   observed_raw = lasso_soln$observed_raw
   
-  pvalus = rep(0, nactive)
+  pvalues = rep(0, nactive)
   ci = matrix(0, nactive, 2)
   for (i in 1:nactive){
     target_transform = selectiveInference:::linear_decomposition(observed_target[i], 
@@ -300,9 +300,8 @@ randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term){
                                                      opt_transform,
                                                      target_transform,
                                                      observed_raw)
-      return(mean((target_sample<observed_target[i])*weights)/mean(weights))
+      return(mean((target_sample+candidate<observed_target[i])*weights)/mean(weights))
     }
-    level = 0.9
     rootU = function(candidate){
       return (pivot(observed_target[i]+candidate)-(1-level)/2)
     }
@@ -310,10 +309,20 @@ randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term){
       return (pivot(observed_target[i]+candidate)-(1+level)/2)
     }
     pvalues[i] = pivot(0)
-    line_min = -10*sd(target_sample)
-    line_max = 10*sd(target_sample)
-    ci[i,1] = uniroot(rootU, c(line_min, line_max))$root+observed_target[i]
-    ci[i,2] = uniroot(rootL, c(line_min, line_max))$root+observed_target[i]
+    line_min = -20*sd(target_sample)
+    line_max = 20*sd(target_sample)
+    if (rootU(line_min)*rootU(line_max)<0){
+      ci[i,2] = uniroot(rootU, c(line_min, line_max))$root+observed_target[i]
+    } else{
+      print("non inv u")
+      ci[i,2]=line_max
+    }
+    if (rootL(line_min)*rootL(line_max)<0){
+      ci[i,1] = uniroot(rootL, c(line_min, line_max))$root+observed_target[i]
+    } else{
+      print("non inv u")
+      ci[i,1] = line_min
+    }
   }
-  return(list(pvalues=pvalues, ci=ci))
+  return(list(active_set=active_set, pvalues=pvalues, ci=ci))
 }
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 3ee447f..7bbc54a 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -25,28 +25,34 @@ gaussian_instance = function(n, p, s, sigma=1, rho=0, signal=6, X=NA,
 }
 
 
-collect_results = function(n,p,s, nsim=10){
-  rho=0.3
+collect_results = function(n,p,s, nsim=10, level=0.9){
+  rho=0.
   lam=1.
   sigma=1
-  sample_pivots = c()
+  sample_pvalues = c()
+  sample_coverage = c()
   for (i in 1:nsim){
     data = gaussian_instance(n=n,p=p,s=s, rho=rho, sigma=sigma)
     X=data$X
     y=data$y
+    beta=data$beta
     ridge_term=sd(y)/sqrt(n)
     noise_scale = sd(y)/2
-    #X = matrix(rnorm(n * p), n, p)
-    #y = rnorm(n)
-    #lam = 20 / sqrt(n)
-    #noise_scale = 0.01 * sqrt(n)
-    #ridge_term = .1 / sqrt(n)
-    result = selectiveInference:::randomized_inference(X,y,sigma,lam,noise_scale,ridge_term)
-    sample_pivots = c(sample_pivots, result$pivots)
+    result = selectiveInference:::randomized_inference(X,y,sigma,lam,noise_scale,ridge_term, level)
+    true_beta = beta[result$active_set]
+    coverage = rep(0, nrow(result$ci))
+    for (i in 1:nrow(result$ci)){
+      if (result$ci[i,1]<true_beta[i] & result$ci[i,2]>true_beta[i]){
+        coverage[i]=1
+      }
+      print(paste("ci", toString(result$ci[i,])))
+    }
+    sample_pvalues = c(sample_pvalues, result$pvalues)
+    sample_coverage = c(sample_coverage, coverage)
   }
-  
+  print(paste("coverage", mean(sample_coverage)))
   jpeg('pivots.jpg')
-  plot(ecdf(sample_pivots), xlim=c(0,1),  main="Empirical CDF of null p-values", xlab="p-values", ylab="ecdf")
+  plot(ecdf(sample_pvalues), xlim=c(0,1),  main="Empirical CDF of null p-values", xlab="p-values", ylab="ecdf")
   abline(0, 1, lty=2)
   dev.off()
 }

From 1115635aa730bf05c7901bcfbaa62c1248058b6f Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Tue, 7 Nov 2017 11:10:11 -0800
Subject: [PATCH 364/493] subgrad condition option added

---
 selectiveInference/R/funs.randomized.R | 9 +++++++--
 tests/randomized/test_instances.R      | 6 +++---
 2 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 78c3a87..95b2a7a 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -258,7 +258,8 @@ conditional_density = function(noise_scale, lasso_soln) {
   return(lasso_soln)
 }
 
-randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term, level=0.9){
+randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term, 
+                                condition_subgrad=FALSE, level=0.9){
 
   n = nrow(X)
   p = ncol(X)
@@ -266,7 +267,11 @@ randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term, level
   active_set = lasso_soln$active_set
   inactive_set = lasso_soln$inactive_set
   nactive = length(active_set)
-
+  
+  if (condition_subgrad==TRUE){
+    lasso_soln=conditional_density(noise_scale,lasso_soln)
+  } 
+    
   dim = length(lasso_soln$observed_opt_state)
   print(paste("chain dim", dim))
   S = selectiveInference:::sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), dim), nsample=10000)
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 7bbc54a..380323d 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -25,8 +25,8 @@ gaussian_instance = function(n, p, s, sigma=1, rho=0, signal=6, X=NA,
 }
 
 
-collect_results = function(n,p,s, nsim=10, level=0.9){
-  rho=0.
+collect_results = function(n,p,s, nsim=100, level=0.9){
+  rho=0.3
   lam=1.
   sigma=1
   sample_pvalues = c()
@@ -38,7 +38,7 @@ collect_results = function(n,p,s, nsim=10, level=0.9){
     beta=data$beta
     ridge_term=sd(y)/sqrt(n)
     noise_scale = sd(y)/2
-    result = selectiveInference:::randomized_inference(X,y,sigma,lam,noise_scale,ridge_term, level)
+    result = selectiveInference:::randomized_inference(X,y,sigma,lam,noise_scale,ridge_term, TRUE, level)
     true_beta = beta[result$active_set]
     coverage = rep(0, nrow(result$ci))
     for (i in 1:nrow(result$ci)){

From e2f45859d8dad938dbdc9dacf0bb1f3f7a277c98 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Tue, 7 Nov 2017 11:43:44 -0800
Subject: [PATCH 365/493] bug

---
 selectiveInference/R/funs.randomized.R | 9 +++------
 tests/randomized/test_instances.R      | 6 +++---
 2 files changed, 6 insertions(+), 9 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 95b2a7a..37b8121 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -258,8 +258,7 @@ conditional_density = function(noise_scale, lasso_soln) {
   return(lasso_soln)
 }
 
-randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term, 
-                                condition_subgrad=FALSE, level=0.9){
+randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term, level=0.9){
 
   n = nrow(X)
   p = ncol(X)
@@ -268,9 +267,7 @@ randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term,
   inactive_set = lasso_soln$inactive_set
   nactive = length(active_set)
   
-  if (condition_subgrad==TRUE){
-    lasso_soln=conditional_density(noise_scale,lasso_soln)
-  } 
+  lasso_soln=conditional_density(noise_scale,lasso_soln)
     
   dim = length(lasso_soln$observed_opt_state)
   print(paste("chain dim", dim))
@@ -296,7 +293,7 @@ randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term,
                                                   target_cov[i,i], 
                                                   cov_target_internal[,i],
                                                   internal_transform)
-    target_sample = rnorm(nrow(opt_samples)) * sqrt(target_cov[i,i])
+    target_sample = rnorm(nrow(as.matrix(opt_samples))) * sqrt(target_cov[i,i])
     
     pivot = function(candidate){
       weights = selectiveInference:::importance_weight(noise_scale,
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 380323d..4e8d529 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -25,9 +25,9 @@ gaussian_instance = function(n, p, s, sigma=1, rho=0, signal=6, X=NA,
 }
 
 
-collect_results = function(n,p,s, nsim=100, level=0.9){
+collect_results = function(n,p,s, nsim=1, level=0.9){
   rho=0.3
-  lam=1.
+  lam=2.
   sigma=1
   sample_pvalues = c()
   sample_coverage = c()
@@ -38,7 +38,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9){
     beta=data$beta
     ridge_term=sd(y)/sqrt(n)
     noise_scale = sd(y)/2
-    result = selectiveInference:::randomized_inference(X,y,sigma,lam,noise_scale,ridge_term, TRUE, level)
+    result = selectiveInference:::randomized_inference(X,y,sigma,lam,noise_scale,ridge_term, level)
     true_beta = beta[result$active_set]
     coverage = rep(0, nrow(result$ci))
     for (i in 1:nrow(result$ci)){

From df777e381fa9f9b3630fc33ea161f1719fa4a394 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 7 Nov 2017 11:55:21 -0800
Subject: [PATCH 366/493] using some default arguments now -- bug in C code for
 densities

---
 selectiveInference/R/RcppExports.R     | 35 ------------
 selectiveInference/R/funs.randomized.R | 73 ++++++++++++++++++--------
 tests/randomized/test_instances.R      |  4 +-
 tests/randomized/test_randomized.R     |  4 +-
 4 files changed, 54 insertions(+), 62 deletions(-)
 delete mode 100644 selectiveInference/R/RcppExports.R

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
deleted file mode 100644
index e927a3a..0000000
--- a/selectiveInference/R/RcppExports.R
+++ /dev/null
@@ -1,35 +0,0 @@
-# Generated by using Rcpp::compileAttributes() -> do not edit by hand
-# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
-
-solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
-    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
-}
-
-solve_QP_wide <- function(X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
-    .Call('selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
-}
-
-update1_ <- function(Q2, w, m, k) {
-    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
-}
-
-downdate1_ <- function(Q1, R, j0, m, n) {
-    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
-}
-
-log_density_gaussian_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
-    .Call('selectiveInference_log_density_gaussian_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
-}
-
-log_density_gaussian_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
-    .Call('selectiveInference_log_density_gaussian_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
-}
-
-log_density_laplace_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
-    .Call('selectiveInference_log_density_laplace_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
-}
-
-log_density_laplace_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
-    .Call('selectiveInference_log_density_laplace_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
-}
-
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 95b2a7a..e065a5e 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -3,11 +3,11 @@
 #
 # min 1/2 || y - \beta_0 - X \beta ||_2^2 + \lambda || \beta ||_1 - \omega^T\beta + \frac{\epsilon}{2} \|\beta\|^2_2
 
-randomizedLASSO = function(X, 
+randomizedLasso = function(X, 
                            y, 
                            lam, 
-                           noise_scale, 
-                           ridge_term, 
+                           noise_scale=NULL, 
+                           ridge_term=NULL, 
                            noise_type=c('gaussian', 'laplace'),
                            max_iter=100,        # how many iterations for each optimization problem
                            kkt_tol=1.e-4,       # tolerance for the KKT conditions
@@ -20,6 +20,21 @@ randomizedLASSO = function(X,
 
     n = nrow(X); p = ncol(X)
     			
+    mean_diag = mean(apply(X^2, 2, sum))
+
+    # default ridge term
+
+    if (is.null(ridge_term)) {
+        ridge_term = sqrt(mean_diag) * sd(y) / sqrt(n)
+    }
+
+    # default noise level
+
+    if (is.null(noise_scale)) {
+        noise_scale = 0.5 * sd(y) * sqrt(mean_diag)
+    }
+
+    print(c(noise_scale, ridge_term))
     noise_type = match.arg(noise_type)
 
     if (noise_scale > 0) {
@@ -246,10 +261,10 @@ conditional_density = function(noise_scale, lasso_soln) {
     if  (sum(opt_state < 0) > 0) {
       return(-Inf)
     }
-    D = selectiveInference:::log_density_gaussian_conditional_(noise_scale,
-                                                               reduced_B,
-                                                               as.matrix(opt_state),
-                                                               reduced_beta_offset)
+    D = log_density_gaussian_conditional_(noise_scale,
+                                          reduced_B,
+                                          as.matrix(opt_state),
+                                          reduced_beta_offset)
     return(D)
   }
   lasso_soln$log_optimization_density = log_condl_optimization_density
@@ -258,12 +273,18 @@ conditional_density = function(noise_scale, lasso_soln) {
   return(lasso_soln)
 }
 
-randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term, 
-                                condition_subgrad=FALSE, level=0.9){
+randomizedLassoInf = function(X, 
+                              y, 
+                              lam, 
+                              sigma=NULL, 
+                              noise_scale=NULL, 
+                              ridge_term=NULL, 
+                              condition_subgrad=TRUE, 
+                              level=0.9) {
 
   n = nrow(X)
   p = ncol(X)
-  lasso_soln = selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
+  lasso_soln = randomizedLasso(X, y, lam, noise_scale, ridge_term)
   active_set = lasso_soln$active_set
   inactive_set = lasso_soln$inactive_set
   nactive = length(active_set)
@@ -274,12 +295,20 @@ randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term,
     
   dim = length(lasso_soln$observed_opt_state)
   print(paste("chain dim", dim))
-  S = selectiveInference:::sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), dim), nsample=10000)
+  S = sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), dim), nsample=10000)
   opt_samples = S$samples[2001:10000,]
   print(paste("dim opt samples", toString(dim(opt_samples))))
   
   X_E = X[, active_set]
   X_minusE = X[, inactive_set]
+
+  # if no sigma given, use OLS estimate
+
+  if (is.null(sigma)) {
+        lm_y = lm(y ~ X[,E] - 1)
+        sigma = sum(resid(lm_y)^2 / lm_y$df.resid)
+  }        
+  print(c(sigma, 'sigma'))
   target_cov = solve(t(X_E) %*% X_E)*sigma^2
   cov_target_internal = rbind(target_cov, matrix(0, nrow=p-nactive, ncol=nactive))
   observed_target = solve(t(X_E) %*% X_E) %*% t(X_E) %*% y
@@ -291,20 +320,20 @@ randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term,
   pvalues = rep(0, nactive)
   ci = matrix(0, nactive, 2)
   for (i in 1:nactive){
-    target_transform = selectiveInference:::linear_decomposition(observed_target[i], 
-                                                  observed_internal, 
-                                                  target_cov[i,i], 
-                                                  cov_target_internal[,i],
-                                                  internal_transform)
+    target_transform = linear_decomposition(observed_target[i], 
+                                            observed_internal, 
+                                            target_cov[i,i], 
+                                            cov_target_internal[,i],
+                                            internal_transform)
     target_sample = rnorm(nrow(opt_samples)) * sqrt(target_cov[i,i])
     
     pivot = function(candidate){
-      weights = selectiveInference:::importance_weight(noise_scale,
-                                                     t(as.matrix(target_sample)) + candidate,
-                                                     t(opt_samples),
-                                                     opt_transform,
-                                                     target_transform,
-                                                     observed_raw)
+      weights = importance_weight(noise_scale,
+                                  t(as.matrix(target_sample)) + candidate,
+                                  t(opt_samples),
+                                  opt_transform,
+                                  target_transform,
+                                  observed_raw)
       return(mean((target_sample+candidate<observed_target[i])*weights)/mean(weights))
     }
     rootU = function(candidate){
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 380323d..404312e 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -36,9 +36,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9){
     X=data$X
     y=data$y
     beta=data$beta
-    ridge_term=sd(y)/sqrt(n)
-    noise_scale = sd(y)/2
-    result = selectiveInference:::randomized_inference(X,y,sigma,lam,noise_scale,ridge_term, TRUE, level)
+    result = selectiveInference:::randomizedLassoInf(X, y, sigma, lam, level=level)
     true_beta = beta[result$active_set]
     coverage = rep(0, nrow(result$ci))
     for (i in 1:nrow(result$ci)){
diff --git a/tests/randomized/test_randomized.R b/tests/randomized/test_randomized.R
index b9e185a..1531d43 100644
--- a/tests/randomized/test_randomized.R
+++ b/tests/randomized/test_randomized.R
@@ -7,7 +7,7 @@ smoke_test = function() {
     lam = 20 / sqrt(n)
     noise_scale = 0.01 * sqrt(n)
     ridge_term = .1 / sqrt(n)
-    selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
+    selectiveInference:::randomizedLasso(X, y, lam, noise_scale, ridge_term)
 }
 
 A = smoke_test()
@@ -20,7 +20,7 @@ sampler_test = function() {
     lam = 20 / sqrt(n)
     noise_scale = 0.01 * sqrt(n)
     ridge_term = .1 / sqrt(n)
-    obj = selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
+    obj = selectiveInference:::randomizedLasso(X, y, lam, noise_scale, ridge_term)
     S = selectiveInference:::sample_opt_variables(obj, jump_scale=rep(1/sqrt(n), p), nsample=10000)
     return(S$samples[2001:10000,])
 }

From ff34a52f8c46ccf1aafc14f95d49b2b5fd7404ce Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Tue, 7 Nov 2017 11:58:06 -0800
Subject: [PATCH 367/493] dealing with zero length active set

---
 selectiveInference/R/funs.randomized.R |  6 +++++-
 tests/randomized/test_instances.R      | 30 +++++++++++++++-----------
 2 files changed, 22 insertions(+), 14 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 37b8121..3d8ddec 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -264,6 +264,9 @@ randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term, level
   p = ncol(X)
   lasso_soln = selectiveInference:::randomizedLASSO(X, y, lam, noise_scale, ridge_term)
   active_set = lasso_soln$active_set
+  if (length(active_set)==0){
+    return (list(active_set=active_set, pvalues=c(), ci=c()))
+  }
   inactive_set = lasso_soln$inactive_set
   nactive = length(active_set)
   
@@ -293,8 +296,9 @@ randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term, level
                                                   target_cov[i,i], 
                                                   cov_target_internal[,i],
                                                   internal_transform)
+  
     target_sample = rnorm(nrow(as.matrix(opt_samples))) * sqrt(target_cov[i,i])
-    
+    print(length(target_sample))
     pivot = function(candidate){
       weights = selectiveInference:::importance_weight(noise_scale,
                                                      t(as.matrix(target_sample)) + candidate,
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 4e8d529..baafb15 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -39,22 +39,26 @@ collect_results = function(n,p,s, nsim=1, level=0.9){
     ridge_term=sd(y)/sqrt(n)
     noise_scale = sd(y)/2
     result = selectiveInference:::randomized_inference(X,y,sigma,lam,noise_scale,ridge_term, level)
-    true_beta = beta[result$active_set]
-    coverage = rep(0, nrow(result$ci))
-    for (i in 1:nrow(result$ci)){
-      if (result$ci[i,1]<true_beta[i] & result$ci[i,2]>true_beta[i]){
-        coverage[i]=1
+    if (length(result$active_set)>0){
+      true_beta = beta[result$active_set]
+      coverage = rep(0, nrow(result$ci))
+      for (i in 1:nrow(result$ci)){
+        if (result$ci[i,1]<true_beta[i] & result$ci[i,2]>true_beta[i]){
+          coverage[i]=1
+        }
+        print(paste("ci", toString(result$ci[i,])))
       }
-      print(paste("ci", toString(result$ci[i,])))
+      sample_pvalues = c(sample_pvalues, result$pvalues)
+      sample_coverage = c(sample_coverage, coverage)
     }
-    sample_pvalues = c(sample_pvalues, result$pvalues)
-    sample_coverage = c(sample_coverage, coverage)
   }
-  print(paste("coverage", mean(sample_coverage)))
-  jpeg('pivots.jpg')
-  plot(ecdf(sample_pvalues), xlim=c(0,1),  main="Empirical CDF of null p-values", xlab="p-values", ylab="ecdf")
-  abline(0, 1, lty=2)
-  dev.off()
+  if (length(sample_coverage)>0){
+    print(paste("coverage", mean(sample_coverage)))
+    jpeg('pivots.jpg')
+    plot(ecdf(sample_pvalues), xlim=c(0,1),  main="Empirical CDF of null p-values", xlab="p-values", ylab="ecdf")
+    abline(0, 1, lty=2)
+    dev.off()
+  }
 }
 
 set.seed(1)

From ab91969dfed3f76417f157faabfb7013d3295c71 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 7 Nov 2017 13:18:30 -0800
Subject: [PATCH 368/493] now nut running after trying to set default arguments

---
 C-software                             | 2 +-
 selectiveInference/R/funs.randomized.R | 2 ++
 tests/randomized/test_instances.R      | 2 +-
 3 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/C-software b/C-software
index ec6a954..8d06a25 160000
--- a/C-software
+++ b/C-software
@@ -1 +1 @@
-Subproject commit ec6a954d6b335439115e961abde91fa5a07a3669
+Subproject commit 8d06a251820689db939246e3362475bf4c56801e
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index e065a5e..cd67f6d 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -65,6 +65,7 @@ randomizedLasso = function(X,
     ever_active = rep(0, p)
     nactive = as.integer(0)
 
+    print('here')
     result = solve_QP_wide(X,                  # design matrix
     	                   lam / n,            # vector of Lagrange multipliers
 		           ridge_term / n,     # ridge_term 
@@ -85,6 +86,7 @@ randomizedLasso = function(X,
 
     sign_soln = sign(result$soln)
 
+    print('now')
     unpenalized = lam == 0
     active = (!unpenalized) & (sign_soln != 0)
     inactive = (!unpenalized) & (sign_soln == 0)
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 404312e..02f6984 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -36,7 +36,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9){
     X=data$X
     y=data$y
     beta=data$beta
-    result = selectiveInference:::randomizedLassoInf(X, y, sigma, lam, level=level)
+    result = selectiveInference:::randomizedLassoInf(X, y, lam, level=level)
     true_beta = beta[result$active_set]
     coverage = rep(0, nrow(result$ci))
     for (i in 1:nrow(result$ci)){

From 5b4bc08d6ccf2c411a0a4fb81f8d1a346afb154b Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 7 Nov 2017 13:23:03 -0800
Subject: [PATCH 369/493] BF: estimate of sigma

---
 selectiveInference/R/funs.randomized.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index cd67f6d..c6b7a23 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -307,8 +307,8 @@ randomizedLassoInf = function(X,
   # if no sigma given, use OLS estimate
 
   if (is.null(sigma)) {
-        lm_y = lm(y ~ X[,E] - 1)
-        sigma = sum(resid(lm_y)^2 / lm_y$df.resid)
+        lm_y = lm(y ~ X_E - 1)
+        sigma = sqrt(sum(resid(lm_y)^2) / lm_y$df.resid)
   }        
   print(c(sigma, 'sigma'))
   target_cov = solve(t(X_E) %*% X_E)*sigma^2

From 46856e1a87fae22dde84b7a07da8c59772161ff8 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 7 Nov 2017 13:25:19 -0800
Subject: [PATCH 370/493] changing arguments

---
 selectiveInference/R/funs.randomized.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index c6b7a23..71a6a71 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -286,7 +286,7 @@ randomizedLassoInf = function(X,
 
   n = nrow(X)
   p = ncol(X)
-  lasso_soln = randomizedLasso(X, y, lam, noise_scale, ridge_term)
+  lasso_soln = randomizedLasso(X, y, lam, noise_scale=noise_scale, ridge_term=ridge_term)
   active_set = lasso_soln$active_set
   inactive_set = lasso_soln$inactive_set
   nactive = length(active_set)

From d1ebca35bfc8e03f0bd6f527dc6f688e07953139 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 7 Nov 2017 14:25:13 -0800
Subject: [PATCH 371/493] BF: noise_scale was NULL

---
 C-software                                 |  2 +-
 selectiveInference/R/funs.randomized.R     | 96 ++++++++++++++--------
 selectiveInference/src/Rcpp-randomized.cpp |  3 +-
 tests/randomized/test_instances.R          |  2 +-
 4 files changed, 67 insertions(+), 36 deletions(-)

diff --git a/C-software b/C-software
index 8d06a25..225d827 160000
--- a/C-software
+++ b/C-software
@@ -1 +1 @@
-Subproject commit 8d06a251820689db939246e3362475bf4c56801e
+Subproject commit 225d8274ec54eea0ccf14d4ef27297dd699b54e5
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 71a6a71..869b277 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -65,7 +65,6 @@ randomizedLasso = function(X,
     ever_active = rep(0, p)
     nactive = as.integer(0)
 
-    print('here')
     result = solve_QP_wide(X,                  # design matrix
     	                   lam / n,            # vector of Lagrange multipliers
 		           ridge_term / n,     # ridge_term 
@@ -86,7 +85,6 @@ randomizedLasso = function(X,
 
     sign_soln = sign(result$soln)
 
-    print('now')
     unpenalized = lam == 0
     active = (!unpenalized) & (sign_soln != 0)
     inactive = (!unpenalized) & (sign_soln == 0)
@@ -156,16 +154,17 @@ randomizedLasso = function(X,
 
     log_optimization_density = function(opt_state 
                                         ) {
-
-
         if ((sum(abs(opt_state[(inactive_start + 1):p]) > inactive_lam) > 0) ||
             (sum(opt_state[(active_start+1):inactive_start] < 0) > 0)) {
             return(-Inf)
         }
-        D = log_density_gaussian_conditional_(noise_scale,
-                                              opt_transform$linear_term,
-                                              as.matrix(opt_state),
-                                              observed_raw+opt_transform$offset_term)
+
+	A = opt_transform$linear_term %*% opt_state + observed_raw + opt_transform$offset_term
+	D = -apply(A^2, 2, sum) / noise_scale^2
+#        D = log_density_gaussian_conditional_(noise_scale,
+#                                              opt_transform$linear_term,
+#                                              as.matrix(opt_state),
+#                                              observed_raw + opt_transform$offset_term)
         return(D)
     }
 
@@ -177,7 +176,8 @@ randomizedLasso = function(X,
                 internal_transform = internal_transform,
                 log_optimization_density = log_optimization_density,
 		observed_opt_state = observed_opt_state,
-                observed_raw = observed_raw
+                observed_raw = observed_raw,
+		noise_scale = noise_scale
                 ))
 
 }
@@ -222,17 +222,31 @@ importance_weight = function(noise_scale,
                              target_transform,
                              observed_raw) {
 
-    log_num = log_density_gaussian_(noise_scale,
-                                    target_transform$linear_term,
-                                    as.matrix(target_sample),
-                                    opt_transform$linear_term,
-                                    as.matrix(opt_sample),
-                                    target_transform$offset_term + opt_transform$offset_term)
-
-    log_den = log_density_gaussian_conditional_(noise_scale,
-                                                opt_transform$linear_term,
-                                                as.matrix(opt_sample),
-                                                observed_raw+opt_transform$offset_term)
+    use_C_code = FALSE
+    if (!use_C_code) {
+        A = (opt_transform$linear_term %*% opt_sample + 
+             target_transform$linear_term %*% target_sample)
+        A = apply(A, 2, function(x) {return(x + target_transform$offset_term + opt_transform$offset_term)})
+        log_num = -apply(A^2, 2, sum) / noise_scale^2
+    } else {
+        log_num = log_density_gaussian_(noise_scale,
+                                        target_transform$linear_term,
+                                        as.matrix(target_sample),
+                                        opt_transform$linear_term,
+                                        as.matrix(opt_sample),
+                                        target_transform$offset_term + opt_transform$offset_term)
+    }
+
+    if (!use_C_code) {
+        A = opt_transform$linear_term %*% opt_sample 
+        A = apply(A, 2, function(x) {return(x + observed_raw + opt_transform$offset_term)})
+        log_den = -apply(A^2, 2, sum) / noise_scale^2
+    } else {
+       log_den = log_density_gaussian_conditional_(noise_scale,
+                                                   opt_transform$linear_term,
+                                                   as.matrix(opt_sample),
+                                                   observed_raw+opt_transform$offset_term)
+    }
     W = log_num - log_den
     W = W - max(W)
     return(exp(W))
@@ -263,11 +277,19 @@ conditional_density = function(noise_scale, lasso_soln) {
     if  (sum(opt_state < 0) > 0) {
       return(-Inf)
     }
-    D = log_density_gaussian_conditional_(noise_scale,
-                                          reduced_B,
-                                          as.matrix(opt_state),
-                                          reduced_beta_offset)
-    return(D)
+
+    use_C_code = FALSE
+    if (!use_C_code) {
+        A = reduced_B %*% as.matrix(opt_state) + reduced_beta_offset
+        A = apply(A, 2, function(x) {x + reduced_beta_offset})
+        log_den = -apply(A^2, 2, sum) / noise_scale^2
+    } else {
+        log_den = log_density_gaussian_conditional_(noise_scale,
+                                                    reduced_B,
+                                                    as.matrix(opt_state),
+                                                    reduced_beta_offset)
+    }
+    return(log_den)
   }
   lasso_soln$log_optimization_density = log_condl_optimization_density
   lasso_soln$observed_opt_state = observed_opt_state[1:nactive]
@@ -282,7 +304,9 @@ randomizedLassoInf = function(X,
                               noise_scale=NULL, 
                               ridge_term=NULL, 
                               condition_subgrad=TRUE, 
-                              level=0.9) {
+                              level=0.9,
+			      nsample=10000,
+			      burnin=2000) {
 
   n = nrow(X)
   p = ncol(X)
@@ -291,14 +315,20 @@ randomizedLassoInf = function(X,
   inactive_set = lasso_soln$inactive_set
   nactive = length(active_set)
   
-  if (condition_subgrad==TRUE){
-    lasso_soln=conditional_density(noise_scale,lasso_soln)
-  } 
+  noise_scale = lasso_soln$noise_scale # set to default value in randomizedLasso
+
+ if (condition_subgrad==TRUE){
+   lasso_soln=conditional_density(noise_scale, lasso_soln)
+ } 
     
   dim = length(lasso_soln$observed_opt_state)
   print(paste("chain dim", dim))
-  S = sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), dim), nsample=10000)
-  opt_samples = S$samples[2001:10000,]
+
+#  print(lasso_soln)
+
+
+  S = sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), dim), nsample=nsample)
+  opt_samples = S$samples[(burnin+1):nsample,]
   print(paste("dim opt samples", toString(dim(opt_samples))))
   
   X_E = X[, active_set]
@@ -331,12 +361,12 @@ randomizedLassoInf = function(X,
     
     pivot = function(candidate){
       weights = importance_weight(noise_scale,
-                                  t(as.matrix(target_sample)) + candidate,
+                                  t(as.matrix(target_sample) + candidate),
                                   t(opt_samples),
                                   opt_transform,
                                   target_transform,
                                   observed_raw)
-      return(mean((target_sample+candidate<observed_target[i])*weights)/mean(weights))
+      return(mean((target_sample + candidate < observed_target[i]) * weights)/mean(weights))
     }
     rootU = function(candidate){
       return (pivot(observed_target[i]+candidate)-(1-level)/2)
diff --git a/selectiveInference/src/Rcpp-randomized.cpp b/selectiveInference/src/Rcpp-randomized.cpp
index b2b2cd2..cf59d77 100644
--- a/selectiveInference/src/Rcpp-randomized.cpp
+++ b/selectiveInference/src/Rcpp-randomized.cpp
@@ -1,6 +1,6 @@
 #include <Rcpp.h>                // need to include the main Rcpp header file 
 #include <randomized_lasso.h>    // where densities are defined
-
+#include <stdio.h>
 // [[Rcpp::export]]
 Rcpp::NumericVector log_density_gaussian_(double noise_scale,                         // Scale of randomization
 					  Rcpp::NumericMatrix internal_linear,        // A_D -- linear part for data
@@ -53,6 +53,7 @@ Rcpp::NumericVector log_density_gaussian_conditional_(double noise_scale,
 
   int ipt;
   for (ipt=0; ipt<npt; ipt++) {
+    // fprintf(stderr, "here 5: %f %d %d %d %d\n", noise_scale, ndim, noptimization, ipt, npt);
     result[ipt] = log_density_gaussian_conditional(noise_scale,
 						   ndim,
 						   noptimization,
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 02f6984..54b219b 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -36,7 +36,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9){
     X=data$X
     y=data$y
     beta=data$beta
-    result = selectiveInference:::randomizedLassoInf(X, y, lam, level=level)
+    result = selectiveInference:::randomizedLassoInf(X, y, lam, level=level, burnin=2000, nsample=4000)
     true_beta = beta[result$active_set]
     coverage = rep(0, nrow(result$ci))
     for (i in 1:nrow(result$ci)){

From 0f1833fdab5d9db47cae5570b8f73d88e7c56ac5 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 7 Nov 2017 14:31:09 -0800
Subject: [PATCH 372/493] seems to be running now with default arguments

---
 C-software                             |  2 +-
 selectiveInference/R/funs.randomized.R | 14 ++++----------
 tests/randomized/test_instances.R      |  1 +
 3 files changed, 6 insertions(+), 11 deletions(-)

diff --git a/C-software b/C-software
index 225d827..563bf1a 160000
--- a/C-software
+++ b/C-software
@@ -1 +1 @@
-Subproject commit 225d8274ec54eea0ccf14d4ef27297dd699b54e5
+Subproject commit 563bf1aa370b55f8343693224717047f1df0d0c3
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 869b277..55bb0f2 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -34,7 +34,6 @@ randomizedLasso = function(X,
         noise_scale = 0.5 * sd(y) * sqrt(mean_diag)
     }
 
-    print(c(noise_scale, ridge_term))
     noise_type = match.arg(noise_type)
 
     if (noise_scale > 0) {
@@ -222,7 +221,7 @@ importance_weight = function(noise_scale,
                              target_transform,
                              observed_raw) {
 
-    use_C_code = FALSE
+    use_C_code = TRUE
     if (!use_C_code) {
         A = (opt_transform$linear_term %*% opt_sample + 
              target_transform$linear_term %*% target_sample)
@@ -278,7 +277,7 @@ conditional_density = function(noise_scale, lasso_soln) {
       return(-Inf)
     }
 
-    use_C_code = FALSE
+    use_C_code = TRUE
     if (!use_C_code) {
         A = reduced_B %*% as.matrix(opt_state) + reduced_beta_offset
         A = apply(A, 2, function(x) {x + reduced_beta_offset})
@@ -321,15 +320,10 @@ randomizedLassoInf = function(X,
    lasso_soln=conditional_density(noise_scale, lasso_soln)
  } 
     
-  dim = length(lasso_soln$observed_opt_state)
-  print(paste("chain dim", dim))
+  ndim = length(lasso_soln$observed_opt_state)
 
-#  print(lasso_soln)
-
-
-  S = sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), dim), nsample=nsample)
+  S = sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), ndim), nsample=nsample)
   opt_samples = S$samples[(burnin+1):nsample,]
-  print(paste("dim opt samples", toString(dim(opt_samples))))
   
   X_E = X[, active_set]
   X_minusE = X[, inactive_set]
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 54b219b..e3e9816 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -47,6 +47,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9){
     }
     sample_pvalues = c(sample_pvalues, result$pvalues)
     sample_coverage = c(sample_coverage, coverage)
+    print(paste("coverage", mean(sample_coverage)))
   }
   print(paste("coverage", mean(sample_coverage)))
   jpeg('pivots.jpg')

From 8187b536f94e4845f6b73d7a56e1f3eb7d444396 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 7 Nov 2017 14:35:31 -0800
Subject: [PATCH 373/493] forgot one bit of C code to use

---
 selectiveInference/R/funs.randomized.R | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 55bb0f2..2591b09 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -158,12 +158,16 @@ randomizedLasso = function(X,
             return(-Inf)
         }
 
-	A = opt_transform$linear_term %*% opt_state + observed_raw + opt_transform$offset_term
-	D = -apply(A^2, 2, sum) / noise_scale^2
-#        D = log_density_gaussian_conditional_(noise_scale,
-#                                              opt_transform$linear_term,
-#                                              as.matrix(opt_state),
-#                                              observed_raw + opt_transform$offset_term)
+        use_C_code = TRUE
+        if (!use_C_code) {
+            A = opt_transform$linear_term %*% opt_state + observed_raw + opt_transform$offset_term
+            D = -apply(A^2, 2, sum) / noise_scale^2
+        } else {
+            D = log_density_gaussian_conditional_(noise_scale,
+                                                  opt_transform$linear_term,
+                                                  as.matrix(opt_state),
+                                                  observed_raw + opt_transform$offset_term)
+        }
         return(D)
     }
 

From 6dae27c96cdfeef4ca8e7238fc83c4e16dc86eb7 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Tue, 7 Nov 2017 14:36:53 -0800
Subject: [PATCH 374/493] weights running in |E|+1 dimensions

---
 selectiveInference/R/funs.randomized.R | 20 ++++++++++++++++----
 tests/randomized/test_instances.R      |  4 ++--
 2 files changed, 18 insertions(+), 6 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 3d8ddec..df53da5 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -296,16 +296,28 @@ randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term, level
                                                   target_cov[i,i], 
                                                   cov_target_internal[,i],
                                                   internal_transform)
-  
+    
+    target_opt_linear = cbind(target_transform$linear_term, opt_transform$linear_term)
+    reduced_target_opt_linear = chol(t(target_opt_linear) %*% target_opt_linear)
+    target_linear = reduced_target_opt_linear[,1]
+    temp = solve(t(reduced_target_opt_linear)) %*% t(target_opt_linear)
+    target_offset = temp %*% target_transform$offset_term
+    target_transform = list(linear_term = as.matrix(target_linear), offset_term = target_offset)
+    print(dim(reduced_target_opt_linear))
+    opt_linear = reduced_target_opt_linear[,2:ncol(reduced_target_opt_linear)]
+    opt_offset = temp %*% opt_transform$offset_term
+    opt_transform_reduced = list(linear_term = as.matrix(opt_linear), offset_term = opt_offset)
+    
+    raw = target_transform$linear_term * observed_target[i] +target_transform$offset_term
+    
     target_sample = rnorm(nrow(as.matrix(opt_samples))) * sqrt(target_cov[i,i])
-    print(length(target_sample))
     pivot = function(candidate){
       weights = selectiveInference:::importance_weight(noise_scale,
                                                      t(as.matrix(target_sample)) + candidate,
                                                      t(opt_samples),
-                                                     opt_transform,
+                                                     opt_transform_reduced,
                                                      target_transform,
-                                                     observed_raw)
+                                                     raw)
       return(mean((target_sample+candidate<observed_target[i])*weights)/mean(weights))
     }
     rootU = function(candidate){
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index baafb15..4b6df00 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -25,9 +25,9 @@ gaussian_instance = function(n, p, s, sigma=1, rho=0, signal=6, X=NA,
 }
 
 
-collect_results = function(n,p,s, nsim=1, level=0.9){
+collect_results = function(n,p,s, nsim=100, level=0.9){
   rho=0.3
-  lam=2.
+  lam=1.
   sigma=1
   sample_pvalues = c()
   sample_coverage = c()

From e90a8f1f9a7d1955b712f730796ba1fefbc3c738 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 7 Nov 2017 15:31:04 -0800
Subject: [PATCH 375/493] BF: opt_transform linear_term was not cast as matrix
 in conditional_density

---
 selectiveInference/R/funs.randomized.R | 33 ++++++++++++++++----------
 tests/randomized/test_instances.R      |  6 ++---
 2 files changed, 23 insertions(+), 16 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 2591b09..de16951 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -109,7 +109,13 @@ randomizedLasso = function(X,
     L_E = t(X) %*% X[,E]
 
     coef_term = L_E
-    coef_term = coef_term %*% diag(c(rep(1, sum(unpenalized)), sign_soln[active]))  # coefficients are non-negative
+    signs_ = c(rep(1, sum(unpenalized)), sign_soln[active])
+    if (length(signs_) == 1) {
+        coef_term = coef_term * signs_
+    } else {
+        coef_term = coef_term %*% diag(signs_)  # scaligns are non-negative
+    }
+
     coef_term[active,] = coef_term[active,] + ridge_term * diag(rep(1, sum(active)))  # ridge term
 
     subgrad_term = matrix(0, p, sum(inactive)) # for subgrad
@@ -151,10 +157,10 @@ randomizedLasso = function(X,
 
     # XXX only for Gaussian so far
 
-    log_optimization_density = function(opt_state 
-                                        ) {
+    log_optimization_density = function(opt_state) {
+
         if ((sum(abs(opt_state[(inactive_start + 1):p]) > inactive_lam) > 0) ||
-            (sum(opt_state[(active_start+1):inactive_start] < 0) > 0)) {
+            (sum(opt_state[(active_start + 1):inactive_start] < 0) > 0)) {
             return(-Inf)
         }
 
@@ -188,7 +194,7 @@ randomizedLasso = function(X,
 sample_opt_variables = function(randomizedLASSO_obj, jump_scale, nsample=10000) {
       return(MCMC(randomizedLASSO_obj$log_optimization_density, 
                   nsample,
-		  randomizedLASSO_obj$observed_opt_state,
+                  randomizedLASSO_obj$observed_opt_state,
                   acc.rate=0.2,
                   scale=jump_scale))
 }
@@ -232,6 +238,7 @@ importance_weight = function(noise_scale,
         A = apply(A, 2, function(x) {return(x + target_transform$offset_term + opt_transform$offset_term)})
         log_num = -apply(A^2, 2, sum) / noise_scale^2
     } else {
+
         log_num = log_density_gaussian_(noise_scale,
                                         target_transform$linear_term,
                                         as.matrix(target_sample),
@@ -264,10 +271,11 @@ conditional_density = function(noise_scale, lasso_soln) {
   observed_opt_state = lasso_soln$observed_opt_state
   
   nactive = length(active_set)
-  B = opt_linear[,1:nactive]
+  B = opt_linear[,1:nactive,drop=FALSE]
   beta_offset = opt_offset
-  p=length(observed_opt_state)
-  if (nactive<p){
+  p = length(observed_opt_state)
+
+  if (nactive < p) {
     beta_offset = beta_offset+(opt_linear[,(nactive+1):p] %*% observed_opt_state[(nactive+1):p])
   }
   opt_transform = list(linear_term=B, 
@@ -313,11 +321,12 @@ randomizedLassoInf = function(X,
 
   n = nrow(X)
   p = ncol(X)
+
   lasso_soln = randomizedLasso(X, y, lam, noise_scale=noise_scale, ridge_term=ridge_term)
   active_set = lasso_soln$active_set
   inactive_set = lasso_soln$inactive_set
   nactive = length(active_set)
-  
+
   noise_scale = lasso_soln$noise_scale # set to default value in randomizedLasso
 
  if (condition_subgrad==TRUE){
@@ -327,7 +336,7 @@ randomizedLassoInf = function(X,
   ndim = length(lasso_soln$observed_opt_state)
 
   S = sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), ndim), nsample=nsample)
-  opt_samples = S$samples[(burnin+1):nsample,]
+  opt_samples = as.matrix(S$samples[(burnin+1):nsample,,drop=FALSE])
   
   X_E = X[, active_set]
   X_minusE = X[, inactive_set]
@@ -338,7 +347,7 @@ randomizedLassoInf = function(X,
         lm_y = lm(y ~ X_E - 1)
         sigma = sqrt(sum(resid(lm_y)^2) / lm_y$df.resid)
   }        
-  print(c(sigma, 'sigma'))
+
   target_cov = solve(t(X_E) %*% X_E)*sigma^2
   cov_target_internal = rbind(target_cov, matrix(0, nrow=p-nactive, ncol=nactive))
   observed_target = solve(t(X_E) %*% X_E) %*% t(X_E) %*% y
@@ -378,13 +387,11 @@ randomizedLassoInf = function(X,
     if (rootU(line_min)*rootU(line_max)<0){
       ci[i,2] = uniroot(rootU, c(line_min, line_max))$root+observed_target[i]
     } else{
-      print("non inv u")
       ci[i,2]=line_max
     }
     if (rootL(line_min)*rootL(line_max)<0){
       ci[i,1] = uniroot(rootL, c(line_min, line_max))$root+observed_target[i]
     } else{
-      print("non inv u")
       ci[i,1] = line_min
     }
   }
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index e3e9816..5fab1e2 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -25,9 +25,9 @@ gaussian_instance = function(n, p, s, sigma=1, rho=0, signal=6, X=NA,
 }
 
 
-collect_results = function(n,p,s, nsim=100, level=0.9){
+collect_results = function(n,p,s, nsim=100, level=0.9, condition_subgrad=TRUE){
   rho=0.3
-  lam=1.
+  lam=1.2
   sigma=1
   sample_pvalues = c()
   sample_coverage = c()
@@ -36,7 +36,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9){
     X=data$X
     y=data$y
     beta=data$beta
-    result = selectiveInference:::randomizedLassoInf(X, y, lam, level=level, burnin=2000, nsample=4000)
+    result = selectiveInference:::randomizedLassoInf(X, y, lam, level=level, burnin=2000, nsample=4000, condition_subgrad=condition_subgrad)
     true_beta = beta[result$active_set]
     coverage = rep(0, nrow(result$ci))
     for (i in 1:nrow(result$ci)){

From 2b49b94cf3febbeb49932009cdc179aefa21c601 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Tue, 7 Nov 2017 16:00:25 -0800
Subject: [PATCH 376/493] working instance

---
 selectiveInference/R/funs.randomized.R | 16 ++++++++++------
 tests/randomized/test_instances.R      |  4 ++--
 2 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index df53da5..99da66d 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -95,6 +95,7 @@ randomizedLASSO = function(X,
     L_E = t(X) %*% X[,E]
 
     coef_term = L_E
+    print(active_set)
     coef_term = coef_term %*% diag(c(rep(1, sum(unpenalized)), sign_soln[active]))  # coefficients are non-negative
     coef_term[active,] = coef_term[active,] + ridge_term * diag(rep(1, sum(active)))  # ridge term
 
@@ -274,8 +275,9 @@ randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term, level
     
   dim = length(lasso_soln$observed_opt_state)
   print(paste("chain dim", dim))
-  S = selectiveInference:::sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), dim), nsample=10000)
-  opt_samples = S$samples[2001:10000,]
+  nsample=4000
+  S = selectiveInference:::sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), dim), nsample=nsample)
+  opt_samples = S$samples[2001:nsample,]
   print(paste("dim opt samples", toString(dim(opt_samples))))
   
   X_E = X[, active_set]
@@ -290,6 +292,7 @@ randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term, level
   
   pvalues = rep(0, nactive)
   ci = matrix(0, nactive, 2)
+  
   for (i in 1:nactive){
     target_transform = selectiveInference:::linear_decomposition(observed_target[i], 
                                                   observed_internal, 
@@ -303,7 +306,6 @@ randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term, level
     temp = solve(t(reduced_target_opt_linear)) %*% t(target_opt_linear)
     target_offset = temp %*% target_transform$offset_term
     target_transform = list(linear_term = as.matrix(target_linear), offset_term = target_offset)
-    print(dim(reduced_target_opt_linear))
     opt_linear = reduced_target_opt_linear[,2:ncol(reduced_target_opt_linear)]
     opt_offset = temp %*% opt_transform$offset_term
     opt_transform_reduced = list(linear_term = as.matrix(opt_linear), offset_term = opt_offset)
@@ -324,11 +326,13 @@ randomized_inference = function(X, y, sigma, lam, noise_scale, ridge_term, level
       return (pivot(observed_target[i]+candidate)-(1-level)/2)
     }
     rootL = function(candidate){
-      return (pivot(observed_target[i]+candidate)-(1+level)/2)
+      return(pivot(observed_target[i]+candidate)-(1+level)/2)
     }
     pvalues[i] = pivot(0)
-    line_min = -20*sd(target_sample)
-    line_max = 20*sd(target_sample)
+    line_min = -10*sd(target_sample)
+    line_max = 10*sd(target_sample)
+    print(rootU(line_min))
+    print(rootU(line_max))
     if (rootU(line_min)*rootU(line_max)<0){
       ci[i,2] = uniroot(rootU, c(line_min, line_max))$root+observed_target[i]
     } else{
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 4b6df00..8c353c9 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -25,9 +25,9 @@ gaussian_instance = function(n, p, s, sigma=1, rho=0, signal=6, X=NA,
 }
 
 
-collect_results = function(n,p,s, nsim=100, level=0.9){
+collect_results = function(n,p,s, nsim=20, level=0.9){
   rho=0.3
-  lam=1.
+  lam=1.2
   sigma=1
   sample_pvalues = c()
   sample_coverage = c()

From 571cf823d4da8fcb099984b92af79e07d4b3beae Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 7 Nov 2017 16:16:10 -0800
Subject: [PATCH 377/493] export of randomized and man page

---
 selectiveInference/NAMESPACE                 |   3 +-
 selectiveInference/man/fixedLassoInf.Rd      |   2 +-
 selectiveInference/man/randomizedLassoInf.Rd | 159 +++++++++++++++++++
 3 files changed, 162 insertions(+), 2 deletions(-)
 create mode 100644 selectiveInference/man/randomizedLassoInf.Rd

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index e8f36e0..da23e38 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -14,7 +14,8 @@ export(lar,fs,
        TG.pvalue, 
        TG.limits, 
        TG.interval,
-       debiasingMatrix
+       debiasingMatrix,
+       randomizedLassoInf
     )
 
 S3method("coef", "lar")
diff --git a/selectiveInference/man/fixedLassoInf.Rd b/selectiveInference/man/fixedLassoInf.Rd
index 445e72a..ac7ca21 100644
--- a/selectiveInference/man/fixedLassoInf.Rd
+++ b/selectiveInference/man/fixedLassoInf.Rd
@@ -113,7 +113,7 @@ is not allowed.
 
 Note that the coefficients and standard errors reported are unregularized.
 Eg for the Gaussian, they are the usual least squares estimates and standard errors
-for the model fit to the actice set from the lasso.
+for the model fit to the active set from the lasso.
 }
 \value{  
 \item{type}{Type of coefficients tested (partial or full)}
diff --git a/selectiveInference/man/randomizedLassoInf.Rd b/selectiveInference/man/randomizedLassoInf.Rd
new file mode 100644
index 0000000..35d0dc4
--- /dev/null
+++ b/selectiveInference/man/randomizedLassoInf.Rd
@@ -0,0 +1,159 @@
+\name{randomizedLassoInf}
+\alias{randomizedLassoInf}
+
+\title{
+Inference for the randomized lasso, with a fixed lambda 
+}
+\description{
+Compute p-values and confidence intervals based on selecting
+an active set with the randomized lasso, at a 
+fixed value of the tuning parameter lambda and using Gaussian
+randomization.
+}
+\usage{
+randomizedLassoInf(X, 
+                   y, 
+                   lam, 
+                   sigma=NULL, 
+                   noise_scale=NULL, 
+                   ridge_term=NULL, 
+                   condition_subgrad=TRUE, 
+                   level=0.9,
+                   nsample=10000,
+                   burnin=2000,
+                   max_iter=100,       
+                   kkt_tol=1.e-4,      
+                   parameter_tol=1.e-8,
+                   objective_tol=1.e-8,
+                   objective_stop=FALSE,
+                   kkt_stop=TRUE,
+                   param_stop=TRUE)
+}
+\arguments{
+  \item{X}{
+Matrix of predictors (n by p); 
+}
+  \item{y}{
+Vector of outcomes (length n)
+}
+  \item{lam}{
+Value of lambda used to compute beta. See the above warning
+ Be careful! This function uses the "standard" lasso objective
+  \deqn{
+    1/2 \|y - x \beta\|_2^2 + \lambda \|\beta\|_1.
+  }
+ In contrast, glmnet multiplies the first term by a factor of 1/n.
+ So after running glmnet, to extract the beta corresponding to a value lambda, 
+ you need to use \code{beta = coef(obj, s=lambda/n)[-1]},
+ where obj is the object returned by glmnet (and [-1] removes the intercept,
+ which glmnet always puts in the first component)
+} 
+\item{sigma}{
+Estimate of error standard deviation. If NULL (default), this is estimated 
+using the mean squared residual of the full least squares based on 
+selected active set.
+}
+\item{noise_scale}{
+Scale of Gaussian noise added to objective. Default is 
+0.5 * sd(y) times the sqrt of the mean of the trace of X^TX.
+}
+\item{ridge_term}{
+A small "elastic net" or ridge penalty is added to ensure
+the randomized problem has a solution. 
+0.5 * sd(y) times the sqrt of the mean of the trace of X^TX divided by
+sqrt(n).
+}
+\item{condition_subgrad}{
+In forming selective confidence intervals and p-values should we condition
+on the inactive coordinates of the subgradient as well?
+Default is TRUE. 
+}
+\item{level}
+{
+Level for confidence intervals.
+}
+\item{nsample}
+{
+Number of samples of optimization variables to sample.
+}
+\item{burnin}
+{
+How many samples of optimization variable to discard (should be less than nsample).
+}
+\item{max_iter}
+{
+How many rounds of updates used of coordinate descent in solving randomized
+LASSO.
+}
+\item{kkt_tol}{
+Tolerance for checking convergence based on KKT conditions.
+}
+\item{parameter_tol}{
+Tolerance for checking convergence based on convergence
+of parameters.
+}
+\item{objective_tol}{
+Tolerance for checking convergence based on convergence
+of objective value.
+}
+\item{kkt_stop}{
+Should we use KKT check to determine when to stop?
+}
+\item{parameter_tol}{
+Should we use convergence of parameters to determine when to stop?
+}
+\item{objective_tol}{
+Should we use convergence of objective value to determine when to stop?
+}
+}
+
+\details{
+This function computes selective p-values and confidence intervals for a
+randomized version of the lasso,
+given a fixed value of the tuning parameter lambda. 
+
+}
+\value{  
+\item{type}{Type of coefficients tested (partial or full)}
+\item{lambda}{Value of tuning parameter lambda used}
+\item{pv}{One-sided P-values for active variables, uses the fact we have conditioned on the sign.}
+\item{ci}{Confidence intervals}
+\item{tailarea}{Realized tail areas (lower and upper) for each confidence interval}
+\item{vlo}{Lower truncation limits for statistics}
+\item{vup}{Upper truncation limits for statistics}
+\item{vmat}{Linear contrasts that define the observed statistics}
+\item{y}{Vector of outcomes}
+\item{vars}{Variables in active set}
+\item{sign}{Signs of active coefficients}
+\item{alpha}{Desired coverage (alpha/2 in each tail)}
+\item{sigma}{Value of error standard deviation (sigma) used}
+\item{call}{The call to lassoInf}
+}
+
+\references{
+Xiaoying Tian, and Jonathan Taylor (2015).
+Selective inference with a randomized response. arxiv.org:1507.06739
+
+Xiaoying Tian, Snigdha Panigrahi, Jelena Markovic, Nan Bi and Jonathan Taylor (2016).
+Selective inference after solving a convex problem. 
+arxiv:1609.05609
+
+}
+\author{Jelena Markovic, Jonathan Taylor}
+
+\examples{
+set.seed(43)
+n = 50
+p = 10
+sigma = 1
+
+x = matrix(rnorm(n*p),n,p)
+x = scale(x,TRUE,TRUE)
+
+beta = c(3,2,rep(0,p-2))
+y = x\%*\%beta + sigma*rnorm(n)
+
+result = randomizedLassoInf(X, y, lam)
+
+}
+ 

From 9adbe9b8d7ff0db6b4c3e44bbab3d298c7be1a45 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 7 Nov 2017 16:16:45 -0800
Subject: [PATCH 378/493] adding resid to namespace

---
 selectiveInference/NAMESPACE | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index da23e38..11a834e 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -41,7 +41,7 @@ import(intervals)
 import(survival)
 importFrom("graphics", abline, axis, matplot)
 importFrom("stats", dnorm, lsfit, pexp, pnorm, predict, 
-           qnorm, rnorm, sd, uniroot, dchisq, model.matrix, pchisq)
+           qnorm, rnorm, sd, uniroot, dchisq, model.matrix, pchisq, resid)
 importFrom("stats", "coef", "df", "lm", "pf")
 importFrom("stats", "glm", "residuals", "vcov")
 importFrom("stats", "rbinom", "rexp")

From eda1482654c33abf23ac0cc6218e4a69266b9ea4 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 7 Nov 2017 16:22:11 -0800
Subject: [PATCH 379/493] fixing to make check pass

---
 selectiveInference/R/funs.randomized.R       | 24 +++++++++++++++++--
 selectiveInference/man/randomizedLassoInf.Rd | 25 +++++++++-----------
 2 files changed, 33 insertions(+), 16 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index de16951..357941a 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -317,12 +317,32 @@ randomizedLassoInf = function(X,
                               condition_subgrad=TRUE, 
                               level=0.9,
 			      nsample=10000,
-			      burnin=2000) {
+			      burnin=2000,
+                              max_iter=100,        # how many iterations for each optimization problem
+                              kkt_tol=1.e-4,       # tolerance for the KKT conditions
+                              parameter_tol=1.e-8, # tolerance for relative convergence of parameter
+                              objective_tol=1.e-8, # tolerance for relative decrease in objective
+                              objective_stop=FALSE,
+                              kkt_stop=TRUE,
+                              param_stop=TRUE)
+ {
 
   n = nrow(X)
   p = ncol(X)
 
-  lasso_soln = randomizedLasso(X, y, lam, noise_scale=noise_scale, ridge_term=ridge_term)
+  lasso_soln = randomizedLasso(X, 
+                               y, 
+                               lam, 
+                               noise_scale=noise_scale, 
+                               ridge_term=ridge_term,
+                               max_iter=max_iter,
+                               kkt_tol=kkt_tol,       
+                               parameter_tol=parameter_tol,
+                               objective_tol=objective_tol,
+                               objective_stop=objective_stop,
+                               kkt_stop=kkt_stop,
+                               param_stop=param_stop)
+
   active_set = lasso_soln$active_set
   inactive_set = lasso_soln$inactive_set
   nactive = length(active_set)
diff --git a/selectiveInference/man/randomizedLassoInf.Rd b/selectiveInference/man/randomizedLassoInf.Rd
index 35d0dc4..15e70f0 100644
--- a/selectiveInference/man/randomizedLassoInf.Rd
+++ b/selectiveInference/man/randomizedLassoInf.Rd
@@ -68,20 +68,16 @@ In forming selective confidence intervals and p-values should we condition
 on the inactive coordinates of the subgradient as well?
 Default is TRUE. 
 }
-\item{level}
-{
+\item{level}{
 Level for confidence intervals.
 }
-\item{nsample}
-{
+\item{nsample}{
 Number of samples of optimization variables to sample.
 }
-\item{burnin}
-{
+\item{burnin}{
 How many samples of optimization variable to discard (should be less than nsample).
 }
-\item{max_iter}
-{
+\item{max_iter}{
 How many rounds of updates used of coordinate descent in solving randomized
 LASSO.
 }
@@ -99,10 +95,10 @@ of objective value.
 \item{kkt_stop}{
 Should we use KKT check to determine when to stop?
 }
-\item{parameter_tol}{
+\item{parameter_stop}{
 Should we use convergence of parameters to determine when to stop?
 }
-\item{objective_tol}{
+\item{objective_stop}{
 Should we use convergence of objective value to determine when to stop?
 }
 }
@@ -145,13 +141,14 @@ arxiv:1609.05609
 set.seed(43)
 n = 50
 p = 10
-sigma = 1
+sigma = 0.2
+lam = 0.5
 
-x = matrix(rnorm(n*p),n,p)
-x = scale(x,TRUE,TRUE)
+X = matrix(rnorm(n*p), n, p)
+X = scale(X, TRUE, TRUE)
 
 beta = c(3,2,rep(0,p-2))
-y = x\%*\%beta + sigma*rnorm(n)
+y = X\%*\%beta + sigma*rnorm(n)
 
 result = randomizedLassoInf(X, y, lam)
 

From 3ca443199d474f740a14f2c252cd181c771dd2e7 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 7 Nov 2017 16:23:34 -0800
Subject: [PATCH 380/493] param_stop

---
 selectiveInference/R/funs.randomized.R       | 8 ++++----
 selectiveInference/man/randomizedLassoInf.Rd | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 357941a..ae8a463 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -15,7 +15,7 @@ randomizedLasso = function(X,
                            objective_tol=1.e-8, # tolerance for relative decrease in objective
                            objective_stop=FALSE,
                            kkt_stop=TRUE,
-                           param_stop=TRUE)
+                           parameter_stop=TRUE)
 {
 
     n = nrow(X); p = ncol(X)
@@ -80,7 +80,7 @@ randomizedLasso = function(X,
                            p,
 		           objective_stop,     # objective_stop
 			   kkt_stop,           # kkt_stop
-			   param_stop)         # param_stop
+			   parameter_stop)         # param_stop
 
     sign_soln = sign(result$soln)
 
@@ -324,7 +324,7 @@ randomizedLassoInf = function(X,
                               objective_tol=1.e-8, # tolerance for relative decrease in objective
                               objective_stop=FALSE,
                               kkt_stop=TRUE,
-                              param_stop=TRUE)
+                              parameter_stop=TRUE)
  {
 
   n = nrow(X)
@@ -341,7 +341,7 @@ randomizedLassoInf = function(X,
                                objective_tol=objective_tol,
                                objective_stop=objective_stop,
                                kkt_stop=kkt_stop,
-                               param_stop=param_stop)
+                               parameter_stop=parameter_stop)
 
   active_set = lasso_soln$active_set
   inactive_set = lasso_soln$inactive_set
diff --git a/selectiveInference/man/randomizedLassoInf.Rd b/selectiveInference/man/randomizedLassoInf.Rd
index 15e70f0..7ce8fb7 100644
--- a/selectiveInference/man/randomizedLassoInf.Rd
+++ b/selectiveInference/man/randomizedLassoInf.Rd
@@ -145,7 +145,7 @@ sigma = 0.2
 lam = 0.5
 
 X = matrix(rnorm(n*p), n, p)
-X = scale(X, TRUE, TRUE)
+X = scale(X, TRUE, TRUE) / sqrt(n-1)
 
 beta = c(3,2,rep(0,p-2))
 y = X\%*\%beta + sigma*rnorm(n)

From 690f0ea59ea4eb1ddf52fa0415584def81af1e14 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 7 Nov 2017 18:19:35 -0800
Subject: [PATCH 381/493] fixing function signature

---
 selectiveInference/man/randomizedLassoInf.Rd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/man/randomizedLassoInf.Rd b/selectiveInference/man/randomizedLassoInf.Rd
index 7ce8fb7..18b9611 100644
--- a/selectiveInference/man/randomizedLassoInf.Rd
+++ b/selectiveInference/man/randomizedLassoInf.Rd
@@ -27,7 +27,7 @@ randomizedLassoInf(X,
                    objective_tol=1.e-8,
                    objective_stop=FALSE,
                    kkt_stop=TRUE,
-                   param_stop=TRUE)
+                   parameter_stop=TRUE)
 }
 \arguments{
   \item{X}{

From 6c1a123fdeee79427b7638939f24111cead6565e Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Tue, 7 Nov 2017 22:59:25 -0800
Subject: [PATCH 382/493] added mean and cov of a gaussian

---
 selectiveInference/R/RcppExports.R     | 35 ++++++++++++++++++++++++++
 selectiveInference/R/funs.randomized.R | 10 +++++++-
 2 files changed, 44 insertions(+), 1 deletion(-)
 create mode 100644 selectiveInference/R/RcppExports.R

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
new file mode 100644
index 0000000..e927a3a
--- /dev/null
+++ b/selectiveInference/R/RcppExports.R
@@ -0,0 +1,35 @@
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
+    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
+}
+
+solve_QP_wide <- function(X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
+    .Call('selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
+}
+
+update1_ <- function(Q2, w, m, k) {
+    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
+}
+
+downdate1_ <- function(Q1, R, j0, m, n) {
+    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
+}
+
+log_density_gaussian_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_gaussian_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
+}
+
+log_density_gaussian_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_gaussian_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
+}
+
+log_density_laplace_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_laplace_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
+}
+
+log_density_laplace_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_laplace_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
+}
+
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 6914fd7..192f648 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -239,7 +239,6 @@ importance_weight = function(noise_scale,
         A = apply(A, 2, function(x) {return(x + target_transform$offset_term + opt_transform$offset_term)})
         log_num = -apply(A^2, 2, sum) / noise_scale^2
     } else {
-
         log_num = log_density_gaussian_(noise_scale,
                                         target_transform$linear_term,
                                         as.matrix(target_sample),
@@ -262,6 +261,15 @@ importance_weight = function(noise_scale,
     W = W - max(W)
     return(exp(W))
 }
+
+get_mean_cov = function(noise_scale, linear_term, offset_term){
+    temp = solve(t(linear_term) %*% linear_term)
+    cov = noise_scale^2*temp
+    mean = temp %*% linear_term %*% offset_term
+    return(list(mean=mean, cov=cov))
+}
+
+
                              
 conditional_density = function(noise_scale, lasso_soln) {
   

From d2a3b9bf075fb76ace427831416e51cf8d322bb8 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Tue, 7 Nov 2017 23:04:22 -0800
Subject: [PATCH 383/493] transpose

---
 selectiveInference/R/funs.randomized.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 192f648..757b555 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -265,7 +265,7 @@ importance_weight = function(noise_scale,
 get_mean_cov = function(noise_scale, linear_term, offset_term){
     temp = solve(t(linear_term) %*% linear_term)
     cov = noise_scale^2*temp
-    mean = temp %*% linear_term %*% offset_term
+    mean = temp %*% t(linear_term) %*% offset_term
     return(list(mean=mean, cov=cov))
 }
 

From e2efbdcbe5b8902113ce34e9d9847ebd7c72077e Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Wed, 8 Nov 2017 18:15:24 -0800
Subject: [PATCH 384/493] working! bugs fixed. new sampler

---
 selectiveInference/R/funs.randomized.R | 83 +++++++++++++++-------
 selectiveInference/R/sampler.R         | 96 ++++++++++++++++++++++++++
 tests/randomized/test_instances.R      | 53 ++++++++++++--
 tests/randomized/test_sampler.R        | 16 +++++
 4 files changed, 217 insertions(+), 31 deletions(-)
 create mode 100644 selectiveInference/R/sampler.R
 create mode 100644 tests/randomized/test_sampler.R

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 757b555..da64f99 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -33,11 +33,15 @@ randomizedLasso = function(X,
     if (is.null(noise_scale)) {
         noise_scale = 0.5 * sd(y) * sqrt(mean_diag)
     }
-
+    
+    print(paste("ridge term", ridge_term))
+    print(paste("noise scale", noise_scale))
+    
     noise_type = match.arg(noise_type)
 
     if (noise_scale > 0) {
         if (noise_type == 'gaussian') {
+            set.seed(1)
             perturb_ = rnorm(p) * noise_scale
         }
         else if (noise_type == 'laplace') {
@@ -65,8 +69,8 @@ randomizedLasso = function(X,
     nactive = as.integer(0)
 
     result = solve_QP_wide(X,                  # design matrix
-    	                   lam / n,            # vector of Lagrange multipliers
-		           ridge_term / n,     # ridge_term 
+    	                     lam / n,            # vector of Lagrange multipliers
+		                       ridge_term / n,     # ridge_term 
                            max_iter, 
                            soln, 
                            linear_func, 
@@ -76,12 +80,12 @@ randomizedLasso = function(X,
                            nactive, 
                            kkt_tol, 
                            objective_tol, 
-			   parameter_tol,
+			                     parameter_tol,
                            p,
-		           objective_stop,     # objective_stop
-			   kkt_stop,           # kkt_stop
-			   parameter_stop)         # param_stop
-
+		                       objective_stop,     # objective_stop
+			                     kkt_stop,           # kkt_stop
+			                     parameter_stop)         # param_stop
+    
     sign_soln = sign(result$soln)
 
     unpenalized = lam == 0
@@ -96,7 +100,11 @@ randomizedLasso = function(X,
 
     observed_scalings = abs(result$soln)[active]
     observed_unpen = result$soln[unpenalized]
-    observed_subgrad = result$gradient[inactive]
+    observed_subgrad = -n*result$gradient[inactive]
+    
+    if (length(which(abs(observed_subgrad)>lam[1]))){
+      print("subgradient eq not satisfied")
+    }
 
     observed_opt_state = c(observed_unpen, observed_scalings, observed_subgrad)
 
@@ -111,14 +119,15 @@ randomizedLasso = function(X,
     coef_term = L_E
 
     signs_ = c(rep(1, sum(unpenalized)), sign_soln[active])
+    
+    coef_term[active,] = coef_term[active,] + ridge_term * diag(rep(1, sum(active)))  # ridge term
+  
     if (length(signs_) == 1) {
-        coef_term = coef_term * signs_
+      coef_term = coef_term * signs_
     } else {
-        coef_term = coef_term %*% diag(signs_)  # scaligns are non-negative
+      coef_term = coef_term %*% diag(signs_)  # scaligns are non-negative
     }
-
-    coef_term[active,] = coef_term[active,] + ridge_term * diag(rep(1, sum(active)))  # ridge term
-
+    
     subgrad_term = matrix(0, p, sum(inactive)) # for subgrad
     for (i in 1:sum(inactive)) {
         subgrad_term[inactive_set[i], i] = 1
@@ -155,7 +164,8 @@ randomizedLasso = function(X,
     inactive_lam = lam[inactive_set]
     inactive_start = sum(unpenalized) + sum(active)
     active_start = sum(unpenalized)
-
+    
+    
     # XXX only for Gaussian so far
 
     log_optimization_density = function(opt_state) {
@@ -185,9 +195,11 @@ randomizedLasso = function(X,
                 optimization_transform = opt_transform,
                 internal_transform = internal_transform,
                 log_optimization_density = log_optimization_density,
-		observed_opt_state = observed_opt_state,
+		            observed_opt_state = observed_opt_state,
                 observed_raw = observed_raw,
-		noise_scale = noise_scale
+		            noise_scale = noise_scale,
+		            soln = result$soln,
+		            perturb = perturb_
                 ))
 
 }
@@ -314,19 +326,22 @@ conditional_density = function(noise_scale, lasso_soln) {
   lasso_soln$log_optimization_density = log_condl_optimization_density
   lasso_soln$observed_opt_state = observed_opt_state[1:nactive]
   lasso_soln$optimization_transform = opt_transform
-  return(lasso_soln)
+  reduced_opt_transform =list(linear_term = reduced_B, offset_term = reduced_beta_offset)
+  return(list(lasso_soln=lasso_soln, 
+              reduced_opt_transform = reduced_opt_transform))
 }
 
 randomizedLassoInf = function(X, 
                               y, 
                               lam, 
+                              sampler="A",
                               sigma=NULL, 
                               noise_scale=NULL, 
                               ridge_term=NULL, 
                               condition_subgrad=TRUE, 
                               level=0.9,
-			      nsample=10000,
-			      burnin=2000,
+			                        nsample=10000,
+			                        burnin=2000,
                               max_iter=100,        # how many iterations for each optimization problem
                               kkt_tol=1.e-4,       # tolerance for the KKT conditions
                               parameter_tol=1.e-8, # tolerance for relative convergence of parameter
@@ -353,22 +368,35 @@ randomizedLassoInf = function(X,
                                parameter_stop=parameter_stop)
 
   active_set = lasso_soln$active_set
-  if (length(active_set)==0){
+  nactive = length(active_set)
+  print(paste("nactive", nactive))
+  if (nactive==0){
     return (list(active_set=active_set, pvalues=c(), ci=c()))
   }
   inactive_set = lasso_soln$inactive_set
-  nactive = length(active_set)
+  
 
   noise_scale = lasso_soln$noise_scale # set to default value in randomizedLasso
 
  if (condition_subgrad==TRUE){
-   lasso_soln=conditional_density(noise_scale, lasso_soln)
+   condl_lasso=conditional_density(noise_scale, lasso_soln)
+   lasso_soln = condl_lasso$lasso_soln
+   reduced_opt_transform = condl_lasso$reduced_opt_transform
  } 
     
   ndim = length(lasso_soln$observed_opt_state)
-
-  S = sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), ndim), nsample=nsample)
-  opt_samples = as.matrix(S$samples[(burnin+1):nsample,,drop=FALSE])
+  
+  if (sampler =="R"){
+    S = sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), ndim), nsample=nsample)
+    opt_samples = as.matrix(S$samples[(burnin+1):nsample,,drop=FALSE])
+  } else if (sampler == "A"){
+    opt_samples = gaussian_sampler(noise_scale, 
+                                 lasso_soln$observed_opt_state, 
+                                 reduced_opt_transform$linear_term,
+                                 reduced_opt_transform$offset_term,
+                                 nsamples=nsample)
+    opt_sample = opt_samples[(burnin+1):nsample,]
+  }
   
   X_E = X[, active_set]
   X_minusE = X[, inactive_set]
@@ -458,3 +486,6 @@ randomizedLassoInf = function(X,
   }
   return(list(active_set=active_set, pvalues=pvalues, ci=ci))
 }
+
+
+
diff --git a/selectiveInference/R/sampler.R b/selectiveInference/R/sampler.R
new file mode 100644
index 0000000..48efccc
--- /dev/null
+++ b/selectiveInference/R/sampler.R
@@ -0,0 +1,96 @@
+
+log_concave_sampler = function(negative_log_density, 
+                               grad_negative_log_density, 
+                               constraints,
+                               observed,
+                               nsamples){
+  #print(constraints)
+  constraints = as.matrix(constraints)
+  dim = nrow(constraints)
+  
+  get_poisson_process = function(state){
+    pos = as.matrix(state$pos)
+    velocity = as.matrix(state$velocity)
+    neg_velocity = velocity<0
+    pos_velocity = velocity>0
+    tau_min = 0
+    tau_max = 10
+    if (sum(neg_velocity)>0){
+      R = (-constraints[neg_velocity,1]+pos[neg_velocity])/(-velocity[neg_velocity])
+      tau_max = min(tau_max, min(R))
+      L = (-constraints[neg_velocity,2]+pos[neg_velocity])/(-velocity[neg_velocity])
+      tau_min = max(tau_min, max(L))
+    }
+    if (sum(pos_velocity)>0){
+      R = (constraints[pos_velocity,2]-pos[pos_velocity])/velocity[pos_velocity]
+      tau_max = min(tau_max, min(R))
+      L = (constraints[pos_velocity,1]-pos[pos_velocity])/velocity[pos_velocity]
+      tau_min = max(tau_min, max(L))
+    }
+    
+    f=function(t){as.numeric(t(velocity) %*% grad_negative_log_density(pos+velocity*t))}
+    tau_star = tau_max
+    if (f(tau_min)*f(tau_max)<0){
+      tau_star = uniroot(f, c(tau_min, tau_max))$root
+    } else{
+      if (negative_log_density(pos+velocity*tau_min)<negative_log_density(pos+velocity*tau_max)){
+        tau_star = tau_min
+      }
+    }
+    
+    tau_min = max(tau_min, tau_star)
+    
+    RHS = negative_log_density(pos+velocity*tau_star)+rexp(1)
+    g = function(t){negative_log_density(pos+velocity*t)-RHS}
+    if (g(tau_min)*g(tau_max)<0){
+      tau = uniroot(g, c(tau_min, tau_max))$root
+    } else{
+      tau = tau_max
+    }
+    return (tau)
+  }
+  
+  update_velocity = function(){
+    Z=rnorm(dim)
+    return(Z/sqrt(t(Z)%*%Z))
+  }
+  
+  compute_next = function(state){
+    bounce_time = get_poisson_process(state)/2
+    #print(paste("bounce time", bounce_time))
+    next_pos = state$pos+state$velocity*bounce_time
+    next_velocity=update_velocity()
+    return(list(pos=next_pos, velocity=next_velocity))
+  }
+  
+  state = list(pos=observed, velocity = update_velocity())
+  samples = matrix(0, nrow = nsamples, ncol = dim)
+  for (i in 1:nsamples){
+    #print(paste("pos", toString(state$pos)))
+    #print(paste("velocity", toString(state$velocity)))
+    samples[i,]=state$pos
+    state = compute_next(state)
+  }
+  return (samples)
+}
+
+gaussian_sampler = function(noise_scale, observed, linear_term, offset_term, nsamples){
+  
+  negative_log_density = function(x) {
+    recon = linear_term %*% x+offset_term
+    return(as.numeric(t(recon)%*%recon/(2*noise_scale^2)))
+  }
+  grad_negative_log_density=function(x){
+    recon = linear_term %*% x+offset_term
+    return(t(linear_term)%*% recon/(noise_scale^2))
+  }
+  dim = length(observed)
+  constraints = matrix(0,dim,2)
+  constraints[,2] = Inf
+  
+  return(log_concave_sampler(negative_log_density, 
+                             grad_negative_log_density,
+                             constraints,
+                             observed,
+                             nsamples))
+}
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 29f1cad..9e2e0d2 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -24,10 +24,46 @@ gaussian_instance = function(n, p, s, sigma=1, rho=0, signal=6, X=NA,
   return(result)
 }
 
+test_randomized_lasso = function(n=100,p=200,s=0){
+  set.seed(1)
+  data = gaussian_instance(n=n,p=p,s=s, rho=0.3, sigma=3)
+  X=data$X
+  y=data$y
+  lam = 2.
+  noise_scale = 0.5
+  ridge_term = 1./sqrt(n)
+  result = selectiveInference:::randomizedLasso(X,y,lam, noise_scale, ridge_term)
+  print(result$soln)
+  print(length(which(result$soln!=0)))
+  print(result$observed_opt_state) # compared with python code
+}
+
+test_KKT=function(){
+  set.seed(1)
+  n=200
+  p=100
+  data = gaussian_instance(n=n,p=p,s=0, rho=0.3, sigma=3)
+  X=data$X
+  y=data$y
+  lam = 2.
+  noise_scale = 0.5
+  ridge_term = 1./sqrt(n)
+  result = selectiveInference:::randomizedLasso(X,y,lam, noise_scale, ridge_term)
+  print("check KKT")
+  opt_linear = result$optimization_transform$linear_term
+  opt_offset = result$optimization_transform$offset_term
+  observed_opt_state=result$observed_opt_state
+  #print(dim(opt_linear))
+  #print(opt_offset)
+  #print(result$perturb)
+  print(opt_linear %*% observed_opt_state+opt_offset+result$observed_raw-result$perturb) ## should be zero
+}
+  
+
 
 collect_results = function(n,p,s, nsim=100, level=0.9, condition_subgrad=TRUE, lam=1.2){
 
-  rho=0.3
+  rho=0.
   sigma=1
   sample_pvalues = c()
   sample_coverage = c()
@@ -36,7 +72,14 @@ collect_results = function(n,p,s, nsim=100, level=0.9, condition_subgrad=TRUE, l
     X=data$X
     y=data$y
     beta=data$beta
-    result = selectiveInference:::randomizedLassoInf(X, y, lam, level=level, burnin=2000, nsample=4000, condition_subgrad=condition_subgrad)
+    result = selectiveInference:::randomizedLassoInf(X, y, 
+                                                     lam=lam, 
+                                                     sigma=sigma,
+                                                     level=level, 
+                                                     sampler = "A",
+                                                     burnin=1000, 
+                                                     nsample=5000, 
+                                                     condition_subgrad=condition_subgrad)
     true_beta = beta[result$active_set]
     coverage = rep(0, nrow(result$ci))
     if (length(result$active_set)>0){
@@ -61,7 +104,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9, condition_subgrad=TRUE, l
 }
 
 set.seed(1)
-collect_results(n=200, p=100, s=0, lam=2)
-
-
+collect_results(n=100, p=2000, s=0, lam=3)
+#test_randomized_lasso()
+#test_KKT()
 
diff --git a/tests/randomized/test_sampler.R b/tests/randomized/test_sampler.R
new file mode 100644
index 0000000..e19a53f
--- /dev/null
+++ b/tests/randomized/test_sampler.R
@@ -0,0 +1,16 @@
+
+test_log_concave_sampler = function(){
+  samples = log_concave_sampler(negative_log_density= function(x){x^2/2}, 
+                                grad_negative_log_density=function(x){x},
+                                constraints = t(as.matrix(c(2,3))),
+                                observed = 2, nsamples=10000)
+  mean(samples)
+  hist(samples)
+}
+
+
+test_gaussian_sampler =function(){
+  samples = gaussian_sampler(1, 1, 1, 0,10000)
+  mean(samples)
+  hist(samples)
+}

From e739255b7e190f4de4f4c75338455656f58f3a6d Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Wed, 8 Nov 2017 21:33:12 -0800
Subject: [PATCH 385/493] active set zero

---
 tests/randomized/test_instances.R | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 9e2e0d2..7f525a4 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -63,7 +63,7 @@ test_KKT=function(){
 
 collect_results = function(n,p,s, nsim=100, level=0.9, condition_subgrad=TRUE, lam=1.2){
 
-  rho=0.
+  rho=0.3
   sigma=1
   sample_pvalues = c()
   sample_coverage = c()
@@ -80,9 +80,9 @@ collect_results = function(n,p,s, nsim=100, level=0.9, condition_subgrad=TRUE, l
                                                      burnin=1000, 
                                                      nsample=5000, 
                                                      condition_subgrad=condition_subgrad)
-    true_beta = beta[result$active_set]
-    coverage = rep(0, nrow(result$ci))
     if (length(result$active_set)>0){
+      true_beta = beta[result$active_set]
+      coverage = rep(0, nrow(result$ci))
       for (i in 1:nrow(result$ci)){
         if (result$ci[i,1]<true_beta[i] & result$ci[i,2]>true_beta[i]){
           coverage[i]=1
@@ -104,7 +104,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9, condition_subgrad=TRUE, l
 }
 
 set.seed(1)
-collect_results(n=100, p=2000, s=0, lam=3)
+collect_results(n=100, p=2000, s=0, lam=2.5)
 #test_randomized_lasso()
 #test_KKT()
 

From b4fd8b521b4f5981250bc749fbfdf1e05df3faae Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Wed, 8 Nov 2017 23:45:39 -0800
Subject: [PATCH 386/493] removed set seed when gen randomization

---
 selectiveInference/R/funs.randomized.R | 1 -
 tests/randomized/test_instances.R      | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index da64f99..a086df9 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -41,7 +41,6 @@ randomizedLasso = function(X,
 
     if (noise_scale > 0) {
         if (noise_type == 'gaussian') {
-            set.seed(1)
             perturb_ = rnorm(p) * noise_scale
         }
         else if (noise_type == 'laplace') {
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 7f525a4..7a89dd7 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -76,7 +76,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9, condition_subgrad=TRUE, l
                                                      lam=lam, 
                                                      sigma=sigma,
                                                      level=level, 
-                                                     sampler = "A",
+                                                     sampler = "R",
                                                      burnin=1000, 
                                                      nsample=5000, 
                                                      condition_subgrad=condition_subgrad)

From d2438c9512802d506f41208c1e54412073079145 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Thu, 9 Nov 2017 12:23:05 -0800
Subject: [PATCH 387/493] amir sampler working with subgrad condition false as
 well

---
 selectiveInference/R/funs.randomized.R | 25 +++++++++++++++++--------
 selectiveInference/R/sampler.R         | 10 ++++++----
 tests/randomized/test_instances.R      |  6 +++---
 3 files changed, 26 insertions(+), 15 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index a086df9..f3c3aac 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -374,14 +374,23 @@ randomizedLassoInf = function(X,
   }
   inactive_set = lasso_soln$inactive_set
   
-
   noise_scale = lasso_soln$noise_scale # set to default value in randomizedLasso
-
- if (condition_subgrad==TRUE){
+  
+  constraints = matrix(0,nactive,2)
+  constraints[,2] = Inf
+  if (condition_subgrad==TRUE){
    condl_lasso=conditional_density(noise_scale, lasso_soln)
    lasso_soln = condl_lasso$lasso_soln
-   reduced_opt_transform = condl_lasso$reduced_opt_transform
- } 
+   cur_opt_transform = condl_lasso$reduced_opt_transform
+   } else{
+   if (nactive<p){
+     subgrad_constraints = matrix(-lam, p-nactive, 2)
+     subgrad_constraints[,2]=lam
+     constraints = rbind(constraints, subgrad_constraints)
+   }
+    cur_opt_transform = list(linear_term = lasso_soln$optimization_transform$linear_term,
+                             offset_term = lasso_soln$optimization_transform$offset_term+lasso_soln$observed_raw)
+  }
     
   ndim = length(lasso_soln$observed_opt_state)
   
@@ -391,8 +400,9 @@ randomizedLassoInf = function(X,
   } else if (sampler == "A"){
     opt_samples = gaussian_sampler(noise_scale, 
                                  lasso_soln$observed_opt_state, 
-                                 reduced_opt_transform$linear_term,
-                                 reduced_opt_transform$offset_term,
+                                 cur_opt_transform$linear_term,
+                                 cur_opt_transform$offset_term,
+                                 constraints,
                                  nsamples=nsample)
     opt_sample = opt_samples[(burnin+1):nsample,]
   }
@@ -439,7 +449,6 @@ randomizedLassoInf = function(X,
         cur_linear = reduced_target_opt_linear[,2:ncol(reduced_target_opt_linear)]
         cur_offset = temp %*% opt_transform$offset_term
         cur_transform = list(linear_term = as.matrix(cur_linear), offset_term = cur_offset)
-
         raw = target_transform$linear_term * observed_target[i] + target_transform$offset_term
     } else {
         cur_transform = opt_transform
diff --git a/selectiveInference/R/sampler.R b/selectiveInference/R/sampler.R
index 48efccc..c9e8ace 100644
--- a/selectiveInference/R/sampler.R
+++ b/selectiveInference/R/sampler.R
@@ -74,7 +74,12 @@ log_concave_sampler = function(negative_log_density,
   return (samples)
 }
 
-gaussian_sampler = function(noise_scale, observed, linear_term, offset_term, nsamples){
+gaussian_sampler = function(noise_scale, 
+                            observed, 
+                            linear_term, 
+                            offset_term, 
+                            constraints,
+                            nsamples){
   
   negative_log_density = function(x) {
     recon = linear_term %*% x+offset_term
@@ -84,9 +89,6 @@ gaussian_sampler = function(noise_scale, observed, linear_term, offset_term, nsa
     recon = linear_term %*% x+offset_term
     return(t(linear_term)%*% recon/(noise_scale^2))
   }
-  dim = length(observed)
-  constraints = matrix(0,dim,2)
-  constraints[,2] = Inf
   
   return(log_concave_sampler(negative_log_density, 
                              grad_negative_log_density,
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 7a89dd7..2255109 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -61,7 +61,7 @@ test_KKT=function(){
   
 
 
-collect_results = function(n,p,s, nsim=100, level=0.9, condition_subgrad=TRUE, lam=1.2){
+collect_results = function(n,p,s, nsim=100, level=0.9, condition_subgrad=FALSE, lam=1.2){
 
   rho=0.3
   sigma=1
@@ -76,7 +76,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9, condition_subgrad=TRUE, l
                                                      lam=lam, 
                                                      sigma=sigma,
                                                      level=level, 
-                                                     sampler = "R",
+                                                     sampler = "A",
                                                      burnin=1000, 
                                                      nsample=5000, 
                                                      condition_subgrad=condition_subgrad)
@@ -104,7 +104,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9, condition_subgrad=TRUE, l
 }
 
 set.seed(1)
-collect_results(n=100, p=2000, s=0, lam=2.5)
+collect_results(n=100, p=20, s=0, lam=1.2)
 #test_randomized_lasso()
 #test_KKT()
 

From ce1317148c0533a2e0e768065d7e842c301d55b5 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Thu, 9 Nov 2017 15:41:32 -0800
Subject: [PATCH 388/493] logistic

---
 selectiveInference/R/funs.randomized.R | 67 ++++++++++++++++++++------
 tests/randomized/test_instances.R      | 31 ++++++++----
 2 files changed, 74 insertions(+), 24 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index f3c3aac..ee414f4 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -6,6 +6,7 @@
 randomizedLasso = function(X, 
                            y, 
                            lam, 
+                           family="gaussian",
                            noise_scale=NULL, 
                            ridge_term=NULL, 
                            noise_type=c('gaussian', 'laplace'),
@@ -17,7 +18,6 @@ randomizedLasso = function(X,
                            kkt_stop=TRUE,
                            parameter_stop=TRUE)
 {
-
     n = nrow(X); p = ncol(X)
     			
     mean_diag = mean(apply(X^2, 2, sum))
@@ -86,7 +86,7 @@ randomizedLasso = function(X,
 			                     parameter_stop)         # param_stop
     
     sign_soln = sign(result$soln)
-
+    
     unpenalized = lam == 0
     active = (!unpenalized) & (sign_soln != 0)
     inactive = (!unpenalized) & (sign_soln == 0)
@@ -113,8 +113,21 @@ randomizedLasso = function(X,
     I = inactive_set
     X_E = X[,E]
     X_I = X[,I]
-    L_E = t(X) %*% X[,E]
-
+    
+    if (family=="binomial"){
+      unpen_reg = glm(y~X_E-1, family="binomial")
+      unpen_est = unpen_reg$coefficients
+      pi_fn = function(beta){
+        temp = X_E %*% as.matrix(beta)
+        return(as.vector(exp(temp)/(1+exp(temp)))) # n-dimensional
+      }
+      pi_vec = pi_fn(unpen_est)
+      W_E = diag(pi_vec*(1-pi_vec))
+    } else if (family=="gaussian"){
+      W_E = diag(rep(1,n))
+    }
+    L_E = t(X) %*% W_E %*% X[,E]
+    
     coef_term = L_E
 
     signs_ = c(rep(1, sum(unpenalized)), sign_soln[active])
@@ -158,8 +171,12 @@ randomizedLasso = function(X,
                               offset_term = offset_term)
 
     # density for sampling optimization variables
-
+    
     observed_raw = -t(X) %*% y
+    if (family=="binomial"){
+      beta_E = result$soln[active_set]
+      observed_raw = observed_raw+t(X)%*%pi_fn(beta_E)-L_E %*% beta_E
+    }
     inactive_lam = lam[inactive_set]
     inactive_start = sum(unpenalized) + sum(active)
     active_start = sum(unpenalized)
@@ -333,6 +350,7 @@ conditional_density = function(noise_scale, lasso_soln) {
 randomizedLassoInf = function(X, 
                               y, 
                               lam, 
+                              family="gaussian",
                               sampler="A",
                               sigma=NULL, 
                               noise_scale=NULL, 
@@ -352,10 +370,11 @@ randomizedLassoInf = function(X,
 
   n = nrow(X)
   p = ncol(X)
-
+  
   lasso_soln = randomizedLasso(X, 
                                y, 
                                lam, 
+                               family=family,
                                noise_scale=noise_scale, 
                                ridge_term=ridge_term,
                                max_iter=max_iter,
@@ -410,17 +429,32 @@ randomizedLassoInf = function(X,
   X_E = X[, active_set]
   X_minusE = X[, inactive_set]
 
-  # if no sigma given, use OLS estimate
-
+  
+  
+  if (family=="gaussian"){
+    lm_y = lm(y ~ X_E - 1)
+    sigma_resid = sqrt(sum(resid(lm_y)^2) / lm_y$df.resid)
+    observed_target = lm_y$coefficients
+    W_E = diag(rep(1,n))
+    observed_internal = c(observed_target, t(X_minusE) %*% (y-X_E%*% observed_target))
+  } else if (family=="binomial"){
+    glm_y = glm(y~X_E-1)
+    sigma_resid = sqrt(sum(resid(glm_y)^2) / glm_y$df.resid)
+    observed_target = as.matrix(glm_y$coefficients)
+    temp = X_E%*%observed_target
+    pi_vec = exp(temp)/(1+exp(temp))
+    observed_internal =  c(observed_target, t(X_minusE) %*% (y-pi_vec))
+    W_E=diag(as.vector(pi_vec *(1-pi_vec)))
+  }
+  
+  # if no sigma given, use the estimate
+  
   if (is.null(sigma)) {
-        lm_y = lm(y ~ X_E - 1)
-        sigma = sqrt(sum(resid(lm_y)^2) / lm_y$df.resid)
+    sigma = sigma_resid
   }        
-
-  target_cov = solve(t(X_E) %*% X_E)*sigma^2
+  
+  target_cov = solve(t(X_E) %*% W_E %*% X_E)*sigma^2
   cov_target_internal = rbind(target_cov, matrix(0, nrow=p-nactive, ncol=nactive))
-  observed_target = solve(t(X_E) %*% X_E) %*% t(X_E) %*% y
-  observed_internal = c(observed_target, t(X_minusE) %*% (y-X_E%*% observed_target))
   internal_transform = lasso_soln$internal_transform
   opt_transform = lasso_soln$optimization_transform
   observed_raw = lasso_soln$observed_raw
@@ -495,5 +529,10 @@ randomizedLassoInf = function(X,
   return(list(active_set=active_set, pvalues=pvalues, ci=ci))
 }
 
+   
+    
+    
+    
+
 
 
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 2255109..dc93ec5 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -1,7 +1,7 @@
 library(selectiveInference)
 
-gaussian_instance = function(n, p, s, sigma=1, rho=0, signal=6, X=NA,
-                             random_signs=TRUE, scale=TRUE, center=TRUE, seed=NA){
+get_instance = function(n, p, s, sigma=1, rho=0, signal=6, family="gaussian",
+                        X=NA, random_signs=TRUE, scale=TRUE, center=TRUE, seed=NA){
   if (!is.na(seed)){
     set.seed(seed)
   }
@@ -19,11 +19,20 @@ gaussian_instance = function(n, p, s, sigma=1, rho=0, signal=6, X=NA,
     signs = sample(c(-1,1), s, replace = TRUE)
     beta = beta * signs
   }
-  y = X %*% beta + rnorm(n)*sigma
+  mu = X %*% beta
+  if (family=="gaussian"){
+    y = mu + rnorm(n)*sigma
+  } else if (family=="binomial"){
+    prob = exp(mu)/(1+exp(mu))
+    y= rbinom(n,1, prob)
+  }
   result = list(X=X,y=y,beta=beta)
   return(result)
 }
 
+
+
+
 test_randomized_lasso = function(n=100,p=200,s=0){
   set.seed(1)
   data = gaussian_instance(n=n,p=p,s=s, rho=0.3, sigma=3)
@@ -61,27 +70,29 @@ test_KKT=function(){
   
 
 
-collect_results = function(n,p,s, nsim=100, level=0.9, condition_subgrad=FALSE, lam=1.2){
+collect_results = function(n,p,s, nsim=100, level=0.9, 
+                           family = "gaussian",
+                           condition_subgrad=FALSE, lam=1.2){
 
   rho=0.3
   sigma=1
   sample_pvalues = c()
   sample_coverage = c()
   for (i in 1:nsim){
-    data = gaussian_instance(n=n,p=p,s=s, rho=rho, sigma=sigma)
+    data = get_instance(n=n,p=p,s=s, rho=rho, sigma=sigma, family=family)
     X=data$X
     y=data$y
-    beta=data$beta
     result = selectiveInference:::randomizedLassoInf(X, y, 
-                                                     lam=lam, 
+                                                     lam, 
+                                                     family = family,
+                                                     sampler = "A",
                                                      sigma=sigma,
                                                      level=level, 
-                                                     sampler = "A",
                                                      burnin=1000, 
                                                      nsample=5000, 
                                                      condition_subgrad=condition_subgrad)
     if (length(result$active_set)>0){
-      true_beta = beta[result$active_set]
+      true_beta = data$beta[result$active_set]
       coverage = rep(0, nrow(result$ci))
       for (i in 1:nrow(result$ci)){
         if (result$ci[i,1]<true_beta[i] & result$ci[i,2]>true_beta[i]){
@@ -104,7 +115,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9, condition_subgrad=FALSE,
 }
 
 set.seed(1)
-collect_results(n=100, p=20, s=0, lam=1.2)
+collect_results(n=100, p=20, s=0, lam=0.5)
 #test_randomized_lasso()
 #test_KKT()
 

From 157c1db486e8d9df877a0cfc7a0221d60776da39 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Thu, 9 Nov 2017 16:02:53 -0800
Subject: [PATCH 389/493] E of zero length

---
 selectiveInference/R/funs.randomized.R | 5 +++++
 tests/randomized/test_instances.R      | 4 ++--
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index ee414f4..9f2e4e2 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -114,6 +114,10 @@ randomizedLasso = function(X,
     X_E = X[,E]
     X_I = X[,I]
     
+    if (length(E)==0){
+      return(list(active_set=c()))
+    }
+    
     if (family=="binomial"){
       unpen_reg = glm(y~X_E-1, family="binomial")
       unpen_est = unpen_reg$coefficients
@@ -126,6 +130,7 @@ randomizedLasso = function(X,
     } else if (family=="gaussian"){
       W_E = diag(rep(1,n))
     }
+    
     L_E = t(X) %*% W_E %*% X[,E]
     
     coef_term = L_E
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index dc93ec5..d5c3eca 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -71,7 +71,7 @@ test_KKT=function(){
 
 
 collect_results = function(n,p,s, nsim=100, level=0.9, 
-                           family = "gaussian",
+                           family = "binomial",
                            condition_subgrad=FALSE, lam=1.2){
 
   rho=0.3
@@ -115,7 +115,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
 }
 
 set.seed(1)
-collect_results(n=100, p=20, s=0, lam=0.5)
+collect_results(n=100, p=20, s=0, lam=0.8)
 #test_randomized_lasso()
 #test_KKT()
 

From 1dfbea8b3596f2f56c9f1ee7e5b29925e13f58cd Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 9 Nov 2017 21:59:39 -0800
Subject: [PATCH 390/493] fixing man page so check passes

---
 selectiveInference/R/RcppExports.R           | 35 --------------------
 selectiveInference/R/funs.randomized.R       | 17 +++++-----
 selectiveInference/man/randomizedLassoInf.Rd |  5 +++
 3 files changed, 13 insertions(+), 44 deletions(-)
 delete mode 100644 selectiveInference/R/RcppExports.R

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
deleted file mode 100644
index e927a3a..0000000
--- a/selectiveInference/R/RcppExports.R
+++ /dev/null
@@ -1,35 +0,0 @@
-# Generated by using Rcpp::compileAttributes() -> do not edit by hand
-# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
-
-solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
-    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
-}
-
-solve_QP_wide <- function(X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
-    .Call('selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
-}
-
-update1_ <- function(Q2, w, m, k) {
-    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
-}
-
-downdate1_ <- function(Q1, R, j0, m, n) {
-    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
-}
-
-log_density_gaussian_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
-    .Call('selectiveInference_log_density_gaussian_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
-}
-
-log_density_gaussian_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
-    .Call('selectiveInference_log_density_gaussian_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
-}
-
-log_density_laplace_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
-    .Call('selectiveInference_log_density_laplace_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
-}
-
-log_density_laplace_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
-    .Call('selectiveInference_log_density_laplace_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
-}
-
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index f3c3aac..f954e1c 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -34,9 +34,6 @@ randomizedLasso = function(X,
         noise_scale = 0.5 * sd(y) * sqrt(mean_diag)
     }
     
-    print(paste("ridge term", ridge_term))
-    print(paste("noise scale", noise_scale))
-    
     noise_type = match.arg(noise_type)
 
     if (noise_scale > 0) {
@@ -333,14 +330,14 @@ conditional_density = function(noise_scale, lasso_soln) {
 randomizedLassoInf = function(X, 
                               y, 
                               lam, 
-                              sampler="A",
                               sigma=NULL, 
                               noise_scale=NULL, 
                               ridge_term=NULL, 
                               condition_subgrad=TRUE, 
                               level=0.9,
-			                        nsample=10000,
-			                        burnin=2000,
+                              sampler=c("norejection", "adaptMCMC"),
+                              nsample=10000,
+                              burnin=2000,
                               max_iter=100,        # how many iterations for each optimization problem
                               kkt_tol=1.e-4,       # tolerance for the KKT conditions
                               parameter_tol=1.e-8, # tolerance for relative convergence of parameter
@@ -368,7 +365,7 @@ randomizedLassoInf = function(X,
 
   active_set = lasso_soln$active_set
   nactive = length(active_set)
-  print(paste("nactive", nactive))
+
   if (nactive==0){
     return (list(active_set=active_set, pvalues=c(), ci=c()))
   }
@@ -394,10 +391,12 @@ randomizedLassoInf = function(X,
     
   ndim = length(lasso_soln$observed_opt_state)
   
-  if (sampler =="R"){
+  sampler = match.arg(sampler)
+
+  if (sampler == "adaptMCMC"){
     S = sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), ndim), nsample=nsample)
     opt_samples = as.matrix(S$samples[(burnin+1):nsample,,drop=FALSE])
-  } else if (sampler == "A"){
+  } else if (sampler == "norejection") {
     opt_samples = gaussian_sampler(noise_scale, 
                                  lasso_soln$observed_opt_state, 
                                  cur_opt_transform$linear_term,
diff --git a/selectiveInference/man/randomizedLassoInf.Rd b/selectiveInference/man/randomizedLassoInf.Rd
index 18b9611..e0f9d44 100644
--- a/selectiveInference/man/randomizedLassoInf.Rd
+++ b/selectiveInference/man/randomizedLassoInf.Rd
@@ -19,6 +19,7 @@ randomizedLassoInf(X,
                    ridge_term=NULL, 
                    condition_subgrad=TRUE, 
                    level=0.9,
+                   sampler=c("norejection", "adaptMCMC"),
                    nsample=10000,
                    burnin=2000,
                    max_iter=100,       
@@ -71,6 +72,10 @@ Default is TRUE.
 \item{level}{
 Level for confidence intervals.
 }
+\item{sampler}{
+Which sampler to use -- default is a no-rejection sampler. Otherwise
+use MCMC from the adaptMCMC package.
+}
 \item{nsample}{
 Number of samples of optimization variables to sample.
 }

From b21260afe2e57afcc4053628591a71393463e3e2 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 9 Nov 2017 22:01:14 -0800
Subject: [PATCH 391/493] renaming file

---
 selectiveInference/R/{sampler.R => funs.sampler.R} | 1 +
 1 file changed, 1 insertion(+)
 rename selectiveInference/R/{sampler.R => funs.sampler.R} (97%)

diff --git a/selectiveInference/R/sampler.R b/selectiveInference/R/funs.sampler.R
similarity index 97%
rename from selectiveInference/R/sampler.R
rename to selectiveInference/R/funs.sampler.R
index c9e8ace..3f7f5fd 100644
--- a/selectiveInference/R/sampler.R
+++ b/selectiveInference/R/funs.sampler.R
@@ -1,3 +1,4 @@
+# A no-rejection MCMC algorithm Jelena and Amir have been working on
 
 log_concave_sampler = function(negative_log_density, 
                                grad_negative_log_density, 

From 183bc3abb230db8a828bf8db9f168dc1afde63e5 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 9 Nov 2017 22:03:47 -0800
Subject: [PATCH 392/493] stop instead of print

---
 selectiveInference/R/funs.randomized.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index f954e1c..5c5f3d1 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -98,8 +98,8 @@ randomizedLasso = function(X,
     observed_unpen = result$soln[unpenalized]
     observed_subgrad = -n*result$gradient[inactive]
     
-    if (length(which(abs(observed_subgrad)>lam[1]))){
-      print("subgradient eq not satisfied")
+    if (sum(abs(observed_subgrad)>lam*(1.001)) > 0){
+      stop("subgradient eq not satisfied")
     }
 
     observed_opt_state = c(observed_unpen, observed_scalings, observed_subgrad)

From 42cd878bddf45a090b1ec54a0ca3aed7e4537bfa Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 9 Nov 2017 22:16:35 -0800
Subject: [PATCH 393/493] WIP: adding logistic option, need to solve logistic
 problem

---
 selectiveInference/R/funs.randomized.R       | 27 ++++++++++----------
 selectiveInference/man/randomizedLassoInf.Rd |  4 +++
 2 files changed, 17 insertions(+), 14 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 1dc835c..020698d 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -6,7 +6,7 @@
 randomizedLasso = function(X, 
                            y, 
                            lam, 
-                           family="gaussian",
+                           family=c("gaussian","binomial"),
                            noise_scale=NULL, 
                            ridge_term=NULL, 
                            noise_type=c('gaussian', 'laplace'),
@@ -18,6 +18,8 @@ randomizedLasso = function(X,
                            kkt_stop=TRUE,
                            parameter_stop=TRUE)
 {
+    family = match.arg(family)
+
     n = nrow(X); p = ncol(X)
     			
     mean_diag = mean(apply(X^2, 2, sum))
@@ -65,8 +67,8 @@ randomizedLasso = function(X,
     nactive = as.integer(0)
 
     result = solve_QP_wide(X,                  # design matrix
-    	                     lam / n,            # vector of Lagrange multipliers
-		                       ridge_term / n,     # ridge_term 
+                           lam / n,            # vector of Lagrange multipliers
+                           ridge_term / n,     # ridge_term 
                            max_iter, 
                            soln, 
                            linear_func, 
@@ -177,7 +179,7 @@ randomizedLasso = function(X,
     observed_raw = -t(X) %*% y
     if (family=="binomial"){
       beta_E = result$soln[active_set]
-      observed_raw = observed_raw+t(X)%*%pi_fn(beta_E)-L_E %*% beta_E
+      observed_raw = observed_raw + t(X)%*%pi_fn(beta_E) - L_E %*% beta_E
     }
     inactive_lam = lam[inactive_set]
     inactive_start = sum(unpenalized) + sum(active)
@@ -213,11 +215,11 @@ randomizedLasso = function(X,
                 optimization_transform = opt_transform,
                 internal_transform = internal_transform,
                 log_optimization_density = log_optimization_density,
-		            observed_opt_state = observed_opt_state,
+                observed_opt_state = observed_opt_state,
                 observed_raw = observed_raw,
-		            noise_scale = noise_scale,
-		            soln = result$soln,
-		            perturb = perturb_
+                noise_scale = noise_scale,
+                soln = result$soln,
+                perturb = perturb_
                 ))
 
 }
@@ -352,8 +354,7 @@ conditional_density = function(noise_scale, lasso_soln) {
 randomizedLassoInf = function(X, 
                               y, 
                               lam, 
-                              family=c("gaussian", "logistic"),
-                              sampler=c("norejection", "adaptMCMC"),
+                              family=c("gaussian", "binomial"),
                               sigma=NULL, 
                               noise_scale=NULL, 
                               ridge_term=NULL, 
@@ -436,15 +437,13 @@ randomizedLassoInf = function(X,
   X_E = X[, active_set]
   X_minusE = X[, inactive_set]
 
-  
-  
-  if (family=="gaussian"){
+  if (family == "gaussian") {
     lm_y = lm(y ~ X_E - 1)
     sigma_resid = sqrt(sum(resid(lm_y)^2) / lm_y$df.resid)
     observed_target = lm_y$coefficients
     W_E = diag(rep(1,n))
     observed_internal = c(observed_target, t(X_minusE) %*% (y-X_E%*% observed_target))
-  } else if (family=="binomial"){
+  } else if (family == "binomial") {
     glm_y = glm(y~X_E-1)
     sigma_resid = sqrt(sum(resid(glm_y)^2) / glm_y$df.resid)
     observed_target = as.matrix(glm_y$coefficients)
diff --git a/selectiveInference/man/randomizedLassoInf.Rd b/selectiveInference/man/randomizedLassoInf.Rd
index e0f9d44..351596e 100644
--- a/selectiveInference/man/randomizedLassoInf.Rd
+++ b/selectiveInference/man/randomizedLassoInf.Rd
@@ -14,6 +14,7 @@ randomization.
 randomizedLassoInf(X, 
                    y, 
                    lam, 
+                   family=c("gaussian", "binomial"),
                    sigma=NULL, 
                    noise_scale=NULL, 
                    ridge_term=NULL, 
@@ -49,6 +50,9 @@ Value of lambda used to compute beta. See the above warning
  where obj is the object returned by glmnet (and [-1] removes the intercept,
  which glmnet always puts in the first component)
 } 
+\item{family}{
+Response type: "gaussian" (default), "binomial".
+}
 \item{sigma}{
 Estimate of error standard deviation. If NULL (default), this is estimated 
 using the mean squared residual of the full least squares based on 

From af3b818cb75b43f1dbc53c1e621fb6a7994027f7 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 10 Nov 2017 08:43:56 -0800
Subject: [PATCH 394/493] fixing empty inactive set indexing problem

---
 selectiveInference/R/funs.randomized.R | 40 +++++++++++++++-----------
 tests/randomized/test_instances.R      |  6 ++--
 2 files changed, 27 insertions(+), 19 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 020698d..5be7938 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -78,11 +78,11 @@ randomizedLasso = function(X,
                            nactive, 
                            kkt_tol, 
                            objective_tol, 
-			                     parameter_tol,
+                           parameter_tol,
                            p,
-		                       objective_stop,     # objective_stop
-			                     kkt_stop,           # kkt_stop
-			                     parameter_stop)         # param_stop
+                           objective_stop,     # objective_stop
+                           kkt_stop,           # kkt_stop
+                           parameter_stop)         # param_stop
     
     sign_soln = sign(result$soln)
     
@@ -100,7 +100,7 @@ randomizedLasso = function(X,
     observed_unpen = result$soln[unpenalized]
     observed_subgrad = -n*result$gradient[inactive]
     
-    if (sum(abs(observed_subgrad)>lam*(1.001)) > 0){
+    if (sum(abs(observed_subgrad)>lam[inactive]*(1.001)) > 0){
       stop("subgradient eq not satisfied")
     }
 
@@ -144,14 +144,17 @@ randomizedLasso = function(X,
       coef_term = coef_term %*% diag(signs_)  # scaligns are non-negative
     }
     
-    subgrad_term = matrix(0, p, sum(inactive)) # for subgrad
-    for (i in 1:sum(inactive)) {
-        subgrad_term[inactive_set[i], i] = 1
-    }
-
-    linear_term = cbind(coef_term,
-                        subgrad_term)
-
+    if (sum(inactive) > 0) {
+        subgrad_term = matrix(0, p, sum(inactive)) # for subgrad
+        for (i in 1:sum(inactive)) {
+            subgrad_term[inactive_set[i], i] = 1
+            }
+
+        linear_term = cbind(coef_term,
+                            subgrad_term)
+    } else {
+        linear_term = coef_term
+    }    
     offset_term = rep(0, p)
     offset_term[active] = lam[active] * sign_soln[active]
 
@@ -167,9 +170,14 @@ randomizedLasso = function(X,
 
     active_term = -L_E                           # for \bar{\beta}_E
 
-    inactive_term = -subgrad_term
-    linear_term = cbind(active_term,
-                        inactive_term)
+    if (sum(inactive) > 0) {
+        inactive_term = -subgrad_term
+        linear_term = cbind(active_term,
+                            inactive_term)
+    } else {
+        linear_term = active_term
+    }
+  
     offset_term = rep(0, p)
     internal_transform = list(linear_term = linear_term,
                               offset_term = offset_term)
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index d5c3eca..8155e5f 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -85,7 +85,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
     result = selectiveInference:::randomizedLassoInf(X, y, 
                                                      lam, 
                                                      family = family,
-                                                     sampler = "A",
+                                                     sampler = "adaptMCMC",
                                                      sigma=sigma,
                                                      level=level, 
                                                      burnin=1000, 
@@ -114,8 +114,8 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
   }
 }
 
-set.seed(1)
-collect_results(n=100, p=20, s=0, lam=0.8)
+#set.seed(1)
+collect_results(n=500, p=200, s=0, lam=0.8)
 #test_randomized_lasso()
 #test_KKT()
 

From 291aa666555952c41503df8cff7662f21d7f9a71 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Fri, 10 Nov 2017 15:24:23 -0800
Subject: [PATCH 395/493] logistic solver

---
 selectiveInference/R/funs.randomized.R | 62 ++++++++++++++++++++++++++
 tests/randomized/test_instances.R      | 27 ++++++++---
 2 files changed, 83 insertions(+), 6 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 5be7938..b94ee53 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -544,8 +544,70 @@ randomizedLassoInf = function(X,
 }
 
    
+solve_logistic=function(X, y, lam, 
+                    ridge_term, 
+                    perturb,
+                    niters=5,
+                    max_iter=100,        # how many iterations for each optimization problem
+                    kkt_tol=1.e-4,       # tolerance for the KKT conditions
+                    parameter_tol=1.e-8, # tolerance for relative convergence of parameter
+                    objective_tol=1.e-8, # tolerance for relative decrease in objective
+                    objective_stop=FALSE,
+                    kkt_stop=TRUE,
+                    parameter_stop=TRUE){
+
+  n=nrow(X); p=ncol(X)
+  soln = rep(0, p)
+  ever_active = rep(0, p)
+  nactive = as.integer(0)
+  
+  pi_fn = function(beta){
+    print(length(as.vector(beta)))
+    print(dim(X))
+    temp = X %*% as.vector(beta)
+    return(as.vector(exp(temp)/(1+exp(temp)))) # n-dimensional
+  }
+  
+  for (i in 1:niters){
+    print(paste("iteration ", i))
+    pi_vec = pi_fn(soln)
+    rootW = diag(sqrt(as.vector(pi_vec*(1-pi_vec))))
+    weighted_X = rootW %*% X
+    Xsoln = weighted_X %*% soln
     
+    gradient = -t(X)%*%(y-pi_vec)-perturb
+    linear_func = gradient-t(weighted_X) %*% weighted_X %*% as.vector(soln)
+    gradient = gradient/n
+    linear_func = linear_func/n
+    print(length(gradient))
+    print(length(linear_func))
+    
+    result = solve_QP_wide(weighted_X,                  # design matrix
+                           lam / n,            # vector of Lagrange multipliers
+                           ridge_term / n,     # ridge_term 
+                           max_iter, 
+                           soln, 
+                           linear_func, 
+                           gradient, 
+                           Xsoln,
+                           ever_active, 
+                           nactive, 
+                           kkt_tol, 
+                           objective_tol, 
+                           parameter_tol,
+                           p,
+                           objective_stop,     # objective_stop
+                           kkt_stop,           # kkt_stop
+                           parameter_stop)         # param_stop
     
+    #soln = result$soln
+    #print(length(soln))
+    print(paste("soln", length(soln)))
+  }
+  return(soln)
+}
+
+
     
 
 
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 8155e5f..35136c2 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -35,7 +35,7 @@ get_instance = function(n, p, s, sigma=1, rho=0, signal=6, family="gaussian",
 
 test_randomized_lasso = function(n=100,p=200,s=0){
   set.seed(1)
-  data = gaussian_instance(n=n,p=p,s=s, rho=0.3, sigma=3)
+  data = get_instance(n=n,p=p,s=s, rho=0.3, sigma=1, family="binomial")
   X=data$X
   y=data$y
   lam = 2.
@@ -47,6 +47,24 @@ test_randomized_lasso = function(n=100,p=200,s=0){
   print(result$observed_opt_state) # compared with python code
 }
 
+test_randomized_logistic = function(n=100,p=20,s=0){
+  set.seed(1)
+  data = get_instance(n=n,p=p,s=s, rho=0.3, sigma=1, family="binomial")
+  X=data$X
+  y=data$y
+  lam = 1.2
+  noise_scale = 0.5
+  ridge_term = 1./sqrt(n)
+  set.seed(1)
+  perturb = rnorm(p)*noise_scale
+  result = selectiveInference:::solve_logistic(X,y,lam, ridge_term, perturb)
+  print(result$soln)
+  print(length(which(result$soln!=0)))
+}
+
+test_randomized_logistic()
+
+
 test_KKT=function(){
   set.seed(1)
   n=200
@@ -69,7 +87,6 @@ test_KKT=function(){
 }
   
 
-
 collect_results = function(n,p,s, nsim=100, level=0.9, 
                            family = "binomial",
                            condition_subgrad=FALSE, lam=1.2){
@@ -114,8 +131,6 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
   }
 }
 
-#set.seed(1)
-collect_results(n=500, p=200, s=0, lam=0.8)
-#test_randomized_lasso()
-#test_KKT()
+set.seed(1)
+collect_results(n=100, p=20, s=0, lam=0.8)
 

From bb5fe6bfeb99bfedb05b08c71f3e6da5093f0ca2 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 10 Nov 2017 15:56:21 -0800
Subject: [PATCH 396/493] bug fixes to logistic solver

---
 selectiveInference/R/funs.randomized.R | 48 +++++++++++++++++---------
 1 file changed, 32 insertions(+), 16 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index b94ee53..ec4fa6b 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -63,7 +63,7 @@ randomizedLasso = function(X,
     linear_func = (- t(X) %*% y - perturb_) / n
 
     gradient = 1. * linear_func
-    ever_active = rep(0, p)
+    ever_active = as.integer(rep(0, p))
     nactive = as.integer(0)
 
     result = solve_QP_wide(X,                  # design matrix
@@ -544,24 +544,31 @@ randomizedLassoInf = function(X,
 }
 
    
-solve_logistic=function(X, y, lam, 
-                    ridge_term, 
-                    perturb,
-                    niters=5,
-                    max_iter=100,        # how many iterations for each optimization problem
-                    kkt_tol=1.e-4,       # tolerance for the KKT conditions
-                    parameter_tol=1.e-8, # tolerance for relative convergence of parameter
-                    objective_tol=1.e-8, # tolerance for relative decrease in objective
-                    objective_stop=FALSE,
-                    kkt_stop=TRUE,
-                    parameter_stop=TRUE){
+solve_logistic=function(X, 
+                        y, 
+                        lam, 
+                        ridge_term, 
+                        perturb,
+                        niters=5,
+                        max_iter=100,        # how many iterations for each optimization problem
+                        kkt_tol=1.e-4,       # tolerance for the KKT conditions
+                        parameter_tol=1.e-8, # tolerance for relative convergence of parameter
+                        objective_tol=1.e-8, # tolerance for relative decrease in objective
+                        objective_stop=FALSE,
+                        kkt_stop=TRUE,
+                        parameter_stop=TRUE){
 
   n=nrow(X); p=ncol(X)
   soln = rep(0, p)
-  ever_active = rep(0, p)
+  ever_active = as.integer(rep(0, p))
   nactive = as.integer(0)
   
-  pi_fn = function(beta){
+    lam = as.numeric(lam)
+    if (length(lam) == 1) {
+       lam = rep(lam, p)
+    }
+
+  pi_fn = function(X, beta){
     print(length(as.vector(beta)))
     print(dim(X))
     temp = X %*% as.vector(beta)
@@ -570,7 +577,7 @@ solve_logistic=function(X, y, lam,
   
   for (i in 1:niters){
     print(paste("iteration ", i))
-    pi_vec = pi_fn(soln)
+    pi_vec = pi_fn(X, soln)
     rootW = diag(sqrt(as.vector(pi_vec*(1-pi_vec))))
     weighted_X = rootW %*% X
     Xsoln = weighted_X %*% soln
@@ -582,6 +589,7 @@ solve_logistic=function(X, y, lam,
     print(length(gradient))
     print(length(linear_func))
     
+    print(paste("soln pre", length(soln)))
     result = solve_QP_wide(weighted_X,                  # design matrix
                            lam / n,            # vector of Lagrange multipliers
                            ridge_term / n,     # ridge_term 
@@ -600,11 +608,19 @@ solve_logistic=function(X, y, lam,
                            kkt_stop,           # kkt_stop
                            parameter_stop)         # param_stop
     
+    
+    pi_vec = pi_fn(X, soln)
+    gradient = -t(X)%*%(y-pi_vec)-perturb
+    print(c("gradient", gradient))
+    print(c("soln", soln))
+    print(c("lam", lam))
+    print(c("nactive", nactive))
+    print(c("ever_active", ever_active))
     #soln = result$soln
     #print(length(soln))
     print(paste("soln", length(soln)))
   }
-  return(soln)
+  return(result)
 }
 
 

From 98e76899192d524fac3daed0c2189158e29a66a6 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 10 Nov 2017 16:06:08 -0800
Subject: [PATCH 397/493] smaller lambda

---
 tests/randomized/test_instances.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 35136c2..2572e3b 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -52,7 +52,7 @@ test_randomized_logistic = function(n=100,p=20,s=0){
   data = get_instance(n=n,p=p,s=s, rho=0.3, sigma=1, family="binomial")
   X=data$X
   y=data$y
-  lam = 1.2
+  lam = 0.5
   noise_scale = 0.5
   ridge_term = 1./sqrt(n)
   set.seed(1)
@@ -132,5 +132,5 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
 }
 
 set.seed(1)
-collect_results(n=100, p=20, s=0, lam=0.8)
+#collect_results(n=100, p=20, s=0, lam=0.8)
 

From 1a27dc830ce1e9f2077f971ac1ad5c009bf8e03b Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Fri, 10 Nov 2017 16:31:17 -0800
Subject: [PATCH 398/493] logistic not working well

---
 selectiveInference/R/RcppExports.R     | 35 +++++++++++++
 selectiveInference/R/funs.randomized.R | 68 ++++++++++++++++----------
 tests/randomized/test_instances.R      |  6 +--
 3 files changed, 79 insertions(+), 30 deletions(-)
 create mode 100644 selectiveInference/R/RcppExports.R

diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
new file mode 100644
index 0000000..e927a3a
--- /dev/null
+++ b/selectiveInference/R/RcppExports.R
@@ -0,0 +1,35 @@
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
+    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
+}
+
+solve_QP_wide <- function(X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
+    .Call('selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
+}
+
+update1_ <- function(Q2, w, m, k) {
+    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
+}
+
+downdate1_ <- function(Q1, R, j0, m, n) {
+    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
+}
+
+log_density_gaussian_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_gaussian_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
+}
+
+log_density_gaussian_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_gaussian_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
+}
+
+log_density_laplace_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_laplace_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
+}
+
+log_density_laplace_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
+    .Call('selectiveInference_log_density_laplace_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
+}
+
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index ec4fa6b..bf958ea 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -58,15 +58,17 @@ randomizedLasso = function(X,
        stop("Lagrange parameter should be single float or of length ncol(X)")
     }    
 
-    soln = rep(0, p)
-    Xsoln = rep(0, n)
-    linear_func = (- t(X) %*% y - perturb_) / n
-
-    gradient = 1. * linear_func
-    ever_active = as.integer(rep(0, p))
-    nactive = as.integer(0)
-
-    result = solve_QP_wide(X,                  # design matrix
+    
+    if (family=="gaussian"){
+      soln = rep(0, p)
+      Xsoln = rep(0, n)
+      linear_func = (- t(X) %*% y - perturb_) / n
+
+      gradient = 1. * linear_func
+      ever_active = as.integer(rep(0, p))
+      nactive = as.integer(0)
+      
+      result = solve_QP_wide(X,                  # design matrix
                            lam / n,            # vector of Lagrange multipliers
                            ridge_term / n,     # ridge_term 
                            max_iter, 
@@ -83,7 +85,21 @@ randomizedLasso = function(X,
                            objective_stop,     # objective_stop
                            kkt_stop,           # kkt_stop
                            parameter_stop)         # param_stop
-    
+    } else if (family=="binomial"){
+      result = solve_logistic(X, 
+                              y, 
+                              lam, 
+                              ridge_term, 
+                              perturb_,
+                              niters=5,
+                              max_iter=max_iter,        # how many iterations for each optimization problem
+                              kkt_tol=kkt_tol,       # tolerance for the KKT conditions
+                              parameter_tol=parameter_tol, # tolerance for relative convergence of parameter
+                              objective_tol=objective_tol, # tolerance for relative decrease in objective
+                              objective_stop=objective_stop,
+                              kkt_stop=kkt_stop,
+                              parameter_stop=parameter_stop)
+    }
     sign_soln = sign(result$soln)
     
     unpenalized = lam == 0
@@ -453,7 +469,7 @@ randomizedLassoInf = function(X,
     observed_internal = c(observed_target, t(X_minusE) %*% (y-X_E%*% observed_target))
   } else if (family == "binomial") {
     glm_y = glm(y~X_E-1)
-    sigma_resid = sqrt(sum(resid(glm_y)^2) / glm_y$df.resid)
+    sigma_resid = 1
     observed_target = as.matrix(glm_y$coefficients)
     temp = X_E%*%observed_target
     pi_vec = exp(temp)/(1+exp(temp))
@@ -484,7 +500,6 @@ randomizedLassoInf = function(X,
                                             cov_target_internal[,i],
                                             internal_transform)
     
-    
     # changing dimension of density evalutaion
 
     if ((condition_subgrad == TRUE) & (nactive < p)) {
@@ -569,14 +584,14 @@ solve_logistic=function(X,
     }
 
   pi_fn = function(X, beta){
-    print(length(as.vector(beta)))
-    print(dim(X))
+    #print(length(as.vector(beta)))
+    #print(dim(X))
     temp = X %*% as.vector(beta)
     return(as.vector(exp(temp)/(1+exp(temp)))) # n-dimensional
   }
   
   for (i in 1:niters){
-    print(paste("iteration ", i))
+    #print(paste("iteration ", i))
     pi_vec = pi_fn(X, soln)
     rootW = diag(sqrt(as.vector(pi_vec*(1-pi_vec))))
     weighted_X = rootW %*% X
@@ -586,10 +601,9 @@ solve_logistic=function(X,
     linear_func = gradient-t(weighted_X) %*% weighted_X %*% as.vector(soln)
     gradient = gradient/n
     linear_func = linear_func/n
-    print(length(gradient))
-    print(length(linear_func))
-    
-    print(paste("soln pre", length(soln)))
+    #print(length(gradient))
+    #print(length(linear_func))
+    #print(paste("soln pre", length(soln)))
     result = solve_QP_wide(weighted_X,                  # design matrix
                            lam / n,            # vector of Lagrange multipliers
                            ridge_term / n,     # ridge_term 
@@ -609,16 +623,16 @@ solve_logistic=function(X,
                            parameter_stop)         # param_stop
     
     
-    pi_vec = pi_fn(X, soln)
-    gradient = -t(X)%*%(y-pi_vec)-perturb
-    print(c("gradient", gradient))
-    print(c("soln", soln))
-    print(c("lam", lam))
-    print(c("nactive", nactive))
-    print(c("ever_active", ever_active))
+    #pi_vec = pi_fn(X, soln)
+    #gradient = -t(X)%*%(y-pi_vec)-perturb
+    #print(c("gradient", gradient))
+    #print(c("soln", soln))
+    #print(c("lam", lam))
+    #print(c("nactive", nactive))
+    #print(c("ever_active", ever_active))
     #soln = result$soln
     #print(length(soln))
-    print(paste("soln", length(soln)))
+    #print(paste("soln", length(soln)))
   }
   return(result)
 }
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 2572e3b..92071dd 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -62,7 +62,7 @@ test_randomized_logistic = function(n=100,p=20,s=0){
   print(length(which(result$soln!=0)))
 }
 
-test_randomized_logistic()
+#test_randomized_logistic()
 
 
 test_KKT=function(){
@@ -102,7 +102,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
     result = selectiveInference:::randomizedLassoInf(X, y, 
                                                      lam, 
                                                      family = family,
-                                                     sampler = "adaptMCMC",
+                                                     sampler = "norejection", #"adaptMCMC",
                                                      sigma=sigma,
                                                      level=level, 
                                                      burnin=1000, 
@@ -132,5 +132,5 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
 }
 
 set.seed(1)
-#collect_results(n=100, p=20, s=0, lam=0.8)
+collect_results(n=100, p=20, s=0, lam=0.8)
 

From aa6b5b7c611ab6b543d3cdfe8b2b60743e38dc2c Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Fri, 10 Nov 2017 17:50:07 -0800
Subject: [PATCH 399/493] randomized logistic working

---
 selectiveInference/R/funs.randomized.R | 59 +++++++++-----------------
 tests/randomized/test_instances.R      |  8 ++--
 2 files changed, 24 insertions(+), 43 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index bf958ea..2aeeee6 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -58,7 +58,6 @@ randomizedLasso = function(X,
        stop("Lagrange parameter should be single float or of length ncol(X)")
     }    
 
-    
     if (family=="gaussian"){
       soln = rep(0, p)
       Xsoln = rep(0, n)
@@ -128,7 +127,7 @@ randomizedLasso = function(X,
     I = inactive_set
     X_E = X[,E]
     X_I = X[,I]
-    
+    print(c("nactive", length(E)))
     if (length(E)==0){
       return(list(active_set=c()))
     }
@@ -136,11 +135,7 @@ randomizedLasso = function(X,
     if (family=="binomial"){
       unpen_reg = glm(y~X_E-1, family="binomial")
       unpen_est = unpen_reg$coefficients
-      pi_fn = function(beta){
-        temp = X_E %*% as.matrix(beta)
-        return(as.vector(exp(temp)/(1+exp(temp)))) # n-dimensional
-      }
-      pi_vec = pi_fn(unpen_est)
+      pi_vec = pi_fn(X_E, unpen_est)
       W_E = diag(pi_vec*(1-pi_vec))
     } else if (family=="gaussian"){
       W_E = diag(rep(1,n))
@@ -200,10 +195,14 @@ randomizedLasso = function(X,
 
     # density for sampling optimization variables
     
-    observed_raw = -t(X) %*% y
+    
     if (family=="binomial"){
-      beta_E = result$soln[active_set]
-      observed_raw = observed_raw + t(X)%*%pi_fn(beta_E) - L_E %*% beta_E
+      #beta_E = result$soln[active_set]
+      #observed_raw = observed_raw + t(X) %*% pi_fn(beta_E) - L_E %*% beta_E
+      unpen_est = as.vector(glm(y~X_E-1, family="binomial")$coefficients)
+      observed_raw = -t(X) %*% (y-pi_fn(X_E, unpen_est))-L_E %*% unpen_est  
+    } else if (family=="gaussian"){
+      observed_raw = -t(X) %*% y
     }
     inactive_lam = lam[inactive_set]
     inactive_start = sum(unpenalized) + sum(active)
@@ -417,7 +416,7 @@ randomizedLassoInf = function(X,
 
   active_set = lasso_soln$active_set
   nactive = length(active_set)
-
+  print(c("nactive", nactive))
   if (nactive==0){
     return (list(active_set=active_set, pvalues=c(), ci=c()))
   }
@@ -468,11 +467,10 @@ randomizedLassoInf = function(X,
     W_E = diag(rep(1,n))
     observed_internal = c(observed_target, t(X_minusE) %*% (y-X_E%*% observed_target))
   } else if (family == "binomial") {
-    glm_y = glm(y~X_E-1)
+    glm_y = glm(y~X_E-1, family="binomial")
     sigma_resid = 1
-    observed_target = as.matrix(glm_y$coefficients)
-    temp = X_E%*%observed_target
-    pi_vec = exp(temp)/(1+exp(temp))
+    observed_target = as.vector(glm_y$coefficients)
+    pi_vec = pi_fn(X_E, observed_target)
     observed_internal =  c(observed_target, t(X_minusE) %*% (y-pi_vec))
     W_E=diag(as.vector(pi_vec *(1-pi_vec)))
   }
@@ -558,6 +556,10 @@ randomizedLassoInf = function(X,
   return(list(active_set=active_set, pvalues=pvalues, ci=ci))
 }
 
+pi_fn = function(X, beta){
+  temp = X %*% as.matrix(beta)
+  return(as.vector(exp(temp)/(1+exp(temp)))) # n-dimensional
+}
    
 solve_logistic=function(X, 
                         y, 
@@ -578,20 +580,12 @@ solve_logistic=function(X,
   ever_active = as.integer(rep(0, p))
   nactive = as.integer(0)
   
-    lam = as.numeric(lam)
-    if (length(lam) == 1) {
+  lam = as.numeric(lam)
+  if (length(lam) == 1) {
        lam = rep(lam, p)
-    }
-
-  pi_fn = function(X, beta){
-    #print(length(as.vector(beta)))
-    #print(dim(X))
-    temp = X %*% as.vector(beta)
-    return(as.vector(exp(temp)/(1+exp(temp)))) # n-dimensional
   }
   
   for (i in 1:niters){
-    #print(paste("iteration ", i))
     pi_vec = pi_fn(X, soln)
     rootW = diag(sqrt(as.vector(pi_vec*(1-pi_vec))))
     weighted_X = rootW %*% X
@@ -601,9 +595,7 @@ solve_logistic=function(X,
     linear_func = gradient-t(weighted_X) %*% weighted_X %*% as.vector(soln)
     gradient = gradient/n
     linear_func = linear_func/n
-    #print(length(gradient))
-    #print(length(linear_func))
-    #print(paste("soln pre", length(soln)))
+    
     result = solve_QP_wide(weighted_X,                  # design matrix
                            lam / n,            # vector of Lagrange multipliers
                            ridge_term / n,     # ridge_term 
@@ -622,17 +614,6 @@ solve_logistic=function(X,
                            kkt_stop,           # kkt_stop
                            parameter_stop)         # param_stop
     
-    
-    #pi_vec = pi_fn(X, soln)
-    #gradient = -t(X)%*%(y-pi_vec)-perturb
-    #print(c("gradient", gradient))
-    #print(c("soln", soln))
-    #print(c("lam", lam))
-    #print(c("nactive", nactive))
-    #print(c("ever_active", ever_active))
-    #soln = result$soln
-    #print(length(soln))
-    #print(paste("soln", length(soln)))
   }
   return(result)
 }
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 92071dd..7ea756c 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -89,9 +89,9 @@ test_KKT=function(){
 
 collect_results = function(n,p,s, nsim=100, level=0.9, 
                            family = "binomial",
-                           condition_subgrad=FALSE, lam=1.2){
+                           condition_subgrad=TRUE, lam=1.2){
 
-  rho=0.3
+  rho=0.
   sigma=1
   sample_pvalues = c()
   sample_coverage = c()
@@ -102,7 +102,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
     result = selectiveInference:::randomizedLassoInf(X, y, 
                                                      lam, 
                                                      family = family,
-                                                     sampler = "norejection", #"adaptMCMC",
+                                                     sampler = "adaptMCMC", #"norejection", #
                                                      sigma=sigma,
                                                      level=level, 
                                                      burnin=1000, 
@@ -132,5 +132,5 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
 }
 
 set.seed(1)
-collect_results(n=100, p=20, s=0, lam=0.8)
+collect_results(n=100, p=20, s=0, lam=0.5)
 

From f3f155e081451917caf99431eccdd7866f308935 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 11 Nov 2017 12:50:36 -0800
Subject: [PATCH 400/493] BF: rob's bug with nactive=p-1

---
 selectiveInference/R/funs.randomized.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index ec4fa6b..cd6c33b 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -325,7 +325,7 @@ conditional_density = function(noise_scale, lasso_soln) {
   p = length(observed_opt_state)
 
   if (nactive < p) {
-    beta_offset = beta_offset+(opt_linear[,(nactive+1):p] %*% observed_opt_state[(nactive+1):p])
+    beta_offset = beta_offset+(opt_linear[,(nactive+1):p,drop=FALSE] %*% observed_opt_state[(nactive+1):p])
   }
   opt_transform = list(linear_term=B, 
                        offset_term = beta_offset)
@@ -487,7 +487,7 @@ randomizedLassoInf = function(X,
     
     # changing dimension of density evalutaion
 
-    if ((condition_subgrad == TRUE) & (nactive < p)) {
+    if ((condition_subgrad == TRUE) & (nactive < p-1)) {
         target_opt_linear = cbind(target_transform$linear_term, opt_transform$linear_term)
         reduced_target_opt_linear = chol(t(target_opt_linear) %*% target_opt_linear)
         target_linear = reduced_target_opt_linear[,1]

From 67e8e2926bc43773ad0fc6cfd0b3603bc59c6c1f Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 11 Nov 2017 12:57:53 -0800
Subject: [PATCH 401/493] removing Rcpp file, few edits to randomized funs

---
 selectiveInference/R/funs.randomized.R | 17 +++++++----------
 1 file changed, 7 insertions(+), 10 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 4f8347b..29b4a9f 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -127,7 +127,7 @@ randomizedLasso = function(X,
     I = inactive_set
     X_E = X[,E]
     X_I = X[,I]
-    print(c("nactive", length(E)))
+
     if (length(E)==0){
       return(list(active_set=c()))
     }
@@ -135,7 +135,7 @@ randomizedLasso = function(X,
     if (family=="binomial"){
       unpen_reg = glm(y~X_E-1, family="binomial")
       unpen_est = unpen_reg$coefficients
-      pi_vec = pi_fn(X_E, unpen_est)
+      pi_vec = logistic_fitted(X_E, unpen_est)
       W_E = diag(pi_vec*(1-pi_vec))
     } else if (family=="gaussian"){
       W_E = diag(rep(1,n))
@@ -195,12 +195,9 @@ randomizedLasso = function(X,
 
     # density for sampling optimization variables
     
-    
     if (family=="binomial"){
-      #beta_E = result$soln[active_set]
-      #observed_raw = observed_raw + t(X) %*% pi_fn(beta_E) - L_E %*% beta_E
       unpen_est = as.vector(glm(y~X_E-1, family="binomial")$coefficients)
-      observed_raw = -t(X) %*% (y-pi_fn(X_E, unpen_est))-L_E %*% unpen_est  
+      observed_raw = -t(X) %*% (y-logistic_fitted(X_E, unpen_est))-L_E %*% unpen_est  
     } else if (family=="gaussian"){
       observed_raw = -t(X) %*% y
     }
@@ -416,7 +413,7 @@ randomizedLassoInf = function(X,
 
   active_set = lasso_soln$active_set
   nactive = length(active_set)
-  print(c("nactive", nactive))
+
   if (nactive==0){
     return (list(active_set=active_set, pvalues=c(), ci=c()))
   }
@@ -470,7 +467,7 @@ randomizedLassoInf = function(X,
     glm_y = glm(y~X_E-1, family="binomial")
     sigma_resid = 1
     observed_target = as.vector(glm_y$coefficients)
-    pi_vec = pi_fn(X_E, observed_target)
+    pi_vec = logistic_fitted(X_E, observed_target)
     observed_internal =  c(observed_target, t(X_minusE) %*% (y-pi_vec))
     W_E=diag(as.vector(pi_vec *(1-pi_vec)))
   }
@@ -556,7 +553,7 @@ randomizedLassoInf = function(X,
   return(list(active_set=active_set, pvalues=pvalues, ci=ci))
 }
 
-pi_fn = function(X, beta){
+logistic_fitted = function(X, beta){
   temp = X %*% as.matrix(beta)
   return(as.vector(exp(temp)/(1+exp(temp)))) # n-dimensional
 }
@@ -586,7 +583,7 @@ solve_logistic=function(X,
   }
   
   for (i in 1:niters){
-    pi_vec = pi_fn(X, soln)
+    pi_vec = logistic_fitted(X, soln)
     rootW = diag(sqrt(as.vector(pi_vec*(1-pi_vec))))
     weighted_X = rootW %*% X
     Xsoln = weighted_X %*% soln

From 86cffd63251d66c707b7ff827b0972e2c63d2161 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sun, 12 Nov 2017 12:47:35 -0800
Subject: [PATCH 402/493] WIP: separating conditional density, and the lasso
 soln as argument to randomizedLassoInf

---
 selectiveInference/R/funs.randomized.R | 219 +++++++++++--------------
 1 file changed, 95 insertions(+), 124 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 29b4a9f..a047146 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -215,39 +215,60 @@ randomizedLasso = function(X,
             return(-Inf)
         }
 
-        use_C_code = TRUE
-        if (!use_C_code) {
-            A = opt_transform$linear_term %*% opt_state + observed_raw + opt_transform$offset_term
-            D = -apply(A^2, 2, sum) / noise_scale^2
-        } else {
-            D = log_density_gaussian_conditional_(noise_scale,
-                                                  opt_transform$linear_term,
-                                                  as.matrix(opt_state),
-                                                  observed_raw + opt_transform$offset_term)
-        }
+        D = log_density_gaussian_conditional_(noise_scale,
+                                              opt_transform$linear_term,
+                                              as.matrix(opt_state),
+                                              observed_raw + opt_transform$offset_term)
+
         return(D)
     }
 
-    return(list(active_set = active_set,
-                inactive_set = inactive_set,
-                unpenalized_set = unpenalized_set,
-                sign_soln = sign_soln,
-                optimization_transform = opt_transform,
-                internal_transform = internal_transform,
-                log_optimization_density = log_optimization_density,
-                observed_opt_state = observed_opt_state,
-                observed_raw = observed_raw,
-                noise_scale = noise_scale,
-                soln = result$soln,
-                perturb = perturb_
+    # work out conditional density and save it as well
+
+    conditional_transform = conditional_opt_transform(noise_scale, 
+                                                      active_set,
+                                                      observed_raw,
+                                                      opt_transform$linear_term,
+                                                      opt_transform$offset_term,
+                                                      observed_opt_state)
+    constraints = matrix(0, length(active_set), 2)
+    constraints[,2] = Inf
+    conditional_transform$constraints = constraints
+
+    # add constraints and density to the full transform
+
+    subgrad_constraints = cbind(-lam[inactive_set], lam[inactive_set])
+    constraints = rbind(constraints, subgrad_constraints)
+    
+    opt_transform$constraints = constraints
+    opt_transform$observed_opt_state = observed_opt_state
+    opt_transform$log_optimization_density = log_optimization_density
+
+
+    return(list(X=X,
+                y=y,
+                lam=lam,
+		family=family,
+                active_set=active_set,
+                inactive_set=inactive_set,
+                unpenalized_set=unpenalized_set,
+                sign_soln=sign_soln,
+                full_transform=opt_transform,
+                conditional_transform=opt_transform,
+                internal_transform=internal_transform,
+                observed_raw=observed_raw,
+                noise_scale=noise_scale,
+                soln=result$soln,
+                perturb=perturb_
                 ))
 
 }
 
-sample_opt_variables = function(randomizedLASSO_obj, jump_scale, nsample=10000) {
-      return(MCMC(randomizedLASSO_obj$log_optimization_density, 
+sample_opt_variables = function(affine_transform, jump_scale, nsample=10000) {
+      print(affine_transform)
+      return(MCMC(affine_transform$log_optimization_density, 
                   nsample,
-                  randomizedLASSO_obj$observed_opt_state,
+                  affine_transform$observed_opt_state,
                   acc.rate=0.2,
                   scale=jump_scale))
 }
@@ -284,31 +305,18 @@ importance_weight = function(noise_scale,
                              target_transform,
                              observed_raw) {
 
-    use_C_code = TRUE
-    if (!use_C_code) {
-        A = (opt_transform$linear_term %*% opt_sample + 
-             target_transform$linear_term %*% target_sample)
-        A = apply(A, 2, function(x) {return(x + target_transform$offset_term + opt_transform$offset_term)})
-        log_num = -apply(A^2, 2, sum) / noise_scale^2
-    } else {
-        log_num = log_density_gaussian_(noise_scale,
-                                        target_transform$linear_term,
-                                        as.matrix(target_sample),
-                                        opt_transform$linear_term,
-                                        as.matrix(opt_sample),
-                                        target_transform$offset_term + opt_transform$offset_term)
-    }
+    log_num = log_density_gaussian_(noise_scale,
+                                    target_transform$linear_term,
+                                    as.matrix(target_sample),
+                                    opt_transform$linear_term,
+                                    as.matrix(opt_sample),
+                                    target_transform$offset_term + opt_transform$offset_term)
+
+    log_den = log_density_gaussian_conditional_(noise_scale,
+                                                opt_transform$linear_term,
+                                                as.matrix(opt_sample),
+                                                observed_raw+opt_transform$offset_term)
 
-    if (!use_C_code) {
-        A = opt_transform$linear_term %*% opt_sample 
-        A = apply(A, 2, function(x) {return(x + observed_raw + opt_transform$offset_term)})
-        log_den = -apply(A^2, 2, sum) / noise_scale^2
-    } else {
-       log_den = log_density_gaussian_conditional_(noise_scale,
-                                                   opt_transform$linear_term,
-                                                   as.matrix(opt_sample),
-                                                   observed_raw+opt_transform$offset_term)
-    }
     W = log_num - log_den
     W = W - max(W)
     return(exp(W))
@@ -320,17 +328,15 @@ get_mean_cov = function(noise_scale, linear_term, offset_term){
     mean = temp %*% t(linear_term) %*% offset_term
     return(list(mean=mean, cov=cov))
 }
-
-
                              
-conditional_density = function(noise_scale, lasso_soln) {
-  
-  active_set = lasso_soln$active_set
-  observed_raw = lasso_soln$observed_raw
-  opt_linear = lasso_soln$optimization_transform$linear_term
-  opt_offset =  lasso_soln$optimization_transform$offset_term
-  observed_opt_state = lasso_soln$observed_opt_state
+conditional_opt_transform = function(noise_scale, 
+                                     active_set,
+                                     observed_raw,
+                                     opt_linear,
+                                     opt_offset,
+                                     observed_opt_state) {
   
+ 
   nactive = length(active_set)
   B = opt_linear[,1:nactive,drop=FALSE]
   beta_offset = opt_offset
@@ -363,54 +369,28 @@ conditional_density = function(noise_scale, lasso_soln) {
     }
     return(log_den)
   }
-  lasso_soln$log_optimization_density = log_condl_optimization_density
-  lasso_soln$observed_opt_state = observed_opt_state[1:nactive]
-  lasso_soln$optimization_transform = opt_transform
+  log_optimization_density = log_condl_optimization_density
+  observed_opt_state = observed_opt_state[1:nactive]
+  optimization_transform = opt_transform
   reduced_opt_transform =list(linear_term = reduced_B, offset_term = reduced_beta_offset)
-  return(list(lasso_soln=lasso_soln, 
-              reduced_opt_transform = reduced_opt_transform))
+  return(list(reduced_opt_transform=reduced_opt_transform,
+              log_optimization_density=log_condl_optimization_density,
+              observed_opt_state=observed_opt_state[1:nactive],
+	      optimization_transform=opt_transform))
 }
 
-randomizedLassoInf = function(X, 
-                              y, 
-                              lam, 
-                              family=c("gaussian", "binomial"),
+randomizedLassoInf = function(lasso_soln,
                               sigma=NULL, 
-                              noise_scale=NULL, 
-                              ridge_term=NULL, 
                               condition_subgrad=TRUE, 
                               level=0.9,
                               sampler=c("norejection", "adaptMCMC"),
                               nsample=10000,
-                              burnin=2000,
-                              max_iter=100,        # how many iterations for each optimization problem
-                              kkt_tol=1.e-4,       # tolerance for the KKT conditions
-                              parameter_tol=1.e-8, # tolerance for relative convergence of parameter
-                              objective_tol=1.e-8, # tolerance for relative decrease in objective
-                              objective_stop=FALSE,
-                              kkt_stop=TRUE,
-                              parameter_stop=TRUE)
+                              burnin=2000)
  {
 
-  n = nrow(X)
-  p = ncol(X)
-  
-  family = match.arg(family)
+  n = nrow(lasso_soln$X)
+  p = ncol(lasso_soln$X)
   
-  lasso_soln = randomizedLasso(X, 
-                               y, 
-                               lam, 
-                               family=family,
-                               noise_scale=noise_scale, 
-                               ridge_term=ridge_term,
-                               max_iter=max_iter,
-                               kkt_tol=kkt_tol,       
-                               parameter_tol=parameter_tol,
-                               objective_tol=objective_tol,
-                               objective_stop=objective_stop,
-                               kkt_stop=kkt_stop,
-                               parameter_stop=parameter_stop)
-
   active_set = lasso_soln$active_set
   nactive = length(active_set)
 
@@ -418,52 +398,43 @@ randomizedLassoInf = function(X,
     return (list(active_set=active_set, pvalues=c(), ci=c()))
   }
   inactive_set = lasso_soln$inactive_set
-  
-  noise_scale = lasso_soln$noise_scale # set to default value in randomizedLasso
-  
-  constraints = matrix(0,nactive,2)
-  constraints[,2] = Inf
-  if (condition_subgrad==TRUE){
-   condl_lasso=conditional_density(noise_scale, lasso_soln)
-   lasso_soln = condl_lasso$lasso_soln
-   cur_opt_transform = condl_lasso$reduced_opt_transform
-   } else{
-   if (nactive<p){
-     subgrad_constraints = matrix(-lam, p-nactive, 2)
-     subgrad_constraints[,2]=lam
-     constraints = rbind(constraints, subgrad_constraints)
-   }
-    cur_opt_transform = list(linear_term = lasso_soln$optimization_transform$linear_term,
-                             offset_term = lasso_soln$optimization_transform$offset_term+lasso_soln$observed_raw)
-  }
-    
+  noise_scale = lasso_soln$noise_scale  # set to default value in randomizedLasso
+   
   ndim = length(lasso_soln$observed_opt_state)
   
   sampler = match.arg(sampler)
 
+  if (condition_subgrad == TRUE) {
+      cur_opt_transform = lasso_soln$conditional_transform
+  } else {
+      cur_opt_transform = lasso_soln$full_transform
+  }       
+
   if (sampler == "adaptMCMC"){
-    S = sample_opt_variables(lasso_soln, jump_scale=rep(1/sqrt(n), ndim), nsample=nsample)
+    S = sample_opt_variables(cur_opt_transform, 
+                             jump_scale=rep(1/sqrt(n), length(cur_opt_transform$observed_opt_state)), nsample=nsample)
     opt_samples = as.matrix(S$samples[(burnin+1):nsample,,drop=FALSE])
   } else if (sampler == "norejection") {
     opt_samples = gaussian_sampler(noise_scale, 
-                                 lasso_soln$observed_opt_state, 
-                                 cur_opt_transform$linear_term,
-                                 cur_opt_transform$offset_term,
-                                 constraints,
-                                 nsamples=nsample)
+                                   cur_opt_transform$observed_opt_state, 
+                                   cur_opt_transform$linear_term,
+                                   cur_opt_transform$offset_term,
+                                   cur_opt_transform$constraints,
+                                   nsamples=nsample)
     opt_sample = opt_samples[(burnin+1):nsample,]
   }
   
-  X_E = X[, active_set]
-  X_minusE = X[, inactive_set]
+  X_E = lasso_soln$X[, active_set]
+  X_minusE = lasso_soln$X[, inactive_set]
+  y = lasso_soln$y
 
-  if (family == "gaussian") {
+  if (lasso_soln$family == "gaussian") {
     lm_y = lm(y ~ X_E - 1)
     sigma_resid = sqrt(sum(resid(lm_y)^2) / lm_y$df.resid)
     observed_target = lm_y$coefficients
     W_E = diag(rep(1,n))
     observed_internal = c(observed_target, t(X_minusE) %*% (y-X_E%*% observed_target))
-  } else if (family == "binomial") {
+  } else if (lasso_soln$family == "binomial") {
     glm_y = glm(y~X_E-1, family="binomial")
     sigma_resid = 1
     observed_target = as.vector(glm_y$coefficients)

From 6f4fc0dc8fec8ae59a81d583904100e5fa3f3847 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sun, 12 Nov 2017 12:54:40 -0800
Subject: [PATCH 403/493] WIP: matrices not right size

---
 selectiveInference/R/funs.randomized.R | 22 ++++++++--------------
 1 file changed, 8 insertions(+), 14 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index a047146..8d9b560 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -356,27 +356,21 @@ conditional_opt_transform = function(noise_scale,
       return(-Inf)
     }
 
-    use_C_code = TRUE
-    if (!use_C_code) {
-        A = reduced_B %*% as.matrix(opt_state) + reduced_beta_offset
-        A = apply(A, 2, function(x) {x + reduced_beta_offset})
-        log_den = -apply(A^2, 2, sum) / noise_scale^2
-    } else {
-        log_den = log_density_gaussian_conditional_(noise_scale,
-                                                    reduced_B,
-                                                    as.matrix(opt_state),
-                                                    reduced_beta_offset)
-    }
+    log_den = log_density_gaussian_conditional_(noise_scale,
+                                                reduced_B,
+                                                as.matrix(opt_state),
+                                                reduced_beta_offset)
     return(log_den)
   }
+
   log_optimization_density = log_condl_optimization_density
   observed_opt_state = observed_opt_state[1:nactive]
   optimization_transform = opt_transform
   reduced_opt_transform =list(linear_term = reduced_B, offset_term = reduced_beta_offset)
-  return(list(reduced_opt_transform=reduced_opt_transform,
+  return(list(optimization_transform=reduced_opt_transform,
               log_optimization_density=log_condl_optimization_density,
               observed_opt_state=observed_opt_state[1:nactive],
-	      optimization_transform=opt_transform))
+	      optimization_transform_alt=opt_transform))
 }
 
 randomizedLassoInf = function(lasso_soln,
@@ -452,7 +446,7 @@ randomizedLassoInf = function(lasso_soln,
   target_cov = solve(t(X_E) %*% W_E %*% X_E)*sigma^2
   cov_target_internal = rbind(target_cov, matrix(0, nrow=p-nactive, ncol=nactive))
   internal_transform = lasso_soln$internal_transform
-  opt_transform = lasso_soln$optimization_transform
+  opt_transform = cur_opt_transform
   observed_raw = lasso_soln$observed_raw
   
   pvalues = rep(0, nactive)

From 3487b71c03436989521c3ef96d656b24260716f5 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sun, 12 Nov 2017 14:11:10 -0800
Subject: [PATCH 404/493] RF: randomizedLassoInf now takes a randomizedLasso
 soln

---
 selectiveInference/R/funs.randomized.R | 67 ++++++++++++++------------
 1 file changed, 36 insertions(+), 31 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 8d9b560..0196a6e 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -225,25 +225,29 @@ randomizedLasso = function(X,
 
     # work out conditional density and save it as well
 
-    conditional_transform = conditional_opt_transform(noise_scale, 
-                                                      active_set,
-                                                      observed_raw,
-                                                      opt_transform$linear_term,
-                                                      opt_transform$offset_term,
-                                                      observed_opt_state)
+    conditional_law = conditional_opt_transform(noise_scale, 
+                                                active_set,
+                                                observed_raw,
+                                                opt_transform$linear_term,
+                                                opt_transform$offset_term,
+                                                observed_opt_state)
     constraints = matrix(0, length(active_set), 2)
     constraints[,2] = Inf
-    conditional_transform$constraints = constraints
+    conditional_law$constraints = constraints
 
     # add constraints and density to the full transform
 
     subgrad_constraints = cbind(-lam[inactive_set], lam[inactive_set])
     constraints = rbind(constraints, subgrad_constraints)
     
-    opt_transform$constraints = constraints
-    opt_transform$observed_opt_state = observed_opt_state
-    opt_transform$log_optimization_density = log_optimization_density
+    # this is for conditional density of opt given data
 
+    full_law = list(sampling_transform=list(linear_term=opt_transform$linear_term,
+                                            offset_term=opt_transform$offset_term + observed_raw),
+                    constraints=constraints,
+                    observed_opt_state=observed_opt_state,
+                    log_optimization_density=log_optimization_density,
+                    importance_transform=opt_transform)
 
     return(list(X=X,
                 y=y,
@@ -253,8 +257,9 @@ randomizedLasso = function(X,
                 inactive_set=inactive_set,
                 unpenalized_set=unpenalized_set,
                 sign_soln=sign_soln,
-                full_transform=opt_transform,
-                conditional_transform=opt_transform,
+                full_law=full_law,
+		#optimization_transform=opt_transform,
+                conditional_law=conditional_law,
                 internal_transform=internal_transform,
                 observed_raw=observed_raw,
                 noise_scale=noise_scale,
@@ -264,11 +269,10 @@ randomizedLasso = function(X,
 
 }
 
-sample_opt_variables = function(affine_transform, jump_scale, nsample=10000) {
-      print(affine_transform)
-      return(MCMC(affine_transform$log_optimization_density, 
+sample_opt_variables = function(law, jump_scale, nsample=10000) {
+      return(MCMC(law$log_optimization_density, 
                   nsample,
-                  affine_transform$observed_opt_state,
+                  law$observed_opt_state,
                   acc.rate=0.2,
                   scale=jump_scale))
 }
@@ -315,7 +319,7 @@ importance_weight = function(noise_scale,
     log_den = log_density_gaussian_conditional_(noise_scale,
                                                 opt_transform$linear_term,
                                                 as.matrix(opt_sample),
-                                                observed_raw+opt_transform$offset_term)
+                                                observed_raw + opt_transform$offset_term)
 
     W = log_num - log_den
     W = W - max(W)
@@ -367,10 +371,10 @@ conditional_opt_transform = function(noise_scale,
   observed_opt_state = observed_opt_state[1:nactive]
   optimization_transform = opt_transform
   reduced_opt_transform =list(linear_term = reduced_B, offset_term = reduced_beta_offset)
-  return(list(optimization_transform=reduced_opt_transform,
+  return(list(sampling_transform=reduced_opt_transform,
               log_optimization_density=log_condl_optimization_density,
               observed_opt_state=observed_opt_state[1:nactive],
-	      optimization_transform_alt=opt_transform))
+	      importance_transform=opt_transform))
 }
 
 randomizedLassoInf = function(lasso_soln,
@@ -399,21 +403,21 @@ randomizedLassoInf = function(lasso_soln,
   sampler = match.arg(sampler)
 
   if (condition_subgrad == TRUE) {
-      cur_opt_transform = lasso_soln$conditional_transform
+      cur_law = lasso_soln$conditional_law
   } else {
-      cur_opt_transform = lasso_soln$full_transform
+      cur_law = lasso_soln$full_law
   }       
 
   if (sampler == "adaptMCMC"){
-    S = sample_opt_variables(cur_opt_transform, 
-                             jump_scale=rep(1/sqrt(n), length(cur_opt_transform$observed_opt_state)), nsample=nsample)
+    S = sample_opt_variables(cur_law, 
+                             jump_scale=rep(1/sqrt(n), length(cur_law$observed_opt_state)), nsample=nsample)
     opt_samples = as.matrix(S$samples[(burnin+1):nsample,,drop=FALSE])
   } else if (sampler == "norejection") {
     opt_samples = gaussian_sampler(noise_scale, 
-                                   cur_opt_transform$observed_opt_state, 
-                                   cur_opt_transform$linear_term,
-                                   cur_opt_transform$offset_term,
-                                   cur_opt_transform$constraints,
+                                   cur_law$observed_opt_state, 
+                                   cur_law$sampling_transform$linear_term,
+                                   cur_law$sampling_transform$offset_term,
+                                   cur_law$constraints,
                                    nsamples=nsample)
     opt_sample = opt_samples[(burnin+1):nsample,]
   }
@@ -446,12 +450,13 @@ randomizedLassoInf = function(lasso_soln,
   target_cov = solve(t(X_E) %*% W_E %*% X_E)*sigma^2
   cov_target_internal = rbind(target_cov, matrix(0, nrow=p-nactive, ncol=nactive))
   internal_transform = lasso_soln$internal_transform
-  opt_transform = cur_opt_transform
   observed_raw = lasso_soln$observed_raw
   
   pvalues = rep(0, nactive)
   ci = matrix(0, nactive, 2)
   
+  importance_transform = cur_law$importance_transform
+
   for (i in 1:nactive){
 
     target_transform = linear_decomposition(observed_target[i], 
@@ -463,18 +468,18 @@ randomizedLassoInf = function(lasso_soln,
     # changing dimension of density evalutaion
 
     if ((condition_subgrad == TRUE) & (nactive < p-1)) {
-        target_opt_linear = cbind(target_transform$linear_term, opt_transform$linear_term)
+        target_opt_linear = cbind(target_transform$linear_term, importance_transform$linear_term)
         reduced_target_opt_linear = chol(t(target_opt_linear) %*% target_opt_linear)
         target_linear = reduced_target_opt_linear[,1]
         temp = solve(t(reduced_target_opt_linear)) %*% t(target_opt_linear)
         target_offset = temp %*% target_transform$offset_term
         target_transform = list(linear_term = as.matrix(target_linear), offset_term = target_offset)
         cur_linear = reduced_target_opt_linear[,2:ncol(reduced_target_opt_linear)]
-        cur_offset = temp %*% opt_transform$offset_term
+        cur_offset = temp %*% importance_transform$offset_term
         cur_transform = list(linear_term = as.matrix(cur_linear), offset_term = cur_offset)
         raw = target_transform$linear_term * observed_target[i] + target_transform$offset_term
     } else {
-        cur_transform = opt_transform
+        cur_transform = importance_transform
         raw = observed_raw
     }   
 

From 8796029a116615fef6beee9c20d31ebb495c6ce9 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sun, 12 Nov 2017 14:26:31 -0800
Subject: [PATCH 405/493] DOC: exported randomizedLasso, and updated docs

---
 .gitignore                                   |   4 +-
 selectiveInference/NAMESPACE                 |   3 +-
 selectiveInference/R/RcppExports.R           |  35 -----
 selectiveInference/R/funs.randomized.R       |  42 +++---
 selectiveInference/man/randomizedLasso.Rd    | 142 +++++++++++++++++++
 selectiveInference/man/randomizedLassoInf.Rd |  76 +---------
 tests/randomized/test_instances.R            |  10 +-
 7 files changed, 177 insertions(+), 135 deletions(-)
 delete mode 100644 selectiveInference/R/RcppExports.R
 create mode 100644 selectiveInference/man/randomizedLasso.Rd

diff --git a/.gitignore b/.gitignore
index 863d843..3b58381 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,4 +6,6 @@
 **.RData
 **.o
 **.so
-forLater/josh/**
\ No newline at end of file
+forLater/josh/**
+*/R/RcppExports.R
+*/src/RcppExports.cpp
\ No newline at end of file
diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index 11a834e..0fa35e1 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -15,7 +15,8 @@ export(lar,fs,
        TG.limits, 
        TG.interval,
        debiasingMatrix,
-       randomizedLassoInf
+       randomizedLassoInf,
+       randomizedLasso
     )
 
 S3method("coef", "lar")
diff --git a/selectiveInference/R/RcppExports.R b/selectiveInference/R/RcppExports.R
deleted file mode 100644
index e927a3a..0000000
--- a/selectiveInference/R/RcppExports.R
+++ /dev/null
@@ -1,35 +0,0 @@
-# Generated by using Rcpp::compileAttributes() -> do not edit by hand
-# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
-
-solve_QP <- function(Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
-    .Call('selectiveInference_solve_QP', PACKAGE = 'selectiveInference', Sigma, bound, maxiter, theta, linear_func, gradient, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
-}
-
-solve_QP_wide <- function(X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop) {
-    .Call('selectiveInference_solve_QP_wide', PACKAGE = 'selectiveInference', X, bound, ridge_term, maxiter, theta, linear_func, gradient, X_theta, ever_active, nactive, kkt_tol, objective_tol, parameter_tol, max_active, kkt_stop, objective_stop, param_stop)
-}
-
-update1_ <- function(Q2, w, m, k) {
-    .Call('selectiveInference_update1_', PACKAGE = 'selectiveInference', Q2, w, m, k)
-}
-
-downdate1_ <- function(Q1, R, j0, m, n) {
-    .Call('selectiveInference_downdate1_', PACKAGE = 'selectiveInference', Q1, R, j0, m, n)
-}
-
-log_density_gaussian_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
-    .Call('selectiveInference_log_density_gaussian_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
-}
-
-log_density_gaussian_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
-    .Call('selectiveInference_log_density_gaussian_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
-}
-
-log_density_laplace_ <- function(noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset) {
-    .Call('selectiveInference_log_density_laplace_', PACKAGE = 'selectiveInference', noise_scale, internal_linear, internal_state, optimization_linear, optimization_state, offset)
-}
-
-log_density_laplace_conditional_ <- function(noise_scale, optimization_linear, optimization_state, offset) {
-    .Call('selectiveInference_log_density_laplace_conditional_', PACKAGE = 'selectiveInference', noise_scale, optimization_linear, optimization_state, offset)
-}
-
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 0196a6e..236e350 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -9,7 +9,6 @@ randomizedLasso = function(X,
                            family=c("gaussian","binomial"),
                            noise_scale=NULL, 
                            ridge_term=NULL, 
-                           noise_type=c('gaussian', 'laplace'),
                            max_iter=100,        # how many iterations for each optimization problem
                            kkt_tol=1.e-4,       # tolerance for the KKT conditions
                            parameter_tol=1.e-8, # tolerance for relative convergence of parameter
@@ -36,15 +35,8 @@ randomizedLasso = function(X,
         noise_scale = 0.5 * sd(y) * sqrt(mean_diag)
     }
     
-    noise_type = match.arg(noise_type)
-
     if (noise_scale > 0) {
-        if (noise_type == 'gaussian') {
-            perturb_ = rnorm(p) * noise_scale
-        }
-        else if (noise_type == 'laplace') {
-            perturb_ = rexp(p) * (2 * rbinom(p, 1, 0.5) - 1) * noise_scale
-        }
+        perturb_ = rnorm(p) * noise_scale
     } else {
         perturb_ = rep(0, p)
     }
@@ -377,7 +369,7 @@ conditional_opt_transform = function(noise_scale,
 	      importance_transform=opt_transform))
 }
 
-randomizedLassoInf = function(lasso_soln,
+randomizedLassoInf = function(rand_lasso_soln,
                               sigma=NULL, 
                               condition_subgrad=TRUE, 
                               level=0.9,
@@ -386,26 +378,26 @@ randomizedLassoInf = function(lasso_soln,
                               burnin=2000)
  {
 
-  n = nrow(lasso_soln$X)
-  p = ncol(lasso_soln$X)
+  n = nrow(rand_lasso_soln$X)
+  p = ncol(rand_lasso_soln$X)
   
-  active_set = lasso_soln$active_set
+  active_set = rand_lasso_soln$active_set
   nactive = length(active_set)
 
   if (nactive==0){
     return (list(active_set=active_set, pvalues=c(), ci=c()))
   }
-  inactive_set = lasso_soln$inactive_set
-  noise_scale = lasso_soln$noise_scale  # set to default value in randomizedLasso
+  inactive_set = rand_lasso_soln$inactive_set
+  noise_scale = rand_lasso_soln$noise_scale  # set to default value in randomizedLasso
    
-  ndim = length(lasso_soln$observed_opt_state)
+  ndim = length(rand_lasso_soln$observed_opt_state)
   
   sampler = match.arg(sampler)
 
   if (condition_subgrad == TRUE) {
-      cur_law = lasso_soln$conditional_law
+      cur_law = rand_lasso_soln$conditional_law
   } else {
-      cur_law = lasso_soln$full_law
+      cur_law = rand_lasso_soln$full_law
   }       
 
   if (sampler == "adaptMCMC"){
@@ -422,17 +414,17 @@ randomizedLassoInf = function(lasso_soln,
     opt_sample = opt_samples[(burnin+1):nsample,]
   }
   
-  X_E = lasso_soln$X[, active_set]
-  X_minusE = lasso_soln$X[, inactive_set]
-  y = lasso_soln$y
+  X_E = rand_lasso_soln$X[, active_set]
+  X_minusE = rand_lasso_soln$X[, inactive_set]
+  y = rand_lasso_soln$y
 
-  if (lasso_soln$family == "gaussian") {
+  if (rand_lasso_soln$family == "gaussian") {
     lm_y = lm(y ~ X_E - 1)
     sigma_resid = sqrt(sum(resid(lm_y)^2) / lm_y$df.resid)
     observed_target = lm_y$coefficients
     W_E = diag(rep(1,n))
     observed_internal = c(observed_target, t(X_minusE) %*% (y-X_E%*% observed_target))
-  } else if (lasso_soln$family == "binomial") {
+  } else if (rand_lasso_soln$family == "binomial") {
     glm_y = glm(y~X_E-1, family="binomial")
     sigma_resid = 1
     observed_target = as.vector(glm_y$coefficients)
@@ -449,8 +441,8 @@ randomizedLassoInf = function(lasso_soln,
   
   target_cov = solve(t(X_E) %*% W_E %*% X_E)*sigma^2
   cov_target_internal = rbind(target_cov, matrix(0, nrow=p-nactive, ncol=nactive))
-  internal_transform = lasso_soln$internal_transform
-  observed_raw = lasso_soln$observed_raw
+  internal_transform = rand_lasso_soln$internal_transform
+  observed_raw = rand_lasso_soln$observed_raw
   
   pvalues = rep(0, nactive)
   ci = matrix(0, nactive, 2)
diff --git a/selectiveInference/man/randomizedLasso.Rd b/selectiveInference/man/randomizedLasso.Rd
new file mode 100644
index 0000000..fd17bfc
--- /dev/null
+++ b/selectiveInference/man/randomizedLasso.Rd
@@ -0,0 +1,142 @@
+\name{randomizedLasso}
+\alias{randomizedLasso}
+
+\title{
+Inference for the randomized lasso, with a fixed lambda 
+}
+\description{
+Solve a randomly perturbed LASSO problem.
+}
+\usage{
+randomizedLasso(X, 
+                y, 
+                lam, 
+                family=c("gaussian", "binomial"),
+                noise_scale=NULL, 
+                ridge_term=NULL, 
+                max_iter=100,       
+                kkt_tol=1.e-4,      
+                parameter_tol=1.e-8,
+                objective_tol=1.e-8,
+                objective_stop=FALSE,
+                kkt_stop=TRUE,
+                parameter_stop=TRUE)
+}
+\arguments{
+  \item{X}{
+Matrix of predictors (n by p); 
+}
+  \item{y}{
+Vector of outcomes (length n)
+}
+  \item{lam}{
+Value of lambda used to compute beta. See the above warning
+ Be careful! This function uses the "standard" lasso objective
+  \deqn{
+    1/2 \|y - x \beta\|_2^2 + \lambda \|\beta\|_1.
+  }
+ In contrast, glmnet multiplies the first term by a factor of 1/n.
+ So after running glmnet, to extract the beta corresponding to a value lambda, 
+ you need to use \code{beta = coef(obj, s=lambda/n)[-1]},
+ where obj is the object returned by glmnet (and [-1] removes the intercept,
+ which glmnet always puts in the first component)
+} 
+\item{family}{
+Response type: "gaussian" (default), "binomial".
+}
+\item{noise_scale}{
+Scale of Gaussian noise added to objective. Default is 
+0.5 * sd(y) times the sqrt of the mean of the trace of X^TX.
+}
+\item{ridge_term}{
+A small "elastic net" or ridge penalty is added to ensure
+the randomized problem has a solution. 
+0.5 * sd(y) times the sqrt of the mean of the trace of X^TX divided by
+sqrt(n).
+}
+\item{max_iter}{
+How many rounds of updates used of coordinate descent in solving randomized
+LASSO.
+}
+\item{kkt_tol}{
+Tolerance for checking convergence based on KKT conditions.
+}
+\item{parameter_tol}{
+Tolerance for checking convergence based on convergence
+of parameters.
+}
+\item{objective_tol}{
+Tolerance for checking convergence based on convergence
+of objective value.
+}
+\item{kkt_stop}{
+Should we use KKT check to determine when to stop?
+}
+\item{parameter_stop}{
+Should we use convergence of parameters to determine when to stop?
+}
+\item{objective_stop}{
+Should we use convergence of objective value to determine when to stop?
+}
+}
+
+\details{For \code{family="gaussian"} this function uses the "standard" lasso objective
+  \deqn{
+    1/2 \|y - x \beta\|_2^2 + \lambda \|\beta\|_1
+  }
+and adds a term 
+  \deqn{
+    - \omega^T\beta + \frac{\epsilon}{2} \|\beta\|^2_2
+  }
+where omega is drawn from IID normals with standard deviation
+\code{noise_scale} and epsilon given by \code{ridge_term}. 
+See below for default values of \code{noise_scale} and \code{ridge_term}.
+
+For \code{family="binomial"}, the squared error loss is replaced by the
+negative of the logistic log-likelihood.
+}
+\value{  
+\item{type}{Type of coefficients tested (partial or full)}
+\item{lambda}{Value of tuning parameter lambda used}
+\item{pv}{One-sided P-values for active variables, uses the fact we have conditioned on the sign.}
+\item{ci}{Confidence intervals}
+\item{tailarea}{Realized tail areas (lower and upper) for each confidence interval}
+\item{vlo}{Lower truncation limits for statistics}
+\item{vup}{Upper truncation limits for statistics}
+\item{vmat}{Linear contrasts that define the observed statistics}
+\item{y}{Vector of outcomes}
+\item{vars}{Variables in active set}
+\item{sign}{Signs of active coefficients}
+\item{alpha}{Desired coverage (alpha/2 in each tail)}
+\item{sigma}{Value of error standard deviation (sigma) used}
+\item{call}{The call to lassoInf}
+}
+
+\references{
+Xiaoying Tian, and Jonathan Taylor (2015).
+Selective inference with a randomized response. arxiv.org:1507.06739
+
+Xiaoying Tian, Snigdha Panigrahi, Jelena Markovic, Nan Bi and Jonathan Taylor (2016).
+Selective inference after solving a convex problem. 
+arxiv:1609.05609
+
+}
+\author{Jelena Markovic, Jonathan Taylor}
+
+\examples{
+set.seed(43)
+n = 50
+p = 10
+sigma = 0.2
+lam = 0.5
+
+X = matrix(rnorm(n*p), n, p)
+X = scale(X, TRUE, TRUE) / sqrt(n-1)
+
+beta = c(3,2,rep(0,p-2))
+y = X\%*\%beta + sigma*rnorm(n)
+
+result = randomizedLasso(X, y, lam)
+
+}
+ 
diff --git a/selectiveInference/man/randomizedLassoInf.Rd b/selectiveInference/man/randomizedLassoInf.Rd
index 351596e..b06b38e 100644
--- a/selectiveInference/man/randomizedLassoInf.Rd
+++ b/selectiveInference/man/randomizedLassoInf.Rd
@@ -11,63 +11,23 @@ fixed value of the tuning parameter lambda and using Gaussian
 randomization.
 }
 \usage{
-randomizedLassoInf(X, 
-                   y, 
-                   lam, 
-                   family=c("gaussian", "binomial"),
+randomizedLassoInf(rand_lasso_soln, 
                    sigma=NULL, 
-                   noise_scale=NULL, 
-                   ridge_term=NULL, 
                    condition_subgrad=TRUE, 
                    level=0.9,
                    sampler=c("norejection", "adaptMCMC"),
                    nsample=10000,
-                   burnin=2000,
-                   max_iter=100,       
-                   kkt_tol=1.e-4,      
-                   parameter_tol=1.e-8,
-                   objective_tol=1.e-8,
-                   objective_stop=FALSE,
-                   kkt_stop=TRUE,
-                   parameter_stop=TRUE)
+                   burnin=2000)
 }
 \arguments{
-  \item{X}{
-Matrix of predictors (n by p); 
-}
-  \item{y}{
-Vector of outcomes (length n)
-}
-  \item{lam}{
-Value of lambda used to compute beta. See the above warning
- Be careful! This function uses the "standard" lasso objective
-  \deqn{
-    1/2 \|y - x \beta\|_2^2 + \lambda \|\beta\|_1.
-  }
- In contrast, glmnet multiplies the first term by a factor of 1/n.
- So after running glmnet, to extract the beta corresponding to a value lambda, 
- you need to use \code{beta = coef(obj, s=lambda/n)[-1]},
- where obj is the object returned by glmnet (and [-1] removes the intercept,
- which glmnet always puts in the first component)
-} 
-\item{family}{
-Response type: "gaussian" (default), "binomial".
+  \item{rand_lasso_soln}{
+A randomized lasso solution as returned by \code{randomizedLasso}.
 }
 \item{sigma}{
 Estimate of error standard deviation. If NULL (default), this is estimated 
 using the mean squared residual of the full least squares based on 
 selected active set.
 }
-\item{noise_scale}{
-Scale of Gaussian noise added to objective. Default is 
-0.5 * sd(y) times the sqrt of the mean of the trace of X^TX.
-}
-\item{ridge_term}{
-A small "elastic net" or ridge penalty is added to ensure
-the randomized problem has a solution. 
-0.5 * sd(y) times the sqrt of the mean of the trace of X^TX divided by
-sqrt(n).
-}
 \item{condition_subgrad}{
 In forming selective confidence intervals and p-values should we condition
 on the inactive coordinates of the subgradient as well?
@@ -86,30 +46,6 @@ Number of samples of optimization variables to sample.
 \item{burnin}{
 How many samples of optimization variable to discard (should be less than nsample).
 }
-\item{max_iter}{
-How many rounds of updates used of coordinate descent in solving randomized
-LASSO.
-}
-\item{kkt_tol}{
-Tolerance for checking convergence based on KKT conditions.
-}
-\item{parameter_tol}{
-Tolerance for checking convergence based on convergence
-of parameters.
-}
-\item{objective_tol}{
-Tolerance for checking convergence based on convergence
-of objective value.
-}
-\item{kkt_stop}{
-Should we use KKT check to determine when to stop?
-}
-\item{parameter_stop}{
-Should we use convergence of parameters to determine when to stop?
-}
-\item{objective_stop}{
-Should we use convergence of objective value to determine when to stop?
-}
 }
 
 \details{
@@ -159,7 +95,7 @@ X = scale(X, TRUE, TRUE) / sqrt(n-1)
 beta = c(3,2,rep(0,p-2))
 y = X\%*\%beta + sigma*rnorm(n)
 
-result = randomizedLassoInf(X, y, lam)
-
+result = randomizedLasso(X, y, lam)
+inf_result = randomizedLassoInf(result)
 }
  
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 7ea756c..4fecf17 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -99,9 +99,13 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
     data = get_instance(n=n,p=p,s=s, rho=rho, sigma=sigma, family=family)
     X=data$X
     y=data$y
-    result = selectiveInference:::randomizedLassoInf(X, y, 
-                                                     lam, 
-                                                     family = family,
+
+    rand_lasso_soln = selectiveInference:::randomizedLasso(X, 
+                                                           y, 
+                                                           lam, 
+                                                           family=family)
+
+    result = selectiveInference:::randomizedLassoInf(rand_lasso_soln,
                                                      sampler = "adaptMCMC", #"norejection", #
                                                      sigma=sigma,
                                                      level=level, 

From b7080813a56847fbb8e21fcb501aa4b714f30f81 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sun, 12 Nov 2017 14:26:53 -0800
Subject: [PATCH 406/493] updating C-software

---
 C-software | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/C-software b/C-software
index 563bf1a..0b35c6e 160000
--- a/C-software
+++ b/C-software
@@ -1 +1 @@
-Subproject commit 563bf1aa370b55f8343693224717047f1df0d0c3
+Subproject commit 0b35c6ed8537cef9aabed526b968b1c63d2f6cb8

From cabc4d98189a21c34e306508127e47959c5fe313 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sun, 12 Nov 2017 16:11:43 -0800
Subject: [PATCH 407/493] NF: targets can be passed to randomizedLassoInf

---
 selectiveInference/R/funs.randomized.R       | 90 ++++++++++----------
 selectiveInference/man/randomizedLassoInf.Rd | 20 +++--
 2 files changed, 61 insertions(+), 49 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 236e350..8ced6f0 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -370,7 +370,7 @@ conditional_opt_transform = function(noise_scale,
 }
 
 randomizedLassoInf = function(rand_lasso_soln,
-                              sigma=NULL, 
+                              targets=NULL,
                               condition_subgrad=TRUE, 
                               level=0.9,
                               sampler=c("norejection", "adaptMCMC"),
@@ -414,52 +414,56 @@ randomizedLassoInf = function(rand_lasso_soln,
     opt_sample = opt_samples[(burnin+1):nsample,]
   }
   
-  X_E = rand_lasso_soln$X[, active_set]
-  X_minusE = rand_lasso_soln$X[, inactive_set]
-  y = rand_lasso_soln$y
-
-  if (rand_lasso_soln$family == "gaussian") {
-    lm_y = lm(y ~ X_E - 1)
-    sigma_resid = sqrt(sum(resid(lm_y)^2) / lm_y$df.resid)
-    observed_target = lm_y$coefficients
-    W_E = diag(rep(1,n))
-    observed_internal = c(observed_target, t(X_minusE) %*% (y-X_E%*% observed_target))
-  } else if (rand_lasso_soln$family == "binomial") {
-    glm_y = glm(y~X_E-1, family="binomial")
-    sigma_resid = 1
-    observed_target = as.vector(glm_y$coefficients)
-    pi_vec = logistic_fitted(X_E, observed_target)
-    observed_internal =  c(observed_target, t(X_minusE) %*% (y-pi_vec))
-    W_E=diag(as.vector(pi_vec *(1-pi_vec)))
-  }
-  
-  # if no sigma given, use the estimate
-  
-  if (is.null(sigma)) {
-    sigma = sigma_resid
-  }        
-  
-  target_cov = solve(t(X_E) %*% W_E %*% X_E)*sigma^2
-  cov_target_internal = rbind(target_cov, matrix(0, nrow=p-nactive, ncol=nactive))
-  internal_transform = rand_lasso_soln$internal_transform
-  observed_raw = rand_lasso_soln$observed_raw
-  
+  if (is.null(targets)) {
+
+      X_E = rand_lasso_soln$X[, active_set]
+      X_minusE = rand_lasso_soln$X[, inactive_set]
+      y = rand_lasso_soln$y
+
+      if (rand_lasso_soln$family == "gaussian") {
+        lm_y = lm(y ~ X_E - 1)
+        sigma_resid = sqrt(sum(resid(lm_y)^2) / lm_y$df.resid)
+        observed_target = lm_y$coefficients
+        W_E = diag(rep(1,n))
+        observed_internal = c(observed_target, t(X_minusE) %*% (y-X_E%*% observed_target))
+      } else if (rand_lasso_soln$family == "binomial") {
+        glm_y = glm(y~X_E-1, family="binomial")
+        sigma_resid = 1
+        observed_target = as.vector(glm_y$coefficients)
+        pi_vec = logistic_fitted(X_E, observed_target)
+        observed_internal =  c(observed_target, t(X_minusE) %*% (y-pi_vec))
+        W_E=diag(as.vector(pi_vec *(1-pi_vec)))
+      }
+
+      cov_target = solve(t(X_E) %*% W_E %*% X_E)*sigma_resid^2
+
+      targets = list(observed_target=observed_target,
+                     cov_target=cov_target,
+                     crosscov_target_internal=rbind(cov_target, matrix(0, nrow=p-nactive, ncol=nactive)))
+
+  } 
+
+  importance_transform = cur_law$importance_transform
+  internal_transform=rand_lasso_soln$internal_transform
+  observed_raw=rand_lasso_soln$observed_raw
+
   pvalues = rep(0, nactive)
   ci = matrix(0, nactive, 2)
   
-  importance_transform = cur_law$importance_transform
-
   for (i in 1:nactive){
 
-    target_transform = linear_decomposition(observed_target[i], 
+    target_transform = linear_decomposition(targets$observed_target[i], 
                                             observed_internal, 
-                                            target_cov[i,i], 
-                                            cov_target_internal[,i],
+                                            targets$cov_target[i,i], 
+                                            targets$crosscov_target_internal[,i],
                                             internal_transform)
     
     # changing dimension of density evalutaion
 
     if ((condition_subgrad == TRUE) & (nactive < p-1)) {
+
+        # A description of why we do this might help
+
         target_opt_linear = cbind(target_transform$linear_term, importance_transform$linear_term)
         reduced_target_opt_linear = chol(t(target_opt_linear) %*% target_opt_linear)
         target_linear = reduced_target_opt_linear[,1]
@@ -469,13 +473,13 @@ randomizedLassoInf = function(rand_lasso_soln,
         cur_linear = reduced_target_opt_linear[,2:ncol(reduced_target_opt_linear)]
         cur_offset = temp %*% importance_transform$offset_term
         cur_transform = list(linear_term = as.matrix(cur_linear), offset_term = cur_offset)
-        raw = target_transform$linear_term * observed_target[i] + target_transform$offset_term
+        raw = target_transform$linear_term * targets$observed_target[i] + target_transform$offset_term
     } else {
         cur_transform = importance_transform
         raw = observed_raw
     }   
 
-    target_sample = rnorm(nrow(as.matrix(opt_samples))) * sqrt(target_cov[i,i])
+    target_sample = rnorm(nrow(as.matrix(opt_samples))) * sqrt(targets$cov_target[i,i])
 
     pivot = function(candidate){
 
@@ -485,16 +489,16 @@ randomizedLassoInf = function(rand_lasso_soln,
                                   cur_transform,
                                   target_transform,
                                   raw)
-      return(mean((target_sample + candidate < observed_target[i]) * weights)/mean(weights))
+      return(mean((target_sample + candidate < targets$observed_target[i]) * weights)/mean(weights))
 
     }
 
     rootU = function(candidate){
-      return (pivot(observed_target[i]+candidate)-(1-level)/2)
+      return (pivot(targets$observed_target[i]+candidate)-(1-level)/2)
     }
 
     rootL = function(candidate){
-      return(pivot(observed_target[i]+candidate)-(1+level)/2)
+      return(pivot(targets$observed_target[i]+candidate)-(1+level)/2)
     }
 
     pvalues[i] = pivot(0)
@@ -502,12 +506,12 @@ randomizedLassoInf = function(rand_lasso_soln,
     line_max = 10*sd(target_sample)
 
     if (rootU(line_min)*rootU(line_max)<0){
-      ci[i,2] = uniroot(rootU, c(line_min, line_max))$root+observed_target[i]
+      ci[i,2] = uniroot(rootU, c(line_min, line_max))$root + targets$observed_target[i]
     } else{
       ci[i,2]=line_max
     }
     if (rootL(line_min)*rootL(line_max)<0){
-      ci[i,1] = uniroot(rootL, c(line_min, line_max))$root+observed_target[i]
+      ci[i,1] = uniroot(rootL, c(line_min, line_max))$root + targets$observed_target[i]
     } else{
       ci[i,1] = line_min
     }
diff --git a/selectiveInference/man/randomizedLassoInf.Rd b/selectiveInference/man/randomizedLassoInf.Rd
index b06b38e..5ecee24 100644
--- a/selectiveInference/man/randomizedLassoInf.Rd
+++ b/selectiveInference/man/randomizedLassoInf.Rd
@@ -12,7 +12,7 @@ randomization.
 }
 \usage{
 randomizedLassoInf(rand_lasso_soln, 
-                   sigma=NULL, 
+		   targets=NULL,
                    condition_subgrad=TRUE, 
                    level=0.9,
                    sampler=c("norejection", "adaptMCMC"),
@@ -20,13 +20,21 @@ randomizedLassoInf(rand_lasso_soln,
                    burnin=2000)
 }
 \arguments{
-  \item{rand_lasso_soln}{
+\item{rand_lasso_soln}{
 A randomized lasso solution as returned by \code{randomizedLasso}.
 }
-\item{sigma}{
-Estimate of error standard deviation. If NULL (default), this is estimated 
-using the mean squared residual of the full least squares based on 
-selected active set.
+\item{targets}{
+If not NULL, should be a list with entries \code{observed_target, cov_target, crosscov_target_internal}.
+The \code{observed_target} should be (pre-selection) asymptotically Gaussian around targeted
+parameters. The quantity \code{cov_target} should be an estimate of the (pre-selection) covariance
+of \code{observed_target}. Finally, \code{crosscov_target_internal} should be an estimate of
+the (pre-selection) covariance of \code{observed_target} and the internal representation of the
+data of the LASSO. For both \code{"gaussian"} and \code{"binomial"}, this is the vector
+  \deqn{
+\hat{\beta}_{E,MLE}, X_{-E}^T(y - \mu(X_E\hat{\beta}_{E,MLE}))
+  } 
+For example, this cross-covariance could be estimated by jointly bootstrapping the target 
+of interest and the above vector.
 }
 \item{condition_subgrad}{
 In forming selective confidence intervals and p-values should we condition

From c9d9ed18faf39daf100fe87f71ce0a850544236f Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sun, 12 Nov 2017 21:36:49 -0800
Subject: [PATCH 408/493] BF: unused sigma argument in test

---
 tests/randomized/test_instances.R | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 4fecf17..a2755e1 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -107,7 +107,6 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
 
     result = selectiveInference:::randomizedLassoInf(rand_lasso_soln,
                                                      sampler = "adaptMCMC", #"norejection", #
-                                                     sigma=sigma,
                                                      level=level, 
                                                      burnin=1000, 
                                                      nsample=5000, 

From b86e1a48be75d0760360bfc15b1a705e423f1547 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sun, 12 Nov 2017 21:44:38 -0800
Subject: [PATCH 409/493] slightly bigger sampler for logistic

---
 tests/randomized/test_instances.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index a2755e1..b3617e6 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -135,5 +135,5 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
 }
 
 set.seed(1)
-collect_results(n=100, p=20, s=0, lam=0.5)
+collect_results(n=500, p=200, s=0, lam=1.)
 

From 306010050ae814ed665984ddc17515b76ca74177 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sun, 12 Nov 2017 21:45:29 -0800
Subject: [PATCH 410/493] possibility of different randomization scales for
 binomial, gaussian

---
 .gitignore                             | 5 ++++-
 selectiveInference/R/funs.randomized.R | 6 +++++-
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/.gitignore b/.gitignore
index 3b58381..46dc0e2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,4 +8,7 @@
 **.so
 forLater/josh/**
 */R/RcppExports.R
-*/src/RcppExports.cpp
\ No newline at end of file
+*/src/RcppExports.cpp
+*~
+*/*~
+*/*/*~
\ No newline at end of file
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 8ced6f0..159956c 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -32,7 +32,11 @@ randomizedLasso = function(X,
     # default noise level
 
     if (is.null(noise_scale)) {
-        noise_scale = 0.5 * sd(y) * sqrt(mean_diag)
+        if (family == "gaussian") {
+            noise_scale = 0.5 * sd(y) * sqrt(mean_diag)
+        } else if (family == "binomial") {
+	    noise_scale = 0.5 * sd(y) * sqrt(mean_diag) 
+        }
     }
     
     if (noise_scale > 0) {

From 08294ca51ba3f5e2d974bf6aa50e9ac52229d234 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sun, 12 Nov 2017 22:01:51 -0800
Subject: [PATCH 411/493] DOC: fixing Rd files for return values

---
 selectiveInference/R/funs.randomized.R       |  3 +-
 selectiveInference/man/randomizedLasso.Rd    | 30 +++++++++++---------
 selectiveInference/man/randomizedLassoInf.Rd | 24 ++++++----------
 3 files changed, 25 insertions(+), 32 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 159956c..504d7ac 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -254,7 +254,6 @@ randomizedLasso = function(X,
                 unpenalized_set=unpenalized_set,
                 sign_soln=sign_soln,
                 full_law=full_law,
-		#optimization_transform=opt_transform,
                 conditional_law=conditional_law,
                 internal_transform=internal_transform,
                 observed_raw=observed_raw,
@@ -520,7 +519,7 @@ randomizedLassoInf = function(rand_lasso_soln,
       ci[i,1] = line_min
     }
   }
-  return(list(active_set=active_set, pvalues=pvalues, ci=ci))
+  return(list(targets=targets, pvalues=pvalues, ci=ci))
 }
 
 logistic_fitted = function(X, beta){
diff --git a/selectiveInference/man/randomizedLasso.Rd b/selectiveInference/man/randomizedLasso.Rd
index fd17bfc..1f69f71 100644
--- a/selectiveInference/man/randomizedLasso.Rd
+++ b/selectiveInference/man/randomizedLasso.Rd
@@ -96,20 +96,22 @@ For \code{family="binomial"}, the squared error loss is replaced by the
 negative of the logistic log-likelihood.
 }
 \value{  
-\item{type}{Type of coefficients tested (partial or full)}
-\item{lambda}{Value of tuning parameter lambda used}
-\item{pv}{One-sided P-values for active variables, uses the fact we have conditioned on the sign.}
-\item{ci}{Confidence intervals}
-\item{tailarea}{Realized tail areas (lower and upper) for each confidence interval}
-\item{vlo}{Lower truncation limits for statistics}
-\item{vup}{Upper truncation limits for statistics}
-\item{vmat}{Linear contrasts that define the observed statistics}
-\item{y}{Vector of outcomes}
-\item{vars}{Variables in active set}
-\item{sign}{Signs of active coefficients}
-\item{alpha}{Desired coverage (alpha/2 in each tail)}
-\item{sigma}{Value of error standard deviation (sigma) used}
-\item{call}{The call to lassoInf}
+\item{X}{Design matrix.}
+\item{y}{Response vector.}
+\item{lam}{Vector of penalty parameters.}
+\item{family}{Family: "gaussian" or "binomial".}
+\item{active_set}{Set of non-zero coefficients in randomized solution that were penalized. Integers from 1:p.}
+\item{inactive_set}{Set of zero coefficients in randomized solution. Integers from 1:p.}
+\item{unpenalized_set}{Set of non-zero coefficients in randomized solution that were not penalized. Integers from 1:p.}
+\item{sign_soln}{The sign pattern of the randomized solution.}
+\item{full_law}{List describing sampling parameters for conditional law of all optimization variables given the data in the LASSO problem.}
+\item{conditional_law}{List describing sampling parameters for conditional law of only the scaling variables given the data and the observed subgradient in the LASSO problem.}
+\item{internal_transform}{Affine transformation describing relationship between internal representation of the data and the data compontent of score of the likelihood at the unregularized MLE based on the sign_vector (a.k.a. relaxed LASSO).}
+\item{observed_raw}{Data component of the score at the unregularized MLE.}
+\item{noise_scale}{SD of Gaussian noise used to draw the perturbed objective.}
+\item{soln}{The randomized solution. Inference is made conditional on its sign vector (so no more snooping of this value is formally permitted.) 
+If \code{condition_subgrad == TRUE} when sampling, then we may snoop on the observed subgradient.}
+\item{perturb}{The random vector in the linear term added to the objective.}
 }
 
 \references{
diff --git a/selectiveInference/man/randomizedLassoInf.Rd b/selectiveInference/man/randomizedLassoInf.Rd
index 5ecee24..3bffbfa 100644
--- a/selectiveInference/man/randomizedLassoInf.Rd
+++ b/selectiveInference/man/randomizedLassoInf.Rd
@@ -63,29 +63,21 @@ given a fixed value of the tuning parameter lambda.
 
 }
 \value{  
-\item{type}{Type of coefficients tested (partial or full)}
-\item{lambda}{Value of tuning parameter lambda used}
-\item{pv}{One-sided P-values for active variables, uses the fact we have conditioned on the sign.}
-\item{ci}{Confidence intervals}
-\item{tailarea}{Realized tail areas (lower and upper) for each confidence interval}
-\item{vlo}{Lower truncation limits for statistics}
-\item{vup}{Upper truncation limits for statistics}
-\item{vmat}{Linear contrasts that define the observed statistics}
-\item{y}{Vector of outcomes}
-\item{vars}{Variables in active set}
-\item{sign}{Signs of active coefficients}
-\item{alpha}{Desired coverage (alpha/2 in each tail)}
-\item{sigma}{Value of error standard deviation (sigma) used}
-\item{call}{The call to lassoInf}
+\item{targets}{A list with entries \code{observed_target, cov_target, crosscov_target_internal}. See argument description above.}
+\item{pvalues}{P-values testing hypotheses that each specific target is 0.}
+\item{ci}{Confidence interval for parameters determined by \code{targets}.}
 }
 
 \references{
-Xiaoying Tian, and Jonathan Taylor (2015).
+Jelena Markovic and Jonathan Taylor (2016).
+Bootstrap inference after using multiple queries for model selection. arxiv.org:1612.07811
+
+Xiaoying Tian and Jonathan Taylor (2015).
 Selective inference with a randomized response. arxiv.org:1507.06739
 
 Xiaoying Tian, Snigdha Panigrahi, Jelena Markovic, Nan Bi and Jonathan Taylor (2016).
 Selective inference after solving a convex problem. 
-arxiv:1609.05609
+arxiv.org:1609.05609
 
 }
 \author{Jelena Markovic, Jonathan Taylor}

From 194dddaf5ed8417b29cb259dc61f739eca1dca62 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 13 Nov 2017 23:10:57 -0800
Subject: [PATCH 412/493] BF: missed some drop=FALSE; RF: using glm to do the
 work for covariance

---
 selectiveInference/R/funs.randomized.R | 26 +++++++++-----------------
 1 file changed, 9 insertions(+), 17 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 504d7ac..d5864f4 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -419,26 +419,18 @@ randomizedLassoInf = function(rand_lasso_soln,
   
   if (is.null(targets)) {
 
-      X_E = rand_lasso_soln$X[, active_set]
-      X_minusE = rand_lasso_soln$X[, inactive_set]
+      X_E = rand_lasso_soln$X[, active_set, drop=FALSE]
+      X_minusE = rand_lasso_soln$X[, inactive_set, drop=FALSE]
       y = rand_lasso_soln$y
 
-      if (rand_lasso_soln$family == "gaussian") {
-        lm_y = lm(y ~ X_E - 1)
-        sigma_resid = sqrt(sum(resid(lm_y)^2) / lm_y$df.resid)
-        observed_target = lm_y$coefficients
-        W_E = diag(rep(1,n))
-        observed_internal = c(observed_target, t(X_minusE) %*% (y-X_E%*% observed_target))
-      } else if (rand_lasso_soln$family == "binomial") {
-        glm_y = glm(y~X_E-1, family="binomial")
-        sigma_resid = 1
-        observed_target = as.vector(glm_y$coefficients)
-        pi_vec = logistic_fitted(X_E, observed_target)
-        observed_internal =  c(observed_target, t(X_minusE) %*% (y-pi_vec))
-        W_E=diag(as.vector(pi_vec *(1-pi_vec)))
+      if (rand_lasso_soln$family == 'gaussian') {
+         glm_y = glm(y ~ X_E-1)
+      } else if (rand_lasso_soln$family == 'binomial') {
+         glm_y = glm(y ~ X_E-1, family=binomial())
       }
-
-      cov_target = solve(t(X_E) %*% W_E %*% X_E)*sigma_resid^2
+      observed_target = as.vector(glm_y$coefficients)
+      observed_internal =  c(observed_target, t(X_minusE) %*% (y - fitted(glm_y)))
+      cov_target = vcov(glm_y)
 
       targets = list(observed_target=observed_target,
                      cov_target=cov_target,

From b26e1523d3e3333975cdfdfe9875ad95fbc69778 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 13 Nov 2017 23:14:08 -0800
Subject: [PATCH 413/493] function imports

---
 selectiveInference/NAMESPACE | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index 0fa35e1..976a667 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -42,7 +42,7 @@ import(intervals)
 import(survival)
 importFrom("graphics", abline, axis, matplot)
 importFrom("stats", dnorm, lsfit, pexp, pnorm, predict, 
-           qnorm, rnorm, sd, uniroot, dchisq, model.matrix, pchisq, resid)
+           qnorm, rnorm, sd, uniroot, dchisq, model.matrix, pchisq, resid, binomial, fitted)
 importFrom("stats", "coef", "df", "lm", "pf")
 importFrom("stats", "glm", "residuals", "vcov")
 importFrom("stats", "rbinom", "rexp")

From 9de1b7c4f7b9544262a7168d1717241841742888 Mon Sep 17 00:00:00 2001
From: tibs <tibs@stanford.edu>
Date: Tue, 14 Nov 2017 21:25:27 -0800
Subject: [PATCH 414/493] rob chanegd NAMESPACE

---
 selectiveInference/NAMESPACE | 1 +
 1 file changed, 1 insertion(+)

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index 11a834e..f6854c5 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -15,6 +15,7 @@ export(lar,fs,
        TG.limits, 
        TG.interval,
        debiasingMatrix,
+       randomizedLasso,
        randomizedLassoInf
     )
 

From 5ea21ba722b8ff590d69b5353c6713ec84bcfd37 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 16 Nov 2017 15:31:05 -0800
Subject: [PATCH 415/493] check for failure to fit relaxed estimator

---
 selectiveInference/R/funs.randomized.R | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index d5864f4..0f29f61 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -432,6 +432,10 @@ randomizedLassoInf = function(rand_lasso_soln,
       observed_internal =  c(observed_target, t(X_minusE) %*% (y - fitted(glm_y)))
       cov_target = vcov(glm_y)
 
+      if (sum(is.na(observed_target)) > 0) {
+          stop("unregularized (relaxed) fit has NA values -- X[,active_set] likely singular")
+      }
+
       targets = list(observed_target=observed_target,
                      cov_target=cov_target,
                      crosscov_target_internal=rbind(cov_target, matrix(0, nrow=p-nactive, ncol=nactive)))

From 84cb2cbb3c8eb1b115c7468eb1f1decf3d11d118 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 16 Nov 2017 19:23:19 -0800
Subject: [PATCH 416/493] BF: trying not to form large matrices

---
 selectiveInference/R/funs.randomized.R | 141 ++++++++++++++-----------
 1 file changed, 81 insertions(+), 60 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 0f29f61..a50993e 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -7,6 +7,7 @@ randomizedLasso = function(X,
                            y, 
                            lam, 
                            family=c("gaussian","binomial"),
+			   condition_subgrad=TRUE,
                            noise_scale=NULL, 
                            ridge_term=NULL, 
                            max_iter=100,        # how many iterations for each optimization problem
@@ -122,7 +123,6 @@ randomizedLasso = function(X,
     E = c(unpenalized_set, active_set)
     I = inactive_set
     X_E = X[,E]
-    X_I = X[,I]
 
     if (length(E)==0){
       return(list(active_set=c()))
@@ -137,9 +137,8 @@ randomizedLasso = function(X,
       W_E = diag(rep(1,n))
     }
     
-    L_E = t(X) %*% W_E %*% X[,E]
-    
-    coef_term = L_E
+    L_E = t(X_E) %*% W_E %*% X
+    coef_term = t(L_E)
 
     signs_ = c(rep(1, sum(unpenalized)), sign_soln[active])
     
@@ -148,23 +147,29 @@ randomizedLasso = function(X,
     if (length(signs_) == 1) {
       coef_term = coef_term * signs_
     } else {
-      coef_term = coef_term %*% diag(signs_)  # scaligns are non-negative
+      coef_term = coef_term %*% diag(signs_)  # scalings are non-negative
     }
     
     if (sum(inactive) > 0) {
-        subgrad_term = matrix(0, p, sum(inactive)) # for subgrad
-        for (i in 1:sum(inactive)) {
-            subgrad_term[inactive_set[i], i] = 1
-            }
-
-        linear_term = cbind(coef_term,
-                            subgrad_term)
+        if (condition_subgrad == FALSE) {
+            subgrad_term = matrix(0, p, sum(inactive)) # for subgrad
+            for (i in 1:sum(inactive)) {
+                subgrad_term[inactive_set[i], i] = 1
+                }
+            linear_term = cbind(coef_term,
+                                subgrad_term)
+        } else {
+            linear_term = coef_term
+        }
     } else {
         linear_term = coef_term
     }    
     offset_term = rep(0, p)
     offset_term[active] = lam[active] * sign_soln[active]
 
+    # if conditional_subgrad == FALSE, linear_term will have E columns
+    # otherwise it will have p columns
+
     opt_transform = list(linear_term=linear_term,
                          offset_term=offset_term)
 
@@ -178,32 +183,37 @@ randomizedLasso = function(X,
     active_term = -L_E                           # for \bar{\beta}_E
 
     if (sum(inactive) > 0) {
-        inactive_term = -subgrad_term
-        linear_term = cbind(active_term,
-                            inactive_term)
+        if (condition_subgrad == FALSE) {
+            inactive_term = -subgrad_term
+            linear_term = cbind(active_term,
+                                inactive_term)
+        } else {
+            linear_term = active_term
+        }
     } else {
         linear_term = active_term
     }
   
     offset_term = rep(0, p)
-    internal_transform = list(linear_term = linear_term,
-                              offset_term = offset_term)
+
+    # if conditional_subgrad == FALSE, linear_term will have E columns
+    # otherwise it will have p columns
+
+    internal_transform = list(linear_term=linear_term,
+                              offset_term=offset_term)
 
     # density for sampling optimization variables
     
     if (family=="binomial"){
       unpen_est = as.vector(glm(y~X_E-1, family="binomial")$coefficients)
-      observed_raw = -t(X) %*% (y-logistic_fitted(X_E, unpen_est))-L_E %*% unpen_est  
+      observed_raw = -as.vector((y-logistic_fitted(X_E, unpen_est)) %*% X) - L_E %*% unpen_est
     } else if (family=="gaussian"){
-      observed_raw = -t(X) %*% y
+      observed_raw = -as.vector(y %*% X)
     }
     inactive_lam = lam[inactive_set]
     inactive_start = sum(unpenalized) + sum(active)
     active_start = sum(unpenalized)
     
-    
-    # XXX only for Gaussian so far
-
     log_optimization_density = function(opt_state) {
 
         if ((sum(abs(opt_state[(inactive_start + 1):p]) > inactive_lam) > 0) ||
@@ -221,30 +231,39 @@ randomizedLasso = function(X,
 
     # work out conditional density and save it as well
 
-    conditional_law = conditional_opt_transform(noise_scale, 
-                                                active_set,
-                                                observed_raw,
-                                                opt_transform$linear_term,
-                                                opt_transform$offset_term,
-                                                observed_opt_state)
-    constraints = matrix(0, length(active_set), 2)
-    constraints[,2] = Inf
-    conditional_law$constraints = constraints
+    if (condition_subgrad == TRUE) {
+
+        constraints = matrix(0, length(active_set), 2)
+        constraints[,2] = Inf
+
+        conditional_law = conditional_opt_transform(noise_scale, 
+                                                    active_set,
+                                                    inactive_set,
+                                                    observed_subgrad,
+                                                    observed_raw,
+                                                    opt_transform$linear_term,
+                                                    opt_transform$offset_term,
+                                                    observed_opt_state)
+        conditional_law$constraints = constraints
+        law = conditional_law
+    } else {
 
-    # add constraints and density to the full transform
+        constraints = matrix(0, length(active_set), 2)
+        constraints[,2] = Inf
 
-    subgrad_constraints = cbind(-lam[inactive_set], lam[inactive_set])
-    constraints = rbind(constraints, subgrad_constraints)
-    
-    # this is for conditional density of opt given data
+        subgrad_constraints = cbind(-lam[inactive_set], lam[inactive_set])
+        constraints = rbind(constraints, subgrad_constraints)
 
-    full_law = list(sampling_transform=list(linear_term=opt_transform$linear_term,
-                                            offset_term=opt_transform$offset_term + observed_raw),
-                    constraints=constraints,
-                    observed_opt_state=observed_opt_state,
-                    log_optimization_density=log_optimization_density,
-                    importance_transform=opt_transform)
+        full_law = list(sampling_transform=list(linear_term=opt_transform$linear_term,
+                                                offset_term=opt_transform$offset_term + observed_raw),
+                        constraints=constraints,
+                        observed_opt_state=observed_opt_state,
+                        log_optimization_density=log_optimization_density,
+                        importance_transform=opt_transform)
+        law = full_law
+    }
 
+    print(active_set)
     return(list(X=X,
                 y=y,
                 lam=lam,
@@ -253,13 +272,13 @@ randomizedLasso = function(X,
                 inactive_set=inactive_set,
                 unpenalized_set=unpenalized_set,
                 sign_soln=sign_soln,
-                full_law=full_law,
-                conditional_law=conditional_law,
+                law=law,
                 internal_transform=internal_transform,
                 observed_raw=observed_raw,
                 noise_scale=noise_scale,
                 soln=result$soln,
-                perturb=perturb_
+                perturb=perturb_,
+                condition_subgrad=condition_subgrad
                 ))
 
 }
@@ -330,6 +349,8 @@ get_mean_cov = function(noise_scale, linear_term, offset_term){
                              
 conditional_opt_transform = function(noise_scale, 
                                      active_set,
+                                     inactive_set,
+                                     observed_inactive_subgrad,
                                      observed_raw,
                                      opt_linear,
                                      opt_offset,
@@ -337,20 +358,21 @@ conditional_opt_transform = function(noise_scale,
   
  
   nactive = length(active_set)
-  B = opt_linear[,1:nactive,drop=FALSE]
+  B = opt_linear
   beta_offset = opt_offset
   p = length(observed_opt_state)
 
   if (nactive < p) {
-    beta_offset = beta_offset+(opt_linear[,(nactive+1):p,drop=FALSE] %*% observed_opt_state[(nactive+1):p])
+    beta_offset[inactive_set] = beta_offset[inactive_set] + observed_inactive_subgrad
   }
   opt_transform = list(linear_term=B, 
-                       offset_term = beta_offset)
+                       offset_term=beta_offset)
   reduced_B = chol(t(B) %*% B)
   beta_offset = beta_offset + observed_raw
   reduced_beta_offset = solve(t(reduced_B)) %*% (t(B) %*% beta_offset)
   
   log_condl_optimization_density = function(opt_state) {
+
     if  (sum(opt_state < 0) > 0) {
       return(-Inf)
     }
@@ -364,7 +386,7 @@ conditional_opt_transform = function(noise_scale,
 
   log_optimization_density = log_condl_optimization_density
   observed_opt_state = observed_opt_state[1:nactive]
-  optimization_transform = opt_transform
+
   reduced_opt_transform =list(linear_term = reduced_B, offset_term = reduced_beta_offset)
   return(list(sampling_transform=reduced_opt_transform,
               log_optimization_density=log_condl_optimization_density,
@@ -397,22 +419,21 @@ randomizedLassoInf = function(rand_lasso_soln,
   
   sampler = match.arg(sampler)
 
-  if (condition_subgrad == TRUE) {
-      cur_law = rand_lasso_soln$conditional_law
-  } else {
-      cur_law = rand_lasso_soln$full_law
-  }       
+  law = rand_lasso_soln$law
+  if (condition_subgrad != soln$condition_subgrad) {
+      stop("condition_subgrad does not agree with value when randomizedLasso was solved -- sampler not properly specified")
+  } 
 
   if (sampler == "adaptMCMC"){
-    S = sample_opt_variables(cur_law, 
+    S = sample_opt_variables(law, 
                              jump_scale=rep(1/sqrt(n), length(cur_law$observed_opt_state)), nsample=nsample)
     opt_samples = as.matrix(S$samples[(burnin+1):nsample,,drop=FALSE])
   } else if (sampler == "norejection") {
     opt_samples = gaussian_sampler(noise_scale, 
-                                   cur_law$observed_opt_state, 
-                                   cur_law$sampling_transform$linear_term,
-                                   cur_law$sampling_transform$offset_term,
-                                   cur_law$constraints,
+                                   law$observed_opt_state, 
+                                   law$sampling_transform$linear_term,
+                                   law$sampling_transform$offset_term,
+                                   law$constraints,
                                    nsamples=nsample)
     opt_sample = opt_samples[(burnin+1):nsample,]
   }
@@ -429,7 +450,7 @@ randomizedLassoInf = function(rand_lasso_soln,
          glm_y = glm(y ~ X_E-1, family=binomial())
       }
       observed_target = as.vector(glm_y$coefficients)
-      observed_internal =  c(observed_target, t(X_minusE) %*% (y - fitted(glm_y)))
+      observed_internal =  c(observed_target, as.vector((y - fitted(glm_y)) %*% X)[inactive_set])
       cov_target = vcov(glm_y)
 
       if (sum(is.na(observed_target)) > 0) {

From 488016f2e7b9cf0a88d416c71a501ddb66b5013a Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 16 Nov 2017 19:35:17 -0800
Subject: [PATCH 417/493] BF: wrong variable name

---
 selectiveInference/R/funs.randomized.R | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index a50993e..37183ec 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -263,7 +263,6 @@ randomizedLasso = function(X,
         law = full_law
     }
 
-    print(active_set)
     return(list(X=X,
                 y=y,
                 lam=lam,
@@ -398,7 +397,7 @@ randomizedLassoInf = function(rand_lasso_soln,
                               targets=NULL,
                               condition_subgrad=TRUE, 
                               level=0.9,
-                              sampler=c("norejection", "adaptMCMC"),
+                              sampler=c("adaptMCMC", "norejection"),
                               nsample=10000,
                               burnin=2000)
  {
@@ -420,7 +419,7 @@ randomizedLassoInf = function(rand_lasso_soln,
   sampler = match.arg(sampler)
 
   law = rand_lasso_soln$law
-  if (condition_subgrad != soln$condition_subgrad) {
+  if (condition_subgrad != rand_lasso_soln$condition_subgrad) {
       stop("condition_subgrad does not agree with value when randomizedLasso was solved -- sampler not properly specified")
   } 
 

From 6ccebc139e92f3a554b324b4edb5a70be1a8dbfd Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 16 Nov 2017 19:38:07 -0800
Subject: [PATCH 418/493] BF: variable name

---
 selectiveInference/R/funs.randomized.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 37183ec..69bc0b4 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -425,7 +425,7 @@ randomizedLassoInf = function(rand_lasso_soln,
 
   if (sampler == "adaptMCMC"){
     S = sample_opt_variables(law, 
-                             jump_scale=rep(1/sqrt(n), length(cur_law$observed_opt_state)), nsample=nsample)
+                             jump_scale=rep(1/sqrt(n), length(law$observed_opt_state)), nsample=nsample)
     opt_samples = as.matrix(S$samples[(burnin+1):nsample,,drop=FALSE])
   } else if (sampler == "norejection") {
     opt_samples = gaussian_sampler(noise_scale, 
@@ -462,7 +462,7 @@ randomizedLassoInf = function(rand_lasso_soln,
 
   } 
 
-  importance_transform = cur_law$importance_transform
+  importance_transform = law$importance_transform
   internal_transform=rand_lasso_soln$internal_transform
   observed_raw=rand_lasso_soln$observed_raw
 

From f7a222d76535ffd0e43ae3b1339827cc7b6d1ff2 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 16 Nov 2017 19:46:32 -0800
Subject: [PATCH 419/493] BF: don't form X_minusE, make a shortcut for X

---
 selectiveInference/man/randomizedLassoInf.Rd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/man/randomizedLassoInf.Rd b/selectiveInference/man/randomizedLassoInf.Rd
index 3bffbfa..78de601 100644
--- a/selectiveInference/man/randomizedLassoInf.Rd
+++ b/selectiveInference/man/randomizedLassoInf.Rd
@@ -15,7 +15,7 @@ randomizedLassoInf(rand_lasso_soln,
 		   targets=NULL,
                    condition_subgrad=TRUE, 
                    level=0.9,
-                   sampler=c("norejection", "adaptMCMC"),
+                   sampler=c("adaptMCMC", "norejection"),
                    nsample=10000,
                    burnin=2000)
 }

From ff4ed8aef3241711c2933c8fe77a825893559f2d Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 16 Nov 2017 23:01:20 -0800
Subject: [PATCH 420/493] timing script runs now -- needs testing to see if
 anything broken

---
 selectiveInference/R/funs.randomized.R | 59 +++++++++++++++-----------
 tests/randomized/test_instances.R      |  3 +-
 2 files changed, 36 insertions(+), 26 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 69bc0b4..e61e50b 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -138,7 +138,8 @@ randomizedLasso = function(X,
     }
     
     L_E = t(X_E) %*% W_E %*% X
-    coef_term = t(L_E)
+    L_E = t(L_E)
+    coef_term = L_E
 
     signs_ = c(rep(1, sum(unpenalized)), sign_soln[active])
     
@@ -395,7 +396,6 @@ conditional_opt_transform = function(noise_scale,
 
 randomizedLassoInf = function(rand_lasso_soln,
                               targets=NULL,
-                              condition_subgrad=TRUE, 
                               level=0.9,
                               sampler=c("adaptMCMC", "norejection"),
                               nsample=10000,
@@ -406,10 +406,11 @@ randomizedLassoInf = function(rand_lasso_soln,
   p = ncol(rand_lasso_soln$X)
   
   active_set = rand_lasso_soln$active_set
-  nactive = length(active_set)
+  unpenalized_set = rand_lasso_soln$unpenalized_set
+  nactive = length(c(active_set, unpenalized_set))
 
   if (nactive==0){
-    return (list(active_set=active_set, pvalues=c(), ci=c()))
+    return (list(active_set=active_set, unpenalized_set=unpenalized_set, pvalues=c(), ci=c()))
   }
   inactive_set = rand_lasso_soln$inactive_set
   noise_scale = rand_lasso_soln$noise_scale  # set to default value in randomizedLasso
@@ -419,9 +420,6 @@ randomizedLassoInf = function(rand_lasso_soln,
   sampler = match.arg(sampler)
 
   law = rand_lasso_soln$law
-  if (condition_subgrad != rand_lasso_soln$condition_subgrad) {
-      stop("condition_subgrad does not agree with value when randomizedLasso was solved -- sampler not properly specified")
-  } 
 
   if (sampler == "adaptMCMC"){
     S = sample_opt_variables(law, 
@@ -437,19 +435,24 @@ randomizedLassoInf = function(rand_lasso_soln,
     opt_sample = opt_samples[(burnin+1):nsample,]
   }
   
-  if (is.null(targets)) {
+  # compute internal representation of the data
 
-      X_E = rand_lasso_soln$X[, active_set, drop=FALSE]
-      X_minusE = rand_lasso_soln$X[, inactive_set, drop=FALSE]
-      y = rand_lasso_soln$y
+  X_E = rand_lasso_soln$X[, c(active_set, unpenalized_set), drop=FALSE]
+
+  y = rand_lasso_soln$y
+  X = rand_lasso_soln$X
+
+  if (rand_lasso_soln$family == 'gaussian') {
+     glm_y = glm(y ~ X_E-1)
+  } else if (rand_lasso_soln$family == 'binomial') {
+     glm_y = glm(y ~ X_E-1, family=binomial())
+  }
+
+  observed_internal =  c(as.vector(glm_y$coefficients), as.vector((y - fitted(glm_y)) %*% X)[inactive_set])
+
+  if (is.null(targets)) {
 
-      if (rand_lasso_soln$family == 'gaussian') {
-         glm_y = glm(y ~ X_E-1)
-      } else if (rand_lasso_soln$family == 'binomial') {
-         glm_y = glm(y ~ X_E-1, family=binomial())
-      }
       observed_target = as.vector(glm_y$coefficients)
-      observed_internal =  c(observed_target, as.vector((y - fitted(glm_y)) %*% X)[inactive_set])
       cov_target = vcov(glm_y)
 
       if (sum(is.na(observed_target)) > 0) {
@@ -471,21 +474,29 @@ randomizedLassoInf = function(rand_lasso_soln,
   
   for (i in 1:nactive){
 
-    target_transform = linear_decomposition(targets$observed_target[i], 
-                                            observed_internal, 
-                                            targets$cov_target[i,i], 
-                                            targets$crosscov_target_internal[,i],
-                                            internal_transform)
+    pre_nuisance = observed_internal - (targets$crosscov_target_internal[,i] *
+                                        targets$observed_target[i] / 
+                                        targets$cov_target[i,i])
+
+    nuisance = internal_transform$linear_term %*% pre_nuisance[1:nactive] 
+    nuisance[inactive_set] = nuisance[inactive_set] + pre_nuisance[(nactive+1):p]
+
+    pre_linear_term = targets$crosscov_target_internal[,i] / targets$cov_target[i,i]
+    linear_term = rep(0, p)
+    linear_term = internal_transform$linear_term %*% pre_linear_term[1:nactive]
+    linear_term[inactive_set] = linear_term[inactive_set] + pre_linear_term[(nactive+1):p]
+    target_transform = list(linear_term=linear_term,
+                            offset_term=nuisance + internal_transform$offset_term) # internal_transform$offset_term is 0...
     
     # changing dimension of density evalutaion
 
-    if ((condition_subgrad == TRUE) & (nactive < p-1)) {
+    if ((rand_lasso_soln$condition_subgrad == TRUE) & (nactive < p-1)) {
 
         # A description of why we do this might help
 
         target_opt_linear = cbind(target_transform$linear_term, importance_transform$linear_term)
         reduced_target_opt_linear = chol(t(target_opt_linear) %*% target_opt_linear)
-        target_linear = reduced_target_opt_linear[,1]
+        target_linear = reduced_target_opt_linear[,1,drop=FALSE]
         temp = solve(t(reduced_target_opt_linear)) %*% t(target_opt_linear)
         target_offset = temp %*% target_transform$offset_term
         target_transform = list(linear_term = as.matrix(target_linear), offset_term = target_offset)
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index b3617e6..c63e88b 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -109,8 +109,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
                                                      sampler = "adaptMCMC", #"norejection", #
                                                      level=level, 
                                                      burnin=1000, 
-                                                     nsample=5000, 
-                                                     condition_subgrad=condition_subgrad)
+                                                     nsample=5000)
     if (length(result$active_set)>0){
       true_beta = data$beta[result$active_set]
       coverage = rep(0, nrow(result$ci))

From 9b9c7b2de47fb7bd0933ee66b68aad1204736f27 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 16 Nov 2017 23:13:52 -0800
Subject: [PATCH 421/493] DOC: fixed to pass check, as.vector for y

---
 selectiveInference/R/funs.randomized.R       | 5 +++--
 selectiveInference/man/randomizedLasso.Rd    | 7 +++++++
 selectiveInference/man/randomizedLassoInf.Rd | 6 ------
 tests/randomized/test_instances.R            | 1 +
 4 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index e61e50b..0f1ac1d 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -21,7 +21,8 @@ randomizedLasso = function(X,
     family = match.arg(family)
 
     n = nrow(X); p = ncol(X)
-    			
+    y = as.vector(y)
+
     mean_diag = mean(apply(X^2, 2, sum))
 
     # default ridge term
@@ -58,7 +59,7 @@ randomizedLasso = function(X,
     if (family=="gaussian"){
       soln = rep(0, p)
       Xsoln = rep(0, n)
-      linear_func = (- t(X) %*% y - perturb_) / n
+      linear_func = (- y %*% X - perturb_) / n
 
       gradient = 1. * linear_func
       ever_active = as.integer(rep(0, p))
diff --git a/selectiveInference/man/randomizedLasso.Rd b/selectiveInference/man/randomizedLasso.Rd
index 1f69f71..bd91cd9 100644
--- a/selectiveInference/man/randomizedLasso.Rd
+++ b/selectiveInference/man/randomizedLasso.Rd
@@ -12,6 +12,7 @@ randomizedLasso(X,
                 y, 
                 lam, 
                 family=c("gaussian", "binomial"),
+		condition_subgrad=TRUE,
                 noise_scale=NULL, 
                 ridge_term=NULL, 
                 max_iter=100,       
@@ -44,6 +45,12 @@ Value of lambda used to compute beta. See the above warning
 \item{family}{
 Response type: "gaussian" (default), "binomial".
 }
+\item{condition_subgrad}{
+In forming selective confidence intervals and p-values should we condition
+on the inactive coordinates of the subgradient as well?
+Default is TRUE. This determines the form of \code{law} -- which
+is used for sampling later.
+}
 \item{noise_scale}{
 Scale of Gaussian noise added to objective. Default is 
 0.5 * sd(y) times the sqrt of the mean of the trace of X^TX.
diff --git a/selectiveInference/man/randomizedLassoInf.Rd b/selectiveInference/man/randomizedLassoInf.Rd
index 78de601..f860188 100644
--- a/selectiveInference/man/randomizedLassoInf.Rd
+++ b/selectiveInference/man/randomizedLassoInf.Rd
@@ -13,7 +13,6 @@ randomization.
 \usage{
 randomizedLassoInf(rand_lasso_soln, 
 		   targets=NULL,
-                   condition_subgrad=TRUE, 
                    level=0.9,
                    sampler=c("adaptMCMC", "norejection"),
                    nsample=10000,
@@ -36,11 +35,6 @@ data of the LASSO. For both \code{"gaussian"} and \code{"binomial"}, this is the
 For example, this cross-covariance could be estimated by jointly bootstrapping the target 
 of interest and the above vector.
 }
-\item{condition_subgrad}{
-In forming selective confidence intervals and p-values should we condition
-on the inactive coordinates of the subgradient as well?
-Default is TRUE. 
-}
 \item{level}{
 Level for confidence intervals.
 }
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index c63e88b..883585e 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -110,6 +110,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
                                                      level=level, 
                                                      burnin=1000, 
                                                      nsample=5000)
+    print(result$active_set)
     if (length(result$active_set)>0){
       true_beta = data$beta[result$active_set]
       coverage = rep(0, nrow(result$ci))

From 74a2ab5128c6a2e247f38cd75abca122d431fddc Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 16 Nov 2017 23:17:59 -0800
Subject: [PATCH 422/493] active_set not returned -- just targets

---
 tests/randomized/test_instances.R | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 883585e..1b58986 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -110,9 +110,8 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
                                                      level=level, 
                                                      burnin=1000, 
                                                      nsample=5000)
-    print(result$active_set)
-    if (length(result$active_set)>0){
-      true_beta = data$beta[result$active_set]
+    if (length(result$pvalues)>0){
+      true_beta = data$beta[rand_lasso_soln$active_set]
       coverage = rep(0, nrow(result$ci))
       for (i in 1:nrow(result$ci)){
         if (result$ci[i,1]<true_beta[i] & result$ci[i,2]>true_beta[i]){

From f127d6fa9932b784a74a66d5fd411acf0356cf5c Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 16 Nov 2017 23:49:33 -0800
Subject: [PATCH 423/493] DOC: changing comment

---
 selectiveInference/R/funs.randomized.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 0f1ac1d..53fa058 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -169,7 +169,7 @@ randomizedLasso = function(X,
     offset_term = rep(0, p)
     offset_term[active] = lam[active] * sign_soln[active]
 
-    # if conditional_subgrad == FALSE, linear_term will have E columns
+    # if conditional_subgrad == TRUE, linear_term will have E columns
     # otherwise it will have p columns
 
     opt_transform = list(linear_term=linear_term,

From 693a138402702c40b51bb97574c51eb918fffbb0 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Fri, 17 Nov 2017 16:32:01 -0800
Subject: [PATCH 424/493] signs changed

---
 selectiveInference/R/funs.randomized.R | 6 +++---
 tests/randomized/test_instances.R      | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 53fa058..905bb5c 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -7,7 +7,7 @@ randomizedLasso = function(X,
                            y, 
                            lam, 
                            family=c("gaussian","binomial"),
-			   condition_subgrad=TRUE,
+			                     condition_subgrad=TRUE,
                            noise_scale=NULL, 
                            ridge_term=NULL, 
                            max_iter=100,        # how many iterations for each optimization problem
@@ -480,12 +480,12 @@ randomizedLassoInf = function(rand_lasso_soln,
                                         targets$cov_target[i,i])
 
     nuisance = internal_transform$linear_term %*% pre_nuisance[1:nactive] 
-    nuisance[inactive_set] = nuisance[inactive_set] + pre_nuisance[(nactive+1):p]
+    nuisance[inactive_set] = nuisance[inactive_set] - pre_nuisance[(nactive+1):p]
 
     pre_linear_term = targets$crosscov_target_internal[,i] / targets$cov_target[i,i]
     linear_term = rep(0, p)
     linear_term = internal_transform$linear_term %*% pre_linear_term[1:nactive]
-    linear_term[inactive_set] = linear_term[inactive_set] + pre_linear_term[(nactive+1):p]
+    linear_term[inactive_set] = linear_term[inactive_set] - pre_linear_term[(nactive+1):p]
     target_transform = list(linear_term=linear_term,
                             offset_term=nuisance + internal_transform$offset_term) # internal_transform$offset_term is 0...
     
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 1b58986..fd39015 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -106,7 +106,7 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
                                                            family=family)
 
     result = selectiveInference:::randomizedLassoInf(rand_lasso_soln,
-                                                     sampler = "adaptMCMC", #"norejection", #
+                                                     sampler = "norejection", #"adaptMCMC", #
                                                      level=level, 
                                                      burnin=1000, 
                                                      nsample=5000)
@@ -134,5 +134,5 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
 }
 
 set.seed(1)
-collect_results(n=500, p=200, s=0, lam=1.)
+collect_results(n=100, p=20, s=0, lam=1.)
 

From e811fd5f85502b3a80bb50ff199a454e2913ff74 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 17 Nov 2017 18:26:48 -0800
Subject: [PATCH 425/493] adding names to pvalues and ci

---
 selectiveInference/R/funs.randomized.R | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 905bb5c..021c95f 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -398,7 +398,7 @@ conditional_opt_transform = function(noise_scale,
 randomizedLassoInf = function(rand_lasso_soln,
                               targets=NULL,
                               level=0.9,
-                              sampler=c("adaptMCMC", "norejection"),
+                              sampler=c("norejection"),
                               nsample=10000,
                               burnin=2000)
  {
@@ -460,6 +460,12 @@ randomizedLassoInf = function(rand_lasso_soln,
           stop("unregularized (relaxed) fit has NA values -- X[,active_set] likely singular")
       }
 
+      if (!is.null(colnames(X))) {
+            names(observed_target) = colnames(X)[rand_lasso_soln$active_set]
+      } else {
+            names(observed_target) = rand_lasso_soln$active_set
+      }
+
       targets = list(observed_target=observed_target,
                      cov_target=cov_target,
                      crosscov_target_internal=rbind(cov_target, matrix(0, nrow=p-nactive, ncol=nactive)))
@@ -473,6 +479,9 @@ randomizedLassoInf = function(rand_lasso_soln,
   pvalues = rep(0, nactive)
   ci = matrix(0, nactive, 2)
   
+  names(pvalues) = names(targets$observed_target)
+  rownames(ci) = names(targets$observed_target)
+
   for (i in 1:nactive){
 
     pre_nuisance = observed_internal - (targets$crosscov_target_internal[,i] *

From f0dc1961d7f08fc0a9ed6ef39095796a094a85c9 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 17 Nov 2017 20:10:38 -0800
Subject: [PATCH 426/493] burnin for noreject sampler

---
 selectiveInference/R/funs.sampler.R | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/selectiveInference/R/funs.sampler.R b/selectiveInference/R/funs.sampler.R
index 3f7f5fd..6c433a2 100644
--- a/selectiveInference/R/funs.sampler.R
+++ b/selectiveInference/R/funs.sampler.R
@@ -4,7 +4,8 @@ log_concave_sampler = function(negative_log_density,
                                grad_negative_log_density, 
                                constraints,
                                observed,
-                               nsamples){
+                               nsamples,
+			       burnin){
   #print(constraints)
   constraints = as.matrix(constraints)
   dim = nrow(constraints)
@@ -65,11 +66,13 @@ log_concave_sampler = function(negative_log_density,
   }
   
   state = list(pos=observed, velocity = update_velocity())
-  samples = matrix(0, nrow = nsamples, ncol = dim)
+  samples = matrix(0, nrow = nsamples - burnin, ncol = dim)
   for (i in 1:nsamples){
     #print(paste("pos", toString(state$pos)))
     #print(paste("velocity", toString(state$velocity)))
-    samples[i,]=state$pos
+    if (i > burnin) {
+        samples[i - burnin,]=state$pos
+    }
     state = compute_next(state)
   }
   return (samples)
@@ -80,7 +83,8 @@ gaussian_sampler = function(noise_scale,
                             linear_term, 
                             offset_term, 
                             constraints,
-                            nsamples){
+                            nsamples=10000,
+			    burnin=2000){
   
   negative_log_density = function(x) {
     recon = linear_term %*% x+offset_term
@@ -95,5 +99,6 @@ gaussian_sampler = function(noise_scale,
                              grad_negative_log_density,
                              constraints,
                              observed,
-                             nsamples))
+                             nsamples,
+			     burnin))
 }

From 5303a72ede612d9a9c291529c81117f2b750c190 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 17 Nov 2017 20:16:07 -0800
Subject: [PATCH 427/493] removing print statement

---
 selectiveInference/R/funs.randomized.R | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 021c95f..e1498a8 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -432,7 +432,8 @@ randomizedLassoInf = function(rand_lasso_soln,
                                    law$sampling_transform$linear_term,
                                    law$sampling_transform$offset_term,
                                    law$constraints,
-                                   nsamples=nsample)
+                                   nsamples=nsample,
+				   burnin=burnin)
     opt_sample = opt_samples[(burnin+1):nsample,]
   }
   

From 657c69d8e9bd2d84d0cc9e167200f67c2bb88f98 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 17 Nov 2017 20:17:24 -0800
Subject: [PATCH 428/493] BF: burnin now handled in samples

---
 selectiveInference/R/funs.randomized.R | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index e1498a8..b82d225 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -427,14 +427,13 @@ randomizedLassoInf = function(rand_lasso_soln,
                              jump_scale=rep(1/sqrt(n), length(law$observed_opt_state)), nsample=nsample)
     opt_samples = as.matrix(S$samples[(burnin+1):nsample,,drop=FALSE])
   } else if (sampler == "norejection") {
-    opt_samples = gaussian_sampler(noise_scale, 
-                                   law$observed_opt_state, 
-                                   law$sampling_transform$linear_term,
-                                   law$sampling_transform$offset_term,
-                                   law$constraints,
-                                   nsamples=nsample,
-				   burnin=burnin)
-    opt_sample = opt_samples[(burnin+1):nsample,]
+    opt_sample = gaussian_sampler(noise_scale, 
+                                  law$observed_opt_state, 
+                                  law$sampling_transform$linear_term,
+                                  law$sampling_transform$offset_term,
+                                  law$constraints,
+                                  nsamples=nsample,
+		                  burnin=burnin)
   }
   
   # compute internal representation of the data

From 42d31aade71fda39f944fd1f89fe3fdbf7ae8524 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 21 Nov 2017 10:59:02 -0800
Subject: [PATCH 429/493] updating C-software

---
 C-software | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/C-software b/C-software
index 0b35c6e..fc60f47 160000
--- a/C-software
+++ b/C-software
@@ -1 +1 @@
-Subproject commit 0b35c6ed8537cef9aabed526b968b1c63d2f6cb8
+Subproject commit fc60f471ecd5fc40b822ee36d46b1a5aaf7ce7e8

From a9820894384ce0b5a78fa728838617e1a4473316 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 21 Nov 2017 10:59:12 -0800
Subject: [PATCH 430/493] speeding up root finding

---
 selectiveInference/R/funs.randomized.R | 50 ++++++++++++++++++--------
 1 file changed, 36 insertions(+), 14 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index b82d225..afd71fe 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -427,15 +427,15 @@ randomizedLassoInf = function(rand_lasso_soln,
                              jump_scale=rep(1/sqrt(n), length(law$observed_opt_state)), nsample=nsample)
     opt_samples = as.matrix(S$samples[(burnin+1):nsample,,drop=FALSE])
   } else if (sampler == "norejection") {
-    opt_sample = gaussian_sampler(noise_scale, 
-                                  law$observed_opt_state, 
-                                  law$sampling_transform$linear_term,
-                                  law$sampling_transform$offset_term,
-                                  law$constraints,
-                                  nsamples=nsample,
-		                  burnin=burnin)
+    opt_samples = gaussian_sampler(noise_scale, 
+                                   law$observed_opt_state, 
+                                   law$sampling_transform$linear_term,
+                                   law$sampling_transform$offset_term,
+                                   law$constraints,
+                                   nsamples=nsample,
+		                   burnin=burnin)
   }
-  
+
   # compute internal representation of the data
 
   X_E = rand_lasso_soln$X[, c(active_set, unpenalized_set), drop=FALSE]
@@ -521,14 +521,36 @@ randomizedLassoInf = function(rand_lasso_soln,
 
     target_sample = rnorm(nrow(as.matrix(opt_samples))) * sqrt(targets$cov_target[i,i])
 
+    # weight in the numerator is of the form
+    # -1/(2 noise_scale^2)\|Do + q + P(t+\theta)\|^2_2 
+    # with D=importance_transform$linear_term
+    #      q=target_transform$offset_term + cur_transform$offset_term
+    #      P=target_transform$linear_term
+
+    # weight in the denominator is of the form
+    # -1/(2 noise_scale^2)\|Do + q_D\|^2_2  
+    # with D=importance_transform$linear_term
+    #      q_D = observed_raw + cur_transform$offset_term
+
+    # reference measure just is the ratio at \theta=0
+    # sufficient statistic is linear term in \theta
+
+    sufficient_statistic = - cur_transform$linear_term %*% t(opt_samples)
+    sufficient_statistic = apply(sufficient_statistic, 2,
+                                 function(x) {return(x - target_transform$offset_term - cur_transform$offset_term)})
+    sufficient_statistic = t(target_transform$linear_term) %*% sufficient_statistic / noise_scale^2
+
+    reference_measure = importance_weight(noise_scale,
+                                          t(as.matrix(target_sample)),
+                                          t(opt_samples),
+                                          cur_transform,
+                                          target_transform,
+                                          raw)
+
     pivot = function(candidate){
 
-      weights = importance_weight(noise_scale,
-                                  t(as.matrix(target_sample) + candidate),
-                                  t(opt_samples),
-                                  cur_transform,
-                                  target_transform,
-                                  raw)
+      weights = reference_measure * exp(candidate * sufficient_statistic)
+      weights = weights / max(weights)
       return(mean((target_sample + candidate < targets$observed_target[i]) * weights)/mean(weights))
 
     }

From a5208c68bd3fc4bf5f8f0e5b499134aa87908037 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 21 Nov 2017 11:21:57 -0800
Subject: [PATCH 431/493] trying to speed up importance weights -- broke it?

---
 selectiveInference/R/funs.randomized.R | 49 ++++++++++++++------------
 1 file changed, 27 insertions(+), 22 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index afd71fe..69eeed1 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -500,24 +500,27 @@ randomizedLassoInf = function(rand_lasso_soln,
     
     # changing dimension of density evalutaion
 
-    if ((rand_lasso_soln$condition_subgrad == TRUE) & (nactive < p-1)) {
-
-        # A description of why we do this might help
-
-        target_opt_linear = cbind(target_transform$linear_term, importance_transform$linear_term)
-        reduced_target_opt_linear = chol(t(target_opt_linear) %*% target_opt_linear)
-        target_linear = reduced_target_opt_linear[,1,drop=FALSE]
-        temp = solve(t(reduced_target_opt_linear)) %*% t(target_opt_linear)
-        target_offset = temp %*% target_transform$offset_term
-        target_transform = list(linear_term = as.matrix(target_linear), offset_term = target_offset)
-        cur_linear = reduced_target_opt_linear[,2:ncol(reduced_target_opt_linear)]
-        cur_offset = temp %*% importance_transform$offset_term
-        cur_transform = list(linear_term = as.matrix(cur_linear), offset_term = cur_offset)
-        raw = target_transform$linear_term * targets$observed_target[i] + target_transform$offset_term
-    } else {
-        cur_transform = importance_transform
-        raw = observed_raw
-    }   
+#     if ((rand_lasso_soln$condition_subgrad == TRUE) & (nactive < p-1)) {
+
+#         # A description of why we do this might help
+
+#         target_opt_linear = cbind(target_transform$linear_term, importance_transform$linear_term)
+#         reduced_target_opt_linear = chol(t(target_opt_linear) %*% target_opt_linear)
+#         target_linear = reduced_target_opt_linear[,1,drop=FALSE]
+#         temp = solve(t(reduced_target_opt_linear)) %*% t(target_opt_linear)
+#         target_offset = temp %*% target_transform$offset_term
+#         target_transform = list(linear_term = as.matrix(target_linear), offset_term = target_offset)
+#         cur_linear = reduced_target_opt_linear[,2:ncol(reduced_target_opt_linear)]
+#         cur_offset = temp %*% importance_transform$offset_term
+#         cur_transform = list(linear_term = as.matrix(cur_linear), offset_term = cur_offset)
+#         raw = target_transform$linear_term * targets$observed_target[i] + target_transform$offset_term
+#     } else {
+#         cur_transform = importance_transform
+#         raw = observed_raw
+#     }   
+
+    cur_transform = importance_transform
+    raw = observed_raw
 
     target_sample = rnorm(nrow(as.matrix(opt_samples))) * sqrt(targets$cov_target[i,i])
 
@@ -546,13 +549,15 @@ randomizedLassoInf = function(rand_lasso_soln,
                                           cur_transform,
                                           target_transform,
                                           raw)
+    log_reference_measure = log(reference_measure)
 
     pivot = function(candidate){
-
-      weights = reference_measure * exp(candidate * sufficient_statistic)
-      weights = weights / max(weights)
+      # instead of uniroot -- maybe use newton?
+      exp_arg = candidate * sufficient_statistic + log_reference_measure
+      exp_arg = exp_arg - max(exp_arg)
+      weights = exp(exp_arg)
+      #weights = pmax(pmin(weights, 1e50), 1e-50)
       return(mean((target_sample + candidate < targets$observed_target[i]) * weights)/mean(weights))
-
     }
 
     rootU = function(candidate){

From d3bbf073ad82ce7931a3f4660ccefcd7143029b6 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 21 Nov 2017 11:26:35 -0800
Subject: [PATCH 432/493] using the cholesky as before to see if it helps

---
 selectiveInference/R/funs.randomized.R | 39 ++++++++++++--------------
 1 file changed, 18 insertions(+), 21 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 69eeed1..fede50a 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -500,27 +500,24 @@ randomizedLassoInf = function(rand_lasso_soln,
     
     # changing dimension of density evalutaion
 
-#     if ((rand_lasso_soln$condition_subgrad == TRUE) & (nactive < p-1)) {
-
-#         # A description of why we do this might help
-
-#         target_opt_linear = cbind(target_transform$linear_term, importance_transform$linear_term)
-#         reduced_target_opt_linear = chol(t(target_opt_linear) %*% target_opt_linear)
-#         target_linear = reduced_target_opt_linear[,1,drop=FALSE]
-#         temp = solve(t(reduced_target_opt_linear)) %*% t(target_opt_linear)
-#         target_offset = temp %*% target_transform$offset_term
-#         target_transform = list(linear_term = as.matrix(target_linear), offset_term = target_offset)
-#         cur_linear = reduced_target_opt_linear[,2:ncol(reduced_target_opt_linear)]
-#         cur_offset = temp %*% importance_transform$offset_term
-#         cur_transform = list(linear_term = as.matrix(cur_linear), offset_term = cur_offset)
-#         raw = target_transform$linear_term * targets$observed_target[i] + target_transform$offset_term
-#     } else {
-#         cur_transform = importance_transform
-#         raw = observed_raw
-#     }   
-
-    cur_transform = importance_transform
-    raw = observed_raw
+    if ((rand_lasso_soln$condition_subgrad == TRUE) & (nactive < p-1)) {
+
+        # A description of why we do this might help
+
+        target_opt_linear = cbind(target_transform$linear_term, importance_transform$linear_term)
+        reduced_target_opt_linear = chol(t(target_opt_linear) %*% target_opt_linear)
+        target_linear = reduced_target_opt_linear[,1,drop=FALSE]
+        temp = solve(t(reduced_target_opt_linear)) %*% t(target_opt_linear)
+        target_offset = temp %*% target_transform$offset_term
+        target_transform = list(linear_term = as.matrix(target_linear), offset_term = target_offset)
+        cur_linear = reduced_target_opt_linear[,2:ncol(reduced_target_opt_linear)]
+        cur_offset = temp %*% importance_transform$offset_term
+        cur_transform = list(linear_term = as.matrix(cur_linear), offset_term = cur_offset)
+        raw = target_transform$linear_term * targets$observed_target[i] + target_transform$offset_term
+    } else {
+        cur_transform = importance_transform
+        raw = observed_raw
+    }   
 
     target_sample = rnorm(nrow(as.matrix(opt_samples))) * sqrt(targets$cov_target[i,i])
 

From a784aa40a0329cf632cc3b40e786b0e675fcad4a Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 21 Nov 2017 11:35:45 -0800
Subject: [PATCH 433/493] looking at other way to compute sufficient_stat

---
 selectiveInference/R/funs.randomized.R | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index fede50a..76a48f2 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -548,6 +548,20 @@ randomizedLassoInf = function(rand_lasso_soln,
                                           raw)
     log_reference_measure = log(reference_measure)
 
+    alt_measure = importance_weight(noise_scale,
+                                    t(as.matrix(target_sample) + 0.1 * sqrt(targets$cov_target[i,i])),
+                                    t(opt_samples),
+                                    cur_transform,
+                                    target_transform,
+                                    raw)
+    sufficient_statistic2 = (log(alt_measure) - log_reference_measure) / (0.1 * sqrt(targets$cov_target[i,i]))
+    sufficient_statistic = as.vector(sufficient_statistic)
+    sufficient_statistic2 = as.vector(sufficient_statistic2)
+    print(dim(as.vector(sufficient_statistic)))
+    print(dim(as.vector(sufficient_statistic2)))
+    print(max(sufficient_statistic - sufficient_statistic2))
+    print(cor(sufficient_statistic, sufficient_statistic2))
+
     pivot = function(candidate){
       # instead of uniroot -- maybe use newton?
       exp_arg = candidate * sufficient_statistic + log_reference_measure

From c3936c16099f4f27082ff7756b980ba760c0f7b1 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 21 Nov 2017 11:37:07 -0800
Subject: [PATCH 434/493] removing print statements

---
 selectiveInference/R/funs.randomized.R | 15 ---------------
 1 file changed, 15 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 76a48f2..70ad818 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -548,26 +548,11 @@ randomizedLassoInf = function(rand_lasso_soln,
                                           raw)
     log_reference_measure = log(reference_measure)
 
-    alt_measure = importance_weight(noise_scale,
-                                    t(as.matrix(target_sample) + 0.1 * sqrt(targets$cov_target[i,i])),
-                                    t(opt_samples),
-                                    cur_transform,
-                                    target_transform,
-                                    raw)
-    sufficient_statistic2 = (log(alt_measure) - log_reference_measure) / (0.1 * sqrt(targets$cov_target[i,i]))
-    sufficient_statistic = as.vector(sufficient_statistic)
-    sufficient_statistic2 = as.vector(sufficient_statistic2)
-    print(dim(as.vector(sufficient_statistic)))
-    print(dim(as.vector(sufficient_statistic2)))
-    print(max(sufficient_statistic - sufficient_statistic2))
-    print(cor(sufficient_statistic, sufficient_statistic2))
-
     pivot = function(candidate){
       # instead of uniroot -- maybe use newton?
       exp_arg = candidate * sufficient_statistic + log_reference_measure
       exp_arg = exp_arg - max(exp_arg)
       weights = exp(exp_arg)
-      #weights = pmax(pmin(weights, 1e50), 1e-50)
       return(mean((target_sample + candidate < targets$observed_target[i]) * weights)/mean(weights))
     }
 

From b2f51ec16bf67e1f5cd93b01dd572161a40ea91b Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 21 Nov 2017 13:44:51 -0800
Subject: [PATCH 435/493] computing sufficient stat directly from importance
 weight

---
 selectiveInference/R/funs.randomized.R | 21 ++++++++++++---------
 1 file changed, 12 insertions(+), 9 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 70ad818..3134924 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -535,24 +535,27 @@ randomizedLassoInf = function(rand_lasso_soln,
     # reference measure just is the ratio at \theta=0
     # sufficient statistic is linear term in \theta
 
-    sufficient_statistic = - cur_transform$linear_term %*% t(opt_samples)
-    sufficient_statistic = apply(sufficient_statistic, 2,
-                                 function(x) {return(x - target_transform$offset_term - cur_transform$offset_term)})
-    sufficient_statistic = t(target_transform$linear_term) %*% sufficient_statistic / noise_scale^2
-
     reference_measure = importance_weight(noise_scale,
                                           t(as.matrix(target_sample)),
                                           t(opt_samples),
                                           cur_transform,
                                           target_transform,
                                           raw)
+
+    alternative_measure = importance_weight(noise_scale,
+                                            t(as.matrix(target_sample) + 0.1 * sqrt(targets$cov_target[i,i])),
+                                            t(opt_samples),
+                                            cur_transform,
+                                            target_transform,
+                                            raw)
+
     log_reference_measure = log(reference_measure)
+    sufficient_stat = (log(alternative_measure) - log_reference_measure) / (0.1 * sqrt(targets$cov_target[i,i]))
 
     pivot = function(candidate){
-      # instead of uniroot -- maybe use newton?
-      exp_arg = candidate * sufficient_statistic + log_reference_measure
-      exp_arg = exp_arg - max(exp_arg)
-      weights = exp(exp_arg)
+      arg_ = candidate * sufficient_stat + log_reference_measure
+      arg_ = arg_ - max(arg_)
+      weights = exp(arg_)
       return(mean((target_sample + candidate < targets$observed_target[i]) * weights)/mean(weights))
     }
 

From 05ab77182980f8488e49720448fd49292f1364db Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 21 Nov 2017 13:45:10 -0800
Subject: [PATCH 436/493] rob's nonnull test -- needs work to run with master

---
 tests/randomized/nonnull.R | 159 +++++++++++++++++++++++++++++++++++++
 1 file changed, 159 insertions(+)
 create mode 100644 tests/randomized/nonnull.R

diff --git a/tests/randomized/nonnull.R b/tests/randomized/nonnull.R
new file mode 100644
index 0000000..660ebc4
--- /dev/null
+++ b/tests/randomized/nonnull.R
@@ -0,0 +1,159 @@
+
+
+library(glmnet)
+#library(R.utils)
+#library(truncnorm)
+library(selectiveInference)
+
+#source("/Users/tibs/Dropbox/git/R-software/selectiveInference-currentCRAN/R/funs.fixed.R")
+source("funs.fixed.R")
+
+#source("/Users/tibs/Dropbox/git/R-software/selectiveInference-currentCRAN/R/funs.inf.R")
+source("funs.inf.R")
+
+
+source("myfuns.R")
+
+pinv=solve
+
+
+options(error=dump.frames)
+set.seed(32103)
+nsim=100
+
+
+
+
+dorand=T
+
+#type="full"
+type="partial"
+
+n=100
+p=12
+
+verbose=F
+sign=rep(NA,p)
+ci=ci0=ci2=ci4=ci5=ci6=vlims0=array(NA,c(p,2,nsim))
+
+betaall=matrix(NA,nsim,p)
+btruepart=matrix(NA,p,nsim)
+betastarall=sestarall=array(NA,c(nsim,p,nboot))
+aall=acterr=rep(NA,nsim)
+mc2=matrix(NA,nsim,p)
+x=matrix(rnorm(n*p),n,p)
+#x=matrix(rnorm(n*p),n,p)+scale(matrix(rnorm(n),n,p),F,sign(rnorm(p)))
+x=scale(x,T,T)/sqrt(n-1)
+sigma=1
+alpha=.1
+zalpha=-qnorm(alpha/2)
+
+
+
+lam=.03 / sqrt(5)  #n=20 p=12
+
+btrue=c(1,4,-4,rep(0,p-3))  #n=20 p=12
+
+ 
+seeds=sample(1:99999,size=nsim)
+
+for(ii in 1:nsim){
+    set.seed(seeds[ii])
+    cat(ii)
+    mutrue=x%*%btrue
+     y=mutrue+sigma*rnorm(n)
+    y=y-mean(y)
+    if(p>1){   
+     a=glmnet(x,y,standardize=F)
+     beta=as.numeric(coef(a,s=lam,exact=T,x=x,y=y))[-1]
+    }
+    if(p==1){
+    coef=lsfit(x,y)$coef[2]
+    beta=sign(coef)*(abs(coef)-n*lam)*(abs(coef)>n*lam)
+    }
+    if(sum(beta!=0)>0){
+       
+        
+  betaall[ii,]=beta
+  a=lsfit(x[,beta!=0],y)
+    aa=ls.diag(a)
+   bhat=a$coef[-1]
+   bhat0=a$coef[1]
+  act=which(beta!=0)
+  se=aa$std.err[-1]
+   btruepart[,ii]=0
+   btruepart[act,ii]=lsfit(x[,act,drop=F],mutrue)$coef[-1]
+#naive intervals
+  ci0[beta!=0,1,ii]=bhat-zalpha*se
+  ci0[beta!=0,2,ii]=bhat+zalpha*se
+
+   #bonf-adj naive
+   alpha4=alpha/p
+   zalpha4=-qnorm(alpha4/2)
+   ci4[beta!=0,1,ii]=bhat-zalpha4*se
+  ci4[beta!=0,2,ii]=bhat+zalpha4*se
+
+#lee et al intervals
+lee=foo(x,y,beta,lam*n,alpha,type=type)
+        ci[,,ii]=lee$ci
+
+ #randomized
+        if(dorand){
+            rand_lasso_soln = randomizedLasso(x, y, n*lam)
+        junk=randomizedLassoInf(rand_lasso_soln, nsample=5000, burnin=1000)
+    ci5[rand_lasso_soln$act,1,ii]=junk$ci[,1]
+    ci5[rand_lasso_soln$act,2,ii]=junk$ci[,2]
+}
+
+}}
+   
+
+
+
+if(type=="partial") btrue=btruepart
+
+mc0=mean(ci0[,1,]>btrue | ci0[,2,]<btrue,na.rm=T)
+len0=mean(ci0[,2,]-ci0[,1,],na.rm=T)
+len0m=median(ci0[,2,]-ci0[,1,],na.rm=T)
+
+ninf=mean(abs(ci)==Inf,na.rm=T)
+ci[abs(ci)==Inf]=NA
+mc=mean(ci[,1,]>btrue | ci[,2,]<btrue,na.rm=T)
+len=mean(ci[,2,]-ci[,1,],na.rm=T)
+lenm=median(ci[,2,]-ci[,1,],na.rm=T)
+
+
+mc4=mean(ci4[,1,]>btrue | ci4[,2,]<btrue,na.rm=T)
+len4=mean(ci4[,2,]-ci4[,1,],na.rm=T)
+len4m=median(ci4[,2,]-ci4[,1,],na.rm=T)
+
+mc5=mean(ci5[,1,]>btrue | ci5[,2,]<btrue,na.rm=T)
+len5=mean(ci5[,2,]-ci5[,1,],na.rm=T)
+len5m=median(ci5[,2,]-ci5[,1,],na.rm=T)
+
+
+
+cat(c("ave# nz",mean(apply(betaall!=0,1,sum,na.rm=T))),fill=T)
+
+miscov=c(mc0,mc,mc4,mc5)
+
+res=
+matrix(
+    c(len0,len0m,0,
+      len,lenm,ninf,
+      len4,len4m,0,
+        len5,len5m,0
+      ),4,3,byrow=T)
+res=cbind(res,miscov)
+
+dimnames(res)=list(c("naive","Lee","bonf-naive","rand"),c("mean","median","propInf","miscov"))
+print(res)
+
+
+#out=cbind(as.vector(ci0[,2,]-ci0[,1,]),
+#             as.vector(ci[,2,]-ci[,1,]),
+#             as.vector(ci2[,2,]-ci2[,1,]),
+##             as.vector(ci3[,2,]-ci3[,1,]),
+#          as.vector(ci4[,2,]-ci4[,1,]),
+#                    as.vector(ci5[,2,]-ci5[,1,] ))
+#print(out)

From b7e70b7c2539f52017003dfcf989d811a5b2ff46 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 21 Nov 2017 14:11:16 -0800
Subject: [PATCH 437/493] rob's nonnull test

---
 tests/randomized/nonnull.R | 251 +++++++++++++++++--------------------
 1 file changed, 115 insertions(+), 136 deletions(-)

diff --git a/tests/randomized/nonnull.R b/tests/randomized/nonnull.R
index 660ebc4..6651ed7 100644
--- a/tests/randomized/nonnull.R
+++ b/tests/randomized/nonnull.R
@@ -1,159 +1,138 @@
-
-
-library(glmnet)
-#library(R.utils)
-#library(truncnorm)
 library(selectiveInference)
-
-#source("/Users/tibs/Dropbox/git/R-software/selectiveInference-currentCRAN/R/funs.fixed.R")
-source("funs.fixed.R")
-
-#source("/Users/tibs/Dropbox/git/R-software/selectiveInference-currentCRAN/R/funs.inf.R")
-source("funs.inf.R")
-
-
-source("myfuns.R")
-
-pinv=solve
-
-
-options(error=dump.frames)
 set.seed(32103)
-nsim=100
 
+# parameters
 
+n=20; p=12; nsim=50
+sigma=1; alpha=.1; lam=0.04
 
+# design
 
-dorand=T
+X=matrix(rnorm(n*p),n,p)
+X=scale(X,TRUE,TRUE) / sqrt(n-1)
 
-#type="full"
-type="partial"
+# truth
 
-n=100
-p=12
+btrue=c(1,4,-4,rep(0,p-3)) 
 
-verbose=F
 sign=rep(NA,p)
 ci=ci0=ci2=ci4=ci5=ci6=vlims0=array(NA,c(p,2,nsim))
 
 betaall=matrix(NA,nsim,p)
 btruepart=matrix(NA,p,nsim)
-betastarall=sestarall=array(NA,c(nsim,p,nboot))
-aall=acterr=rep(NA,nsim)
 mc2=matrix(NA,nsim,p)
-x=matrix(rnorm(n*p),n,p)
-#x=matrix(rnorm(n*p),n,p)+scale(matrix(rnorm(n),n,p),F,sign(rnorm(p)))
-x=scale(x,T,T)/sqrt(n-1)
-sigma=1
-alpha=.1
 zalpha=-qnorm(alpha/2)
 
 
-
-lam=.03 / sqrt(5)  #n=20 p=12
-
-btrue=c(1,4,-4,rep(0,p-3))  #n=20 p=12
-
- 
-seeds=sample(1:99999,size=nsim)
+seeds=sample(1:99999, size=nsim)
 
 for(ii in 1:nsim){
     set.seed(seeds[ii])
-    cat(ii)
-    mutrue=x%*%btrue
-     y=mutrue+sigma*rnorm(n)
-    y=y-mean(y)
-    if(p>1){   
-     a=glmnet(x,y,standardize=F)
-     beta=as.numeric(coef(a,s=lam,exact=T,x=x,y=y))[-1]
+    mutrue = X%*%btrue
+    y = mutrue+sigma*rnorm(n)
+    y = y - mean(y)
+
+    if (p>1) {   
+        G = glmnet(X,y,standardize=FALSE)
+        beta=as.numeric(coef(G, s=lam, exact=TRUE, x=X, y=y))[-1]
+    } else if (p==1) {
+        coef=lsfit(x,y)$coef[2]
+        beta=sign(coef)*(abs(coef)-n*lam)*(abs(coef)>n*lam)
     }
-    if(p==1){
-    coef=lsfit(x,y)$coef[2]
-    beta=sign(coef)*(abs(coef)-n*lam)*(abs(coef)>n*lam)
+
+    # any active
+    if (sum(beta!=0) > 0){
+        betaall[ii,]=beta
+        a=lsfit(X[,beta!=0], y)
+        aa=ls.diag(a)
+        bhat=a$coef[-1]
+        bhat0=a$coef[1]
+        act=which(beta!=0)
+        se=aa$std.err[-1]
+        btruepart[,ii]=0
+        btruepart[act,ii]=lsfit(X[,act,drop=F],mutrue)$coef[-1]
+
+        #naive intervals
+        ci0[beta!=0,1,ii]=bhat-zalpha*se
+        ci0[beta!=0,2,ii]=bhat+zalpha*se
+
+        #bonf-adj naive
+        alpha4=alpha/p
+        zalpha4=-qnorm(alpha4/2)
+        ci4[beta!=0,1,ii]=bhat-zalpha4*se
+        ci4[beta!=0,2,ii]=bhat+zalpha4*se
+
+        #lee et al intervals
+        lee = fixedLassoInf(X,
+                            y,
+                            beta,
+                            lam*n,
+			    alpha=alpha, 
+			    family='gaussian',
+                            type='partial', 
+                            sigma=sigma)
+        ci_lee = matrix(NA, p, 2)
+	ci_lee[lee$vars,] = lee$ci    
+        ci[,,ii] = ci_lee
+
+        #randomized
+
+        rand_lasso_soln = randomizedLasso(X, y, n*lam, noise_scale=0.5*sigma, ridge_term=0.5*sigma/sqrt(n))
+        rand_E = rand_lasso_soln$active_set
+        X_rand_E = X[,rand_E,drop=FALSE]
+        rand_target = coef(glm(y~X_rand_E-1))
+        cov_rand_E = solve(t(X_rand_E) %*% X_rand_E) * sigma^2
+        cross_cov = matrix(0, p, length(rand_E))
+        cross_cov[1:length(rand_E),1:length(rand_E)] = cov_rand_E
+        targets = list(observed_target=rand_target,
+                       cov_target=cov_rand_E, 
+                       crosscov_target_internal=cross_cov)
+        rand_inf = randomizedLassoInf(rand_lasso_soln, nsample=5000, burnin=1000, targets=targets)
+        ci5[rand_lasso_soln$act,1,ii] = rand_inf$ci[,1]
+        ci5[rand_lasso_soln$act,2,ii] = rand_inf$ci[,2]
     }
-    if(sum(beta!=0)>0){
-       
-        
-  betaall[ii,]=beta
-  a=lsfit(x[,beta!=0],y)
-    aa=ls.diag(a)
-   bhat=a$coef[-1]
-   bhat0=a$coef[1]
-  act=which(beta!=0)
-  se=aa$std.err[-1]
-   btruepart[,ii]=0
-   btruepart[act,ii]=lsfit(x[,act,drop=F],mutrue)$coef[-1]
-#naive intervals
-  ci0[beta!=0,1,ii]=bhat-zalpha*se
-  ci0[beta!=0,2,ii]=bhat+zalpha*se
-
-   #bonf-adj naive
-   alpha4=alpha/p
-   zalpha4=-qnorm(alpha4/2)
-   ci4[beta!=0,1,ii]=bhat-zalpha4*se
-  ci4[beta!=0,2,ii]=bhat+zalpha4*se
-
-#lee et al intervals
-lee=foo(x,y,beta,lam*n,alpha,type=type)
-        ci[,,ii]=lee$ci
-
- #randomized
-        if(dorand){
-            rand_lasso_soln = randomizedLasso(x, y, n*lam)
-        junk=randomizedLassoInf(rand_lasso_soln, nsample=5000, burnin=1000)
-    ci5[rand_lasso_soln$act,1,ii]=junk$ci[,1]
-    ci5[rand_lasso_soln$act,2,ii]=junk$ci[,2]
-}
-
-}}
-   
-
-
-
-if(type=="partial") btrue=btruepart
-
-mc0=mean(ci0[,1,]>btrue | ci0[,2,]<btrue,na.rm=T)
-len0=mean(ci0[,2,]-ci0[,1,],na.rm=T)
-len0m=median(ci0[,2,]-ci0[,1,],na.rm=T)
-
-ninf=mean(abs(ci)==Inf,na.rm=T)
-ci[abs(ci)==Inf]=NA
-mc=mean(ci[,1,]>btrue | ci[,2,]<btrue,na.rm=T)
-len=mean(ci[,2,]-ci[,1,],na.rm=T)
-lenm=median(ci[,2,]-ci[,1,],na.rm=T)
-
-
-mc4=mean(ci4[,1,]>btrue | ci4[,2,]<btrue,na.rm=T)
-len4=mean(ci4[,2,]-ci4[,1,],na.rm=T)
-len4m=median(ci4[,2,]-ci4[,1,],na.rm=T)
-
-mc5=mean(ci5[,1,]>btrue | ci5[,2,]<btrue,na.rm=T)
-len5=mean(ci5[,2,]-ci5[,1,],na.rm=T)
-len5m=median(ci5[,2,]-ci5[,1,],na.rm=T)
-
-
-
-cat(c("ave# nz",mean(apply(betaall!=0,1,sum,na.rm=T))),fill=T)
-
-miscov=c(mc0,mc,mc4,mc5)
-
-res=
-matrix(
-    c(len0,len0m,0,
-      len,lenm,ninf,
-      len4,len4m,0,
-        len5,len5m,0
-      ),4,3,byrow=T)
-res=cbind(res,miscov)
-
-dimnames(res)=list(c("naive","Lee","bonf-naive","rand"),c("mean","median","propInf","miscov"))
-print(res)
-
-
-#out=cbind(as.vector(ci0[,2,]-ci0[,1,]),
-#             as.vector(ci[,2,]-ci[,1,]),
-#             as.vector(ci2[,2,]-ci2[,1,]),
-##             as.vector(ci3[,2,]-ci3[,1,]),
-#          as.vector(ci4[,2,]-ci4[,1,]),
-#                    as.vector(ci5[,2,]-ci5[,1,] ))
-#print(out)
+
+
+
+
+    mc0=mean(ci0[,1,1:ii]>btruepart[,1:ii] | ci0[,2,1:ii]<btruepart[,1:ii],na.rm=T)
+    len0=mean(ci0[,2,1:ii]-ci0[,1,1:ii],na.rm=T)
+    len0m=median(ci0[,2,1:ii]-ci0[,1,1:ii],na.rm=T)
+
+    ninf=mean(abs(ci)==Inf,na.rm=T)
+    ci[abs(ci)==Inf]=NA
+    mc=mean(ci[,1,1:ii]>btruepart[,1:ii] | ci[,2,1:ii]<btruepart[,1:ii],na.rm=T)
+    len=mean(ci[,2,1:ii]-ci[,1,1:ii],na.rm=T)
+    lenm=median(ci[,2,1:ii]-ci[,1,1:ii],na.rm=T)
+
+
+    mc4=mean(ci4[,1,1:ii]>btruepart[,1:ii] | ci4[,2,1:ii]<btruepart[,1:ii],na.rm=T)
+    len4=mean(ci4[,2,1:ii]-ci4[,1,1:ii],na.rm=T)
+    len4m=median(ci4[,2,1:ii]-ci4[,1,1:ii],na.rm=T)
+
+    mc5=mean(ci5[,1,1:ii]>btruepart[,1:ii] | ci5[,2,1:ii]<btruepart[,1:ii],na.rm=T)
+    len5=mean(ci5[,2,1:ii]-ci5[,1,1:ii],na.rm=T)
+    len5m=median(ci5[,2,1:ii]-ci5[,1,1:ii],na.rm=T)
+
+
+
+    cat(c("trial ", ii, " (cumulative)\n"))
+    cat(c("ave# nz",mean(apply(betaall[1:ii,,drop=FALSE]!=0,1,sum,na.rm=T))),fill=T)
+
+    miscov=c(mc0,mc,mc4,mc5)
+
+    res=
+    matrix(
+        c(len0,len0m,0,
+          len,lenm,ninf,
+          len4,len4m,0,
+            len5,len5m,0
+          ),4,3,byrow=T)
+    res=cbind(res,miscov)
+
+    dimnames(res)=list(c("naive","Lee","bonf-naive","rand"),c("mean","median","propInf","miscov"))
+
+    print(res)
+
+
+}  

From b988ba60a6ea209670a50de602d7522aa90f96e0 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 21 Nov 2017 14:15:30 -0800
Subject: [PATCH 438/493] no longer need second cholesky

---
 selectiveInference/R/funs.randomized.R | 29 +++++---------------------
 1 file changed, 5 insertions(+), 24 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 3134924..48d10f2 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -498,26 +498,7 @@ randomizedLassoInf = function(rand_lasso_soln,
     target_transform = list(linear_term=linear_term,
                             offset_term=nuisance + internal_transform$offset_term) # internal_transform$offset_term is 0...
     
-    # changing dimension of density evalutaion
-
-    if ((rand_lasso_soln$condition_subgrad == TRUE) & (nactive < p-1)) {
-
-        # A description of why we do this might help
-
-        target_opt_linear = cbind(target_transform$linear_term, importance_transform$linear_term)
-        reduced_target_opt_linear = chol(t(target_opt_linear) %*% target_opt_linear)
-        target_linear = reduced_target_opt_linear[,1,drop=FALSE]
-        temp = solve(t(reduced_target_opt_linear)) %*% t(target_opt_linear)
-        target_offset = temp %*% target_transform$offset_term
-        target_transform = list(linear_term = as.matrix(target_linear), offset_term = target_offset)
-        cur_linear = reduced_target_opt_linear[,2:ncol(reduced_target_opt_linear)]
-        cur_offset = temp %*% importance_transform$offset_term
-        cur_transform = list(linear_term = as.matrix(cur_linear), offset_term = cur_offset)
-        raw = target_transform$linear_term * targets$observed_target[i] + target_transform$offset_term
-    } else {
-        cur_transform = importance_transform
-        raw = observed_raw
-    }   
+    # compute sufficient statistic for root finding
 
     target_sample = rnorm(nrow(as.matrix(opt_samples))) * sqrt(targets$cov_target[i,i])
 
@@ -538,16 +519,16 @@ randomizedLassoInf = function(rand_lasso_soln,
     reference_measure = importance_weight(noise_scale,
                                           t(as.matrix(target_sample)),
                                           t(opt_samples),
-                                          cur_transform,
+                                          importance_transform,
                                           target_transform,
-                                          raw)
+                                          observed_raw)
 
     alternative_measure = importance_weight(noise_scale,
                                             t(as.matrix(target_sample) + 0.1 * sqrt(targets$cov_target[i,i])),
                                             t(opt_samples),
-                                            cur_transform,
+                                            importance_transform,
                                             target_transform,
-                                            raw)
+                                            observed_raw)
 
     log_reference_measure = log(reference_measure)
     sufficient_stat = (log(alternative_measure) - log_reference_measure) / (0.1 * sqrt(targets$cov_target[i,i]))

From 2150080ca850e3ec57671d452de725f7f9a3d9fb Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 22 Nov 2017 09:38:59 -0800
Subject: [PATCH 439/493] rewriting sufficient stat to avoid NaN

---
 selectiveInference/R/funs.randomized.R       | 41 ++++++++++++++++----
 selectiveInference/man/randomizedLassoInf.Rd |  2 +-
 2 files changed, 34 insertions(+), 9 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 48d10f2..2d4fc3b 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -338,7 +338,9 @@ importance_weight = function(noise_scale,
 
     W = log_num - log_den
     W = W - max(W)
-    return(exp(W))
+    W = exp(W)
+    W = W / sum(W)
+    return(W)
 }
 
 get_mean_cov = function(noise_scale, linear_term, offset_term){
@@ -398,7 +400,7 @@ conditional_opt_transform = function(noise_scale,
 randomizedLassoInf = function(rand_lasso_soln,
                               targets=NULL,
                               level=0.9,
-                              sampler=c("norejection"),
+                              sampler=c("norejection", "adaptMCMC"),
                               nsample=10000,
                               burnin=2000)
  {
@@ -496,7 +498,7 @@ randomizedLassoInf = function(rand_lasso_soln,
     linear_term = internal_transform$linear_term %*% pre_linear_term[1:nactive]
     linear_term[inactive_set] = linear_term[inactive_set] - pre_linear_term[(nactive+1):p]
     target_transform = list(linear_term=linear_term,
-                            offset_term=nuisance + internal_transform$offset_term) # internal_transform$offset_term is 0...
+                            offset_term=as.vector(nuisance + internal_transform$offset_term)) # internal_transform$offset_term is 0...
     
     # compute sufficient statistic for root finding
 
@@ -505,17 +507,31 @@ randomizedLassoInf = function(rand_lasso_soln,
     # weight in the numerator is of the form
     # -1/(2 noise_scale^2)\|Do + q + P(t+\theta)\|^2_2 
     # with D=importance_transform$linear_term
-    #      q=target_transform$offset_term + cur_transform$offset_term
+    #      q=target_transform$offset_term + importance_transform$offset_term
     #      P=target_transform$linear_term
 
     # weight in the denominator is of the form
     # -1/(2 noise_scale^2)\|Do + q_D\|^2_2  
     # with D=importance_transform$linear_term
-    #      q_D = observed_raw + cur_transform$offset_term
+    #      q_D = observed_raw + importance_transform$offset_term
 
     # reference measure just is the ratio at \theta=0
     # sufficient statistic is linear term in \theta
 
+    den = importance_transform$linear_term %*% t(opt_samples) + observed_raw + importance_transform$offset_term
+
+    num1 = (importance_transform$linear_term %*% t(opt_samples) + 
+            target_transform$linear_term %*% t(as.matrix(target_sample)) + 
+            importance_transform$offset_term +
+            target_transform$offset_term)
+    num2 = (importance_transform$linear_term %*% t(opt_samples) + 
+            target_transform$linear_term %*% t(as.matrix(target_sample) + 1) +
+            importance_transform$offset_term +
+            target_transform$offset_term)
+
+    suff_stat2 = -apply(num2^2 - num1^2, 2, sum) / (2 * noise_scale^2)
+    suff_stat2 = suff_stat2
+	       
     reference_measure = importance_weight(noise_scale,
                                           t(as.matrix(target_sample)),
                                           t(opt_samples),
@@ -533,6 +549,9 @@ randomizedLassoInf = function(rand_lasso_soln,
     log_reference_measure = log(reference_measure)
     sufficient_stat = (log(alternative_measure) - log_reference_measure) / (0.1 * sqrt(targets$cov_target[i,i]))
 
+    print(cor(as.vector(suff_stat2), as.vector(sufficient_stat)))
+    print(summary(lm(as.vector(suff_stat2) ~ as.vector(sufficient_stat))))
+
     pivot = function(candidate){
       arg_ = candidate * sufficient_stat + log_reference_measure
       arg_ = arg_ - max(arg_)
@@ -549,9 +568,15 @@ randomizedLassoInf = function(rand_lasso_soln,
     }
 
     pvalues[i] = pivot(0)
-    line_min = -10*sd(target_sample)
-    line_max = 10*sd(target_sample)
-
+    line_min = -10*sd(target_sample) + targets$observed_target[i]
+    line_max = 10*sd(target_sample) + targets$observed_target[i]
+
+    print(min(log_reference_measure))
+    print(max(log_reference_measure))
+    print(min(sufficient_stat))
+    print(max(sufficient_stat))
+    print(rootU(line_min))
+    print(rootU(line_max))
     if (rootU(line_min)*rootU(line_max)<0){
       ci[i,2] = uniroot(rootU, c(line_min, line_max))$root + targets$observed_target[i]
     } else{
diff --git a/selectiveInference/man/randomizedLassoInf.Rd b/selectiveInference/man/randomizedLassoInf.Rd
index f860188..e7f94b1 100644
--- a/selectiveInference/man/randomizedLassoInf.Rd
+++ b/selectiveInference/man/randomizedLassoInf.Rd
@@ -14,7 +14,7 @@ randomization.
 randomizedLassoInf(rand_lasso_soln, 
 		   targets=NULL,
                    level=0.9,
-                   sampler=c("adaptMCMC", "norejection"),
+                   sampler=c("norejection", "adaptMCMC"),
                    nsample=10000,
                    burnin=2000)
 }

From bb910dcd0f18b76708d19fcb86bd84009690bc14 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 22 Nov 2017 09:39:18 -0800
Subject: [PATCH 440/493] removing debug statements

---
 selectiveInference/R/funs.randomized.R | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 2d4fc3b..cbdd1bf 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -549,9 +549,6 @@ randomizedLassoInf = function(rand_lasso_soln,
     log_reference_measure = log(reference_measure)
     sufficient_stat = (log(alternative_measure) - log_reference_measure) / (0.1 * sqrt(targets$cov_target[i,i]))
 
-    print(cor(as.vector(suff_stat2), as.vector(sufficient_stat)))
-    print(summary(lm(as.vector(suff_stat2) ~ as.vector(sufficient_stat))))
-
     pivot = function(candidate){
       arg_ = candidate * sufficient_stat + log_reference_measure
       arg_ = arg_ - max(arg_)
@@ -571,12 +568,6 @@ randomizedLassoInf = function(rand_lasso_soln,
     line_min = -10*sd(target_sample) + targets$observed_target[i]
     line_max = 10*sd(target_sample) + targets$observed_target[i]
 
-    print(min(log_reference_measure))
-    print(max(log_reference_measure))
-    print(min(sufficient_stat))
-    print(max(sufficient_stat))
-    print(rootU(line_min))
-    print(rootU(line_max))
     if (rootU(line_min)*rootU(line_max)<0){
       ci[i,2] = uniroot(rootU, c(line_min, line_max))$root + targets$observed_target[i]
     } else{

From b01a0af84a627aa42ab48214fd2bcf0fb412cdaa Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 22 Nov 2017 09:45:11 -0800
Subject: [PATCH 441/493] BF: finished new sufficient stat

---
 selectiveInference/R/funs.randomized.R | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index cbdd1bf..4e3ecb0 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -529,25 +529,25 @@ randomizedLassoInf = function(rand_lasso_soln,
             importance_transform$offset_term +
             target_transform$offset_term)
 
-    suff_stat2 = -apply(num2^2 - num1^2, 2, sum) / (2 * noise_scale^2)
-    suff_stat2 = suff_stat2
-	       
+    sufficient_stat = -apply(num2^2 - num1^2, 2, sum) / (2 * noise_scale^2)
+
     reference_measure = importance_weight(noise_scale,
                                           t(as.matrix(target_sample)),
                                           t(opt_samples),
                                           importance_transform,
                                           target_transform,
                                           observed_raw)
+    log_reference_measure = log(reference_measure)	       
+
+#     alternative_measure = importance_weight(noise_scale,
+#                                             t(as.matrix(target_sample) + 0.1 * sqrt(targets$cov_target[i,i])),
+#                                             t(opt_samples),
+#                                             importance_transform,
+#                                             target_transform,
+#                                             observed_raw)
 
-    alternative_measure = importance_weight(noise_scale,
-                                            t(as.matrix(target_sample) + 0.1 * sqrt(targets$cov_target[i,i])),
-                                            t(opt_samples),
-                                            importance_transform,
-                                            target_transform,
-                                            observed_raw)
 
-    log_reference_measure = log(reference_measure)
-    sufficient_stat = (log(alternative_measure) - log_reference_measure) / (0.1 * sqrt(targets$cov_target[i,i]))
+#     sufficient_stat = (log(alternative_measure) - log_reference_measure) / (0.1 * sqrt(targets$cov_target[i,i]))
 
     pivot = function(candidate){
       arg_ = candidate * sufficient_stat + log_reference_measure

From 62d5cbcc7bdfd02aef58cfd78f786dbadc5e69f5 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 22 Nov 2017 09:45:20 -0800
Subject: [PATCH 442/493] two new timing scripts

---
 tests/randomized/timing.R         | 28 ++++++++++++++++++++++++++++
 tests/randomized/timing_smaller.R | 26 ++++++++++++++++++++++++++
 2 files changed, 54 insertions(+)
 create mode 100644 tests/randomized/timing.R
 create mode 100644 tests/randomized/timing_smaller.R

diff --git a/tests/randomized/timing.R b/tests/randomized/timing.R
new file mode 100644
index 0000000..787152b
--- /dev/null
+++ b/tests/randomized/timing.R
@@ -0,0 +1,28 @@
+library(selectiveInference)
+library(glmnet)
+set.seed(32)
+
+n=6000; p=50000
+
+coverage = c()
+for (i in 1:100) {
+print("creating X")
+print(system.time(x <- matrix(rnorm(n*p),n,p)))
+print(system.time(x <- scale(x,T,T)/sqrt(n-1)))
+print('running glmnet')
+y=rnorm(n)
+print(system.time(G <- glmnet(x,y,intercept=F,standardize=F)))
+lam=G$lam[5] #for p< 1000
+lam=G$lam[10] #for p=1000
+lam=G$lam[2]  #for p=50,000
+print("solving randomized LASSO")
+print(system.time(rand_lasso <- randomizedLasso(x, y, n*lam)))
+print(rand_lasso$active_set)
+print("inference for randomizedLasso")
+tim=system.time(rand_lasso_inf<-randomizedLassoInf(rand_lasso, sampler='norejection', nsample=20000, burnin=10000))[1]
+print(rand_lasso_inf$pvalues)
+print(names(rand_lasso_inf$pvalues))
+print(rand_lasso_inf$ci)
+coverage = c(coverage, (rand_lasso_inf$ci[,1] <= 0) * (rand_lasso_inf$ci[,2] >= 0))
+print(mean(coverage))
+}
\ No newline at end of file
diff --git a/tests/randomized/timing_smaller.R b/tests/randomized/timing_smaller.R
new file mode 100644
index 0000000..53ff6e7
--- /dev/null
+++ b/tests/randomized/timing_smaller.R
@@ -0,0 +1,26 @@
+library(selectiveInference)
+library(glmnet)
+set.seed(32)
+
+# smaller data sets
+n=500; p=4000
+
+coverage = c()
+for (i in 1:100) {
+print("creating X")
+print(system.time(x <- matrix(rnorm(n*p),n,p)))
+print(system.time(x <- scale(x,T,T)/sqrt(n-1)))
+print('running glmnet')
+y=rnorm(n)
+print(system.time(G <- glmnet(x,y,intercept=F,standardize=F)))
+lam=G$lam[5] #for p< 1000
+lam=G$lam[10] #for p=1000
+lam=G$lam[2]  #for p=50,000
+print("solving randomized LASSO")
+print(system.time(rand_lasso <- randomizedLasso(x, y, n*lam)))
+print("inference for randomizedLasso")
+print(system.time(rand_lasso_inf<-randomizedLassoInf(rand_lasso, sampler='norejection', nsample=15000, burnin=10000)))
+print(rand_lasso_inf$ci)
+coverage = c(coverage, (rand_lasso_inf$ci[,1] <= 0) * (rand_lasso_inf$ci[,2] >= 0))
+print(mean(coverage))
+}
\ No newline at end of file

From c9dffae9d3820f20be69527f33629fee98d9f664 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Sun, 26 Nov 2017 15:48:50 -0800
Subject: [PATCH 443/493] added randomized debiased

---
 selectiveInference/R/funs.fixed.R      |  20 ++--
 selectiveInference/R/funs.randomized.R | 143 ++++++++++++++++++-------
 tests/randomized/test_instances.R      |  14 ++-
 3 files changed, 123 insertions(+), 54 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 5df84d3..64dd026 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -318,20 +318,20 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
                            is_wide,
                            nsample, 
                            rows, 
- 		           verbose=FALSE, 
-		           bound=NULL,             # starting value of bound
-   			   linesearch=TRUE,     # do a linesearch?
-   		           scaling_factor=1.5,  # multiplicative factor for linesearch
-			   max_active=NULL,     # how big can active set get?
-			   max_try=10,          # how many steps in linesearch?
-			   warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
-			   max_iter=50,         # how many iterations for each optimization problem
+ 		                       verbose=FALSE, 
+		                       bound=NULL,             # starting value of bound
+   			                   linesearch=TRUE,     # do a linesearch?
+   		                     scaling_factor=1.5,  # multiplicative factor for linesearch
+			                     max_active=NULL,     # how big can active set get?
+			                     max_try=10,          # how many steps in linesearch?
+			                     warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
+			                     max_iter=50,         # how many iterations for each optimization problem
                            kkt_stop=TRUE,       # stop based on KKT conditions?
                            parameter_stop=TRUE, # stop based on relative convergence of parameter?
-			   objective_stop=TRUE, # stop based on relative decrease in objective?
+			                     objective_stop=TRUE, # stop based on relative decrease in objective?
                            kkt_tol=1.e-4,       # tolerance for the KKT conditions
                            parameter_tol=1.e-4, # tolerance for relative convergence of parameter
-			   objective_tol=1.e-4  # tolerance for relative decrease in objective
+			                     objective_tol=1.e-4  # tolerance for relative decrease in objective
                            ) {
 
 
diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 4e3ecb0..d7be44a 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -268,7 +268,7 @@ randomizedLasso = function(X,
     return(list(X=X,
                 y=y,
                 lam=lam,
-		family=family,
+		            family=family,
                 active_set=active_set,
                 inactive_set=inactive_set,
                 unpenalized_set=unpenalized_set,
@@ -397,8 +397,105 @@ conditional_opt_transform = function(noise_scale,
 	      importance_transform=opt_transform))
 }
 
+set.target.and.internal = function(rand_lasso_soln, type){
+  
+  # compute internal representation of the data
+  y = rand_lasso_soln$y
+  X = rand_lasso_soln$X
+  p=ncol(X); n=nrow(X);
+  active_set = rand_lasso_soln$active_set
+  unpenalized_set = rand_lasso_soln$unpenalized_set
+  nactive = length(c(active_set, unpenalized_set))
+  inactive_set = rand_lasso_soln$inactive_set
+  
+  X_E = rand_lasso_soln$X[, c(active_set, unpenalized_set), drop=FALSE]
+  
+  if (rand_lasso_soln$family == 'gaussian') {
+    glm_y = glm(y ~ X_E-1)
+  } else if (rand_lasso_soln$family == 'binomial') {
+    glm_y = glm(y ~ X_E-1, family=binomial())
+  }
+  
+  observed_internal =  c(as.vector(glm_y$coefficients), as.vector((y - fitted(glm_y)) %*% X)[inactive_set])
+  
+  if (type=="partial"){
+    
+    observed_target = as.vector(glm_y$coefficients)
+    cov_target = vcov(glm_y)
+    
+    if (sum(is.na(observed_target)) > 0) {
+      stop("unregularized (relaxed) fit has NA values -- X[,active_set] likely singular")
+    }
+    
+    crosscov_target_internal=rbind(cov_target, matrix(0, nrow=p-nactive, ncol=nactive))
+  } 
+  
+  if (type=="full"){
+    
+    lasso.est = rand_lasso_soln$soln
+    X_active = X[, active_set]
+    X_inactive = X[, inactive_set]
+    
+    if (n<p){
+  
+      Xordered = X[,c(active_set,inactive_set,recursive=T)]
+      hsigmaS = 1/n*(t(X_active)%*%X_active) # hsigma[S,S]
+      hsigmaSinv = solve(hsigmaS) # pinv(hsigmaS)
+      FS = rbind(diag(nactive),matrix(0,p-nactive,nactive))
+      GS = cbind(diag(nactive),matrix(0,nactive,p-nactive))
+      hsigma = 1/n*(t(Xordered)%*%Xordered)
+      is_wide = n < (2 * p)
+      
+      if (!is_wide) {
+        hsigma = 1/n*(t(Xordered)%*%Xordered)
+        htheta = selectiveInference:::debiasingMatrix(hsigma, is_wide, n, 1:nactive)
+        ithetasigma = (GS-(htheta%*%hsigma))
+      } else {
+        htheta = selectiveInference:::debiasingMatrix(Xordered, is_wide, n, 1:nactive)
+        ithetasigma = (GS-((htheta%*%t(Xordered)) %*% Xordered)/n)
+      }
+      
+      M_active <- ((htheta%*%t(Xordered))+ithetasigma%*%FS%*%hsigmaSinv%*%t(X_active))/n
+      M_inactive  =  (htheta[, (nactive+1):p]%*%t(X[,inactive_set])/n)
+                       #+ithetasigma_inactive%*%FS%*%hsigmaSinv%*%t(X_active))/n)
+    }
+    else{
+      pseudo_invX = pinv(crossprod(X))
+      M_active = pseudo_invX[active_set,] %*% t(X)
+      M_inactive = (pseudo_invX[,inactive_set] %*% t(X_inactive))[active_set,]
+    }
+    #print(c("M_active size", dim(M_active)))
+    #print(c("M_inactive size", dim(M_inactive)))
+    #print(M_inactive[,1:10])
+    #pseudo_invX = pinv(crossprod(X))
+    #M_inactive = (pseudo_invX[,inactive_set] %*% t(X_inactive))[active_set,]
+    #print(M_inactive[,1:10])
+    
+    print(c("M_active size", dim(M_active)))
+    print(c("M_inactive size", dim(M_inactive)))
+    residuals = y-X%*%lasso.est
+    scalar = 1 #sqrt(n)
+    observed_target = lasso.est[active_set]+scalar*M_active %*% residuals
+    cov_target = vcov(glm_y) + scalar^2*M_inactive %*% t(M_inactive)
+    crosscov_target_internal = rbind(vcov(glm_y), scalar*t(M_inactive))
+    print(c("dim",dim(crosscov_target_internal)))
+  }
+  
+  if (!is.null(colnames(X))) {
+    names(observed_target) = colnames(X)[active_set]
+  } else {
+    names(observed_target) = active_set
+  }
+  
+  targets = list(observed_target=observed_target,
+                 cov_target=cov_target,
+                 crosscov_target_internal=crosscov_target_internal)
+  return(list(targets=targets, observed_internal=observed_internal))
+}
+
+
 randomizedLassoInf = function(rand_lasso_soln,
-                              targets=NULL,
+                              type="partial",
                               level=0.9,
                               sampler=c("norejection", "adaptMCMC"),
                               nsample=10000,
@@ -435,45 +532,13 @@ randomizedLassoInf = function(rand_lasso_soln,
                                    law$sampling_transform$offset_term,
                                    law$constraints,
                                    nsamples=nsample,
-		                   burnin=burnin)
-  }
-
-  # compute internal representation of the data
-
-  X_E = rand_lasso_soln$X[, c(active_set, unpenalized_set), drop=FALSE]
-
-  y = rand_lasso_soln$y
-  X = rand_lasso_soln$X
-
-  if (rand_lasso_soln$family == 'gaussian') {
-     glm_y = glm(y ~ X_E-1)
-  } else if (rand_lasso_soln$family == 'binomial') {
-     glm_y = glm(y ~ X_E-1, family=binomial())
+		                               burnin=burnin)
   }
 
-  observed_internal =  c(as.vector(glm_y$coefficients), as.vector((y - fitted(glm_y)) %*% X)[inactive_set])
-
-  if (is.null(targets)) {
-
-      observed_target = as.vector(glm_y$coefficients)
-      cov_target = vcov(glm_y)
-
-      if (sum(is.na(observed_target)) > 0) {
-          stop("unregularized (relaxed) fit has NA values -- X[,active_set] likely singular")
-      }
-
-      if (!is.null(colnames(X))) {
-            names(observed_target) = colnames(X)[rand_lasso_soln$active_set]
-      } else {
-            names(observed_target) = rand_lasso_soln$active_set
-      }
-
-      targets = list(observed_target=observed_target,
-                     cov_target=cov_target,
-                     crosscov_target_internal=rbind(cov_target, matrix(0, nrow=p-nactive, ncol=nactive)))
-
-  } 
-
+  targets.and.internal = set.target.and.internal(rand_lasso_soln, type)
+  targets = targets.and.internal$targets
+  observed_internal = targets.and.internal$observed_internal
+  
   importance_transform = law$importance_transform
   internal_transform=rand_lasso_soln$internal_transform
   observed_raw=rand_lasso_soln$observed_raw
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index fd39015..1c1688b 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -85,11 +85,13 @@ test_KKT=function(){
   #print(result$perturb)
   print(opt_linear %*% observed_opt_state+opt_offset+result$observed_raw-result$perturb) ## should be zero
 }
-  
+
 
 collect_results = function(n,p,s, nsim=100, level=0.9, 
-                           family = "binomial",
-                           condition_subgrad=TRUE, lam=1.2){
+                           family = "gaussian",
+                           condition_subgrad=TRUE, 
+                           type="full",
+                           lam=1.2){
 
   rho=0.
   sigma=1
@@ -103,9 +105,11 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
     rand_lasso_soln = selectiveInference:::randomizedLasso(X, 
                                                            y, 
                                                            lam, 
-                                                           family=family)
-
+                                                           family=family,
+                                                           condition_subgrad=condition_subgrad)
+    
     result = selectiveInference:::randomizedLassoInf(rand_lasso_soln,
+                                                     type=type,
                                                      sampler = "norejection", #"adaptMCMC", #
                                                      level=level, 
                                                      burnin=1000, 

From e90c9a6e0abf8b735ff436309ba6bde3a2721655 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Mon, 27 Nov 2017 10:21:59 -0800
Subject: [PATCH 444/493] back to targets

---
 selectiveInference/R/funs.randomized.R | 21 +++++++++++++--------
 tests/randomized/test_instances.R      |  4 +++-
 2 files changed, 16 insertions(+), 9 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index d7be44a..019da4b 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -136,8 +136,12 @@ randomizedLasso = function(X,
       W_E = diag(pi_vec*(1-pi_vec))
     } else if (family=="gaussian"){
       W_E = diag(rep(1,n))
+      unpen_reg = glm(y ~ X_E-1)
     }
     
+    observed_internal =  c(as.vector(unpen_reg$coefficients), as.vector((y - fitted(unpen_reg)) %*% X)[inactive_set])
+    
+    
     L_E = t(X_E) %*% W_E %*% X
     L_E = t(L_E)
     coef_term = L_E
@@ -275,6 +279,7 @@ randomizedLasso = function(X,
                 sign_soln=sign_soln,
                 law=law,
                 internal_transform=internal_transform,
+		            observed_internal=observed_internal,
                 observed_raw=observed_raw,
                 noise_scale=noise_scale,
                 soln=result$soln,
@@ -397,7 +402,7 @@ conditional_opt_transform = function(noise_scale,
 	      importance_transform=opt_transform))
 }
 
-set.target.and.internal = function(rand_lasso_soln, type){
+set.target = function(rand_lasso_soln, type){
   
   # compute internal representation of the data
   y = rand_lasso_soln$y
@@ -416,8 +421,6 @@ set.target.and.internal = function(rand_lasso_soln, type){
     glm_y = glm(y ~ X_E-1, family=binomial())
   }
   
-  observed_internal =  c(as.vector(glm_y$coefficients), as.vector((y - fitted(glm_y)) %*% X)[inactive_set])
-  
   if (type=="partial"){
     
     observed_target = as.vector(glm_y$coefficients)
@@ -490,12 +493,12 @@ set.target.and.internal = function(rand_lasso_soln, type){
   targets = list(observed_target=observed_target,
                  cov_target=cov_target,
                  crosscov_target_internal=crosscov_target_internal)
-  return(list(targets=targets, observed_internal=observed_internal))
+  return(targets)
 }
 
 
 randomizedLassoInf = function(rand_lasso_soln,
-                              type="partial",
+                              targets=NULL,
                               level=0.9,
                               sampler=c("norejection", "adaptMCMC"),
                               nsample=10000,
@@ -535,9 +538,11 @@ randomizedLassoInf = function(rand_lasso_soln,
 		                               burnin=burnin)
   }
 
-  targets.and.internal = set.target.and.internal(rand_lasso_soln, type)
-  targets = targets.and.internal$targets
-  observed_internal = targets.and.internal$observed_internal
+  if (is.null(targets)){
+    targets=set.target(rand_lasso_soln, type="partial")
+  }
+  
+  observed_internal = rand_lasso_soln$observed_internal
   
   importance_transform = law$importance_transform
   internal_transform=rand_lasso_soln$internal_transform
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 1c1688b..355dcfb 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -108,8 +108,10 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
                                                            family=family,
                                                            condition_subgrad=condition_subgrad)
     
+    targets=selectiveInference:::set.target(rand_lasso_soln,type=type)
+    
     result = selectiveInference:::randomizedLassoInf(rand_lasso_soln,
-                                                     type=type,
+                                                     targets=targets,
                                                      sampler = "norejection", #"adaptMCMC", #
                                                      level=level, 
                                                      burnin=1000, 

From 049a7ee7aab2574279007aee51b1f3fbece3e8a2 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Tue, 28 Nov 2017 16:35:38 -0800
Subject: [PATCH 445/493] cross cov was wrong

---
 selectiveInference/R/funs.randomized.R | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 019da4b..c03ceee 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -113,6 +113,7 @@ randomizedLasso = function(X,
     observed_unpen = result$soln[unpenalized]
     observed_subgrad = -n*result$gradient[inactive]
     
+    print(c("nactive", length(active_set)))
     if (sum(abs(observed_subgrad)>lam[inactive]*(1.001)) > 0){
       stop("subgradient eq not satisfied")
     }
@@ -457,10 +458,10 @@ set.target = function(rand_lasso_soln, type){
         htheta = selectiveInference:::debiasingMatrix(Xordered, is_wide, n, 1:nactive)
         ithetasigma = (GS-((htheta%*%t(Xordered)) %*% Xordered)/n)
       }
-      
       M_active <- ((htheta%*%t(Xordered))+ithetasigma%*%FS%*%hsigmaSinv%*%t(X_active))/n
       M_inactive  =  (htheta[, (nactive+1):p]%*%t(X[,inactive_set])/n)
                        #+ithetasigma_inactive%*%FS%*%hsigmaSinv%*%t(X_active))/n)
+      M_inactive_full = htheta[, (nactive+1)]
     }
     else{
       pseudo_invX = pinv(crossprod(X))
@@ -474,14 +475,14 @@ set.target = function(rand_lasso_soln, type){
     #M_inactive = (pseudo_invX[,inactive_set] %*% t(X_inactive))[active_set,]
     #print(M_inactive[,1:10])
     
-    print(c("M_active size", dim(M_active)))
-    print(c("M_inactive size", dim(M_inactive)))
+    #print(c("M_active size", dim(M_active)))
+    #print(c("M_inactive size", dim(M_inactive)))
     residuals = y-X%*%lasso.est
     scalar = 1 #sqrt(n)
     observed_target = lasso.est[active_set]+scalar*M_active %*% residuals
     cov_target = vcov(glm_y) + scalar^2*M_inactive %*% t(M_inactive)
-    crosscov_target_internal = rbind(vcov(glm_y), scalar*t(M_inactive))
-    print(c("dim",dim(crosscov_target_internal)))
+    crosscov_target_internal = rbind(vcov(glm_y), scalar*t(X_inactive) %*% t(M_inactive))
+    
   }
   
   if (!is.null(colnames(X))) {
@@ -555,11 +556,10 @@ randomizedLassoInf = function(rand_lasso_soln,
   rownames(ci) = names(targets$observed_target)
 
   for (i in 1:nactive){
-
-    pre_nuisance = observed_internal - (targets$crosscov_target_internal[,i] *
+    pre_nuisance = observed_internal - (as.vector(targets$crosscov_target_internal[,i]) *
                                         targets$observed_target[i] / 
                                         targets$cov_target[i,i])
-
+    
     nuisance = internal_transform$linear_term %*% pre_nuisance[1:nactive] 
     nuisance[inactive_set] = nuisance[inactive_set] - pre_nuisance[(nactive+1):p]
 
@@ -637,7 +637,7 @@ randomizedLassoInf = function(rand_lasso_soln,
     pvalues[i] = pivot(0)
     line_min = -10*sd(target_sample) + targets$observed_target[i]
     line_max = 10*sd(target_sample) + targets$observed_target[i]
-
+    
     if (rootU(line_min)*rootU(line_max)<0){
       ci[i,2] = uniroot(rootU, c(line_min, line_max))$root + targets$observed_target[i]
     } else{

From df37d54bb57fde65fb2feda5e1b08df54de08844 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Tue, 28 Nov 2017 19:57:51 -0800
Subject: [PATCH 446/493] changed pivot

---
 selectiveInference/R/funs.randomized.R | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index c03ceee..839529d 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -623,7 +623,8 @@ randomizedLassoInf = function(rand_lasso_soln,
       arg_ = candidate * sufficient_stat + log_reference_measure
       arg_ = arg_ - max(arg_)
       weights = exp(arg_)
-      return(mean((target_sample + candidate < targets$observed_target[i]) * weights)/mean(weights))
+      p = mean((target_sample + candidate < targets$observed_target[i]) * weights)/mean(weights)
+      return(2*min(p, 1-p))
     }
 
     rootU = function(candidate){

From ca4a341a2b984b8fcf90557a1148cd89e6b4c82c Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Thu, 30 Nov 2017 13:02:56 -0800
Subject: [PATCH 447/493] added twosided pivots option and constructing ci and
 pvalues as options

---
 selectiveInference/R/funs.randomized.R | 70 ++++++++++++++++++--------
 tests/randomized/test_instances.R      |  4 +-
 2 files changed, 52 insertions(+), 22 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 839529d..5d9f42c 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -403,7 +403,10 @@ conditional_opt_transform = function(noise_scale,
 	      importance_transform=opt_transform))
 }
 
-set.target = function(rand_lasso_soln, type){
+set.target = function(rand_lasso_soln, 
+                      type, 
+                      construct_pvalues=NULL,
+                      construct_ci=NULL){
   
   # compute internal representation of the data
   y = rand_lasso_soln$y
@@ -494,16 +497,28 @@ set.target = function(rand_lasso_soln, type){
   targets = list(observed_target=observed_target,
                  cov_target=cov_target,
                  crosscov_target_internal=crosscov_target_internal)
-  return(targets)
+  
+  if (is.null(construct_ci)){
+    construct_ci=rep(1,nactive)
+  }
+  if (is.null(construct_pvalues)){
+    construct_pvalues=rep(1, nactive)
+  }
+  
+  
+  return(list(targets=targets,
+              construct_ci=construct_ci, 
+              construct_pvalues=construct_pvalues))
 }
 
 
 randomizedLassoInf = function(rand_lasso_soln,
-                              targets=NULL,
+                              full_targets=NULL,
                               level=0.9,
                               sampler=c("norejection", "adaptMCMC"),
                               nsample=10000,
-                              burnin=2000)
+                              burnin=2000,
+                              alternative = c("two-sided", "greated", "less"))
  {
 
   n = nrow(rand_lasso_soln$X)
@@ -539,10 +554,14 @@ randomizedLassoInf = function(rand_lasso_soln,
 		                               burnin=burnin)
   }
 
-  if (is.null(targets)){
-    targets=set.target(rand_lasso_soln, type="partial")
+  if (is.null(full_targets)){
+    full_targets=set.target(rand_lasso_soln, type="partial")
   }
   
+  targets=full_targets$targets
+  construct_ci=full_targets$construct_ci
+  construct_pvalues = full_targets$construct_pvalues
+  
   observed_internal = rand_lasso_soln$observed_internal
   
   importance_transform = law$importance_transform
@@ -624,7 +643,7 @@ randomizedLassoInf = function(rand_lasso_soln,
       arg_ = arg_ - max(arg_)
       weights = exp(arg_)
       p = mean((target_sample + candidate < targets$observed_target[i]) * weights)/mean(weights)
-      return(2*min(p, 1-p))
+      return(p)
     }
 
     rootU = function(candidate){
@@ -634,23 +653,34 @@ randomizedLassoInf = function(rand_lasso_soln,
     rootL = function(candidate){
       return(pivot(targets$observed_target[i]+candidate)-(1+level)/2)
     }
-
-    pvalues[i] = pivot(0)
-    line_min = -10*sd(target_sample) + targets$observed_target[i]
-    line_max = 10*sd(target_sample) + targets$observed_target[i]
     
-    if (rootU(line_min)*rootU(line_max)<0){
-      ci[i,2] = uniroot(rootU, c(line_min, line_max))$root + targets$observed_target[i]
-    } else{
-      ci[i,2]=line_max
+    if (construct_pvalues[i]==1){
+      pvalues[i] = pivot(0)
+      if (alternative=="two-sided"){
+        pvalues[i] = 2*min(pvalues[i], 1-pvalues[i])
+      } else if (alternative=="greater"){
+        pvalues[i]= 1-pvalues[i]
+      } 
     }
-    if (rootL(line_min)*rootL(line_max)<0){
-      ci[i,1] = uniroot(rootL, c(line_min, line_max))$root + targets$observed_target[i]
-    } else{
-      ci[i,1] = line_min
+    
+    if (construct_ci[i]==1){
+      
+      line_min = -10*sd(target_sample) + targets$observed_target[i]
+      line_max = 10*sd(target_sample) + targets$observed_target[i]
+    
+      if (rootU(line_min)*rootU(line_max)<0){
+        ci[i,2] = uniroot(rootU, c(line_min, line_max))$root + targets$observed_target[i]
+      } else{
+        ci[i,2]=line_max
+      }
+      if (rootL(line_min)*rootL(line_max)<0){
+        ci[i,1] = uniroot(rootL, c(line_min, line_max))$root + targets$observed_target[i]
+      } else{
+        ci[i,1] = line_min
+      }
     }
   }
-  return(list(targets=targets, pvalues=pvalues, ci=ci))
+  return(list(full_targets=full_targets, pvalues=pvalues, ci=ci))
 }
 
 logistic_fitted = function(X, beta){
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index 355dcfb..bb2c66a 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -108,10 +108,10 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
                                                            family=family,
                                                            condition_subgrad=condition_subgrad)
     
-    targets=selectiveInference:::set.target(rand_lasso_soln,type=type)
+    full_targets=selectiveInference:::set.target(rand_lasso_soln,type=type)
     
     result = selectiveInference:::randomizedLassoInf(rand_lasso_soln,
-                                                     targets=targets,
+                                                     full_targets=full_targets,
                                                      sampler = "norejection", #"adaptMCMC", #
                                                      level=level, 
                                                      burnin=1000, 

From c9a77521b27a124cdfd569de2df5fc04b5e4adec Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 12 Feb 2018 17:08:25 -0800
Subject: [PATCH 448/493] including conditional mean and covariance

---
 selectiveInference/R/funs.randomized.R | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 53fa058..cd02200 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -186,7 +186,7 @@ randomizedLasso = function(X,
 
     if (sum(inactive) > 0) {
         if (condition_subgrad == FALSE) {
-            inactive_term = -subgrad_term
+            inactive_term = subgrad_term
             linear_term = cbind(active_term,
                                 inactive_term)
         } else {
@@ -279,7 +279,8 @@ randomizedLasso = function(X,
                 noise_scale=noise_scale,
                 soln=result$soln,
                 perturb=perturb_,
-                condition_subgrad=condition_subgrad
+                condition_subgrad=condition_subgrad,
+		ridge_term=ridge_term
                 ))
 
 }
@@ -370,8 +371,12 @@ conditional_opt_transform = function(noise_scale,
                        offset_term=beta_offset)
   reduced_B = chol(t(B) %*% B)
   beta_offset = beta_offset + observed_raw
+
   reduced_beta_offset = solve(t(reduced_B)) %*% (t(B) %*% beta_offset)
-  
+
+  cond_cov = solve(t(B) %*% B) * noise_scale^2
+  cond_mean = - cond_cov %*% t(B) %*% beta_offset / noise_scale^2
+
   log_condl_optimization_density = function(opt_state) {
 
     if  (sum(opt_state < 0) > 0) {
@@ -392,7 +397,9 @@ conditional_opt_transform = function(noise_scale,
   return(list(sampling_transform=reduced_opt_transform,
               log_optimization_density=log_condl_optimization_density,
               observed_opt_state=observed_opt_state[1:nactive],
-	      importance_transform=opt_transform))
+	      importance_transform=opt_transform,
+	      cond_cov=cond_cov,
+	      cond_mean=cond_mean))
 }
 
 randomizedLassoInf = function(rand_lasso_soln,

From 6d829966ae4d7a01f2e0a1d35f3227493af0cd35 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Tue, 20 Mar 2018 00:05:33 -0700
Subject: [PATCH 449/493] added full code

---
 selectiveInference/R/funs.full.high.dim.R   | 528 ++++++++++++++++++++
 tests/high_dim_full/test_lasso.R            |  74 +++
 tests/high_dim_full/test_linear_contrasts.R | 108 ++++
 tests/high_dim_full/test_logistic.R         | 115 +++++
 4 files changed, 825 insertions(+)
 create mode 100644 selectiveInference/R/funs.full.high.dim.R
 create mode 100644 tests/high_dim_full/test_lasso.R
 create mode 100644 tests/high_dim_full/test_linear_contrasts.R
 create mode 100644 tests/high_dim_full/test_logistic.R

diff --git a/selectiveInference/R/funs.full.high.dim.R b/selectiveInference/R/funs.full.high.dim.R
new file mode 100644
index 0000000..ffbc63c
--- /dev/null
+++ b/selectiveInference/R/funs.full.high.dim.R
@@ -0,0 +1,528 @@
+
+family_label = function(loss){
+  if (loss=="ls"){
+    return("gaussian")
+  } else if (loss=="logit"){
+    return("binomial")
+  }
+}
+
+# solves full lasso problem via glmnet
+solve_problem_glmnet = function(X, y, lambda, penalty_factor, loss){
+  if (is.null(lambda)){
+    cv = cv.glmnet(x=X, y=y, family=family_label(loss), penalty.factor=penalty_factor, intercept=FALSE,  thresh = 1e-12)
+    beta_hat = coef(cv, s="lambda.min")
+  }
+  else {
+    lasso = glmnet(x=X, y=y, family=family_label(loss), penalty.factor=penalty_factor,alpha=1,
+               standardize=FALSE, intercept=FALSE, thresh = 1e-20)
+    beta_hat = coef(lasso, s=lambda)
+  }
+  return(beta_hat[-1])
+}
+
+# solves full group lasso problem via gglasso
+solve_problem_gglasso = function(X, y, groups, lambda, penalty_factor, loss){
+  if (is.null(lambda)){
+    cv <- cv.gglasso(x=X, y=y, group=groups, loss=loss, pf=penalty_factor, intercept=FALSE, eps=1e-12)
+    beta_hat = coef(cv, s="lambda.min")
+  }
+  else {
+      # gglasso for logit loss needs the response to be in {-1,1}
+      if (loss=="logit"){
+        y_pm1 = rep(y)
+        y_pm1[which(y==0)]=-1
+      } else if (loss=="ls"){
+       y_pm1 = rep(y)
+      }
+      m <- gglasso(x=X, y=y_pm1, group=groups, loss=loss, pf=penalty_factor, intercept = FALSE, eps=1e-20)
+      beta_hat = coef(m, s=lambda)
+  }
+    #which(beta_hat!=0)
+    #beta_hat[which(beta_hat!=0)]
+  return(beta_hat[-1])
+}
+
+# solves the restricted problem
+solve_restricted_problem = function(X, y, groups, group, lambda, penalty_factor, loss, algo){
+  ngroups = length(unique(groups))
+  if ((ngroups<ncol(X)) && algo=="glmnet"){
+    algo="gglasso"
+  }
+  if (algo=="glmnet"){
+    restricted_soln=rep(0, ncol(X))
+    restricted_soln[-group] = solve_problem_glmnet(X[,-group], y, lambda, penalty_factor[-group], loss=loss)
+  } else if (algo=="gglasso"){
+    penalty_factor_rest = rep(penalty_factor)
+    penalty_factor_rest[group] = 10^10
+    restricted_soln = solve_problem_gglasso(X,y,groups, lambda, penalty_factor=penalty_factor_rest, loss=loss)
+  }
+  return(restricted_soln)
+}
+
+solve_problem_Q = function(Q_sq, Qbeta_bar, lambda, penalty_factor,
+                           max_iter=50,
+                           kkt_tol=1.e-4, 
+                           objective_tol=1.e-4, 
+                           parameter_tol=1.e-4,
+                           kkt_stop=TRUE,
+                           objective_stop=TRUE,	
+                           parameter_stop=TRUE){
+  n=nrow(Q_sq)
+  p=ncol(Q_sq)
+
+  Xinfo = Q_sq
+  linear_func = -as.numeric(Qbeta_bar)
+  soln = as.numeric(rep(0., p))
+  ever_active = as.integer(rep(0, p))
+  ever_active[1] = 1
+  ever_active = as.integer(ever_active)
+  nactive = as.integer(1)
+  Xsoln = as.numeric(rep(0, nrow(Xinfo)))
+  gradient = 1. * linear_func 
+  max_active=as.integer(p)
+  
+  linear_func = linear_func/n
+  gradient = gradient/n
+  
+  #solve_QP_wide solves n*slinear_func^T\beta+\beta^T Xinfo\beta+\sum\lambda_i|\beta_i|
+  result = selectiveInference:::solve_QP_wide(Xinfo,         # this is a design matrix
+                         as.numeric(penalty_factor*lambda),  # vector of Lagrange multipliers
+                         0,                          # ridge_term 
+                         max_iter, 
+                         soln, 
+                         linear_func, 
+                         gradient, 
+                         Xsoln,
+                         ever_active, 
+                         nactive, 
+                         kkt_tol, 
+                         objective_tol, 
+                         parameter_tol,
+                         max_active,
+                         kkt_stop,
+                         objective_stop,	
+                         parameter_stop)
+  
+  return(result$soln)
+}
+
+
+# the selection event is |sigma_est^2*(target_cov)^{-1}Z+center|>radius
+truncation_set = function(X, y, Qbeta_bar, Q, Q_sq, target_cov, sigma_est, group, target_stat, 
+                          groups, lambda, penalty_factor, loss, algo){
+  
+  if (algo=="Q"){
+    penalty_factor_rest = rep(penalty_factor)
+    penalty_factor_rest[group] = 10^10
+    restricted_soln = solve_problem_Q(Q_sq, Qbeta_bar, lambda, penalty_factor=penalty_factor_rest)
+  } else {
+    restricted_soln = solve_restricted_problem(X, y, groups, group, lambda, penalty_factor=penalty_factor, 
+                                             loss=loss, algo=algo)
+  }
+  #print("restricted soln")
+  #print(restricted_soln)
+  n = nrow(X)
+  p = length(Qbeta_bar)
+  I = diag(p) * sigma_est^2
+  group_vars = which(groups==group)
+  nuisance = (Qbeta_bar - I[,group_vars] %*% solve(target_cov) %*% target_stat)/n
+  center = nuisance[group_vars] - (Q[group_vars,] %*% restricted_soln/n)
+  radius = penalty_factor[group]*lambda
+  return(list(target_cov=target_cov, center=center*n, radius=radius*n))
+}
+
+
+linear_contrast = function(i, target_stat, target_cov, sigma_est,center, radius){
+  target_cov_inv = solve(target_cov)
+  target_stat_scaled = target_cov_inv %*% target_stat
+  I=diag(length(target_stat)) * sigma_est^2
+  nuisance = target_stat_scaled - I[,i]*target_stat[i]/target_cov[i,i]
+  new_center = center[i]+ nuisance[i]
+  new_radius = sqrt(max(radius^2-norm(nuisance[-i])^2,0))
+  return(list(target_cov=target_cov[i,i], center=new_center, radius=new_radius))
+}
+
+# the pvalue if prob(Z>obs given  |sigma_est^2/variance * Z+center|>radius)
+# where Z~N(param, variance)
+test_TG = function(param, observed, variance, sigma_est, center, radius, alt){
+  st.error = sqrt(variance)
+  lower = variance*(-center-radius)/(sigma_est^2)
+  upper = variance*(-center+radius)/(sigma_est^2)
+  if (observed<=lower){
+     case=1
+     num = pnorm(upper, mean=param, sd=st.error, lower.tail = FALSE)+pnorm(lower, mean=param, sd=st.error)-pnorm(observed, mean=param, sd=st.error)
+  } else if (observed>=upper){
+    case=2
+    num = pnorm(observed, mean=param, sd=st.error, lower.tail = FALSE)
+  } else{
+    case=3
+    num = pnorm(upper, mean=param, sd=st.error, lower.tail = FALSE)
+    return(NULL)
+    #stop("observation in the unallowed range")
+  }
+  den = pnorm(upper, mean=param, sd=st.error, lower.tail = FALSE)+pnorm(lower, mean=param, sd=st.error)
+  pivot = num/den
+  if (alt=="two-sided"){
+    return(2*pmin(pivot, 1-pivot))
+  } else if (alt=="upper"){
+    return(pivot)
+  } else if (alt=="lower"){
+    return(1-pivot)
+  }
+}
+
+
+# returns P(Z > z | z in union of intervals)
+tnorm.union.surv = function(z, mean, sd, intervals, bits = NULL){
+  # intervals is a I x 2 matrix of disjoint intervals where the first column contains the lower endpoint
+  
+  pval = matrix(NA, nr = dim(intervals)[1], nc = length(mean))
+  for(jj in 1:dim(intervals)[1]){
+    if(z <= intervals[jj,1]){
+      pval[jj,] = 1
+    }else if(z >= intervals[jj,2]){
+      pval[jj,] = 0
+    }else{
+      pval[jj,] = selectiveInference:::tnorm.surv(z, mean, sd, intervals[jj,1], intervals[jj,2], bits = bits)
+    }
+  }
+  
+  ww = matrix(NA, nr = dim(intervals)[1], nc = length(as.vector(mean)))
+  for(jj in 1:dim(intervals)[1]){
+    ww[jj,] = pnorm(intervals[jj,2], mean = mean, sd = sd) - pnorm(intervals[jj,1], mean = mean, sd = sd)
+  }
+  
+  ww = ww%*%diag(as.vector(1/apply(ww,2,sum)), nrow=ncol(ww))
+  
+  pval = apply(pval*ww,2,sum)
+  
+  return(as.numeric(pval))
+}
+
+create_tnorm_interval = function(z, sd, alpha, intervals, gridrange=c(-20,20), gridpts = 10000, 
+                                 griddepth = 2, bits = NULL){
+  
+  grid = seq(gridrange[1]*sd,gridrange[2]*sd,length=gridpts)
+  fun = function(x) { return(tnorm.union.surv(z, x, sd, intervals, bits)) }
+  
+  int = selectiveInference:::grid.search(grid, fun, alpha/2, 1-alpha/2, gridpts, griddepth)
+  #print(int)
+  return(int)
+}
+
+selective_CI = function(observed, variance, sigma_est, center, radius,
+                        alpha=0.1, gridrange=c(-20,20), gridpts=10000, griddepth=2){
+  
+  pivot = function(param){
+    return(test_TG(param, observed, variance, sigma_est, center, radius, alt="upper"))
+  }
+  st.error = sqrt(variance)
+  param_grid = seq(observed+gridrange[1] * st.error, observed+gridrange[2] * st.error, length=gridpts)
+  interval = selectiveInference:::grid.search(param_grid, pivot, alpha/2, 1-alpha/2, gridpts, griddepth)
+  return(interval)
+}
+
+
+norm = function(x){sqrt(sum(x*x))}
+
+
+pvalue_naive_linear = function(observed, variance){
+  pval = pnorm(observed, mean=0, sd = sqrt(variance), lower.tail = FALSE)
+  return(2*min(pval, 1-pval))
+}
+
+naive_CI = function(observed, variance, alpha=0.1){
+  quantile = abs(qnorm(alpha/2, mean=0, sd = 1))
+  st.error = sqrt(variance)
+  interval = c(observed - st.error*quantile, observed + st.error*quantile)
+  return(interval)
+}
+
+
+# theoretical lambda for glmnet
+# glmnet solves: 1/2n*\|y-X\beta\|_2^2+\lambda_1\|\beta\|_1
+theoretical.lambda = function(X, loss="ls", sigma=1){
+  n = nrow(X); p = ncol(X)
+  nsamples= 1000
+  if (loss=="ls"){
+    empirical = apply(abs(t(X) %*% matrix(rnorm(n*nsamples), nrow=n)), 2, max)
+  } else if (loss=="logit"){
+    empirical = apply(abs(t(X) %*% matrix(sample(c(0,1), n*nsamples, replace = TRUE), nrow=n)), 2, max)
+  }
+  lam = mean(empirical)*sigma/n
+  return(lam)
+}
+
+
+compute_coverage = function(ci, beta){
+  nactive=length(beta)
+  coverage_vector = rep(0, nactive)
+  for (i in 1:nactive){
+    if (beta[i]>=ci[1,i] && beta[i]<=ci[2,i]){
+      coverage_vector[i]=1
+    }
+  }
+  return(coverage_vector)
+}
+
+
+
+gradient = function(X,y,beta,loss){
+  fit = X %*% beta
+  if (loss=="logit"){
+    fit=exp(fit)/(1+exp(fit))
+  }
+  return(-t(X)%*%(y-fit))
+}
+
+hessian = function(X, beta, loss){
+  if (loss=="logit"){
+    fit = X%*%beta
+    W=diag(as.vector(exp(fit)/((1+exp(fit))^2)))
+  } else if (loss=="ls"){
+    W=diag(nrow(X))
+  }
+  return(t(X) %*% W %*% X)
+}
+
+mle=function(X,y,loss){
+  reg = glm(y~X-1, family=family_label(loss))
+  return(reg$coefficients)
+}
+
+approximate = function(X, active_set){
+  n=nrow(X)
+  p=ncol(X)
+  nactive=length(active_set)
+  inactive_set=setdiff(1:p, active_set)
+  X_active = X[, active_set]
+  X_inactive = X[, inactive_set]
+  
+  Xordered = X[,c(active_set,inactive_set,recursive=T)]
+  hsigmaS = 1/n*(t(X_active)%*%X_active) # hsigma[S,S]
+  hsigmaSinv =  ginv(hsigmaS) # generalized inverse solve(hsigmaS) #
+  FS = rbind(diag(nactive),matrix(0,p-nactive,nactive))
+  GS = cbind(diag(nactive),matrix(0,nactive,p-nactive))
+  hsigma = 1/n*(t(Xordered)%*%Xordered)
+  is_wide = n < (2 * p)
+  
+  if (!is_wide) {
+    hsigma = 1/n*(t(Xordered)%*%Xordered)
+    htheta = selectiveInference:::debiasingMatrix(hsigma, is_wide, n, 1:nactive)
+    ithetasigma = (GS-(htheta%*%hsigma))
+  } else {
+    htheta = selectiveInference:::debiasingMatrix(Xordered, is_wide, n, 1:nactive)
+    ithetasigma = (GS-((htheta%*%t(Xordered)) %*% Xordered)/n)
+  }
+  M_active <- ((htheta%*%t(Xordered))+ithetasigma%*%FS%*%hsigmaSinv%*%t(X_active))/n
+  #M_inactive  =  (htheta[, (nactive+1):p]%*%t(X[,inactive_set])/n)
+  #M=matrix(nrow=p, ncol=n)
+  #M[active_set, ]= M_active
+  #M[inactive_set, ]=matrix(0,nrow=length(inactive_set), ncol=n)
+  return(M_active)
+}
+
+estimate_sigma = function(X, y, beta_hat_cv){
+  n=nrow(X)
+  p=ncol(X)
+  if (n<p){
+    # Reid et al. (2016) "A study of error variance estimation in lasso regression"
+    residuals = y-X%*% beta_hat_cv
+    nactive=length(which(beta_hat_cv!=0))
+    sigma_est_sq = drop(t(residuals) %*% residuals/(nrow(X)-nactive))
+    sigma_est=sqrt(sigma_est_sq)
+  } else{
+    m = lm(y~X-1)
+    sigma_est = summary(m)$sigma
+  }
+  return(sigma_est)
+}
+
+
+get_QB = function(X, y, soln, active_set, loss){
+  n=nrow(X)
+  p=ncol(X)
+  
+  fit = X%*%soln
+  if (loss=="ls"){
+    diagonal = rep(1,n)
+    W_root=diag(as.vector(diagonal))
+    residuals = y-fit
+  }
+  if (loss=="logit"){
+    diagonal = exp(fit/2)/(1+exp(fit))  ## sqrt(pi*(1-pi))
+    W_root = diag(as.vector(diagonal))
+    residuals = y-(exp(fit)/(1+exp(fit)))
+  } 
+  
+  if (n>p){
+    Q=hessian(X, soln, loss=loss)
+    Qi=solve(Q)
+    Q_sq =  W_root %*% X
+    #beta_bar = mle(X,y,loss=loss)
+    #print("mle")
+    #print(mle(X,y,loss=loss))
+    beta_bar = soln - Qi %*% gradient(X,y,soln, loss=loss)
+    #beta_bar = (soln - Qi %*% gradient(X,y,soln, loss=loss)+mle(X,y,loss))/2
+    #print("one step")
+    #print(beta_bar)
+    Qbeta_bar = Q%*%soln - gradient(X,y,soln, loss=loss)
+    beta_barE = beta_bar[active_set]
+    QiE = Qi[active_set, active_set]
+  } else{
+    
+    M_active = approximate(W_root %*% X, active_set) ## this should be the active rows of \Sigma_i (W^{1/2} X)^T
+    Q = hessian(X, soln, loss)
+    Q_sq = W_root %*% X
+    QiE = M_active %*% t(M_active)
+    beta_barE = soln[active_set] + M_active %*% diag(as.vector(1/diagonal)) %*% residuals
+    Qbeta_bar = Q%*%soln-gradient(X,y,soln,loss=loss)
+  }
+  
+  return(list(Q=Q, Q_sq=Q_sq, Qbeta_bar=Qbeta_bar, QiE=QiE, beta_barE=beta_barE))  
+}
+
+
+
+inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sigma_est,
+                                 loss, algo, construct_ci){
+  
+  active_vars = which(soln!=0)
+  print("active_vars")
+  print(active_vars)
+  if (length(active_vars)==0){
+    return(list(pvalues=NULL, naive_pvalues=NULL))
+  }
+  active_groups = unique(groups[active_vars])
+  nactive_groups = length(active_groups)
+  print(c("nactive groups", nactive_groups))
+
+  setup_params = get_QB(X=X, y=y, soln=soln, active_set=active_vars, loss=loss)
+  Q=setup_params$Q
+  Q_sq=setup_params$Q_sq
+  QiE=as.matrix(setup_params$QiE)
+  beta_barE = setup_params$beta_barE
+  Qbeta_bar = setup_params$Qbeta_bar
+  
+  QiE = QiE * sigma_est^2
+  
+  pvalues = NULL
+  naive_pvalues = NULL
+  selected_vars=NULL
+  sel_intervals = NULL
+  naive_intervals= NULL
+  
+  
+  for (i in 1:nactive_groups){
+    group=active_groups[i]
+    group_vars = which(groups==group)
+    cat("selected group:", group_vars, "\n")
+    
+    group_varsE = match(group_vars, active_vars)
+    target_stat = beta_barE[group_varsE]
+    target_cov = as.matrix(QiE)[group_varsE,group_varsE]
+    
+    TS =  truncation_set(X=X, y=y, Qbeta_bar=Qbeta_bar, Q=Q, Q_sq=Q_sq, target_cov=target_cov, sigma_est=sigma_est, 
+                         group=group, target_stat=target_stat, groups=groups, lambda=lambda, 
+                         penalty_factor=penalty_factor, loss=loss, algo=algo)
+    center = TS$center
+    radius = TS$radius
+    
+    #print(c("center", center))
+    #print(c("radius", radius))
+    
+    if (length(target_stat)>1){
+      for (i in 1:length(target_stat)){
+        LC = linear_contrast(i=i, target_stat=target_stat, target_cov=target_cov, 
+                             sigma_est=sigma_est, center=center, radius=radius)
+        lower = target_cov[i,i]*(-LC$center-LC$radius)/(sigma_est^2)
+        upper = target_cov[i,i]*(-LC$center+LC$radius)/(sigma_est^2)
+        
+        if (target_stat[i]<=lower | target_stat[i]>=upper)
+        {
+          intervals = matrix(nrow=2, ncol=2)
+          intervals[1,] = c(-Inf, lower)
+          intervals[2,] = c(upper, Inf)
+          pval = tnorm.union.surv(target_stat[i], mean=0, sd=sqrt(target_cov[i,i]), intervals)
+          #pval = test_TG(0, target_stat[i], target_cov[i,i], sigma_est, center, radius, alt="two-sided")
+          pval = 2*min(pval, 1-pval)
+          print(c("pval", pval))
+          pvalues = c(pvalues, pval)
+          
+          naive_pval =  pvalue_naive_linear(target_stat[i], target_cov[i,i])
+          naive_pvalues = c(naive_pvalues, naive_pval)
+          
+          selected_vars = c(selected_vars, group_vars[i])
+          
+          if (construct_ci){
+            sel_int = create_tnorm_interval(z=target_stat[i], sd=sqrt(target_cov[i,i]), alpha=0.1, intervals=intervals)
+            #print(c("tg intervals", sel_int))
+            #sel_int = selective_CI(target_stat, target_cov, sigma_est, center, radius)
+            #print(c("jelena int", sel_int))
+            naive_int = naive_CI(target_stat[i], target_cov[i,i])
+            cat("sel interval", sel_int, "\n")
+            cat("naive interval", naive_int, "\n")
+            sel_intervals = cbind(sel_intervals, sel_int)
+            naive_intervals = cbind(naive_intervals, naive_int)
+          }
+        } else{
+          print("observation not within the truncation limits!")
+        }
+      }
+    } else{
+      
+      lower = target_cov*(-center-radius)/(sigma_est^2)
+      upper = target_cov*(-center+radius)/(sigma_est^2)
+      
+      if (target_stat<=lower | target_stat>=upper)
+      {
+        intervals = matrix(nrow=2, ncol=2)
+        intervals[1,] = c(-Inf, lower)
+        intervals[2,] = c(upper, Inf)
+        pval = tnorm.union.surv(target_stat, mean=0, sd=sqrt(target_cov), intervals)
+        #pval = test_TG(0, target_stat, target_cov, sigma_est, center, radius, alt="two-sided")
+        pval = 2*min(pval, 1-pval)
+        print(c("pval", pval))
+        pvalues = c(pvalues, pval)
+        
+        selected_vars = c(selected_vars, group_vars)
+        
+        naive_pval =  pvalue_naive_linear(target_stat, target_cov)
+        naive_pvalues = c(naive_pvalues, naive_pval)
+        
+        if (construct_ci){
+          sel_int = create_tnorm_interval(z=target_stat, sd=sqrt(target_cov), alpha=0.1, intervals=intervals)
+          #print(c("tg intervals", sel_int))
+          #sel_int = selective_CI(target_stat, target_cov, sigma_est, center, radius)
+          #print(c("jelena int", sel_int))
+          naive_int = naive_CI(target_stat, target_cov)
+          cat("sel interval", sel_int, "\n")
+          cat("naive interval", naive_int, "\n")
+          sel_intervals = cbind(sel_intervals, sel_int)
+          naive_intervals = cbind(naive_intervals, naive_int)
+        }
+      } else{
+        print("observation not within the truncation limits!")
+      }
+      
+    }
+    
+    
+    #reference = get_reference(center, radius, target_stat, solve(target_cov))
+    #samples = sampling(target_cov, reference, center, radius, n=nrow(X), nsample=nsample)
+    #tilting_weights = compute_tilting_weights(reference, solve(target_cov), samples)
+    #pvalues[i] = weighted_pvalue(samples, tilting_weights, target_stat)
+    #naive_pvalues[i] = p_value(mvrnorm(n=nsample, mu=rep(0, length(target_stat)), Sigma = target_cov), target_stat)
+    #print(c("sampling", pvalues[i]))
+  }
+  
+  return(list(pvalues=pvalues, naive_pvalues=naive_pvalues, active_vars=selected_vars,
+              sel_intervals = sel_intervals, naive_intervals=naive_intervals))
+}
+
+
+
+
+
+
diff --git a/tests/high_dim_full/test_lasso.R b/tests/high_dim_full/test_lasso.R
new file mode 100644
index 0000000..1f43b95
--- /dev/null
+++ b/tests/high_dim_full/test_lasso.R
@@ -0,0 +1,74 @@
+library(gglasso)
+library(MASS)
+library(selectiveInference)
+library(glmnet)
+#source("/Users/Jelena/GitHub Jelena/full/code/group_lasso_fns.R")
+
+test_high_dim_lasso = function(nrep=10){
+  
+  set.seed(1)
+  loss="ls"
+  construct_ci=TRUE
+  n=100
+  p=300
+  penalty_factor = rep(1, p)
+  
+  pvalues = NULL
+  naive_pvalues = NULL
+  sel_intervals=NULL
+  naive_intervals=NULL
+  sel_coverages=NULL
+  naive_coverages=NULL
+  sel_lengths=NULL
+  naive_lengths=NULL
+  
+  for (i in 1:nrep){
+    X=matrix(rnorm(n*p), nrow=n)
+    beta = rep(0,p)
+    #beta[1:10]=1
+    y = X%*%beta+rnorm(n)
+    
+    CV = cv.glmnet(X, y, standardize=FALSE, intercept=FALSE, family=selectiveInference:::family_label(loss))
+    # print(CV$lambda.min)
+    # lambda via randomized cv 
+    # lambda = CV$lambda[which.min(CV$cvm+rnorm(length(CV$cvm))/sqrt(n))]
+    # sigma via Reid et al.
+    sigma_est=selectiveInference:::estimate_sigma(X,y,coef(CV, s="lambda.min")[-1])
+    print(c("sigma est", sigma_est))
+    # theoretical lambda
+    lambda = 0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est)
+    
+    soln = selectiveInference:::solve_problem_glmnet(X, y, lambda, penalty_factor=penalty_factor, loss=loss)
+    #soln = solve_problem_gglasso(X, y, groups=1:ncol(X), lambda, penalty_factor=penalty_factor, loss=loss)
+    PVS = selectiveInference:::inference_group_lasso(X, y, soln, groups=1:ncol(X), lambda=lambda, penalty_factor=penalty_factor, 
+                                sigma_est, loss=loss, algo="Q", construct_ci = construct_ci)
+    active_vars=PVS$active_vars
+    pvalues = c(pvalues, PVS$pvalues)
+    naive_pvalues = c(naive_pvalues, PVS$naive_pvalues)
+    sel_intervals = cbind(sel_intervals, PVS$sel_intervals)
+    naive_intervals = cbind(naive_intervals, PVS$naive_intervals)
+    
+    if (length(pvalues)>0){
+      plot(ecdf(pvalues))
+      lines(ecdf(naive_pvalues), col="red")
+      abline(0,1)
+    }
+    if (construct_ci && length(active_vars)>0){
+      
+      sel_coverages=c(sel_coverages, selectiveInference:::compute_coverage(PVS$sel_intervals, beta[active_vars]))
+      naive_coverages=c(naive_coverages, selectiveInference:::compute_coverage(PVS$naive_intervals, beta[active_vars]))
+      sel_lengths=c(sel_lengths, mean(PVS$sel_intervals[2,]-PVS$sel_intervals[1,]))
+      naive_lengths=c(naive_lengths, mean(PVS$naive_intervals[2,]-PVS$naive_intervals[1,]))
+      print(c("selective coverage:", mean(sel_coverages)))
+      print(c("naive coverage:", mean(naive_coverages)))
+      print(c("selective length:", mean(sel_lengths)))
+      print(c("naive length:", mean(naive_lengths)))
+    }
+    
+  }
+  
+  return(list(pvalues=pvalues, naive_pvalues=naive_pvalues))
+}
+
+test_high_dim_lasso()
+
diff --git a/tests/high_dim_full/test_linear_contrasts.R b/tests/high_dim_full/test_linear_contrasts.R
new file mode 100644
index 0000000..e3bee64
--- /dev/null
+++ b/tests/high_dim_full/test_linear_contrasts.R
@@ -0,0 +1,108 @@
+library(gglasso)
+library(MASS)
+library(selectiveInference)
+library(glmnet)
+#.libPaths("/Users/Jelena/anaconda/lib/R/library")
+#.libPaths("/Library/Frameworks/R.framework/Versions/3.4/Resources/library")
+#source("/Users/Jelena/GitHub Jelena/full/code/group_lasso_fns.R")
+
+
+gaussian_instance = function(n, p, s, sigma=1, rho=0, snr=7, 
+                             random_signs=TRUE, scale=FALSE, center=FALSE){
+  X = sqrt(1-rho)*matrix(rnorm(n*p),n) + sqrt(rho)*matrix(rep(rnorm(n), p), nrow = n)
+  if (scale==TRUE){
+    X = scale(X)
+    X = X/sqrt(n)
+  }
+  beta = rep(0, p)
+  beta[1:s]=snr
+  if (random_signs==TRUE && s>0){
+    signs = sample(c(-1,1), s, replace = TRUE)
+    beta[1:s] = beta[1:s] * signs
+  }
+  beta=sample(beta)
+  y = X %*% beta + rnorm(n)*sigma 
+  result <- list(X=X,y=y,beta=beta)
+  return(result)
+}
+
+
+test_group_lasso = function(nrep=10){
+  
+  set.seed(1)
+  loss="ls"
+  construct_ci=TRUE
+  n=500
+  p=100
+  s=10
+  ngroups=50
+  lambda=60/n
+  snr=0.3
+  groups = rep(1:ngroups,each=p/ngroups)
+  group_sizes = rep(p/ngroups,ngroups)
+  penalty_factor = rep(1, length(group_sizes))
+  
+  
+  pvalues = NULL
+  naive_pvalues = NULL
+  sel_intervals=NULL
+  naive_intervals=NULL
+  sel_coverages=NULL
+  naive_coverages=NULL
+  sel_lengths=NULL
+  naive_lengths=NULL
+  
+  
+  for (i in 1:nrep){
+    data = gaussian_instance(n,p,s, snr=snr)
+    X=data$X
+    y=data$y
+    beta=data$beta
+    #print("beta")
+    #print(which(beta!=0))
+    CV = cv.glmnet(X, y, standardize=FALSE, intercept=FALSE, family=selectiveInference:::family_label(loss))
+    sigma_est=selectiveInference:::estimate_sigma(X,y, coef(CV, s="lambda.min")[-1])
+    print(c("sigma est", sigma_est))
+    
+    soln = selectiveInference:::solve_problem_gglasso(X, y, groups, lambda, penalty_factor=penalty_factor, loss=loss)
+    PVS = selectiveInference:::inference_group_lasso(X,y, soln,groups, lambda=lambda, penalty_factor=penalty_factor, 
+                                        sigma_est=sigma_est, loss=loss, algo="glmnet", construct_ci = construct_ci)
+    
+    active_vars = PVS$active_vars
+    print("active_vars")
+    print(active_vars)
+    null_active_vars = match(which(beta==0), active_vars)
+    null_active_vars = null_active_vars[!is.na(null_active_vars)]
+    print("null_active_vars")
+    print(null_active_vars)
+    
+    if (length(null_active_vars)>0){
+      print(PVS$pvalues)
+      print(PVS$pvalues[null_active_vars])
+      print(PVS$naive_pvalues[null_active_vars])
+      pvalues = c(pvalues, PVS$pvalues[null_active_vars])
+      naive_pvalues = c(naive_pvalues, PVS$naive_pvalues[null_active_vars])
+      plot(ecdf(pvalues))
+      lines(ecdf(naive_pvalues), col="red")
+      abline(0,1)
+    }
+    
+    if (construct_ci && length(active_vars)>0){
+      
+      sel_coverages=c(sel_coverages, selectiveInference:::compute_coverage(PVS$sel_intervals, beta[active_vars]))
+      naive_coverages=c(naive_coverages, selectiveInference:::compute_coverage(PVS$naive_intervals, beta[active_vars]))
+      sel_lengths=c(sel_lengths, mean(PVS$sel_intervals[2,]-PVS$sel_intervals[1,]))
+      naive_lengths=c(naive_lengths, mean(PVS$naive_intervals[2,]-PVS$naive_intervals[1,]))
+      print(c("selective covarage:", mean(sel_coverages)))
+      print(c("naive coverage:", mean(naive_coverages)))
+      print(c("selective length:", mean(sel_lengths)))
+      print(c("naive length:", mean(naive_lengths)))
+    }
+    
+  }
+  
+  return(list(pvalues=pvalues, naive_pvalues=naive_pvalues))
+}
+
+
+test_group_lasso()
diff --git a/tests/high_dim_full/test_logistic.R b/tests/high_dim_full/test_logistic.R
new file mode 100644
index 0000000..06b0186
--- /dev/null
+++ b/tests/high_dim_full/test_logistic.R
@@ -0,0 +1,115 @@
+#library(gglasso)
+library(MASS)
+library(selectiveInference)
+library(glmnet)
+#source("/Users/Jelena/GitHub Jelena/full/code/group_lasso_fns.R")
+
+
+logistic_instance = function(n, p, s, sigma=1, rho=0, snr=7, 
+                             random_signs=TRUE, scale=FALSE, center=FALSE){
+  
+  X = sqrt(1-rho)*matrix(rnorm(n*p),n) + sqrt(rho)*matrix(rep(rnorm(n), p), nrow = n)
+  if (scale==TRUE){
+    X = scale(X)
+    X = X/sqrt(n)
+  }
+  beta = rep(0, p)
+  beta[1:s]=snr
+  if (random_signs==TRUE && s>0){
+    signs = sample(c(-1,1), s, replace = TRUE)
+    beta[1:s] = beta[1:s] * signs
+  }
+  beta=sample(beta)
+  mu = X %*% beta
+  prob = exp(mu)/(1+exp(mu))
+  y = rbinom(n, 1, prob)
+  
+  result <- list(X=X,y=y,beta=beta)
+  return(result)
+}
+
+
+test_logistic = function(nrep=10){
+  
+  set.seed(1)
+  loss="logit"
+  construct_ci=TRUE
+  n=300
+  p=50
+  s=10
+ 
+  snr=2/sqrt(n)
+  
+  penalty_factor = rep(1, p)
+  
+  pvalues = NULL
+  naive_pvalues = NULL
+  sel_intervals=NULL
+  naive_intervals=NULL
+  sel_coverages=NULL
+  naive_coverages=NULL
+  sel_lengths=NULL
+  naive_lengths=NULL
+  
+  
+  for (i in 1:nrep){
+    #data = logistic_instance(n, p, s, snr=snr)
+    #X=data$X
+    #y=data$y
+    #beta=data$beta
+    X = matrix(rnorm(n*p),n)
+    beta=rep(0,p)
+    beta[1:s]=snr
+    mu = X %*% beta
+    prob = exp(mu)/(1+exp(mu))
+    y=rbinom(n,1, prob)
+    
+    print("beta")
+    print(which(beta!=0))
+    
+    lambda=10/n
+    #lambda = 0.7*selectiveInference:::theoretical.lambda(X, loss, sigma=1)
+    
+    soln = selectiveInference:::solve_problem_glmnet(X, y, lambda, penalty_factor=penalty_factor, loss=loss)
+    
+    PVS = selectiveInference:::inference_group_lasso(X,y, soln, groups=1:ncol(X), lambda=lambda, penalty_factor=penalty_factor,
+                                sigma_est=1, loss=loss, algo="glmnet", construct_ci = construct_ci)
+    
+    active_vars = PVS$active_vars
+    print("active_vars")
+    print(active_vars)
+    null_active_vars = match(which(beta==0), active_vars)
+    null_active_vars = null_active_vars[!is.na(null_active_vars)]
+    #print("null_active_vars")
+    #print(null_active_vars)
+    
+    if (length(null_active_vars)>0){
+      pvalues = c(pvalues, PVS$pvalues[null_active_vars])
+      naive_pvalues = c(naive_pvalues, PVS$naive_pvalues[null_active_vars])
+      plot(ecdf(pvalues))
+      lines(ecdf(naive_pvalues), col="red")
+      abline(0,1)
+    }
+    
+    if (construct_ci && length(active_vars)>0){
+      
+      sel_coverages=c(sel_coverages, selectiveInference:::compute_coverage(PVS$sel_intervals, beta[active_vars]))
+      naive_coverages=c(naive_coverages, selectiveInference:::compute_coverage(PVS$naive_intervals, beta[active_vars]))
+      
+      #sel_coverages=c(sel_coverages, compute_coverage(PVS$sel_intervals[,null_active_vars], rep(0, length(null_active_vars))))
+      #naive_coverages=c(naive_coverages,compute_coverage(PVS$naive_intervals[,null_active_vars], rep(0, length(null_active_vars))))
+      
+      sel_lengths=c(sel_lengths, mean(PVS$sel_intervals[2,]-PVS$sel_intervals[1,]))
+      naive_lengths=c(naive_lengths, mean(PVS$naive_intervals[2,]-PVS$naive_intervals[1,]))
+      print(c("selective coverage:", mean(sel_coverages)))
+      print(c("naive coverage:", mean(naive_coverages)))
+      print(c("selective length:", mean(sel_lengths)))
+      print(c("naive length:", mean(naive_lengths)))
+    }
+  }
+  
+  return(list(pvalues=pvalues, naive_pvalues=naive_pvalues))
+}
+
+test_logistic()
+

From 47d26d53a622ec81be7681134b22a1cff9c87385 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Tue, 20 Mar 2018 20:45:27 -0700
Subject: [PATCH 450/493] comparisons

---
 liu_full.rds                                  | Bin 0 -> 9781 bytes
 selectiveInference/R/funs.full.high.dim.R     |  64 +-
 selectiveInference/src/debias.h               |  79 +++
 selectiveInference/src/matrixcomps.c          | 266 ++++++++
 selectiveInference/src/matrixcomps.h          |  12 +
 selectiveInference/src/quadratic_program.c    | 441 +++++++++++++
 .../src/quadratic_program_wide.c              | 597 ++++++++++++++++++
 selectiveInference/src/randomized_lasso.c     | 411 ++++++++++++
 selectiveInference/src/randomized_lasso.h     |  54 ++
 tests/comparisons/lee_full.rds                | Bin 0 -> 23430 bytes
 tests/comparisons/liu_full.rds                | Bin 0 -> 9781 bytes
 tests/comparisons/plot_coverages.R            |  38 ++
 tests/lee_high_dim_full/test_lee_full.R       |  73 +++
 .../test_linear_contrasts.R                   |   0
 .../test_liu_full.R}                          |  51 +-
 .../test_logistic.R                           |   0
 16 files changed, 2050 insertions(+), 36 deletions(-)
 create mode 100644 liu_full.rds
 create mode 100644 selectiveInference/src/debias.h
 create mode 100644 selectiveInference/src/matrixcomps.c
 create mode 100644 selectiveInference/src/matrixcomps.h
 create mode 100644 selectiveInference/src/quadratic_program.c
 create mode 100644 selectiveInference/src/quadratic_program_wide.c
 create mode 100644 selectiveInference/src/randomized_lasso.c
 create mode 100644 selectiveInference/src/randomized_lasso.h
 create mode 100644 tests/comparisons/lee_full.rds
 create mode 100644 tests/comparisons/liu_full.rds
 create mode 100644 tests/comparisons/plot_coverages.R
 create mode 100644 tests/lee_high_dim_full/test_lee_full.R
 rename tests/{high_dim_full => liu_high_dim_full}/test_linear_contrasts.R (100%)
 rename tests/{high_dim_full/test_lasso.R => liu_high_dim_full/test_liu_full.R} (59%)
 rename tests/{high_dim_full => liu_high_dim_full}/test_logistic.R (100%)

diff --git a/liu_full.rds b/liu_full.rds
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HcmV?d00001

diff --git a/selectiveInference/R/funs.full.high.dim.R b/selectiveInference/R/funs.full.high.dim.R
index ffbc63c..6b0856e 100644
--- a/selectiveInference/R/funs.full.high.dim.R
+++ b/selectiveInference/R/funs.full.high.dim.R
@@ -1,4 +1,48 @@
 
+AR_design = function(n, p, rho, scale=FALSE){
+  times = c(1:p)
+  cov_mat <- rho^abs(outer(times, times, "-"))
+  chol_mat = chol(cov_mat) # t(chol_mat) %*% chol_mat = cov_mat
+  X=matrix(rnorm(n*p), nrow=n) %*% t(chol_mat)
+  if (scale==TRUE){
+    X = scale(X)
+    X = X/sqrt(n)
+  }
+  return(X)
+}
+
+equicorrelated_design = function(n, p, rho, scale=FALSE){
+  X = sqrt(1-rho)*matrix(rnorm(n*p),n) + sqrt(rho)*matrix(rep(rnorm(n), p), nrow = n)
+  if (scale==TRUE){
+    X = scale(X)
+    X = X/sqrt(n)
+  }
+  return(X)
+}
+
+gaussian_instance = function(n, p, s, rho, sigma, snr, random_signs=TRUE, scale=FALSE, design="AR"){
+  
+  if (design=="AR"){
+    X=AR_design(n,p,rho, scale)
+  } else if (design=="equicorrelated"){
+    X=equicorrelated_design(n,p, rho, scale)
+  }
+  
+  beta = rep(0, p)
+  beta[1:s]=snr
+  
+  if (random_signs==TRUE && s>0){
+    signs = sample(c(-1,1), s, replace = TRUE)
+    beta[1:s] = beta[1:s] * signs
+  }
+  
+  beta=sample(beta)
+  y = X %*% beta + rnorm(n)*sigma 
+  result <- list(X=X,y=y,beta=beta)
+  return(result)
+}
+
+
 family_label = function(loss){
   if (loss=="ls"){
     return("gaussian")
@@ -389,14 +433,12 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
                                  loss, algo, construct_ci){
   
   active_vars = which(soln!=0)
-  print("active_vars")
-  print(active_vars)
+  
   if (length(active_vars)==0){
     return(list(pvalues=NULL, naive_pvalues=NULL))
   }
   active_groups = unique(groups[active_vars])
   nactive_groups = length(active_groups)
-  print(c("nactive groups", nactive_groups))
 
   setup_params = get_QB(X=X, y=y, soln=soln, active_set=active_vars, loss=loss)
   Q=setup_params$Q
@@ -429,9 +471,6 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
     center = TS$center
     radius = TS$radius
     
-    #print(c("center", center))
-    #print(c("radius", radius))
-    
     if (length(target_stat)>1){
       for (i in 1:length(target_stat)){
         LC = linear_contrast(i=i, target_stat=target_stat, target_cov=target_cov, 
@@ -447,7 +486,6 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
           pval = tnorm.union.surv(target_stat[i], mean=0, sd=sqrt(target_cov[i,i]), intervals)
           #pval = test_TG(0, target_stat[i], target_cov[i,i], sigma_est, center, radius, alt="two-sided")
           pval = 2*min(pval, 1-pval)
-          print(c("pval", pval))
           pvalues = c(pvalues, pval)
           
           naive_pval =  pvalue_naive_linear(target_stat[i], target_cov[i,i])
@@ -457,12 +495,10 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
           
           if (construct_ci){
             sel_int = create_tnorm_interval(z=target_stat[i], sd=sqrt(target_cov[i,i]), alpha=0.1, intervals=intervals)
-            #print(c("tg intervals", sel_int))
             #sel_int = selective_CI(target_stat, target_cov, sigma_est, center, radius)
-            #print(c("jelena int", sel_int))
             naive_int = naive_CI(target_stat[i], target_cov[i,i])
-            cat("sel interval", sel_int, "\n")
-            cat("naive interval", naive_int, "\n")
+            #cat("sel interval", sel_int, "\n")
+            #cat("naive interval", naive_int, "\n")
             sel_intervals = cbind(sel_intervals, sel_int)
             naive_intervals = cbind(naive_intervals, naive_int)
           }
@@ -483,7 +519,7 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
         pval = tnorm.union.surv(target_stat, mean=0, sd=sqrt(target_cov), intervals)
         #pval = test_TG(0, target_stat, target_cov, sigma_est, center, radius, alt="two-sided")
         pval = 2*min(pval, 1-pval)
-        print(c("pval", pval))
+        #print(c("pval", pval))
         pvalues = c(pvalues, pval)
         
         selected_vars = c(selected_vars, group_vars)
@@ -497,8 +533,8 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
           #sel_int = selective_CI(target_stat, target_cov, sigma_est, center, radius)
           #print(c("jelena int", sel_int))
           naive_int = naive_CI(target_stat, target_cov)
-          cat("sel interval", sel_int, "\n")
-          cat("naive interval", naive_int, "\n")
+          #cat("sel interval", sel_int, "\n")
+          #cat("naive interval", naive_int, "\n")
           sel_intervals = cbind(sel_intervals, sel_int)
           naive_intervals = cbind(naive_intervals, naive_int)
         }
diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
new file mode 100644
index 0000000..da27467
--- /dev/null
+++ b/selectiveInference/src/debias.h
@@ -0,0 +1,79 @@
+#ifdef __cplusplus
+extern "C"
+{
+#endif /* __cplusplus */
+
+void multiply_by_2(double *X, int nval);
+
+int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
+	     double *linear_func_ptr,    /* Linear term in objective */
+	     double *nndef_diag_ptr,     /* Diagonal of nndef */
+	     double *gradient_ptr,       /* nndef times theta */
+	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
+	     int *nactive_ptr,           /* Size of ever active set */
+	     int nfeature,               /* How many features in nndef */
+	     double bound,               /* feasibility parameter */
+	     double *theta,              /* current value */
+	     double *theta_old,          /* previous value */
+	     int maxiter,                /* max number of iterations */
+	     double kkt_tol,             /* precision for checking KKT conditions */
+	     double objective_tol,       /* precision for checking relative decrease in objective value */
+	     double parameter_tol,       /* precision for checking relative convergence of parameter */
+	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
+             int kkt_stop,               /* Break based on KKT? */
+	     int objective_stop,         /* Break based on convergence of objective value? */
+	     int param_stop);            /* Break based on parameter convergence? */
+
+int check_KKT_qp(double *theta,        /* current theta */
+		 double *gradient_ptr, /* nndef times theta + linear_func */
+		 int nrow,             /* how many rows in nndef */
+		 double bound,         /* Lagrange multipler for \ell_1 */
+		 double tol);          /* precision for checking KKT conditions */        
+
+int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
+	       double *X_theta_ptr,        /* Fitted values   */
+	       double *linear_func_ptr,    /* Linear term in objective */
+	       double *nndef_diag_ptr,     /* Diagonal entries of non-neg def matrix */
+	       double *gradient_ptr,       /* X^TX/ncase times theta + linear_func*/
+	       int *need_update_ptr,       /* Keeps track of updated gradient coords */
+	       int *ever_active_ptr,       /* Ever active set: 1-based */ 
+	       int *nactive_ptr,           /* Size of ever active set */
+	       int ncase,                  /* How many rows in X */
+	       int nfeature,               /* How many columns in X */
+	       double *bound_ptr,          /* Lagrange multipliers */
+	       double ridge_term,          /* Ridge / ENet term */
+	       double *theta_ptr,          /* current value */
+	       double *theta_old_ptr,      /* previous value */
+	       int maxiter,                /* max number of iterations */
+	       double kkt_tol,             /* precision for checking KKT conditions */
+	       double objective_tol,       /* precision for checking relative decrease in objective value */
+	       double parameter_tol,       /* precision for checking relative convergence of parameter */
+	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
+	       int kkt_stop,               /* Break based on KKT? */
+	       int objective_stop,         /* Break based on convergence of objective value? */
+	       int param_stop);            /* Break based on parameter convergence? */
+
+int check_KKT_wide(double *theta_ptr,        /* current theta */
+		   double *gradient_ptr,     /* X^TX/ncase times theta + linear_func*/
+		   double *X_theta_ptr,      /* Current fitted values */
+		   double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
+		   double *linear_func_ptr,  /* Linear term in objective */   
+		   int *need_update_ptr,     /* Which coordinates need to be updated? */
+		   int nfeature,             /* how many columns in X */
+		   int ncase,                /* how many rows in X */
+		   double *bound_ptr,        /* Lagrange multiplers for \ell_1 */
+		   double ridge_term,        /* Ridge / ENet term */
+		   double tol);              /* precision for checking KKT conditions */        
+  
+void update_gradient_wide(double *gradient_ptr,     /* X^TX/ncase times theta + linear_func */
+			  double *X_theta_ptr,      /* Current fitted values */
+			  double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
+			  double *linear_func_ptr,  /* Linear term in objective */   
+			  int *need_update_ptr,     /* Which coordinates need to be updated? */
+			  int nfeature,             /* how many columns in X */
+			  int ncase);               /* how many rows in X */
+
+
+#ifdef __cplusplus
+}  /* extern "C" */
+#endif /* __cplusplus */
diff --git a/selectiveInference/src/matrixcomps.c b/selectiveInference/src/matrixcomps.c
new file mode 100644
index 0000000..b7e8bbc
--- /dev/null
+++ b/selectiveInference/src/matrixcomps.c
@@ -0,0 +1,266 @@
+#include <math.h>
+
+// Matrices are stored as vectors, in column-major order
+
+// Givens rotation of a and b, stored in c and s
+void givens(double a, double b, double *c, double *s) {
+  if (b==0) {
+    *c = 1; 
+    *s = 0; 
+  }
+  else {
+    if (fabs(b)>fabs(a)) {
+      double t = -a/b;
+      *s = 1/sqrt(1+t*t);
+      *c = (*s)*t;
+    }
+    else {
+      double t = -b/a;
+      *c = 1/sqrt(1+t*t);
+      *s = (*c)*t;
+    }
+  }
+}
+
+// Givens rotation applied to rows i1 and i2 of the m x n
+// matrix A, on the subset of columns j1 through j2
+void rowrot(double *A, int i1, int i2, int m, int n, int j1, int j2, double c, double s) {
+  int j;
+  double t1,t2;
+  for (j=j1; j<=j2; j++) {
+    t1 = A[i1+j*m]; 
+    t2 = A[i2+j*m];
+    A[i1+j*m] = c*t1-s*t2;
+    A[i2+j*m] = s*t1+c*t2;
+  }
+}
+
+// Givens rotation applied to columns j1 and j2 of the m x n
+// matrix A, on the subset of rows i1 through i2
+void colrot(double *A, int j1, int j2, int m, int n, int i1, int i2, double c, double s) {
+  int i;
+  double t1,t2;
+  for (i=i1; i<=i2; i++) {
+    t1 = A[i+j1*m];
+    t2 = A[i+j2*m];
+    A[i+j1*m] = c*t1-s*t2;
+    A[i+j2*m] = s*t1+c*t2;
+  }
+}
+
+// Downdate the QR factorization after deleting column j0, 
+// where Q1 is m x n and R is n x n. The other part of 
+// the Q matrix, Q2 m x (m-n), isn't needed so it isn't 
+// passed for efficiency
+void downdate1(double *Q1, double *R, int j0, int m, int n) {
+  int j;
+
+  double c,s;
+  for (j=j0+1; j<n; j++) {
+    // Compute the appropriate c and s
+    givens(R[j-1+j*n],R[j+j*n],&c,&s);
+
+    // Pre-multiply R
+    rowrot(R,j-1,j,n,n,j,n-1,c,s);
+
+    // Post-mutiply Q1
+    colrot(Q1,j-1,j,m,n,0,m-1,c,s);
+  }
+}
+
+// Update the QR factorization after adding a column z.
+// For convenience, we are given w=Q2'z. Here Q2 is m x 
+// (m-n). The other part of the Q matrix, Q1 m x n, and 
+// R are not needed, so they aren't passed for efficiency 
+void update1(double *Q2, double *w, int m, int k) {
+  int j;
+
+  double c,s;
+  for (j=k-1; j>=1; j--) {
+    // Compute the appropriate c and s
+    givens(w[j-1],w[j],&c,&s);
+
+    // Pre-multiply w
+    rowrot(w,j-1,j,k,1,0,0,c,s);
+
+    // Post-multiply Q2
+    colrot(Q2,j-1,j,m,k,0,m-1,c,s);
+  }
+}
+
+// Downdate the QR factorization after deleting the first row, 
+// where Q is m x m and R is m x n
+void downdate2(double *Q, double *R, int *mp, int *np) {
+  int m,n,i;
+  m = *mp;
+  n = *np;
+
+  double c,s;
+  for (i=m-1; i>=1; i--) {
+    // Compute the appropriate c and s
+    givens(Q[(i-1)*m],Q[i*m],&c,&s);
+
+    // Post-mutiply Q
+    colrot(Q,i-1,i,m,m,0,m-1,c,s);
+
+    // Pre-multiply R
+    if (i<=n) rowrot(R,i-1,i,m,n,i-1,n-1,c,s); 
+  }
+}
+
+// Update the QR factorization after adding the last row,
+// where Q is m x m and R is m x n. For efficiency, Q is not
+// passed, and only the first row of R is passed. Not counting
+// its first row, the first q columns of R are zero
+void update2(double *y, double *D, double *r, int *mp, int *np, int *qp) {
+  int m,n,q,j;
+  m = *mp;
+  n = *np;
+  q = *qp;
+
+  double c,s;
+  for (j=0; j<q-1; j++) {
+    // Compute the appropriate c and s
+    givens(r[j+1],r[j],&c,&s);
+
+    // Pre-multiply r
+    rowrot(r,j+1,j,n,1,0,0,c,s);
+
+    // Post-mutiply D
+    colrot(D,j+1,j,m,n,0,m-1,c,s);
+
+    // Pre-multiply y
+    rowrot(y,j+1,j,n,1,0,0,c,s);
+  }
+}
+
+// Make the R factor upper triangular, by Givens rotating its
+// columns. Here A is m x n, and R is n x n with rank(R) = n-k
+void maketri1(double *y, double *A, double *R, int *mp, int *np, int *kp) {
+  int m,n,k,i,j;
+  m = *mp;
+  n = *np;
+  k = *kp; 
+  
+  double c,s;
+  for (i=n-k-1; i>=0; i--) {
+    for (j=i; j<i+k; j++) {
+      // Compute the appropriate c and s
+      givens(R[i+(j+1)*n],R[i+j*n],&c,&s);
+      
+      // Post-multiply R
+      colrot(R,j+1,j,n,n,0,i,c,s);
+
+      // Post-multiply D
+      colrot(A,j+1,j,m,n,0,m-1,c,s);
+    
+      // Pre-multiply y
+      rowrot(y,j+1,j,n,1,0,0,c,s);
+    }
+  }
+}
+
+// Make the R factor upper triangular, by Givens rotating its 
+// columns. Here A is m x n, and R is m x n with rank(R) = min(m,n)-k
+void maketri2(double *y, double *A, double *R, int *mp, int *np, int *kp) {
+  int m,n,k,i,j;
+  m = *mp;
+  n = *np;
+  k = *kp;
+ 
+  int r=m;
+  if (n < m) {
+    r = n;
+  }
+  int d = 0;
+  if (n-m>0) {
+    d = n-m;
+  }
+
+  double c,s;
+  for (i=r-k-1; i>=0; i--) {
+    for (j=i; j<i+k+d; j++) {
+      // Compute the appropriate c and s
+      givens(R[i+(j+1)*m],R[i+j*m],&c,&s);
+      
+      // Post-multiply R
+      colrot(R,j+1,j,m,n,0,i,c,s);
+
+      // Post-multiply D
+      colrot(A,j+1,j,m,n,0,m-1,c,s);
+    
+      // Pre-multiply y
+      rowrot(y,j+1,j,n,1,0,0,c,s);
+    }
+  }
+}
+
+// Make the R factor upper triangular, by Givens rotating
+// its columns. Here A is m1 x n, and R is m2 x n with 
+// rank(R) = n-q-1. The first q columns of R are zero
+void maketri3(double *y, double *A, double *R, int *m1p, int *m2p, int *np, int *qp) {
+  int m1,m2,n,q,i;
+  m1 = *m1p;
+  m2 = *m2p;
+  n = *np;
+  q = *qp;
+  
+  double c,s;
+  for (i=n-q-2; i>=0; i--) {
+    // Compute the appropriate c and s
+    givens(R[i+(i+q+1)*m2],R[i+(i+q)*m2],&c,&s);
+      
+    // Post-multiply R
+    colrot(R,i+q+1,i+q,m2,n,0,i,c,s);
+
+    // Post-multiply D
+    colrot(A,i+q+1,i+q,m1,n,0,m1-1,c,s);
+    
+    // Pre-multiply y
+    rowrot(y,i+q+1,i+q,n,1,0,0,c,s);
+  }
+}
+
+// Make the R factor upper triangular, by Givens rotating
+// its columns and rows, appropriately. Here A is m1 x n, 
+// Q is m2 x m2, and R is m2 x n with rank(R) = n-q-1. The 
+// first q columns of R are zero. The kth row of R is the 
+// last row with a zero element on the diagonal    
+void maketri4(double *y, double *A, double *Q, double *R, int *m1p, int *m2p, int *np, int *qp, int *kp) {
+  int m1,m2,n,q,k,i,j;
+  m1 = *m1p;
+  m2 = *m2p;
+  n = *np; 
+  q = *qp;
+  k = *kp;
+
+  double c,s;
+
+  // First rotate the columns
+  for (i=k-1; i>=0; i--) {
+    // Compute the appropriate c and s
+    givens(R[i+(i+q+1)*m2],R[i+(i+q)*m2],&c,&s);
+      
+    // Post-multiply R
+    colrot(R,i+q+1,i+q,m2,n,0,i,c,s);
+
+    // Post-multiply D
+    colrot(A,i+q+1,i+q,m1,n,0,m1-1,c,s);
+    
+    // Pre-multiply y
+    rowrot(y,i+q+1,i+q,n,1,0,0,c,s);
+  }
+
+  // Next rotate the rows
+  for (j=k+q+1; j<n; j++) {
+    // Compute the appropriate c and s
+    givens(R[j-q-1+j*m2],R[j-q+j*m2],&c,&s);
+
+    // Pre-multiply R
+    rowrot(R,j-q-1,j-q,m2,n,j,n-1,c,s);
+
+    // Post-multiply Q
+    colrot(Q,j-q-1,j-q,m2,m2,0,m2-1,c,s);
+  }
+}
+
diff --git a/selectiveInference/src/matrixcomps.h b/selectiveInference/src/matrixcomps.h
new file mode 100644
index 0000000..8632e3e
--- /dev/null
+++ b/selectiveInference/src/matrixcomps.h
@@ -0,0 +1,12 @@
+#ifdef __cplusplus
+extern "C"
+{
+#endif /* __cplusplus */
+
+void update1(double *Q2, double *w, int m, int k);
+
+void downdate1(double *Q1, double *R, int j0, int m, int n);
+
+#ifdef __cplusplus
+}  /* extern "C" */
+#endif /* __cplusplus */
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
new file mode 100644
index 0000000..c851f7d
--- /dev/null
+++ b/selectiveInference/src/quadratic_program.c
@@ -0,0 +1,441 @@
+#include <math.h> // for fabs
+
+// Find an approximate row of \hat{nndef}^{-1}
+
+// Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
+
+// Dual problem: \text{min}_{\theta} 1/2 \theta^T \Sigma \theta - l^T\theta + \mu \|\theta\|_1
+// where l is `linear_func` below
+
+// This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
+// Therefore we don't have to negate the answer to get theta.
+// Update one coordinate 
+
+double objective_qp(double *nndef_ptr,       /* A non-negative definite matrix */
+		    double *linear_func_ptr, /* Linear term in objective */
+		    int *ever_active_ptr,    /* Ever active set: 0-based */ 
+		    int *nactive_ptr,        /* Size of ever active set */
+		    int nrow,                /* how many rows in nndef */
+		    double bound,            /* Lagrange multipler for \ell_1 */
+		    double *theta_ptr)           /* current value */
+{
+  int irow, icol;
+  double value = 0;
+  double *nndef_ptr_tmp = nndef_ptr;
+  double *linear_func_ptr_tmp = linear_func_ptr;
+  double *theta_row_ptr, *theta_col_ptr;
+  int *active_row_ptr, *active_col_ptr;
+  int active_row, active_col;
+  int nactive = *nactive_ptr;
+
+  theta_row_ptr = theta_ptr;
+  theta_col_ptr = theta_ptr;
+
+  for (irow=0; irow<nactive; irow++) {
+
+    active_row_ptr = ((int *) ever_active_ptr + irow);
+    active_row = *active_row_ptr - 1;          // Ever-active is 1-based
+    theta_row_ptr = ((double *) theta_ptr + active_row);
+
+    for (icol=0; icol<nactive; icol++) {
+      
+      active_col_ptr = ((int *) ever_active_ptr + icol);
+      active_col = *active_col_ptr - 1;          // Ever-active is 1-based
+      theta_col_ptr = ((double *) theta_ptr + active_col);
+
+      nndef_ptr_tmp = ((double *) nndef_ptr + nrow * active_col + active_row); // Matrices are column-major order
+
+      value += 0.5 * (*nndef_ptr_tmp) * (*theta_row_ptr) * (*theta_col_ptr);
+    }
+    value += bound * fabs((*theta_row_ptr)); // the \ell_1 term
+
+    // The linear term in the objective
+
+    linear_func_ptr_tmp = ((double *) linear_func_ptr + active_row);
+    value += (*linear_func_ptr_tmp) * (*theta_row_ptr); 
+
+  }
+  
+  return(value);
+}
+
+// Ever-active is 1-based
+// coord is 0-based
+int update_ever_active_qp(int coord,
+			  int *ever_active_ptr,
+			  int *nactive_ptr) {
+  int iactive;
+  int active_var;
+  int nactive = *nactive_ptr;
+  int *ever_active_ptr_tmp = ever_active_ptr;
+
+  for (iactive=0; iactive<nactive; iactive++) {
+    ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
+    active_var = *ever_active_ptr_tmp;
+    if (active_var - 1 == coord) {          // Ever-active is 1-based
+      return(1);
+    }
+  }
+  
+  // If we haven't returned yet, this means the coord was not in 
+  // ever_active.
+
+  // Add it to the active set and increment the 
+  // number of active variables
+
+  ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
+  *ever_active_ptr_tmp = coord + 1;         // Ever-active is 1-based
+  *nactive_ptr += 1;
+
+  return(0);
+}
+
+int check_KKT_qp(double *theta_ptr,        /* current theta */
+		 double *gradient_ptr, /* nndef times theta + linear_func */
+		 int nfeature,             /* how many features in nndef */
+		 double bound,         /* Lagrange multipler for \ell_1 */
+		 double tol)           /* precision for checking KKT conditions */        
+{
+  // First check inactive
+
+  int ifeature;
+  double *theta_ptr_tmp, *gradient_ptr_tmp;
+  double gradient;
+
+  for (ifeature=0; ifeature<nfeature; ifeature++) {
+    theta_ptr_tmp = ((double *) theta_ptr + ifeature);
+    gradient_ptr_tmp = ((double *) gradient_ptr + ifeature);
+
+    // Compute this coordinate of the gradient
+
+    gradient = *gradient_ptr_tmp;
+
+    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
+      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
+	return(0);
+      }
+      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
+	return(0);
+      }
+    }
+    else {
+      if (fabs(gradient) > (1. + tol) * bound) {
+	return(0);
+      }
+    }
+  }
+
+  return(1);
+}
+
+int check_KKT_qp_active(int *ever_active_ptr,           /* Ever active set: 0-based */ 
+		        int *nactive_ptr,               /* Size of ever active set */
+			double *theta_ptr,        /* current theta */
+			double *gradient_ptr, /* nndef times theta + linear_func */
+			int nfeature,             /* how many features in nndef */
+			double bound,         /* Lagrange multipler for \ell_1 */
+			double tol)           /* precision for checking KKT conditions */        
+{
+  // First check inactive
+
+  int iactive;
+  double *theta_ptr_tmp;
+  double gradient;
+  double *gradient_ptr_tmp;
+  int nactive = *nactive_ptr;
+  int active_feature;
+  int *active_feature_ptr;
+
+  for (iactive=0; iactive<nactive; iactive++) {
+
+    active_feature_ptr = ((int *) ever_active_ptr + iactive);
+    active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
+    theta_ptr_tmp = ((double *) theta_ptr + active_feature);
+
+    gradient_ptr_tmp = ((double *) gradient_ptr + active_feature);
+
+    // Compute this coordinate of the gradient
+
+    gradient = *gradient_ptr_tmp;
+
+    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
+
+      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
+	return(0);
+      }
+      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
+	return(0);
+      }
+
+    }
+    else {
+      if (fabs(gradient) > (1. + tol) * bound) {
+	return(0);
+      }
+    }
+  }
+
+  return(1);
+}
+
+
+double update_one_coord_qp(double *nndef_ptr,           /* A non-negative definite matrix */
+			   double *linear_func_ptr,     /* Linear term in objective */
+			   double *nndef_diag_ptr,      /* Diagonal of nndef */
+			   double *gradient_ptr,        /* nndef times theta + linear_func */
+			   int *ever_active_ptr,        /* Ever active set: 1-based */ 
+			   int *nactive_ptr,            /* Size of ever active set */
+			   int nfeature,                    /* How many features in nndef */
+			   double bound,                /* feasibility parameter */
+			   double *theta_ptr,               /* current value */
+			   int coord,                   /* which coordinate to update: 0-based */
+			   int is_active)               /* Is this coord in ever_active */     
+{
+
+  double delta;
+  double linear_term = 0;
+  double value = 0;
+  double old_value;
+  double *nndef_ptr_tmp;
+  double *gradient_ptr_tmp;
+  double *theta_ptr_tmp;
+  int icol = 0;
+
+  double *quadratic_ptr = ((double *) nndef_diag_ptr + coord);
+  double quadratic_term = *quadratic_ptr;
+
+  gradient_ptr_tmp = ((double *) gradient_ptr + coord);
+  linear_term = *gradient_ptr_tmp;
+
+  theta_ptr_tmp = ((double *) theta_ptr + coord);
+  old_value = *theta_ptr_tmp;
+
+  // The coord entry of gradient_ptr term has a diagonal term in it:
+  // nndef[coord, coord] * theta[coord]
+  // This removes it. 
+
+  linear_term -= quadratic_term * old_value;
+
+  // Now soft-threshold the coord entry of theta 
+
+  // Objective is t \mapsto q/2 * t^2 + l * t + bound |t|
+  // with q=quadratic_term and l=linear_term
+
+  // With a negative linear term, solution should be
+  // positive
+
+  if (linear_term < -bound) {
+    value = (-linear_term - bound) / quadratic_term;
+  }
+  else if (linear_term > bound) {
+    value = -(linear_term - bound) / quadratic_term;
+  }
+
+  // Add to active set if necessary
+
+  if ((is_active == 0) && (value != 0)) {
+    update_ever_active_qp(coord, ever_active_ptr, nactive_ptr);
+  }
+
+  // Update the linear term
+
+  if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
+
+    delta = value - old_value;
+    nndef_ptr_tmp = ((double *) nndef_ptr + coord * nfeature);
+    gradient_ptr_tmp = ((double *) gradient_ptr);
+
+    for (icol=0; icol<nfeature; icol++) {
+      *gradient_ptr_tmp = *gradient_ptr_tmp + delta * (*nndef_ptr_tmp);
+      gradient_ptr_tmp += 1;
+      nndef_ptr_tmp += 1;
+    }
+
+    theta_ptr_tmp = ((double *) theta_ptr + coord);
+    *theta_ptr_tmp = value;
+
+  }
+
+  return(value);
+
+}
+
+int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
+	     double *linear_func_ptr,    /* Linear term in objective */
+	     double *nndef_diag_ptr,     /* Diagonal of nndef */
+	     double *gradient_ptr,       /* nndef times theta */
+	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
+	     int *nactive_ptr,           /* Size of ever active set */
+	     int nfeature,               /* How many features in nndef */
+	     double bound,               /* feasibility parameter */
+	     double *theta,              /* current value */
+	     double *theta_old,          /* previous value */
+	     int maxiter,                /* max number of iterations */
+	     double kkt_tol,             /* precision for checking KKT conditions */
+	     double objective_tol,       /* precision for checking relative decrease in objective value */
+	     double parameter_tol,       /* precision for checking relative convergence of parameter */
+	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
+             int kkt_stop,               /* Break based on KKT? */
+	     int objective_stop,         /* Break based on convergence of objective value? */
+	     int param_stop)             /* Break based on parameter convergence? */
+{
+
+  int iter = 0;
+  int iter_old = 1;
+  int ifeature = 0;
+  int iactive = 0;
+  int *active_ptr;
+
+  int iter_active;
+  int niter_active=5;
+
+  double old_value, new_value; 
+  double norm_diff = 1.;
+  double norm_last = 1.;
+  double delta;
+  double *theta_ptr, *theta_old_ptr;
+
+  if (objective_stop) {
+
+    old_value = objective_qp(nndef_ptr,
+			     linear_func_ptr,
+			     ever_active_ptr,
+			     nactive_ptr,
+			     nfeature,
+			     bound,
+			     theta);
+
+  }
+
+
+  for (iter=0; iter<maxiter; iter++) {
+
+    // Update the active variables first -- do this niter_active times
+
+    for (iter_active=0; iter_active<niter_active; iter_active++) { 
+
+        active_ptr = (int *) ever_active_ptr;
+        for (iactive=0; iactive < *nactive_ptr; iactive++) {
+
+	  update_one_coord_qp(nndef_ptr,
+			      linear_func_ptr,
+			      nndef_diag_ptr,
+			      gradient_ptr,
+			      ever_active_ptr,
+			      nactive_ptr,
+			      nfeature,
+			      bound,
+			      theta,
+			      *active_ptr - 1,   // Ever-active is 1-based
+			      1);
+	  active_ptr++;
+	}
+
+	// Check KKT of active subproblem
+
+	if (check_KKT_qp_active(ever_active_ptr,
+				nactive_ptr,
+				theta,
+				gradient_ptr,
+				nfeature,
+				bound,
+				kkt_tol) == 1) {
+	  break;
+	}
+    }
+
+    // Check KKT
+
+    if (kkt_stop) {
+      if (check_KKT_qp(theta, 
+		       gradient_ptr,
+		       nfeature,
+		       bound,
+		       kkt_tol) == 1) {
+	break;
+      }
+    }
+
+    // Update all variables 
+
+    for (ifeature=0; ifeature<nfeature; ifeature++) {
+
+      update_one_coord_qp(nndef_ptr,
+			  linear_func_ptr,
+			  nndef_diag_ptr,
+			  gradient_ptr,
+			  ever_active_ptr,
+			  nactive_ptr,
+			  nfeature,
+			  bound,
+			  theta,
+			  ifeature,
+			  0);
+    }
+
+    // Check KKT
+
+    if (kkt_stop) {
+      if (check_KKT_qp(theta, 
+		       gradient_ptr,
+		       nfeature,
+		       bound,
+		       kkt_tol) == 1) {
+	break;
+      }
+    }
+					  
+
+    if (iter == 2 * iter_old) { // Geometric iterations from Adel's code
+
+      // Check based on norm 
+
+      if (param_stop) { 
+	iter_old = iter;
+	norm_diff = 0;
+	norm_last = 0;
+	for (ifeature=0; ifeature<nfeature; ifeature++) {
+	  theta_old_ptr = ((double *) theta_old + ifeature);
+	  theta_ptr = ((double *) theta + ifeature);
+	  delta = (*theta_ptr - *theta_old_ptr);
+	  norm_diff += delta * delta;
+	  norm_last += (*theta_ptr) * (*theta_ptr);
+	  *theta_old_ptr = (*theta_ptr);
+	}
+	norm_diff = sqrt(norm_diff);
+	norm_last = sqrt(norm_last);
+	
+	if (norm_diff < parameter_tol * norm_last) {
+	  break;
+	}
+      }
+
+      // Check relative decrease of objective
+
+      if (objective_stop) {
+	new_value = objective_qp(nndef_ptr,
+				 linear_func_ptr,
+				 ever_active_ptr,
+				 nactive_ptr,
+				 nfeature,
+				 bound,
+				 theta);
+
+	if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
+	  break;
+	}
+	old_value = new_value;
+      }
+
+    }
+
+    // Check size of active set
+
+    if (*nactive_ptr >= max_active) {
+      break;
+    }
+
+  }
+  return(iter);
+}
+
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
new file mode 100644
index 0000000..ce7284b
--- /dev/null
+++ b/selectiveInference/src/quadratic_program_wide.c
@@ -0,0 +1,597 @@
+#include <math.h> // for fabs
+
+// Find an approximate row of \hat{nndef}^{-1}
+
+// Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
+
+// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2/n + l^T\theta + \mu \|\theta\|_1 + \frac{\epsilon}{2} \|\theta\|^2_2
+// where l is `linear_func` below
+
+// This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
+// Therefore we don't have to negate the answer to get theta.
+// Update one coordinate 
+
+// Throughout X is a design matrix
+
+double objective_wide(double *X_theta_ptr,     /* Fitted values */
+		      double *linear_func_ptr, /* Linear term in objective */
+		      int *ever_active_ptr,    /* Ever active set: 0-based */ 
+		      int *nactive_ptr,        /* Size of ever active set */
+		      int ncase,               /* how many rows in X */
+		      int nfeature,            /* how many columns in X */
+		      double *bound_ptr,       /* Lagrange multiplers for \ell_1 */
+		      double ridge_term,       /* Ridge / ENet term */
+		      double *theta_ptr)       /* current value */
+{
+  int icase, iactive;
+  double value = 0;
+  double *bound_ptr_tmp;
+  double *X_theta_ptr_tmp = X_theta_ptr;
+  double *linear_func_ptr_tmp = linear_func_ptr;
+  double *theta_ptr_tmp;
+  int *active_feature_ptr;
+  int active_feature;
+  int nactive = *nactive_ptr;
+
+  // The term \|X\theta\|^2_2/n, with n=ncase
+
+  for (icase=0; icase<ncase; icase++) {
+
+    X_theta_ptr_tmp = ((double *) X_theta_ptr + icase);
+    value += (*(X_theta_ptr_tmp)) * (*(X_theta_ptr_tmp));
+
+  }
+
+  value *= 0.5 / ncase;
+
+  for (iactive=0; iactive<nactive; iactive++) {
+
+    // The linear term in the objective
+
+    active_feature_ptr = ((int *) ever_active_ptr + iactive);
+    active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
+
+    theta_ptr_tmp = ((double *) theta_ptr + active_feature);
+    linear_func_ptr_tmp = ((double *) linear_func_ptr + active_feature);
+    value += (*linear_func_ptr_tmp) * (*theta_ptr_tmp); 
+
+    // The \ell_1 term
+
+    bound_ptr_tmp = ((double *) bound_ptr + active_feature);
+    value += (*bound_ptr_tmp) * fabs((*theta_ptr_tmp));
+
+    // The ridge term
+
+    value += 0.5 * ridge_term * (*theta_ptr_tmp) * (*theta_ptr_tmp);
+  }
+  
+  return(value);
+}
+
+// Compute, update and return one coordinate of the gradient of \|X\theta\|^2_2/2n
+
+double compute_gradient_coord(double *gradient_ptr,        /* Gradient -- one coordinate will be updated if needed */
+			      double *X_theta_ptr,         /* Current fitted values */
+			      double *X_ptr,               /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
+			      double *linear_func_ptr,     /* Linear term in objective */   
+			      int *need_update_ptr,        /* Which coordinates need to be updated? */
+			      int coord,                   /* Coordinate we are trying to update */
+			      int ncase,                   /* How many rows in X */
+			      int nfeature)                /* How many columns in X */
+{
+
+  double *gradient_ptr_tmp;
+  double *X_theta_ptr_tmp;
+  double *X_ptr_tmp;  
+  double *linear_func_ptr_tmp;  
+  int *need_update_ptr_tmp;
+  int icase;
+  double value = 0.;
+
+  need_update_ptr_tmp = ((int *) need_update_ptr + coord);
+
+  // Check if this coordinate needs updating
+  if (*need_update_ptr_tmp == 1) {
+
+    for (icase=0; icase<ncase; icase++) {
+#ifdef COLUMN_MAJOR_ORDER
+      X_ptr_tmp = ((double *) X_ptr + coord * ncase + icase);
+#else
+      X_ptr_tmp = ((double *) X_ptr + icase * nfeature + coord);
+#endif
+      X_theta_ptr_tmp = ((double *) X_theta_ptr + icase);
+      value += (*X_ptr_tmp) * (*X_theta_ptr_tmp);
+    }
+
+    value /= ncase;
+
+    linear_func_ptr_tmp = ((double *) linear_func_ptr + coord);
+    value += *linear_func_ptr_tmp;
+
+    gradient_ptr_tmp = ((double *) gradient_ptr + coord);
+    *gradient_ptr_tmp = value;
+
+    *need_update_ptr_tmp = 0;
+  }
+  else {
+    gradient_ptr_tmp = ((double *) gradient_ptr + coord);
+    value = *gradient_ptr_tmp;
+  }
+
+  return(value);
+}
+
+
+// Ever-active is 1-based
+// coord is 0-based
+int update_ever_active_wide(int coord,
+			    int *ever_active_ptr,
+			    int *nactive_ptr) {
+  int iactive;
+  int active_var;
+  int nactive = *nactive_ptr;
+  int *ever_active_ptr_tmp = ever_active_ptr;
+
+  for (iactive=0; iactive<nactive; iactive++) {
+    ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
+    active_var = *ever_active_ptr_tmp;
+
+    if (active_var - 1 == coord) {          // Ever-active is 1-based
+      return(1);
+    }
+  }
+  
+  // If we haven't returned yet, this means the coord was not in 
+  // ever_active.
+
+  // Add it to the active set and increment the 
+  // number of active variables
+
+  ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
+  *ever_active_ptr_tmp = coord + 1;         // Ever-active is 1-based
+  *nactive_ptr += 1;
+
+  return(0);
+}
+
+void update_gradient_wide(double *gradient_ptr,     /* X^TX/ncase times theta + linear_func */
+			  double *X_theta_ptr,      /* Current fitted values */
+			  double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
+			  double *linear_func_ptr,  /* Linear term in objective */   
+			  int *need_update_ptr,     /* Which coordinates need to be updated? */
+			  int ncase,             /* how many rows in X */
+			  int nfeature)                /* how many columns in X */
+{
+  int ifeature;
+
+  for (ifeature=0; ifeature<nfeature; ifeature++) {
+    compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, ifeature, ncase, nfeature);
+  }
+
+}
+
+int check_KKT_wide(double *theta_ptr,        /* current theta */
+		   double *gradient_ptr,     /* X^TX/ncase times theta + linear_func*/
+		   double *X_theta_ptr,      /* Current fitted values */
+		   double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
+		   double *linear_func_ptr,  /* Linear term in objective */   
+		   int *need_update_ptr,     /* Which coordinates need to be updated? */
+		   int ncase,                /* how many rows in X */
+		   int nfeature,             /* how many columns in X */
+		   double *bound_ptr,        /* Lagrange multiplers for \ell_1 */
+		   double ridge_term,        /* Ridge / ENet term */
+		   double tol)               /* precision for checking KKT conditions */        
+{
+  // First check inactive
+
+  int ifeature;
+  double *theta_ptr_tmp;
+  double *bound_ptr_tmp;
+  double bound;
+  double gradient;
+
+  for (ifeature=0; ifeature<nfeature; ifeature++) {
+
+    theta_ptr_tmp = ((double *) theta_ptr + ifeature);
+    bound_ptr_tmp = ((double *) bound_ptr + ifeature);
+    bound = *bound_ptr_tmp;
+
+    // Compute this coordinate of the gradient
+
+    gradient = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, ifeature, ncase, nfeature);
+
+    if ((*theta_ptr_tmp != 0) && (bound != 0)) { // these coordinates of gradients should be equal to -bound
+      
+      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + ridge_term * (*theta_ptr_tmp) + bound) > tol * bound)) {
+	return(0);
+      }
+      else if ((*theta_ptr_tmp < 0) && (fabs(gradient + ridge_term * (*theta_ptr_tmp) - bound) > tol * bound)) {
+	return(0);
+      }
+      
+    }
+    else if (bound != 0) {
+      if (fabs(gradient) > (1. + tol) * bound) {
+	return(0);
+      }
+    }
+  }
+
+  return(1);
+}
+
+int check_KKT_wide_active(int *ever_active_ptr,           /* Ever active set: 0-based */ 
+			  int *nactive_ptr,               /* Size of ever active set */
+			  double *theta_ptr,              /* current theta */
+			  double *gradient_ptr,           /* X^TX/ncase times theta + linear_func*/
+			  double *X_theta_ptr,            /* Current fitted values */
+			  double *X_ptr,                  /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
+			  double *linear_func_ptr,        /* Linear term in objective */   
+			  int *need_update_ptr,           /* Which coordinates need to be updated? */
+			  int ncase,                      /* how many rows in X */
+			  int nfeature,                   /* how many columns in X */
+			  double *bound_ptr,              /* Lagrange multipliers for \ell_1 */
+			  double ridge_term,              /* Ridge / ENet term */
+			  double tol)                     /* precision for checking KKT conditions */        
+{
+  // First check inactive
+
+  int iactive;
+  double *theta_ptr_tmp;
+  double *bound_ptr_tmp;
+  double bound;
+  double gradient;
+  int nactive = *nactive_ptr;
+  int active_feature;
+  int *active_feature_ptr;
+
+  for (iactive=0; iactive<nactive; iactive++) {
+
+    active_feature_ptr = ((int *) ever_active_ptr + iactive);
+    active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
+
+    theta_ptr_tmp = ((double *) theta_ptr + active_feature);
+    bound_ptr_tmp = ((double *) bound_ptr + active_feature);
+    bound = *bound_ptr_tmp;
+
+    // Compute this coordinate of the gradient
+
+    gradient = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, active_feature, ncase, nfeature);
+
+    if ((*theta_ptr_tmp != 0) && (bound != 0)) { // these coordinates of gradients should be equal to -bound
+
+      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + ridge_term * (*theta_ptr_tmp) + bound) > tol * bound)) {
+	return(0);
+      }
+      else if ((*theta_ptr_tmp < 0) && (fabs(gradient + ridge_term * (*theta_ptr_tmp) - bound) > tol * bound)) {
+	return(0);
+      }
+
+    }
+    else if (bound != 0) {
+      if (fabs(gradient) > (1. + tol) * bound) {
+	return(0);
+      }
+    }
+  }
+
+  return(1);
+}
+
+double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
+			     double *linear_func_ptr,     /* Linear term in objective */
+			     double *nndef_diag_ptr,      /* Diagonal of nndef */
+			     double *gradient_ptr,        /* X^TX/ncase times theta + linear_func*/
+			     int *ever_active_ptr,        /* Ever active set: 1-based */ 
+			     int *nactive_ptr,            /* Size of ever active set */
+			     double *X_theta_ptr,         /* X\theta -- fitted values */
+			     int *need_update_ptr,        /* Whether a gradient coordinate needs update or not */
+			     int ncase,                   /* How many rows in X */
+			     int nfeature,                /* How many rows in X */
+			     double *bound_ptr,           /* Lagrange multipliers */
+			     double ridge_term,           /* Ridge / ENet term */
+			     double *theta_ptr,           /* current value */
+			     int coord,                   /* which coordinate to update: 0-based */
+			     int is_active)               /* Is this coord in ever_active */     
+{
+
+  double delta;
+  double linear_term = 0;
+  double value = 0;
+  double old_value;
+  double *X_ptr_tmp;
+  double *X_theta_ptr_tmp;
+  int *need_update_ptr_tmp;
+  double *theta_ptr_tmp;
+  double *bound_ptr_tmp;
+  double bound;
+  int ifeature, icase;
+
+  double *diagonal_ptr = ((double *) nndef_diag_ptr + coord);
+  double diagonal_entry = *diagonal_ptr;
+
+  linear_term = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, coord, ncase, nfeature);
+
+  theta_ptr_tmp = ((double *) theta_ptr + coord);
+  old_value = *theta_ptr_tmp;
+
+  bound_ptr_tmp = ((double *) bound_ptr + coord);
+  bound = *bound_ptr_tmp;
+
+  // The coord entry of gradient_ptr term has a diagonal term in it:
+  // (X^TX)[coord, coord] * theta[coord] / ncase
+  // This removes it. 
+
+  linear_term -= diagonal_entry * old_value;
+
+  // Now soft-threshold the coord entry of theta 
+
+  // Objective is t \mapsto (q+eps)/2 * t^2 + l * t + bound |t| + 
+  // with q=diagonal_entry and l=linear_term and eps=ridge_Term
+
+  // With a negative linear term, solution should be
+  // positive
+
+  if (linear_term < -bound) {
+    value = (-linear_term - bound) / (diagonal_entry + ridge_term);
+  }
+  else if (linear_term > bound) {
+    value = -(linear_term - bound) / (diagonal_entry + ridge_term);
+  }
+
+  // Add to active set if necessary
+
+  if ((is_active == 0) && (value != 0)) {
+    update_ever_active_wide(coord, ever_active_ptr, nactive_ptr);
+  }
+
+  // Update X\theta if changed
+
+  if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
+
+    // Set the update_gradient_ptr to 1
+
+    need_update_ptr_tmp = need_update_ptr;
+    for (ifeature=0; ifeature<nfeature; ifeature++) {
+      *need_update_ptr_tmp = 1;
+      need_update_ptr_tmp++;
+    }
+
+    // Update X\theta
+
+    delta = value - old_value;
+
+    for (icase=0; icase<ncase; icase++) {
+      X_theta_ptr_tmp = ((double *) X_theta_ptr + icase);
+#ifdef COLUMN_MAJOR_ORDER
+      X_ptr_tmp = ((double *) X_ptr + coord * ncase + icase);
+#else
+      X_ptr_tmp = ((double *) X_ptr + icase * nfeature + coord);
+#endif
+      *X_theta_ptr_tmp = (*X_theta_ptr_tmp) + delta * (*X_ptr_tmp);
+    }
+
+    theta_ptr_tmp = ((double *) theta_ptr + coord);
+    *theta_ptr_tmp = value;
+
+    theta_ptr_tmp = theta_ptr;
+    for (ifeature=0; ifeature<nfeature; ifeature++) {
+      theta_ptr_tmp++;
+    }
+
+  }
+
+  return(value);
+
+}
+
+int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
+	       double *X_theta_ptr,        /* Fitted values   */
+	       double *linear_func_ptr,    /* Linear term in objective */
+	       double *nndef_diag_ptr,     /* Diagonal entries of non-neg def matrix */
+	       double *gradient_ptr,       /* X^TX/ncase times theta + linear_func*/
+	       int *need_update_ptr,       /* Keeps track of updated gradient coords */
+	       int *ever_active_ptr,       /* Ever active set: 1-based */ 
+	       int *nactive_ptr,           /* Size of ever active set */
+	       int ncase,                  /* How many rows in X */
+	       int nfeature,               /* How many columns in X */
+	       double *bound_ptr,          /* Lagrange multipliers */
+	       double ridge_term,          /* Ridge / ENet term */
+	       double *theta_ptr,          /* current value */
+	       double *theta_old_ptr,      /* previous value */
+	       int maxiter,                /* max number of iterations */
+	       double kkt_tol,             /* precision for checking KKT conditions */
+	       double objective_tol,       /* precision for checking relative decrease in objective value */
+	       double parameter_tol,       /* precision for checking relative convergence of parameter */
+	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
+	       int kkt_stop,               /* Break based on KKT? */
+	       int objective_stop,         /* Break based on convergence of objective value? */
+	       int param_stop)             /* Break based on parameter convergence? */
+{
+
+  int iter = 0;
+  int iter_old = 1;
+  int ifeature = 0;
+  int iactive = 0;
+  int *active_ptr;
+
+  double old_value, new_value; 
+  int niter_active = 5;
+  int iter_active;
+
+  double norm_diff = 1.;
+  double norm_last = 1.;
+  double delta;
+  double *theta_ptr_tmp, *theta_old_ptr_tmp;
+
+  new_value = objective_wide(X_theta_ptr,
+			     linear_func_ptr,
+			     ever_active_ptr,
+			     nactive_ptr,
+			     ncase,
+			     nfeature,
+			     bound_ptr,
+			     ridge_term,
+			     theta_ptr);
+
+  old_value = new_value + 2000000000; // hack for a big number... should do better
+
+
+  for (iter=0; iter<maxiter; iter++) {
+
+    // Update the active variables first -- do this niter_active times
+
+    for (iter_active=0; iter_active<niter_active; iter_active++) { 
+
+        active_ptr = (int *) ever_active_ptr;
+        for (iactive=0; iactive < *nactive_ptr; iactive++) {
+
+          update_one_coord_wide(X_ptr,
+    			        linear_func_ptr,
+				nndef_diag_ptr,
+				gradient_ptr,
+				ever_active_ptr,
+				nactive_ptr,
+				X_theta_ptr,
+				need_update_ptr,
+				ncase,
+				nfeature,
+				bound_ptr,
+				ridge_term,
+				theta_ptr,
+				*active_ptr - 1,   // Ever-active is 1-based
+				1);
+	  active_ptr++;
+	}
+
+	// Check KKT of active subproblem
+
+	if (check_KKT_wide_active(ever_active_ptr,
+				  nactive_ptr,
+				  theta_ptr,
+				  gradient_ptr,
+				  X_theta_ptr,
+				  X_ptr,
+				  linear_func_ptr,
+				  need_update_ptr,
+				  ncase,
+				  nfeature,
+				  bound_ptr,
+				  ridge_term,
+				  kkt_tol) == 1) {
+	  break;
+	}
+
+    }
+
+    // Check KKT
+
+    if (kkt_stop) { 
+      if (check_KKT_wide(theta_ptr,
+			 gradient_ptr,
+			 X_theta_ptr,
+			 X_ptr,
+			 linear_func_ptr,
+			 need_update_ptr,
+			 ncase,
+			 nfeature,
+			 bound_ptr,
+			 ridge_term,
+			 kkt_tol) == 1) {
+	break;
+      }
+    }
+
+    // Update all variables 
+
+    for (ifeature=0; ifeature<nfeature; ifeature++) {
+
+      update_one_coord_wide(X_ptr,
+			    linear_func_ptr,
+			    nndef_diag_ptr,
+			    gradient_ptr,
+			    ever_active_ptr,
+			    nactive_ptr,
+			    X_theta_ptr,
+			    need_update_ptr,
+			    ncase,
+			    nfeature,
+			    bound_ptr,
+			    ridge_term,
+			    theta_ptr,
+			    ifeature,
+			    0);
+    }
+
+    // Check KKT
+
+    if (kkt_stop) {
+      if (check_KKT_wide(theta_ptr,
+			 gradient_ptr,
+			 X_theta_ptr,
+			 X_ptr,
+			 linear_func_ptr,
+			 need_update_ptr,
+			 ncase,
+			 nfeature,
+			 bound_ptr,
+			 ridge_term,
+			 kkt_tol) == 1) {
+	break;
+      }
+    }
+
+    if (iter == 2 * iter_old) { // Geometric iterations from Adel's code
+
+      // Check based on norm 
+
+      if (param_stop) {
+	iter_old = iter;
+	norm_diff = 0;
+	norm_last = 0;
+	for (ifeature=0; ifeature<nfeature; ifeature++) {
+	  theta_old_ptr_tmp = ((double *) theta_old_ptr + ifeature);
+	  theta_ptr_tmp = ((double *) theta_ptr + ifeature);
+	  delta = (*theta_ptr_tmp - *theta_old_ptr_tmp);
+	  norm_diff += delta * delta;
+	  norm_last += (*theta_ptr_tmp) * (*theta_ptr_tmp);
+	  *theta_old_ptr = (*theta_ptr_tmp);
+	}
+	norm_diff = sqrt(norm_diff);
+	norm_last = sqrt(norm_last);
+	
+	if (norm_diff < parameter_tol * norm_last) {
+	  break;
+	}
+      }
+
+      // Check relative decrease of objective
+
+      if (objective_stop) {
+	new_value = objective_wide(X_theta_ptr,
+				   linear_func_ptr,
+				   ever_active_ptr,
+				   nactive_ptr,
+				   ncase,
+				   nfeature,
+				   bound_ptr,
+				   ridge_term,
+				   theta_ptr);
+
+	if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
+	  break;
+	}
+	old_value = new_value;
+      }
+    }
+
+    // Check size of active set
+
+    if (*nactive_ptr >= max_active) {
+      break;
+    }
+
+  }
+  return(iter);
+}
+
diff --git a/selectiveInference/src/randomized_lasso.c b/selectiveInference/src/randomized_lasso.c
new file mode 100644
index 0000000..5729408
--- /dev/null
+++ b/selectiveInference/src/randomized_lasso.c
@@ -0,0 +1,411 @@
+#include <math.h> // for fabs
+#include <stdio.h>
+
+// Augmented density for randomized LASSO after
+// Gaussian randomization
+
+// Described in https://arxiv.org/abs/1609.05609
+
+// Gaussian is product of IID N(0, noise_scale^2) density
+// Evaluated at A_D D + A_O O + h
+
+// Laplace is product of IID Laplace with scale noise_scale
+// Also evaluated at A_D D + A_O O + h
+
+// Matrices are assumed in column major order! 
+
+double log_density_gaussian(double noise_scale,             // Scale of randomization
+			    int ndim,                       // Number of features -- "p"
+			    int ninternal,                  // Dimension of internal data representation often 1
+			    int noptimization,              // Dimension of optimization variables -- "p"
+			    double *internal_linear,        // A_D -- linear part for data
+			    double *internal_state,         // D -- data state
+			    double *optimization_linear,    // A_O -- linear part for optimization variables
+			    double *optimization_state,     // O -- optimization state
+			    double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double denom = 2 * noise_scale * noise_scale;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Internal (i.e. data) contribution
+    for (icol=0; icol<ninternal; icol++) {
+      
+      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
+      internal_state_ptr = ((double *) internal_state + icol);
+
+      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
+    }
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= (reconstruction * reconstruction) / denom;
+  }
+
+  return(value);
+}
+
+double log_density_laplace(double noise_scale,             // Scale of randomization
+			   int ndim,                       // Number of features -- "p"
+			   int ninternal,                  // Dimension of internal data representation often 1
+			   int noptimization,              // Dimension of optimization variables -- "p"
+			   double *internal_linear,        // A_D -- linear part for data
+			   double *internal_state,         // D -- data state
+			   double *optimization_linear,    // A_O -- linear part for optimization variables
+			   double *optimization_state,     // O -- optimization state
+			   double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *internal_linear_ptr;
+  double *internal_state_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Internal (i.e. data) contribution
+    for (icol=0; icol<ninternal; icol++) {
+      
+      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
+      internal_state_ptr = ((double *) internal_state + icol);
+
+      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
+    }
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= fabs(reconstruction) / noise_scale;
+  }
+
+  return(value);
+}
+
+// Keeping internal (data) state fixed
+
+double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
+					int ndim,                       // Number of features -- "p"
+					int noptimization,              // Dimension of optimization variables -- "p"
+					double *optimization_linear,    // A_O -- linear part for optimization variables
+					double *optimization_state,     // O -- optimization state
+					double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double denom = 2 * noise_scale * noise_scale;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= reconstruction * reconstruction / denom;
+  }
+
+  return(value);
+}
+
+double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
+				       int ndim,                       // Number of features -- "p"
+				       int noptimization,              // Dimension of optimization variables -- "p"
+				       double *optimization_linear,    // A_O -- linear part for optimization variables
+				       double *optimization_state,     // O -- optimization state
+				       double *offset)                 // h -- offset in affine transform -- "p" dimensional 
+{
+  int irow, icol;
+  double value = 0;
+  double reconstruction = 0;
+  double *offset_ptr;
+  double *optimization_linear_ptr;
+  double *optimization_state_ptr;
+
+  for (irow=0; irow<ndim; irow++) {
+
+    // Compute the irow-th entry of the ndim vector
+
+    offset_ptr = ((double *) offset + irow);
+    reconstruction = *offset_ptr;
+
+    // Optimization contribution
+    for (icol=0; icol<noptimization; icol++) {
+      
+      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
+      optimization_state_ptr = ((double *) optimization_state + icol);
+
+      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
+    }
+
+    value -= fabs(reconstruction) / noise_scale;
+  }
+
+  return(value);
+}
+
+//    objective = lambda u: -u.T.dot(conjugate_arg) + u.T.dot(precision).dot(u)/2. + np.log(1.+ 1./(u / scaling)).sum()
+
+double barrier_objective(double *opt_variable,               // Optimization variable
+			 double *conjugate_arg,              // Argument to conjugate of Gaussian
+			 double *precision,                  // Precision matrix of Gaussian
+			 double *scaling,                    // Diagonal scaling matrix for log barrier
+			 int ndim)                           // Dimension of conjugate_arg, precision
+{
+  int idim, jdim;
+  double *conjugate_arg_ptr;
+  double *opt_variable_ptr;
+  double *precision_ptr;
+  double *scaling_ptr;
+  double product_entry, value;
+
+  value = 0.;
+  for (idim=0; idim<ndim; idim++) {
+    
+    // Compute an entry of precision.dot(conjugate_arg)
+
+    product_entry = 0;
+    for (jdim=0; jdim<ndim; jdim++) {
+    
+      precision_ptr = ((double *) precision + idim * ndim + jdim); // precision is a symmetric matrix
+      opt_variable_ptr = ((double *) opt_variable + jdim);
+      product_entry += (*precision_ptr) * (*opt_variable_ptr);
+    }
+      
+    opt_variable_ptr = ((double *) opt_variable + idim);
+    value += 0.5 * (*opt_variable_ptr) * product_entry;
+
+    // now add linear term
+
+    conjugate_arg_ptr = ((double *) conjugate_arg + idim);
+    value -= (*conjugate_arg_ptr) * (*opt_variable_ptr);
+
+    // now log term
+
+    scaling_ptr = ((double *) scaling + idim);
+    value += log(((*opt_variable_ptr) + (*scaling_ptr)) / (*opt_variable_ptr));
+
+  }
+
+  return(value);
+}
+
+//    grad = lambda u: -conjugate_arg + precision.dot(u) + (1./(scaling + u) - 1./u)
+
+void barrier_gradient(double *gradient,                   // Gradient vector
+		      double *opt_variable,               // Optimization variable
+		      double *conjugate_arg,              // Argument to conjugate of Gaussian
+		      double *precision,                  // Precision matrix of Gaussian
+		      double *scaling,                    // Diagonal scaling matrix for log barrier
+		      int ndim)                           // Dimension of conjugate_arg, precision
+{
+  int idim, jdim;
+  double *gradient_ptr;
+  double *conjugate_arg_ptr;
+  double *opt_variable_ptr;
+  double *precision_ptr;
+  double *scaling_ptr;
+  double product_entry;
+
+  for (idim=0; idim<ndim; idim++) {
+    
+    gradient_ptr = ((double *) gradient + idim);
+
+    // Compute an entry of precision.dot(conjugate_arg)
+
+    product_entry = 0;
+    for (jdim=0; jdim<ndim; jdim++) {
+    
+      precision_ptr = ((double *) precision + idim * ndim + jdim); // precision is a symmetric matrix
+      opt_variable_ptr = ((double *) opt_variable + jdim);
+      product_entry += (*precision_ptr) * (*opt_variable_ptr);
+    }
+      
+    opt_variable_ptr = ((double *) opt_variable + idim);
+    *gradient_ptr = product_entry;
+
+    // now add linear term
+
+    conjugate_arg_ptr = ((double *) conjugate_arg + idim);
+    *gradient_ptr -= (*conjugate_arg_ptr);
+
+    // now log term
+
+    scaling_ptr = ((double *) scaling + idim);
+    *gradient_ptr = *gradient_ptr + 1. / ((*opt_variable_ptr) + (*scaling_ptr)) - 1. / (*opt_variable_ptr);
+  }
+
+}
+
+double barrier_gradient_step(double *gradient,                   // Gradient vector
+			     double *opt_variable,               // Optimization variable
+			     double *opt_proposed,               // Proposed value of optimization variable
+			     double *conjugate_arg,              // Argument to conjugate of Gaussian
+			     double *precision,                  // Precision matrix of Gaussian
+			     double *scaling,                    // Diagonal scaling matrix for log barrier
+			     double step,                        // Step size for gradient step
+			     int ndim)                           // Dimension of conjugate_arg, precision
+{
+  int idim;
+  double *gradient_ptr;
+  double *opt_variable_ptr;
+  double *opt_proposed_ptr;
+
+  for (idim=0; idim<ndim; idim++) {
+    opt_variable_ptr = ((double *) opt_variable + idim);
+    opt_proposed_ptr = ((double *) opt_proposed + idim);
+    gradient_ptr = ((double *) gradient + idim);
+    *opt_proposed_ptr = (*opt_variable_ptr) - step * (*gradient_ptr);
+  }
+
+  return barrier_objective(opt_proposed,
+			   conjugate_arg,         
+			   precision,             
+			   scaling,               
+			   ndim);
+}
+
+double barrier_solve(double *gradient,                   // Gradient vector
+		     double *opt_variable,               // Optimization variable
+		     double *opt_proposed,               // New value of optimization variable
+		     double *conjugate_arg,              // Argument to conjugate of Gaussian
+		     double *precision,                  // Precision matrix of Gaussian
+		     double *scaling,                    // Diagonal scaling matrix for log barrier
+		     int ndim,                           // Dimension of conjugate_arg, precision
+		     int max_iter,                       // Maximum number of iterations
+		     double value_tol,                   // Tolerance for convergence based on value
+		     double initial_step)                // Initial step size
+{
+  int iter, idim, istep;
+  double *gradient_ptr;
+  double *opt_variable_ptr;
+  double *opt_proposed_ptr;
+  double current_value = barrier_objective(opt_variable,
+					   conjugate_arg,         
+					   precision,             
+					   scaling,               
+					   ndim);
+  double proposed_value;
+  double step = initial_step;
+  int any_negative;
+
+  for (iter=0; iter<max_iter; iter++) {
+    
+    // Compute the gradient
+
+    barrier_gradient(gradient,    
+		     opt_variable,
+		     conjugate_arg,
+		     precision,    
+		     scaling,
+		     ndim);
+    
+    // Find a valid step size
+
+    istep = 0;
+    while (1) {
+      any_negative = 0;
+      for (idim=0; idim<ndim; idim++) {
+	opt_variable_ptr = ((double *) opt_variable + idim);
+	gradient_ptr = ((double *) gradient + idim);
+	if ((*opt_variable) - step * (*gradient_ptr) < 0) {
+	  any_negative += 1;
+	}
+      }
+      if (any_negative == 0) {
+	break;
+      }
+      step = step * 0.5;
+      istep++;
+      if (istep == 50) {
+	break;            // Terminate eventually -- but this will be a failure.
+	                  // Should mean that opt_variable is in feasible.
+      }
+    }
+
+    // Find a step size that is a descent
+
+    istep = 0;
+    while (1) {
+      proposed_value = barrier_gradient_step(gradient,
+					     opt_variable,
+					     opt_proposed,
+					     conjugate_arg,         
+					     precision,             
+					     scaling,               
+					     step,
+					     ndim);
+      if (proposed_value < current_value) {
+	for (idim=0; idim<ndim; idim++) {
+	  opt_variable_ptr = ((double *) opt_variable + idim);
+	  opt_proposed_ptr = ((double *) opt_proposed + idim);
+	  *opt_variable_ptr = *opt_proposed_ptr;
+	}
+	break;
+      }
+      step = step * 0.5;
+      istep++;
+      if (istep == 50) {
+	break;            // Terminate eventually -- this will mean no descent.
+	                  // We've solved the problem
+      }
+    }
+
+    if (fabs(current_value - proposed_value) < value_tol * fabs(current_value)) {
+      current_value = proposed_value;
+      break;
+    }
+    current_value = proposed_value;
+    
+  }
+
+  return(current_value);
+
+}
+
diff --git a/selectiveInference/src/randomized_lasso.h b/selectiveInference/src/randomized_lasso.h
new file mode 100644
index 0000000..268e071
--- /dev/null
+++ b/selectiveInference/src/randomized_lasso.h
@@ -0,0 +1,54 @@
+#ifdef __cplusplus
+extern "C"
+{
+#endif /* __cplusplus */
+
+double log_density_gaussian(double noise_scale,             // Scale of randomization
+			    int ndim,                       // Number of features -- "p"
+			    int ninternal,                  // Dimension of internal data representation often 1
+			    int noptimization,              // Dimension of optimization variables -- "p"
+			    double *internal_linear,        // A_D -- linear part for data
+			    double *internal_state,         // D -- data state
+			    double *optimization_linear,    // A_O -- linear part for optimization variables
+			    double *optimization_state,     // O -- optimization state
+			    double *offset);                // h -- offset in affine transform -- "p" dimensional 
+  
+double log_density_laplace(double noise_scale,             // Scale of randomization
+			   int ndim,                       // Number of features -- "p"
+			   int ninternal,                  // Dimension of internal data representation often 1
+			   int noptimization,              // Dimension of optimization variables -- "p"
+			   double *internal_linear,        // A_D -- linear part for data
+			   double *internal_state,         // D -- data state
+			   double *optimization_linear,    // A_O -- linear part for optimization variables
+			   double *optimization_state,     // O -- optimization state
+			   double *offset);                // h -- offset in affine transform -- "p" dimensional 
+
+double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
+					int ndim,                       // Number of features -- "p"
+					int noptimization,              // Dimension of optimization variables -- "p"
+					double *optimization_linear,    // A_O -- linear part for optimization variables
+					double *optimization_state,     // O -- optimization state
+					double *offset);                // h -- offset in affine transform -- "p" dimensional 
+
+double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
+				       int ndim,                       // Number of features -- "p"
+				       int noptimization,              // Dimension of optimization variables -- "p"
+				       double *optimization_linear,    // A_O -- linear part for optimization variables
+				       double *optimization_state,     // O -- optimization state
+				       double *offset);                // h -- offset in affine transform -- "p" dimensional 
+
+
+double barrier_solve(double *gradient,                   // Gradient vector
+		     double *opt_variable,               // Optimization variable
+		     double *opt_proposed,               // New value of optimization variable
+		     double *conjugate_arg,              // Argument to conjugate of Gaussian
+		     double *precision,                  // Precision matrix of Gaussian
+		     double *scaling,                    // Diagonal scaling matrix for log barrier
+		     int ndim,                           // Dimension of conjugate_arg, precision
+		     int max_iter,                       // Maximum number of iterations
+		     double value_tol,                   // Tolerance for convergence based on value
+		     double initial_step);               // Initial stepsize 
+
+#ifdef __cplusplus
+}  /* extern "C" */
+#endif /* __cplusplus */
diff --git a/tests/comparisons/lee_full.rds b/tests/comparisons/lee_full.rds
new file mode 100644
index 0000000000000000000000000000000000000000..6191396115b8bc090391d8dd8fd6a95f90225b16
GIT binary patch
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diff --git a/tests/comparisons/liu_full.rds b/tests/comparisons/liu_full.rds
new file mode 100644
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diff --git a/tests/comparisons/plot_coverages.R b/tests/comparisons/plot_coverages.R
new file mode 100644
index 0000000..aa686e4
--- /dev/null
+++ b/tests/comparisons/plot_coverages.R
@@ -0,0 +1,38 @@
+
+lee = readRDS(file="lee_full.rds")
+liu = readRDS(file="liu_full.rds")
+
+n=lee$n
+p=lee$p
+s=lee$s
+snr=lee$snr
+rhp=lee$rho
+
+lee_cov = mean(lee$sel_coverages)
+lee_len = median(lee$sel_lengths)
+
+liu_cov=mean(liu$sel_coverages)
+liu_len=median(liu$sel_lengths)
+
+naive_cov = mean(liu$naive_coverages)
+naive_len=median(liu$naive_lengths)
+
+labels = rep("",3)
+labels[1] = paste("len: ", round(lee_len,2), ", cov: ", round(lee_cov,2), sep = "")
+labels[2] = paste("len: ", round(liu_len,2), ", cov: ", round(liu_cov,2), sep = "")
+labels[3] = paste("len: ", round(naive_len,2), ", cov: ", round(naive_cov,2), sep = "")
+
+title = paste("n=", n,", p=", p, ", s=", s, ", signal=", snr, ", rho=", rho, sep = "")
+
+boxplot(lee$sel_lengths[which(lee$sel_lengths!=Inf)], liu$sel_lengths, liu$naive_lengths,
+        at = seq(1,11,4) - 0.25, col = "pink", las = 2, outline = F,
+        names = c("Lee", "Liu", "naive"), boxwex = 0.6, range = 0.5,
+        log="x", horizontal = T, xlim = c(0,12.5), cex.axis = 1)
+
+title(title, cex.main = 1, font.main = 1, line = 0.2)
+text(labels = labels, x = c(lee_len, liu_len, naive_len)+0.3, y = seq(2,11,4) - 0.25, cex = 1)
+
+ninf_lee = length(which(lee$sel_lengths==Inf))/length(lee$sel_lengths)
+ninf_liu = length(which(liu$sel_lengths==Inf))/length(liu$sel_lengths)
+c(ninf_lee, ninf_liu)
+
diff --git a/tests/lee_high_dim_full/test_lee_full.R b/tests/lee_high_dim_full/test_lee_full.R
new file mode 100644
index 0000000..995f42d
--- /dev/null
+++ b/tests/lee_high_dim_full/test_lee_full.R
@@ -0,0 +1,73 @@
+library(selectiveInference)
+library(glmnet)
+
+# testing Lee et al type=full in high dimensional settings -- uses debiasing matrix
+
+test_lee_full = function(nrep=50, n=100, p=300, s=10, rho=0){
+  
+  snr = sqrt(2*log(p)/n)
+  
+  set.seed(1)
+  loss="ls"
+  construct_ci=TRUE
+  penalty_factor = rep(1, p)
+  
+  pvalues = NULL
+  sel_intervals=NULL
+  sel_coverages=NULL
+  sel_lengths=NULL
+  
+  for (i in 1:nrep){
+    data = selectiveInference:::gaussian_instance(n=n, p=p, s=s, rho=rho, sigma=1, snr=snr)
+    X=data$X
+    y=data$y
+    beta=data$beta
+    cat("true nonzero:", which(beta!=0), "\n")
+    
+    CV = cv.glmnet(X, y, standardize=FALSE, intercept=FALSE, family=selectiveInference:::family_label(loss))
+    
+    sigma_est=selectiveInference:::estimate_sigma(X,y,coef(CV, s="lambda.min")[-1])  # sigma via Reid et al.
+    print(c("sigma est", sigma_est))
+    
+    # lambda = CV$lambda[which.min(CV$cvm+rnorm(length(CV$cvm))/sqrt(n))] # lambda via randomized cv 
+    lambda = 0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est) # theoretical lambda
+    
+    lasso = glmnet(X, y, family=selectiveInference:::family_label(loss), alpha=1, standardize=FALSE, intercept=FALSE, thresh=1e-12)
+    soln = as.numeric(coef(lasso,x=X,y=y, family=selectiveInference:::family_label(loss), s=lambda, exact=TRUE))[-1]
+
+    PVS = selectiveInference:::fixedLassoInf(X,y,soln, intercept=FALSE, lambda*n, family=selectiveInference:::family_label(loss),
+                                             type="full",sigma=sigma_est)
+    
+    abs_soln = abs(soln)
+    beta_threshold = abs_soln[order(abs_soln,decreasing=TRUE)][length(PVS$pv)]
+    active_vars = which(abs_soln>=beta_threshold)
+    cat("nactive:", length(active_vars), "\n")
+    cat("active vars:", active_vars, "\n")
+    
+    pvalues = c(pvalues, PVS$pv)
+    sel_intervals = cbind(sel_intervals, t(PVS$ci))
+
+    if (length(pvalues)>0){
+      plot(ecdf(pvalues))
+      abline(0,1)
+    }
+    
+    if (construct_ci && length(active_vars)>0){
+      sel_coverages=c(sel_coverages, selectiveInference:::compute_coverage(t(PVS$ci), beta[active_vars]))
+      sel_lengths=c(sel_lengths, as.vector(PVS$ci[,2]-PVS$ci[,1]))
+      print(c("selective coverage:", mean(sel_coverages)))
+      print(c("selective length mean:", mean(sel_lengths)))
+      print(c("selective length median:", median(sel_lengths)))
+    }
+  }
+  
+  saveRDS(list(sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths, 
+               n=n,p=p, s=s, snr=snr, rho=rho), file="lee_full.rds")
+  
+  return(list(pvalues=pvalues))
+}
+
+test_lee_full()
+
+
+
diff --git a/tests/high_dim_full/test_linear_contrasts.R b/tests/liu_high_dim_full/test_linear_contrasts.R
similarity index 100%
rename from tests/high_dim_full/test_linear_contrasts.R
rename to tests/liu_high_dim_full/test_linear_contrasts.R
diff --git a/tests/high_dim_full/test_lasso.R b/tests/liu_high_dim_full/test_liu_full.R
similarity index 59%
rename from tests/high_dim_full/test_lasso.R
rename to tests/liu_high_dim_full/test_liu_full.R
index 1f43b95..5661eae 100644
--- a/tests/high_dim_full/test_lasso.R
+++ b/tests/liu_high_dim_full/test_liu_full.R
@@ -2,15 +2,16 @@ library(gglasso)
 library(MASS)
 library(selectiveInference)
 library(glmnet)
-#source("/Users/Jelena/GitHub Jelena/full/code/group_lasso_fns.R")
 
-test_high_dim_lasso = function(nrep=10){
+# testing Liu et al type=full in high dimensional settings -- uses debiasing matrix
+
+test_liu_full = function(nrep=50, n=100, p=200, s=10, rho=0){
+  
+  snr = sqrt(2*log(p)/n)
   
   set.seed(1)
   loss="ls"
   construct_ci=TRUE
-  n=100
-  p=300
   penalty_factor = rep(1, p)
   
   pvalues = NULL
@@ -23,29 +24,29 @@ test_high_dim_lasso = function(nrep=10){
   naive_lengths=NULL
   
   for (i in 1:nrep){
-    X=matrix(rnorm(n*p), nrow=n)
-    beta = rep(0,p)
-    #beta[1:10]=1
-    y = X%*%beta+rnorm(n)
+    data = selectiveInference:::gaussian_instance(n=n, p=p, s=s, rho=rho, sigma=1, snr=snr)
+    X=data$X
+    y=data$y
+    beta=data$beta
+    cat("true nonzero:", which(beta!=0), "\n")
     
     CV = cv.glmnet(X, y, standardize=FALSE, intercept=FALSE, family=selectiveInference:::family_label(loss))
-    # print(CV$lambda.min)
-    # lambda via randomized cv 
-    # lambda = CV$lambda[which.min(CV$cvm+rnorm(length(CV$cvm))/sqrt(n))]
-    # sigma via Reid et al.
-    sigma_est=selectiveInference:::estimate_sigma(X,y,coef(CV, s="lambda.min")[-1])
+    
+    sigma_est=selectiveInference:::estimate_sigma(X,y,coef(CV, s="lambda.min")[-1]) # sigma via Reid et al.
     print(c("sigma est", sigma_est))
-    # theoretical lambda
-    lambda = 0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est)
+    #sigma_est=1
+    # lambda = CV$lambda[which.min(CV$cvm+rnorm(length(CV$cvm))/sqrt(n))]  # lambda via randomized cv 
+    lambda = 0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est)  # theoretical lambda
     
     soln = selectiveInference:::solve_problem_glmnet(X, y, lambda, penalty_factor=penalty_factor, loss=loss)
     #soln = solve_problem_gglasso(X, y, groups=1:ncol(X), lambda, penalty_factor=penalty_factor, loss=loss)
     PVS = selectiveInference:::inference_group_lasso(X, y, soln, groups=1:ncol(X), lambda=lambda, penalty_factor=penalty_factor, 
                                 sigma_est, loss=loss, algo="Q", construct_ci = construct_ci)
     active_vars=PVS$active_vars
+    cat("active_vars:",active_vars,"\n")
     pvalues = c(pvalues, PVS$pvalues)
     naive_pvalues = c(naive_pvalues, PVS$naive_pvalues)
-    sel_intervals = cbind(sel_intervals, PVS$sel_intervals)
+    sel_intervals = cbind(sel_intervals, PVS$sel_intervals)  # matrix with two rows
     naive_intervals = cbind(naive_intervals, PVS$naive_intervals)
     
     if (length(pvalues)>0){
@@ -53,22 +54,28 @@ test_high_dim_lasso = function(nrep=10){
       lines(ecdf(naive_pvalues), col="red")
       abline(0,1)
     }
+    
     if (construct_ci && length(active_vars)>0){
       
       sel_coverages=c(sel_coverages, selectiveInference:::compute_coverage(PVS$sel_intervals, beta[active_vars]))
       naive_coverages=c(naive_coverages, selectiveInference:::compute_coverage(PVS$naive_intervals, beta[active_vars]))
-      sel_lengths=c(sel_lengths, mean(PVS$sel_intervals[2,]-PVS$sel_intervals[1,]))
-      naive_lengths=c(naive_lengths, mean(PVS$naive_intervals[2,]-PVS$naive_intervals[1,]))
+      sel_lengths=c(sel_lengths, as.vector(PVS$sel_intervals[2,]-PVS$sel_intervals[1,]))
+      naive_lengths=c(naive_lengths, as.vector(PVS$naive_intervals[2,]-PVS$naive_intervals[1,]))
       print(c("selective coverage:", mean(sel_coverages)))
       print(c("naive coverage:", mean(naive_coverages)))
-      print(c("selective length:", mean(sel_lengths)))
-      print(c("naive length:", mean(naive_lengths)))
+      print(c("selective length mean:", mean(sel_lengths)))
+      print(c("selective length median:", median(sel_lengths)))
+      print(c("naive length mean:", mean(naive_lengths)))
+      print(c("naive length median:", median(naive_lengths)))
     }
-    
   }
   
+  saveRDS(list(sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
+               naive_intervals=naive_intervals, naive_coverages=naive_coverages, naive_lengths=naive_lengths,
+               n=n,p=p, s=s, snr=snr, rho=rho), file="liu_full.rds")
+  
   return(list(pvalues=pvalues, naive_pvalues=naive_pvalues))
 }
 
-test_high_dim_lasso()
+test_liu_full()
 
diff --git a/tests/high_dim_full/test_logistic.R b/tests/liu_high_dim_full/test_logistic.R
similarity index 100%
rename from tests/high_dim_full/test_logistic.R
rename to tests/liu_high_dim_full/test_logistic.R

From daa5125ef7de140a8f6d13dea0c072a49f89835b Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Wed, 21 Mar 2018 17:16:30 -0700
Subject: [PATCH 451/493] tests

---
 tests/knockoffs/test_knockoffs.R        | 65 +++++++++++++++++++++++++
 tests/lee_high_dim_full/test_lee_full.R | 23 +++++++--
 tests/liu_high_dim_full/test_liu_full.R | 30 +++++++++---
 3 files changed, 106 insertions(+), 12 deletions(-)
 create mode 100644 tests/knockoffs/test_knockoffs.R

diff --git a/tests/knockoffs/test_knockoffs.R b/tests/knockoffs/test_knockoffs.R
new file mode 100644
index 0000000..87c855f
--- /dev/null
+++ b/tests/knockoffs/test_knockoffs.R
@@ -0,0 +1,65 @@
+library(knockoff)
+
+compute.knockoff = function(data, method, q=0.2, model.free=TRUE){
+  X=data$X 
+  y=data$y
+  n = nrow(X)
+  
+  true.nonzero = which(data$beta!=0)
+  s=length(true.nonzero)
+  offset=0
+  if (method=="knockoff+"){
+    offset=1
+  }
+  
+  if (model.free==TRUE){
+    filter = knockoff.filter(X, y, fdr=q, offset=offset)
+  } else{
+    filter = knockoff.filter(X, y, knockoffs = create.fixed, fdr=q, offset=offset)
+  }
+  
+  rejected = filter$selected
+  nrejected = length(rejected)
+  TP = length(intersect(rejected, true.nonzero))
+  power = TP/s
+  FDR = (nrejected-TP)/max(1, nrejected)
+  
+  return(list(power=power, 
+              FDR=FDR, 
+              nrejected = nrejected))
+}
+
+
+
+test_knockoffs = function(nrep=50, n=100, p=200, s=10, rho=0.5){
+  
+  method = "knockoff"
+  snr = sqrt(2*log(p)/n)
+  
+  set.seed(1)
+
+  FDR_sample = NULL
+  power_sample=NULL
+  
+  for (i in 1:nrep){
+    data = selectiveInference:::gaussian_instance(n=n, p=p, s=s, rho=rho, sigma=1, snr=snr)
+    
+    cat("true nonzero:", which(data$beta!=0), "\n")
+    
+    mc = compute.knockoff(data, method=method)
+    FDR_sample=c(FDR_sample, mc$FDR)
+    power_sample=c(power_sample, mc$power)
+    
+    if (length(FDR_sample)>0){
+      print(c("FDR:", mean(FDR_sample)))
+      print(c("power:", mean(power_sample)))
+    }
+  }
+  
+  saveRDS(list(FDR_sample=FDR_sample, power_sample=power_sample,
+               n=n,p=p, s=s, snr=snr, rho=rho), file=paste(method, ".rds", sep=""))
+  
+  return(list(FDR_sample=FDR_sample, power_sample=power_sample))
+}
+
+test_knockoffs()
diff --git a/tests/lee_high_dim_full/test_lee_full.R b/tests/lee_high_dim_full/test_lee_full.R
index 995f42d..3983738 100644
--- a/tests/lee_high_dim_full/test_lee_full.R
+++ b/tests/lee_high_dim_full/test_lee_full.R
@@ -3,7 +3,7 @@ library(glmnet)
 
 # testing Lee et al type=full in high dimensional settings -- uses debiasing matrix
 
-test_lee_full = function(nrep=50, n=100, p=300, s=10, rho=0){
+test_lee_full = function(nrep=50, n=100, p=200, s=10, rho=0.5){
   
   snr = sqrt(2*log(p)/n)
   
@@ -17,6 +17,9 @@ test_lee_full = function(nrep=50, n=100, p=300, s=10, rho=0){
   sel_coverages=NULL
   sel_lengths=NULL
   
+  FDR_sample = NULL
+  power_sample=NULL
+  
   for (i in 1:nrep){
     data = selectiveInference:::gaussian_instance(n=n, p=p, s=s, rho=rho, sigma=1, snr=snr)
     X=data$X
@@ -26,8 +29,9 @@ test_lee_full = function(nrep=50, n=100, p=300, s=10, rho=0){
     
     CV = cv.glmnet(X, y, standardize=FALSE, intercept=FALSE, family=selectiveInference:::family_label(loss))
     
-    sigma_est=selectiveInference:::estimate_sigma(X,y,coef(CV, s="lambda.min")[-1])  # sigma via Reid et al.
-    print(c("sigma est", sigma_est))
+    #sigma_est=selectiveInference:::estimate_sigma(X,y,coef(CV, s="lambda.min")[-1])  # sigma via Reid et al.
+    #print(c("sigma est", sigma_est))
+    sigma_est=1
     
     # lambda = CV$lambda[which.min(CV$cvm+rnorm(length(CV$cvm))/sqrt(n))] # lambda via randomized cv 
     lambda = 0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est) # theoretical lambda
@@ -59,9 +63,20 @@ test_lee_full = function(nrep=50, n=100, p=300, s=10, rho=0){
       print(c("selective length mean:", mean(sel_lengths)))
       print(c("selective length median:", median(sel_lengths)))
     }
+    
+    mc = selectiveInference:::selective.plus.BH(beta, active_vars, PVS$pv, q=0.2)
+    FDR_sample=c(FDR_sample, mc$FDR)
+    power_sample=c(power_sample, mc$power)
+    
+    if (length(FDR_sample)>0){
+      print(c("FDR:", mean(FDR_sample)))
+      print(c("power:", mean(power_sample)))
+    }
   }
   
-  saveRDS(list(sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths, 
+  saveRDS(list(sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
+               pvalues=pvalues,
+               FDR_sample=FDR_sample, power_sample=power_sample,
                n=n,p=p, s=s, snr=snr, rho=rho), file="lee_full.rds")
   
   return(list(pvalues=pvalues))
diff --git a/tests/liu_high_dim_full/test_liu_full.R b/tests/liu_high_dim_full/test_liu_full.R
index 5661eae..495f7ac 100644
--- a/tests/liu_high_dim_full/test_liu_full.R
+++ b/tests/liu_high_dim_full/test_liu_full.R
@@ -5,7 +5,7 @@ library(glmnet)
 
 # testing Liu et al type=full in high dimensional settings -- uses debiasing matrix
 
-test_liu_full = function(nrep=50, n=100, p=200, s=10, rho=0){
+test_liu_full = function(nrep=50, n=100, p=200, s=10, rho=0.5){
   
   snr = sqrt(2*log(p)/n)
   
@@ -15,14 +15,17 @@ test_liu_full = function(nrep=50, n=100, p=200, s=10, rho=0){
   penalty_factor = rep(1, p)
   
   pvalues = NULL
-  naive_pvalues = NULL
   sel_intervals=NULL
-  naive_intervals=NULL
   sel_coverages=NULL
-  naive_coverages=NULL
   sel_lengths=NULL
+  naive_pvalues = NULL
+  naive_intervals=NULL
+  naive_coverages=NULL
   naive_lengths=NULL
   
+  FDR_sample = NULL
+  power_sample=NULL
+  
   for (i in 1:nrep){
     data = selectiveInference:::gaussian_instance(n=n, p=p, s=s, rho=rho, sigma=1, snr=snr)
     X=data$X
@@ -32,9 +35,9 @@ test_liu_full = function(nrep=50, n=100, p=200, s=10, rho=0){
     
     CV = cv.glmnet(X, y, standardize=FALSE, intercept=FALSE, family=selectiveInference:::family_label(loss))
     
-    sigma_est=selectiveInference:::estimate_sigma(X,y,coef(CV, s="lambda.min")[-1]) # sigma via Reid et al.
-    print(c("sigma est", sigma_est))
-    #sigma_est=1
+    #sigma_est=selectiveInference:::estimate_sigma(X,y,coef(CV, s="lambda.min")[-1]) # sigma via Reid et al.
+    #print(c("sigma est", sigma_est))
+    sigma_est=1
     # lambda = CV$lambda[which.min(CV$cvm+rnorm(length(CV$cvm))/sqrt(n))]  # lambda via randomized cv 
     lambda = 0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est)  # theoretical lambda
     
@@ -68,11 +71,22 @@ test_liu_full = function(nrep=50, n=100, p=200, s=10, rho=0){
       print(c("naive length mean:", mean(naive_lengths)))
       print(c("naive length median:", median(naive_lengths)))
     }
+    
+    mc = selectiveInference:::selective.plus.BH(beta, active_vars, PVS$pvalues, q=0.2)
+    FDR_sample=c(FDR_sample, mc$FDR)
+    power_sample=c(power_sample, mc$power)
+    
+    if (length(FDR_sample)>0){
+      print(c("FDR:", mean(FDR_sample)))
+      print(c("power:", mean(power_sample)))
+    }
   }
   
   saveRDS(list(sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
                naive_intervals=naive_intervals, naive_coverages=naive_coverages, naive_lengths=naive_lengths,
-               n=n,p=p, s=s, snr=snr, rho=rho), file="liu_full.rds")
+               pvalues=pvalues, naive_pvalues=naive_pvalues,
+               FDR_sample=FDR_sample, power_sample=power_sample,
+               n=n, p=p, s=s, snr=snr, rho=rho), file="liu_full_empty.rds")
   
   return(list(pvalues=pvalues, naive_pvalues=naive_pvalues))
 }

From ba79191f73b3acf4a5bb9e404a3b83e831ffd02b Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Wed, 21 Mar 2018 17:17:03 -0700
Subject: [PATCH 452/493] selective plus BH added

---
 selectiveInference/R/funs.full.high.dim.R | 49 ++++++++++++++++++++++-
 1 file changed, 48 insertions(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.full.high.dim.R b/selectiveInference/R/funs.full.high.dim.R
index 6b0856e..81bd1b0 100644
--- a/selectiveInference/R/funs.full.high.dim.R
+++ b/selectiveInference/R/funs.full.high.dim.R
@@ -1,4 +1,52 @@
 
+selective.plus.BH = function(beta, selected.vars, pvalues, q){
+  
+  if (is.null(selected.vars)){
+    return(list(power=NA, FDR=NA, pvalues=NULL, null.pvalues=NULL, ci=NULL, nselected=0))
+  }
+  
+  nselected = length(selected.vars)
+  p.adjust.BH = p.adjust(pvalues, method = "BH", n = nselected)
+  rejected = selected.vars[which(p.adjust.BH<q)]
+  nrejected=length(rejected)
+  print(paste("sel+BH rejected", nrejected, "vars:",toString(rejected)))
+  
+  true.nonzero = which(beta!=0)
+  true.nulls = which(beta==0)
+  print(paste("true nonzero", length(true.nonzero), "vars:", toString(true.nonzero)))
+  
+  TP = length(intersect(rejected, true.nonzero))
+  s = length(true.nonzero)
+  if (s==0){
+    power = NA
+  } else{
+    power = TP/s
+  }
+  
+  FDR = (nrejected-TP)/max(1, nrejected)
+  
+  selected.nulls = NULL
+  for (i in 1:nselected){
+    if (any(true.nulls==selected.vars[i])){
+      selected.nulls = c(selected.nulls, i)
+    }
+  }
+  
+  null.pvalues=NA
+  if (length(selected.nulls)>0){
+    null.pvalues = pvalues[selected.nulls]
+    print(paste("selected nulls", length(selected.nulls), "vars:",toString(selected.vars[selected.nulls])))
+  }
+  
+  return(list(power=power, 
+              FDR=FDR, 
+              pvalues=pvalues, 
+              null.pvalues=null.pvalues, 
+              nselected=nselected, 
+              nrejected=nrejected))
+}
+
+
 AR_design = function(n, p, rho, scale=FALSE){
   times = c(1:p)
   cov_mat <- rho^abs(outer(times, times, "-"))
@@ -544,7 +592,6 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
       
     }
     
-    
     #reference = get_reference(center, radius, target_stat, solve(target_cov))
     #samples = sampling(target_cov, reference, center, radius, n=nrow(X), nsample=nsample)
     #tilting_weights = compute_tilting_weights(reference, solve(target_cov), samples)

From 24c8fc796da1d5e1d7b0e8ce5724770f252b8267 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Wed, 21 Mar 2018 17:18:07 -0700
Subject: [PATCH 453/493] randomized test

---
 tests/randomized/test_randomized_full.R | 94 +++++++++++++++++++++++++
 1 file changed, 94 insertions(+)
 create mode 100644 tests/randomized/test_randomized_full.R

diff --git a/tests/randomized/test_randomized_full.R b/tests/randomized/test_randomized_full.R
new file mode 100644
index 0000000..03038b4
--- /dev/null
+++ b/tests/randomized/test_randomized_full.R
@@ -0,0 +1,94 @@
+
+
+
+test_randomized_full = function(nrep=50, n=100, p=200, s=10, rho=0){
+  
+  snr = sqrt(2*log(p)/n)
+  
+  set.seed(1)
+  loss="ls"
+  construct_ci=TRUE
+  penalty_factor = rep(1, p)
+  
+  pvalues = NULL
+  sel_intervals=NULL
+  sel_coverages=NULL
+  sel_lengths=NULL
+  
+  FDR_sample = NULL
+  power_sample=NULL
+  
+  for (i in 1:nrep){
+    data = selectiveInference:::gaussian_instance(n=n, p=p, s=s, rho=rho, sigma=1, snr=snr)
+    X=data$X
+    y=data$y
+    beta=data$beta
+    cat("true nonzero:", which(beta!=0), "\n")
+    
+    #CV = cv.glmnet(X, y, standardize=FALSE, intercept=FALSE, family=selectiveInference:::family_label(loss))
+    #sigma_est=selectiveInference:::estimate_sigma(X,y,coef(CV, s="lambda.min")[-1]) # sigma via Reid et al.
+    #print(c("sigma est", sigma_est))
+    sigma_est=1
+    # lambda = CV$lambda[which.min(CV$cvm+rnorm(length(CV$cvm))/sqrt(n))]  # lambda via randomized cv 
+    lambda = 0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est)  # theoretical lambda
+    
+    
+    rand_lasso_soln = selectiveInference:::randomizedLasso(X, 
+                                                           y, 
+                                                           lambda*n, 
+                                                           family=selectiveInference:::family_label(loss),
+                                                           condition_subgrad=TRUE)
+    
+    full_targets=selectiveInference:::set.target(rand_lasso_soln, type="full")
+    
+    PVS = selectiveInference:::randomizedLassoInf(rand_lasso_soln,
+                                                     full_targets=full_targets,
+                                                     sampler = "norejection", #"adaptMCMC", #
+                                                     level=0.9, 
+                                                     burnin=1000, 
+                                                     nsample=10000)
+    active_vars=rand_lasso_soln$active_set
+    cat("active_vars:",active_vars,"\n")
+    pvalues = c(pvalues, PVS$pvalues)
+    sel_intervals = cbind(sel_intervals, t(PVS$ci))  # matrix with two rows
+    
+    
+    if (length(pvalues)>0){
+      plot(ecdf(pvalues))
+      #lines(ecdf(naive_pvalues), col="red")
+      abline(0,1)
+    }
+    
+    if (construct_ci && length(active_vars)>0){
+      
+      sel_coverages=c(sel_coverages, selectiveInference:::compute_coverage(t(PVS$ci), beta[active_vars]))
+      sel_lengths=c(sel_lengths, as.vector(PVS$ci[,2]-PVS$ci[,1]))
+      print(c("selective coverage:", mean(sel_coverages)))
+      print(c("selective length mean:", mean(sel_lengths)))
+      print(c("selective length median:", median(sel_lengths)))
+      #naive_coverages=c(naive_coverages, selectiveInference:::compute_coverage(PVS$naive_intervals, beta[active_vars]))
+      #naive_lengths=c(naive_lengths, as.vector(PVS$naive_intervals[2,]-PVS$naive_intervals[1,]))
+      #print(c("naive coverage:", mean(naive_coverages)))
+      #print(c("naive length mean:", mean(naive_lengths)))
+      #print(c("naive length median:", median(naive_lengths)))
+    }
+    
+    mc = selectiveInference:::selective.plus.BH(beta, active_vars, PVS$pvalues, q=0.2)
+    FDR_sample=c(FDR_sample, mc$FDR)
+    power_sample=c(power_sample, mc$power)
+    
+    if (length(FDR_sample)>0){
+      print(c("FDR:", mean(FDR_sample)))
+      print(c("power:", mean(power_sample)))
+    }
+  }
+  
+  saveRDS(list(sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
+               pvalues=pvalues,
+               FDR_sample=FDR_sample, power_sample=power_sample,
+               n=n,p=p, s=s, snr=snr, rho=rho), file="randomized_full.rds")
+  
+  return(list(pvalues=pvalues))
+}
+
+test_randomized_full()

From f0db0326bedc37eaa7f7424f87b7e3ccfd9ad5f2 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Wed, 21 Mar 2018 21:00:46 -0700
Subject: [PATCH 454/493] cluster files

---
 selectiveInference/R/funs.randomized.R  | 10 +++++++---
 tests/comparisons/compare_FDR_power.R   | 13 +++++++++++++
 tests/comparisons/plot_coverages.R      |  5 ++++-
 tests/knockoffs/test_knockoffs.R        |  2 +-
 tests/liu_high_dim_full/cluster.R       | 13 +++++++++++++
 tests/liu_high_dim_full/test_liu_full.R | 12 ++++++++----
 tests/randomized/test_randomized_full.R | 14 +++++++-------
 7 files changed, 53 insertions(+), 16 deletions(-)
 create mode 100644 tests/comparisons/compare_FDR_power.R
 create mode 100644 tests/liu_high_dim_full/cluster.R

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 5d9f42c..5d2f7f7 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -405,6 +405,7 @@ conditional_opt_transform = function(noise_scale,
 
 set.target = function(rand_lasso_soln, 
                       type, 
+                      sigma_est=1,
                       construct_pvalues=NULL,
                       construct_ci=NULL){
   
@@ -421,14 +422,17 @@ set.target = function(rand_lasso_soln,
   
   if (rand_lasso_soln$family == 'gaussian') {
     glm_y = glm(y ~ X_E-1)
+    sigma_res = sigma(glm_y)
+    glm_cov = vcov(glm_y)*sigma_est^2/(sigma_res^2)
   } else if (rand_lasso_soln$family == 'binomial') {
     glm_y = glm(y ~ X_E-1, family=binomial())
+    glm_cov = vcov(glm_y)
   }
   
   if (type=="partial"){
     
     observed_target = as.vector(glm_y$coefficients)
-    cov_target = vcov(glm_y)
+    cov_target = glm_cov
     
     if (sum(is.na(observed_target)) > 0) {
       stop("unregularized (relaxed) fit has NA values -- X[,active_set] likely singular")
@@ -483,8 +487,8 @@ set.target = function(rand_lasso_soln,
     residuals = y-X%*%lasso.est
     scalar = 1 #sqrt(n)
     observed_target = lasso.est[active_set]+scalar*M_active %*% residuals
-    cov_target = vcov(glm_y) + scalar^2*M_inactive %*% t(M_inactive)
-    crosscov_target_internal = rbind(vcov(glm_y), scalar*t(X_inactive) %*% t(M_inactive))
+    cov_target = glm_cov + sigma_est^2*scalar^2*M_inactive %*% t(M_inactive)
+    crosscov_target_internal = rbind(glm_cov, sigma_est^2*scalar*t(X_inactive) %*% t(M_inactive))
     
   }
   
diff --git a/tests/comparisons/compare_FDR_power.R b/tests/comparisons/compare_FDR_power.R
new file mode 100644
index 0000000..f5148c9
--- /dev/null
+++ b/tests/comparisons/compare_FDR_power.R
@@ -0,0 +1,13 @@
+lee = readRDS(file="lee_full.rds")
+liu = readRDS(file="liu_full.rds")
+kn = readRDS(file="knockoff.rds")
+kn_plus = readRDS(file="knockoff+.rds")
+
+
+table = matrix(c(mean(lee$FDR_sample), mean(lee$power_sample), 
+                 mean(liu$FDR_sample), mean(liu$power_sample),
+                 mean(kn$FDR_sample), mean(kn$power_sample),
+                 mean(kn_plus$FDR_sample), mean(kn_plus$power_sample)), ncol=2, byrow=TRUE)
+colnames(table)=c("FDR", "power")
+rownames(table)=c("Lee", "Liu", "knockoff", "knockoff+")
+table  
diff --git a/tests/comparisons/plot_coverages.R b/tests/comparisons/plot_coverages.R
index aa686e4..fda6921 100644
--- a/tests/comparisons/plot_coverages.R
+++ b/tests/comparisons/plot_coverages.R
@@ -22,7 +22,7 @@ labels[1] = paste("len: ", round(lee_len,2), ", cov: ", round(lee_cov,2), sep =
 labels[2] = paste("len: ", round(liu_len,2), ", cov: ", round(liu_cov,2), sep = "")
 labels[3] = paste("len: ", round(naive_len,2), ", cov: ", round(naive_cov,2), sep = "")
 
-title = paste("n=", n,", p=", p, ", s=", s, ", signal=", snr, ", rho=", rho, sep = "")
+title = paste("n=", n,", p=", p, ", s=", s, ", signal=", round(snr,2), ", rho=", rho, sep = "")
 
 boxplot(lee$sel_lengths[which(lee$sel_lengths!=Inf)], liu$sel_lengths, liu$naive_lengths,
         at = seq(1,11,4) - 0.25, col = "pink", las = 2, outline = F,
@@ -36,3 +36,6 @@ ninf_lee = length(which(lee$sel_lengths==Inf))/length(lee$sel_lengths)
 ninf_liu = length(which(liu$sel_lengths==Inf))/length(liu$sel_lengths)
 c(ninf_lee, ninf_liu)
 
+plot(ecdf(lee$pvalues))
+lines(ecdf(liu$pvalues))
+abline(c(0,1))
diff --git a/tests/knockoffs/test_knockoffs.R b/tests/knockoffs/test_knockoffs.R
index 87c855f..b40b8f6 100644
--- a/tests/knockoffs/test_knockoffs.R
+++ b/tests/knockoffs/test_knockoffs.R
@@ -33,7 +33,7 @@ compute.knockoff = function(data, method, q=0.2, model.free=TRUE){
 
 test_knockoffs = function(nrep=50, n=100, p=200, s=10, rho=0.5){
   
-  method = "knockoff"
+  method = "knockoff+"
   snr = sqrt(2*log(p)/n)
   
   set.seed(1)
diff --git a/tests/liu_high_dim_full/cluster.R b/tests/liu_high_dim_full/cluster.R
new file mode 100644
index 0000000..d8a6ce3
--- /dev/null
+++ b/tests/liu_high_dim_full/cluster.R
@@ -0,0 +1,13 @@
+setwd(getwd())
+source("test_liu_full.R")
+
+args = commandArgs(trailingOnly=TRUE)
+seed = round(as.numeric(args[1]))
+
+outdir = "/scratch/users/jelenam/full/"
+outdir = paste(outdir, method, "_n", n, "p", p,"_", regen, sep='')
+dir.create(outdir)
+outfile = file.path(outdir, paste(sep="","list_result_", toString(seed), ".rda"))
+
+test_liu_full(seed, outfile)
+
diff --git a/tests/liu_high_dim_full/test_liu_full.R b/tests/liu_high_dim_full/test_liu_full.R
index 495f7ac..a01d781 100644
--- a/tests/liu_high_dim_full/test_liu_full.R
+++ b/tests/liu_high_dim_full/test_liu_full.R
@@ -5,11 +5,11 @@ library(glmnet)
 
 # testing Liu et al type=full in high dimensional settings -- uses debiasing matrix
 
-test_liu_full = function(nrep=50, n=100, p=200, s=10, rho=0.5){
+test_liu_full = function(seed=1, outfile=NULL, nrep=10, n=1000, p=2000, s=30, rho=0.){
   
   snr = sqrt(2*log(p)/n)
   
-  set.seed(1)
+  set.seed(seed)
   loss="ls"
   construct_ci=TRUE
   penalty_factor = rep(1, p)
@@ -82,14 +82,18 @@ test_liu_full = function(nrep=50, n=100, p=200, s=10, rho=0.5){
     }
   }
   
+  if (is.null(outfile)){
+    outfile="liu_full.rds"
+  }
+  
   saveRDS(list(sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
                naive_intervals=naive_intervals, naive_coverages=naive_coverages, naive_lengths=naive_lengths,
                pvalues=pvalues, naive_pvalues=naive_pvalues,
                FDR_sample=FDR_sample, power_sample=power_sample,
-               n=n, p=p, s=s, snr=snr, rho=rho), file="liu_full_empty.rds")
+               n=n, p=p, s=s, snr=snr, rho=rho), file=oufile)
   
   return(list(pvalues=pvalues, naive_pvalues=naive_pvalues))
 }
 
-test_liu_full()
+#test_liu_full()
 
diff --git a/tests/randomized/test_randomized_full.R b/tests/randomized/test_randomized_full.R
index 03038b4..b03caf0 100644
--- a/tests/randomized/test_randomized_full.R
+++ b/tests/randomized/test_randomized_full.R
@@ -1,7 +1,7 @@
 
 
 
-test_randomized_full = function(nrep=50, n=100, p=200, s=10, rho=0){
+test_randomized_full = function(nrep=50, n=100, p=200, s=10, rho=0.){
   
   snr = sqrt(2*log(p)/n)
   
@@ -39,14 +39,14 @@ test_randomized_full = function(nrep=50, n=100, p=200, s=10, rho=0){
                                                            family=selectiveInference:::family_label(loss),
                                                            condition_subgrad=TRUE)
     
-    full_targets=selectiveInference:::set.target(rand_lasso_soln, type="full")
+    full_targets=selectiveInference:::set.target(rand_lasso_soln, type="full", sigma_est=sigma_est)
     
     PVS = selectiveInference:::randomizedLassoInf(rand_lasso_soln,
-                                                     full_targets=full_targets,
-                                                     sampler = "norejection", #"adaptMCMC", #
-                                                     level=0.9, 
-                                                     burnin=1000, 
-                                                     nsample=10000)
+                                                  full_targets=full_targets,
+                                                  sampler = "norejection", #"adaptMCMC", #
+                                                  level=0.9, 
+                                                  burnin=1000, 
+                                                  nsample=10000)
     active_vars=rand_lasso_soln$active_set
     cat("active_vars:",active_vars,"\n")
     pvalues = c(pvalues, PVS$pvalues)

From b1fbe5ebf2a8fcf7e281c809b7f212176c3bd809 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Wed, 21 Mar 2018 21:05:16 -0700
Subject: [PATCH 455/493] cluster files again

---
 tests/liu_high_dim_full/single_R_run.sbatch | 38 +++++++++++++++++++++
 tests/liu_high_dim_full/submit_R_jobs.sh    | 10 ++++++
 2 files changed, 48 insertions(+)
 create mode 100644 tests/liu_high_dim_full/single_R_run.sbatch
 create mode 100644 tests/liu_high_dim_full/submit_R_jobs.sh

diff --git a/tests/liu_high_dim_full/single_R_run.sbatch b/tests/liu_high_dim_full/single_R_run.sbatch
new file mode 100644
index 0000000..f7c3008
--- /dev/null
+++ b/tests/liu_high_dim_full/single_R_run.sbatch
@@ -0,0 +1,38 @@
+#!/bin/bash 
+#
+#all commands that start with SBATCH contain commands that are just used by SLURM for scheduling
+#################
+#set a job name  
+#SBATCH --job-name=job
+#################  
+#a file for job output, you can check job progress, append the job ID with %j to make it unique
+#SBATCH --output=jobs/%j.out
+#################
+# a file for errors from the job
+#SBATCH --error=jobs/%j.err
+#################
+#time you think you need; default is 2 hours
+#format could be dd-hh:mm:ss, hh:mm:ss, mm:ss, or mm
+#SBATCH --time=5:00:00
+#################
+#SBATCH --qos=normal
+#SBATCH -p normal 
+#################
+#number of nodes you are requesting, the more you ask for the longer you wait
+#SBATCH --nodes=1
+#################
+#SBATCH --mem=4000
+
+# You can use srun if your job is parallel
+#srun R CMD BATCH  ./rtest.R 
+# otherwise: 
+
+SEED=$1
+
+# cd to program directory
+cd /home/jelenam/R-selective/tests/liu_high_dim_full
+
+module load R/3.4.0
+
+Rscript cluster.R $SEED 
+
diff --git a/tests/liu_high_dim_full/submit_R_jobs.sh b/tests/liu_high_dim_full/submit_R_jobs.sh
new file mode 100644
index 0000000..4d4b4a1
--- /dev/null
+++ b/tests/liu_high_dim_full/submit_R_jobs.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+# Setup bash job headers
+
+# load local environment
+
+
+for i in {1..10}
+do
+	sbatch single_R_run.sbatch $i
+done
\ No newline at end of file

From 4bead7cfc771b104d1722383972f1ee1f036ef88 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Wed, 21 Mar 2018 21:37:58 -0700
Subject: [PATCH 456/493] cluster again

---
 liu_full.rds                                  | Bin 9781 -> 0 bytes
 randomized_full.rds                           | Bin 0 -> 35141 bytes
 tests/X.csv                                   |  50 ------------------
 tests/Y.csv                                   |  50 ------------------
 tests/comparisons/lee_full.rds                | Bin 23430 -> 21222 bytes
 tests/comparisons/liu_full.rds                | Bin 9781 -> 42553 bytes
 tests/comparisons/small corr/knockoff+.rds    | Bin 0 -> 273 bytes
 tests/comparisons/small corr/knockoff.rds     | Bin 0 -> 350 bytes
 tests/comparisons/small corr/lee_full.rds     | Bin 0 -> 21222 bytes
 tests/comparisons/small corr/liu_full.rds     | Bin 0 -> 42553 bytes
 tests/comparisons/small uncorr/knockoff+.rds  | Bin 0 -> 271 bytes
 tests/comparisons/small uncorr/knockoff.rds   | Bin 0 -> 357 bytes
 tests/comparisons/small uncorr/lee_full.rds   | Bin 0 -> 15719 bytes
 tests/comparisons/small uncorr/liu_full.rds   | Bin 0 -> 31871 bytes
 .../small uncorr/randomized_full.rds          | Bin 0 -> 35115 bytes
 tests/knockoffs/test_knockoffs.R              |   2 +-
 tests/liu_high_dim_full/cluster.R             |   2 -
 tests/liu_high_dim_full/single_R_run.sbatch   |   2 +-
 18 files changed, 2 insertions(+), 104 deletions(-)
 delete mode 100644 liu_full.rds
 create mode 100644 randomized_full.rds
 delete mode 100644 tests/X.csv
 delete mode 100644 tests/Y.csv
 create mode 100644 tests/comparisons/small corr/knockoff+.rds
 create mode 100644 tests/comparisons/small corr/knockoff.rds
 create mode 100644 tests/comparisons/small corr/lee_full.rds
 create mode 100644 tests/comparisons/small corr/liu_full.rds
 create mode 100644 tests/comparisons/small uncorr/knockoff+.rds
 create mode 100644 tests/comparisons/small uncorr/knockoff.rds
 create mode 100644 tests/comparisons/small uncorr/lee_full.rds
 create mode 100644 tests/comparisons/small uncorr/liu_full.rds
 create mode 100644 tests/comparisons/small uncorr/randomized_full.rds

diff --git a/liu_full.rds b/liu_full.rds
deleted file mode 100644
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GIT binary patch
literal 0
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diff --git a/tests/X.csv b/tests/X.csv
deleted file mode 100644
index 9e3bf14..0000000
--- a/tests/X.csv
+++ /dev/null
@@ -1,50 +0,0 @@
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diff --git a/tests/Y.csv b/tests/Y.csv
deleted file mode 100644
index 468003d..0000000
--- a/tests/Y.csv
+++ /dev/null
@@ -1,50 +0,0 @@
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--1.072378967160714680e+00
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diff --git a/tests/comparisons/lee_full.rds b/tests/comparisons/lee_full.rds
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diff --git a/tests/comparisons/liu_full.rds b/tests/comparisons/liu_full.rds
index e1499ba842759d56034e5734ff762dd37d84401c..affe4ef5e2cb81c202775cb9aae040d280f8025e 100644
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diff --git a/tests/comparisons/small corr/liu_full.rds b/tests/comparisons/small corr/liu_full.rds
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diff --git a/tests/comparisons/small uncorr/knockoff+.rds b/tests/comparisons/small uncorr/knockoff+.rds
new file mode 100644
index 0000000000000000000000000000000000000000..44bae6624c4c4c251b61d01f2dfd2a6982f0ae16
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diff --git a/tests/comparisons/small uncorr/knockoff.rds b/tests/comparisons/small uncorr/knockoff.rds
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diff --git a/tests/comparisons/small uncorr/lee_full.rds b/tests/comparisons/small uncorr/lee_full.rds
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diff --git a/tests/comparisons/small uncorr/liu_full.rds b/tests/comparisons/small uncorr/liu_full.rds
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diff --git a/tests/comparisons/small uncorr/randomized_full.rds b/tests/comparisons/small uncorr/randomized_full.rds
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fBm!#>GJkYfC8?12UG4oKgRFx^b>dFNiWUC`hd`He

literal 0
HcmV?d00001

diff --git a/tests/knockoffs/test_knockoffs.R b/tests/knockoffs/test_knockoffs.R
index b40b8f6..e4b56ed 100644
--- a/tests/knockoffs/test_knockoffs.R
+++ b/tests/knockoffs/test_knockoffs.R
@@ -31,7 +31,7 @@ compute.knockoff = function(data, method, q=0.2, model.free=TRUE){
 
 
 
-test_knockoffs = function(nrep=50, n=100, p=200, s=10, rho=0.5){
+test_knockoffs = function(nrep=50, n=1000, p=2000, s=30, rho=0.5){
   
   method = "knockoff+"
   snr = sqrt(2*log(p)/n)
diff --git a/tests/liu_high_dim_full/cluster.R b/tests/liu_high_dim_full/cluster.R
index d8a6ce3..bc4ddfb 100644
--- a/tests/liu_high_dim_full/cluster.R
+++ b/tests/liu_high_dim_full/cluster.R
@@ -5,8 +5,6 @@ args = commandArgs(trailingOnly=TRUE)
 seed = round(as.numeric(args[1]))
 
 outdir = "/scratch/users/jelenam/full/"
-outdir = paste(outdir, method, "_n", n, "p", p,"_", regen, sep='')
-dir.create(outdir)
 outfile = file.path(outdir, paste(sep="","list_result_", toString(seed), ".rda"))
 
 test_liu_full(seed, outfile)
diff --git a/tests/liu_high_dim_full/single_R_run.sbatch b/tests/liu_high_dim_full/single_R_run.sbatch
index f7c3008..2d8e326 100644
--- a/tests/liu_high_dim_full/single_R_run.sbatch
+++ b/tests/liu_high_dim_full/single_R_run.sbatch
@@ -30,7 +30,7 @@
 SEED=$1
 
 # cd to program directory
-cd /home/jelenam/R-selective/tests/liu_high_dim_full
+cd /home/users/jelenam/R-selective/tests/liu_high_dim_full
 
 module load R/3.4.0
 

From bb1f92fce464d3e3a043eda3854445f045539f73 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Wed, 21 Mar 2018 22:23:40 -0700
Subject: [PATCH 457/493] added cluster files for all methods

---
 tests/knockoffs/cluster.R                   | 11 ++++++
 tests/knockoffs/single_R_run.sbatch         | 38 +++++++++++++++++++++
 tests/knockoffs/submit_R_jobs.sh            | 10 ++++++
 tests/knockoffs/test_knockoffs.R            | 13 ++++---
 tests/lee_high_dim_full/cluster.R           | 11 ++++++
 tests/lee_high_dim_full/single_R_run.sbatch | 38 +++++++++++++++++++++
 tests/lee_high_dim_full/submit_R_jobs.sh    | 10 ++++++
 tests/lee_high_dim_full/test_lee_full.R     | 12 ++++---
 tests/liu_high_dim_full/cluster.R           |  2 +-
 tests/liu_high_dim_full/test_liu_full.R     |  4 +--
 10 files changed, 138 insertions(+), 11 deletions(-)
 create mode 100644 tests/knockoffs/cluster.R
 create mode 100644 tests/knockoffs/single_R_run.sbatch
 create mode 100644 tests/knockoffs/submit_R_jobs.sh
 create mode 100644 tests/lee_high_dim_full/cluster.R
 create mode 100644 tests/lee_high_dim_full/single_R_run.sbatch
 create mode 100644 tests/lee_high_dim_full/submit_R_jobs.sh

diff --git a/tests/knockoffs/cluster.R b/tests/knockoffs/cluster.R
new file mode 100644
index 0000000..352d2e8
--- /dev/null
+++ b/tests/knockoffs/cluster.R
@@ -0,0 +1,11 @@
+setwd(getwd())
+source("test_knockoffs.R")
+
+args = commandArgs(trailingOnly=TRUE)
+seed = round(as.numeric(args[1]))
+
+outdir = "/scratch/users/jelenam/full/"
+outfile = file.path(outdir, paste(sep="","knockoff_result_", toString(seed), ".rda"))
+
+test_knockoffs(seed, outfile, method="knockoff")
+
diff --git a/tests/knockoffs/single_R_run.sbatch b/tests/knockoffs/single_R_run.sbatch
new file mode 100644
index 0000000..2d8e326
--- /dev/null
+++ b/tests/knockoffs/single_R_run.sbatch
@@ -0,0 +1,38 @@
+#!/bin/bash 
+#
+#all commands that start with SBATCH contain commands that are just used by SLURM for scheduling
+#################
+#set a job name  
+#SBATCH --job-name=job
+#################  
+#a file for job output, you can check job progress, append the job ID with %j to make it unique
+#SBATCH --output=jobs/%j.out
+#################
+# a file for errors from the job
+#SBATCH --error=jobs/%j.err
+#################
+#time you think you need; default is 2 hours
+#format could be dd-hh:mm:ss, hh:mm:ss, mm:ss, or mm
+#SBATCH --time=5:00:00
+#################
+#SBATCH --qos=normal
+#SBATCH -p normal 
+#################
+#number of nodes you are requesting, the more you ask for the longer you wait
+#SBATCH --nodes=1
+#################
+#SBATCH --mem=4000
+
+# You can use srun if your job is parallel
+#srun R CMD BATCH  ./rtest.R 
+# otherwise: 
+
+SEED=$1
+
+# cd to program directory
+cd /home/users/jelenam/R-selective/tests/liu_high_dim_full
+
+module load R/3.4.0
+
+Rscript cluster.R $SEED 
+
diff --git a/tests/knockoffs/submit_R_jobs.sh b/tests/knockoffs/submit_R_jobs.sh
new file mode 100644
index 0000000..4d4b4a1
--- /dev/null
+++ b/tests/knockoffs/submit_R_jobs.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+# Setup bash job headers
+
+# load local environment
+
+
+for i in {1..10}
+do
+	sbatch single_R_run.sbatch $i
+done
\ No newline at end of file
diff --git a/tests/knockoffs/test_knockoffs.R b/tests/knockoffs/test_knockoffs.R
index e4b56ed..3ecb2e3 100644
--- a/tests/knockoffs/test_knockoffs.R
+++ b/tests/knockoffs/test_knockoffs.R
@@ -1,6 +1,7 @@
 library(knockoff)
 
 compute.knockoff = function(data, method, q=0.2, model.free=TRUE){
+  
   X=data$X 
   y=data$y
   n = nrow(X)
@@ -31,12 +32,12 @@ compute.knockoff = function(data, method, q=0.2, model.free=TRUE){
 
 
 
-test_knockoffs = function(nrep=50, n=1000, p=2000, s=30, rho=0.5){
+test_knockoffs = function(seed=1, outfile=NULL, method = "knockoff", 
+                          nrep=1, n=1000, p=2000, s=30, rho=0.){
   
-  method = "knockoff+"
   snr = sqrt(2*log(p)/n)
   
-  set.seed(1)
+  set.seed(seed)
 
   FDR_sample = NULL
   power_sample=NULL
@@ -56,8 +57,12 @@ test_knockoffs = function(nrep=50, n=1000, p=2000, s=30, rho=0.5){
     }
   }
   
+  if (is.null(outfile)){
+    outfile=paste(method, ".rds", sep="")
+  }
+  
   saveRDS(list(FDR_sample=FDR_sample, power_sample=power_sample,
-               n=n,p=p, s=s, snr=snr, rho=rho), file=paste(method, ".rds", sep=""))
+               n=n,p=p, s=s, snr=snr, rho=rho), file=outfile)
   
   return(list(FDR_sample=FDR_sample, power_sample=power_sample))
 }
diff --git a/tests/lee_high_dim_full/cluster.R b/tests/lee_high_dim_full/cluster.R
new file mode 100644
index 0000000..892ff47
--- /dev/null
+++ b/tests/lee_high_dim_full/cluster.R
@@ -0,0 +1,11 @@
+setwd(getwd())
+source("test_lee_full.R")
+
+args = commandArgs(trailingOnly=TRUE)
+seed = round(as.numeric(args[1]))
+
+outdir = "/scratch/users/jelenam/full/"
+outfile = file.path(outdir, paste(sep="","lee_result_", toString(seed), ".rda"))
+
+test_lee_full(seed, outfile)
+
diff --git a/tests/lee_high_dim_full/single_R_run.sbatch b/tests/lee_high_dim_full/single_R_run.sbatch
new file mode 100644
index 0000000..2d8e326
--- /dev/null
+++ b/tests/lee_high_dim_full/single_R_run.sbatch
@@ -0,0 +1,38 @@
+#!/bin/bash 
+#
+#all commands that start with SBATCH contain commands that are just used by SLURM for scheduling
+#################
+#set a job name  
+#SBATCH --job-name=job
+#################  
+#a file for job output, you can check job progress, append the job ID with %j to make it unique
+#SBATCH --output=jobs/%j.out
+#################
+# a file for errors from the job
+#SBATCH --error=jobs/%j.err
+#################
+#time you think you need; default is 2 hours
+#format could be dd-hh:mm:ss, hh:mm:ss, mm:ss, or mm
+#SBATCH --time=5:00:00
+#################
+#SBATCH --qos=normal
+#SBATCH -p normal 
+#################
+#number of nodes you are requesting, the more you ask for the longer you wait
+#SBATCH --nodes=1
+#################
+#SBATCH --mem=4000
+
+# You can use srun if your job is parallel
+#srun R CMD BATCH  ./rtest.R 
+# otherwise: 
+
+SEED=$1
+
+# cd to program directory
+cd /home/users/jelenam/R-selective/tests/liu_high_dim_full
+
+module load R/3.4.0
+
+Rscript cluster.R $SEED 
+
diff --git a/tests/lee_high_dim_full/submit_R_jobs.sh b/tests/lee_high_dim_full/submit_R_jobs.sh
new file mode 100644
index 0000000..4d4b4a1
--- /dev/null
+++ b/tests/lee_high_dim_full/submit_R_jobs.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+# Setup bash job headers
+
+# load local environment
+
+
+for i in {1..10}
+do
+	sbatch single_R_run.sbatch $i
+done
\ No newline at end of file
diff --git a/tests/lee_high_dim_full/test_lee_full.R b/tests/lee_high_dim_full/test_lee_full.R
index 3983738..5d2184f 100644
--- a/tests/lee_high_dim_full/test_lee_full.R
+++ b/tests/lee_high_dim_full/test_lee_full.R
@@ -3,11 +3,11 @@ library(glmnet)
 
 # testing Lee et al type=full in high dimensional settings -- uses debiasing matrix
 
-test_lee_full = function(nrep=50, n=100, p=200, s=10, rho=0.5){
+test_lee_full = function(seed=1, outfile=NULL, nrep=1, n=1000, p=2000, s=30, rho=0.){
   
   snr = sqrt(2*log(p)/n)
   
-  set.seed(1)
+  set.seed(seed)
   loss="ls"
   construct_ci=TRUE
   penalty_factor = rep(1, p)
@@ -74,15 +74,19 @@ test_lee_full = function(nrep=50, n=100, p=200, s=10, rho=0.5){
     }
   }
   
+  if (is.null(outfile)){
+    outfile="lee_full.rds"
+  }
+  
   saveRDS(list(sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
                pvalues=pvalues,
                FDR_sample=FDR_sample, power_sample=power_sample,
-               n=n,p=p, s=s, snr=snr, rho=rho), file="lee_full.rds")
+               n=n,p=p, s=s, snr=snr, rho=rho), file=outfile)
   
   return(list(pvalues=pvalues))
 }
 
-test_lee_full()
+#test_lee_full()
 
 
 
diff --git a/tests/liu_high_dim_full/cluster.R b/tests/liu_high_dim_full/cluster.R
index bc4ddfb..4605ee9 100644
--- a/tests/liu_high_dim_full/cluster.R
+++ b/tests/liu_high_dim_full/cluster.R
@@ -5,7 +5,7 @@ args = commandArgs(trailingOnly=TRUE)
 seed = round(as.numeric(args[1]))
 
 outdir = "/scratch/users/jelenam/full/"
-outfile = file.path(outdir, paste(sep="","list_result_", toString(seed), ".rda"))
+outfile = file.path(outdir, paste(sep="","liu_result_", toString(seed), ".rda"))
 
 test_liu_full(seed, outfile)
 
diff --git a/tests/liu_high_dim_full/test_liu_full.R b/tests/liu_high_dim_full/test_liu_full.R
index a01d781..7d4a5e2 100644
--- a/tests/liu_high_dim_full/test_liu_full.R
+++ b/tests/liu_high_dim_full/test_liu_full.R
@@ -5,7 +5,7 @@ library(glmnet)
 
 # testing Liu et al type=full in high dimensional settings -- uses debiasing matrix
 
-test_liu_full = function(seed=1, outfile=NULL, nrep=10, n=1000, p=2000, s=30, rho=0.){
+test_liu_full = function(seed=1, outfile=NULL, nrep=1, n=1000, p=2000, s=30, rho=0.){
   
   snr = sqrt(2*log(p)/n)
   
@@ -90,7 +90,7 @@ test_liu_full = function(seed=1, outfile=NULL, nrep=10, n=1000, p=2000, s=30, rh
                naive_intervals=naive_intervals, naive_coverages=naive_coverages, naive_lengths=naive_lengths,
                pvalues=pvalues, naive_pvalues=naive_pvalues,
                FDR_sample=FDR_sample, power_sample=power_sample,
-               n=n, p=p, s=s, snr=snr, rho=rho), file=oufile)
+               n=n, p=p, s=s, snr=snr, rho=rho), file=outfile)
   
   return(list(pvalues=pvalues, naive_pvalues=naive_pvalues))
 }

From ca4e19aca82d425287bf19e090c700d749bced48 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Wed, 21 Mar 2018 22:26:29 -0700
Subject: [PATCH 458/493] added cluster files for all methods

---
 tests/randomized/cluster.R              | 11 +++++++
 tests/randomized/single_R_run.sbatch    | 38 +++++++++++++++++++++++++
 tests/randomized/submit_R_jobs.sh       | 10 +++++++
 tests/randomized/test_randomized_full.R | 12 +++++---
 4 files changed, 67 insertions(+), 4 deletions(-)
 create mode 100644 tests/randomized/cluster.R
 create mode 100644 tests/randomized/single_R_run.sbatch
 create mode 100644 tests/randomized/submit_R_jobs.sh

diff --git a/tests/randomized/cluster.R b/tests/randomized/cluster.R
new file mode 100644
index 0000000..3795c8b
--- /dev/null
+++ b/tests/randomized/cluster.R
@@ -0,0 +1,11 @@
+setwd(getwd())
+source("test_randomized_full.R")
+
+args = commandArgs(trailingOnly=TRUE)
+seed = round(as.numeric(args[1]))
+
+outdir = "/scratch/users/jelenam/full/"
+outfile = file.path(outdir, paste(sep="","randomized_result_", toString(seed), ".rda"))
+
+test_randomized_full(seed, outfile)
+
diff --git a/tests/randomized/single_R_run.sbatch b/tests/randomized/single_R_run.sbatch
new file mode 100644
index 0000000..2d8e326
--- /dev/null
+++ b/tests/randomized/single_R_run.sbatch
@@ -0,0 +1,38 @@
+#!/bin/bash 
+#
+#all commands that start with SBATCH contain commands that are just used by SLURM for scheduling
+#################
+#set a job name  
+#SBATCH --job-name=job
+#################  
+#a file for job output, you can check job progress, append the job ID with %j to make it unique
+#SBATCH --output=jobs/%j.out
+#################
+# a file for errors from the job
+#SBATCH --error=jobs/%j.err
+#################
+#time you think you need; default is 2 hours
+#format could be dd-hh:mm:ss, hh:mm:ss, mm:ss, or mm
+#SBATCH --time=5:00:00
+#################
+#SBATCH --qos=normal
+#SBATCH -p normal 
+#################
+#number of nodes you are requesting, the more you ask for the longer you wait
+#SBATCH --nodes=1
+#################
+#SBATCH --mem=4000
+
+# You can use srun if your job is parallel
+#srun R CMD BATCH  ./rtest.R 
+# otherwise: 
+
+SEED=$1
+
+# cd to program directory
+cd /home/users/jelenam/R-selective/tests/liu_high_dim_full
+
+module load R/3.4.0
+
+Rscript cluster.R $SEED 
+
diff --git a/tests/randomized/submit_R_jobs.sh b/tests/randomized/submit_R_jobs.sh
new file mode 100644
index 0000000..4d4b4a1
--- /dev/null
+++ b/tests/randomized/submit_R_jobs.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+# Setup bash job headers
+
+# load local environment
+
+
+for i in {1..10}
+do
+	sbatch single_R_run.sbatch $i
+done
\ No newline at end of file
diff --git a/tests/randomized/test_randomized_full.R b/tests/randomized/test_randomized_full.R
index b03caf0..f4f96f3 100644
--- a/tests/randomized/test_randomized_full.R
+++ b/tests/randomized/test_randomized_full.R
@@ -1,11 +1,11 @@
 
 
 
-test_randomized_full = function(nrep=50, n=100, p=200, s=10, rho=0.){
+test_randomized_full = function(seed=1, outfile, nrep=1, n=1000, p=2000, s=30, rho=0.){
   
   snr = sqrt(2*log(p)/n)
   
-  set.seed(1)
+  set.seed(seed)
   loss="ls"
   construct_ci=TRUE
   penalty_factor = rep(1, p)
@@ -83,12 +83,16 @@ test_randomized_full = function(nrep=50, n=100, p=200, s=10, rho=0.){
     }
   }
   
+  if (is.null(outfile)){
+    outfile="randomized_full.rds"
+  }
+  
   saveRDS(list(sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
                pvalues=pvalues,
                FDR_sample=FDR_sample, power_sample=power_sample,
-               n=n,p=p, s=s, snr=snr, rho=rho), file="randomized_full.rds")
+               n=n,p=p, s=s, snr=snr, rho=rho), file=outfile)
   
   return(list(pvalues=pvalues))
 }
 
-test_randomized_full()
+#test_randomized_full()

From e3d4b2db6a4ebd2e6297f9610f9decd27369ab1f Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Thu, 22 Mar 2018 12:07:43 -0700
Subject: [PATCH 459/493] cluster can do all six methods now

---
 randomized_full.rds                         | Bin 35141 -> 0 bytes
 tests/comparisons/compare_FDR_power.R       |   8 +-
 tests/comparisons/knockoff.rds              | Bin 0 -> 803 bytes
 tests/comparisons/lee.rds                   | Bin 0 -> 116403 bytes
 tests/comparisons/lee_full.rds              | Bin 21222 -> 0 bytes
 tests/comparisons/liu.rds                   | Bin 0 -> 239089 bytes
 tests/comparisons/liu_full.rds              | Bin 42553 -> 0 bytes
 tests/comparisons/randomized_full.rds       | Bin 0 -> 197511 bytes
 tests/comparisons/read_files.R              |  50 +++++
 tests/knockoffs/cluster.R                   |   8 +-
 tests/knockoffs/single_R_run.sbatch         |   5 +-
 tests/knockoffs/submit_R_jobs.sh            |   5 +-
 tests/knockoffs/test_knockoffs.R            |   4 +-
 tests/lee_high_dim_full/single_R_run.sbatch |   2 +-
 tests/lee_high_dim_full/test_lee_full.R     |   2 +-
 tests/liu_high_dim_full/test_liu_full.R     |   2 +-
 tests/randomized/cluster.R                  |  10 +-
 tests/randomized/single_R_run.sbatch        |   5 +-
 tests/randomized/submit_R_jobs.sh           |   5 +-
 tests/randomized/test_randomized.R          | 208 +++++++++-----------
 tests/randomized/test_randomized_full.R     |  98 ---------
 tests/randomized/test_randomized_simple.R   | 119 +++++++++++
 22 files changed, 297 insertions(+), 234 deletions(-)
 delete mode 100644 randomized_full.rds
 create mode 100644 tests/comparisons/knockoff.rds
 create mode 100644 tests/comparisons/lee.rds
 delete mode 100644 tests/comparisons/lee_full.rds
 create mode 100644 tests/comparisons/liu.rds
 delete mode 100644 tests/comparisons/liu_full.rds
 create mode 100644 tests/comparisons/randomized_full.rds
 create mode 100644 tests/comparisons/read_files.R
 delete mode 100644 tests/randomized/test_randomized_full.R
 create mode 100644 tests/randomized/test_randomized_simple.R

diff --git a/randomized_full.rds b/randomized_full.rds
deleted file mode 100644
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diff --git a/tests/comparisons/compare_FDR_power.R b/tests/comparisons/compare_FDR_power.R
index f5148c9..6ca3f4d 100644
--- a/tests/comparisons/compare_FDR_power.R
+++ b/tests/comparisons/compare_FDR_power.R
@@ -1,7 +1,9 @@
-lee = readRDS(file="lee_full.rds")
-liu = readRDS(file="liu_full.rds")
+lee = readRDS(file="lee.rds")
+liu = readRDS(file="liu.rds")
 kn = readRDS(file="knockoff.rds")
 kn_plus = readRDS(file="knockoff+.rds")
+randomized_full=readRDS(file="randomized_full.rds")
+randomized_partial=readRDS(file="randomized_partial.rds")
 
 
 table = matrix(c(mean(lee$FDR_sample), mean(lee$power_sample), 
@@ -9,5 +11,5 @@ table = matrix(c(mean(lee$FDR_sample), mean(lee$power_sample),
                  mean(kn$FDR_sample), mean(kn$power_sample),
                  mean(kn_plus$FDR_sample), mean(kn_plus$power_sample)), ncol=2, byrow=TRUE)
 colnames(table)=c("FDR", "power")
-rownames(table)=c("Lee", "Liu", "knockoff", "knockoff+")
+rownames(table)=c("Lee", "Liu", "knockoff", "knockoff+", "randomized full", "randomized partial")
 table  
diff --git a/tests/comparisons/knockoff.rds b/tests/comparisons/knockoff.rds
new file mode 100644
index 0000000000000000000000000000000000000000..eda883fe0f6501e1dd9b9b345a4099031dd00e95
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literal 0
HcmV?d00001

diff --git a/tests/comparisons/lee.rds b/tests/comparisons/lee.rds
new file mode 100644
index 0000000000000000000000000000000000000000..dbf3400698a2f9bd76a4fa4e7b6fa05e9697aa6d
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diff --git a/tests/comparisons/lee_full.rds b/tests/comparisons/lee_full.rds
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diff --git a/tests/comparisons/liu_full.rds b/tests/comparisons/liu_full.rds
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diff --git a/tests/comparisons/read_files.R b/tests/comparisons/read_files.R
new file mode 100644
index 0000000..1a35426
--- /dev/null
+++ b/tests/comparisons/read_files.R
@@ -0,0 +1,50 @@
+result.path = "/Users/Jelena/Documents/collect_cluster_results/full/"
+setwd(result.path)
+
+#method="liu"
+#method="lee"
+#method="knockoff"
+method="randomized"
+txt_files = list.files(pattern = paste(method, "*", sep=""))
+
+i = 0
+
+pvalues = NULL
+sel_intervals=NULL
+sel_coverages=NULL
+sel_lengths=NULL
+naive_pvalues = NULL
+naive_intervals=NULL
+naive_coverages=NULL
+naive_lengths=NULL
+FDR_sample = NULL
+power_sample=NULL
+
+for (file in txt_files){
+  result = readRDS(file)
+  pvalues = c(pvalues, result$pvalues)
+  sel_intervals=c(sel_intervals, result$sel_intervals)
+  sel_coverages=c(sel_coverages, result$sel_coverages)
+  sel_lengths=c(sel_lengths, result$sel_lengths)
+  naive_pvalues = c(naive_pvalues, result$naive_pvalues)
+  naive_intervals= c(naive_intervals, result$naive_intervals)
+  naive_coverages=c(naive_coverages, result$naive_coverages)
+  naive_lengths=c(naive_lengths, result$naive_lengths)
+  FDR_sample =c(FDR_sample, result$FDR_sample)
+  power_sample=c(power_sample, result$power_sample)
+  i=i+1
+}
+
+print(i)
+
+setwd("/Users/Jelena/GitHub Jelena/R-selective/tests/comparisons/")
+
+saveRDS(list(pvalues=pvalues, 
+             sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
+             naive_pvalues=naive_pvalues, 
+             naive_intervals=naive_intervals, naive_coverages=naive_coverages, naive_lengths=naive_lengths,
+             FDR_sample=FDR_sample, power_sample=power_sample), file=paste(method,".rds", sep=""))
+
+
+
+
diff --git a/tests/knockoffs/cluster.R b/tests/knockoffs/cluster.R
index 352d2e8..178114e 100644
--- a/tests/knockoffs/cluster.R
+++ b/tests/knockoffs/cluster.R
@@ -3,9 +3,11 @@ source("test_knockoffs.R")
 
 args = commandArgs(trailingOnly=TRUE)
 seed = round(as.numeric(args[1]))
-
+type=toString(args[2])
+  
 outdir = "/scratch/users/jelenam/full/"
-outfile = file.path(outdir, paste(sep="","knockoff_result_", toString(seed), ".rda"))
+label=paste(type,"_result_", sep="")
+outfile = file.path(outdir, paste(sep="",label, toString(seed), ".rda"))
 
-test_knockoffs(seed, outfile, method="knockoff")
+test_knockoffs(seed, outfile, method=type)
 
diff --git a/tests/knockoffs/single_R_run.sbatch b/tests/knockoffs/single_R_run.sbatch
index 2d8e326..9cb1ef1 100644
--- a/tests/knockoffs/single_R_run.sbatch
+++ b/tests/knockoffs/single_R_run.sbatch
@@ -28,11 +28,12 @@
 # otherwise: 
 
 SEED=$1
+type=$2
 
 # cd to program directory
-cd /home/users/jelenam/R-selective/tests/liu_high_dim_full
+cd /home/users/jelenam/R-selective/tests/knockoffs
 
 module load R/3.4.0
 
-Rscript cluster.R $SEED 
+Rscript cluster.R $SEED $type
 
diff --git a/tests/knockoffs/submit_R_jobs.sh b/tests/knockoffs/submit_R_jobs.sh
index 4d4b4a1..0729d58 100644
--- a/tests/knockoffs/submit_R_jobs.sh
+++ b/tests/knockoffs/submit_R_jobs.sh
@@ -3,8 +3,11 @@
 
 # load local environment
 
+type1=knockoff
+type2=knockoff+
 
 for i in {1..10}
 do
-	sbatch single_R_run.sbatch $i
+	sbatch single_R_run.sbatch $i $type1
+	sbatch single_R_run.sbatch $i $type2
 done
\ No newline at end of file
diff --git a/tests/knockoffs/test_knockoffs.R b/tests/knockoffs/test_knockoffs.R
index 3ecb2e3..15e0f73 100644
--- a/tests/knockoffs/test_knockoffs.R
+++ b/tests/knockoffs/test_knockoffs.R
@@ -33,7 +33,7 @@ compute.knockoff = function(data, method, q=0.2, model.free=TRUE){
 
 
 test_knockoffs = function(seed=1, outfile=NULL, method = "knockoff", 
-                          nrep=1, n=1000, p=2000, s=30, rho=0.){
+                          nrep=10, n=1000, p=2000, s=30, rho=0.){
   
   snr = sqrt(2*log(p)/n)
   
@@ -67,4 +67,4 @@ test_knockoffs = function(seed=1, outfile=NULL, method = "knockoff",
   return(list(FDR_sample=FDR_sample, power_sample=power_sample))
 }
 
-test_knockoffs()
+#test_knockoffs()
diff --git a/tests/lee_high_dim_full/single_R_run.sbatch b/tests/lee_high_dim_full/single_R_run.sbatch
index 2d8e326..75d08e4 100644
--- a/tests/lee_high_dim_full/single_R_run.sbatch
+++ b/tests/lee_high_dim_full/single_R_run.sbatch
@@ -30,7 +30,7 @@
 SEED=$1
 
 # cd to program directory
-cd /home/users/jelenam/R-selective/tests/liu_high_dim_full
+cd /home/users/jelenam/R-selective/tests/lee_high_dim_full
 
 module load R/3.4.0
 
diff --git a/tests/lee_high_dim_full/test_lee_full.R b/tests/lee_high_dim_full/test_lee_full.R
index 5d2184f..b4869fb 100644
--- a/tests/lee_high_dim_full/test_lee_full.R
+++ b/tests/lee_high_dim_full/test_lee_full.R
@@ -3,7 +3,7 @@ library(glmnet)
 
 # testing Lee et al type=full in high dimensional settings -- uses debiasing matrix
 
-test_lee_full = function(seed=1, outfile=NULL, nrep=1, n=1000, p=2000, s=30, rho=0.){
+test_lee_full = function(seed=1, outfile=NULL, nrep=10, n=1000, p=2000, s=30, rho=0.){
   
   snr = sqrt(2*log(p)/n)
   
diff --git a/tests/liu_high_dim_full/test_liu_full.R b/tests/liu_high_dim_full/test_liu_full.R
index 7d4a5e2..85a1770 100644
--- a/tests/liu_high_dim_full/test_liu_full.R
+++ b/tests/liu_high_dim_full/test_liu_full.R
@@ -5,7 +5,7 @@ library(glmnet)
 
 # testing Liu et al type=full in high dimensional settings -- uses debiasing matrix
 
-test_liu_full = function(seed=1, outfile=NULL, nrep=1, n=1000, p=2000, s=30, rho=0.){
+test_liu_full = function(seed=1, outfile=NULL, nrep=10, n=1000, p=2000, s=30, rho=0.){
   
   snr = sqrt(2*log(p)/n)
   
diff --git a/tests/randomized/cluster.R b/tests/randomized/cluster.R
index 3795c8b..28c1c08 100644
--- a/tests/randomized/cluster.R
+++ b/tests/randomized/cluster.R
@@ -1,11 +1,13 @@
 setwd(getwd())
-source("test_randomized_full.R")
+source("test_randomized.R")
 
 args = commandArgs(trailingOnly=TRUE)
 seed = round(as.numeric(args[1]))
-
+type = toString(args[2])
+  
 outdir = "/scratch/users/jelenam/full/"
-outfile = file.path(outdir, paste(sep="","randomized_result_", toString(seed), ".rda"))
+label = paste("randomized_", type, "_result_", sep="")
+outfile = file.path(outdir, paste(sep="",label, toString(seed), ".rda"))
 
-test_randomized_full(seed, outfile)
+test_randomized(seed, outfile, type=type)
 
diff --git a/tests/randomized/single_R_run.sbatch b/tests/randomized/single_R_run.sbatch
index 2d8e326..20be012 100644
--- a/tests/randomized/single_R_run.sbatch
+++ b/tests/randomized/single_R_run.sbatch
@@ -28,11 +28,12 @@
 # otherwise: 
 
 SEED=$1
+type=$2
 
 # cd to program directory
-cd /home/users/jelenam/R-selective/tests/liu_high_dim_full
+cd /home/users/jelenam/R-selective/tests/randomized
 
 module load R/3.4.0
 
-Rscript cluster.R $SEED 
+Rscript cluster.R $SEED $type
 
diff --git a/tests/randomized/submit_R_jobs.sh b/tests/randomized/submit_R_jobs.sh
index 4d4b4a1..84a1db4 100644
--- a/tests/randomized/submit_R_jobs.sh
+++ b/tests/randomized/submit_R_jobs.sh
@@ -3,8 +3,11 @@
 
 # load local environment
 
+type1=full
+type2=partial
 
 for i in {1..10}
 do
-	sbatch single_R_run.sbatch $i
+	sbatch single_R_run.sbatch $i $type1
+	sbatch single_R_run.sbatch $i $type2
 done
\ No newline at end of file
diff --git a/tests/randomized/test_randomized.R b/tests/randomized/test_randomized.R
index 1531d43..d10be74 100644
--- a/tests/randomized/test_randomized.R
+++ b/tests/randomized/test_randomized.R
@@ -1,119 +1,97 @@
-library(selectiveInference)
 
-smoke_test = function() {
-    n = 100; p = 50
-    X = matrix(rnorm(n * p), n, p)
-    y = rnorm(n)
-    lam = 20 / sqrt(n)
-    noise_scale = 0.01 * sqrt(n)
-    ridge_term = .1 / sqrt(n)
-    selectiveInference:::randomizedLasso(X, y, lam, noise_scale, ridge_term)
-}
-
-A = smoke_test()
-
-sampler_test = function() {
-
-    n = 100; p = 50
-    X = matrix(rnorm(n * p), n, p)
-    y = rnorm(n)
-    lam = 20 / sqrt(n)
-    noise_scale = 0.01 * sqrt(n)
-    ridge_term = .1 / sqrt(n)
-    obj = selectiveInference:::randomizedLasso(X, y, lam, noise_scale, ridge_term)
-    S = selectiveInference:::sample_opt_variables(obj, jump_scale=rep(1/sqrt(n), p), nsample=10000)
-    return(S$samples[2001:10000,])
-}
-B = sampler_test()
-
-gaussian_density_test = function() {
-
-    noise_scale = 10.
-    random_lasso = smoke_test()
-    p = nrow(random_lasso$internal_transform$linear_term)
-    internal_state = matrix(rnorm(p * 20), p, 20)
-    optimization_state = matrix(rnorm(p * 20), p, 20)
-    offset = rnorm(p)
-
-    V1 = selectiveInference:::log_density_gaussian_(noise_scale,
-                                                    random_lasso$internal_transform$linear_term,
-                                                    internal_state,
-                                                    random_lasso$optimization_transform$linear_term,
-                                                    optimization_state,
-                                                    offset)
-    A1 = random_lasso$internal_transform$linear_term
-    A2 = random_lasso$optimization_transform$linear_term
-    arg = A1 %*% internal_state + A2 %*% optimization_state + offset
-    V2 = -apply(arg^2, 2, sum) / (2 * noise_scale^2)
-    print(sqrt(sum((V1-V2)^2) / sum(V1^2)))
-
-    U1 = selectiveInference:::log_density_gaussian_conditional_(noise_scale,
-                                                                random_lasso$optimization_transform$linear_term,
-                                                                optimization_state,
-                                                                offset)
-    arg = A2 %*% optimization_state + offset
-    U2 = -apply(arg^2, 2, sum) / (2 * noise_scale^2)
-    print(sqrt(sum((U1-U2)^2) / sum(U1^2)))
-
-    # test that a single column matrix works -- numeric should not
-
-    print(selectiveInference:::log_density_gaussian_conditional_(noise_scale,
-                                                                 random_lasso$optimization_transform$linear_term,
-                                                                 optimization_state[,1,drop=FALSE],
-                                                                 offset))
-    print(selectiveInference:::log_density_gaussian_(noise_scale,
-                                                     random_lasso$internal_transform$linear_term,
-                                                     internal_state[,1,drop=FALSE],
-                                                     random_lasso$optimization_transform$linear_term,
-                                                     optimization_state[,1,drop=FALSE],
-                                                     offset))
-
-}
-
-gaussian_density_test()
-
-laplace_density_test = function() {
-
-    noise_scale = 10.
-    random_lasso = smoke_test()
-    p = nrow(random_lasso$internal_transform$linear_term)
-    internal_state = matrix(rnorm(p * 20), p, 20)
-    optimization_state = matrix(rnorm(p * 20), p, 20)
-    offset = rnorm(p)
-
-    V1 = selectiveInference:::log_density_laplace_(noise_scale,
-                                                    random_lasso$internal_transform$linear_term,
-                                                    internal_state,
-                                                    random_lasso$optimization_transform$linear_term,
-                                                    optimization_state,
-                                                    offset)
-    A1 = random_lasso$internal_transform$linear_term
-    A2 = random_lasso$optimization_transform$linear_term
-    arg = A1 %*% internal_state + A2 %*% optimization_state + offset
-    V2 = -apply(abs(arg), 2, sum) / noise_scale
-    print(sqrt(sum((V1-V2)^2) / sum(V1^2)))
-
-    U1 = selectiveInference:::log_density_laplace_conditional_(noise_scale,
-                                                                random_lasso$optimization_transform$linear_term,
-                                                                optimization_state,
-                                                                offset)
-    arg = A2 %*% optimization_state + offset
-    U2 = -apply(abs(arg), 2, sum) / noise_scale
-    print(sqrt(sum((U1-U2)^2) / sum(U1^2)))
-
-    # test that a single column matrix works -- numeric should not
-
-    print(selectiveInference:::log_density_laplace_conditional_(noise_scale,
-                                                                 random_lasso$optimization_transform$linear_term,
-                                                                 optimization_state[,1,drop=FALSE],
-                                                                 offset))
-    print(selectiveInference:::log_density_laplace_(noise_scale,
-                                                     random_lasso$internal_transform$linear_term,
-                                                     internal_state[,1,drop=FALSE],
-                                                     random_lasso$optimization_transform$linear_term,
-                                                     optimization_state[,1,drop=FALSE],
-                                                     offset))
 
+test_randomized = function(seed=1, outfile, type="full", nrep=10, n=1000, p=2000, s=30, rho=0.){
+  
+  snr = sqrt(2*log(p)/n)
+  
+  set.seed(seed)
+  loss="ls"
+  construct_ci=TRUE
+  penalty_factor = rep(1, p)
+  
+  pvalues = NULL
+  sel_intervals=NULL
+  sel_coverages=NULL
+  sel_lengths=NULL
+  
+  FDR_sample = NULL
+  power_sample=NULL
+  
+  for (i in 1:nrep){
+    data = selectiveInference:::gaussian_instance(n=n, p=p, s=s, rho=rho, sigma=1, snr=snr)
+    X=data$X
+    y=data$y
+    beta=data$beta
+    cat("true nonzero:", which(beta!=0), "\n")
+    
+    #CV = cv.glmnet(X, y, standardize=FALSE, intercept=FALSE, family=selectiveInference:::family_label(loss))
+    #sigma_est=selectiveInference:::estimate_sigma(X,y,coef(CV, s="lambda.min")[-1]) # sigma via Reid et al.
+    #print(c("sigma est", sigma_est))
+    sigma_est=1
+    # lambda = CV$lambda[which.min(CV$cvm+rnorm(length(CV$cvm))/sqrt(n))]  # lambda via randomized cv 
+    lambda = 0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est)  # theoretical lambda
+    
+    
+    rand_lasso_soln = selectiveInference:::randomizedLasso(X, 
+                                                           y, 
+                                                           lambda*n, 
+                                                           family=selectiveInference:::family_label(loss),
+                                                           condition_subgrad=TRUE)
+    
+    full_targets=selectiveInference:::set.target(rand_lasso_soln, type=type, sigma_est=sigma_est)
+    
+    PVS = selectiveInference:::randomizedLassoInf(rand_lasso_soln,
+                                                  full_targets=full_targets,
+                                                  sampler = "norejection", #"adaptMCMC", #
+                                                  level=0.9, 
+                                                  burnin=1000, 
+                                                  nsample=10000)
+    active_vars=rand_lasso_soln$active_set
+    cat("active_vars:",active_vars,"\n")
+    pvalues = c(pvalues, PVS$pvalues)
+    sel_intervals = cbind(sel_intervals, t(PVS$ci))  # matrix with two rows
+    
+    
+    if (length(pvalues)>0){
+      plot(ecdf(pvalues))
+      #lines(ecdf(naive_pvalues), col="red")
+      abline(0,1)
+    }
+    
+    if (construct_ci && length(active_vars)>0){
+      
+      sel_coverages=c(sel_coverages, selectiveInference:::compute_coverage(t(PVS$ci), beta[active_vars]))
+      sel_lengths=c(sel_lengths, as.vector(PVS$ci[,2]-PVS$ci[,1]))
+      print(c("selective coverage:", mean(sel_coverages)))
+      print(c("selective length mean:", mean(sel_lengths)))
+      print(c("selective length median:", median(sel_lengths)))
+      #naive_coverages=c(naive_coverages, selectiveInference:::compute_coverage(PVS$naive_intervals, beta[active_vars]))
+      #naive_lengths=c(naive_lengths, as.vector(PVS$naive_intervals[2,]-PVS$naive_intervals[1,]))
+      #print(c("naive coverage:", mean(naive_coverages)))
+      #print(c("naive length mean:", mean(naive_lengths)))
+      #print(c("naive length median:", median(naive_lengths)))
+    }
+    
+    mc = selectiveInference:::selective.plus.BH(beta, active_vars, PVS$pvalues, q=0.2)
+    FDR_sample=c(FDR_sample, mc$FDR)
+    power_sample=c(power_sample, mc$power)
+    
+    if (length(FDR_sample)>0){
+      print(c("FDR:", mean(FDR_sample)))
+      print(c("power:", mean(power_sample)))
+    }
+  }
+  
+  if (is.null(outfile)){
+    outfile="randomized_full.rds"
+  }
+  
+  saveRDS(list(sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
+               pvalues=pvalues,
+               FDR_sample=FDR_sample, power_sample=power_sample,
+               n=n,p=p, s=s, snr=snr, rho=rho), file=outfile)
+  
+  return(list(pvalues=pvalues))
 }
 
-laplace_density_test()
+#test_randomized()
diff --git a/tests/randomized/test_randomized_full.R b/tests/randomized/test_randomized_full.R
deleted file mode 100644
index f4f96f3..0000000
--- a/tests/randomized/test_randomized_full.R
+++ /dev/null
@@ -1,98 +0,0 @@
-
-
-
-test_randomized_full = function(seed=1, outfile, nrep=1, n=1000, p=2000, s=30, rho=0.){
-  
-  snr = sqrt(2*log(p)/n)
-  
-  set.seed(seed)
-  loss="ls"
-  construct_ci=TRUE
-  penalty_factor = rep(1, p)
-  
-  pvalues = NULL
-  sel_intervals=NULL
-  sel_coverages=NULL
-  sel_lengths=NULL
-  
-  FDR_sample = NULL
-  power_sample=NULL
-  
-  for (i in 1:nrep){
-    data = selectiveInference:::gaussian_instance(n=n, p=p, s=s, rho=rho, sigma=1, snr=snr)
-    X=data$X
-    y=data$y
-    beta=data$beta
-    cat("true nonzero:", which(beta!=0), "\n")
-    
-    #CV = cv.glmnet(X, y, standardize=FALSE, intercept=FALSE, family=selectiveInference:::family_label(loss))
-    #sigma_est=selectiveInference:::estimate_sigma(X,y,coef(CV, s="lambda.min")[-1]) # sigma via Reid et al.
-    #print(c("sigma est", sigma_est))
-    sigma_est=1
-    # lambda = CV$lambda[which.min(CV$cvm+rnorm(length(CV$cvm))/sqrt(n))]  # lambda via randomized cv 
-    lambda = 0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est)  # theoretical lambda
-    
-    
-    rand_lasso_soln = selectiveInference:::randomizedLasso(X, 
-                                                           y, 
-                                                           lambda*n, 
-                                                           family=selectiveInference:::family_label(loss),
-                                                           condition_subgrad=TRUE)
-    
-    full_targets=selectiveInference:::set.target(rand_lasso_soln, type="full", sigma_est=sigma_est)
-    
-    PVS = selectiveInference:::randomizedLassoInf(rand_lasso_soln,
-                                                  full_targets=full_targets,
-                                                  sampler = "norejection", #"adaptMCMC", #
-                                                  level=0.9, 
-                                                  burnin=1000, 
-                                                  nsample=10000)
-    active_vars=rand_lasso_soln$active_set
-    cat("active_vars:",active_vars,"\n")
-    pvalues = c(pvalues, PVS$pvalues)
-    sel_intervals = cbind(sel_intervals, t(PVS$ci))  # matrix with two rows
-    
-    
-    if (length(pvalues)>0){
-      plot(ecdf(pvalues))
-      #lines(ecdf(naive_pvalues), col="red")
-      abline(0,1)
-    }
-    
-    if (construct_ci && length(active_vars)>0){
-      
-      sel_coverages=c(sel_coverages, selectiveInference:::compute_coverage(t(PVS$ci), beta[active_vars]))
-      sel_lengths=c(sel_lengths, as.vector(PVS$ci[,2]-PVS$ci[,1]))
-      print(c("selective coverage:", mean(sel_coverages)))
-      print(c("selective length mean:", mean(sel_lengths)))
-      print(c("selective length median:", median(sel_lengths)))
-      #naive_coverages=c(naive_coverages, selectiveInference:::compute_coverage(PVS$naive_intervals, beta[active_vars]))
-      #naive_lengths=c(naive_lengths, as.vector(PVS$naive_intervals[2,]-PVS$naive_intervals[1,]))
-      #print(c("naive coverage:", mean(naive_coverages)))
-      #print(c("naive length mean:", mean(naive_lengths)))
-      #print(c("naive length median:", median(naive_lengths)))
-    }
-    
-    mc = selectiveInference:::selective.plus.BH(beta, active_vars, PVS$pvalues, q=0.2)
-    FDR_sample=c(FDR_sample, mc$FDR)
-    power_sample=c(power_sample, mc$power)
-    
-    if (length(FDR_sample)>0){
-      print(c("FDR:", mean(FDR_sample)))
-      print(c("power:", mean(power_sample)))
-    }
-  }
-  
-  if (is.null(outfile)){
-    outfile="randomized_full.rds"
-  }
-  
-  saveRDS(list(sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
-               pvalues=pvalues,
-               FDR_sample=FDR_sample, power_sample=power_sample,
-               n=n,p=p, s=s, snr=snr, rho=rho), file=outfile)
-  
-  return(list(pvalues=pvalues))
-}
-
-#test_randomized_full()
diff --git a/tests/randomized/test_randomized_simple.R b/tests/randomized/test_randomized_simple.R
new file mode 100644
index 0000000..1531d43
--- /dev/null
+++ b/tests/randomized/test_randomized_simple.R
@@ -0,0 +1,119 @@
+library(selectiveInference)
+
+smoke_test = function() {
+    n = 100; p = 50
+    X = matrix(rnorm(n * p), n, p)
+    y = rnorm(n)
+    lam = 20 / sqrt(n)
+    noise_scale = 0.01 * sqrt(n)
+    ridge_term = .1 / sqrt(n)
+    selectiveInference:::randomizedLasso(X, y, lam, noise_scale, ridge_term)
+}
+
+A = smoke_test()
+
+sampler_test = function() {
+
+    n = 100; p = 50
+    X = matrix(rnorm(n * p), n, p)
+    y = rnorm(n)
+    lam = 20 / sqrt(n)
+    noise_scale = 0.01 * sqrt(n)
+    ridge_term = .1 / sqrt(n)
+    obj = selectiveInference:::randomizedLasso(X, y, lam, noise_scale, ridge_term)
+    S = selectiveInference:::sample_opt_variables(obj, jump_scale=rep(1/sqrt(n), p), nsample=10000)
+    return(S$samples[2001:10000,])
+}
+B = sampler_test()
+
+gaussian_density_test = function() {
+
+    noise_scale = 10.
+    random_lasso = smoke_test()
+    p = nrow(random_lasso$internal_transform$linear_term)
+    internal_state = matrix(rnorm(p * 20), p, 20)
+    optimization_state = matrix(rnorm(p * 20), p, 20)
+    offset = rnorm(p)
+
+    V1 = selectiveInference:::log_density_gaussian_(noise_scale,
+                                                    random_lasso$internal_transform$linear_term,
+                                                    internal_state,
+                                                    random_lasso$optimization_transform$linear_term,
+                                                    optimization_state,
+                                                    offset)
+    A1 = random_lasso$internal_transform$linear_term
+    A2 = random_lasso$optimization_transform$linear_term
+    arg = A1 %*% internal_state + A2 %*% optimization_state + offset
+    V2 = -apply(arg^2, 2, sum) / (2 * noise_scale^2)
+    print(sqrt(sum((V1-V2)^2) / sum(V1^2)))
+
+    U1 = selectiveInference:::log_density_gaussian_conditional_(noise_scale,
+                                                                random_lasso$optimization_transform$linear_term,
+                                                                optimization_state,
+                                                                offset)
+    arg = A2 %*% optimization_state + offset
+    U2 = -apply(arg^2, 2, sum) / (2 * noise_scale^2)
+    print(sqrt(sum((U1-U2)^2) / sum(U1^2)))
+
+    # test that a single column matrix works -- numeric should not
+
+    print(selectiveInference:::log_density_gaussian_conditional_(noise_scale,
+                                                                 random_lasso$optimization_transform$linear_term,
+                                                                 optimization_state[,1,drop=FALSE],
+                                                                 offset))
+    print(selectiveInference:::log_density_gaussian_(noise_scale,
+                                                     random_lasso$internal_transform$linear_term,
+                                                     internal_state[,1,drop=FALSE],
+                                                     random_lasso$optimization_transform$linear_term,
+                                                     optimization_state[,1,drop=FALSE],
+                                                     offset))
+
+}
+
+gaussian_density_test()
+
+laplace_density_test = function() {
+
+    noise_scale = 10.
+    random_lasso = smoke_test()
+    p = nrow(random_lasso$internal_transform$linear_term)
+    internal_state = matrix(rnorm(p * 20), p, 20)
+    optimization_state = matrix(rnorm(p * 20), p, 20)
+    offset = rnorm(p)
+
+    V1 = selectiveInference:::log_density_laplace_(noise_scale,
+                                                    random_lasso$internal_transform$linear_term,
+                                                    internal_state,
+                                                    random_lasso$optimization_transform$linear_term,
+                                                    optimization_state,
+                                                    offset)
+    A1 = random_lasso$internal_transform$linear_term
+    A2 = random_lasso$optimization_transform$linear_term
+    arg = A1 %*% internal_state + A2 %*% optimization_state + offset
+    V2 = -apply(abs(arg), 2, sum) / noise_scale
+    print(sqrt(sum((V1-V2)^2) / sum(V1^2)))
+
+    U1 = selectiveInference:::log_density_laplace_conditional_(noise_scale,
+                                                                random_lasso$optimization_transform$linear_term,
+                                                                optimization_state,
+                                                                offset)
+    arg = A2 %*% optimization_state + offset
+    U2 = -apply(abs(arg), 2, sum) / noise_scale
+    print(sqrt(sum((U1-U2)^2) / sum(U1^2)))
+
+    # test that a single column matrix works -- numeric should not
+
+    print(selectiveInference:::log_density_laplace_conditional_(noise_scale,
+                                                                 random_lasso$optimization_transform$linear_term,
+                                                                 optimization_state[,1,drop=FALSE],
+                                                                 offset))
+    print(selectiveInference:::log_density_laplace_(noise_scale,
+                                                     random_lasso$internal_transform$linear_term,
+                                                     internal_state[,1,drop=FALSE],
+                                                     random_lasso$optimization_transform$linear_term,
+                                                     optimization_state[,1,drop=FALSE],
+                                                     offset))
+
+}
+
+laplace_density_test()

From 9b3a094dc7e925a1c7f8e6ad2edd5bb5ffaf31a8 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Sun, 25 Mar 2018 19:19:33 -0700
Subject: [PATCH 460/493] real data analysis

---
 knockoff.rds                                  | Bin 0 -> 204 bytes
 liu_full.rds                                  | Bin 0 -> 234 bytes
 liu_logistic_full.rds                         | Bin 0 -> 1546 bytes
 selectiveInference/R/funs.full.high.dim.R     |  26 +++-
 .../comparisons/big cor/knockoff+_result.rds  | Bin 0 -> 782 bytes
 tests/comparisons/big cor/knockoff.rds        | Bin 0 -> 1114 bytes
 tests/comparisons/big cor/knockoff_result.rds | Bin 0 -> 782 bytes
 tests/comparisons/big cor/lee.rds             | Bin 0 -> 122793 bytes
 tests/comparisons/big cor/liu.rds             | Bin 0 -> 246824 bytes
 tests/comparisons/big cor/randomized_full.rds | Bin 0 -> 199392 bytes
 .../big cor/randomized_partial.rds            | Bin 0 -> 195272 bytes
 tests/comparisons/big uncorr/knockoff+.rds    | Bin 0 -> 193795 bytes
 .../comparisons/{ => big uncorr}/knockoff.rds | Bin
 tests/comparisons/{ => big uncorr}/lee.rds    | Bin
 tests/comparisons/{ => big uncorr}/liu.rds    | Bin
 tests/comparisons/big uncorr/params.txt       |   3 +
 .../{ => big uncorr}/randomized_full.rds      | Bin
 .../big uncorr/randomized_partial.rds         | Bin 0 -> 193795 bytes
 tests/comparisons/compare_FDR_power.R         |   4 +-
 tests/comparisons/plot_coverages.R            |  50 +++++---
 tests/comparisons/read_files.R                |  23 ++--
 tests/comparisons/small corr/params.txt       |   0
 tests/comparisons/small uncorr/params.txt     |   0
 tests/knockoffs/single_R_run.sbatch           |   2 +-
 tests/knockoffs/test_knockoffs.R              |  12 +-
 tests/lee_high_dim_full/single_R_run.sbatch   |   2 +-
 tests/lee_high_dim_full/test_lee_full.R       |   4 +-
 tests/liu_high_dim_full/single_R_run.sbatch   |   2 +-
 tests/liu_high_dim_full/test_liu_full.R       |   2 +-
 tests/liu_high_dim_full/test_logistic.R       |  84 ++++++-------
 tests/randomized/single_R_run.sbatch          |   2 +-
 tests/randomized/test_randomized.R            |   2 +-
 tests/real_data_analysis/estimate_sigma.R     |  30 +++++
 tests/real_data_analysis/real_data_analysis.R | 114 ++++++++++++++++++
 tests/real_data_analysis/single_R_run.sbatch  |  38 ++++++
 tests/real_data_analysis/submit_R_jobs.sh     |  21 ++++
 36 files changed, 339 insertions(+), 82 deletions(-)
 create mode 100644 knockoff.rds
 create mode 100644 liu_full.rds
 create mode 100644 liu_logistic_full.rds
 create mode 100644 tests/comparisons/big cor/knockoff+_result.rds
 create mode 100644 tests/comparisons/big cor/knockoff.rds
 create mode 100644 tests/comparisons/big cor/knockoff_result.rds
 create mode 100644 tests/comparisons/big cor/lee.rds
 create mode 100644 tests/comparisons/big cor/liu.rds
 create mode 100644 tests/comparisons/big cor/randomized_full.rds
 create mode 100644 tests/comparisons/big cor/randomized_partial.rds
 create mode 100644 tests/comparisons/big uncorr/knockoff+.rds
 rename tests/comparisons/{ => big uncorr}/knockoff.rds (100%)
 rename tests/comparisons/{ => big uncorr}/lee.rds (100%)
 rename tests/comparisons/{ => big uncorr}/liu.rds (100%)
 create mode 100644 tests/comparisons/big uncorr/params.txt
 rename tests/comparisons/{ => big uncorr}/randomized_full.rds (100%)
 create mode 100644 tests/comparisons/big uncorr/randomized_partial.rds
 create mode 100644 tests/comparisons/small corr/params.txt
 create mode 100644 tests/comparisons/small uncorr/params.txt
 create mode 100644 tests/real_data_analysis/estimate_sigma.R
 create mode 100644 tests/real_data_analysis/real_data_analysis.R
 create mode 100644 tests/real_data_analysis/single_R_run.sbatch
 create mode 100644 tests/real_data_analysis/submit_R_jobs.sh

diff --git a/knockoff.rds b/knockoff.rds
new file mode 100644
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diff --git a/liu_full.rds b/liu_full.rds
new file mode 100644
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diff --git a/liu_logistic_full.rds b/liu_logistic_full.rds
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diff --git a/selectiveInference/R/funs.full.high.dim.R b/selectiveInference/R/funs.full.high.dim.R
index 81bd1b0..355a7e0 100644
--- a/selectiveInference/R/funs.full.high.dim.R
+++ b/selectiveInference/R/funs.full.high.dim.R
@@ -1,4 +1,5 @@
 
+
 selective.plus.BH = function(beta, selected.vars, pvalues, q){
   
   if (is.null(selected.vars)){
@@ -90,6 +91,29 @@ gaussian_instance = function(n, p, s, rho, sigma, snr, random_signs=TRUE, scale=
   return(result)
 }
 
+logistic_instance = function(n, p, s, rho, sigma, snr, random_signs=TRUE, scale=FALSE, design="AR"){
+  
+  if (design=="AR"){
+    X=AR_design(n,p,rho, scale)
+  } else if (design=="equicorrelated"){
+    X=equicorrelated_design(n,p, rho, scale)
+  }
+  
+  beta = rep(0, p)
+  beta[1:s]=snr
+  if (random_signs==TRUE && s>0){
+    signs = sample(c(-1,1), s, replace = TRUE)
+    beta[1:s] = beta[1:s] * signs
+  }
+  beta=sample(beta)
+  mu = X %*% beta
+  prob = exp(mu)/(1+exp(mu))
+  y = rbinom(n, 1, prob)
+  
+  result <- list(X=X,y=y,beta=beta)
+  return(result)
+}
+
 
 family_label = function(loss){
   if (loss=="ls"){
@@ -393,7 +417,7 @@ approximate = function(X, active_set){
   
   Xordered = X[,c(active_set,inactive_set,recursive=T)]
   hsigmaS = 1/n*(t(X_active)%*%X_active) # hsigma[S,S]
-  hsigmaSinv =  ginv(hsigmaS) # generalized inverse solve(hsigmaS) #
+  hsigmaSinv =  ginv(hsigmaS) # generalized inverse solve(hsigmaS) # ginv
   FS = rbind(diag(nactive),matrix(0,p-nactive,nactive))
   GS = cbind(diag(nactive),matrix(0,nactive,p-nactive))
   hsigma = 1/n*(t(Xordered)%*%Xordered)
diff --git a/tests/comparisons/big cor/knockoff+_result.rds b/tests/comparisons/big cor/knockoff+_result.rds
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diff --git a/tests/comparisons/big cor/knockoff.rds b/tests/comparisons/big cor/knockoff.rds
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diff --git a/tests/comparisons/big cor/knockoff_result.rds b/tests/comparisons/big cor/knockoff_result.rds
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diff --git a/tests/comparisons/big cor/lee.rds b/tests/comparisons/big cor/lee.rds
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diff --git a/tests/comparisons/big cor/randomized_full.rds b/tests/comparisons/big cor/randomized_full.rds
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diff --git a/tests/comparisons/big uncorr/knockoff+.rds b/tests/comparisons/big uncorr/knockoff+.rds
new file mode 100644
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diff --git a/tests/comparisons/knockoff.rds b/tests/comparisons/big uncorr/knockoff.rds
similarity index 100%
rename from tests/comparisons/knockoff.rds
rename to tests/comparisons/big uncorr/knockoff.rds
diff --git a/tests/comparisons/lee.rds b/tests/comparisons/big uncorr/lee.rds
similarity index 100%
rename from tests/comparisons/lee.rds
rename to tests/comparisons/big uncorr/lee.rds
diff --git a/tests/comparisons/liu.rds b/tests/comparisons/big uncorr/liu.rds
similarity index 100%
rename from tests/comparisons/liu.rds
rename to tests/comparisons/big uncorr/liu.rds
diff --git a/tests/comparisons/big uncorr/params.txt b/tests/comparisons/big uncorr/params.txt
new file mode 100644
index 0000000..dca935c
--- /dev/null
+++ b/tests/comparisons/big uncorr/params.txt	
@@ -0,0 +1,3 @@
+n=1000, p=2000, rho=0,
+snr = sqrt(2*log(p)/n)
+nrep=100
\ No newline at end of file
diff --git a/tests/comparisons/randomized_full.rds b/tests/comparisons/big uncorr/randomized_full.rds
similarity index 100%
rename from tests/comparisons/randomized_full.rds
rename to tests/comparisons/big uncorr/randomized_full.rds
diff --git a/tests/comparisons/big uncorr/randomized_partial.rds b/tests/comparisons/big uncorr/randomized_partial.rds
new file mode 100644
index 0000000000000000000000000000000000000000..a4365b6a31dc3ce497c86f3e281ef96cee1389f1
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diff --git a/tests/comparisons/compare_FDR_power.R b/tests/comparisons/compare_FDR_power.R
index 6ca3f4d..2349d22 100644
--- a/tests/comparisons/compare_FDR_power.R
+++ b/tests/comparisons/compare_FDR_power.R
@@ -9,7 +9,9 @@ randomized_partial=readRDS(file="randomized_partial.rds")
 table = matrix(c(mean(lee$FDR_sample), mean(lee$power_sample), 
                  mean(liu$FDR_sample), mean(liu$power_sample),
                  mean(kn$FDR_sample), mean(kn$power_sample),
-                 mean(kn_plus$FDR_sample), mean(kn_plus$power_sample)), ncol=2, byrow=TRUE)
+                 mean(kn_plus$FDR_sample), mean(kn_plus$power_sample),
+                 mean(randomized_full$FDR_sample), mean(randomized_full$power_sample),
+                 mean(randomized_partial$FDR_sample), mean(randomized_partial$power_sample)), ncol=2, byrow=TRUE)
 colnames(table)=c("FDR", "power")
 rownames(table)=c("Lee", "Liu", "knockoff", "knockoff+", "randomized full", "randomized partial")
 table  
diff --git a/tests/comparisons/plot_coverages.R b/tests/comparisons/plot_coverages.R
index fda6921..a73e5d7 100644
--- a/tests/comparisons/plot_coverages.R
+++ b/tests/comparisons/plot_coverages.R
@@ -1,12 +1,14 @@
 
-lee = readRDS(file="lee_full.rds")
-liu = readRDS(file="liu_full.rds")
+lee = readRDS(file="lee.rds")
+liu = readRDS(file="liu.rds")
+rand_full=readRDS(file="randomized_full.rds")
+rand_partial=readRDS(file="randomized_partial.rds")
 
 n=lee$n
 p=lee$p
 s=lee$s
 snr=lee$snr
-rhp=lee$rho
+rho=lee$rho
 
 lee_cov = mean(lee$sel_coverages)
 lee_len = median(lee$sel_lengths)
@@ -17,25 +19,45 @@ liu_len=median(liu$sel_lengths)
 naive_cov = mean(liu$naive_coverages)
 naive_len=median(liu$naive_lengths)
 
-labels = rep("",3)
-labels[1] = paste("len: ", round(lee_len,2), ", cov: ", round(lee_cov,2), sep = "")
-labels[2] = paste("len: ", round(liu_len,2), ", cov: ", round(liu_cov,2), sep = "")
+rand_full_cov = mean(rand_full$sel_coverages)
+rand_full_len = median(rand_full$sel_lengths)
+
+rand_partial_cov=mean(rand_partial$sel_coverages)
+rand_partial_len=median(rand_partial$sel_lengths)
+
+ninf_lee = length(which(lee$sel_lengths==Inf))/length(lee$sel_lengths)
+ninf_liu = length(which(liu$sel_lengths==Inf))/length(liu$sel_lengths)
+c(ninf_lee, ninf_liu)
+
+labels = rep("",5)
+labels[1] = paste("len: ", round(lee_len,2), ", cov: ", round(lee_cov,2), ", ninf: ", round(ninf_lee,2), sep = "")
+labels[2] = paste("len: ", round(liu_len,2), ", cov: ", round(liu_cov,2), ", ninf: ", round(ninf_liu,2), sep = "")
 labels[3] = paste("len: ", round(naive_len,2), ", cov: ", round(naive_cov,2), sep = "")
+labels[4] = paste("len: ", round(rand_full_len,2), ", cov: ", round(rand_full_cov,2), sep = "")
+labels[5] = paste("len: ", round(rand_partial_len,2), ", cov: ", round(rand_partial_cov,2), sep = "")
+
 
 title = paste("n=", n,", p=", p, ", s=", s, ", signal=", round(snr,2), ", rho=", rho, sep = "")
 
 boxplot(lee$sel_lengths[which(lee$sel_lengths!=Inf)], liu$sel_lengths, liu$naive_lengths,
-        at = seq(1,11,4) - 0.25, col = "pink", las = 2, outline = F,
-        names = c("Lee", "Liu", "naive"), boxwex = 0.6, range = 0.5,
+        rand_full$sel_lengths, rand_partial$sel_lengths, 
+        at = seq(1,11,2.5) - 0.25, col = "pink", las = 2, outline = F,
+        names = c("Lee \n (full)", "Liu \n (full)", "naive", "rand \n (full)", "rand \n (partial)"), 
+        boxwex = 0.6, range = 0.5,
         log="x", horizontal = T, xlim = c(0,12.5), cex.axis = 1)
 
 title(title, cex.main = 1, font.main = 1, line = 0.2)
-text(labels = labels, x = c(lee_len, liu_len, naive_len)+0.3, y = seq(2,11,4) - 0.25, cex = 1)
+text(labels = labels, x = c(lee_len, liu_len, naive_len, rand_full_len,rand_partial_len)+0.05, y = seq(1,11,2.5) + 0.5, cex = 1)
+
+
 
-ninf_lee = length(which(lee$sel_lengths==Inf))/length(lee$sel_lengths)
-ninf_liu = length(which(liu$sel_lengths==Inf))/length(liu$sel_lengths)
-c(ninf_lee, ninf_liu)
 
-plot(ecdf(lee$pvalues))
-lines(ecdf(liu$pvalues))
+plot(ecdf(lee$pvalues), xlab="values", ylab="Empirical cdf", main="Selective pvalues", 
+     col="red")
+lines(ecdf(liu$pvalues), col="blue")
 abline(c(0,1))
+legend("bottomright", legend=c("Lee et al", "Liu et al"),
+       col=c("red", "blue"), lty=1:1, cex=1.5)
+
+
+
diff --git a/tests/comparisons/read_files.R b/tests/comparisons/read_files.R
index 1a35426..e5845a8 100644
--- a/tests/comparisons/read_files.R
+++ b/tests/comparisons/read_files.R
@@ -3,8 +3,11 @@ setwd(result.path)
 
 #method="liu"
 #method="lee"
-#method="knockoff"
-method="randomized"
+#method="knockoff_result"
+#method="knockoff+_result"
+#method="randomized_full"
+#method="randomized_partial"
+
 txt_files = list.files(pattern = paste(method, "*", sep=""))
 
 i = 0
@@ -21,17 +24,22 @@ FDR_sample = NULL
 power_sample=NULL
 
 for (file in txt_files){
-  result = readRDS(file)
-  pvalues = c(pvalues, result$pvalues)
+  result=readRDS(file)
+  pvalues=c(pvalues, result$pvalues)
   sel_intervals=c(sel_intervals, result$sel_intervals)
   sel_coverages=c(sel_coverages, result$sel_coverages)
   sel_lengths=c(sel_lengths, result$sel_lengths)
-  naive_pvalues = c(naive_pvalues, result$naive_pvalues)
-  naive_intervals= c(naive_intervals, result$naive_intervals)
+  naive_pvalues=c(naive_pvalues, result$naive_pvalues)
+  naive_intervals=c(naive_intervals, result$naive_intervals)
   naive_coverages=c(naive_coverages, result$naive_coverages)
   naive_lengths=c(naive_lengths, result$naive_lengths)
-  FDR_sample =c(FDR_sample, result$FDR_sample)
+  FDR_sample=c(FDR_sample, result$FDR_sample)
   power_sample=c(power_sample, result$power_sample)
+  n=result$n
+  p=result$p
+  s=result$s
+  rho=result$rho
+  snr=result$snr
   i=i+1
 }
 
@@ -43,6 +51,7 @@ saveRDS(list(pvalues=pvalues,
              sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
              naive_pvalues=naive_pvalues, 
              naive_intervals=naive_intervals, naive_coverages=naive_coverages, naive_lengths=naive_lengths,
+             n=n, p=p, s=s, rho=rho, snr=snr,
              FDR_sample=FDR_sample, power_sample=power_sample), file=paste(method,".rds", sep=""))
 
 
diff --git a/tests/comparisons/small corr/params.txt b/tests/comparisons/small corr/params.txt
new file mode 100644
index 0000000..e69de29
diff --git a/tests/comparisons/small uncorr/params.txt b/tests/comparisons/small uncorr/params.txt
new file mode 100644
index 0000000..e69de29
diff --git a/tests/knockoffs/single_R_run.sbatch b/tests/knockoffs/single_R_run.sbatch
index 9cb1ef1..77c877d 100644
--- a/tests/knockoffs/single_R_run.sbatch
+++ b/tests/knockoffs/single_R_run.sbatch
@@ -21,7 +21,7 @@
 #number of nodes you are requesting, the more you ask for the longer you wait
 #SBATCH --nodes=1
 #################
-#SBATCH --mem=4000
+#SBATCH --mem=16000
 
 # You can use srun if your job is parallel
 #srun R CMD BATCH  ./rtest.R 
diff --git a/tests/knockoffs/test_knockoffs.R b/tests/knockoffs/test_knockoffs.R
index 15e0f73..0536d64 100644
--- a/tests/knockoffs/test_knockoffs.R
+++ b/tests/knockoffs/test_knockoffs.R
@@ -25,7 +25,8 @@ compute.knockoff = function(data, method, q=0.2, model.free=TRUE){
   power = TP/s
   FDR = (nrejected-TP)/max(1, nrejected)
   
-  return(list(power=power, 
+  return(list(rejected=rejected,
+              power=power, 
               FDR=FDR, 
               nrejected = nrejected))
 }
@@ -33,9 +34,9 @@ compute.knockoff = function(data, method, q=0.2, model.free=TRUE){
 
 
 test_knockoffs = function(seed=1, outfile=NULL, method = "knockoff", 
-                          nrep=10, n=1000, p=2000, s=30, rho=0.){
+                          nrep=10, n=100, p=200, s=10, rho=0.){
   
-  snr = sqrt(2*log(p)/n)
+  snr = 4*sqrt(2*log(p)/n)
   
   set.seed(seed)
 
@@ -43,7 +44,8 @@ test_knockoffs = function(seed=1, outfile=NULL, method = "knockoff",
   power_sample=NULL
   
   for (i in 1:nrep){
-    data = selectiveInference:::gaussian_instance(n=n, p=p, s=s, rho=rho, sigma=1, snr=snr)
+    #data = selectiveInference:::gaussian_instance(n=n, p=p, s=s, rho=rho, sigma=1, snr=snr)
+    data = selectiveInference:::logistic_instance(n=n, p=p, s=s, rho=rho, sigma=1, snr=snr)
     
     cat("true nonzero:", which(data$beta!=0), "\n")
     
@@ -67,4 +69,4 @@ test_knockoffs = function(seed=1, outfile=NULL, method = "knockoff",
   return(list(FDR_sample=FDR_sample, power_sample=power_sample))
 }
 
-#test_knockoffs()
+test_knockoffs()
diff --git a/tests/lee_high_dim_full/single_R_run.sbatch b/tests/lee_high_dim_full/single_R_run.sbatch
index 75d08e4..d3d3d45 100644
--- a/tests/lee_high_dim_full/single_R_run.sbatch
+++ b/tests/lee_high_dim_full/single_R_run.sbatch
@@ -21,7 +21,7 @@
 #number of nodes you are requesting, the more you ask for the longer you wait
 #SBATCH --nodes=1
 #################
-#SBATCH --mem=4000
+#SBATCH --mem=16000
 
 # You can use srun if your job is parallel
 #srun R CMD BATCH  ./rtest.R 
diff --git a/tests/lee_high_dim_full/test_lee_full.R b/tests/lee_high_dim_full/test_lee_full.R
index b4869fb..f955d52 100644
--- a/tests/lee_high_dim_full/test_lee_full.R
+++ b/tests/lee_high_dim_full/test_lee_full.R
@@ -3,7 +3,7 @@ library(glmnet)
 
 # testing Lee et al type=full in high dimensional settings -- uses debiasing matrix
 
-test_lee_full = function(seed=1, outfile=NULL, nrep=10, n=1000, p=2000, s=30, rho=0.){
+test_lee_full = function(seed=1, outfile=NULL, nrep=1, n=1000, p=10000, s=50, rho=0.){
   
   snr = sqrt(2*log(p)/n)
   
@@ -81,7 +81,7 @@ test_lee_full = function(seed=1, outfile=NULL, nrep=10, n=1000, p=2000, s=30, rh
   saveRDS(list(sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
                pvalues=pvalues,
                FDR_sample=FDR_sample, power_sample=power_sample,
-               n=n,p=p, s=s, snr=snr, rho=rho), file=outfile)
+               n=n, p=p, s=s, snr=snr, rho=rho), file=outfile)
   
   return(list(pvalues=pvalues))
 }
diff --git a/tests/liu_high_dim_full/single_R_run.sbatch b/tests/liu_high_dim_full/single_R_run.sbatch
index 2d8e326..0ae7ce7 100644
--- a/tests/liu_high_dim_full/single_R_run.sbatch
+++ b/tests/liu_high_dim_full/single_R_run.sbatch
@@ -21,7 +21,7 @@
 #number of nodes you are requesting, the more you ask for the longer you wait
 #SBATCH --nodes=1
 #################
-#SBATCH --mem=4000
+#SBATCH --mem=16000
 
 # You can use srun if your job is parallel
 #srun R CMD BATCH  ./rtest.R 
diff --git a/tests/liu_high_dim_full/test_liu_full.R b/tests/liu_high_dim_full/test_liu_full.R
index 85a1770..da8aad3 100644
--- a/tests/liu_high_dim_full/test_liu_full.R
+++ b/tests/liu_high_dim_full/test_liu_full.R
@@ -5,7 +5,7 @@ library(glmnet)
 
 # testing Liu et al type=full in high dimensional settings -- uses debiasing matrix
 
-test_liu_full = function(seed=1, outfile=NULL, nrep=10, n=1000, p=2000, s=30, rho=0.){
+test_liu_full = function(seed=1, outfile=NULL, nrep=1, n=1000, p=10000, s=50, rho=0.){
   
   snr = sqrt(2*log(p)/n)
   
diff --git a/tests/liu_high_dim_full/test_logistic.R b/tests/liu_high_dim_full/test_logistic.R
index 06b0186..b6e7374 100644
--- a/tests/liu_high_dim_full/test_logistic.R
+++ b/tests/liu_high_dim_full/test_logistic.R
@@ -1,44 +1,23 @@
-#library(gglasso)
 library(MASS)
 library(selectiveInference)
 library(glmnet)
-#source("/Users/Jelena/GitHub Jelena/full/code/group_lasso_fns.R")
 
 
-logistic_instance = function(n, p, s, sigma=1, rho=0, snr=7, 
-                             random_signs=TRUE, scale=FALSE, center=FALSE){
+test_logistic = function(seed=1, outfile=NULL, nrep=10, n=1000, p=2000, s=30, rho=0.){
   
-  X = sqrt(1-rho)*matrix(rnorm(n*p),n) + sqrt(rho)*matrix(rep(rnorm(n), p), nrow = n)
-  if (scale==TRUE){
-    X = scale(X)
-    X = X/sqrt(n)
-  }
-  beta = rep(0, p)
-  beta[1:s]=snr
-  if (random_signs==TRUE && s>0){
-    signs = sample(c(-1,1), s, replace = TRUE)
-    beta[1:s] = beta[1:s] * signs
-  }
-  beta=sample(beta)
-  mu = X %*% beta
-  prob = exp(mu)/(1+exp(mu))
-  y = rbinom(n, 1, prob)
+  snr = sqrt(2*log(p)/n)
+  snr = 2*snr
+  #print(snr)
+  #empirical=empirical = apply(abs(t(matrix(rnorm(n*p), nrow=n)) %*% matrix(rnorm(n*1000), nrow=n)), 2, max)
+  #print(mean(empirical)/n)
   
-  result <- list(X=X,y=y,beta=beta)
-  return(result)
-}
-
-
-test_logistic = function(nrep=10){
+  #empirical = apply(abs(t(matrix(rnorm(n*p), nrow=n)) %*% matrix(sample(c(0,1), n*1000, replace = TRUE), nrow=n)), 2, max)
+  #snr=mean(empirical)/n
+  #print(snr)
   
-  set.seed(1)
+  set.seed(seed)
   loss="logit"
   construct_ci=TRUE
-  n=300
-  p=50
-  s=10
- 
-  snr=2/sqrt(n)
   
   penalty_factor = rep(1, p)
   
@@ -51,27 +30,20 @@ test_logistic = function(nrep=10){
   sel_lengths=NULL
   naive_lengths=NULL
   
+  FDR_sample = NULL
+  power_sample=NULL
   
   for (i in 1:nrep){
-    #data = logistic_instance(n, p, s, snr=snr)
-    #X=data$X
-    #y=data$y
-    #beta=data$beta
-    X = matrix(rnorm(n*p),n)
-    beta=rep(0,p)
-    beta[1:s]=snr
-    mu = X %*% beta
-    prob = exp(mu)/(1+exp(mu))
-    y=rbinom(n,1, prob)
+    data = selectiveInference:::logistic_instance(n=n, p=p, s=s, rho=rho, sigma=1, snr=snr)
+    X=data$X
+    y=data$y
+    beta=data$beta
     
-    print("beta")
-    print(which(beta!=0))
+    cat("true nonzero:", which(beta!=0), "\n")
     
-    lambda=10/n
-    #lambda = 0.7*selectiveInference:::theoretical.lambda(X, loss, sigma=1)
+    lambda = 0.4*selectiveInference:::theoretical.lambda(X, loss, sigma=1)
     
     soln = selectiveInference:::solve_problem_glmnet(X, y, lambda, penalty_factor=penalty_factor, loss=loss)
-    
     PVS = selectiveInference:::inference_group_lasso(X,y, soln, groups=1:ncol(X), lambda=lambda, penalty_factor=penalty_factor,
                                 sigma_est=1, loss=loss, algo="glmnet", construct_ci = construct_ci)
     
@@ -106,8 +78,28 @@ test_logistic = function(nrep=10){
       print(c("selective length:", mean(sel_lengths)))
       print(c("naive length:", mean(naive_lengths)))
     }
+    
+    mc = selectiveInference:::selective.plus.BH(beta, active_vars, PVS$pvalues, q=0.2)
+    FDR_sample=c(FDR_sample, mc$FDR)
+    power_sample=c(power_sample, mc$power)
+    
+    if (length(FDR_sample)>0){
+      print(c("FDR:", mean(FDR_sample)))
+      print(c("power:", mean(power_sample)))
+    }
+  }
+  
+  if (is.null(outfile)){
+    outfile="liu_logistic_full.rds"
   }
   
+  saveRDS(list(sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
+               naive_intervals=naive_intervals, naive_coverages=naive_coverages, naive_lengths=naive_lengths,
+               pvalues=pvalues, naive_pvalues=naive_pvalues,
+               FDR_sample=FDR_sample, power_sample=power_sample,
+               n=n, p=p, s=s, snr=snr, rho=rho), file=outfile)
+  
+  
   return(list(pvalues=pvalues, naive_pvalues=naive_pvalues))
 }
 
diff --git a/tests/randomized/single_R_run.sbatch b/tests/randomized/single_R_run.sbatch
index 20be012..fd05e12 100644
--- a/tests/randomized/single_R_run.sbatch
+++ b/tests/randomized/single_R_run.sbatch
@@ -21,7 +21,7 @@
 #number of nodes you are requesting, the more you ask for the longer you wait
 #SBATCH --nodes=1
 #################
-#SBATCH --mem=4000
+#SBATCH --mem=16000
 
 # You can use srun if your job is parallel
 #srun R CMD BATCH  ./rtest.R 
diff --git a/tests/randomized/test_randomized.R b/tests/randomized/test_randomized.R
index d10be74..7bd5b1b 100644
--- a/tests/randomized/test_randomized.R
+++ b/tests/randomized/test_randomized.R
@@ -1,6 +1,6 @@
 
 
-test_randomized = function(seed=1, outfile, type="full", nrep=10, n=1000, p=2000, s=30, rho=0.){
+test_randomized = function(seed=1, outfile, type="full", nrep=1, n=1000, p=10000, s=50, rho=0.){
   
   snr = sqrt(2*log(p)/n)
   
diff --git a/tests/real_data_analysis/estimate_sigma.R b/tests/real_data_analysis/estimate_sigma.R
new file mode 100644
index 0000000..df5a39a
--- /dev/null
+++ b/tests/real_data_analysis/estimate_sigma.R
@@ -0,0 +1,30 @@
+library(glmnet)
+library(selectiveInference)
+
+setwd("/Users/Jelena/Dropbox/kevin/jelena/real_data")
+data=readRDS("real_data.rds")
+X=data$X
+y=data$y
+
+set.seed(1)
+
+CV = cv.glmnet(X, y, standardize=FALSE, intercept=FALSE, family="gaussian")
+sigma_est=selectiveInference:::estimate_sigma(X,y,coef(CV, s="lambda.min")[-1]) # sigma via Reid et al.
+print(c("sigma est via Reid et al.", sigma_est))  "sigma est via Reid et al." "0.620021797352246" 
+
+print(c("std error of y", sqrt(var(y))))  "std error of y"    "0.488729008112197"
+
+n=nrow(X)
+m=floor(n/2)
+subsample = sample(1:n,m, replace=FALSE)
+leftover = setdiff(1:n, subsample)
+CV = cv.glmnet(X[subsample,], y[subsample], standardize=FALSE, intercept=FALSE, family="gaussian")
+beta_hat = coef(CV, s="lambda.min")[-1]
+selected = which(beta_hat!=0)
+LM = lm(y[leftover]~X[leftover,][,selected])
+sigma_est = sigma(LM)
+print(c("sigma est from data splitting", sigma_est)) "0.481421791225578"  
+
+
+
+
diff --git a/tests/real_data_analysis/real_data_analysis.R b/tests/real_data_analysis/real_data_analysis.R
new file mode 100644
index 0000000..aae0cad
--- /dev/null
+++ b/tests/real_data_analysis/real_data_analysis.R
@@ -0,0 +1,114 @@
+library(glmnet)
+library(selectiveInference)
+library(MASS)
+
+setwd(getwd())
+args = commandArgs(trailingOnly=TRUE)
+method = toString(args[1])
+
+outdir = "/scratch/users/jelenam/full/"
+label = paste("randomized_", method, "_results", sep="")
+outfile = file.path(outdir, paste(sep="",label, ".rds"))
+
+#setwd("/Users/Jelena/Dropbox/kevin/jelena/real_data")
+data=readRDS("real_data.rds")
+X=data$X
+y=data$y
+
+set.seed(1)
+
+penalty_factor = rep(1, ncol(X))
+loss="ls"
+sigma_est = 0.62
+lambda =  0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est)
+print(c("lambda", lambda))
+
+
+liu_full = function(outfile){
+
+  soln = selectiveInference:::solve_problem_glmnet(X, y, lambda, penalty_factor=penalty_factor, loss=loss)
+  PVS = selectiveInference:::inference_group_lasso(X, y, soln, groups=1:ncol(X), lambda=lambda, penalty_factor=penalty_factor, 
+                                                 sigma_est, loss=loss, algo="Q", construct_ci = TRUE)
+  saveRDS(list(active_vars=PVS$active_vars, 
+               sel_intervals=PVS$sel_intervals, naive_intervals=PVS$naive_intervals,
+               pvalues=PVS$pvalues, naive_pvalues=PVS$naive_pvalues), file=outfile)
+  
+  return(NULL)
+}
+
+
+
+lee_full = function(outfile){
+  
+  lasso = glmnet(X, y, family=selectiveInference:::family_label(loss), alpha=1, standardize=FALSE, intercept=FALSE, thresh=1e-12)
+  soln = as.numeric(coef(lasso,x=X,y=y, family=selectiveInference:::family_label(loss), s=lambda, exact=TRUE))[-1]
+  
+  PVS = selectiveInference:::fixedLassoInf(X,y,soln, intercept=FALSE, lambda*n, family=selectiveInference:::family_label(loss),
+                                           type="full",sigma=sigma_est)
+  
+  abs_soln = abs(soln)
+  beta_threshold = abs_soln[order(abs_soln,decreasing=TRUE)][length(PVS$pv)]
+  active_vars = which(abs_soln>=beta_threshold)
+  cat("nactive:", length(active_vars), "\n")
+  cat("active vars:", active_vars, "\n")
+  
+  pvalues = PVS$pv
+  sel_intervals = t(PVS$ci)
+  
+  saveRDS(list(active_vars=active_vars, sel_intervals=sel_intervals, pvalues=pvalues), file=outfile)
+  
+}
+
+
+knockoff = function(method, outfile){
+  offset=0
+  if (method=="knockoff+"){
+    offset=1
+  }
+  filter = knockoff.filter(X, y, fdr=q, offset=offset)
+  saveRDS(list(active_vars = filter$rejected), file=outfile)
+}
+
+
+randomized = function(type, outfile){
+  rand_lasso_soln = selectiveInference:::randomizedLasso(X, 
+                                                         y, 
+                                                         lambda*n, 
+                                                         family=selectiveInference:::family_label(loss),
+                                                         condition_subgrad=TRUE)
+  
+  full_targets=selectiveInference:::set.target(rand_lasso_soln, type=type, sigma_est=sigma_est)
+  
+  PVS = selectiveInference:::randomizedLassoInf(rand_lasso_soln,
+                                                full_targets=full_targets,
+                                                sampler = "norejection", #"adaptMCMC", #
+                                                level=0.9, 
+                                                burnin=1000, 
+                                                nsample=10000)
+  active_vars=rand_lasso_soln$active_set
+  cat("active_vars:",active_vars,"\n")
+  pvalues = PVS$pvalues
+  sel_intervals = t(PVS$ci)  # matrix with two rows
+  saveRDS(list(active_vars=active_vars, sel_intervals=sel_intervals, pvalues=pvalues), file=outfile)
+  
+  return(NULL)
+}
+
+if (method=="liu"){
+  liu_full(outfile)
+} else if (method=="lee"){
+  lee_full(outfile)
+} else if (method=="knockoff"){
+  knockoff(method, outfile)
+} else if (method=="knockoff+"){
+  knockoff(method, outfile)
+} else if (method=="randomized_full"){
+  randomized("full", outfile)
+} else if (method=="randomized_partial"){
+  randomized("partial", outfile)
+}
+
+
+
+
+
diff --git a/tests/real_data_analysis/single_R_run.sbatch b/tests/real_data_analysis/single_R_run.sbatch
new file mode 100644
index 0000000..79198d2
--- /dev/null
+++ b/tests/real_data_analysis/single_R_run.sbatch
@@ -0,0 +1,38 @@
+#!/bin/bash 
+#
+#all commands that start with SBATCH contain commands that are just used by SLURM for scheduling
+#################
+#set a job name  
+#SBATCH --job-name=job
+#################  
+#a file for job output, you can check job progress, append the job ID with %j to make it unique
+#SBATCH --output=jobs/%j.out
+#################
+# a file for errors from the job
+#SBATCH --error=jobs/%j.err
+#################
+#time you think you need; default is 2 hours
+#format could be dd-hh:mm:ss, hh:mm:ss, mm:ss, or mm
+#SBATCH --time=5:00:00
+#################
+#SBATCH --qos=normal
+#SBATCH -p normal 
+#################
+#number of nodes you are requesting, the more you ask for the longer you wait
+#SBATCH --nodes=1
+#################
+#SBATCH --mem=32000
+
+# You can use srun if your job is parallel
+#srun R CMD BATCH  ./rtest.R 
+# otherwise: 
+
+method=$1
+
+# cd to program directory
+cd /home/users/jelenam/R-selective/tests/real_data_analysis
+
+module load R/3.4.0
+
+Rscript real_data_analysis.R $method
+
diff --git a/tests/real_data_analysis/submit_R_jobs.sh b/tests/real_data_analysis/submit_R_jobs.sh
new file mode 100644
index 0000000..8000519
--- /dev/null
+++ b/tests/real_data_analysis/submit_R_jobs.sh
@@ -0,0 +1,21 @@
+#!/bin/bash
+# Setup bash job headers
+
+# load local environment
+
+method1=liu
+method2=lee
+method3=knockoff
+method4=knockoff+
+method5=randomized_full
+method6=randomized_partial
+
+for i in {1..10}
+do
+	sbatch single_R_run.sbatch $method1
+	sbatch single_R_run.sbatch $method2
+	sbatch single_R_run.sbatch $method3
+	sbatch single_R_run.sbatch $method4
+	sbatch single_R_run.sbatch $method5
+	sbatch single_R_run.sbatch $method6
+done
\ No newline at end of file

From a0977190bfd8badc6623fa89c90f114a51a0880b Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 26 Mar 2018 08:01:24 -0700
Subject: [PATCH 461/493] BF: doc mismatch

---
 selectiveInference/R/funs.randomized.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 5d9f42c..23b90fc 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -513,7 +513,7 @@ set.target = function(rand_lasso_soln,
 
 
 randomizedLassoInf = function(rand_lasso_soln,
-                              full_targets=NULL,
+                              targets=NULL,
                               level=0.9,
                               sampler=c("norejection", "adaptMCMC"),
                               nsample=10000,

From 5eccc1fba17898e533b0e5e60f0ca447d5b369b4 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 26 Mar 2018 08:07:50 -0700
Subject: [PATCH 462/493] BF in C code

---
 C-software | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/C-software b/C-software
index fc60f47..aca77f1 160000
--- a/C-software
+++ b/C-software
@@ -1 +1 @@
-Subproject commit fc60f471ecd5fc40b822ee36d46b1a5aaf7ce7e8
+Subproject commit aca77f1e320dafba6041c4dc44cf9ffc049edec8

From 6d827900986eddc5d676226b85f6a784155d9cdd Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Mon, 26 Mar 2018 08:08:25 -0700
Subject: [PATCH 463/493] BF in C code

---
 C-software | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/C-software b/C-software
index fc60f47..aca77f1 160000
--- a/C-software
+++ b/C-software
@@ -1 +1 @@
-Subproject commit fc60f471ecd5fc40b822ee36d46b1a5aaf7ce7e8
+Subproject commit aca77f1e320dafba6041c4dc44cf9ffc049edec8

From c10004e8280e758df4684b2cb3bf88d66393a13a Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Mon, 26 Mar 2018 13:29:47 -0700
Subject: [PATCH 464/493] real data

---
 liu_full.rds                                  | Bin 234 -> 3232 bytes
 ...{funs.full.high.dim.R => funs.full.main.R} | 162 ++----------------
 selectiveInference/R/funs.full.side.R         | 148 ++++++++++++++++
 tests/knockoffs/single_R_run.sbatch           |   4 +-
 tests/liu_high_dim_full/test_liu_full.R       |  13 +-
 tests/randomized/single_R_run.sbatch          |   4 +-
 tests/real_data_analysis/real_data_analysis.R |  47 +++--
 tests/real_data_analysis/single_R_run.sbatch  |   4 +-
 tests/real_data_analysis/submit_R_jobs.sh     |   2 +-
 9 files changed, 210 insertions(+), 174 deletions(-)
 rename selectiveInference/R/{funs.full.high.dim.R => funs.full.main.R} (81%)
 create mode 100644 selectiveInference/R/funs.full.side.R

diff --git a/liu_full.rds b/liu_full.rds
index 7b7f89dd9b1abfad0a3bb670c80462fd83e143ca..29ef9b1f8561d63f7d1dd79c575e80cc0ad473cf 100644
GIT binary patch
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diff --git a/selectiveInference/R/funs.full.high.dim.R b/selectiveInference/R/funs.full.main.R
similarity index 81%
rename from selectiveInference/R/funs.full.high.dim.R
rename to selectiveInference/R/funs.full.main.R
index 355a7e0..fcfeaf4 100644
--- a/selectiveInference/R/funs.full.high.dim.R
+++ b/selectiveInference/R/funs.full.main.R
@@ -1,120 +1,5 @@
 
 
-selective.plus.BH = function(beta, selected.vars, pvalues, q){
-  
-  if (is.null(selected.vars)){
-    return(list(power=NA, FDR=NA, pvalues=NULL, null.pvalues=NULL, ci=NULL, nselected=0))
-  }
-  
-  nselected = length(selected.vars)
-  p.adjust.BH = p.adjust(pvalues, method = "BH", n = nselected)
-  rejected = selected.vars[which(p.adjust.BH<q)]
-  nrejected=length(rejected)
-  print(paste("sel+BH rejected", nrejected, "vars:",toString(rejected)))
-  
-  true.nonzero = which(beta!=0)
-  true.nulls = which(beta==0)
-  print(paste("true nonzero", length(true.nonzero), "vars:", toString(true.nonzero)))
-  
-  TP = length(intersect(rejected, true.nonzero))
-  s = length(true.nonzero)
-  if (s==0){
-    power = NA
-  } else{
-    power = TP/s
-  }
-  
-  FDR = (nrejected-TP)/max(1, nrejected)
-  
-  selected.nulls = NULL
-  for (i in 1:nselected){
-    if (any(true.nulls==selected.vars[i])){
-      selected.nulls = c(selected.nulls, i)
-    }
-  }
-  
-  null.pvalues=NA
-  if (length(selected.nulls)>0){
-    null.pvalues = pvalues[selected.nulls]
-    print(paste("selected nulls", length(selected.nulls), "vars:",toString(selected.vars[selected.nulls])))
-  }
-  
-  return(list(power=power, 
-              FDR=FDR, 
-              pvalues=pvalues, 
-              null.pvalues=null.pvalues, 
-              nselected=nselected, 
-              nrejected=nrejected))
-}
-
-
-AR_design = function(n, p, rho, scale=FALSE){
-  times = c(1:p)
-  cov_mat <- rho^abs(outer(times, times, "-"))
-  chol_mat = chol(cov_mat) # t(chol_mat) %*% chol_mat = cov_mat
-  X=matrix(rnorm(n*p), nrow=n) %*% t(chol_mat)
-  if (scale==TRUE){
-    X = scale(X)
-    X = X/sqrt(n)
-  }
-  return(X)
-}
-
-equicorrelated_design = function(n, p, rho, scale=FALSE){
-  X = sqrt(1-rho)*matrix(rnorm(n*p),n) + sqrt(rho)*matrix(rep(rnorm(n), p), nrow = n)
-  if (scale==TRUE){
-    X = scale(X)
-    X = X/sqrt(n)
-  }
-  return(X)
-}
-
-gaussian_instance = function(n, p, s, rho, sigma, snr, random_signs=TRUE, scale=FALSE, design="AR"){
-  
-  if (design=="AR"){
-    X=AR_design(n,p,rho, scale)
-  } else if (design=="equicorrelated"){
-    X=equicorrelated_design(n,p, rho, scale)
-  }
-  
-  beta = rep(0, p)
-  beta[1:s]=snr
-  
-  if (random_signs==TRUE && s>0){
-    signs = sample(c(-1,1), s, replace = TRUE)
-    beta[1:s] = beta[1:s] * signs
-  }
-  
-  beta=sample(beta)
-  y = X %*% beta + rnorm(n)*sigma 
-  result <- list(X=X,y=y,beta=beta)
-  return(result)
-}
-
-logistic_instance = function(n, p, s, rho, sigma, snr, random_signs=TRUE, scale=FALSE, design="AR"){
-  
-  if (design=="AR"){
-    X=AR_design(n,p,rho, scale)
-  } else if (design=="equicorrelated"){
-    X=equicorrelated_design(n,p, rho, scale)
-  }
-  
-  beta = rep(0, p)
-  beta[1:s]=snr
-  if (random_signs==TRUE && s>0){
-    signs = sample(c(-1,1), s, replace = TRUE)
-    beta[1:s] = beta[1:s] * signs
-  }
-  beta=sample(beta)
-  mu = X %*% beta
-  prob = exp(mu)/(1+exp(mu))
-  y = rbinom(n, 1, prob)
-  
-  result <- list(X=X,y=y,beta=beta)
-  return(result)
-}
-
-
 family_label = function(loss){
   if (loss=="ls"){
     return("gaussian")
@@ -356,21 +241,6 @@ naive_CI = function(observed, variance, alpha=0.1){
 }
 
 
-# theoretical lambda for glmnet
-# glmnet solves: 1/2n*\|y-X\beta\|_2^2+\lambda_1\|\beta\|_1
-theoretical.lambda = function(X, loss="ls", sigma=1){
-  n = nrow(X); p = ncol(X)
-  nsamples= 1000
-  if (loss=="ls"){
-    empirical = apply(abs(t(X) %*% matrix(rnorm(n*nsamples), nrow=n)), 2, max)
-  } else if (loss=="logit"){
-    empirical = apply(abs(t(X) %*% matrix(sample(c(0,1), n*nsamples, replace = TRUE), nrow=n)), 2, max)
-  }
-  lam = mean(empirical)*sigma/n
-  return(lam)
-}
-
-
 compute_coverage = function(ci, beta){
   nactive=length(beta)
   coverage_vector = rep(0, nactive)
@@ -439,21 +309,6 @@ approximate = function(X, active_set){
   return(M_active)
 }
 
-estimate_sigma = function(X, y, beta_hat_cv){
-  n=nrow(X)
-  p=ncol(X)
-  if (n<p){
-    # Reid et al. (2016) "A study of error variance estimation in lasso regression"
-    residuals = y-X%*% beta_hat_cv
-    nactive=length(which(beta_hat_cv!=0))
-    sigma_est_sq = drop(t(residuals) %*% residuals/(nrow(X)-nactive))
-    sigma_est=sqrt(sigma_est_sq)
-  } else{
-    m = lm(y~X-1)
-    sigma_est = summary(m)$sigma
-  }
-  return(sigma_est)
-}
 
 
 get_QB = function(X, y, soln, active_set, loss){
@@ -489,10 +344,11 @@ get_QB = function(X, y, soln, active_set, loss){
   } else{
     
     M_active = approximate(W_root %*% X, active_set) ## this should be the active rows of \Sigma_i (W^{1/2} X)^T
-    Q = hessian(X, soln, loss)
-    Q_sq = W_root %*% X
     QiE = M_active %*% t(M_active)
     beta_barE = soln[active_set] + M_active %*% diag(as.vector(1/diagonal)) %*% residuals
+    
+    Q = hessian(X, soln, loss)
+    Q_sq = W_root %*% X
     Qbeta_bar = Q%*%soln-gradient(X,y,soln,loss=loss)
   }
   
@@ -511,15 +367,19 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
   }
   active_groups = unique(groups[active_vars])
   nactive_groups = length(active_groups)
-
+  
+  print(c("nactive", nactive_groups))
+  
+  begin_setup = Sys.time()
   setup_params = get_QB(X=X, y=y, soln=soln, active_set=active_vars, loss=loss)
   Q=setup_params$Q
   Q_sq=setup_params$Q_sq
   QiE=as.matrix(setup_params$QiE)
   beta_barE = setup_params$beta_barE
   Qbeta_bar = setup_params$Qbeta_bar
-  
   QiE = QiE * sigma_est^2
+  end_setup = Sys.time()
+  cat("setup time", end_setup-begin_setup, "\n")
   
   pvalues = NULL
   naive_pvalues = NULL
@@ -537,9 +397,13 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
     target_stat = beta_barE[group_varsE]
     target_cov = as.matrix(QiE)[group_varsE,group_varsE]
     
+    begin_TS = Sys.time()
     TS =  truncation_set(X=X, y=y, Qbeta_bar=Qbeta_bar, Q=Q, Q_sq=Q_sq, target_cov=target_cov, sigma_est=sigma_est, 
                          group=group, target_stat=target_stat, groups=groups, lambda=lambda, 
                          penalty_factor=penalty_factor, loss=loss, algo=algo)
+    end_TS = Sys.time()
+    cat("TS time", end_TS-begin_TS, "\n")
+    
     center = TS$center
     radius = TS$radius
     
diff --git a/selectiveInference/R/funs.full.side.R b/selectiveInference/R/funs.full.side.R
new file mode 100644
index 0000000..965c266
--- /dev/null
+++ b/selectiveInference/R/funs.full.side.R
@@ -0,0 +1,148 @@
+
+selective.plus.BH = function(beta, selected.vars, pvalues, q){
+  
+  if (is.null(selected.vars)){
+    return(list(power=NA, FDR=NA, pvalues=NULL, null.pvalues=NULL, ci=NULL, nselected=0))
+  }
+  
+  nselected = length(selected.vars)
+  p.adjust.BH = p.adjust(pvalues, method = "BH", n = nselected)
+  rejected = selected.vars[which(p.adjust.BH<q)]
+  nrejected=length(rejected)
+  print(paste("sel+BH rejected", nrejected, "vars:",toString(rejected)))
+  
+  true.nonzero = which(beta!=0)
+  true.nulls = which(beta==0)
+  print(paste("true nonzero", length(true.nonzero), "vars:", toString(true.nonzero)))
+  
+  TP = length(intersect(rejected, true.nonzero))
+  s = length(true.nonzero)
+  if (s==0){
+    power = NA
+  } else{
+    power = TP/s
+  }
+  
+  FDR = (nrejected-TP)/max(1, nrejected)
+  
+  selected.nulls = NULL
+  for (i in 1:nselected){
+    if (any(true.nulls==selected.vars[i])){
+      selected.nulls = c(selected.nulls, i)
+    }
+  }
+  
+  null.pvalues=NA
+  if (length(selected.nulls)>0){
+    null.pvalues = pvalues[selected.nulls]
+    print(paste("selected nulls", length(selected.nulls), "vars:",toString(selected.vars[selected.nulls])))
+  }
+  
+  return(list(power=power, 
+              FDR=FDR, 
+              pvalues=pvalues, 
+              null.pvalues=null.pvalues, 
+              nselected=nselected, 
+              nrejected=nrejected))
+}
+
+
+AR_design = function(n, p, rho, scale=FALSE){
+  times = c(1:p)
+  cov_mat <- rho^abs(outer(times, times, "-"))
+  chol_mat = chol(cov_mat) # t(chol_mat) %*% chol_mat = cov_mat
+  X=matrix(rnorm(n*p), nrow=n) %*% t(chol_mat)
+  if (scale==TRUE){
+    X = scale(X)
+    X = X/sqrt(n)
+  }
+  return(X)
+}
+
+
+equicorrelated_design = function(n, p, rho, scale=FALSE){
+  X = sqrt(1-rho)*matrix(rnorm(n*p),n) + sqrt(rho)*matrix(rep(rnorm(n), p), nrow = n)
+  if (scale==TRUE){
+    X = scale(X)
+    X = X/sqrt(n)
+  }
+  return(X)
+}
+
+gaussian_instance = function(n, p, s, rho, sigma, snr, random_signs=TRUE, scale=FALSE, design="AR"){
+  
+  if (design=="AR"){
+    X=AR_design(n,p,rho, scale)
+  } else if (design=="equicorrelated"){
+    X=equicorrelated_design(n,p, rho, scale)
+  }
+  
+  beta = rep(0, p)
+  beta[1:s]=snr
+  
+  if (random_signs==TRUE && s>0){
+    signs = sample(c(-1,1), s, replace = TRUE)
+    beta[1:s] = beta[1:s] * signs
+  }
+  
+  beta=sample(beta)
+  y = X %*% beta + rnorm(n)*sigma 
+  result <- list(X=X,y=y,beta=beta)
+  return(result)
+}
+
+logistic_instance = function(n, p, s, rho, sigma, snr, random_signs=TRUE, scale=FALSE, design="AR"){
+  
+  if (design=="AR"){
+    X=AR_design(n,p,rho, scale)
+  } else if (design=="equicorrelated"){
+    X=equicorrelated_design(n,p, rho, scale)
+  }
+  
+  beta = rep(0, p)
+  beta[1:s]=snr
+  if (random_signs==TRUE && s>0){
+    signs = sample(c(-1,1), s, replace = TRUE)
+    beta[1:s] = beta[1:s] * signs
+  }
+  beta=sample(beta)
+  mu = X %*% beta
+  prob = exp(mu)/(1+exp(mu))
+  y = rbinom(n, 1, prob)
+  
+  result <- list(X=X,y=y,beta=beta)
+  return(result)
+}
+
+
+estimate_sigma = function(X, y, beta_hat_cv){
+  n=nrow(X)
+  p=ncol(X)
+  if (n<p){
+    # Reid et al. (2016) "A study of error variance estimation in lasso regression"
+    residuals = y-X%*% beta_hat_cv
+    nactive=length(which(beta_hat_cv!=0))
+    sigma_est_sq = drop(t(residuals) %*% residuals/(nrow(X)-nactive))
+    sigma_est=sqrt(sigma_est_sq)
+  } else{
+    m = lm(y~X-1)
+    sigma_est = summary(m)$sigma
+  }
+  return(sigma_est)
+}
+
+
+
+# theoretical lambda for glmnet
+# glmnet solves: 1/2n*\|y-X\beta\|_2^2+\lambda_1\|\beta\|_1
+theoretical.lambda = function(X, loss="ls", sigma=1){
+  n = nrow(X); p = ncol(X)
+  nsamples= 1000
+  if (loss=="ls"){
+    empirical = apply(abs(t(X) %*% matrix(rnorm(n*nsamples), nrow=n)), 2, max)
+  } else if (loss=="logit"){
+    empirical = apply(abs(t(X) %*% matrix(sample(c(0,1), n*nsamples, replace = TRUE), nrow=n)), 2, max)
+  }
+  lam = mean(empirical)*sigma/n
+  return(lam)
+}
diff --git a/tests/knockoffs/single_R_run.sbatch b/tests/knockoffs/single_R_run.sbatch
index 77c877d..c3440b1 100644
--- a/tests/knockoffs/single_R_run.sbatch
+++ b/tests/knockoffs/single_R_run.sbatch
@@ -13,7 +13,7 @@
 #################
 #time you think you need; default is 2 hours
 #format could be dd-hh:mm:ss, hh:mm:ss, mm:ss, or mm
-#SBATCH --time=5:00:00
+#SBATCH --time=10:00:00
 #################
 #SBATCH --qos=normal
 #SBATCH -p normal 
@@ -21,7 +21,7 @@
 #number of nodes you are requesting, the more you ask for the longer you wait
 #SBATCH --nodes=1
 #################
-#SBATCH --mem=16000
+#SBATCH --mem=32000
 
 # You can use srun if your job is parallel
 #srun R CMD BATCH  ./rtest.R 
diff --git a/tests/liu_high_dim_full/test_liu_full.R b/tests/liu_high_dim_full/test_liu_full.R
index da8aad3..ec55813 100644
--- a/tests/liu_high_dim_full/test_liu_full.R
+++ b/tests/liu_high_dim_full/test_liu_full.R
@@ -5,7 +5,7 @@ library(glmnet)
 
 # testing Liu et al type=full in high dimensional settings -- uses debiasing matrix
 
-test_liu_full = function(seed=1, outfile=NULL, nrep=1, n=1000, p=10000, s=50, rho=0.){
+test_liu_full = function(seed=1, outfile=NULL, nrep=1, n=1000, p=10000, s=20, rho=0.){
   
   snr = sqrt(2*log(p)/n)
   
@@ -33,13 +33,14 @@ test_liu_full = function(seed=1, outfile=NULL, nrep=1, n=1000, p=10000, s=50, rh
     beta=data$beta
     cat("true nonzero:", which(beta!=0), "\n")
     
-    CV = cv.glmnet(X, y, standardize=FALSE, intercept=FALSE, family=selectiveInference:::family_label(loss))
-    
-    #sigma_est=selectiveInference:::estimate_sigma(X,y,coef(CV, s="lambda.min")[-1]) # sigma via Reid et al.
-    #print(c("sigma est", sigma_est))
+    # CV = cv.glmnet(X, y, standardize=FALSE, intercept=FALSE, family=selectiveInference:::family_label(loss))
+    # sigma_est=selectiveInference:::estimate_sigma(X,y,coef(CV, s="lambda.min")[-1]) # sigma via Reid et al.
     sigma_est=1
+    print(c("sigma est", sigma_est))
+    
     # lambda = CV$lambda[which.min(CV$cvm+rnorm(length(CV$cvm))/sqrt(n))]  # lambda via randomized cv 
     lambda = 0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est)  # theoretical lambda
+    print(c("lambda", lambda))
     
     soln = selectiveInference:::solve_problem_glmnet(X, y, lambda, penalty_factor=penalty_factor, loss=loss)
     #soln = solve_problem_gglasso(X, y, groups=1:ncol(X), lambda, penalty_factor=penalty_factor, loss=loss)
@@ -95,5 +96,5 @@ test_liu_full = function(seed=1, outfile=NULL, nrep=1, n=1000, p=10000, s=50, rh
   return(list(pvalues=pvalues, naive_pvalues=naive_pvalues))
 }
 
-#test_liu_full()
+test_liu_full()
 
diff --git a/tests/randomized/single_R_run.sbatch b/tests/randomized/single_R_run.sbatch
index fd05e12..d578a79 100644
--- a/tests/randomized/single_R_run.sbatch
+++ b/tests/randomized/single_R_run.sbatch
@@ -13,7 +13,7 @@
 #################
 #time you think you need; default is 2 hours
 #format could be dd-hh:mm:ss, hh:mm:ss, mm:ss, or mm
-#SBATCH --time=5:00:00
+#SBATCH --time=10:00:00
 #################
 #SBATCH --qos=normal
 #SBATCH -p normal 
@@ -21,7 +21,7 @@
 #number of nodes you are requesting, the more you ask for the longer you wait
 #SBATCH --nodes=1
 #################
-#SBATCH --mem=16000
+#SBATCH --mem=32000
 
 # You can use srun if your job is parallel
 #srun R CMD BATCH  ./rtest.R 
diff --git a/tests/real_data_analysis/real_data_analysis.R b/tests/real_data_analysis/real_data_analysis.R
index aae0cad..0e1af3e 100644
--- a/tests/real_data_analysis/real_data_analysis.R
+++ b/tests/real_data_analysis/real_data_analysis.R
@@ -1,31 +1,32 @@
 library(glmnet)
 library(selectiveInference)
 library(MASS)
+library(knockoff)
 
 setwd(getwd())
 args = commandArgs(trailingOnly=TRUE)
 method = toString(args[1])
 
 outdir = "/scratch/users/jelenam/full/"
-label = paste("randomized_", method, "_results", sep="")
+label = paste(method, "_results", sep="")
 outfile = file.path(outdir, paste(sep="",label, ".rds"))
 
 #setwd("/Users/Jelena/Dropbox/kevin/jelena/real_data")
-data=readRDS("real_data.rds")
-X=data$X
-y=data$y
-
 set.seed(1)
-
-penalty_factor = rep(1, ncol(X))
 loss="ls"
 sigma_est = 0.62
-lambda =  0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est)
-print(c("lambda", lambda))
+lambda = 0.0352479
+#lambda =  0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est) # "lambda" "0.0352479112219816"
+#print(c("lambda", lambda))
 
 
 liu_full = function(outfile){
-
+  data=readRDS("real_data1.rds")
+  X=data$X
+  y=data$y
+  n=nrow(X)
+  penalty_factor = rep(1, ncol(X))
+  
   soln = selectiveInference:::solve_problem_glmnet(X, y, lambda, penalty_factor=penalty_factor, loss=loss)
   PVS = selectiveInference:::inference_group_lasso(X, y, soln, groups=1:ncol(X), lambda=lambda, penalty_factor=penalty_factor, 
                                                  sigma_est, loss=loss, algo="Q", construct_ci = TRUE)
@@ -39,10 +40,13 @@ liu_full = function(outfile){
 
 
 lee_full = function(outfile){
-  
+  data=readRDS("real_data2.rds")
+  X=data$X
+  y=data$y
+  n=nrow(X)
+
   lasso = glmnet(X, y, family=selectiveInference:::family_label(loss), alpha=1, standardize=FALSE, intercept=FALSE, thresh=1e-12)
   soln = as.numeric(coef(lasso,x=X,y=y, family=selectiveInference:::family_label(loss), s=lambda, exact=TRUE))[-1]
-  
   PVS = selectiveInference:::fixedLassoInf(X,y,soln, intercept=FALSE, lambda*n, family=selectiveInference:::family_label(loss),
                                            type="full",sigma=sigma_est)
   
@@ -61,6 +65,15 @@ lee_full = function(outfile){
 
 
 knockoff = function(method, outfile){
+  if (method=="knockoff"){
+    data=readRDS("real_data3.rds")
+  } else if (method=="knockoff+"){
+    data=readRDS("real_data4.rds")
+  }
+  
+  X=data$X
+  y=data$y
+  
   offset=0
   if (method=="knockoff+"){
     offset=1
@@ -71,6 +84,16 @@ knockoff = function(method, outfile){
 
 
 randomized = function(type, outfile){
+  if (type=="full"){
+    data=readRDS("real_data5.rds")
+  } else if (type=="partial"){
+    data=readRDS("real_data6.rds")
+  }
+  
+  X=data$X
+  y=data$y
+  n=nrow(X)
+  
   rand_lasso_soln = selectiveInference:::randomizedLasso(X, 
                                                          y, 
                                                          lambda*n, 
diff --git a/tests/real_data_analysis/single_R_run.sbatch b/tests/real_data_analysis/single_R_run.sbatch
index 79198d2..dd9de8e 100644
--- a/tests/real_data_analysis/single_R_run.sbatch
+++ b/tests/real_data_analysis/single_R_run.sbatch
@@ -13,7 +13,7 @@
 #################
 #time you think you need; default is 2 hours
 #format could be dd-hh:mm:ss, hh:mm:ss, mm:ss, or mm
-#SBATCH --time=5:00:00
+#SBATCH --time=10:00:00
 #################
 #SBATCH --qos=normal
 #SBATCH -p normal 
@@ -21,7 +21,7 @@
 #number of nodes you are requesting, the more you ask for the longer you wait
 #SBATCH --nodes=1
 #################
-#SBATCH --mem=32000
+#SBATCH --mem=128000
 
 # You can use srun if your job is parallel
 #srun R CMD BATCH  ./rtest.R 
diff --git a/tests/real_data_analysis/submit_R_jobs.sh b/tests/real_data_analysis/submit_R_jobs.sh
index 8000519..16a908c 100644
--- a/tests/real_data_analysis/submit_R_jobs.sh
+++ b/tests/real_data_analysis/submit_R_jobs.sh
@@ -10,7 +10,7 @@ method4=knockoff+
 method5=randomized_full
 method6=randomized_partial
 
-for i in {1..10}
+for i in {1..1}
 do
 	sbatch single_R_run.sbatch $method1
 	sbatch single_R_run.sbatch $method2

From d5ec2aa208de2b4f9d0672625555ad0d05439e64 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Mon, 26 Mar 2018 14:04:56 -0700
Subject: [PATCH 465/493] got rid of Q

---
 selectiveInference/R/funs.full.main.R         | 46 +++++++++++++------
 tests/liu_high_dim_full/test_liu_full.R       |  4 +-
 tests/real_data_analysis/real_data_analysis.R |  2 +-
 3 files changed, 35 insertions(+), 17 deletions(-)

diff --git a/selectiveInference/R/funs.full.main.R b/selectiveInference/R/funs.full.main.R
index fcfeaf4..aa6c33d 100644
--- a/selectiveInference/R/funs.full.main.R
+++ b/selectiveInference/R/funs.full.main.R
@@ -110,8 +110,10 @@ solve_problem_Q = function(Q_sq, Qbeta_bar, lambda, penalty_factor,
 
 
 # the selection event is |sigma_est^2*(target_cov)^{-1}Z+center|>radius
-truncation_set = function(X, y, Qbeta_bar, Q, Q_sq, target_cov, sigma_est, group, target_stat, 
-                          groups, lambda, penalty_factor, loss, algo){
+truncation_set = function(X, y, Qbeta_bar, QE, sigma_est, 
+                          target_stat, target_cov,
+                          group, groups, active_vars,
+                          lambda, penalty_factor, loss, algo){
   
   if (algo=="Q"){
     penalty_factor_rest = rep(penalty_factor)
@@ -127,8 +129,9 @@ truncation_set = function(X, y, Qbeta_bar, Q, Q_sq, target_cov, sigma_est, group
   p = length(Qbeta_bar)
   I = diag(p) * sigma_est^2
   group_vars = which(groups==group)
+  group_varsE = match(group_vars, active_vars)
   nuisance = (Qbeta_bar - I[,group_vars] %*% solve(target_cov) %*% target_stat)/n
-  center = nuisance[group_vars] - (Q[group_vars,] %*% restricted_soln/n)
+  center = nuisance[group_vars] - (QE[group_varsE,] %*% restricted_soln/n)
   radius = penalty_factor[group]*lambda
   return(list(target_cov=target_cov, center=center*n, radius=radius*n))
 }
@@ -272,6 +275,18 @@ hessian = function(X, beta, loss){
   return(t(X) %*% W %*% X)
 }
 
+
+hessian_active = function(X, beta, loss, active_set){
+  if (loss=="logit"){
+    fit = X%*%beta
+    W=diag(as.vector(exp(fit)/((1+exp(fit))^2)))
+  } else if (loss=="ls"){
+    W=diag(nrow(X))
+  }
+  return(t(X[, active_set]) %*% W %*% X)
+}
+
+
 mle=function(X,y,loss){
   reg = glm(y~X-1, family=family_label(loss))
   return(reg$coefficients)
@@ -329,8 +344,10 @@ get_QB = function(X, y, soln, active_set, loss){
   
   if (n>p){
     Q=hessian(X, soln, loss=loss)
+    QE=Q[active_set,]
     Qi=solve(Q)
-    Q_sq =  W_root %*% X
+    QiE=Qi[active_set, active_set]
+    #Q_sq =  W_root %*% X
     #beta_bar = mle(X,y,loss=loss)
     #print("mle")
     #print(mle(X,y,loss=loss))
@@ -340,19 +357,19 @@ get_QB = function(X, y, soln, active_set, loss){
     #print(beta_bar)
     Qbeta_bar = Q%*%soln - gradient(X,y,soln, loss=loss)
     beta_barE = beta_bar[active_set]
-    QiE = Qi[active_set, active_set]
+    
   } else{
     
     M_active = approximate(W_root %*% X, active_set) ## this should be the active rows of \Sigma_i (W^{1/2} X)^T
     QiE = M_active %*% t(M_active)
     beta_barE = soln[active_set] + M_active %*% diag(as.vector(1/diagonal)) %*% residuals
     
-    Q = hessian(X, soln, loss)
-    Q_sq = W_root %*% X
-    Qbeta_bar = Q%*%soln-gradient(X,y,soln,loss=loss)
+    QE = hessian_active(X, soln, loss, active_set)
+    #Q_sq = W_root %*% X
+    Qbeta_bar = t(QE)%*%soln[active_set]-gradient(X,y,soln,loss=loss)
   }
   
-  return(list(Q=Q, Q_sq=Q_sq, Qbeta_bar=Qbeta_bar, QiE=QiE, beta_barE=beta_barE))  
+  return(list(QE=QE, Qbeta_bar=Qbeta_bar, QiE=QiE, beta_barE=beta_barE))  
 }
 
 
@@ -372,8 +389,8 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
   
   begin_setup = Sys.time()
   setup_params = get_QB(X=X, y=y, soln=soln, active_set=active_vars, loss=loss)
-  Q=setup_params$Q
-  Q_sq=setup_params$Q_sq
+  QE=as.matrix(setup_params$QE)
+  #Q_sq=setup_params$Q_sq
   QiE=as.matrix(setup_params$QiE)
   beta_barE = setup_params$beta_barE
   Qbeta_bar = setup_params$Qbeta_bar
@@ -398,9 +415,10 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
     target_cov = as.matrix(QiE)[group_varsE,group_varsE]
     
     begin_TS = Sys.time()
-    TS =  truncation_set(X=X, y=y, Qbeta_bar=Qbeta_bar, Q=Q, Q_sq=Q_sq, target_cov=target_cov, sigma_est=sigma_est, 
-                         group=group, target_stat=target_stat, groups=groups, lambda=lambda, 
-                         penalty_factor=penalty_factor, loss=loss, algo=algo)
+    TS =  truncation_set(X=X, y=y, Qbeta_bar=Qbeta_bar, QE=QE, sigma_est=sigma_est, 
+                         target_cov=target_cov, target_stat=target_stat, 
+                         group=group, groups=groups, active_vars=active_vars,
+                         lambda=lambda, penalty_factor=penalty_factor, loss=loss, algo=algo)
     end_TS = Sys.time()
     cat("TS time", end_TS-begin_TS, "\n")
     
diff --git a/tests/liu_high_dim_full/test_liu_full.R b/tests/liu_high_dim_full/test_liu_full.R
index ec55813..3f75937 100644
--- a/tests/liu_high_dim_full/test_liu_full.R
+++ b/tests/liu_high_dim_full/test_liu_full.R
@@ -5,7 +5,7 @@ library(glmnet)
 
 # testing Liu et al type=full in high dimensional settings -- uses debiasing matrix
 
-test_liu_full = function(seed=1, outfile=NULL, nrep=1, n=1000, p=10000, s=20, rho=0.){
+test_liu_full = function(seed=1, outfile=NULL, nrep=1, n=100, p=200, s=20, rho=0.){
   
   snr = sqrt(2*log(p)/n)
   
@@ -45,7 +45,7 @@ test_liu_full = function(seed=1, outfile=NULL, nrep=1, n=1000, p=10000, s=20, rh
     soln = selectiveInference:::solve_problem_glmnet(X, y, lambda, penalty_factor=penalty_factor, loss=loss)
     #soln = solve_problem_gglasso(X, y, groups=1:ncol(X), lambda, penalty_factor=penalty_factor, loss=loss)
     PVS = selectiveInference:::inference_group_lasso(X, y, soln, groups=1:ncol(X), lambda=lambda, penalty_factor=penalty_factor, 
-                                sigma_est, loss=loss, algo="Q", construct_ci = construct_ci)
+                                sigma_est, loss=loss, algo="glmnet", construct_ci = construct_ci)
     active_vars=PVS$active_vars
     cat("active_vars:",active_vars,"\n")
     pvalues = c(pvalues, PVS$pvalues)
diff --git a/tests/real_data_analysis/real_data_analysis.R b/tests/real_data_analysis/real_data_analysis.R
index 0e1af3e..c5132a7 100644
--- a/tests/real_data_analysis/real_data_analysis.R
+++ b/tests/real_data_analysis/real_data_analysis.R
@@ -29,7 +29,7 @@ liu_full = function(outfile){
   
   soln = selectiveInference:::solve_problem_glmnet(X, y, lambda, penalty_factor=penalty_factor, loss=loss)
   PVS = selectiveInference:::inference_group_lasso(X, y, soln, groups=1:ncol(X), lambda=lambda, penalty_factor=penalty_factor, 
-                                                 sigma_est, loss=loss, algo="Q", construct_ci = TRUE)
+                                                 sigma_est, loss=loss, algo="glmnet", construct_ci = TRUE)
   saveRDS(list(active_vars=PVS$active_vars, 
                sel_intervals=PVS$sel_intervals, naive_intervals=PVS$naive_intervals,
                pvalues=PVS$pvalues, naive_pvalues=PVS$naive_pvalues), file=outfile)

From d61ddcfed83f3beb85ce6f1b6d0a1fabaed64ad0 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 28 Mar 2018 13:40:23 -0700
Subject: [PATCH 466/493] fixing gitignore to ignore selectiveInference/src
 code

---
 .gitignore | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 46dc0e2..ebb4ab7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,4 +11,9 @@ forLater/josh/**
 */src/RcppExports.cpp
 *~
 */*~
-*/*/*~
\ No newline at end of file
+*/*/*~
+# this code is copied from C-software
+selectiveInference/src/matrixcomps.*
+selectiveInference/src/debias.*
+selectiveInference/src/randomized_lasso.*
+selectiveInference/src/quadratic_program*.c
\ No newline at end of file

From 737b48a7f3fa34c20edbe9f3da67bb33df48392b Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 29 Mar 2018 14:45:15 -0700
Subject: [PATCH 467/493] removing reference to self library

---
 selectiveInference/R/funs.randomized.R | 67 +++++++++++++-------------
 tests/randomized/test_instances.R      | 10 ++--
 tests/randomized/test_randomized.R     | 15 ++++--
 3 files changed, 51 insertions(+), 41 deletions(-)

diff --git a/selectiveInference/R/funs.randomized.R b/selectiveInference/R/funs.randomized.R
index 180a17d..0457ad9 100644
--- a/selectiveInference/R/funs.randomized.R
+++ b/selectiveInference/R/funs.randomized.R
@@ -7,7 +7,7 @@ randomizedLasso = function(X,
                            y, 
                            lam, 
                            family=c("gaussian","binomial"),
-			                     condition_subgrad=TRUE,
+                           condition_subgrad=TRUE,
                            noise_scale=NULL, 
                            ridge_term=NULL, 
                            max_iter=100,        # how many iterations for each optimization problem
@@ -113,7 +113,6 @@ randomizedLasso = function(X,
     observed_unpen = result$soln[unpenalized]
     observed_subgrad = -n*result$gradient[inactive]
     
-    print(c("nactive", length(active_set)))
     if (sum(abs(observed_subgrad)>lam[inactive]*(1.001)) > 0){
       stop("subgradient eq not satisfied")
     }
@@ -273,14 +272,14 @@ randomizedLasso = function(X,
     return(list(X=X,
                 y=y,
                 lam=lam,
-		            family=family,
+                family=family,
                 active_set=active_set,
                 inactive_set=inactive_set,
                 unpenalized_set=unpenalized_set,
                 sign_soln=sign_soln,
                 law=law,
                 internal_transform=internal_transform,
-		            observed_internal=observed_internal,
+	        observed_internal=observed_internal,
                 observed_raw=observed_raw,
                 noise_scale=noise_scale,
                 soln=result$soln,
@@ -410,10 +409,10 @@ conditional_opt_transform = function(noise_scale,
 	      cond_mean=cond_mean))
 }
 
-set.target = function(rand_lasso_soln, 
-                      type, 
-                      construct_pvalues=NULL,
-                      construct_ci=NULL){
+compute_target = function(rand_lasso_soln, 
+                          type, 
+                          construct_pvalues=NULL,
+                          construct_ci=NULL){
   
   # compute internal representation of the data
   y = rand_lasso_soln$y
@@ -444,6 +443,15 @@ set.target = function(rand_lasso_soln,
     crosscov_target_internal=rbind(cov_target, matrix(0, nrow=p-nactive, ncol=nactive))
   } 
   
+  alternatives = c()
+  for (i in 1:length(rand_lasso_soln$sign_soln)) {
+      if (rand_lasso_soln$sign_soln[i] == 1) {
+          alternatives = c(alternatives, 'greater')
+      } else {
+          alternatives = c(alternatives, 'less')
+      }
+  }
+
   if (type=="full"){
     
     lasso.est = rand_lasso_soln$soln
@@ -462,10 +470,10 @@ set.target = function(rand_lasso_soln,
       
       if (!is_wide) {
         hsigma = 1/n*(t(Xordered)%*%Xordered)
-        htheta = selectiveInference:::debiasingMatrix(hsigma, is_wide, n, 1:nactive)
+        htheta = debiasingMatrix(hsigma, is_wide, n, 1:nactive)
         ithetasigma = (GS-(htheta%*%hsigma))
       } else {
-        htheta = selectiveInference:::debiasingMatrix(Xordered, is_wide, n, 1:nactive)
+        htheta = debiasingMatrix(Xordered, is_wide, n, 1:nactive)
         ithetasigma = (GS-((htheta%*%t(Xordered)) %*% Xordered)/n)
       }
       M_active <- ((htheta%*%t(Xordered))+ithetasigma%*%FS%*%hsigmaSinv%*%t(X_active))/n
@@ -478,17 +486,9 @@ set.target = function(rand_lasso_soln,
       M_active = pseudo_invX[active_set,] %*% t(X)
       M_inactive = (pseudo_invX[,inactive_set] %*% t(X_inactive))[active_set,]
     }
-    #print(c("M_active size", dim(M_active)))
-    #print(c("M_inactive size", dim(M_inactive)))
-    #print(M_inactive[,1:10])
-    #pseudo_invX = pinv(crossprod(X))
-    #M_inactive = (pseudo_invX[,inactive_set] %*% t(X_inactive))[active_set,]
-    #print(M_inactive[,1:10])
-    
-    #print(c("M_active size", dim(M_active)))
-    #print(c("M_inactive size", dim(M_inactive)))
+
     residuals = y-X%*%lasso.est
-    scalar = 1 #sqrt(n)
+    scalar = 1 #sqrt(n) # JT: this is sigma?
     observed_target = lasso.est[active_set]+scalar*M_active %*% residuals
     cov_target = vcov(glm_y) + scalar^2*M_inactive %*% t(M_inactive)
     crosscov_target_internal = rbind(vcov(glm_y), scalar*t(X_inactive) %*% t(M_inactive))
@@ -515,7 +515,8 @@ set.target = function(rand_lasso_soln,
   
   return(list(targets=targets,
               construct_ci=construct_ci, 
-              construct_pvalues=construct_pvalues))
+              construct_pvalues=construct_pvalues,
+              alternatives=alternatives))
 }
 
 
@@ -524,8 +525,7 @@ randomizedLassoInf = function(rand_lasso_soln,
                               level=0.9,
                               sampler=c("norejection", "adaptMCMC"),
                               nsample=10000,
-                              burnin=2000,
-                              alternative = c("two-sided", "greated", "less"))
+                              burnin=2000)
  {
 
   n = nrow(rand_lasso_soln$X)
@@ -558,17 +558,18 @@ randomizedLassoInf = function(rand_lasso_soln,
                                    law$sampling_transform$offset_term,
                                    law$constraints,
                                    nsamples=nsample,
-		                               burnin=burnin)
+		                   burnin=burnin)
   }
 
-  if (is.null(full_targets)){
-    full_targets=set.target(rand_lasso_soln, type="partial")
+  if (is.null(targets)){
+    targets = compute_target(rand_lasso_soln, type="partial")
   }
   
-  targets=full_targets$targets
-  construct_ci=full_targets$construct_ci
-  construct_pvalues = full_targets$construct_pvalues
-  
+  alternatives = targets$alternatives
+  construct_ci = targets$construct_ci
+  construct_pvalues = targets$construct_pvalues
+  targets = targets$targets
+     		  
   observed_internal = rand_lasso_soln$observed_internal
   
   importance_transform = law$importance_transform
@@ -663,9 +664,9 @@ randomizedLassoInf = function(rand_lasso_soln,
     
     if (construct_pvalues[i]==1){
       pvalues[i] = pivot(0)
-      if (alternative=="two-sided"){
+      if (alternatives[i]=="two-sided"){
         pvalues[i] = 2*min(pvalues[i], 1-pvalues[i])
-      } else if (alternative=="greater"){
+      } else if (alternatives[i]=="greater"){
         pvalues[i]= 1-pvalues[i]
       } 
     }
@@ -687,7 +688,7 @@ randomizedLassoInf = function(rand_lasso_soln,
       }
     }
   }
-  return(list(full_targets=full_targets, pvalues=pvalues, ci=ci))
+  return(list(targets=targets, pvalues=pvalues, ci=ci))
 }
 
 logistic_fitted = function(X, beta){
diff --git a/tests/randomized/test_instances.R b/tests/randomized/test_instances.R
index bb2c66a..ae0736f 100644
--- a/tests/randomized/test_instances.R
+++ b/tests/randomized/test_instances.R
@@ -41,7 +41,7 @@ test_randomized_lasso = function(n=100,p=200,s=0){
   lam = 2.
   noise_scale = 0.5
   ridge_term = 1./sqrt(n)
-  result = selectiveInference:::randomizedLasso(X,y,lam, noise_scale, ridge_term)
+  result = selectiveInference:::randomizedLasso(X, y, lam, noise_scale, ridge_term)
   print(result$soln)
   print(length(which(result$soln!=0)))
   print(result$observed_opt_state) # compared with python code
@@ -108,11 +108,11 @@ collect_results = function(n,p,s, nsim=100, level=0.9,
                                                            family=family,
                                                            condition_subgrad=condition_subgrad)
     
-    full_targets=selectiveInference:::set.target(rand_lasso_soln,type=type)
-    
+    targets=selectiveInference:::compute_target(rand_lasso_soln,type=type)
+    print(targets$construct_pvalues)    
     result = selectiveInference:::randomizedLassoInf(rand_lasso_soln,
-                                                     full_targets=full_targets,
-                                                     sampler = "norejection", #"adaptMCMC", #
+                                                     targets=targets,
+                                                     sampler="norejection", #"adaptMCMC", #
                                                      level=level, 
                                                      burnin=1000, 
                                                      nsample=5000)
diff --git a/tests/randomized/test_randomized.R b/tests/randomized/test_randomized.R
index 1531d43..d4344b0 100644
--- a/tests/randomized/test_randomized.R
+++ b/tests/randomized/test_randomized.R
@@ -7,7 +7,11 @@ smoke_test = function() {
     lam = 20 / sqrt(n)
     noise_scale = 0.01 * sqrt(n)
     ridge_term = .1 / sqrt(n)
-    selectiveInference:::randomizedLasso(X, y, lam, noise_scale, ridge_term)
+    selectiveInference:::randomizedLasso(X, 
+                                         y, 
+                                         lam, 
+                                         noise_scale=noise_scale, 
+                                         ridge_term=ridge_term)
 }
 
 A = smoke_test()
@@ -20,8 +24,13 @@ sampler_test = function() {
     lam = 20 / sqrt(n)
     noise_scale = 0.01 * sqrt(n)
     ridge_term = .1 / sqrt(n)
-    obj = selectiveInference:::randomizedLasso(X, y, lam, noise_scale, ridge_term)
-    S = selectiveInference:::sample_opt_variables(obj, jump_scale=rep(1/sqrt(n), p), nsample=10000)
+    obj = selectiveInference:::randomizedLasso(X, 
+                                               y, 
+                                               lam, 
+                                               noise_scale=noise_scale, 
+                                               ridge_term=ridge_term,
+ 					       condition_subgrad=FALSE)
+    S = selectiveInference:::sample_opt_variables(obj$law, jump_scale=rep(1/sqrt(n), p), nsample=10000)
     return(S$samples[2001:10000,])
 }
 B = sampler_test()

From af2698f7ffcb4456aac3f069fe3152a93ff84356 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 29 Mar 2018 15:05:46 -0700
Subject: [PATCH 468/493] adding Jelena as an author

---
 README.md                      | 2 +-
 selectiveInference/DESCRIPTION | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 0a60855..11770b5 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # R-software
 R software for [selective inference](http://cran.r-project.org/web/packages/selectiveInference/).  
-Authors: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid  
+Authors: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor, Joshua Loftus, Stephen Reid, Jelena Markovic  
 Maintainer: Rob Tibshirani <tibs@stanford.edu>
 
 New tools for inference after selection, for use with forward stepwise regression, least angle regression, the lasso, and the many means problem. The package is available on [CRAN](http://cran.r-project.org/web/packages/selectiveInference/). See [this paper](http://www.pnas.org/content/112/25/7629.full) for a high level introduction to selective inference.
diff --git a/selectiveInference/DESCRIPTION b/selectiveInference/DESCRIPTION
index c765663..535abea 100644
--- a/selectiveInference/DESCRIPTION
+++ b/selectiveInference/DESCRIPTION
@@ -4,7 +4,7 @@ Title: Tools for Post-Selection Inference
 Version: 1.2.2
 Date: 2016-07-3
 Author: Ryan Tibshirani, Rob Tibshirani, Jonathan Taylor,
-    Joshua Loftus, Stephen Reid
+    Joshua Loftus, Stephen Reid, Jelena Markovic
 Maintainer: Rob Tibshirani <tibs@stanford.edu>
 Depends:
     glmnet,

From 224681bf0e16fc6e71be25c729bb88e8122c2f74 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 29 Mar 2018 15:21:20 -0700
Subject: [PATCH 469/493] ignore csv and rds files under tests

---
 .gitignore | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/.gitignore b/.gitignore
index ebb4ab7..c24c638 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,10 @@
+*.rds
+tests/*.rds
+tests/*/*.rds
+tests/*/*/*.rds
+tests/*.csv
+tests/*/*.csv
+tests/*/*/*.csv
 **DS_Store**
 **Rcheck**
 **tar.gz

From 5014544916320cb070f5f3bbe3f842ecaa6434de Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 29 Mar 2018 15:29:04 -0700
Subject: [PATCH 470/493] trying to add path to Makevars

---
 selectiveInference/src/Makevars | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/src/Makevars b/selectiveInference/src/Makevars
index 22c4c7d..06dc3b8 100644
--- a/selectiveInference/src/Makevars
+++ b/selectiveInference/src/Makevars
@@ -8,4 +8,4 @@ clean:
 	rm -f *o
 
 Rcpp:
-	Rscript -e "library(Rcpp); Rcpp::compileAttributes('..')"
\ No newline at end of file
+	$(R_HOME)/bin/Rscript -e "library(Rcpp); Rcpp::compileAttributes('..')"
\ No newline at end of file

From 67dafd761b537f6e5b260bcc48e3af3b9d47e9b1 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 29 Mar 2018 20:46:21 -0700
Subject: [PATCH 471/493] disabling old for now due to Rmpfr not installing on
 old travis

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 76ab515..f9d313d 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -2,7 +2,7 @@ language: r
 cache: packages
 sudo: false
 r:
-  - oldrel
+  #- oldrel
   - release
   - devel
 addons:

From 3f9600092aec09cecd07bd03e5bc82c71665910b Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 29 Mar 2018 20:50:57 -0700
Subject: [PATCH 472/493] removing data files, and C files that are synced
 automatically from C-software

---
 selectiveInference/src/debias.h               |  79 ---
 selectiveInference/src/matrixcomps.c          | 266 --------
 selectiveInference/src/matrixcomps.h          |  12 -
 selectiveInference/src/quadratic_program.c    | 441 -------------
 .../src/quadratic_program_wide.c              | 597 ------------------
 selectiveInference/src/randomized_lasso.c     | 411 ------------
 selectiveInference/src/randomized_lasso.h     |  54 --
 .../comparisons/big cor/knockoff+_result.rds  | Bin 782 -> 0 bytes
 tests/comparisons/big cor/knockoff.rds        | Bin 1114 -> 0 bytes
 tests/comparisons/big cor/knockoff_result.rds | Bin 782 -> 0 bytes
 tests/comparisons/big cor/lee.rds             | Bin 122793 -> 0 bytes
 tests/comparisons/big cor/liu.rds             | Bin 246824 -> 0 bytes
 tests/comparisons/big cor/randomized_full.rds | Bin 199392 -> 0 bytes
 .../big cor/randomized_partial.rds            | Bin 195272 -> 0 bytes
 tests/comparisons/big uncorr/knockoff+.rds    | Bin 193795 -> 0 bytes
 tests/comparisons/big uncorr/knockoff.rds     | Bin 803 -> 0 bytes
 tests/comparisons/big uncorr/lee.rds          | Bin 116403 -> 0 bytes
 tests/comparisons/big uncorr/liu.rds          | Bin 239089 -> 0 bytes
 .../big uncorr/randomized_full.rds            | Bin 197511 -> 0 bytes
 .../big uncorr/randomized_partial.rds         | Bin 193795 -> 0 bytes
 tests/comparisons/small corr/knockoff+.rds    | Bin 273 -> 0 bytes
 tests/comparisons/small corr/knockoff.rds     | Bin 350 -> 0 bytes
 tests/comparisons/small corr/lee_full.rds     | Bin 21222 -> 0 bytes
 tests/comparisons/small corr/liu_full.rds     | Bin 42553 -> 0 bytes
 tests/comparisons/small uncorr/knockoff+.rds  | Bin 271 -> 0 bytes
 tests/comparisons/small uncorr/knockoff.rds   | Bin 357 -> 0 bytes
 tests/comparisons/small uncorr/lee_full.rds   | Bin 15719 -> 0 bytes
 tests/comparisons/small uncorr/liu_full.rds   | Bin 31871 -> 0 bytes
 .../small uncorr/randomized_full.rds          | Bin 35115 -> 0 bytes
 29 files changed, 1860 deletions(-)
 delete mode 100644 selectiveInference/src/debias.h
 delete mode 100644 selectiveInference/src/matrixcomps.c
 delete mode 100644 selectiveInference/src/matrixcomps.h
 delete mode 100644 selectiveInference/src/quadratic_program.c
 delete mode 100644 selectiveInference/src/quadratic_program_wide.c
 delete mode 100644 selectiveInference/src/randomized_lasso.c
 delete mode 100644 selectiveInference/src/randomized_lasso.h
 delete mode 100644 tests/comparisons/big cor/knockoff+_result.rds
 delete mode 100644 tests/comparisons/big cor/knockoff.rds
 delete mode 100644 tests/comparisons/big cor/knockoff_result.rds
 delete mode 100644 tests/comparisons/big cor/lee.rds
 delete mode 100644 tests/comparisons/big cor/liu.rds
 delete mode 100644 tests/comparisons/big cor/randomized_full.rds
 delete mode 100644 tests/comparisons/big cor/randomized_partial.rds
 delete mode 100644 tests/comparisons/big uncorr/knockoff+.rds
 delete mode 100644 tests/comparisons/big uncorr/knockoff.rds
 delete mode 100644 tests/comparisons/big uncorr/lee.rds
 delete mode 100644 tests/comparisons/big uncorr/liu.rds
 delete mode 100644 tests/comparisons/big uncorr/randomized_full.rds
 delete mode 100644 tests/comparisons/big uncorr/randomized_partial.rds
 delete mode 100644 tests/comparisons/small corr/knockoff+.rds
 delete mode 100644 tests/comparisons/small corr/knockoff.rds
 delete mode 100644 tests/comparisons/small corr/lee_full.rds
 delete mode 100644 tests/comparisons/small corr/liu_full.rds
 delete mode 100644 tests/comparisons/small uncorr/knockoff+.rds
 delete mode 100644 tests/comparisons/small uncorr/knockoff.rds
 delete mode 100644 tests/comparisons/small uncorr/lee_full.rds
 delete mode 100644 tests/comparisons/small uncorr/liu_full.rds
 delete mode 100644 tests/comparisons/small uncorr/randomized_full.rds

diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
deleted file mode 100644
index da27467..0000000
--- a/selectiveInference/src/debias.h
+++ /dev/null
@@ -1,79 +0,0 @@
-#ifdef __cplusplus
-extern "C"
-{
-#endif /* __cplusplus */
-
-void multiply_by_2(double *X, int nval);
-
-int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
-	     double *linear_func_ptr,    /* Linear term in objective */
-	     double *nndef_diag_ptr,     /* Diagonal of nndef */
-	     double *gradient_ptr,       /* nndef times theta */
-	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
-	     int *nactive_ptr,           /* Size of ever active set */
-	     int nfeature,               /* How many features in nndef */
-	     double bound,               /* feasibility parameter */
-	     double *theta,              /* current value */
-	     double *theta_old,          /* previous value */
-	     int maxiter,                /* max number of iterations */
-	     double kkt_tol,             /* precision for checking KKT conditions */
-	     double objective_tol,       /* precision for checking relative decrease in objective value */
-	     double parameter_tol,       /* precision for checking relative convergence of parameter */
-	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
-             int kkt_stop,               /* Break based on KKT? */
-	     int objective_stop,         /* Break based on convergence of objective value? */
-	     int param_stop);            /* Break based on parameter convergence? */
-
-int check_KKT_qp(double *theta,        /* current theta */
-		 double *gradient_ptr, /* nndef times theta + linear_func */
-		 int nrow,             /* how many rows in nndef */
-		 double bound,         /* Lagrange multipler for \ell_1 */
-		 double tol);          /* precision for checking KKT conditions */        
-
-int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-	       double *X_theta_ptr,        /* Fitted values   */
-	       double *linear_func_ptr,    /* Linear term in objective */
-	       double *nndef_diag_ptr,     /* Diagonal entries of non-neg def matrix */
-	       double *gradient_ptr,       /* X^TX/ncase times theta + linear_func*/
-	       int *need_update_ptr,       /* Keeps track of updated gradient coords */
-	       int *ever_active_ptr,       /* Ever active set: 1-based */ 
-	       int *nactive_ptr,           /* Size of ever active set */
-	       int ncase,                  /* How many rows in X */
-	       int nfeature,               /* How many columns in X */
-	       double *bound_ptr,          /* Lagrange multipliers */
-	       double ridge_term,          /* Ridge / ENet term */
-	       double *theta_ptr,          /* current value */
-	       double *theta_old_ptr,      /* previous value */
-	       int maxiter,                /* max number of iterations */
-	       double kkt_tol,             /* precision for checking KKT conditions */
-	       double objective_tol,       /* precision for checking relative decrease in objective value */
-	       double parameter_tol,       /* precision for checking relative convergence of parameter */
-	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
-	       int kkt_stop,               /* Break based on KKT? */
-	       int objective_stop,         /* Break based on convergence of objective value? */
-	       int param_stop);            /* Break based on parameter convergence? */
-
-int check_KKT_wide(double *theta_ptr,        /* current theta */
-		   double *gradient_ptr,     /* X^TX/ncase times theta + linear_func*/
-		   double *X_theta_ptr,      /* Current fitted values */
-		   double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-		   double *linear_func_ptr,  /* Linear term in objective */   
-		   int *need_update_ptr,     /* Which coordinates need to be updated? */
-		   int nfeature,             /* how many columns in X */
-		   int ncase,                /* how many rows in X */
-		   double *bound_ptr,        /* Lagrange multiplers for \ell_1 */
-		   double ridge_term,        /* Ridge / ENet term */
-		   double tol);              /* precision for checking KKT conditions */        
-  
-void update_gradient_wide(double *gradient_ptr,     /* X^TX/ncase times theta + linear_func */
-			  double *X_theta_ptr,      /* Current fitted values */
-			  double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-			  double *linear_func_ptr,  /* Linear term in objective */   
-			  int *need_update_ptr,     /* Which coordinates need to be updated? */
-			  int nfeature,             /* how many columns in X */
-			  int ncase);               /* how many rows in X */
-
-
-#ifdef __cplusplus
-}  /* extern "C" */
-#endif /* __cplusplus */
diff --git a/selectiveInference/src/matrixcomps.c b/selectiveInference/src/matrixcomps.c
deleted file mode 100644
index b7e8bbc..0000000
--- a/selectiveInference/src/matrixcomps.c
+++ /dev/null
@@ -1,266 +0,0 @@
-#include <math.h>
-
-// Matrices are stored as vectors, in column-major order
-
-// Givens rotation of a and b, stored in c and s
-void givens(double a, double b, double *c, double *s) {
-  if (b==0) {
-    *c = 1; 
-    *s = 0; 
-  }
-  else {
-    if (fabs(b)>fabs(a)) {
-      double t = -a/b;
-      *s = 1/sqrt(1+t*t);
-      *c = (*s)*t;
-    }
-    else {
-      double t = -b/a;
-      *c = 1/sqrt(1+t*t);
-      *s = (*c)*t;
-    }
-  }
-}
-
-// Givens rotation applied to rows i1 and i2 of the m x n
-// matrix A, on the subset of columns j1 through j2
-void rowrot(double *A, int i1, int i2, int m, int n, int j1, int j2, double c, double s) {
-  int j;
-  double t1,t2;
-  for (j=j1; j<=j2; j++) {
-    t1 = A[i1+j*m]; 
-    t2 = A[i2+j*m];
-    A[i1+j*m] = c*t1-s*t2;
-    A[i2+j*m] = s*t1+c*t2;
-  }
-}
-
-// Givens rotation applied to columns j1 and j2 of the m x n
-// matrix A, on the subset of rows i1 through i2
-void colrot(double *A, int j1, int j2, int m, int n, int i1, int i2, double c, double s) {
-  int i;
-  double t1,t2;
-  for (i=i1; i<=i2; i++) {
-    t1 = A[i+j1*m];
-    t2 = A[i+j2*m];
-    A[i+j1*m] = c*t1-s*t2;
-    A[i+j2*m] = s*t1+c*t2;
-  }
-}
-
-// Downdate the QR factorization after deleting column j0, 
-// where Q1 is m x n and R is n x n. The other part of 
-// the Q matrix, Q2 m x (m-n), isn't needed so it isn't 
-// passed for efficiency
-void downdate1(double *Q1, double *R, int j0, int m, int n) {
-  int j;
-
-  double c,s;
-  for (j=j0+1; j<n; j++) {
-    // Compute the appropriate c and s
-    givens(R[j-1+j*n],R[j+j*n],&c,&s);
-
-    // Pre-multiply R
-    rowrot(R,j-1,j,n,n,j,n-1,c,s);
-
-    // Post-mutiply Q1
-    colrot(Q1,j-1,j,m,n,0,m-1,c,s);
-  }
-}
-
-// Update the QR factorization after adding a column z.
-// For convenience, we are given w=Q2'z. Here Q2 is m x 
-// (m-n). The other part of the Q matrix, Q1 m x n, and 
-// R are not needed, so they aren't passed for efficiency 
-void update1(double *Q2, double *w, int m, int k) {
-  int j;
-
-  double c,s;
-  for (j=k-1; j>=1; j--) {
-    // Compute the appropriate c and s
-    givens(w[j-1],w[j],&c,&s);
-
-    // Pre-multiply w
-    rowrot(w,j-1,j,k,1,0,0,c,s);
-
-    // Post-multiply Q2
-    colrot(Q2,j-1,j,m,k,0,m-1,c,s);
-  }
-}
-
-// Downdate the QR factorization after deleting the first row, 
-// where Q is m x m and R is m x n
-void downdate2(double *Q, double *R, int *mp, int *np) {
-  int m,n,i;
-  m = *mp;
-  n = *np;
-
-  double c,s;
-  for (i=m-1; i>=1; i--) {
-    // Compute the appropriate c and s
-    givens(Q[(i-1)*m],Q[i*m],&c,&s);
-
-    // Post-mutiply Q
-    colrot(Q,i-1,i,m,m,0,m-1,c,s);
-
-    // Pre-multiply R
-    if (i<=n) rowrot(R,i-1,i,m,n,i-1,n-1,c,s); 
-  }
-}
-
-// Update the QR factorization after adding the last row,
-// where Q is m x m and R is m x n. For efficiency, Q is not
-// passed, and only the first row of R is passed. Not counting
-// its first row, the first q columns of R are zero
-void update2(double *y, double *D, double *r, int *mp, int *np, int *qp) {
-  int m,n,q,j;
-  m = *mp;
-  n = *np;
-  q = *qp;
-
-  double c,s;
-  for (j=0; j<q-1; j++) {
-    // Compute the appropriate c and s
-    givens(r[j+1],r[j],&c,&s);
-
-    // Pre-multiply r
-    rowrot(r,j+1,j,n,1,0,0,c,s);
-
-    // Post-mutiply D
-    colrot(D,j+1,j,m,n,0,m-1,c,s);
-
-    // Pre-multiply y
-    rowrot(y,j+1,j,n,1,0,0,c,s);
-  }
-}
-
-// Make the R factor upper triangular, by Givens rotating its
-// columns. Here A is m x n, and R is n x n with rank(R) = n-k
-void maketri1(double *y, double *A, double *R, int *mp, int *np, int *kp) {
-  int m,n,k,i,j;
-  m = *mp;
-  n = *np;
-  k = *kp; 
-  
-  double c,s;
-  for (i=n-k-1; i>=0; i--) {
-    for (j=i; j<i+k; j++) {
-      // Compute the appropriate c and s
-      givens(R[i+(j+1)*n],R[i+j*n],&c,&s);
-      
-      // Post-multiply R
-      colrot(R,j+1,j,n,n,0,i,c,s);
-
-      // Post-multiply D
-      colrot(A,j+1,j,m,n,0,m-1,c,s);
-    
-      // Pre-multiply y
-      rowrot(y,j+1,j,n,1,0,0,c,s);
-    }
-  }
-}
-
-// Make the R factor upper triangular, by Givens rotating its 
-// columns. Here A is m x n, and R is m x n with rank(R) = min(m,n)-k
-void maketri2(double *y, double *A, double *R, int *mp, int *np, int *kp) {
-  int m,n,k,i,j;
-  m = *mp;
-  n = *np;
-  k = *kp;
- 
-  int r=m;
-  if (n < m) {
-    r = n;
-  }
-  int d = 0;
-  if (n-m>0) {
-    d = n-m;
-  }
-
-  double c,s;
-  for (i=r-k-1; i>=0; i--) {
-    for (j=i; j<i+k+d; j++) {
-      // Compute the appropriate c and s
-      givens(R[i+(j+1)*m],R[i+j*m],&c,&s);
-      
-      // Post-multiply R
-      colrot(R,j+1,j,m,n,0,i,c,s);
-
-      // Post-multiply D
-      colrot(A,j+1,j,m,n,0,m-1,c,s);
-    
-      // Pre-multiply y
-      rowrot(y,j+1,j,n,1,0,0,c,s);
-    }
-  }
-}
-
-// Make the R factor upper triangular, by Givens rotating
-// its columns. Here A is m1 x n, and R is m2 x n with 
-// rank(R) = n-q-1. The first q columns of R are zero
-void maketri3(double *y, double *A, double *R, int *m1p, int *m2p, int *np, int *qp) {
-  int m1,m2,n,q,i;
-  m1 = *m1p;
-  m2 = *m2p;
-  n = *np;
-  q = *qp;
-  
-  double c,s;
-  for (i=n-q-2; i>=0; i--) {
-    // Compute the appropriate c and s
-    givens(R[i+(i+q+1)*m2],R[i+(i+q)*m2],&c,&s);
-      
-    // Post-multiply R
-    colrot(R,i+q+1,i+q,m2,n,0,i,c,s);
-
-    // Post-multiply D
-    colrot(A,i+q+1,i+q,m1,n,0,m1-1,c,s);
-    
-    // Pre-multiply y
-    rowrot(y,i+q+1,i+q,n,1,0,0,c,s);
-  }
-}
-
-// Make the R factor upper triangular, by Givens rotating
-// its columns and rows, appropriately. Here A is m1 x n, 
-// Q is m2 x m2, and R is m2 x n with rank(R) = n-q-1. The 
-// first q columns of R are zero. The kth row of R is the 
-// last row with a zero element on the diagonal    
-void maketri4(double *y, double *A, double *Q, double *R, int *m1p, int *m2p, int *np, int *qp, int *kp) {
-  int m1,m2,n,q,k,i,j;
-  m1 = *m1p;
-  m2 = *m2p;
-  n = *np; 
-  q = *qp;
-  k = *kp;
-
-  double c,s;
-
-  // First rotate the columns
-  for (i=k-1; i>=0; i--) {
-    // Compute the appropriate c and s
-    givens(R[i+(i+q+1)*m2],R[i+(i+q)*m2],&c,&s);
-      
-    // Post-multiply R
-    colrot(R,i+q+1,i+q,m2,n,0,i,c,s);
-
-    // Post-multiply D
-    colrot(A,i+q+1,i+q,m1,n,0,m1-1,c,s);
-    
-    // Pre-multiply y
-    rowrot(y,i+q+1,i+q,n,1,0,0,c,s);
-  }
-
-  // Next rotate the rows
-  for (j=k+q+1; j<n; j++) {
-    // Compute the appropriate c and s
-    givens(R[j-q-1+j*m2],R[j-q+j*m2],&c,&s);
-
-    // Pre-multiply R
-    rowrot(R,j-q-1,j-q,m2,n,j,n-1,c,s);
-
-    // Post-multiply Q
-    colrot(Q,j-q-1,j-q,m2,m2,0,m2-1,c,s);
-  }
-}
-
diff --git a/selectiveInference/src/matrixcomps.h b/selectiveInference/src/matrixcomps.h
deleted file mode 100644
index 8632e3e..0000000
--- a/selectiveInference/src/matrixcomps.h
+++ /dev/null
@@ -1,12 +0,0 @@
-#ifdef __cplusplus
-extern "C"
-{
-#endif /* __cplusplus */
-
-void update1(double *Q2, double *w, int m, int k);
-
-void downdate1(double *Q1, double *R, int j0, int m, int n);
-
-#ifdef __cplusplus
-}  /* extern "C" */
-#endif /* __cplusplus */
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
deleted file mode 100644
index c851f7d..0000000
--- a/selectiveInference/src/quadratic_program.c
+++ /dev/null
@@ -1,441 +0,0 @@
-#include <math.h> // for fabs
-
-// Find an approximate row of \hat{nndef}^{-1}
-
-// Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
-
-// Dual problem: \text{min}_{\theta} 1/2 \theta^T \Sigma \theta - l^T\theta + \mu \|\theta\|_1
-// where l is `linear_func` below
-
-// This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
-// Therefore we don't have to negate the answer to get theta.
-// Update one coordinate 
-
-double objective_qp(double *nndef_ptr,       /* A non-negative definite matrix */
-		    double *linear_func_ptr, /* Linear term in objective */
-		    int *ever_active_ptr,    /* Ever active set: 0-based */ 
-		    int *nactive_ptr,        /* Size of ever active set */
-		    int nrow,                /* how many rows in nndef */
-		    double bound,            /* Lagrange multipler for \ell_1 */
-		    double *theta_ptr)           /* current value */
-{
-  int irow, icol;
-  double value = 0;
-  double *nndef_ptr_tmp = nndef_ptr;
-  double *linear_func_ptr_tmp = linear_func_ptr;
-  double *theta_row_ptr, *theta_col_ptr;
-  int *active_row_ptr, *active_col_ptr;
-  int active_row, active_col;
-  int nactive = *nactive_ptr;
-
-  theta_row_ptr = theta_ptr;
-  theta_col_ptr = theta_ptr;
-
-  for (irow=0; irow<nactive; irow++) {
-
-    active_row_ptr = ((int *) ever_active_ptr + irow);
-    active_row = *active_row_ptr - 1;          // Ever-active is 1-based
-    theta_row_ptr = ((double *) theta_ptr + active_row);
-
-    for (icol=0; icol<nactive; icol++) {
-      
-      active_col_ptr = ((int *) ever_active_ptr + icol);
-      active_col = *active_col_ptr - 1;          // Ever-active is 1-based
-      theta_col_ptr = ((double *) theta_ptr + active_col);
-
-      nndef_ptr_tmp = ((double *) nndef_ptr + nrow * active_col + active_row); // Matrices are column-major order
-
-      value += 0.5 * (*nndef_ptr_tmp) * (*theta_row_ptr) * (*theta_col_ptr);
-    }
-    value += bound * fabs((*theta_row_ptr)); // the \ell_1 term
-
-    // The linear term in the objective
-
-    linear_func_ptr_tmp = ((double *) linear_func_ptr + active_row);
-    value += (*linear_func_ptr_tmp) * (*theta_row_ptr); 
-
-  }
-  
-  return(value);
-}
-
-// Ever-active is 1-based
-// coord is 0-based
-int update_ever_active_qp(int coord,
-			  int *ever_active_ptr,
-			  int *nactive_ptr) {
-  int iactive;
-  int active_var;
-  int nactive = *nactive_ptr;
-  int *ever_active_ptr_tmp = ever_active_ptr;
-
-  for (iactive=0; iactive<nactive; iactive++) {
-    ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
-    active_var = *ever_active_ptr_tmp;
-    if (active_var - 1 == coord) {          // Ever-active is 1-based
-      return(1);
-    }
-  }
-  
-  // If we haven't returned yet, this means the coord was not in 
-  // ever_active.
-
-  // Add it to the active set and increment the 
-  // number of active variables
-
-  ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
-  *ever_active_ptr_tmp = coord + 1;         // Ever-active is 1-based
-  *nactive_ptr += 1;
-
-  return(0);
-}
-
-int check_KKT_qp(double *theta_ptr,        /* current theta */
-		 double *gradient_ptr, /* nndef times theta + linear_func */
-		 int nfeature,             /* how many features in nndef */
-		 double bound,         /* Lagrange multipler for \ell_1 */
-		 double tol)           /* precision for checking KKT conditions */        
-{
-  // First check inactive
-
-  int ifeature;
-  double *theta_ptr_tmp, *gradient_ptr_tmp;
-  double gradient;
-
-  for (ifeature=0; ifeature<nfeature; ifeature++) {
-    theta_ptr_tmp = ((double *) theta_ptr + ifeature);
-    gradient_ptr_tmp = ((double *) gradient_ptr + ifeature);
-
-    // Compute this coordinate of the gradient
-
-    gradient = *gradient_ptr_tmp;
-
-    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
-	return(0);
-      }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
-	return(0);
-      }
-    }
-    else {
-      if (fabs(gradient) > (1. + tol) * bound) {
-	return(0);
-      }
-    }
-  }
-
-  return(1);
-}
-
-int check_KKT_qp_active(int *ever_active_ptr,           /* Ever active set: 0-based */ 
-		        int *nactive_ptr,               /* Size of ever active set */
-			double *theta_ptr,        /* current theta */
-			double *gradient_ptr, /* nndef times theta + linear_func */
-			int nfeature,             /* how many features in nndef */
-			double bound,         /* Lagrange multipler for \ell_1 */
-			double tol)           /* precision for checking KKT conditions */        
-{
-  // First check inactive
-
-  int iactive;
-  double *theta_ptr_tmp;
-  double gradient;
-  double *gradient_ptr_tmp;
-  int nactive = *nactive_ptr;
-  int active_feature;
-  int *active_feature_ptr;
-
-  for (iactive=0; iactive<nactive; iactive++) {
-
-    active_feature_ptr = ((int *) ever_active_ptr + iactive);
-    active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
-    theta_ptr_tmp = ((double *) theta_ptr + active_feature);
-
-    gradient_ptr_tmp = ((double *) gradient_ptr + active_feature);
-
-    // Compute this coordinate of the gradient
-
-    gradient = *gradient_ptr_tmp;
-
-    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
-
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
-	return(0);
-      }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
-	return(0);
-      }
-
-    }
-    else {
-      if (fabs(gradient) > (1. + tol) * bound) {
-	return(0);
-      }
-    }
-  }
-
-  return(1);
-}
-
-
-double update_one_coord_qp(double *nndef_ptr,           /* A non-negative definite matrix */
-			   double *linear_func_ptr,     /* Linear term in objective */
-			   double *nndef_diag_ptr,      /* Diagonal of nndef */
-			   double *gradient_ptr,        /* nndef times theta + linear_func */
-			   int *ever_active_ptr,        /* Ever active set: 1-based */ 
-			   int *nactive_ptr,            /* Size of ever active set */
-			   int nfeature,                    /* How many features in nndef */
-			   double bound,                /* feasibility parameter */
-			   double *theta_ptr,               /* current value */
-			   int coord,                   /* which coordinate to update: 0-based */
-			   int is_active)               /* Is this coord in ever_active */     
-{
-
-  double delta;
-  double linear_term = 0;
-  double value = 0;
-  double old_value;
-  double *nndef_ptr_tmp;
-  double *gradient_ptr_tmp;
-  double *theta_ptr_tmp;
-  int icol = 0;
-
-  double *quadratic_ptr = ((double *) nndef_diag_ptr + coord);
-  double quadratic_term = *quadratic_ptr;
-
-  gradient_ptr_tmp = ((double *) gradient_ptr + coord);
-  linear_term = *gradient_ptr_tmp;
-
-  theta_ptr_tmp = ((double *) theta_ptr + coord);
-  old_value = *theta_ptr_tmp;
-
-  // The coord entry of gradient_ptr term has a diagonal term in it:
-  // nndef[coord, coord] * theta[coord]
-  // This removes it. 
-
-  linear_term -= quadratic_term * old_value;
-
-  // Now soft-threshold the coord entry of theta 
-
-  // Objective is t \mapsto q/2 * t^2 + l * t + bound |t|
-  // with q=quadratic_term and l=linear_term
-
-  // With a negative linear term, solution should be
-  // positive
-
-  if (linear_term < -bound) {
-    value = (-linear_term - bound) / quadratic_term;
-  }
-  else if (linear_term > bound) {
-    value = -(linear_term - bound) / quadratic_term;
-  }
-
-  // Add to active set if necessary
-
-  if ((is_active == 0) && (value != 0)) {
-    update_ever_active_qp(coord, ever_active_ptr, nactive_ptr);
-  }
-
-  // Update the linear term
-
-  if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
-
-    delta = value - old_value;
-    nndef_ptr_tmp = ((double *) nndef_ptr + coord * nfeature);
-    gradient_ptr_tmp = ((double *) gradient_ptr);
-
-    for (icol=0; icol<nfeature; icol++) {
-      *gradient_ptr_tmp = *gradient_ptr_tmp + delta * (*nndef_ptr_tmp);
-      gradient_ptr_tmp += 1;
-      nndef_ptr_tmp += 1;
-    }
-
-    theta_ptr_tmp = ((double *) theta_ptr + coord);
-    *theta_ptr_tmp = value;
-
-  }
-
-  return(value);
-
-}
-
-int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
-	     double *linear_func_ptr,    /* Linear term in objective */
-	     double *nndef_diag_ptr,     /* Diagonal of nndef */
-	     double *gradient_ptr,       /* nndef times theta */
-	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
-	     int *nactive_ptr,           /* Size of ever active set */
-	     int nfeature,               /* How many features in nndef */
-	     double bound,               /* feasibility parameter */
-	     double *theta,              /* current value */
-	     double *theta_old,          /* previous value */
-	     int maxiter,                /* max number of iterations */
-	     double kkt_tol,             /* precision for checking KKT conditions */
-	     double objective_tol,       /* precision for checking relative decrease in objective value */
-	     double parameter_tol,       /* precision for checking relative convergence of parameter */
-	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
-             int kkt_stop,               /* Break based on KKT? */
-	     int objective_stop,         /* Break based on convergence of objective value? */
-	     int param_stop)             /* Break based on parameter convergence? */
-{
-
-  int iter = 0;
-  int iter_old = 1;
-  int ifeature = 0;
-  int iactive = 0;
-  int *active_ptr;
-
-  int iter_active;
-  int niter_active=5;
-
-  double old_value, new_value; 
-  double norm_diff = 1.;
-  double norm_last = 1.;
-  double delta;
-  double *theta_ptr, *theta_old_ptr;
-
-  if (objective_stop) {
-
-    old_value = objective_qp(nndef_ptr,
-			     linear_func_ptr,
-			     ever_active_ptr,
-			     nactive_ptr,
-			     nfeature,
-			     bound,
-			     theta);
-
-  }
-
-
-  for (iter=0; iter<maxiter; iter++) {
-
-    // Update the active variables first -- do this niter_active times
-
-    for (iter_active=0; iter_active<niter_active; iter_active++) { 
-
-        active_ptr = (int *) ever_active_ptr;
-        for (iactive=0; iactive < *nactive_ptr; iactive++) {
-
-	  update_one_coord_qp(nndef_ptr,
-			      linear_func_ptr,
-			      nndef_diag_ptr,
-			      gradient_ptr,
-			      ever_active_ptr,
-			      nactive_ptr,
-			      nfeature,
-			      bound,
-			      theta,
-			      *active_ptr - 1,   // Ever-active is 1-based
-			      1);
-	  active_ptr++;
-	}
-
-	// Check KKT of active subproblem
-
-	if (check_KKT_qp_active(ever_active_ptr,
-				nactive_ptr,
-				theta,
-				gradient_ptr,
-				nfeature,
-				bound,
-				kkt_tol) == 1) {
-	  break;
-	}
-    }
-
-    // Check KKT
-
-    if (kkt_stop) {
-      if (check_KKT_qp(theta, 
-		       gradient_ptr,
-		       nfeature,
-		       bound,
-		       kkt_tol) == 1) {
-	break;
-      }
-    }
-
-    // Update all variables 
-
-    for (ifeature=0; ifeature<nfeature; ifeature++) {
-
-      update_one_coord_qp(nndef_ptr,
-			  linear_func_ptr,
-			  nndef_diag_ptr,
-			  gradient_ptr,
-			  ever_active_ptr,
-			  nactive_ptr,
-			  nfeature,
-			  bound,
-			  theta,
-			  ifeature,
-			  0);
-    }
-
-    // Check KKT
-
-    if (kkt_stop) {
-      if (check_KKT_qp(theta, 
-		       gradient_ptr,
-		       nfeature,
-		       bound,
-		       kkt_tol) == 1) {
-	break;
-      }
-    }
-					  
-
-    if (iter == 2 * iter_old) { // Geometric iterations from Adel's code
-
-      // Check based on norm 
-
-      if (param_stop) { 
-	iter_old = iter;
-	norm_diff = 0;
-	norm_last = 0;
-	for (ifeature=0; ifeature<nfeature; ifeature++) {
-	  theta_old_ptr = ((double *) theta_old + ifeature);
-	  theta_ptr = ((double *) theta + ifeature);
-	  delta = (*theta_ptr - *theta_old_ptr);
-	  norm_diff += delta * delta;
-	  norm_last += (*theta_ptr) * (*theta_ptr);
-	  *theta_old_ptr = (*theta_ptr);
-	}
-	norm_diff = sqrt(norm_diff);
-	norm_last = sqrt(norm_last);
-	
-	if (norm_diff < parameter_tol * norm_last) {
-	  break;
-	}
-      }
-
-      // Check relative decrease of objective
-
-      if (objective_stop) {
-	new_value = objective_qp(nndef_ptr,
-				 linear_func_ptr,
-				 ever_active_ptr,
-				 nactive_ptr,
-				 nfeature,
-				 bound,
-				 theta);
-
-	if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
-	  break;
-	}
-	old_value = new_value;
-      }
-
-    }
-
-    // Check size of active set
-
-    if (*nactive_ptr >= max_active) {
-      break;
-    }
-
-  }
-  return(iter);
-}
-
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
deleted file mode 100644
index ce7284b..0000000
--- a/selectiveInference/src/quadratic_program_wide.c
+++ /dev/null
@@ -1,597 +0,0 @@
-#include <math.h> // for fabs
-
-// Find an approximate row of \hat{nndef}^{-1}
-
-// Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
-
-// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2/n + l^T\theta + \mu \|\theta\|_1 + \frac{\epsilon}{2} \|\theta\|^2_2
-// where l is `linear_func` below
-
-// This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
-// Therefore we don't have to negate the answer to get theta.
-// Update one coordinate 
-
-// Throughout X is a design matrix
-
-double objective_wide(double *X_theta_ptr,     /* Fitted values */
-		      double *linear_func_ptr, /* Linear term in objective */
-		      int *ever_active_ptr,    /* Ever active set: 0-based */ 
-		      int *nactive_ptr,        /* Size of ever active set */
-		      int ncase,               /* how many rows in X */
-		      int nfeature,            /* how many columns in X */
-		      double *bound_ptr,       /* Lagrange multiplers for \ell_1 */
-		      double ridge_term,       /* Ridge / ENet term */
-		      double *theta_ptr)       /* current value */
-{
-  int icase, iactive;
-  double value = 0;
-  double *bound_ptr_tmp;
-  double *X_theta_ptr_tmp = X_theta_ptr;
-  double *linear_func_ptr_tmp = linear_func_ptr;
-  double *theta_ptr_tmp;
-  int *active_feature_ptr;
-  int active_feature;
-  int nactive = *nactive_ptr;
-
-  // The term \|X\theta\|^2_2/n, with n=ncase
-
-  for (icase=0; icase<ncase; icase++) {
-
-    X_theta_ptr_tmp = ((double *) X_theta_ptr + icase);
-    value += (*(X_theta_ptr_tmp)) * (*(X_theta_ptr_tmp));
-
-  }
-
-  value *= 0.5 / ncase;
-
-  for (iactive=0; iactive<nactive; iactive++) {
-
-    // The linear term in the objective
-
-    active_feature_ptr = ((int *) ever_active_ptr + iactive);
-    active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
-
-    theta_ptr_tmp = ((double *) theta_ptr + active_feature);
-    linear_func_ptr_tmp = ((double *) linear_func_ptr + active_feature);
-    value += (*linear_func_ptr_tmp) * (*theta_ptr_tmp); 
-
-    // The \ell_1 term
-
-    bound_ptr_tmp = ((double *) bound_ptr + active_feature);
-    value += (*bound_ptr_tmp) * fabs((*theta_ptr_tmp));
-
-    // The ridge term
-
-    value += 0.5 * ridge_term * (*theta_ptr_tmp) * (*theta_ptr_tmp);
-  }
-  
-  return(value);
-}
-
-// Compute, update and return one coordinate of the gradient of \|X\theta\|^2_2/2n
-
-double compute_gradient_coord(double *gradient_ptr,        /* Gradient -- one coordinate will be updated if needed */
-			      double *X_theta_ptr,         /* Current fitted values */
-			      double *X_ptr,               /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-			      double *linear_func_ptr,     /* Linear term in objective */   
-			      int *need_update_ptr,        /* Which coordinates need to be updated? */
-			      int coord,                   /* Coordinate we are trying to update */
-			      int ncase,                   /* How many rows in X */
-			      int nfeature)                /* How many columns in X */
-{
-
-  double *gradient_ptr_tmp;
-  double *X_theta_ptr_tmp;
-  double *X_ptr_tmp;  
-  double *linear_func_ptr_tmp;  
-  int *need_update_ptr_tmp;
-  int icase;
-  double value = 0.;
-
-  need_update_ptr_tmp = ((int *) need_update_ptr + coord);
-
-  // Check if this coordinate needs updating
-  if (*need_update_ptr_tmp == 1) {
-
-    for (icase=0; icase<ncase; icase++) {
-#ifdef COLUMN_MAJOR_ORDER
-      X_ptr_tmp = ((double *) X_ptr + coord * ncase + icase);
-#else
-      X_ptr_tmp = ((double *) X_ptr + icase * nfeature + coord);
-#endif
-      X_theta_ptr_tmp = ((double *) X_theta_ptr + icase);
-      value += (*X_ptr_tmp) * (*X_theta_ptr_tmp);
-    }
-
-    value /= ncase;
-
-    linear_func_ptr_tmp = ((double *) linear_func_ptr + coord);
-    value += *linear_func_ptr_tmp;
-
-    gradient_ptr_tmp = ((double *) gradient_ptr + coord);
-    *gradient_ptr_tmp = value;
-
-    *need_update_ptr_tmp = 0;
-  }
-  else {
-    gradient_ptr_tmp = ((double *) gradient_ptr + coord);
-    value = *gradient_ptr_tmp;
-  }
-
-  return(value);
-}
-
-
-// Ever-active is 1-based
-// coord is 0-based
-int update_ever_active_wide(int coord,
-			    int *ever_active_ptr,
-			    int *nactive_ptr) {
-  int iactive;
-  int active_var;
-  int nactive = *nactive_ptr;
-  int *ever_active_ptr_tmp = ever_active_ptr;
-
-  for (iactive=0; iactive<nactive; iactive++) {
-    ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
-    active_var = *ever_active_ptr_tmp;
-
-    if (active_var - 1 == coord) {          // Ever-active is 1-based
-      return(1);
-    }
-  }
-  
-  // If we haven't returned yet, this means the coord was not in 
-  // ever_active.
-
-  // Add it to the active set and increment the 
-  // number of active variables
-
-  ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
-  *ever_active_ptr_tmp = coord + 1;         // Ever-active is 1-based
-  *nactive_ptr += 1;
-
-  return(0);
-}
-
-void update_gradient_wide(double *gradient_ptr,     /* X^TX/ncase times theta + linear_func */
-			  double *X_theta_ptr,      /* Current fitted values */
-			  double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-			  double *linear_func_ptr,  /* Linear term in objective */   
-			  int *need_update_ptr,     /* Which coordinates need to be updated? */
-			  int ncase,             /* how many rows in X */
-			  int nfeature)                /* how many columns in X */
-{
-  int ifeature;
-
-  for (ifeature=0; ifeature<nfeature; ifeature++) {
-    compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, ifeature, ncase, nfeature);
-  }
-
-}
-
-int check_KKT_wide(double *theta_ptr,        /* current theta */
-		   double *gradient_ptr,     /* X^TX/ncase times theta + linear_func*/
-		   double *X_theta_ptr,      /* Current fitted values */
-		   double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-		   double *linear_func_ptr,  /* Linear term in objective */   
-		   int *need_update_ptr,     /* Which coordinates need to be updated? */
-		   int ncase,                /* how many rows in X */
-		   int nfeature,             /* how many columns in X */
-		   double *bound_ptr,        /* Lagrange multiplers for \ell_1 */
-		   double ridge_term,        /* Ridge / ENet term */
-		   double tol)               /* precision for checking KKT conditions */        
-{
-  // First check inactive
-
-  int ifeature;
-  double *theta_ptr_tmp;
-  double *bound_ptr_tmp;
-  double bound;
-  double gradient;
-
-  for (ifeature=0; ifeature<nfeature; ifeature++) {
-
-    theta_ptr_tmp = ((double *) theta_ptr + ifeature);
-    bound_ptr_tmp = ((double *) bound_ptr + ifeature);
-    bound = *bound_ptr_tmp;
-
-    // Compute this coordinate of the gradient
-
-    gradient = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, ifeature, ncase, nfeature);
-
-    if ((*theta_ptr_tmp != 0) && (bound != 0)) { // these coordinates of gradients should be equal to -bound
-      
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + ridge_term * (*theta_ptr_tmp) + bound) > tol * bound)) {
-	return(0);
-      }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient + ridge_term * (*theta_ptr_tmp) - bound) > tol * bound)) {
-	return(0);
-      }
-      
-    }
-    else if (bound != 0) {
-      if (fabs(gradient) > (1. + tol) * bound) {
-	return(0);
-      }
-    }
-  }
-
-  return(1);
-}
-
-int check_KKT_wide_active(int *ever_active_ptr,           /* Ever active set: 0-based */ 
-			  int *nactive_ptr,               /* Size of ever active set */
-			  double *theta_ptr,              /* current theta */
-			  double *gradient_ptr,           /* X^TX/ncase times theta + linear_func*/
-			  double *X_theta_ptr,            /* Current fitted values */
-			  double *X_ptr,                  /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-			  double *linear_func_ptr,        /* Linear term in objective */   
-			  int *need_update_ptr,           /* Which coordinates need to be updated? */
-			  int ncase,                      /* how many rows in X */
-			  int nfeature,                   /* how many columns in X */
-			  double *bound_ptr,              /* Lagrange multipliers for \ell_1 */
-			  double ridge_term,              /* Ridge / ENet term */
-			  double tol)                     /* precision for checking KKT conditions */        
-{
-  // First check inactive
-
-  int iactive;
-  double *theta_ptr_tmp;
-  double *bound_ptr_tmp;
-  double bound;
-  double gradient;
-  int nactive = *nactive_ptr;
-  int active_feature;
-  int *active_feature_ptr;
-
-  for (iactive=0; iactive<nactive; iactive++) {
-
-    active_feature_ptr = ((int *) ever_active_ptr + iactive);
-    active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
-
-    theta_ptr_tmp = ((double *) theta_ptr + active_feature);
-    bound_ptr_tmp = ((double *) bound_ptr + active_feature);
-    bound = *bound_ptr_tmp;
-
-    // Compute this coordinate of the gradient
-
-    gradient = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, active_feature, ncase, nfeature);
-
-    if ((*theta_ptr_tmp != 0) && (bound != 0)) { // these coordinates of gradients should be equal to -bound
-
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + ridge_term * (*theta_ptr_tmp) + bound) > tol * bound)) {
-	return(0);
-      }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient + ridge_term * (*theta_ptr_tmp) - bound) > tol * bound)) {
-	return(0);
-      }
-
-    }
-    else if (bound != 0) {
-      if (fabs(gradient) > (1. + tol) * bound) {
-	return(0);
-      }
-    }
-  }
-
-  return(1);
-}
-
-double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
-			     double *linear_func_ptr,     /* Linear term in objective */
-			     double *nndef_diag_ptr,      /* Diagonal of nndef */
-			     double *gradient_ptr,        /* X^TX/ncase times theta + linear_func*/
-			     int *ever_active_ptr,        /* Ever active set: 1-based */ 
-			     int *nactive_ptr,            /* Size of ever active set */
-			     double *X_theta_ptr,         /* X\theta -- fitted values */
-			     int *need_update_ptr,        /* Whether a gradient coordinate needs update or not */
-			     int ncase,                   /* How many rows in X */
-			     int nfeature,                /* How many rows in X */
-			     double *bound_ptr,           /* Lagrange multipliers */
-			     double ridge_term,           /* Ridge / ENet term */
-			     double *theta_ptr,           /* current value */
-			     int coord,                   /* which coordinate to update: 0-based */
-			     int is_active)               /* Is this coord in ever_active */     
-{
-
-  double delta;
-  double linear_term = 0;
-  double value = 0;
-  double old_value;
-  double *X_ptr_tmp;
-  double *X_theta_ptr_tmp;
-  int *need_update_ptr_tmp;
-  double *theta_ptr_tmp;
-  double *bound_ptr_tmp;
-  double bound;
-  int ifeature, icase;
-
-  double *diagonal_ptr = ((double *) nndef_diag_ptr + coord);
-  double diagonal_entry = *diagonal_ptr;
-
-  linear_term = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, coord, ncase, nfeature);
-
-  theta_ptr_tmp = ((double *) theta_ptr + coord);
-  old_value = *theta_ptr_tmp;
-
-  bound_ptr_tmp = ((double *) bound_ptr + coord);
-  bound = *bound_ptr_tmp;
-
-  // The coord entry of gradient_ptr term has a diagonal term in it:
-  // (X^TX)[coord, coord] * theta[coord] / ncase
-  // This removes it. 
-
-  linear_term -= diagonal_entry * old_value;
-
-  // Now soft-threshold the coord entry of theta 
-
-  // Objective is t \mapsto (q+eps)/2 * t^2 + l * t + bound |t| + 
-  // with q=diagonal_entry and l=linear_term and eps=ridge_Term
-
-  // With a negative linear term, solution should be
-  // positive
-
-  if (linear_term < -bound) {
-    value = (-linear_term - bound) / (diagonal_entry + ridge_term);
-  }
-  else if (linear_term > bound) {
-    value = -(linear_term - bound) / (diagonal_entry + ridge_term);
-  }
-
-  // Add to active set if necessary
-
-  if ((is_active == 0) && (value != 0)) {
-    update_ever_active_wide(coord, ever_active_ptr, nactive_ptr);
-  }
-
-  // Update X\theta if changed
-
-  if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
-
-    // Set the update_gradient_ptr to 1
-
-    need_update_ptr_tmp = need_update_ptr;
-    for (ifeature=0; ifeature<nfeature; ifeature++) {
-      *need_update_ptr_tmp = 1;
-      need_update_ptr_tmp++;
-    }
-
-    // Update X\theta
-
-    delta = value - old_value;
-
-    for (icase=0; icase<ncase; icase++) {
-      X_theta_ptr_tmp = ((double *) X_theta_ptr + icase);
-#ifdef COLUMN_MAJOR_ORDER
-      X_ptr_tmp = ((double *) X_ptr + coord * ncase + icase);
-#else
-      X_ptr_tmp = ((double *) X_ptr + icase * nfeature + coord);
-#endif
-      *X_theta_ptr_tmp = (*X_theta_ptr_tmp) + delta * (*X_ptr_tmp);
-    }
-
-    theta_ptr_tmp = ((double *) theta_ptr + coord);
-    *theta_ptr_tmp = value;
-
-    theta_ptr_tmp = theta_ptr;
-    for (ifeature=0; ifeature<nfeature; ifeature++) {
-      theta_ptr_tmp++;
-    }
-
-  }
-
-  return(value);
-
-}
-
-int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-	       double *X_theta_ptr,        /* Fitted values   */
-	       double *linear_func_ptr,    /* Linear term in objective */
-	       double *nndef_diag_ptr,     /* Diagonal entries of non-neg def matrix */
-	       double *gradient_ptr,       /* X^TX/ncase times theta + linear_func*/
-	       int *need_update_ptr,       /* Keeps track of updated gradient coords */
-	       int *ever_active_ptr,       /* Ever active set: 1-based */ 
-	       int *nactive_ptr,           /* Size of ever active set */
-	       int ncase,                  /* How many rows in X */
-	       int nfeature,               /* How many columns in X */
-	       double *bound_ptr,          /* Lagrange multipliers */
-	       double ridge_term,          /* Ridge / ENet term */
-	       double *theta_ptr,          /* current value */
-	       double *theta_old_ptr,      /* previous value */
-	       int maxiter,                /* max number of iterations */
-	       double kkt_tol,             /* precision for checking KKT conditions */
-	       double objective_tol,       /* precision for checking relative decrease in objective value */
-	       double parameter_tol,       /* precision for checking relative convergence of parameter */
-	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
-	       int kkt_stop,               /* Break based on KKT? */
-	       int objective_stop,         /* Break based on convergence of objective value? */
-	       int param_stop)             /* Break based on parameter convergence? */
-{
-
-  int iter = 0;
-  int iter_old = 1;
-  int ifeature = 0;
-  int iactive = 0;
-  int *active_ptr;
-
-  double old_value, new_value; 
-  int niter_active = 5;
-  int iter_active;
-
-  double norm_diff = 1.;
-  double norm_last = 1.;
-  double delta;
-  double *theta_ptr_tmp, *theta_old_ptr_tmp;
-
-  new_value = objective_wide(X_theta_ptr,
-			     linear_func_ptr,
-			     ever_active_ptr,
-			     nactive_ptr,
-			     ncase,
-			     nfeature,
-			     bound_ptr,
-			     ridge_term,
-			     theta_ptr);
-
-  old_value = new_value + 2000000000; // hack for a big number... should do better
-
-
-  for (iter=0; iter<maxiter; iter++) {
-
-    // Update the active variables first -- do this niter_active times
-
-    for (iter_active=0; iter_active<niter_active; iter_active++) { 
-
-        active_ptr = (int *) ever_active_ptr;
-        for (iactive=0; iactive < *nactive_ptr; iactive++) {
-
-          update_one_coord_wide(X_ptr,
-    			        linear_func_ptr,
-				nndef_diag_ptr,
-				gradient_ptr,
-				ever_active_ptr,
-				nactive_ptr,
-				X_theta_ptr,
-				need_update_ptr,
-				ncase,
-				nfeature,
-				bound_ptr,
-				ridge_term,
-				theta_ptr,
-				*active_ptr - 1,   // Ever-active is 1-based
-				1);
-	  active_ptr++;
-	}
-
-	// Check KKT of active subproblem
-
-	if (check_KKT_wide_active(ever_active_ptr,
-				  nactive_ptr,
-				  theta_ptr,
-				  gradient_ptr,
-				  X_theta_ptr,
-				  X_ptr,
-				  linear_func_ptr,
-				  need_update_ptr,
-				  ncase,
-				  nfeature,
-				  bound_ptr,
-				  ridge_term,
-				  kkt_tol) == 1) {
-	  break;
-	}
-
-    }
-
-    // Check KKT
-
-    if (kkt_stop) { 
-      if (check_KKT_wide(theta_ptr,
-			 gradient_ptr,
-			 X_theta_ptr,
-			 X_ptr,
-			 linear_func_ptr,
-			 need_update_ptr,
-			 ncase,
-			 nfeature,
-			 bound_ptr,
-			 ridge_term,
-			 kkt_tol) == 1) {
-	break;
-      }
-    }
-
-    // Update all variables 
-
-    for (ifeature=0; ifeature<nfeature; ifeature++) {
-
-      update_one_coord_wide(X_ptr,
-			    linear_func_ptr,
-			    nndef_diag_ptr,
-			    gradient_ptr,
-			    ever_active_ptr,
-			    nactive_ptr,
-			    X_theta_ptr,
-			    need_update_ptr,
-			    ncase,
-			    nfeature,
-			    bound_ptr,
-			    ridge_term,
-			    theta_ptr,
-			    ifeature,
-			    0);
-    }
-
-    // Check KKT
-
-    if (kkt_stop) {
-      if (check_KKT_wide(theta_ptr,
-			 gradient_ptr,
-			 X_theta_ptr,
-			 X_ptr,
-			 linear_func_ptr,
-			 need_update_ptr,
-			 ncase,
-			 nfeature,
-			 bound_ptr,
-			 ridge_term,
-			 kkt_tol) == 1) {
-	break;
-      }
-    }
-
-    if (iter == 2 * iter_old) { // Geometric iterations from Adel's code
-
-      // Check based on norm 
-
-      if (param_stop) {
-	iter_old = iter;
-	norm_diff = 0;
-	norm_last = 0;
-	for (ifeature=0; ifeature<nfeature; ifeature++) {
-	  theta_old_ptr_tmp = ((double *) theta_old_ptr + ifeature);
-	  theta_ptr_tmp = ((double *) theta_ptr + ifeature);
-	  delta = (*theta_ptr_tmp - *theta_old_ptr_tmp);
-	  norm_diff += delta * delta;
-	  norm_last += (*theta_ptr_tmp) * (*theta_ptr_tmp);
-	  *theta_old_ptr = (*theta_ptr_tmp);
-	}
-	norm_diff = sqrt(norm_diff);
-	norm_last = sqrt(norm_last);
-	
-	if (norm_diff < parameter_tol * norm_last) {
-	  break;
-	}
-      }
-
-      // Check relative decrease of objective
-
-      if (objective_stop) {
-	new_value = objective_wide(X_theta_ptr,
-				   linear_func_ptr,
-				   ever_active_ptr,
-				   nactive_ptr,
-				   ncase,
-				   nfeature,
-				   bound_ptr,
-				   ridge_term,
-				   theta_ptr);
-
-	if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
-	  break;
-	}
-	old_value = new_value;
-      }
-    }
-
-    // Check size of active set
-
-    if (*nactive_ptr >= max_active) {
-      break;
-    }
-
-  }
-  return(iter);
-}
-
diff --git a/selectiveInference/src/randomized_lasso.c b/selectiveInference/src/randomized_lasso.c
deleted file mode 100644
index 5729408..0000000
--- a/selectiveInference/src/randomized_lasso.c
+++ /dev/null
@@ -1,411 +0,0 @@
-#include <math.h> // for fabs
-#include <stdio.h>
-
-// Augmented density for randomized LASSO after
-// Gaussian randomization
-
-// Described in https://arxiv.org/abs/1609.05609
-
-// Gaussian is product of IID N(0, noise_scale^2) density
-// Evaluated at A_D D + A_O O + h
-
-// Laplace is product of IID Laplace with scale noise_scale
-// Also evaluated at A_D D + A_O O + h
-
-// Matrices are assumed in column major order! 
-
-double log_density_gaussian(double noise_scale,             // Scale of randomization
-			    int ndim,                       // Number of features -- "p"
-			    int ninternal,                  // Dimension of internal data representation often 1
-			    int noptimization,              // Dimension of optimization variables -- "p"
-			    double *internal_linear,        // A_D -- linear part for data
-			    double *internal_state,         // D -- data state
-			    double *optimization_linear,    // A_O -- linear part for optimization variables
-			    double *optimization_state,     // O -- optimization state
-			    double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double denom = 2 * noise_scale * noise_scale;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Internal (i.e. data) contribution
-    for (icol=0; icol<ninternal; icol++) {
-      
-      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
-      internal_state_ptr = ((double *) internal_state + icol);
-
-      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
-    }
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= (reconstruction * reconstruction) / denom;
-  }
-
-  return(value);
-}
-
-double log_density_laplace(double noise_scale,             // Scale of randomization
-			   int ndim,                       // Number of features -- "p"
-			   int ninternal,                  // Dimension of internal data representation often 1
-			   int noptimization,              // Dimension of optimization variables -- "p"
-			   double *internal_linear,        // A_D -- linear part for data
-			   double *internal_state,         // D -- data state
-			   double *optimization_linear,    // A_O -- linear part for optimization variables
-			   double *optimization_state,     // O -- optimization state
-			   double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Internal (i.e. data) contribution
-    for (icol=0; icol<ninternal; icol++) {
-      
-      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
-      internal_state_ptr = ((double *) internal_state + icol);
-
-      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
-    }
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= fabs(reconstruction) / noise_scale;
-  }
-
-  return(value);
-}
-
-// Keeping internal (data) state fixed
-
-double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
-					int ndim,                       // Number of features -- "p"
-					int noptimization,              // Dimension of optimization variables -- "p"
-					double *optimization_linear,    // A_O -- linear part for optimization variables
-					double *optimization_state,     // O -- optimization state
-					double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double denom = 2 * noise_scale * noise_scale;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= reconstruction * reconstruction / denom;
-  }
-
-  return(value);
-}
-
-double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
-				       int ndim,                       // Number of features -- "p"
-				       int noptimization,              // Dimension of optimization variables -- "p"
-				       double *optimization_linear,    // A_O -- linear part for optimization variables
-				       double *optimization_state,     // O -- optimization state
-				       double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= fabs(reconstruction) / noise_scale;
-  }
-
-  return(value);
-}
-
-//    objective = lambda u: -u.T.dot(conjugate_arg) + u.T.dot(precision).dot(u)/2. + np.log(1.+ 1./(u / scaling)).sum()
-
-double barrier_objective(double *opt_variable,               // Optimization variable
-			 double *conjugate_arg,              // Argument to conjugate of Gaussian
-			 double *precision,                  // Precision matrix of Gaussian
-			 double *scaling,                    // Diagonal scaling matrix for log barrier
-			 int ndim)                           // Dimension of conjugate_arg, precision
-{
-  int idim, jdim;
-  double *conjugate_arg_ptr;
-  double *opt_variable_ptr;
-  double *precision_ptr;
-  double *scaling_ptr;
-  double product_entry, value;
-
-  value = 0.;
-  for (idim=0; idim<ndim; idim++) {
-    
-    // Compute an entry of precision.dot(conjugate_arg)
-
-    product_entry = 0;
-    for (jdim=0; jdim<ndim; jdim++) {
-    
-      precision_ptr = ((double *) precision + idim * ndim + jdim); // precision is a symmetric matrix
-      opt_variable_ptr = ((double *) opt_variable + jdim);
-      product_entry += (*precision_ptr) * (*opt_variable_ptr);
-    }
-      
-    opt_variable_ptr = ((double *) opt_variable + idim);
-    value += 0.5 * (*opt_variable_ptr) * product_entry;
-
-    // now add linear term
-
-    conjugate_arg_ptr = ((double *) conjugate_arg + idim);
-    value -= (*conjugate_arg_ptr) * (*opt_variable_ptr);
-
-    // now log term
-
-    scaling_ptr = ((double *) scaling + idim);
-    value += log(((*opt_variable_ptr) + (*scaling_ptr)) / (*opt_variable_ptr));
-
-  }
-
-  return(value);
-}
-
-//    grad = lambda u: -conjugate_arg + precision.dot(u) + (1./(scaling + u) - 1./u)
-
-void barrier_gradient(double *gradient,                   // Gradient vector
-		      double *opt_variable,               // Optimization variable
-		      double *conjugate_arg,              // Argument to conjugate of Gaussian
-		      double *precision,                  // Precision matrix of Gaussian
-		      double *scaling,                    // Diagonal scaling matrix for log barrier
-		      int ndim)                           // Dimension of conjugate_arg, precision
-{
-  int idim, jdim;
-  double *gradient_ptr;
-  double *conjugate_arg_ptr;
-  double *opt_variable_ptr;
-  double *precision_ptr;
-  double *scaling_ptr;
-  double product_entry;
-
-  for (idim=0; idim<ndim; idim++) {
-    
-    gradient_ptr = ((double *) gradient + idim);
-
-    // Compute an entry of precision.dot(conjugate_arg)
-
-    product_entry = 0;
-    for (jdim=0; jdim<ndim; jdim++) {
-    
-      precision_ptr = ((double *) precision + idim * ndim + jdim); // precision is a symmetric matrix
-      opt_variable_ptr = ((double *) opt_variable + jdim);
-      product_entry += (*precision_ptr) * (*opt_variable_ptr);
-    }
-      
-    opt_variable_ptr = ((double *) opt_variable + idim);
-    *gradient_ptr = product_entry;
-
-    // now add linear term
-
-    conjugate_arg_ptr = ((double *) conjugate_arg + idim);
-    *gradient_ptr -= (*conjugate_arg_ptr);
-
-    // now log term
-
-    scaling_ptr = ((double *) scaling + idim);
-    *gradient_ptr = *gradient_ptr + 1. / ((*opt_variable_ptr) + (*scaling_ptr)) - 1. / (*opt_variable_ptr);
-  }
-
-}
-
-double barrier_gradient_step(double *gradient,                   // Gradient vector
-			     double *opt_variable,               // Optimization variable
-			     double *opt_proposed,               // Proposed value of optimization variable
-			     double *conjugate_arg,              // Argument to conjugate of Gaussian
-			     double *precision,                  // Precision matrix of Gaussian
-			     double *scaling,                    // Diagonal scaling matrix for log barrier
-			     double step,                        // Step size for gradient step
-			     int ndim)                           // Dimension of conjugate_arg, precision
-{
-  int idim;
-  double *gradient_ptr;
-  double *opt_variable_ptr;
-  double *opt_proposed_ptr;
-
-  for (idim=0; idim<ndim; idim++) {
-    opt_variable_ptr = ((double *) opt_variable + idim);
-    opt_proposed_ptr = ((double *) opt_proposed + idim);
-    gradient_ptr = ((double *) gradient + idim);
-    *opt_proposed_ptr = (*opt_variable_ptr) - step * (*gradient_ptr);
-  }
-
-  return barrier_objective(opt_proposed,
-			   conjugate_arg,         
-			   precision,             
-			   scaling,               
-			   ndim);
-}
-
-double barrier_solve(double *gradient,                   // Gradient vector
-		     double *opt_variable,               // Optimization variable
-		     double *opt_proposed,               // New value of optimization variable
-		     double *conjugate_arg,              // Argument to conjugate of Gaussian
-		     double *precision,                  // Precision matrix of Gaussian
-		     double *scaling,                    // Diagonal scaling matrix for log barrier
-		     int ndim,                           // Dimension of conjugate_arg, precision
-		     int max_iter,                       // Maximum number of iterations
-		     double value_tol,                   // Tolerance for convergence based on value
-		     double initial_step)                // Initial step size
-{
-  int iter, idim, istep;
-  double *gradient_ptr;
-  double *opt_variable_ptr;
-  double *opt_proposed_ptr;
-  double current_value = barrier_objective(opt_variable,
-					   conjugate_arg,         
-					   precision,             
-					   scaling,               
-					   ndim);
-  double proposed_value;
-  double step = initial_step;
-  int any_negative;
-
-  for (iter=0; iter<max_iter; iter++) {
-    
-    // Compute the gradient
-
-    barrier_gradient(gradient,    
-		     opt_variable,
-		     conjugate_arg,
-		     precision,    
-		     scaling,
-		     ndim);
-    
-    // Find a valid step size
-
-    istep = 0;
-    while (1) {
-      any_negative = 0;
-      for (idim=0; idim<ndim; idim++) {
-	opt_variable_ptr = ((double *) opt_variable + idim);
-	gradient_ptr = ((double *) gradient + idim);
-	if ((*opt_variable) - step * (*gradient_ptr) < 0) {
-	  any_negative += 1;
-	}
-      }
-      if (any_negative == 0) {
-	break;
-      }
-      step = step * 0.5;
-      istep++;
-      if (istep == 50) {
-	break;            // Terminate eventually -- but this will be a failure.
-	                  // Should mean that opt_variable is in feasible.
-      }
-    }
-
-    // Find a step size that is a descent
-
-    istep = 0;
-    while (1) {
-      proposed_value = barrier_gradient_step(gradient,
-					     opt_variable,
-					     opt_proposed,
-					     conjugate_arg,         
-					     precision,             
-					     scaling,               
-					     step,
-					     ndim);
-      if (proposed_value < current_value) {
-	for (idim=0; idim<ndim; idim++) {
-	  opt_variable_ptr = ((double *) opt_variable + idim);
-	  opt_proposed_ptr = ((double *) opt_proposed + idim);
-	  *opt_variable_ptr = *opt_proposed_ptr;
-	}
-	break;
-      }
-      step = step * 0.5;
-      istep++;
-      if (istep == 50) {
-	break;            // Terminate eventually -- this will mean no descent.
-	                  // We've solved the problem
-      }
-    }
-
-    if (fabs(current_value - proposed_value) < value_tol * fabs(current_value)) {
-      current_value = proposed_value;
-      break;
-    }
-    current_value = proposed_value;
-    
-  }
-
-  return(current_value);
-
-}
-
diff --git a/selectiveInference/src/randomized_lasso.h b/selectiveInference/src/randomized_lasso.h
deleted file mode 100644
index 268e071..0000000
--- a/selectiveInference/src/randomized_lasso.h
+++ /dev/null
@@ -1,54 +0,0 @@
-#ifdef __cplusplus
-extern "C"
-{
-#endif /* __cplusplus */
-
-double log_density_gaussian(double noise_scale,             // Scale of randomization
-			    int ndim,                       // Number of features -- "p"
-			    int ninternal,                  // Dimension of internal data representation often 1
-			    int noptimization,              // Dimension of optimization variables -- "p"
-			    double *internal_linear,        // A_D -- linear part for data
-			    double *internal_state,         // D -- data state
-			    double *optimization_linear,    // A_O -- linear part for optimization variables
-			    double *optimization_state,     // O -- optimization state
-			    double *offset);                // h -- offset in affine transform -- "p" dimensional 
-  
-double log_density_laplace(double noise_scale,             // Scale of randomization
-			   int ndim,                       // Number of features -- "p"
-			   int ninternal,                  // Dimension of internal data representation often 1
-			   int noptimization,              // Dimension of optimization variables -- "p"
-			   double *internal_linear,        // A_D -- linear part for data
-			   double *internal_state,         // D -- data state
-			   double *optimization_linear,    // A_O -- linear part for optimization variables
-			   double *optimization_state,     // O -- optimization state
-			   double *offset);                // h -- offset in affine transform -- "p" dimensional 
-
-double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
-					int ndim,                       // Number of features -- "p"
-					int noptimization,              // Dimension of optimization variables -- "p"
-					double *optimization_linear,    // A_O -- linear part for optimization variables
-					double *optimization_state,     // O -- optimization state
-					double *offset);                // h -- offset in affine transform -- "p" dimensional 
-
-double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
-				       int ndim,                       // Number of features -- "p"
-				       int noptimization,              // Dimension of optimization variables -- "p"
-				       double *optimization_linear,    // A_O -- linear part for optimization variables
-				       double *optimization_state,     // O -- optimization state
-				       double *offset);                // h -- offset in affine transform -- "p" dimensional 
-
-
-double barrier_solve(double *gradient,                   // Gradient vector
-		     double *opt_variable,               // Optimization variable
-		     double *opt_proposed,               // New value of optimization variable
-		     double *conjugate_arg,              // Argument to conjugate of Gaussian
-		     double *precision,                  // Precision matrix of Gaussian
-		     double *scaling,                    // Diagonal scaling matrix for log barrier
-		     int ndim,                           // Dimension of conjugate_arg, precision
-		     int max_iter,                       // Maximum number of iterations
-		     double value_tol,                   // Tolerance for convergence based on value
-		     double initial_step);               // Initial stepsize 
-
-#ifdef __cplusplus
-}  /* extern "C" */
-#endif /* __cplusplus */
diff --git a/tests/comparisons/big cor/knockoff+_result.rds b/tests/comparisons/big cor/knockoff+_result.rds
deleted file mode 100644
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diff --git a/tests/comparisons/big cor/knockoff.rds b/tests/comparisons/big cor/knockoff.rds
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diff --git a/tests/comparisons/big uncorr/knockoff+.rds b/tests/comparisons/big uncorr/knockoff+.rds
deleted file mode 100644
index a4365b6a31dc3ce497c86f3e281ef96cee1389f1..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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From 6ea54a577d7ab4dd7aa50e635dda080a34e70cc5 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 29 Mar 2018 20:54:21 -0700
Subject: [PATCH 473/493] disambgiuating argument

---
 selectiveInference/R/funs.full.main.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.full.main.R b/selectiveInference/R/funs.full.main.R
index aa6c33d..5047772 100644
--- a/selectiveInference/R/funs.full.main.R
+++ b/selectiveInference/R/funs.full.main.R
@@ -181,7 +181,7 @@ test_TG = function(param, observed, variance, sigma_est, center, radius, alt){
 tnorm.union.surv = function(z, mean, sd, intervals, bits = NULL){
   # intervals is a I x 2 matrix of disjoint intervals where the first column contains the lower endpoint
   
-  pval = matrix(NA, nr = dim(intervals)[1], nc = length(mean))
+  pval = matrix(NA, nrow = dim(intervals)[1], ncol = length(mean))
   for(jj in 1:dim(intervals)[1]){
     if(z <= intervals[jj,1]){
       pval[jj,] = 1
@@ -192,7 +192,7 @@ tnorm.union.surv = function(z, mean, sd, intervals, bits = NULL){
     }
   }
   
-  ww = matrix(NA, nr = dim(intervals)[1], nc = length(as.vector(mean)))
+  ww = matrix(NA, nrow = dim(intervals)[1], ncol = length(as.vector(mean)))
   for(jj in 1:dim(intervals)[1]){
     ww[jj,] = pnorm(intervals[jj,2], mean = mean, sd = sd) - pnorm(intervals[jj,1], mean = mean, sd = sd)
   }

From 1caebe270b88dbd5af94f7e0d6f7a29dcdc78454 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 29 Mar 2018 20:56:30 -0700
Subject: [PATCH 474/493] fixing import of p.adjust

---
 selectiveInference/NAMESPACE | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index c17a163..024a0a7 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -42,7 +42,7 @@ import(intervals)
 import(survival)
 importFrom("graphics", abline, axis, matplot)
 importFrom("stats", dnorm, lsfit, pexp, pnorm, predict, 
-           qnorm, rnorm, sd, uniroot, dchisq, model.matrix, pchisq, resid, binomial, fitted)
+           qnorm, rnorm, sd, uniroot, dchisq, model.matrix, pchisq, resid, binomial, fitted, p.adjust)
 importFrom("stats", "coef", "df", "lm", "pf")
 importFrom("stats", "glm", "residuals", "vcov")
 importFrom("stats", "rbinom", "rexp")

From 05f4d6cd1afdc6be20d245e7d161addab8e74f70 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 29 Mar 2018 20:58:54 -0700
Subject: [PATCH 475/493] sigma import

---
 selectiveInference/NAMESPACE | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index 024a0a7..b0aa8b5 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -42,7 +42,8 @@ import(intervals)
 import(survival)
 importFrom("graphics", abline, axis, matplot)
 importFrom("stats", dnorm, lsfit, pexp, pnorm, predict, 
-           qnorm, rnorm, sd, uniroot, dchisq, model.matrix, pchisq, resid, binomial, fitted, p.adjust)
+           qnorm, rnorm, sd, uniroot, dchisq, model.matrix, pchisq, resid, binomial, fitted, 
+	   p.adjust, sigma)
 importFrom("stats", "coef", "df", "lm", "pf")
 importFrom("stats", "glm", "residuals", "vcov")
 importFrom("stats", "rbinom", "rexp")

From 791a7c86159120a7e2a07d9c32bc96a3992ccd85 Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Thu, 29 Mar 2018 21:14:49 -1000
Subject: [PATCH 476/493] removed rds files

---
 tests/comparisons/big cor/knockoff+_result.rds | Bin 782 -> 0 bytes
 tests/comparisons/big cor/knockoff.rds         | Bin 1114 -> 0 bytes
 tests/comparisons/big cor/knockoff_result.rds  | Bin 782 -> 0 bytes
 tests/comparisons/big cor/lee.rds              | Bin 122793 -> 0 bytes
 tests/comparisons/big cor/liu.rds              | Bin 246824 -> 0 bytes
 tests/comparisons/big cor/randomized_full.rds  | Bin 199392 -> 0 bytes
 .../comparisons/big cor/randomized_partial.rds | Bin 195272 -> 0 bytes
 tests/comparisons/big uncorr/knockoff+.rds     | Bin 193795 -> 0 bytes
 tests/comparisons/big uncorr/knockoff.rds      | Bin 803 -> 0 bytes
 tests/comparisons/big uncorr/lee.rds           | Bin 116403 -> 0 bytes
 tests/comparisons/big uncorr/liu.rds           | Bin 239089 -> 0 bytes
 tests/comparisons/big uncorr/params.txt        |   3 ---
 .../comparisons/big uncorr/randomized_full.rds | Bin 197511 -> 0 bytes
 .../big uncorr/randomized_partial.rds          | Bin 193795 -> 0 bytes
 tests/comparisons/small corr/knockoff+.rds     | Bin 273 -> 0 bytes
 tests/comparisons/small corr/knockoff.rds      | Bin 350 -> 0 bytes
 tests/comparisons/small corr/lee_full.rds      | Bin 21222 -> 0 bytes
 tests/comparisons/small corr/liu_full.rds      | Bin 42553 -> 0 bytes
 tests/comparisons/small corr/params.txt        |   0
 tests/comparisons/small uncorr/knockoff+.rds   | Bin 271 -> 0 bytes
 tests/comparisons/small uncorr/knockoff.rds    | Bin 357 -> 0 bytes
 tests/comparisons/small uncorr/lee_full.rds    | Bin 15719 -> 0 bytes
 tests/comparisons/small uncorr/liu_full.rds    | Bin 31871 -> 0 bytes
 tests/comparisons/small uncorr/params.txt      |   0
 .../small uncorr/randomized_full.rds           | Bin 35115 -> 0 bytes
 25 files changed, 3 deletions(-)
 delete mode 100644 tests/comparisons/big cor/knockoff+_result.rds
 delete mode 100644 tests/comparisons/big cor/knockoff.rds
 delete mode 100644 tests/comparisons/big cor/knockoff_result.rds
 delete mode 100644 tests/comparisons/big cor/lee.rds
 delete mode 100644 tests/comparisons/big cor/liu.rds
 delete mode 100644 tests/comparisons/big cor/randomized_full.rds
 delete mode 100644 tests/comparisons/big cor/randomized_partial.rds
 delete mode 100644 tests/comparisons/big uncorr/knockoff+.rds
 delete mode 100644 tests/comparisons/big uncorr/knockoff.rds
 delete mode 100644 tests/comparisons/big uncorr/lee.rds
 delete mode 100644 tests/comparisons/big uncorr/liu.rds
 delete mode 100644 tests/comparisons/big uncorr/params.txt
 delete mode 100644 tests/comparisons/big uncorr/randomized_full.rds
 delete mode 100644 tests/comparisons/big uncorr/randomized_partial.rds
 delete mode 100644 tests/comparisons/small corr/knockoff+.rds
 delete mode 100644 tests/comparisons/small corr/knockoff.rds
 delete mode 100644 tests/comparisons/small corr/lee_full.rds
 delete mode 100644 tests/comparisons/small corr/liu_full.rds
 delete mode 100644 tests/comparisons/small corr/params.txt
 delete mode 100644 tests/comparisons/small uncorr/knockoff+.rds
 delete mode 100644 tests/comparisons/small uncorr/knockoff.rds
 delete mode 100644 tests/comparisons/small uncorr/lee_full.rds
 delete mode 100644 tests/comparisons/small uncorr/liu_full.rds
 delete mode 100644 tests/comparisons/small uncorr/params.txt
 delete mode 100644 tests/comparisons/small uncorr/randomized_full.rds

diff --git a/tests/comparisons/big cor/knockoff+_result.rds b/tests/comparisons/big cor/knockoff+_result.rds
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diff --git a/tests/comparisons/big uncorr/lee.rds b/tests/comparisons/big uncorr/lee.rds
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diff --git a/tests/comparisons/big uncorr/params.txt b/tests/comparisons/big uncorr/params.txt
deleted file mode 100644
index dca935c..0000000
--- a/tests/comparisons/big uncorr/params.txt	
+++ /dev/null
@@ -1,3 +0,0 @@
-n=1000, p=2000, rho=0,
-snr = sqrt(2*log(p)/n)
-nrep=100
\ No newline at end of file
diff --git a/tests/comparisons/big uncorr/randomized_full.rds b/tests/comparisons/big uncorr/randomized_full.rds
deleted file mode 100644
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diff --git a/tests/comparisons/small uncorr/params.txt b/tests/comparisons/small uncorr/params.txt
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index e69de29..0000000
diff --git a/tests/comparisons/small uncorr/randomized_full.rds b/tests/comparisons/small uncorr/randomized_full.rds
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From eed2e4acfb4ee3b4dd7b74f66e57fdd9708c92a7 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 30 Mar 2018 08:03:31 -0700
Subject: [PATCH 477/493] adding gglasso dependence, using solve instead of
 ginv

---
 selectiveInference/DESCRIPTION        |  1 +
 selectiveInference/NAMESPACE          |  2 ++
 selectiveInference/R/funs.full.main.R | 23 +++++++++--------------
 selectiveInference/src/.gitignore     |  3 +++
 4 files changed, 15 insertions(+), 14 deletions(-)
 create mode 100644 selectiveInference/src/.gitignore

diff --git a/selectiveInference/DESCRIPTION b/selectiveInference/DESCRIPTION
index 535abea..57108c2 100644
--- a/selectiveInference/DESCRIPTION
+++ b/selectiveInference/DESCRIPTION
@@ -11,6 +11,7 @@ Depends:
     intervals,
     survival,
     adaptMCMC,
+    gglasso
 Suggests:
     Rmpfr
 Description: New tools for post-selection inference, for use with forward
diff --git a/selectiveInference/NAMESPACE b/selectiveInference/NAMESPACE
index b0aa8b5..5e0d4d3 100644
--- a/selectiveInference/NAMESPACE
+++ b/selectiveInference/NAMESPACE
@@ -49,3 +49,5 @@ importFrom("stats", "glm", "residuals", "vcov")
 importFrom("stats", "rbinom", "rexp")
 importFrom("Rcpp", "sourceCpp")
 importFrom("adaptMCMC", "MCMC")
+importFrom("gglasso", "gglasso")
+importFrom("gglasso", "cv.gglasso")
diff --git a/selectiveInference/R/funs.full.main.R b/selectiveInference/R/funs.full.main.R
index 5047772..375bb03 100644
--- a/selectiveInference/R/funs.full.main.R
+++ b/selectiveInference/R/funs.full.main.R
@@ -87,7 +87,7 @@ solve_problem_Q = function(Q_sq, Qbeta_bar, lambda, penalty_factor,
   gradient = gradient/n
   
   #solve_QP_wide solves n*slinear_func^T\beta+\beta^T Xinfo\beta+\sum\lambda_i|\beta_i|
-  result = selectiveInference:::solve_QP_wide(Xinfo,         # this is a design matrix
+  result = solve_QP_wide(Xinfo,         # this is a design matrix
                          as.numeric(penalty_factor*lambda),  # vector of Lagrange multipliers
                          0,                          # ridge_term 
                          max_iter, 
@@ -123,8 +123,7 @@ truncation_set = function(X, y, Qbeta_bar, QE, sigma_est,
     restricted_soln = solve_restricted_problem(X, y, groups, group, lambda, penalty_factor=penalty_factor, 
                                              loss=loss, algo=algo)
   }
-  #print("restricted soln")
-  #print(restricted_soln)
+
   n = nrow(X)
   p = length(Qbeta_bar)
   I = diag(p) * sigma_est^2
@@ -188,7 +187,7 @@ tnorm.union.surv = function(z, mean, sd, intervals, bits = NULL){
     }else if(z >= intervals[jj,2]){
       pval[jj,] = 0
     }else{
-      pval[jj,] = selectiveInference:::tnorm.surv(z, mean, sd, intervals[jj,1], intervals[jj,2], bits = bits)
+      pval[jj,] = tnorm.surv(z, mean, sd, intervals[jj,1], intervals[jj,2], bits = bits)
     }
   }
   
@@ -210,8 +209,8 @@ create_tnorm_interval = function(z, sd, alpha, intervals, gridrange=c(-20,20), g
   grid = seq(gridrange[1]*sd,gridrange[2]*sd,length=gridpts)
   fun = function(x) { return(tnorm.union.surv(z, x, sd, intervals, bits)) }
   
-  int = selectiveInference:::grid.search(grid, fun, alpha/2, 1-alpha/2, gridpts, griddepth)
-  #print(int)
+  int = grid.search(grid, fun, alpha/2, 1-alpha/2, gridpts, griddepth)
+
   return(int)
 }
 
@@ -223,7 +222,7 @@ selective_CI = function(observed, variance, sigma_est, center, radius,
   }
   st.error = sqrt(variance)
   param_grid = seq(observed+gridrange[1] * st.error, observed+gridrange[2] * st.error, length=gridpts)
-  interval = selectiveInference:::grid.search(param_grid, pivot, alpha/2, 1-alpha/2, gridpts, griddepth)
+  interval = grid.search(param_grid, pivot, alpha/2, 1-alpha/2, gridpts, griddepth)
   return(interval)
 }
 
@@ -302,7 +301,7 @@ approximate = function(X, active_set){
   
   Xordered = X[,c(active_set,inactive_set,recursive=T)]
   hsigmaS = 1/n*(t(X_active)%*%X_active) # hsigma[S,S]
-  hsigmaSinv =  ginv(hsigmaS) # generalized inverse solve(hsigmaS) # ginv
+  hsigmaSinv =  solve(hsigmaS) # inverse of hsigma[S,S]
   FS = rbind(diag(nactive),matrix(0,p-nactive,nactive))
   GS = cbind(diag(nactive),matrix(0,nactive,p-nactive))
   hsigma = 1/n*(t(Xordered)%*%Xordered)
@@ -310,17 +309,13 @@ approximate = function(X, active_set){
   
   if (!is_wide) {
     hsigma = 1/n*(t(Xordered)%*%Xordered)
-    htheta = selectiveInference:::debiasingMatrix(hsigma, is_wide, n, 1:nactive)
+    htheta = debiasingMatrix(hsigma, is_wide, n, 1:nactive)
     ithetasigma = (GS-(htheta%*%hsigma))
   } else {
-    htheta = selectiveInference:::debiasingMatrix(Xordered, is_wide, n, 1:nactive)
+    htheta = debiasingMatrix(Xordered, is_wide, n, 1:nactive)
     ithetasigma = (GS-((htheta%*%t(Xordered)) %*% Xordered)/n)
   }
   M_active <- ((htheta%*%t(Xordered))+ithetasigma%*%FS%*%hsigmaSinv%*%t(X_active))/n
-  #M_inactive  =  (htheta[, (nactive+1):p]%*%t(X[,inactive_set])/n)
-  #M=matrix(nrow=p, ncol=n)
-  #M[active_set, ]= M_active
-  #M[inactive_set, ]=matrix(0,nrow=length(inactive_set), ncol=n)
   return(M_active)
 }
 
diff --git a/selectiveInference/src/.gitignore b/selectiveInference/src/.gitignore
new file mode 100644
index 0000000..22034c4
--- /dev/null
+++ b/selectiveInference/src/.gitignore
@@ -0,0 +1,3 @@
+*.o
+*.so
+*.dll

From b4068160ff1ce5a4528c75e74e7e2dca70ec2b19 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 30 Mar 2018 08:12:04 -0700
Subject: [PATCH 478/493] removing C code

---
 selectiveInference/src/debias.h               |  79 ---
 selectiveInference/src/matrixcomps.c          | 266 --------
 selectiveInference/src/matrixcomps.h          |  12 -
 selectiveInference/src/quadratic_program.c    | 441 -------------
 .../src/quadratic_program_wide.c              | 597 ------------------
 selectiveInference/src/randomized_lasso.c     | 411 ------------
 selectiveInference/src/randomized_lasso.h     |  54 --
 7 files changed, 1860 deletions(-)
 delete mode 100644 selectiveInference/src/debias.h
 delete mode 100644 selectiveInference/src/matrixcomps.c
 delete mode 100644 selectiveInference/src/matrixcomps.h
 delete mode 100644 selectiveInference/src/quadratic_program.c
 delete mode 100644 selectiveInference/src/quadratic_program_wide.c
 delete mode 100644 selectiveInference/src/randomized_lasso.c
 delete mode 100644 selectiveInference/src/randomized_lasso.h

diff --git a/selectiveInference/src/debias.h b/selectiveInference/src/debias.h
deleted file mode 100644
index da27467..0000000
--- a/selectiveInference/src/debias.h
+++ /dev/null
@@ -1,79 +0,0 @@
-#ifdef __cplusplus
-extern "C"
-{
-#endif /* __cplusplus */
-
-void multiply_by_2(double *X, int nval);
-
-int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
-	     double *linear_func_ptr,    /* Linear term in objective */
-	     double *nndef_diag_ptr,     /* Diagonal of nndef */
-	     double *gradient_ptr,       /* nndef times theta */
-	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
-	     int *nactive_ptr,           /* Size of ever active set */
-	     int nfeature,               /* How many features in nndef */
-	     double bound,               /* feasibility parameter */
-	     double *theta,              /* current value */
-	     double *theta_old,          /* previous value */
-	     int maxiter,                /* max number of iterations */
-	     double kkt_tol,             /* precision for checking KKT conditions */
-	     double objective_tol,       /* precision for checking relative decrease in objective value */
-	     double parameter_tol,       /* precision for checking relative convergence of parameter */
-	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
-             int kkt_stop,               /* Break based on KKT? */
-	     int objective_stop,         /* Break based on convergence of objective value? */
-	     int param_stop);            /* Break based on parameter convergence? */
-
-int check_KKT_qp(double *theta,        /* current theta */
-		 double *gradient_ptr, /* nndef times theta + linear_func */
-		 int nrow,             /* how many rows in nndef */
-		 double bound,         /* Lagrange multipler for \ell_1 */
-		 double tol);          /* precision for checking KKT conditions */        
-
-int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-	       double *X_theta_ptr,        /* Fitted values   */
-	       double *linear_func_ptr,    /* Linear term in objective */
-	       double *nndef_diag_ptr,     /* Diagonal entries of non-neg def matrix */
-	       double *gradient_ptr,       /* X^TX/ncase times theta + linear_func*/
-	       int *need_update_ptr,       /* Keeps track of updated gradient coords */
-	       int *ever_active_ptr,       /* Ever active set: 1-based */ 
-	       int *nactive_ptr,           /* Size of ever active set */
-	       int ncase,                  /* How many rows in X */
-	       int nfeature,               /* How many columns in X */
-	       double *bound_ptr,          /* Lagrange multipliers */
-	       double ridge_term,          /* Ridge / ENet term */
-	       double *theta_ptr,          /* current value */
-	       double *theta_old_ptr,      /* previous value */
-	       int maxiter,                /* max number of iterations */
-	       double kkt_tol,             /* precision for checking KKT conditions */
-	       double objective_tol,       /* precision for checking relative decrease in objective value */
-	       double parameter_tol,       /* precision for checking relative convergence of parameter */
-	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
-	       int kkt_stop,               /* Break based on KKT? */
-	       int objective_stop,         /* Break based on convergence of objective value? */
-	       int param_stop);            /* Break based on parameter convergence? */
-
-int check_KKT_wide(double *theta_ptr,        /* current theta */
-		   double *gradient_ptr,     /* X^TX/ncase times theta + linear_func*/
-		   double *X_theta_ptr,      /* Current fitted values */
-		   double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-		   double *linear_func_ptr,  /* Linear term in objective */   
-		   int *need_update_ptr,     /* Which coordinates need to be updated? */
-		   int nfeature,             /* how many columns in X */
-		   int ncase,                /* how many rows in X */
-		   double *bound_ptr,        /* Lagrange multiplers for \ell_1 */
-		   double ridge_term,        /* Ridge / ENet term */
-		   double tol);              /* precision for checking KKT conditions */        
-  
-void update_gradient_wide(double *gradient_ptr,     /* X^TX/ncase times theta + linear_func */
-			  double *X_theta_ptr,      /* Current fitted values */
-			  double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-			  double *linear_func_ptr,  /* Linear term in objective */   
-			  int *need_update_ptr,     /* Which coordinates need to be updated? */
-			  int nfeature,             /* how many columns in X */
-			  int ncase);               /* how many rows in X */
-
-
-#ifdef __cplusplus
-}  /* extern "C" */
-#endif /* __cplusplus */
diff --git a/selectiveInference/src/matrixcomps.c b/selectiveInference/src/matrixcomps.c
deleted file mode 100644
index b7e8bbc..0000000
--- a/selectiveInference/src/matrixcomps.c
+++ /dev/null
@@ -1,266 +0,0 @@
-#include <math.h>
-
-// Matrices are stored as vectors, in column-major order
-
-// Givens rotation of a and b, stored in c and s
-void givens(double a, double b, double *c, double *s) {
-  if (b==0) {
-    *c = 1; 
-    *s = 0; 
-  }
-  else {
-    if (fabs(b)>fabs(a)) {
-      double t = -a/b;
-      *s = 1/sqrt(1+t*t);
-      *c = (*s)*t;
-    }
-    else {
-      double t = -b/a;
-      *c = 1/sqrt(1+t*t);
-      *s = (*c)*t;
-    }
-  }
-}
-
-// Givens rotation applied to rows i1 and i2 of the m x n
-// matrix A, on the subset of columns j1 through j2
-void rowrot(double *A, int i1, int i2, int m, int n, int j1, int j2, double c, double s) {
-  int j;
-  double t1,t2;
-  for (j=j1; j<=j2; j++) {
-    t1 = A[i1+j*m]; 
-    t2 = A[i2+j*m];
-    A[i1+j*m] = c*t1-s*t2;
-    A[i2+j*m] = s*t1+c*t2;
-  }
-}
-
-// Givens rotation applied to columns j1 and j2 of the m x n
-// matrix A, on the subset of rows i1 through i2
-void colrot(double *A, int j1, int j2, int m, int n, int i1, int i2, double c, double s) {
-  int i;
-  double t1,t2;
-  for (i=i1; i<=i2; i++) {
-    t1 = A[i+j1*m];
-    t2 = A[i+j2*m];
-    A[i+j1*m] = c*t1-s*t2;
-    A[i+j2*m] = s*t1+c*t2;
-  }
-}
-
-// Downdate the QR factorization after deleting column j0, 
-// where Q1 is m x n and R is n x n. The other part of 
-// the Q matrix, Q2 m x (m-n), isn't needed so it isn't 
-// passed for efficiency
-void downdate1(double *Q1, double *R, int j0, int m, int n) {
-  int j;
-
-  double c,s;
-  for (j=j0+1; j<n; j++) {
-    // Compute the appropriate c and s
-    givens(R[j-1+j*n],R[j+j*n],&c,&s);
-
-    // Pre-multiply R
-    rowrot(R,j-1,j,n,n,j,n-1,c,s);
-
-    // Post-mutiply Q1
-    colrot(Q1,j-1,j,m,n,0,m-1,c,s);
-  }
-}
-
-// Update the QR factorization after adding a column z.
-// For convenience, we are given w=Q2'z. Here Q2 is m x 
-// (m-n). The other part of the Q matrix, Q1 m x n, and 
-// R are not needed, so they aren't passed for efficiency 
-void update1(double *Q2, double *w, int m, int k) {
-  int j;
-
-  double c,s;
-  for (j=k-1; j>=1; j--) {
-    // Compute the appropriate c and s
-    givens(w[j-1],w[j],&c,&s);
-
-    // Pre-multiply w
-    rowrot(w,j-1,j,k,1,0,0,c,s);
-
-    // Post-multiply Q2
-    colrot(Q2,j-1,j,m,k,0,m-1,c,s);
-  }
-}
-
-// Downdate the QR factorization after deleting the first row, 
-// where Q is m x m and R is m x n
-void downdate2(double *Q, double *R, int *mp, int *np) {
-  int m,n,i;
-  m = *mp;
-  n = *np;
-
-  double c,s;
-  for (i=m-1; i>=1; i--) {
-    // Compute the appropriate c and s
-    givens(Q[(i-1)*m],Q[i*m],&c,&s);
-
-    // Post-mutiply Q
-    colrot(Q,i-1,i,m,m,0,m-1,c,s);
-
-    // Pre-multiply R
-    if (i<=n) rowrot(R,i-1,i,m,n,i-1,n-1,c,s); 
-  }
-}
-
-// Update the QR factorization after adding the last row,
-// where Q is m x m and R is m x n. For efficiency, Q is not
-// passed, and only the first row of R is passed. Not counting
-// its first row, the first q columns of R are zero
-void update2(double *y, double *D, double *r, int *mp, int *np, int *qp) {
-  int m,n,q,j;
-  m = *mp;
-  n = *np;
-  q = *qp;
-
-  double c,s;
-  for (j=0; j<q-1; j++) {
-    // Compute the appropriate c and s
-    givens(r[j+1],r[j],&c,&s);
-
-    // Pre-multiply r
-    rowrot(r,j+1,j,n,1,0,0,c,s);
-
-    // Post-mutiply D
-    colrot(D,j+1,j,m,n,0,m-1,c,s);
-
-    // Pre-multiply y
-    rowrot(y,j+1,j,n,1,0,0,c,s);
-  }
-}
-
-// Make the R factor upper triangular, by Givens rotating its
-// columns. Here A is m x n, and R is n x n with rank(R) = n-k
-void maketri1(double *y, double *A, double *R, int *mp, int *np, int *kp) {
-  int m,n,k,i,j;
-  m = *mp;
-  n = *np;
-  k = *kp; 
-  
-  double c,s;
-  for (i=n-k-1; i>=0; i--) {
-    for (j=i; j<i+k; j++) {
-      // Compute the appropriate c and s
-      givens(R[i+(j+1)*n],R[i+j*n],&c,&s);
-      
-      // Post-multiply R
-      colrot(R,j+1,j,n,n,0,i,c,s);
-
-      // Post-multiply D
-      colrot(A,j+1,j,m,n,0,m-1,c,s);
-    
-      // Pre-multiply y
-      rowrot(y,j+1,j,n,1,0,0,c,s);
-    }
-  }
-}
-
-// Make the R factor upper triangular, by Givens rotating its 
-// columns. Here A is m x n, and R is m x n with rank(R) = min(m,n)-k
-void maketri2(double *y, double *A, double *R, int *mp, int *np, int *kp) {
-  int m,n,k,i,j;
-  m = *mp;
-  n = *np;
-  k = *kp;
- 
-  int r=m;
-  if (n < m) {
-    r = n;
-  }
-  int d = 0;
-  if (n-m>0) {
-    d = n-m;
-  }
-
-  double c,s;
-  for (i=r-k-1; i>=0; i--) {
-    for (j=i; j<i+k+d; j++) {
-      // Compute the appropriate c and s
-      givens(R[i+(j+1)*m],R[i+j*m],&c,&s);
-      
-      // Post-multiply R
-      colrot(R,j+1,j,m,n,0,i,c,s);
-
-      // Post-multiply D
-      colrot(A,j+1,j,m,n,0,m-1,c,s);
-    
-      // Pre-multiply y
-      rowrot(y,j+1,j,n,1,0,0,c,s);
-    }
-  }
-}
-
-// Make the R factor upper triangular, by Givens rotating
-// its columns. Here A is m1 x n, and R is m2 x n with 
-// rank(R) = n-q-1. The first q columns of R are zero
-void maketri3(double *y, double *A, double *R, int *m1p, int *m2p, int *np, int *qp) {
-  int m1,m2,n,q,i;
-  m1 = *m1p;
-  m2 = *m2p;
-  n = *np;
-  q = *qp;
-  
-  double c,s;
-  for (i=n-q-2; i>=0; i--) {
-    // Compute the appropriate c and s
-    givens(R[i+(i+q+1)*m2],R[i+(i+q)*m2],&c,&s);
-      
-    // Post-multiply R
-    colrot(R,i+q+1,i+q,m2,n,0,i,c,s);
-
-    // Post-multiply D
-    colrot(A,i+q+1,i+q,m1,n,0,m1-1,c,s);
-    
-    // Pre-multiply y
-    rowrot(y,i+q+1,i+q,n,1,0,0,c,s);
-  }
-}
-
-// Make the R factor upper triangular, by Givens rotating
-// its columns and rows, appropriately. Here A is m1 x n, 
-// Q is m2 x m2, and R is m2 x n with rank(R) = n-q-1. The 
-// first q columns of R are zero. The kth row of R is the 
-// last row with a zero element on the diagonal    
-void maketri4(double *y, double *A, double *Q, double *R, int *m1p, int *m2p, int *np, int *qp, int *kp) {
-  int m1,m2,n,q,k,i,j;
-  m1 = *m1p;
-  m2 = *m2p;
-  n = *np; 
-  q = *qp;
-  k = *kp;
-
-  double c,s;
-
-  // First rotate the columns
-  for (i=k-1; i>=0; i--) {
-    // Compute the appropriate c and s
-    givens(R[i+(i+q+1)*m2],R[i+(i+q)*m2],&c,&s);
-      
-    // Post-multiply R
-    colrot(R,i+q+1,i+q,m2,n,0,i,c,s);
-
-    // Post-multiply D
-    colrot(A,i+q+1,i+q,m1,n,0,m1-1,c,s);
-    
-    // Pre-multiply y
-    rowrot(y,i+q+1,i+q,n,1,0,0,c,s);
-  }
-
-  // Next rotate the rows
-  for (j=k+q+1; j<n; j++) {
-    // Compute the appropriate c and s
-    givens(R[j-q-1+j*m2],R[j-q+j*m2],&c,&s);
-
-    // Pre-multiply R
-    rowrot(R,j-q-1,j-q,m2,n,j,n-1,c,s);
-
-    // Post-multiply Q
-    colrot(Q,j-q-1,j-q,m2,m2,0,m2-1,c,s);
-  }
-}
-
diff --git a/selectiveInference/src/matrixcomps.h b/selectiveInference/src/matrixcomps.h
deleted file mode 100644
index 8632e3e..0000000
--- a/selectiveInference/src/matrixcomps.h
+++ /dev/null
@@ -1,12 +0,0 @@
-#ifdef __cplusplus
-extern "C"
-{
-#endif /* __cplusplus */
-
-void update1(double *Q2, double *w, int m, int k);
-
-void downdate1(double *Q1, double *R, int j0, int m, int n);
-
-#ifdef __cplusplus
-}  /* extern "C" */
-#endif /* __cplusplus */
diff --git a/selectiveInference/src/quadratic_program.c b/selectiveInference/src/quadratic_program.c
deleted file mode 100644
index c851f7d..0000000
--- a/selectiveInference/src/quadratic_program.c
+++ /dev/null
@@ -1,441 +0,0 @@
-#include <math.h> // for fabs
-
-// Find an approximate row of \hat{nndef}^{-1}
-
-// Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
-
-// Dual problem: \text{min}_{\theta} 1/2 \theta^T \Sigma \theta - l^T\theta + \mu \|\theta\|_1
-// where l is `linear_func` below
-
-// This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
-// Therefore we don't have to negate the answer to get theta.
-// Update one coordinate 
-
-double objective_qp(double *nndef_ptr,       /* A non-negative definite matrix */
-		    double *linear_func_ptr, /* Linear term in objective */
-		    int *ever_active_ptr,    /* Ever active set: 0-based */ 
-		    int *nactive_ptr,        /* Size of ever active set */
-		    int nrow,                /* how many rows in nndef */
-		    double bound,            /* Lagrange multipler for \ell_1 */
-		    double *theta_ptr)           /* current value */
-{
-  int irow, icol;
-  double value = 0;
-  double *nndef_ptr_tmp = nndef_ptr;
-  double *linear_func_ptr_tmp = linear_func_ptr;
-  double *theta_row_ptr, *theta_col_ptr;
-  int *active_row_ptr, *active_col_ptr;
-  int active_row, active_col;
-  int nactive = *nactive_ptr;
-
-  theta_row_ptr = theta_ptr;
-  theta_col_ptr = theta_ptr;
-
-  for (irow=0; irow<nactive; irow++) {
-
-    active_row_ptr = ((int *) ever_active_ptr + irow);
-    active_row = *active_row_ptr - 1;          // Ever-active is 1-based
-    theta_row_ptr = ((double *) theta_ptr + active_row);
-
-    for (icol=0; icol<nactive; icol++) {
-      
-      active_col_ptr = ((int *) ever_active_ptr + icol);
-      active_col = *active_col_ptr - 1;          // Ever-active is 1-based
-      theta_col_ptr = ((double *) theta_ptr + active_col);
-
-      nndef_ptr_tmp = ((double *) nndef_ptr + nrow * active_col + active_row); // Matrices are column-major order
-
-      value += 0.5 * (*nndef_ptr_tmp) * (*theta_row_ptr) * (*theta_col_ptr);
-    }
-    value += bound * fabs((*theta_row_ptr)); // the \ell_1 term
-
-    // The linear term in the objective
-
-    linear_func_ptr_tmp = ((double *) linear_func_ptr + active_row);
-    value += (*linear_func_ptr_tmp) * (*theta_row_ptr); 
-
-  }
-  
-  return(value);
-}
-
-// Ever-active is 1-based
-// coord is 0-based
-int update_ever_active_qp(int coord,
-			  int *ever_active_ptr,
-			  int *nactive_ptr) {
-  int iactive;
-  int active_var;
-  int nactive = *nactive_ptr;
-  int *ever_active_ptr_tmp = ever_active_ptr;
-
-  for (iactive=0; iactive<nactive; iactive++) {
-    ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
-    active_var = *ever_active_ptr_tmp;
-    if (active_var - 1 == coord) {          // Ever-active is 1-based
-      return(1);
-    }
-  }
-  
-  // If we haven't returned yet, this means the coord was not in 
-  // ever_active.
-
-  // Add it to the active set and increment the 
-  // number of active variables
-
-  ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
-  *ever_active_ptr_tmp = coord + 1;         // Ever-active is 1-based
-  *nactive_ptr += 1;
-
-  return(0);
-}
-
-int check_KKT_qp(double *theta_ptr,        /* current theta */
-		 double *gradient_ptr, /* nndef times theta + linear_func */
-		 int nfeature,             /* how many features in nndef */
-		 double bound,         /* Lagrange multipler for \ell_1 */
-		 double tol)           /* precision for checking KKT conditions */        
-{
-  // First check inactive
-
-  int ifeature;
-  double *theta_ptr_tmp, *gradient_ptr_tmp;
-  double gradient;
-
-  for (ifeature=0; ifeature<nfeature; ifeature++) {
-    theta_ptr_tmp = ((double *) theta_ptr + ifeature);
-    gradient_ptr_tmp = ((double *) gradient_ptr + ifeature);
-
-    // Compute this coordinate of the gradient
-
-    gradient = *gradient_ptr_tmp;
-
-    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
-	return(0);
-      }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
-	return(0);
-      }
-    }
-    else {
-      if (fabs(gradient) > (1. + tol) * bound) {
-	return(0);
-      }
-    }
-  }
-
-  return(1);
-}
-
-int check_KKT_qp_active(int *ever_active_ptr,           /* Ever active set: 0-based */ 
-		        int *nactive_ptr,               /* Size of ever active set */
-			double *theta_ptr,        /* current theta */
-			double *gradient_ptr, /* nndef times theta + linear_func */
-			int nfeature,             /* how many features in nndef */
-			double bound,         /* Lagrange multipler for \ell_1 */
-			double tol)           /* precision for checking KKT conditions */        
-{
-  // First check inactive
-
-  int iactive;
-  double *theta_ptr_tmp;
-  double gradient;
-  double *gradient_ptr_tmp;
-  int nactive = *nactive_ptr;
-  int active_feature;
-  int *active_feature_ptr;
-
-  for (iactive=0; iactive<nactive; iactive++) {
-
-    active_feature_ptr = ((int *) ever_active_ptr + iactive);
-    active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
-    theta_ptr_tmp = ((double *) theta_ptr + active_feature);
-
-    gradient_ptr_tmp = ((double *) gradient_ptr + active_feature);
-
-    // Compute this coordinate of the gradient
-
-    gradient = *gradient_ptr_tmp;
-
-    if (*theta_ptr_tmp != 0) { // these coordinates of gradients should be equal to -bound
-
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + bound) > tol * bound)) {
-	return(0);
-      }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient - bound) > tol * bound)) {
-	return(0);
-      }
-
-    }
-    else {
-      if (fabs(gradient) > (1. + tol) * bound) {
-	return(0);
-      }
-    }
-  }
-
-  return(1);
-}
-
-
-double update_one_coord_qp(double *nndef_ptr,           /* A non-negative definite matrix */
-			   double *linear_func_ptr,     /* Linear term in objective */
-			   double *nndef_diag_ptr,      /* Diagonal of nndef */
-			   double *gradient_ptr,        /* nndef times theta + linear_func */
-			   int *ever_active_ptr,        /* Ever active set: 1-based */ 
-			   int *nactive_ptr,            /* Size of ever active set */
-			   int nfeature,                    /* How many features in nndef */
-			   double bound,                /* feasibility parameter */
-			   double *theta_ptr,               /* current value */
-			   int coord,                   /* which coordinate to update: 0-based */
-			   int is_active)               /* Is this coord in ever_active */     
-{
-
-  double delta;
-  double linear_term = 0;
-  double value = 0;
-  double old_value;
-  double *nndef_ptr_tmp;
-  double *gradient_ptr_tmp;
-  double *theta_ptr_tmp;
-  int icol = 0;
-
-  double *quadratic_ptr = ((double *) nndef_diag_ptr + coord);
-  double quadratic_term = *quadratic_ptr;
-
-  gradient_ptr_tmp = ((double *) gradient_ptr + coord);
-  linear_term = *gradient_ptr_tmp;
-
-  theta_ptr_tmp = ((double *) theta_ptr + coord);
-  old_value = *theta_ptr_tmp;
-
-  // The coord entry of gradient_ptr term has a diagonal term in it:
-  // nndef[coord, coord] * theta[coord]
-  // This removes it. 
-
-  linear_term -= quadratic_term * old_value;
-
-  // Now soft-threshold the coord entry of theta 
-
-  // Objective is t \mapsto q/2 * t^2 + l * t + bound |t|
-  // with q=quadratic_term and l=linear_term
-
-  // With a negative linear term, solution should be
-  // positive
-
-  if (linear_term < -bound) {
-    value = (-linear_term - bound) / quadratic_term;
-  }
-  else if (linear_term > bound) {
-    value = -(linear_term - bound) / quadratic_term;
-  }
-
-  // Add to active set if necessary
-
-  if ((is_active == 0) && (value != 0)) {
-    update_ever_active_qp(coord, ever_active_ptr, nactive_ptr);
-  }
-
-  // Update the linear term
-
-  if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
-
-    delta = value - old_value;
-    nndef_ptr_tmp = ((double *) nndef_ptr + coord * nfeature);
-    gradient_ptr_tmp = ((double *) gradient_ptr);
-
-    for (icol=0; icol<nfeature; icol++) {
-      *gradient_ptr_tmp = *gradient_ptr_tmp + delta * (*nndef_ptr_tmp);
-      gradient_ptr_tmp += 1;
-      nndef_ptr_tmp += 1;
-    }
-
-    theta_ptr_tmp = ((double *) theta_ptr + coord);
-    *theta_ptr_tmp = value;
-
-  }
-
-  return(value);
-
-}
-
-int solve_qp(double *nndef_ptr,          /* A non-negative definite matrix */
-	     double *linear_func_ptr,    /* Linear term in objective */
-	     double *nndef_diag_ptr,     /* Diagonal of nndef */
-	     double *gradient_ptr,       /* nndef times theta */
-	     int *ever_active_ptr,       /* Ever active set: 1-based */ 
-	     int *nactive_ptr,           /* Size of ever active set */
-	     int nfeature,               /* How many features in nndef */
-	     double bound,               /* feasibility parameter */
-	     double *theta,              /* current value */
-	     double *theta_old,          /* previous value */
-	     int maxiter,                /* max number of iterations */
-	     double kkt_tol,             /* precision for checking KKT conditions */
-	     double objective_tol,       /* precision for checking relative decrease in objective value */
-	     double parameter_tol,       /* precision for checking relative convergence of parameter */
-	     int max_active,             /* Upper limit for size of active set -- otherwise break */ 
-             int kkt_stop,               /* Break based on KKT? */
-	     int objective_stop,         /* Break based on convergence of objective value? */
-	     int param_stop)             /* Break based on parameter convergence? */
-{
-
-  int iter = 0;
-  int iter_old = 1;
-  int ifeature = 0;
-  int iactive = 0;
-  int *active_ptr;
-
-  int iter_active;
-  int niter_active=5;
-
-  double old_value, new_value; 
-  double norm_diff = 1.;
-  double norm_last = 1.;
-  double delta;
-  double *theta_ptr, *theta_old_ptr;
-
-  if (objective_stop) {
-
-    old_value = objective_qp(nndef_ptr,
-			     linear_func_ptr,
-			     ever_active_ptr,
-			     nactive_ptr,
-			     nfeature,
-			     bound,
-			     theta);
-
-  }
-
-
-  for (iter=0; iter<maxiter; iter++) {
-
-    // Update the active variables first -- do this niter_active times
-
-    for (iter_active=0; iter_active<niter_active; iter_active++) { 
-
-        active_ptr = (int *) ever_active_ptr;
-        for (iactive=0; iactive < *nactive_ptr; iactive++) {
-
-	  update_one_coord_qp(nndef_ptr,
-			      linear_func_ptr,
-			      nndef_diag_ptr,
-			      gradient_ptr,
-			      ever_active_ptr,
-			      nactive_ptr,
-			      nfeature,
-			      bound,
-			      theta,
-			      *active_ptr - 1,   // Ever-active is 1-based
-			      1);
-	  active_ptr++;
-	}
-
-	// Check KKT of active subproblem
-
-	if (check_KKT_qp_active(ever_active_ptr,
-				nactive_ptr,
-				theta,
-				gradient_ptr,
-				nfeature,
-				bound,
-				kkt_tol) == 1) {
-	  break;
-	}
-    }
-
-    // Check KKT
-
-    if (kkt_stop) {
-      if (check_KKT_qp(theta, 
-		       gradient_ptr,
-		       nfeature,
-		       bound,
-		       kkt_tol) == 1) {
-	break;
-      }
-    }
-
-    // Update all variables 
-
-    for (ifeature=0; ifeature<nfeature; ifeature++) {
-
-      update_one_coord_qp(nndef_ptr,
-			  linear_func_ptr,
-			  nndef_diag_ptr,
-			  gradient_ptr,
-			  ever_active_ptr,
-			  nactive_ptr,
-			  nfeature,
-			  bound,
-			  theta,
-			  ifeature,
-			  0);
-    }
-
-    // Check KKT
-
-    if (kkt_stop) {
-      if (check_KKT_qp(theta, 
-		       gradient_ptr,
-		       nfeature,
-		       bound,
-		       kkt_tol) == 1) {
-	break;
-      }
-    }
-					  
-
-    if (iter == 2 * iter_old) { // Geometric iterations from Adel's code
-
-      // Check based on norm 
-
-      if (param_stop) { 
-	iter_old = iter;
-	norm_diff = 0;
-	norm_last = 0;
-	for (ifeature=0; ifeature<nfeature; ifeature++) {
-	  theta_old_ptr = ((double *) theta_old + ifeature);
-	  theta_ptr = ((double *) theta + ifeature);
-	  delta = (*theta_ptr - *theta_old_ptr);
-	  norm_diff += delta * delta;
-	  norm_last += (*theta_ptr) * (*theta_ptr);
-	  *theta_old_ptr = (*theta_ptr);
-	}
-	norm_diff = sqrt(norm_diff);
-	norm_last = sqrt(norm_last);
-	
-	if (norm_diff < parameter_tol * norm_last) {
-	  break;
-	}
-      }
-
-      // Check relative decrease of objective
-
-      if (objective_stop) {
-	new_value = objective_qp(nndef_ptr,
-				 linear_func_ptr,
-				 ever_active_ptr,
-				 nactive_ptr,
-				 nfeature,
-				 bound,
-				 theta);
-
-	if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
-	  break;
-	}
-	old_value = new_value;
-      }
-
-    }
-
-    // Check size of active set
-
-    if (*nactive_ptr >= max_active) {
-      break;
-    }
-
-  }
-  return(iter);
-}
-
diff --git a/selectiveInference/src/quadratic_program_wide.c b/selectiveInference/src/quadratic_program_wide.c
deleted file mode 100644
index ce7284b..0000000
--- a/selectiveInference/src/quadratic_program_wide.c
+++ /dev/null
@@ -1,597 +0,0 @@
-#include <math.h> // for fabs
-
-// Find an approximate row of \hat{nndef}^{-1}
-
-// Solves a dual version of problem (4) of https://arxiv.org/pdf/1306.3171.pdf
-
-// Dual problem: \text{min}_{\theta} 1/2 \|X\theta\|^2/n + l^T\theta + \mu \|\theta\|_1 + \frac{\epsilon}{2} \|\theta\|^2_2
-// where l is `linear_func` below
-
-// This is the "negative" of the problem as in https://gist.github.com/jonathan-taylor/07774d209173f8bc4e42aa37712339bf
-// Therefore we don't have to negate the answer to get theta.
-// Update one coordinate 
-
-// Throughout X is a design matrix
-
-double objective_wide(double *X_theta_ptr,     /* Fitted values */
-		      double *linear_func_ptr, /* Linear term in objective */
-		      int *ever_active_ptr,    /* Ever active set: 0-based */ 
-		      int *nactive_ptr,        /* Size of ever active set */
-		      int ncase,               /* how many rows in X */
-		      int nfeature,            /* how many columns in X */
-		      double *bound_ptr,       /* Lagrange multiplers for \ell_1 */
-		      double ridge_term,       /* Ridge / ENet term */
-		      double *theta_ptr)       /* current value */
-{
-  int icase, iactive;
-  double value = 0;
-  double *bound_ptr_tmp;
-  double *X_theta_ptr_tmp = X_theta_ptr;
-  double *linear_func_ptr_tmp = linear_func_ptr;
-  double *theta_ptr_tmp;
-  int *active_feature_ptr;
-  int active_feature;
-  int nactive = *nactive_ptr;
-
-  // The term \|X\theta\|^2_2/n, with n=ncase
-
-  for (icase=0; icase<ncase; icase++) {
-
-    X_theta_ptr_tmp = ((double *) X_theta_ptr + icase);
-    value += (*(X_theta_ptr_tmp)) * (*(X_theta_ptr_tmp));
-
-  }
-
-  value *= 0.5 / ncase;
-
-  for (iactive=0; iactive<nactive; iactive++) {
-
-    // The linear term in the objective
-
-    active_feature_ptr = ((int *) ever_active_ptr + iactive);
-    active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
-
-    theta_ptr_tmp = ((double *) theta_ptr + active_feature);
-    linear_func_ptr_tmp = ((double *) linear_func_ptr + active_feature);
-    value += (*linear_func_ptr_tmp) * (*theta_ptr_tmp); 
-
-    // The \ell_1 term
-
-    bound_ptr_tmp = ((double *) bound_ptr + active_feature);
-    value += (*bound_ptr_tmp) * fabs((*theta_ptr_tmp));
-
-    // The ridge term
-
-    value += 0.5 * ridge_term * (*theta_ptr_tmp) * (*theta_ptr_tmp);
-  }
-  
-  return(value);
-}
-
-// Compute, update and return one coordinate of the gradient of \|X\theta\|^2_2/2n
-
-double compute_gradient_coord(double *gradient_ptr,        /* Gradient -- one coordinate will be updated if needed */
-			      double *X_theta_ptr,         /* Current fitted values */
-			      double *X_ptr,               /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-			      double *linear_func_ptr,     /* Linear term in objective */   
-			      int *need_update_ptr,        /* Which coordinates need to be updated? */
-			      int coord,                   /* Coordinate we are trying to update */
-			      int ncase,                   /* How many rows in X */
-			      int nfeature)                /* How many columns in X */
-{
-
-  double *gradient_ptr_tmp;
-  double *X_theta_ptr_tmp;
-  double *X_ptr_tmp;  
-  double *linear_func_ptr_tmp;  
-  int *need_update_ptr_tmp;
-  int icase;
-  double value = 0.;
-
-  need_update_ptr_tmp = ((int *) need_update_ptr + coord);
-
-  // Check if this coordinate needs updating
-  if (*need_update_ptr_tmp == 1) {
-
-    for (icase=0; icase<ncase; icase++) {
-#ifdef COLUMN_MAJOR_ORDER
-      X_ptr_tmp = ((double *) X_ptr + coord * ncase + icase);
-#else
-      X_ptr_tmp = ((double *) X_ptr + icase * nfeature + coord);
-#endif
-      X_theta_ptr_tmp = ((double *) X_theta_ptr + icase);
-      value += (*X_ptr_tmp) * (*X_theta_ptr_tmp);
-    }
-
-    value /= ncase;
-
-    linear_func_ptr_tmp = ((double *) linear_func_ptr + coord);
-    value += *linear_func_ptr_tmp;
-
-    gradient_ptr_tmp = ((double *) gradient_ptr + coord);
-    *gradient_ptr_tmp = value;
-
-    *need_update_ptr_tmp = 0;
-  }
-  else {
-    gradient_ptr_tmp = ((double *) gradient_ptr + coord);
-    value = *gradient_ptr_tmp;
-  }
-
-  return(value);
-}
-
-
-// Ever-active is 1-based
-// coord is 0-based
-int update_ever_active_wide(int coord,
-			    int *ever_active_ptr,
-			    int *nactive_ptr) {
-  int iactive;
-  int active_var;
-  int nactive = *nactive_ptr;
-  int *ever_active_ptr_tmp = ever_active_ptr;
-
-  for (iactive=0; iactive<nactive; iactive++) {
-    ever_active_ptr_tmp = ((int *) ever_active_ptr + iactive);
-    active_var = *ever_active_ptr_tmp;
-
-    if (active_var - 1 == coord) {          // Ever-active is 1-based
-      return(1);
-    }
-  }
-  
-  // If we haven't returned yet, this means the coord was not in 
-  // ever_active.
-
-  // Add it to the active set and increment the 
-  // number of active variables
-
-  ever_active_ptr_tmp = ((int *) ever_active_ptr + *nactive_ptr);
-  *ever_active_ptr_tmp = coord + 1;         // Ever-active is 1-based
-  *nactive_ptr += 1;
-
-  return(0);
-}
-
-void update_gradient_wide(double *gradient_ptr,     /* X^TX/ncase times theta + linear_func */
-			  double *X_theta_ptr,      /* Current fitted values */
-			  double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-			  double *linear_func_ptr,  /* Linear term in objective */   
-			  int *need_update_ptr,     /* Which coordinates need to be updated? */
-			  int ncase,             /* how many rows in X */
-			  int nfeature)                /* how many columns in X */
-{
-  int ifeature;
-
-  for (ifeature=0; ifeature<nfeature; ifeature++) {
-    compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, ifeature, ncase, nfeature);
-  }
-
-}
-
-int check_KKT_wide(double *theta_ptr,        /* current theta */
-		   double *gradient_ptr,     /* X^TX/ncase times theta + linear_func*/
-		   double *X_theta_ptr,      /* Current fitted values */
-		   double *X_ptr,            /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-		   double *linear_func_ptr,  /* Linear term in objective */   
-		   int *need_update_ptr,     /* Which coordinates need to be updated? */
-		   int ncase,                /* how many rows in X */
-		   int nfeature,             /* how many columns in X */
-		   double *bound_ptr,        /* Lagrange multiplers for \ell_1 */
-		   double ridge_term,        /* Ridge / ENet term */
-		   double tol)               /* precision for checking KKT conditions */        
-{
-  // First check inactive
-
-  int ifeature;
-  double *theta_ptr_tmp;
-  double *bound_ptr_tmp;
-  double bound;
-  double gradient;
-
-  for (ifeature=0; ifeature<nfeature; ifeature++) {
-
-    theta_ptr_tmp = ((double *) theta_ptr + ifeature);
-    bound_ptr_tmp = ((double *) bound_ptr + ifeature);
-    bound = *bound_ptr_tmp;
-
-    // Compute this coordinate of the gradient
-
-    gradient = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, ifeature, ncase, nfeature);
-
-    if ((*theta_ptr_tmp != 0) && (bound != 0)) { // these coordinates of gradients should be equal to -bound
-      
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + ridge_term * (*theta_ptr_tmp) + bound) > tol * bound)) {
-	return(0);
-      }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient + ridge_term * (*theta_ptr_tmp) - bound) > tol * bound)) {
-	return(0);
-      }
-      
-    }
-    else if (bound != 0) {
-      if (fabs(gradient) > (1. + tol) * bound) {
-	return(0);
-      }
-    }
-  }
-
-  return(1);
-}
-
-int check_KKT_wide_active(int *ever_active_ptr,           /* Ever active set: 0-based */ 
-			  int *nactive_ptr,               /* Size of ever active set */
-			  double *theta_ptr,              /* current theta */
-			  double *gradient_ptr,           /* X^TX/ncase times theta + linear_func*/
-			  double *X_theta_ptr,            /* Current fitted values */
-			  double *X_ptr,                  /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-			  double *linear_func_ptr,        /* Linear term in objective */   
-			  int *need_update_ptr,           /* Which coordinates need to be updated? */
-			  int ncase,                      /* how many rows in X */
-			  int nfeature,                   /* how many columns in X */
-			  double *bound_ptr,              /* Lagrange multipliers for \ell_1 */
-			  double ridge_term,              /* Ridge / ENet term */
-			  double tol)                     /* precision for checking KKT conditions */        
-{
-  // First check inactive
-
-  int iactive;
-  double *theta_ptr_tmp;
-  double *bound_ptr_tmp;
-  double bound;
-  double gradient;
-  int nactive = *nactive_ptr;
-  int active_feature;
-  int *active_feature_ptr;
-
-  for (iactive=0; iactive<nactive; iactive++) {
-
-    active_feature_ptr = ((int *) ever_active_ptr + iactive);
-    active_feature = *active_feature_ptr - 1;          // Ever-active is 1-based
-
-    theta_ptr_tmp = ((double *) theta_ptr + active_feature);
-    bound_ptr_tmp = ((double *) bound_ptr + active_feature);
-    bound = *bound_ptr_tmp;
-
-    // Compute this coordinate of the gradient
-
-    gradient = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, active_feature, ncase, nfeature);
-
-    if ((*theta_ptr_tmp != 0) && (bound != 0)) { // these coordinates of gradients should be equal to -bound
-
-      if ((*theta_ptr_tmp > 0) &&  (fabs(gradient + ridge_term * (*theta_ptr_tmp) + bound) > tol * bound)) {
-	return(0);
-      }
-      else if ((*theta_ptr_tmp < 0) && (fabs(gradient + ridge_term * (*theta_ptr_tmp) - bound) > tol * bound)) {
-	return(0);
-      }
-
-    }
-    else if (bound != 0) {
-      if (fabs(gradient) > (1. + tol) * bound) {
-	return(0);
-      }
-    }
-  }
-
-  return(1);
-}
-
-double update_one_coord_wide(double *X_ptr,               /* A design matrix*/
-			     double *linear_func_ptr,     /* Linear term in objective */
-			     double *nndef_diag_ptr,      /* Diagonal of nndef */
-			     double *gradient_ptr,        /* X^TX/ncase times theta + linear_func*/
-			     int *ever_active_ptr,        /* Ever active set: 1-based */ 
-			     int *nactive_ptr,            /* Size of ever active set */
-			     double *X_theta_ptr,         /* X\theta -- fitted values */
-			     int *need_update_ptr,        /* Whether a gradient coordinate needs update or not */
-			     int ncase,                   /* How many rows in X */
-			     int nfeature,                /* How many rows in X */
-			     double *bound_ptr,           /* Lagrange multipliers */
-			     double ridge_term,           /* Ridge / ENet term */
-			     double *theta_ptr,           /* current value */
-			     int coord,                   /* which coordinate to update: 0-based */
-			     int is_active)               /* Is this coord in ever_active */     
-{
-
-  double delta;
-  double linear_term = 0;
-  double value = 0;
-  double old_value;
-  double *X_ptr_tmp;
-  double *X_theta_ptr_tmp;
-  int *need_update_ptr_tmp;
-  double *theta_ptr_tmp;
-  double *bound_ptr_tmp;
-  double bound;
-  int ifeature, icase;
-
-  double *diagonal_ptr = ((double *) nndef_diag_ptr + coord);
-  double diagonal_entry = *diagonal_ptr;
-
-  linear_term = compute_gradient_coord(gradient_ptr, X_theta_ptr, X_ptr, linear_func_ptr, need_update_ptr, coord, ncase, nfeature);
-
-  theta_ptr_tmp = ((double *) theta_ptr + coord);
-  old_value = *theta_ptr_tmp;
-
-  bound_ptr_tmp = ((double *) bound_ptr + coord);
-  bound = *bound_ptr_tmp;
-
-  // The coord entry of gradient_ptr term has a diagonal term in it:
-  // (X^TX)[coord, coord] * theta[coord] / ncase
-  // This removes it. 
-
-  linear_term -= diagonal_entry * old_value;
-
-  // Now soft-threshold the coord entry of theta 
-
-  // Objective is t \mapsto (q+eps)/2 * t^2 + l * t + bound |t| + 
-  // with q=diagonal_entry and l=linear_term and eps=ridge_Term
-
-  // With a negative linear term, solution should be
-  // positive
-
-  if (linear_term < -bound) {
-    value = (-linear_term - bound) / (diagonal_entry + ridge_term);
-  }
-  else if (linear_term > bound) {
-    value = -(linear_term - bound) / (diagonal_entry + ridge_term);
-  }
-
-  // Add to active set if necessary
-
-  if ((is_active == 0) && (value != 0)) {
-    update_ever_active_wide(coord, ever_active_ptr, nactive_ptr);
-  }
-
-  // Update X\theta if changed
-
-  if (fabs(old_value - value) > 1.e-6 * (fabs(value) + fabs(old_value))) { 
-
-    // Set the update_gradient_ptr to 1
-
-    need_update_ptr_tmp = need_update_ptr;
-    for (ifeature=0; ifeature<nfeature; ifeature++) {
-      *need_update_ptr_tmp = 1;
-      need_update_ptr_tmp++;
-    }
-
-    // Update X\theta
-
-    delta = value - old_value;
-
-    for (icase=0; icase<ncase; icase++) {
-      X_theta_ptr_tmp = ((double *) X_theta_ptr + icase);
-#ifdef COLUMN_MAJOR_ORDER
-      X_ptr_tmp = ((double *) X_ptr + coord * ncase + icase);
-#else
-      X_ptr_tmp = ((double *) X_ptr + icase * nfeature + coord);
-#endif
-      *X_theta_ptr_tmp = (*X_theta_ptr_tmp) + delta * (*X_ptr_tmp);
-    }
-
-    theta_ptr_tmp = ((double *) theta_ptr + coord);
-    *theta_ptr_tmp = value;
-
-    theta_ptr_tmp = theta_ptr;
-    for (ifeature=0; ifeature<nfeature; ifeature++) {
-      theta_ptr_tmp++;
-    }
-
-  }
-
-  return(value);
-
-}
-
-int solve_wide(double *X_ptr,              /* Sqrt of non-neg def matrix -- X^TX/ncase = nndef */
-	       double *X_theta_ptr,        /* Fitted values   */
-	       double *linear_func_ptr,    /* Linear term in objective */
-	       double *nndef_diag_ptr,     /* Diagonal entries of non-neg def matrix */
-	       double *gradient_ptr,       /* X^TX/ncase times theta + linear_func*/
-	       int *need_update_ptr,       /* Keeps track of updated gradient coords */
-	       int *ever_active_ptr,       /* Ever active set: 1-based */ 
-	       int *nactive_ptr,           /* Size of ever active set */
-	       int ncase,                  /* How many rows in X */
-	       int nfeature,               /* How many columns in X */
-	       double *bound_ptr,          /* Lagrange multipliers */
-	       double ridge_term,          /* Ridge / ENet term */
-	       double *theta_ptr,          /* current value */
-	       double *theta_old_ptr,      /* previous value */
-	       int maxiter,                /* max number of iterations */
-	       double kkt_tol,             /* precision for checking KKT conditions */
-	       double objective_tol,       /* precision for checking relative decrease in objective value */
-	       double parameter_tol,       /* precision for checking relative convergence of parameter */
-	       int max_active,             /* Upper limit for size of active set -- otherwise break */ 
-	       int kkt_stop,               /* Break based on KKT? */
-	       int objective_stop,         /* Break based on convergence of objective value? */
-	       int param_stop)             /* Break based on parameter convergence? */
-{
-
-  int iter = 0;
-  int iter_old = 1;
-  int ifeature = 0;
-  int iactive = 0;
-  int *active_ptr;
-
-  double old_value, new_value; 
-  int niter_active = 5;
-  int iter_active;
-
-  double norm_diff = 1.;
-  double norm_last = 1.;
-  double delta;
-  double *theta_ptr_tmp, *theta_old_ptr_tmp;
-
-  new_value = objective_wide(X_theta_ptr,
-			     linear_func_ptr,
-			     ever_active_ptr,
-			     nactive_ptr,
-			     ncase,
-			     nfeature,
-			     bound_ptr,
-			     ridge_term,
-			     theta_ptr);
-
-  old_value = new_value + 2000000000; // hack for a big number... should do better
-
-
-  for (iter=0; iter<maxiter; iter++) {
-
-    // Update the active variables first -- do this niter_active times
-
-    for (iter_active=0; iter_active<niter_active; iter_active++) { 
-
-        active_ptr = (int *) ever_active_ptr;
-        for (iactive=0; iactive < *nactive_ptr; iactive++) {
-
-          update_one_coord_wide(X_ptr,
-    			        linear_func_ptr,
-				nndef_diag_ptr,
-				gradient_ptr,
-				ever_active_ptr,
-				nactive_ptr,
-				X_theta_ptr,
-				need_update_ptr,
-				ncase,
-				nfeature,
-				bound_ptr,
-				ridge_term,
-				theta_ptr,
-				*active_ptr - 1,   // Ever-active is 1-based
-				1);
-	  active_ptr++;
-	}
-
-	// Check KKT of active subproblem
-
-	if (check_KKT_wide_active(ever_active_ptr,
-				  nactive_ptr,
-				  theta_ptr,
-				  gradient_ptr,
-				  X_theta_ptr,
-				  X_ptr,
-				  linear_func_ptr,
-				  need_update_ptr,
-				  ncase,
-				  nfeature,
-				  bound_ptr,
-				  ridge_term,
-				  kkt_tol) == 1) {
-	  break;
-	}
-
-    }
-
-    // Check KKT
-
-    if (kkt_stop) { 
-      if (check_KKT_wide(theta_ptr,
-			 gradient_ptr,
-			 X_theta_ptr,
-			 X_ptr,
-			 linear_func_ptr,
-			 need_update_ptr,
-			 ncase,
-			 nfeature,
-			 bound_ptr,
-			 ridge_term,
-			 kkt_tol) == 1) {
-	break;
-      }
-    }
-
-    // Update all variables 
-
-    for (ifeature=0; ifeature<nfeature; ifeature++) {
-
-      update_one_coord_wide(X_ptr,
-			    linear_func_ptr,
-			    nndef_diag_ptr,
-			    gradient_ptr,
-			    ever_active_ptr,
-			    nactive_ptr,
-			    X_theta_ptr,
-			    need_update_ptr,
-			    ncase,
-			    nfeature,
-			    bound_ptr,
-			    ridge_term,
-			    theta_ptr,
-			    ifeature,
-			    0);
-    }
-
-    // Check KKT
-
-    if (kkt_stop) {
-      if (check_KKT_wide(theta_ptr,
-			 gradient_ptr,
-			 X_theta_ptr,
-			 X_ptr,
-			 linear_func_ptr,
-			 need_update_ptr,
-			 ncase,
-			 nfeature,
-			 bound_ptr,
-			 ridge_term,
-			 kkt_tol) == 1) {
-	break;
-      }
-    }
-
-    if (iter == 2 * iter_old) { // Geometric iterations from Adel's code
-
-      // Check based on norm 
-
-      if (param_stop) {
-	iter_old = iter;
-	norm_diff = 0;
-	norm_last = 0;
-	for (ifeature=0; ifeature<nfeature; ifeature++) {
-	  theta_old_ptr_tmp = ((double *) theta_old_ptr + ifeature);
-	  theta_ptr_tmp = ((double *) theta_ptr + ifeature);
-	  delta = (*theta_ptr_tmp - *theta_old_ptr_tmp);
-	  norm_diff += delta * delta;
-	  norm_last += (*theta_ptr_tmp) * (*theta_ptr_tmp);
-	  *theta_old_ptr = (*theta_ptr_tmp);
-	}
-	norm_diff = sqrt(norm_diff);
-	norm_last = sqrt(norm_last);
-	
-	if (norm_diff < parameter_tol * norm_last) {
-	  break;
-	}
-      }
-
-      // Check relative decrease of objective
-
-      if (objective_stop) {
-	new_value = objective_wide(X_theta_ptr,
-				   linear_func_ptr,
-				   ever_active_ptr,
-				   nactive_ptr,
-				   ncase,
-				   nfeature,
-				   bound_ptr,
-				   ridge_term,
-				   theta_ptr);
-
-	if ((fabs(old_value - new_value) < objective_tol * fabs(new_value)) && (iter > 0)) {
-	  break;
-	}
-	old_value = new_value;
-      }
-    }
-
-    // Check size of active set
-
-    if (*nactive_ptr >= max_active) {
-      break;
-    }
-
-  }
-  return(iter);
-}
-
diff --git a/selectiveInference/src/randomized_lasso.c b/selectiveInference/src/randomized_lasso.c
deleted file mode 100644
index 5729408..0000000
--- a/selectiveInference/src/randomized_lasso.c
+++ /dev/null
@@ -1,411 +0,0 @@
-#include <math.h> // for fabs
-#include <stdio.h>
-
-// Augmented density for randomized LASSO after
-// Gaussian randomization
-
-// Described in https://arxiv.org/abs/1609.05609
-
-// Gaussian is product of IID N(0, noise_scale^2) density
-// Evaluated at A_D D + A_O O + h
-
-// Laplace is product of IID Laplace with scale noise_scale
-// Also evaluated at A_D D + A_O O + h
-
-// Matrices are assumed in column major order! 
-
-double log_density_gaussian(double noise_scale,             // Scale of randomization
-			    int ndim,                       // Number of features -- "p"
-			    int ninternal,                  // Dimension of internal data representation often 1
-			    int noptimization,              // Dimension of optimization variables -- "p"
-			    double *internal_linear,        // A_D -- linear part for data
-			    double *internal_state,         // D -- data state
-			    double *optimization_linear,    // A_O -- linear part for optimization variables
-			    double *optimization_state,     // O -- optimization state
-			    double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double denom = 2 * noise_scale * noise_scale;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Internal (i.e. data) contribution
-    for (icol=0; icol<ninternal; icol++) {
-      
-      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
-      internal_state_ptr = ((double *) internal_state + icol);
-
-      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
-    }
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= (reconstruction * reconstruction) / denom;
-  }
-
-  return(value);
-}
-
-double log_density_laplace(double noise_scale,             // Scale of randomization
-			   int ndim,                       // Number of features -- "p"
-			   int ninternal,                  // Dimension of internal data representation often 1
-			   int noptimization,              // Dimension of optimization variables -- "p"
-			   double *internal_linear,        // A_D -- linear part for data
-			   double *internal_state,         // D -- data state
-			   double *optimization_linear,    // A_O -- linear part for optimization variables
-			   double *optimization_state,     // O -- optimization state
-			   double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *internal_linear_ptr;
-  double *internal_state_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Internal (i.e. data) contribution
-    for (icol=0; icol<ninternal; icol++) {
-      
-      internal_linear_ptr = ((double *) internal_linear + icol * ndim + irow);
-      internal_state_ptr = ((double *) internal_state + icol);
-
-      reconstruction += (*internal_linear_ptr) * (*internal_state_ptr);
-    }
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= fabs(reconstruction) / noise_scale;
-  }
-
-  return(value);
-}
-
-// Keeping internal (data) state fixed
-
-double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
-					int ndim,                       // Number of features -- "p"
-					int noptimization,              // Dimension of optimization variables -- "p"
-					double *optimization_linear,    // A_O -- linear part for optimization variables
-					double *optimization_state,     // O -- optimization state
-					double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double denom = 2 * noise_scale * noise_scale;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= reconstruction * reconstruction / denom;
-  }
-
-  return(value);
-}
-
-double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
-				       int ndim,                       // Number of features -- "p"
-				       int noptimization,              // Dimension of optimization variables -- "p"
-				       double *optimization_linear,    // A_O -- linear part for optimization variables
-				       double *optimization_state,     // O -- optimization state
-				       double *offset)                 // h -- offset in affine transform -- "p" dimensional 
-{
-  int irow, icol;
-  double value = 0;
-  double reconstruction = 0;
-  double *offset_ptr;
-  double *optimization_linear_ptr;
-  double *optimization_state_ptr;
-
-  for (irow=0; irow<ndim; irow++) {
-
-    // Compute the irow-th entry of the ndim vector
-
-    offset_ptr = ((double *) offset + irow);
-    reconstruction = *offset_ptr;
-
-    // Optimization contribution
-    for (icol=0; icol<noptimization; icol++) {
-      
-      optimization_linear_ptr = ((double *) optimization_linear + icol * ndim + irow);
-      optimization_state_ptr = ((double *) optimization_state + icol);
-
-      reconstruction += (*optimization_linear_ptr) * (*optimization_state_ptr);
-    }
-
-    value -= fabs(reconstruction) / noise_scale;
-  }
-
-  return(value);
-}
-
-//    objective = lambda u: -u.T.dot(conjugate_arg) + u.T.dot(precision).dot(u)/2. + np.log(1.+ 1./(u / scaling)).sum()
-
-double barrier_objective(double *opt_variable,               // Optimization variable
-			 double *conjugate_arg,              // Argument to conjugate of Gaussian
-			 double *precision,                  // Precision matrix of Gaussian
-			 double *scaling,                    // Diagonal scaling matrix for log barrier
-			 int ndim)                           // Dimension of conjugate_arg, precision
-{
-  int idim, jdim;
-  double *conjugate_arg_ptr;
-  double *opt_variable_ptr;
-  double *precision_ptr;
-  double *scaling_ptr;
-  double product_entry, value;
-
-  value = 0.;
-  for (idim=0; idim<ndim; idim++) {
-    
-    // Compute an entry of precision.dot(conjugate_arg)
-
-    product_entry = 0;
-    for (jdim=0; jdim<ndim; jdim++) {
-    
-      precision_ptr = ((double *) precision + idim * ndim + jdim); // precision is a symmetric matrix
-      opt_variable_ptr = ((double *) opt_variable + jdim);
-      product_entry += (*precision_ptr) * (*opt_variable_ptr);
-    }
-      
-    opt_variable_ptr = ((double *) opt_variable + idim);
-    value += 0.5 * (*opt_variable_ptr) * product_entry;
-
-    // now add linear term
-
-    conjugate_arg_ptr = ((double *) conjugate_arg + idim);
-    value -= (*conjugate_arg_ptr) * (*opt_variable_ptr);
-
-    // now log term
-
-    scaling_ptr = ((double *) scaling + idim);
-    value += log(((*opt_variable_ptr) + (*scaling_ptr)) / (*opt_variable_ptr));
-
-  }
-
-  return(value);
-}
-
-//    grad = lambda u: -conjugate_arg + precision.dot(u) + (1./(scaling + u) - 1./u)
-
-void barrier_gradient(double *gradient,                   // Gradient vector
-		      double *opt_variable,               // Optimization variable
-		      double *conjugate_arg,              // Argument to conjugate of Gaussian
-		      double *precision,                  // Precision matrix of Gaussian
-		      double *scaling,                    // Diagonal scaling matrix for log barrier
-		      int ndim)                           // Dimension of conjugate_arg, precision
-{
-  int idim, jdim;
-  double *gradient_ptr;
-  double *conjugate_arg_ptr;
-  double *opt_variable_ptr;
-  double *precision_ptr;
-  double *scaling_ptr;
-  double product_entry;
-
-  for (idim=0; idim<ndim; idim++) {
-    
-    gradient_ptr = ((double *) gradient + idim);
-
-    // Compute an entry of precision.dot(conjugate_arg)
-
-    product_entry = 0;
-    for (jdim=0; jdim<ndim; jdim++) {
-    
-      precision_ptr = ((double *) precision + idim * ndim + jdim); // precision is a symmetric matrix
-      opt_variable_ptr = ((double *) opt_variable + jdim);
-      product_entry += (*precision_ptr) * (*opt_variable_ptr);
-    }
-      
-    opt_variable_ptr = ((double *) opt_variable + idim);
-    *gradient_ptr = product_entry;
-
-    // now add linear term
-
-    conjugate_arg_ptr = ((double *) conjugate_arg + idim);
-    *gradient_ptr -= (*conjugate_arg_ptr);
-
-    // now log term
-
-    scaling_ptr = ((double *) scaling + idim);
-    *gradient_ptr = *gradient_ptr + 1. / ((*opt_variable_ptr) + (*scaling_ptr)) - 1. / (*opt_variable_ptr);
-  }
-
-}
-
-double barrier_gradient_step(double *gradient,                   // Gradient vector
-			     double *opt_variable,               // Optimization variable
-			     double *opt_proposed,               // Proposed value of optimization variable
-			     double *conjugate_arg,              // Argument to conjugate of Gaussian
-			     double *precision,                  // Precision matrix of Gaussian
-			     double *scaling,                    // Diagonal scaling matrix for log barrier
-			     double step,                        // Step size for gradient step
-			     int ndim)                           // Dimension of conjugate_arg, precision
-{
-  int idim;
-  double *gradient_ptr;
-  double *opt_variable_ptr;
-  double *opt_proposed_ptr;
-
-  for (idim=0; idim<ndim; idim++) {
-    opt_variable_ptr = ((double *) opt_variable + idim);
-    opt_proposed_ptr = ((double *) opt_proposed + idim);
-    gradient_ptr = ((double *) gradient + idim);
-    *opt_proposed_ptr = (*opt_variable_ptr) - step * (*gradient_ptr);
-  }
-
-  return barrier_objective(opt_proposed,
-			   conjugate_arg,         
-			   precision,             
-			   scaling,               
-			   ndim);
-}
-
-double barrier_solve(double *gradient,                   // Gradient vector
-		     double *opt_variable,               // Optimization variable
-		     double *opt_proposed,               // New value of optimization variable
-		     double *conjugate_arg,              // Argument to conjugate of Gaussian
-		     double *precision,                  // Precision matrix of Gaussian
-		     double *scaling,                    // Diagonal scaling matrix for log barrier
-		     int ndim,                           // Dimension of conjugate_arg, precision
-		     int max_iter,                       // Maximum number of iterations
-		     double value_tol,                   // Tolerance for convergence based on value
-		     double initial_step)                // Initial step size
-{
-  int iter, idim, istep;
-  double *gradient_ptr;
-  double *opt_variable_ptr;
-  double *opt_proposed_ptr;
-  double current_value = barrier_objective(opt_variable,
-					   conjugate_arg,         
-					   precision,             
-					   scaling,               
-					   ndim);
-  double proposed_value;
-  double step = initial_step;
-  int any_negative;
-
-  for (iter=0; iter<max_iter; iter++) {
-    
-    // Compute the gradient
-
-    barrier_gradient(gradient,    
-		     opt_variable,
-		     conjugate_arg,
-		     precision,    
-		     scaling,
-		     ndim);
-    
-    // Find a valid step size
-
-    istep = 0;
-    while (1) {
-      any_negative = 0;
-      for (idim=0; idim<ndim; idim++) {
-	opt_variable_ptr = ((double *) opt_variable + idim);
-	gradient_ptr = ((double *) gradient + idim);
-	if ((*opt_variable) - step * (*gradient_ptr) < 0) {
-	  any_negative += 1;
-	}
-      }
-      if (any_negative == 0) {
-	break;
-      }
-      step = step * 0.5;
-      istep++;
-      if (istep == 50) {
-	break;            // Terminate eventually -- but this will be a failure.
-	                  // Should mean that opt_variable is in feasible.
-      }
-    }
-
-    // Find a step size that is a descent
-
-    istep = 0;
-    while (1) {
-      proposed_value = barrier_gradient_step(gradient,
-					     opt_variable,
-					     opt_proposed,
-					     conjugate_arg,         
-					     precision,             
-					     scaling,               
-					     step,
-					     ndim);
-      if (proposed_value < current_value) {
-	for (idim=0; idim<ndim; idim++) {
-	  opt_variable_ptr = ((double *) opt_variable + idim);
-	  opt_proposed_ptr = ((double *) opt_proposed + idim);
-	  *opt_variable_ptr = *opt_proposed_ptr;
-	}
-	break;
-      }
-      step = step * 0.5;
-      istep++;
-      if (istep == 50) {
-	break;            // Terminate eventually -- this will mean no descent.
-	                  // We've solved the problem
-      }
-    }
-
-    if (fabs(current_value - proposed_value) < value_tol * fabs(current_value)) {
-      current_value = proposed_value;
-      break;
-    }
-    current_value = proposed_value;
-    
-  }
-
-  return(current_value);
-
-}
-
diff --git a/selectiveInference/src/randomized_lasso.h b/selectiveInference/src/randomized_lasso.h
deleted file mode 100644
index 268e071..0000000
--- a/selectiveInference/src/randomized_lasso.h
+++ /dev/null
@@ -1,54 +0,0 @@
-#ifdef __cplusplus
-extern "C"
-{
-#endif /* __cplusplus */
-
-double log_density_gaussian(double noise_scale,             // Scale of randomization
-			    int ndim,                       // Number of features -- "p"
-			    int ninternal,                  // Dimension of internal data representation often 1
-			    int noptimization,              // Dimension of optimization variables -- "p"
-			    double *internal_linear,        // A_D -- linear part for data
-			    double *internal_state,         // D -- data state
-			    double *optimization_linear,    // A_O -- linear part for optimization variables
-			    double *optimization_state,     // O -- optimization state
-			    double *offset);                // h -- offset in affine transform -- "p" dimensional 
-  
-double log_density_laplace(double noise_scale,             // Scale of randomization
-			   int ndim,                       // Number of features -- "p"
-			   int ninternal,                  // Dimension of internal data representation often 1
-			   int noptimization,              // Dimension of optimization variables -- "p"
-			   double *internal_linear,        // A_D -- linear part for data
-			   double *internal_state,         // D -- data state
-			   double *optimization_linear,    // A_O -- linear part for optimization variables
-			   double *optimization_state,     // O -- optimization state
-			   double *offset);                // h -- offset in affine transform -- "p" dimensional 
-
-double log_density_gaussian_conditional(double noise_scale,             // Scale of randomization
-					int ndim,                       // Number of features -- "p"
-					int noptimization,              // Dimension of optimization variables -- "p"
-					double *optimization_linear,    // A_O -- linear part for optimization variables
-					double *optimization_state,     // O -- optimization state
-					double *offset);                // h -- offset in affine transform -- "p" dimensional 
-
-double log_density_laplace_conditional(double noise_scale,             // Scale of randomization
-				       int ndim,                       // Number of features -- "p"
-				       int noptimization,              // Dimension of optimization variables -- "p"
-				       double *optimization_linear,    // A_O -- linear part for optimization variables
-				       double *optimization_state,     // O -- optimization state
-				       double *offset);                // h -- offset in affine transform -- "p" dimensional 
-
-
-double barrier_solve(double *gradient,                   // Gradient vector
-		     double *opt_variable,               // Optimization variable
-		     double *opt_proposed,               // New value of optimization variable
-		     double *conjugate_arg,              // Argument to conjugate of Gaussian
-		     double *precision,                  // Precision matrix of Gaussian
-		     double *scaling,                    // Diagonal scaling matrix for log barrier
-		     int ndim,                           // Dimension of conjugate_arg, precision
-		     int max_iter,                       // Maximum number of iterations
-		     double value_tol,                   // Tolerance for convergence based on value
-		     double initial_step);               // Initial stepsize 
-
-#ifdef __cplusplus
-}  /* extern "C" */
-#endif /* __cplusplus */

From 523216fff44972e62d34ccd6e8f4b9b8611488cb Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 30 Mar 2018 08:14:25 -0700
Subject: [PATCH 479/493] more rds files

---
 knockoff.rds          | Bin 204 -> 0 bytes
 liu_full.rds          | Bin 3232 -> 0 bytes
 liu_logistic_full.rds | Bin 1546 -> 0 bytes
 3 files changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 knockoff.rds
 delete mode 100644 liu_full.rds
 delete mode 100644 liu_logistic_full.rds

diff --git a/knockoff.rds b/knockoff.rds
deleted file mode 100644
index 25aad7dc22a9ac7f1a5a30669cef4f63f8836d3d..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 204
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diff --git a/liu_full.rds b/liu_full.rds
deleted file mode 100644
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GIT binary patch
literal 0
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diff --git a/liu_logistic_full.rds b/liu_logistic_full.rds
deleted file mode 100644
index 13d35adcca774b4cadfcace0b00416a6a519f17b..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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From 97f87e04f4837fff36f073cff2d6db11db263a52 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 30 Mar 2018 08:23:50 -0700
Subject: [PATCH 480/493] using Q_sq NULL arg

---
 selectiveInference/R/funs.full.main.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.full.main.R b/selectiveInference/R/funs.full.main.R
index 7d63b6d..c7f37c1 100644
--- a/selectiveInference/R/funs.full.main.R
+++ b/selectiveInference/R/funs.full.main.R
@@ -113,11 +113,11 @@ solve_problem_Q = function(Q_sq, Qbeta_bar, lambda, penalty_factor,
 truncation_set = function(X, y, Qbeta_bar, QE, sigma_est, 
                           target_stat, target_cov,
                           group, groups, active_vars,
-                          lambda, penalty_factor, loss, algo){
+                          lambda, penalty_factor, loss, algo, Q_sq=NULL){
   
   if (algo=="Q"){
     penalty_factor_rest = rep(penalty_factor)
-    penalty_factor_rest[group] = 10^10
+    penalty_factor_rest[group] = 10^10 * penalty_factor
     restricted_soln = solve_problem_Q(Q_sq, Qbeta_bar, lambda, penalty_factor=penalty_factor_rest)
   } else {
     restricted_soln = solve_restricted_problem(X, y, groups, group, lambda, penalty_factor=penalty_factor, 

From cfb70446955ac9d3f4a1146315d58a6f01f2da9c Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 30 Mar 2018 08:25:26 -0700
Subject: [PATCH 481/493] adding line about testing

---
 README.md | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 11770b5..c681eb1 100644
--- a/README.md
+++ b/README.md
@@ -28,4 +28,12 @@ You will have to run
 
 ```
 make Rcpp
-```
\ No newline at end of file
+```
+
+## To test
+
+```
+make check
+```
+
+These tests are also run on [travis](http://travis-ci.org) based on .travis.yml setup. 
\ No newline at end of file

From a0b732fb0df8eb2d0d2c26f7d41d39a7d5092e30 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Fri, 30 Mar 2018 08:33:32 -0700
Subject: [PATCH 482/493] deleting test scripts

---
 tests/comparisons/big uncorr/params.txt       |   3 -
 tests/comparisons/compare_FDR_power.R         |  17 ---
 tests/comparisons/plot_coverages.R            |  63 --------
 tests/comparisons/read_files.R                |  59 --------
 tests/comparisons/small corr/params.txt       |   0
 tests/comparisons/small uncorr/params.txt     |   0
 tests/knockoffs/cluster.R                     |  13 --
 tests/knockoffs/single_R_run.sbatch           |  39 -----
 tests/knockoffs/submit_R_jobs.sh              |  13 --
 tests/knockoffs/test_knockoffs.R              |  72 ---------
 tests/lee_high_dim_full/cluster.R             |  11 --
 tests/lee_high_dim_full/single_R_run.sbatch   |  38 -----
 tests/lee_high_dim_full/submit_R_jobs.sh      |  10 --
 tests/lee_high_dim_full/test_lee_full.R       |  92 ------------
 tests/liu_high_dim_full/cluster.R             |  11 --
 tests/liu_high_dim_full/single_R_run.sbatch   |  38 -----
 tests/liu_high_dim_full/submit_R_jobs.sh      |  10 --
 .../liu_high_dim_full/test_linear_contrasts.R | 108 --------------
 tests/liu_high_dim_full/test_liu_full.R       | 100 -------------
 tests/liu_high_dim_full/test_logistic.R       | 107 --------------
 tests/real_data_analysis/estimate_sigma.R     |  30 ----
 tests/real_data_analysis/real_data_analysis.R | 137 ------------------
 tests/real_data_analysis/single_R_run.sbatch  |  38 -----
 tests/real_data_analysis/submit_R_jobs.sh     |  21 ---
 24 files changed, 1030 deletions(-)
 delete mode 100644 tests/comparisons/big uncorr/params.txt
 delete mode 100644 tests/comparisons/compare_FDR_power.R
 delete mode 100644 tests/comparisons/plot_coverages.R
 delete mode 100644 tests/comparisons/read_files.R
 delete mode 100644 tests/comparisons/small corr/params.txt
 delete mode 100644 tests/comparisons/small uncorr/params.txt
 delete mode 100644 tests/knockoffs/cluster.R
 delete mode 100644 tests/knockoffs/single_R_run.sbatch
 delete mode 100644 tests/knockoffs/submit_R_jobs.sh
 delete mode 100644 tests/knockoffs/test_knockoffs.R
 delete mode 100644 tests/lee_high_dim_full/cluster.R
 delete mode 100644 tests/lee_high_dim_full/single_R_run.sbatch
 delete mode 100644 tests/lee_high_dim_full/submit_R_jobs.sh
 delete mode 100644 tests/lee_high_dim_full/test_lee_full.R
 delete mode 100644 tests/liu_high_dim_full/cluster.R
 delete mode 100644 tests/liu_high_dim_full/single_R_run.sbatch
 delete mode 100644 tests/liu_high_dim_full/submit_R_jobs.sh
 delete mode 100644 tests/liu_high_dim_full/test_linear_contrasts.R
 delete mode 100644 tests/liu_high_dim_full/test_liu_full.R
 delete mode 100644 tests/liu_high_dim_full/test_logistic.R
 delete mode 100644 tests/real_data_analysis/estimate_sigma.R
 delete mode 100644 tests/real_data_analysis/real_data_analysis.R
 delete mode 100644 tests/real_data_analysis/single_R_run.sbatch
 delete mode 100644 tests/real_data_analysis/submit_R_jobs.sh

diff --git a/tests/comparisons/big uncorr/params.txt b/tests/comparisons/big uncorr/params.txt
deleted file mode 100644
index dca935c..0000000
--- a/tests/comparisons/big uncorr/params.txt	
+++ /dev/null
@@ -1,3 +0,0 @@
-n=1000, p=2000, rho=0,
-snr = sqrt(2*log(p)/n)
-nrep=100
\ No newline at end of file
diff --git a/tests/comparisons/compare_FDR_power.R b/tests/comparisons/compare_FDR_power.R
deleted file mode 100644
index 2349d22..0000000
--- a/tests/comparisons/compare_FDR_power.R
+++ /dev/null
@@ -1,17 +0,0 @@
-lee = readRDS(file="lee.rds")
-liu = readRDS(file="liu.rds")
-kn = readRDS(file="knockoff.rds")
-kn_plus = readRDS(file="knockoff+.rds")
-randomized_full=readRDS(file="randomized_full.rds")
-randomized_partial=readRDS(file="randomized_partial.rds")
-
-
-table = matrix(c(mean(lee$FDR_sample), mean(lee$power_sample), 
-                 mean(liu$FDR_sample), mean(liu$power_sample),
-                 mean(kn$FDR_sample), mean(kn$power_sample),
-                 mean(kn_plus$FDR_sample), mean(kn_plus$power_sample),
-                 mean(randomized_full$FDR_sample), mean(randomized_full$power_sample),
-                 mean(randomized_partial$FDR_sample), mean(randomized_partial$power_sample)), ncol=2, byrow=TRUE)
-colnames(table)=c("FDR", "power")
-rownames(table)=c("Lee", "Liu", "knockoff", "knockoff+", "randomized full", "randomized partial")
-table  
diff --git a/tests/comparisons/plot_coverages.R b/tests/comparisons/plot_coverages.R
deleted file mode 100644
index a73e5d7..0000000
--- a/tests/comparisons/plot_coverages.R
+++ /dev/null
@@ -1,63 +0,0 @@
-
-lee = readRDS(file="lee.rds")
-liu = readRDS(file="liu.rds")
-rand_full=readRDS(file="randomized_full.rds")
-rand_partial=readRDS(file="randomized_partial.rds")
-
-n=lee$n
-p=lee$p
-s=lee$s
-snr=lee$snr
-rho=lee$rho
-
-lee_cov = mean(lee$sel_coverages)
-lee_len = median(lee$sel_lengths)
-
-liu_cov=mean(liu$sel_coverages)
-liu_len=median(liu$sel_lengths)
-
-naive_cov = mean(liu$naive_coverages)
-naive_len=median(liu$naive_lengths)
-
-rand_full_cov = mean(rand_full$sel_coverages)
-rand_full_len = median(rand_full$sel_lengths)
-
-rand_partial_cov=mean(rand_partial$sel_coverages)
-rand_partial_len=median(rand_partial$sel_lengths)
-
-ninf_lee = length(which(lee$sel_lengths==Inf))/length(lee$sel_lengths)
-ninf_liu = length(which(liu$sel_lengths==Inf))/length(liu$sel_lengths)
-c(ninf_lee, ninf_liu)
-
-labels = rep("",5)
-labels[1] = paste("len: ", round(lee_len,2), ", cov: ", round(lee_cov,2), ", ninf: ", round(ninf_lee,2), sep = "")
-labels[2] = paste("len: ", round(liu_len,2), ", cov: ", round(liu_cov,2), ", ninf: ", round(ninf_liu,2), sep = "")
-labels[3] = paste("len: ", round(naive_len,2), ", cov: ", round(naive_cov,2), sep = "")
-labels[4] = paste("len: ", round(rand_full_len,2), ", cov: ", round(rand_full_cov,2), sep = "")
-labels[5] = paste("len: ", round(rand_partial_len,2), ", cov: ", round(rand_partial_cov,2), sep = "")
-
-
-title = paste("n=", n,", p=", p, ", s=", s, ", signal=", round(snr,2), ", rho=", rho, sep = "")
-
-boxplot(lee$sel_lengths[which(lee$sel_lengths!=Inf)], liu$sel_lengths, liu$naive_lengths,
-        rand_full$sel_lengths, rand_partial$sel_lengths, 
-        at = seq(1,11,2.5) - 0.25, col = "pink", las = 2, outline = F,
-        names = c("Lee \n (full)", "Liu \n (full)", "naive", "rand \n (full)", "rand \n (partial)"), 
-        boxwex = 0.6, range = 0.5,
-        log="x", horizontal = T, xlim = c(0,12.5), cex.axis = 1)
-
-title(title, cex.main = 1, font.main = 1, line = 0.2)
-text(labels = labels, x = c(lee_len, liu_len, naive_len, rand_full_len,rand_partial_len)+0.05, y = seq(1,11,2.5) + 0.5, cex = 1)
-
-
-
-
-plot(ecdf(lee$pvalues), xlab="values", ylab="Empirical cdf", main="Selective pvalues", 
-     col="red")
-lines(ecdf(liu$pvalues), col="blue")
-abline(c(0,1))
-legend("bottomright", legend=c("Lee et al", "Liu et al"),
-       col=c("red", "blue"), lty=1:1, cex=1.5)
-
-
-
diff --git a/tests/comparisons/read_files.R b/tests/comparisons/read_files.R
deleted file mode 100644
index e5845a8..0000000
--- a/tests/comparisons/read_files.R
+++ /dev/null
@@ -1,59 +0,0 @@
-result.path = "/Users/Jelena/Documents/collect_cluster_results/full/"
-setwd(result.path)
-
-#method="liu"
-#method="lee"
-#method="knockoff_result"
-#method="knockoff+_result"
-#method="randomized_full"
-#method="randomized_partial"
-
-txt_files = list.files(pattern = paste(method, "*", sep=""))
-
-i = 0
-
-pvalues = NULL
-sel_intervals=NULL
-sel_coverages=NULL
-sel_lengths=NULL
-naive_pvalues = NULL
-naive_intervals=NULL
-naive_coverages=NULL
-naive_lengths=NULL
-FDR_sample = NULL
-power_sample=NULL
-
-for (file in txt_files){
-  result=readRDS(file)
-  pvalues=c(pvalues, result$pvalues)
-  sel_intervals=c(sel_intervals, result$sel_intervals)
-  sel_coverages=c(sel_coverages, result$sel_coverages)
-  sel_lengths=c(sel_lengths, result$sel_lengths)
-  naive_pvalues=c(naive_pvalues, result$naive_pvalues)
-  naive_intervals=c(naive_intervals, result$naive_intervals)
-  naive_coverages=c(naive_coverages, result$naive_coverages)
-  naive_lengths=c(naive_lengths, result$naive_lengths)
-  FDR_sample=c(FDR_sample, result$FDR_sample)
-  power_sample=c(power_sample, result$power_sample)
-  n=result$n
-  p=result$p
-  s=result$s
-  rho=result$rho
-  snr=result$snr
-  i=i+1
-}
-
-print(i)
-
-setwd("/Users/Jelena/GitHub Jelena/R-selective/tests/comparisons/")
-
-saveRDS(list(pvalues=pvalues, 
-             sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
-             naive_pvalues=naive_pvalues, 
-             naive_intervals=naive_intervals, naive_coverages=naive_coverages, naive_lengths=naive_lengths,
-             n=n, p=p, s=s, rho=rho, snr=snr,
-             FDR_sample=FDR_sample, power_sample=power_sample), file=paste(method,".rds", sep=""))
-
-
-
-
diff --git a/tests/comparisons/small corr/params.txt b/tests/comparisons/small corr/params.txt
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/comparisons/small uncorr/params.txt b/tests/comparisons/small uncorr/params.txt
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/knockoffs/cluster.R b/tests/knockoffs/cluster.R
deleted file mode 100644
index 178114e..0000000
--- a/tests/knockoffs/cluster.R
+++ /dev/null
@@ -1,13 +0,0 @@
-setwd(getwd())
-source("test_knockoffs.R")
-
-args = commandArgs(trailingOnly=TRUE)
-seed = round(as.numeric(args[1]))
-type=toString(args[2])
-  
-outdir = "/scratch/users/jelenam/full/"
-label=paste(type,"_result_", sep="")
-outfile = file.path(outdir, paste(sep="",label, toString(seed), ".rda"))
-
-test_knockoffs(seed, outfile, method=type)
-
diff --git a/tests/knockoffs/single_R_run.sbatch b/tests/knockoffs/single_R_run.sbatch
deleted file mode 100644
index c3440b1..0000000
--- a/tests/knockoffs/single_R_run.sbatch
+++ /dev/null
@@ -1,39 +0,0 @@
-#!/bin/bash 
-#
-#all commands that start with SBATCH contain commands that are just used by SLURM for scheduling
-#################
-#set a job name  
-#SBATCH --job-name=job
-#################  
-#a file for job output, you can check job progress, append the job ID with %j to make it unique
-#SBATCH --output=jobs/%j.out
-#################
-# a file for errors from the job
-#SBATCH --error=jobs/%j.err
-#################
-#time you think you need; default is 2 hours
-#format could be dd-hh:mm:ss, hh:mm:ss, mm:ss, or mm
-#SBATCH --time=10:00:00
-#################
-#SBATCH --qos=normal
-#SBATCH -p normal 
-#################
-#number of nodes you are requesting, the more you ask for the longer you wait
-#SBATCH --nodes=1
-#################
-#SBATCH --mem=32000
-
-# You can use srun if your job is parallel
-#srun R CMD BATCH  ./rtest.R 
-# otherwise: 
-
-SEED=$1
-type=$2
-
-# cd to program directory
-cd /home/users/jelenam/R-selective/tests/knockoffs
-
-module load R/3.4.0
-
-Rscript cluster.R $SEED $type
-
diff --git a/tests/knockoffs/submit_R_jobs.sh b/tests/knockoffs/submit_R_jobs.sh
deleted file mode 100644
index 0729d58..0000000
--- a/tests/knockoffs/submit_R_jobs.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/bin/bash
-# Setup bash job headers
-
-# load local environment
-
-type1=knockoff
-type2=knockoff+
-
-for i in {1..10}
-do
-	sbatch single_R_run.sbatch $i $type1
-	sbatch single_R_run.sbatch $i $type2
-done
\ No newline at end of file
diff --git a/tests/knockoffs/test_knockoffs.R b/tests/knockoffs/test_knockoffs.R
deleted file mode 100644
index 0536d64..0000000
--- a/tests/knockoffs/test_knockoffs.R
+++ /dev/null
@@ -1,72 +0,0 @@
-library(knockoff)
-
-compute.knockoff = function(data, method, q=0.2, model.free=TRUE){
-  
-  X=data$X 
-  y=data$y
-  n = nrow(X)
-  
-  true.nonzero = which(data$beta!=0)
-  s=length(true.nonzero)
-  offset=0
-  if (method=="knockoff+"){
-    offset=1
-  }
-  
-  if (model.free==TRUE){
-    filter = knockoff.filter(X, y, fdr=q, offset=offset)
-  } else{
-    filter = knockoff.filter(X, y, knockoffs = create.fixed, fdr=q, offset=offset)
-  }
-  
-  rejected = filter$selected
-  nrejected = length(rejected)
-  TP = length(intersect(rejected, true.nonzero))
-  power = TP/s
-  FDR = (nrejected-TP)/max(1, nrejected)
-  
-  return(list(rejected=rejected,
-              power=power, 
-              FDR=FDR, 
-              nrejected = nrejected))
-}
-
-
-
-test_knockoffs = function(seed=1, outfile=NULL, method = "knockoff", 
-                          nrep=10, n=100, p=200, s=10, rho=0.){
-  
-  snr = 4*sqrt(2*log(p)/n)
-  
-  set.seed(seed)
-
-  FDR_sample = NULL
-  power_sample=NULL
-  
-  for (i in 1:nrep){
-    #data = selectiveInference:::gaussian_instance(n=n, p=p, s=s, rho=rho, sigma=1, snr=snr)
-    data = selectiveInference:::logistic_instance(n=n, p=p, s=s, rho=rho, sigma=1, snr=snr)
-    
-    cat("true nonzero:", which(data$beta!=0), "\n")
-    
-    mc = compute.knockoff(data, method=method)
-    FDR_sample=c(FDR_sample, mc$FDR)
-    power_sample=c(power_sample, mc$power)
-    
-    if (length(FDR_sample)>0){
-      print(c("FDR:", mean(FDR_sample)))
-      print(c("power:", mean(power_sample)))
-    }
-  }
-  
-  if (is.null(outfile)){
-    outfile=paste(method, ".rds", sep="")
-  }
-  
-  saveRDS(list(FDR_sample=FDR_sample, power_sample=power_sample,
-               n=n,p=p, s=s, snr=snr, rho=rho), file=outfile)
-  
-  return(list(FDR_sample=FDR_sample, power_sample=power_sample))
-}
-
-test_knockoffs()
diff --git a/tests/lee_high_dim_full/cluster.R b/tests/lee_high_dim_full/cluster.R
deleted file mode 100644
index 892ff47..0000000
--- a/tests/lee_high_dim_full/cluster.R
+++ /dev/null
@@ -1,11 +0,0 @@
-setwd(getwd())
-source("test_lee_full.R")
-
-args = commandArgs(trailingOnly=TRUE)
-seed = round(as.numeric(args[1]))
-
-outdir = "/scratch/users/jelenam/full/"
-outfile = file.path(outdir, paste(sep="","lee_result_", toString(seed), ".rda"))
-
-test_lee_full(seed, outfile)
-
diff --git a/tests/lee_high_dim_full/single_R_run.sbatch b/tests/lee_high_dim_full/single_R_run.sbatch
deleted file mode 100644
index d3d3d45..0000000
--- a/tests/lee_high_dim_full/single_R_run.sbatch
+++ /dev/null
@@ -1,38 +0,0 @@
-#!/bin/bash 
-#
-#all commands that start with SBATCH contain commands that are just used by SLURM for scheduling
-#################
-#set a job name  
-#SBATCH --job-name=job
-#################  
-#a file for job output, you can check job progress, append the job ID with %j to make it unique
-#SBATCH --output=jobs/%j.out
-#################
-# a file for errors from the job
-#SBATCH --error=jobs/%j.err
-#################
-#time you think you need; default is 2 hours
-#format could be dd-hh:mm:ss, hh:mm:ss, mm:ss, or mm
-#SBATCH --time=5:00:00
-#################
-#SBATCH --qos=normal
-#SBATCH -p normal 
-#################
-#number of nodes you are requesting, the more you ask for the longer you wait
-#SBATCH --nodes=1
-#################
-#SBATCH --mem=16000
-
-# You can use srun if your job is parallel
-#srun R CMD BATCH  ./rtest.R 
-# otherwise: 
-
-SEED=$1
-
-# cd to program directory
-cd /home/users/jelenam/R-selective/tests/lee_high_dim_full
-
-module load R/3.4.0
-
-Rscript cluster.R $SEED 
-
diff --git a/tests/lee_high_dim_full/submit_R_jobs.sh b/tests/lee_high_dim_full/submit_R_jobs.sh
deleted file mode 100644
index 4d4b4a1..0000000
--- a/tests/lee_high_dim_full/submit_R_jobs.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/bin/bash
-# Setup bash job headers
-
-# load local environment
-
-
-for i in {1..10}
-do
-	sbatch single_R_run.sbatch $i
-done
\ No newline at end of file
diff --git a/tests/lee_high_dim_full/test_lee_full.R b/tests/lee_high_dim_full/test_lee_full.R
deleted file mode 100644
index f955d52..0000000
--- a/tests/lee_high_dim_full/test_lee_full.R
+++ /dev/null
@@ -1,92 +0,0 @@
-library(selectiveInference)
-library(glmnet)
-
-# testing Lee et al type=full in high dimensional settings -- uses debiasing matrix
-
-test_lee_full = function(seed=1, outfile=NULL, nrep=1, n=1000, p=10000, s=50, rho=0.){
-  
-  snr = sqrt(2*log(p)/n)
-  
-  set.seed(seed)
-  loss="ls"
-  construct_ci=TRUE
-  penalty_factor = rep(1, p)
-  
-  pvalues = NULL
-  sel_intervals=NULL
-  sel_coverages=NULL
-  sel_lengths=NULL
-  
-  FDR_sample = NULL
-  power_sample=NULL
-  
-  for (i in 1:nrep){
-    data = selectiveInference:::gaussian_instance(n=n, p=p, s=s, rho=rho, sigma=1, snr=snr)
-    X=data$X
-    y=data$y
-    beta=data$beta
-    cat("true nonzero:", which(beta!=0), "\n")
-    
-    CV = cv.glmnet(X, y, standardize=FALSE, intercept=FALSE, family=selectiveInference:::family_label(loss))
-    
-    #sigma_est=selectiveInference:::estimate_sigma(X,y,coef(CV, s="lambda.min")[-1])  # sigma via Reid et al.
-    #print(c("sigma est", sigma_est))
-    sigma_est=1
-    
-    # lambda = CV$lambda[which.min(CV$cvm+rnorm(length(CV$cvm))/sqrt(n))] # lambda via randomized cv 
-    lambda = 0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est) # theoretical lambda
-    
-    lasso = glmnet(X, y, family=selectiveInference:::family_label(loss), alpha=1, standardize=FALSE, intercept=FALSE, thresh=1e-12)
-    soln = as.numeric(coef(lasso,x=X,y=y, family=selectiveInference:::family_label(loss), s=lambda, exact=TRUE))[-1]
-
-    PVS = selectiveInference:::fixedLassoInf(X,y,soln, intercept=FALSE, lambda*n, family=selectiveInference:::family_label(loss),
-                                             type="full",sigma=sigma_est)
-    
-    abs_soln = abs(soln)
-    beta_threshold = abs_soln[order(abs_soln,decreasing=TRUE)][length(PVS$pv)]
-    active_vars = which(abs_soln>=beta_threshold)
-    cat("nactive:", length(active_vars), "\n")
-    cat("active vars:", active_vars, "\n")
-    
-    pvalues = c(pvalues, PVS$pv)
-    sel_intervals = cbind(sel_intervals, t(PVS$ci))
-
-    if (length(pvalues)>0){
-      plot(ecdf(pvalues))
-      abline(0,1)
-    }
-    
-    if (construct_ci && length(active_vars)>0){
-      sel_coverages=c(sel_coverages, selectiveInference:::compute_coverage(t(PVS$ci), beta[active_vars]))
-      sel_lengths=c(sel_lengths, as.vector(PVS$ci[,2]-PVS$ci[,1]))
-      print(c("selective coverage:", mean(sel_coverages)))
-      print(c("selective length mean:", mean(sel_lengths)))
-      print(c("selective length median:", median(sel_lengths)))
-    }
-    
-    mc = selectiveInference:::selective.plus.BH(beta, active_vars, PVS$pv, q=0.2)
-    FDR_sample=c(FDR_sample, mc$FDR)
-    power_sample=c(power_sample, mc$power)
-    
-    if (length(FDR_sample)>0){
-      print(c("FDR:", mean(FDR_sample)))
-      print(c("power:", mean(power_sample)))
-    }
-  }
-  
-  if (is.null(outfile)){
-    outfile="lee_full.rds"
-  }
-  
-  saveRDS(list(sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
-               pvalues=pvalues,
-               FDR_sample=FDR_sample, power_sample=power_sample,
-               n=n, p=p, s=s, snr=snr, rho=rho), file=outfile)
-  
-  return(list(pvalues=pvalues))
-}
-
-#test_lee_full()
-
-
-
diff --git a/tests/liu_high_dim_full/cluster.R b/tests/liu_high_dim_full/cluster.R
deleted file mode 100644
index 4605ee9..0000000
--- a/tests/liu_high_dim_full/cluster.R
+++ /dev/null
@@ -1,11 +0,0 @@
-setwd(getwd())
-source("test_liu_full.R")
-
-args = commandArgs(trailingOnly=TRUE)
-seed = round(as.numeric(args[1]))
-
-outdir = "/scratch/users/jelenam/full/"
-outfile = file.path(outdir, paste(sep="","liu_result_", toString(seed), ".rda"))
-
-test_liu_full(seed, outfile)
-
diff --git a/tests/liu_high_dim_full/single_R_run.sbatch b/tests/liu_high_dim_full/single_R_run.sbatch
deleted file mode 100644
index 0ae7ce7..0000000
--- a/tests/liu_high_dim_full/single_R_run.sbatch
+++ /dev/null
@@ -1,38 +0,0 @@
-#!/bin/bash 
-#
-#all commands that start with SBATCH contain commands that are just used by SLURM for scheduling
-#################
-#set a job name  
-#SBATCH --job-name=job
-#################  
-#a file for job output, you can check job progress, append the job ID with %j to make it unique
-#SBATCH --output=jobs/%j.out
-#################
-# a file for errors from the job
-#SBATCH --error=jobs/%j.err
-#################
-#time you think you need; default is 2 hours
-#format could be dd-hh:mm:ss, hh:mm:ss, mm:ss, or mm
-#SBATCH --time=5:00:00
-#################
-#SBATCH --qos=normal
-#SBATCH -p normal 
-#################
-#number of nodes you are requesting, the more you ask for the longer you wait
-#SBATCH --nodes=1
-#################
-#SBATCH --mem=16000
-
-# You can use srun if your job is parallel
-#srun R CMD BATCH  ./rtest.R 
-# otherwise: 
-
-SEED=$1
-
-# cd to program directory
-cd /home/users/jelenam/R-selective/tests/liu_high_dim_full
-
-module load R/3.4.0
-
-Rscript cluster.R $SEED 
-
diff --git a/tests/liu_high_dim_full/submit_R_jobs.sh b/tests/liu_high_dim_full/submit_R_jobs.sh
deleted file mode 100644
index 4d4b4a1..0000000
--- a/tests/liu_high_dim_full/submit_R_jobs.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/bin/bash
-# Setup bash job headers
-
-# load local environment
-
-
-for i in {1..10}
-do
-	sbatch single_R_run.sbatch $i
-done
\ No newline at end of file
diff --git a/tests/liu_high_dim_full/test_linear_contrasts.R b/tests/liu_high_dim_full/test_linear_contrasts.R
deleted file mode 100644
index e3bee64..0000000
--- a/tests/liu_high_dim_full/test_linear_contrasts.R
+++ /dev/null
@@ -1,108 +0,0 @@
-library(gglasso)
-library(MASS)
-library(selectiveInference)
-library(glmnet)
-#.libPaths("/Users/Jelena/anaconda/lib/R/library")
-#.libPaths("/Library/Frameworks/R.framework/Versions/3.4/Resources/library")
-#source("/Users/Jelena/GitHub Jelena/full/code/group_lasso_fns.R")
-
-
-gaussian_instance = function(n, p, s, sigma=1, rho=0, snr=7, 
-                             random_signs=TRUE, scale=FALSE, center=FALSE){
-  X = sqrt(1-rho)*matrix(rnorm(n*p),n) + sqrt(rho)*matrix(rep(rnorm(n), p), nrow = n)
-  if (scale==TRUE){
-    X = scale(X)
-    X = X/sqrt(n)
-  }
-  beta = rep(0, p)
-  beta[1:s]=snr
-  if (random_signs==TRUE && s>0){
-    signs = sample(c(-1,1), s, replace = TRUE)
-    beta[1:s] = beta[1:s] * signs
-  }
-  beta=sample(beta)
-  y = X %*% beta + rnorm(n)*sigma 
-  result <- list(X=X,y=y,beta=beta)
-  return(result)
-}
-
-
-test_group_lasso = function(nrep=10){
-  
-  set.seed(1)
-  loss="ls"
-  construct_ci=TRUE
-  n=500
-  p=100
-  s=10
-  ngroups=50
-  lambda=60/n
-  snr=0.3
-  groups = rep(1:ngroups,each=p/ngroups)
-  group_sizes = rep(p/ngroups,ngroups)
-  penalty_factor = rep(1, length(group_sizes))
-  
-  
-  pvalues = NULL
-  naive_pvalues = NULL
-  sel_intervals=NULL
-  naive_intervals=NULL
-  sel_coverages=NULL
-  naive_coverages=NULL
-  sel_lengths=NULL
-  naive_lengths=NULL
-  
-  
-  for (i in 1:nrep){
-    data = gaussian_instance(n,p,s, snr=snr)
-    X=data$X
-    y=data$y
-    beta=data$beta
-    #print("beta")
-    #print(which(beta!=0))
-    CV = cv.glmnet(X, y, standardize=FALSE, intercept=FALSE, family=selectiveInference:::family_label(loss))
-    sigma_est=selectiveInference:::estimate_sigma(X,y, coef(CV, s="lambda.min")[-1])
-    print(c("sigma est", sigma_est))
-    
-    soln = selectiveInference:::solve_problem_gglasso(X, y, groups, lambda, penalty_factor=penalty_factor, loss=loss)
-    PVS = selectiveInference:::inference_group_lasso(X,y, soln,groups, lambda=lambda, penalty_factor=penalty_factor, 
-                                        sigma_est=sigma_est, loss=loss, algo="glmnet", construct_ci = construct_ci)
-    
-    active_vars = PVS$active_vars
-    print("active_vars")
-    print(active_vars)
-    null_active_vars = match(which(beta==0), active_vars)
-    null_active_vars = null_active_vars[!is.na(null_active_vars)]
-    print("null_active_vars")
-    print(null_active_vars)
-    
-    if (length(null_active_vars)>0){
-      print(PVS$pvalues)
-      print(PVS$pvalues[null_active_vars])
-      print(PVS$naive_pvalues[null_active_vars])
-      pvalues = c(pvalues, PVS$pvalues[null_active_vars])
-      naive_pvalues = c(naive_pvalues, PVS$naive_pvalues[null_active_vars])
-      plot(ecdf(pvalues))
-      lines(ecdf(naive_pvalues), col="red")
-      abline(0,1)
-    }
-    
-    if (construct_ci && length(active_vars)>0){
-      
-      sel_coverages=c(sel_coverages, selectiveInference:::compute_coverage(PVS$sel_intervals, beta[active_vars]))
-      naive_coverages=c(naive_coverages, selectiveInference:::compute_coverage(PVS$naive_intervals, beta[active_vars]))
-      sel_lengths=c(sel_lengths, mean(PVS$sel_intervals[2,]-PVS$sel_intervals[1,]))
-      naive_lengths=c(naive_lengths, mean(PVS$naive_intervals[2,]-PVS$naive_intervals[1,]))
-      print(c("selective covarage:", mean(sel_coverages)))
-      print(c("naive coverage:", mean(naive_coverages)))
-      print(c("selective length:", mean(sel_lengths)))
-      print(c("naive length:", mean(naive_lengths)))
-    }
-    
-  }
-  
-  return(list(pvalues=pvalues, naive_pvalues=naive_pvalues))
-}
-
-
-test_group_lasso()
diff --git a/tests/liu_high_dim_full/test_liu_full.R b/tests/liu_high_dim_full/test_liu_full.R
deleted file mode 100644
index 3f75937..0000000
--- a/tests/liu_high_dim_full/test_liu_full.R
+++ /dev/null
@@ -1,100 +0,0 @@
-library(gglasso)
-library(MASS)
-library(selectiveInference)
-library(glmnet)
-
-# testing Liu et al type=full in high dimensional settings -- uses debiasing matrix
-
-test_liu_full = function(seed=1, outfile=NULL, nrep=1, n=100, p=200, s=20, rho=0.){
-  
-  snr = sqrt(2*log(p)/n)
-  
-  set.seed(seed)
-  loss="ls"
-  construct_ci=TRUE
-  penalty_factor = rep(1, p)
-  
-  pvalues = NULL
-  sel_intervals=NULL
-  sel_coverages=NULL
-  sel_lengths=NULL
-  naive_pvalues = NULL
-  naive_intervals=NULL
-  naive_coverages=NULL
-  naive_lengths=NULL
-  
-  FDR_sample = NULL
-  power_sample=NULL
-  
-  for (i in 1:nrep){
-    data = selectiveInference:::gaussian_instance(n=n, p=p, s=s, rho=rho, sigma=1, snr=snr)
-    X=data$X
-    y=data$y
-    beta=data$beta
-    cat("true nonzero:", which(beta!=0), "\n")
-    
-    # CV = cv.glmnet(X, y, standardize=FALSE, intercept=FALSE, family=selectiveInference:::family_label(loss))
-    # sigma_est=selectiveInference:::estimate_sigma(X,y,coef(CV, s="lambda.min")[-1]) # sigma via Reid et al.
-    sigma_est=1
-    print(c("sigma est", sigma_est))
-    
-    # lambda = CV$lambda[which.min(CV$cvm+rnorm(length(CV$cvm))/sqrt(n))]  # lambda via randomized cv 
-    lambda = 0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est)  # theoretical lambda
-    print(c("lambda", lambda))
-    
-    soln = selectiveInference:::solve_problem_glmnet(X, y, lambda, penalty_factor=penalty_factor, loss=loss)
-    #soln = solve_problem_gglasso(X, y, groups=1:ncol(X), lambda, penalty_factor=penalty_factor, loss=loss)
-    PVS = selectiveInference:::inference_group_lasso(X, y, soln, groups=1:ncol(X), lambda=lambda, penalty_factor=penalty_factor, 
-                                sigma_est, loss=loss, algo="glmnet", construct_ci = construct_ci)
-    active_vars=PVS$active_vars
-    cat("active_vars:",active_vars,"\n")
-    pvalues = c(pvalues, PVS$pvalues)
-    naive_pvalues = c(naive_pvalues, PVS$naive_pvalues)
-    sel_intervals = cbind(sel_intervals, PVS$sel_intervals)  # matrix with two rows
-    naive_intervals = cbind(naive_intervals, PVS$naive_intervals)
-    
-    if (length(pvalues)>0){
-      plot(ecdf(pvalues))
-      lines(ecdf(naive_pvalues), col="red")
-      abline(0,1)
-    }
-    
-    if (construct_ci && length(active_vars)>0){
-      
-      sel_coverages=c(sel_coverages, selectiveInference:::compute_coverage(PVS$sel_intervals, beta[active_vars]))
-      naive_coverages=c(naive_coverages, selectiveInference:::compute_coverage(PVS$naive_intervals, beta[active_vars]))
-      sel_lengths=c(sel_lengths, as.vector(PVS$sel_intervals[2,]-PVS$sel_intervals[1,]))
-      naive_lengths=c(naive_lengths, as.vector(PVS$naive_intervals[2,]-PVS$naive_intervals[1,]))
-      print(c("selective coverage:", mean(sel_coverages)))
-      print(c("naive coverage:", mean(naive_coverages)))
-      print(c("selective length mean:", mean(sel_lengths)))
-      print(c("selective length median:", median(sel_lengths)))
-      print(c("naive length mean:", mean(naive_lengths)))
-      print(c("naive length median:", median(naive_lengths)))
-    }
-    
-    mc = selectiveInference:::selective.plus.BH(beta, active_vars, PVS$pvalues, q=0.2)
-    FDR_sample=c(FDR_sample, mc$FDR)
-    power_sample=c(power_sample, mc$power)
-    
-    if (length(FDR_sample)>0){
-      print(c("FDR:", mean(FDR_sample)))
-      print(c("power:", mean(power_sample)))
-    }
-  }
-  
-  if (is.null(outfile)){
-    outfile="liu_full.rds"
-  }
-  
-  saveRDS(list(sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
-               naive_intervals=naive_intervals, naive_coverages=naive_coverages, naive_lengths=naive_lengths,
-               pvalues=pvalues, naive_pvalues=naive_pvalues,
-               FDR_sample=FDR_sample, power_sample=power_sample,
-               n=n, p=p, s=s, snr=snr, rho=rho), file=outfile)
-  
-  return(list(pvalues=pvalues, naive_pvalues=naive_pvalues))
-}
-
-test_liu_full()
-
diff --git a/tests/liu_high_dim_full/test_logistic.R b/tests/liu_high_dim_full/test_logistic.R
deleted file mode 100644
index b6e7374..0000000
--- a/tests/liu_high_dim_full/test_logistic.R
+++ /dev/null
@@ -1,107 +0,0 @@
-library(MASS)
-library(selectiveInference)
-library(glmnet)
-
-
-test_logistic = function(seed=1, outfile=NULL, nrep=10, n=1000, p=2000, s=30, rho=0.){
-  
-  snr = sqrt(2*log(p)/n)
-  snr = 2*snr
-  #print(snr)
-  #empirical=empirical = apply(abs(t(matrix(rnorm(n*p), nrow=n)) %*% matrix(rnorm(n*1000), nrow=n)), 2, max)
-  #print(mean(empirical)/n)
-  
-  #empirical = apply(abs(t(matrix(rnorm(n*p), nrow=n)) %*% matrix(sample(c(0,1), n*1000, replace = TRUE), nrow=n)), 2, max)
-  #snr=mean(empirical)/n
-  #print(snr)
-  
-  set.seed(seed)
-  loss="logit"
-  construct_ci=TRUE
-  
-  penalty_factor = rep(1, p)
-  
-  pvalues = NULL
-  naive_pvalues = NULL
-  sel_intervals=NULL
-  naive_intervals=NULL
-  sel_coverages=NULL
-  naive_coverages=NULL
-  sel_lengths=NULL
-  naive_lengths=NULL
-  
-  FDR_sample = NULL
-  power_sample=NULL
-  
-  for (i in 1:nrep){
-    data = selectiveInference:::logistic_instance(n=n, p=p, s=s, rho=rho, sigma=1, snr=snr)
-    X=data$X
-    y=data$y
-    beta=data$beta
-    
-    cat("true nonzero:", which(beta!=0), "\n")
-    
-    lambda = 0.4*selectiveInference:::theoretical.lambda(X, loss, sigma=1)
-    
-    soln = selectiveInference:::solve_problem_glmnet(X, y, lambda, penalty_factor=penalty_factor, loss=loss)
-    PVS = selectiveInference:::inference_group_lasso(X,y, soln, groups=1:ncol(X), lambda=lambda, penalty_factor=penalty_factor,
-                                sigma_est=1, loss=loss, algo="glmnet", construct_ci = construct_ci)
-    
-    active_vars = PVS$active_vars
-    print("active_vars")
-    print(active_vars)
-    null_active_vars = match(which(beta==0), active_vars)
-    null_active_vars = null_active_vars[!is.na(null_active_vars)]
-    #print("null_active_vars")
-    #print(null_active_vars)
-    
-    if (length(null_active_vars)>0){
-      pvalues = c(pvalues, PVS$pvalues[null_active_vars])
-      naive_pvalues = c(naive_pvalues, PVS$naive_pvalues[null_active_vars])
-      plot(ecdf(pvalues))
-      lines(ecdf(naive_pvalues), col="red")
-      abline(0,1)
-    }
-    
-    if (construct_ci && length(active_vars)>0){
-      
-      sel_coverages=c(sel_coverages, selectiveInference:::compute_coverage(PVS$sel_intervals, beta[active_vars]))
-      naive_coverages=c(naive_coverages, selectiveInference:::compute_coverage(PVS$naive_intervals, beta[active_vars]))
-      
-      #sel_coverages=c(sel_coverages, compute_coverage(PVS$sel_intervals[,null_active_vars], rep(0, length(null_active_vars))))
-      #naive_coverages=c(naive_coverages,compute_coverage(PVS$naive_intervals[,null_active_vars], rep(0, length(null_active_vars))))
-      
-      sel_lengths=c(sel_lengths, mean(PVS$sel_intervals[2,]-PVS$sel_intervals[1,]))
-      naive_lengths=c(naive_lengths, mean(PVS$naive_intervals[2,]-PVS$naive_intervals[1,]))
-      print(c("selective coverage:", mean(sel_coverages)))
-      print(c("naive coverage:", mean(naive_coverages)))
-      print(c("selective length:", mean(sel_lengths)))
-      print(c("naive length:", mean(naive_lengths)))
-    }
-    
-    mc = selectiveInference:::selective.plus.BH(beta, active_vars, PVS$pvalues, q=0.2)
-    FDR_sample=c(FDR_sample, mc$FDR)
-    power_sample=c(power_sample, mc$power)
-    
-    if (length(FDR_sample)>0){
-      print(c("FDR:", mean(FDR_sample)))
-      print(c("power:", mean(power_sample)))
-    }
-  }
-  
-  if (is.null(outfile)){
-    outfile="liu_logistic_full.rds"
-  }
-  
-  saveRDS(list(sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
-               naive_intervals=naive_intervals, naive_coverages=naive_coverages, naive_lengths=naive_lengths,
-               pvalues=pvalues, naive_pvalues=naive_pvalues,
-               FDR_sample=FDR_sample, power_sample=power_sample,
-               n=n, p=p, s=s, snr=snr, rho=rho), file=outfile)
-  
-  
-  return(list(pvalues=pvalues, naive_pvalues=naive_pvalues))
-}
-
-test_logistic()
-
diff --git a/tests/real_data_analysis/estimate_sigma.R b/tests/real_data_analysis/estimate_sigma.R
deleted file mode 100644
index df5a39a..0000000
--- a/tests/real_data_analysis/estimate_sigma.R
+++ /dev/null
@@ -1,30 +0,0 @@
-library(glmnet)
-library(selectiveInference)
-
-setwd("/Users/Jelena/Dropbox/kevin/jelena/real_data")
-data=readRDS("real_data.rds")
-X=data$X
-y=data$y
-
-set.seed(1)
-
-CV = cv.glmnet(X, y, standardize=FALSE, intercept=FALSE, family="gaussian")
-sigma_est=selectiveInference:::estimate_sigma(X,y,coef(CV, s="lambda.min")[-1]) # sigma via Reid et al.
-print(c("sigma est via Reid et al.", sigma_est))  "sigma est via Reid et al." "0.620021797352246" 
-
-print(c("std error of y", sqrt(var(y))))  "std error of y"    "0.488729008112197"
-
-n=nrow(X)
-m=floor(n/2)
-subsample = sample(1:n,m, replace=FALSE)
-leftover = setdiff(1:n, subsample)
-CV = cv.glmnet(X[subsample,], y[subsample], standardize=FALSE, intercept=FALSE, family="gaussian")
-beta_hat = coef(CV, s="lambda.min")[-1]
-selected = which(beta_hat!=0)
-LM = lm(y[leftover]~X[leftover,][,selected])
-sigma_est = sigma(LM)
-print(c("sigma est from data splitting", sigma_est)) "0.481421791225578"  
-
-
-
-
diff --git a/tests/real_data_analysis/real_data_analysis.R b/tests/real_data_analysis/real_data_analysis.R
deleted file mode 100644
index c5132a7..0000000
--- a/tests/real_data_analysis/real_data_analysis.R
+++ /dev/null
@@ -1,137 +0,0 @@
-library(glmnet)
-library(selectiveInference)
-library(MASS)
-library(knockoff)
-
-setwd(getwd())
-args = commandArgs(trailingOnly=TRUE)
-method = toString(args[1])
-
-outdir = "/scratch/users/jelenam/full/"
-label = paste(method, "_results", sep="")
-outfile = file.path(outdir, paste(sep="",label, ".rds"))
-
-#setwd("/Users/Jelena/Dropbox/kevin/jelena/real_data")
-set.seed(1)
-loss="ls"
-sigma_est = 0.62
-lambda = 0.0352479
-#lambda =  0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est) # "lambda" "0.0352479112219816"
-#print(c("lambda", lambda))
-
-
-liu_full = function(outfile){
-  data=readRDS("real_data1.rds")
-  X=data$X
-  y=data$y
-  n=nrow(X)
-  penalty_factor = rep(1, ncol(X))
-  
-  soln = selectiveInference:::solve_problem_glmnet(X, y, lambda, penalty_factor=penalty_factor, loss=loss)
-  PVS = selectiveInference:::inference_group_lasso(X, y, soln, groups=1:ncol(X), lambda=lambda, penalty_factor=penalty_factor, 
-                                                 sigma_est, loss=loss, algo="glmnet", construct_ci = TRUE)
-  saveRDS(list(active_vars=PVS$active_vars, 
-               sel_intervals=PVS$sel_intervals, naive_intervals=PVS$naive_intervals,
-               pvalues=PVS$pvalues, naive_pvalues=PVS$naive_pvalues), file=outfile)
-  
-  return(NULL)
-}
-
-
-
-lee_full = function(outfile){
-  data=readRDS("real_data2.rds")
-  X=data$X
-  y=data$y
-  n=nrow(X)
-
-  lasso = glmnet(X, y, family=selectiveInference:::family_label(loss), alpha=1, standardize=FALSE, intercept=FALSE, thresh=1e-12)
-  soln = as.numeric(coef(lasso,x=X,y=y, family=selectiveInference:::family_label(loss), s=lambda, exact=TRUE))[-1]
-  PVS = selectiveInference:::fixedLassoInf(X,y,soln, intercept=FALSE, lambda*n, family=selectiveInference:::family_label(loss),
-                                           type="full",sigma=sigma_est)
-  
-  abs_soln = abs(soln)
-  beta_threshold = abs_soln[order(abs_soln,decreasing=TRUE)][length(PVS$pv)]
-  active_vars = which(abs_soln>=beta_threshold)
-  cat("nactive:", length(active_vars), "\n")
-  cat("active vars:", active_vars, "\n")
-  
-  pvalues = PVS$pv
-  sel_intervals = t(PVS$ci)
-  
-  saveRDS(list(active_vars=active_vars, sel_intervals=sel_intervals, pvalues=pvalues), file=outfile)
-  
-}
-
-
-knockoff = function(method, outfile){
-  if (method=="knockoff"){
-    data=readRDS("real_data3.rds")
-  } else if (method=="knockoff+"){
-    data=readRDS("real_data4.rds")
-  }
-  
-  X=data$X
-  y=data$y
-  
-  offset=0
-  if (method=="knockoff+"){
-    offset=1
-  }
-  filter = knockoff.filter(X, y, fdr=q, offset=offset)
-  saveRDS(list(active_vars = filter$rejected), file=outfile)
-}
-
-
-randomized = function(type, outfile){
-  if (type=="full"){
-    data=readRDS("real_data5.rds")
-  } else if (type=="partial"){
-    data=readRDS("real_data6.rds")
-  }
-  
-  X=data$X
-  y=data$y
-  n=nrow(X)
-  
-  rand_lasso_soln = selectiveInference:::randomizedLasso(X, 
-                                                         y, 
-                                                         lambda*n, 
-                                                         family=selectiveInference:::family_label(loss),
-                                                         condition_subgrad=TRUE)
-  
-  full_targets=selectiveInference:::set.target(rand_lasso_soln, type=type, sigma_est=sigma_est)
-  
-  PVS = selectiveInference:::randomizedLassoInf(rand_lasso_soln,
-                                                full_targets=full_targets,
-                                                sampler = "norejection", #"adaptMCMC", #
-                                                level=0.9, 
-                                                burnin=1000, 
-                                                nsample=10000)
-  active_vars=rand_lasso_soln$active_set
-  cat("active_vars:",active_vars,"\n")
-  pvalues = PVS$pvalues
-  sel_intervals = t(PVS$ci)  # matrix with two rows
-  saveRDS(list(active_vars=active_vars, sel_intervals=sel_intervals, pvalues=pvalues), file=outfile)
-  
-  return(NULL)
-}
-
-if (method=="liu"){
-  liu_full(outfile)
-} else if (method=="lee"){
-  lee_full(outfile)
-} else if (method=="knockoff"){
-  knockoff(method, outfile)
-} else if (method=="knockoff+"){
-  knockoff(method, outfile)
-} else if (method=="randomized_full"){
-  randomized("full", outfile)
-} else if (method=="randomized_partial"){
-  randomized("partial", outfile)
-}
-
-
-
-
-
diff --git a/tests/real_data_analysis/single_R_run.sbatch b/tests/real_data_analysis/single_R_run.sbatch
deleted file mode 100644
index dd9de8e..0000000
--- a/tests/real_data_analysis/single_R_run.sbatch
+++ /dev/null
@@ -1,38 +0,0 @@
-#!/bin/bash 
-#
-#all commands that start with SBATCH contain commands that are just used by SLURM for scheduling
-#################
-#set a job name  
-#SBATCH --job-name=job
-#################  
-#a file for job output, you can check job progress, append the job ID with %j to make it unique
-#SBATCH --output=jobs/%j.out
-#################
-# a file for errors from the job
-#SBATCH --error=jobs/%j.err
-#################
-#time you think you need; default is 2 hours
-#format could be dd-hh:mm:ss, hh:mm:ss, mm:ss, or mm
-#SBATCH --time=10:00:00
-#################
-#SBATCH --qos=normal
-#SBATCH -p normal 
-#################
-#number of nodes you are requesting, the more you ask for the longer you wait
-#SBATCH --nodes=1
-#################
-#SBATCH --mem=128000
-
-# You can use srun if your job is parallel
-#srun R CMD BATCH  ./rtest.R 
-# otherwise: 
-
-method=$1
-
-# cd to program directory
-cd /home/users/jelenam/R-selective/tests/real_data_analysis
-
-module load R/3.4.0
-
-Rscript real_data_analysis.R $method
-
diff --git a/tests/real_data_analysis/submit_R_jobs.sh b/tests/real_data_analysis/submit_R_jobs.sh
deleted file mode 100644
index 16a908c..0000000
--- a/tests/real_data_analysis/submit_R_jobs.sh
+++ /dev/null
@@ -1,21 +0,0 @@
-#!/bin/bash
-# Setup bash job headers
-
-# load local environment
-
-method1=liu
-method2=lee
-method3=knockoff
-method4=knockoff+
-method5=randomized_full
-method6=randomized_partial
-
-for i in {1..1}
-do
-	sbatch single_R_run.sbatch $method1
-	sbatch single_R_run.sbatch $method2
-	sbatch single_R_run.sbatch $method3
-	sbatch single_R_run.sbatch $method4
-	sbatch single_R_run.sbatch $method5
-	sbatch single_R_run.sbatch $method6
-done
\ No newline at end of file

From c8bd4ea1980f2e31c2dd64f77ce642a3592f6cda Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Mon, 2 Apr 2018 14:41:07 -0700
Subject: [PATCH 483/493] Q_sq added back

---
 selectiveInference/R/funs.full.main.R         | 12 ++++++------
 tests/liu_high_dim_full/test_liu_full.R       |  2 +-
 tests/real_data_analysis/real_data_analysis.R |  6 +++---
 3 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/selectiveInference/R/funs.full.main.R b/selectiveInference/R/funs.full.main.R
index 6650201..e085094 100644
--- a/selectiveInference/R/funs.full.main.R
+++ b/selectiveInference/R/funs.full.main.R
@@ -110,7 +110,7 @@ solve_problem_Q = function(Q_sq, Qbeta_bar, lambda, penalty_factor,
 
 
 # the selection event is |sigma_est^2*(target_cov)^{-1}Z+center|>radius
-truncation_set = function(X, y, Qbeta_bar, QE, sigma_est, 
+truncation_set = function(X, y, Qbeta_bar, QE, Q_sq=Q_sq, sigma_est, 
                           target_stat, target_cov,
                           group, groups, active_vars,
                           lambda, penalty_factor, loss, algo){
@@ -350,7 +350,7 @@ get_QB = function(X, y, soln, active_set, loss){
     QE=Q[active_set,]
     Qi=solve(Q)
     QiE=Qi[active_set, active_set]
-    #Q_sq =  W_root %*% X
+    Q_sq =  W_root %*% X
     #beta_bar = mle(X,y,loss=loss)
     #print("mle")
     #print(mle(X,y,loss=loss))
@@ -368,11 +368,11 @@ get_QB = function(X, y, soln, active_set, loss){
     beta_barE = soln[active_set] + M_active %*% diag(as.vector(1/diagonal)) %*% residuals
     
     QE = hessian_active(X, soln, loss, active_set)
-    #Q_sq = W_root %*% X
+    Q_sq = W_root %*% X
     Qbeta_bar = t(QE)%*%soln[active_set]-gradient(X,y,soln,loss=loss)
   }
   
-  return(list(QE=QE, Qbeta_bar=Qbeta_bar, QiE=QiE, beta_barE=beta_barE))  
+  return(list(QE=QE, Q_sq=Q_sq, Qbeta_bar=Qbeta_bar, QiE=QiE, beta_barE=beta_barE))  
 }
 
 
@@ -393,7 +393,7 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
   begin_setup = Sys.time()
   setup_params = get_QB(X=X, y=y, soln=soln, active_set=active_vars, loss=loss)
   QE=as.matrix(setup_params$QE)
-  #Q_sq=setup_params$Q_sq
+  Q_sq=setup_params$Q_sq
   QiE=as.matrix(setup_params$QiE)
   beta_barE = setup_params$beta_barE
   Qbeta_bar = setup_params$Qbeta_bar
@@ -418,7 +418,7 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
     target_cov = as.matrix(QiE)[group_varsE,group_varsE]
     
     begin_TS = Sys.time()
-    TS =  truncation_set(X=X, y=y, Qbeta_bar=Qbeta_bar, QE=QE, sigma_est=sigma_est, 
+    TS =  truncation_set(X=X, y=y, Qbeta_bar=Qbeta_bar, QE=QE, Q_sq=Q_sq, sigma_est=sigma_est, 
                          target_cov=target_cov, target_stat=target_stat, 
                          group=group, groups=groups, active_vars=active_vars,
                          lambda=lambda, penalty_factor=penalty_factor, loss=loss, algo=algo)
diff --git a/tests/liu_high_dim_full/test_liu_full.R b/tests/liu_high_dim_full/test_liu_full.R
index 3f75937..dd062ad 100644
--- a/tests/liu_high_dim_full/test_liu_full.R
+++ b/tests/liu_high_dim_full/test_liu_full.R
@@ -5,7 +5,7 @@ library(glmnet)
 
 # testing Liu et al type=full in high dimensional settings -- uses debiasing matrix
 
-test_liu_full = function(seed=1, outfile=NULL, nrep=1, n=100, p=200, s=20, rho=0.){
+test_liu_full = function(seed=1, outfile=NULL, nrep=1, n=4000, p=70000, s=20, rho=0.){
   
   snr = sqrt(2*log(p)/n)
   
diff --git a/tests/real_data_analysis/real_data_analysis.R b/tests/real_data_analysis/real_data_analysis.R
index c5132a7..01524bd 100644
--- a/tests/real_data_analysis/real_data_analysis.R
+++ b/tests/real_data_analysis/real_data_analysis.R
@@ -14,9 +14,9 @@ outfile = file.path(outdir, paste(sep="",label, ".rds"))
 #setwd("/Users/Jelena/Dropbox/kevin/jelena/real_data")
 set.seed(1)
 loss="ls"
-sigma_est = 0.62
-lambda = 0.0352479
-#lambda =  0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est) # "lambda" "0.0352479112219816"
+sigma_est = 0.4814
+lambda =  0.02735951
+#lambda =  0.8*selectiveInference:::theoretical.lambda(X, loss, sigma_est)
 #print(c("lambda", lambda))
 
 

From 09731d89f3fd298b7d407545ee86ae48500b89ee Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Mon, 2 Apr 2018 17:25:43 -0700
Subject: [PATCH 484/493] moved comparisons to the dropbox

---
 tests/comparisons/compare_FDR_power.R   | 17 -------
 tests/comparisons/plot_coverages.R      | 63 -------------------------
 tests/comparisons/read_files.R          | 59 -----------------------
 tests/liu_high_dim_full/test_liu_full.R |  2 +-
 4 files changed, 1 insertion(+), 140 deletions(-)
 delete mode 100644 tests/comparisons/compare_FDR_power.R
 delete mode 100644 tests/comparisons/plot_coverages.R
 delete mode 100644 tests/comparisons/read_files.R

diff --git a/tests/comparisons/compare_FDR_power.R b/tests/comparisons/compare_FDR_power.R
deleted file mode 100644
index 2349d22..0000000
--- a/tests/comparisons/compare_FDR_power.R
+++ /dev/null
@@ -1,17 +0,0 @@
-lee = readRDS(file="lee.rds")
-liu = readRDS(file="liu.rds")
-kn = readRDS(file="knockoff.rds")
-kn_plus = readRDS(file="knockoff+.rds")
-randomized_full=readRDS(file="randomized_full.rds")
-randomized_partial=readRDS(file="randomized_partial.rds")
-
-
-table = matrix(c(mean(lee$FDR_sample), mean(lee$power_sample), 
-                 mean(liu$FDR_sample), mean(liu$power_sample),
-                 mean(kn$FDR_sample), mean(kn$power_sample),
-                 mean(kn_plus$FDR_sample), mean(kn_plus$power_sample),
-                 mean(randomized_full$FDR_sample), mean(randomized_full$power_sample),
-                 mean(randomized_partial$FDR_sample), mean(randomized_partial$power_sample)), ncol=2, byrow=TRUE)
-colnames(table)=c("FDR", "power")
-rownames(table)=c("Lee", "Liu", "knockoff", "knockoff+", "randomized full", "randomized partial")
-table  
diff --git a/tests/comparisons/plot_coverages.R b/tests/comparisons/plot_coverages.R
deleted file mode 100644
index a73e5d7..0000000
--- a/tests/comparisons/plot_coverages.R
+++ /dev/null
@@ -1,63 +0,0 @@
-
-lee = readRDS(file="lee.rds")
-liu = readRDS(file="liu.rds")
-rand_full=readRDS(file="randomized_full.rds")
-rand_partial=readRDS(file="randomized_partial.rds")
-
-n=lee$n
-p=lee$p
-s=lee$s
-snr=lee$snr
-rho=lee$rho
-
-lee_cov = mean(lee$sel_coverages)
-lee_len = median(lee$sel_lengths)
-
-liu_cov=mean(liu$sel_coverages)
-liu_len=median(liu$sel_lengths)
-
-naive_cov = mean(liu$naive_coverages)
-naive_len=median(liu$naive_lengths)
-
-rand_full_cov = mean(rand_full$sel_coverages)
-rand_full_len = median(rand_full$sel_lengths)
-
-rand_partial_cov=mean(rand_partial$sel_coverages)
-rand_partial_len=median(rand_partial$sel_lengths)
-
-ninf_lee = length(which(lee$sel_lengths==Inf))/length(lee$sel_lengths)
-ninf_liu = length(which(liu$sel_lengths==Inf))/length(liu$sel_lengths)
-c(ninf_lee, ninf_liu)
-
-labels = rep("",5)
-labels[1] = paste("len: ", round(lee_len,2), ", cov: ", round(lee_cov,2), ", ninf: ", round(ninf_lee,2), sep = "")
-labels[2] = paste("len: ", round(liu_len,2), ", cov: ", round(liu_cov,2), ", ninf: ", round(ninf_liu,2), sep = "")
-labels[3] = paste("len: ", round(naive_len,2), ", cov: ", round(naive_cov,2), sep = "")
-labels[4] = paste("len: ", round(rand_full_len,2), ", cov: ", round(rand_full_cov,2), sep = "")
-labels[5] = paste("len: ", round(rand_partial_len,2), ", cov: ", round(rand_partial_cov,2), sep = "")
-
-
-title = paste("n=", n,", p=", p, ", s=", s, ", signal=", round(snr,2), ", rho=", rho, sep = "")
-
-boxplot(lee$sel_lengths[which(lee$sel_lengths!=Inf)], liu$sel_lengths, liu$naive_lengths,
-        rand_full$sel_lengths, rand_partial$sel_lengths, 
-        at = seq(1,11,2.5) - 0.25, col = "pink", las = 2, outline = F,
-        names = c("Lee \n (full)", "Liu \n (full)", "naive", "rand \n (full)", "rand \n (partial)"), 
-        boxwex = 0.6, range = 0.5,
-        log="x", horizontal = T, xlim = c(0,12.5), cex.axis = 1)
-
-title(title, cex.main = 1, font.main = 1, line = 0.2)
-text(labels = labels, x = c(lee_len, liu_len, naive_len, rand_full_len,rand_partial_len)+0.05, y = seq(1,11,2.5) + 0.5, cex = 1)
-
-
-
-
-plot(ecdf(lee$pvalues), xlab="values", ylab="Empirical cdf", main="Selective pvalues", 
-     col="red")
-lines(ecdf(liu$pvalues), col="blue")
-abline(c(0,1))
-legend("bottomright", legend=c("Lee et al", "Liu et al"),
-       col=c("red", "blue"), lty=1:1, cex=1.5)
-
-
-
diff --git a/tests/comparisons/read_files.R b/tests/comparisons/read_files.R
deleted file mode 100644
index e5845a8..0000000
--- a/tests/comparisons/read_files.R
+++ /dev/null
@@ -1,59 +0,0 @@
-result.path = "/Users/Jelena/Documents/collect_cluster_results/full/"
-setwd(result.path)
-
-#method="liu"
-#method="lee"
-#method="knockoff_result"
-#method="knockoff+_result"
-#method="randomized_full"
-#method="randomized_partial"
-
-txt_files = list.files(pattern = paste(method, "*", sep=""))
-
-i = 0
-
-pvalues = NULL
-sel_intervals=NULL
-sel_coverages=NULL
-sel_lengths=NULL
-naive_pvalues = NULL
-naive_intervals=NULL
-naive_coverages=NULL
-naive_lengths=NULL
-FDR_sample = NULL
-power_sample=NULL
-
-for (file in txt_files){
-  result=readRDS(file)
-  pvalues=c(pvalues, result$pvalues)
-  sel_intervals=c(sel_intervals, result$sel_intervals)
-  sel_coverages=c(sel_coverages, result$sel_coverages)
-  sel_lengths=c(sel_lengths, result$sel_lengths)
-  naive_pvalues=c(naive_pvalues, result$naive_pvalues)
-  naive_intervals=c(naive_intervals, result$naive_intervals)
-  naive_coverages=c(naive_coverages, result$naive_coverages)
-  naive_lengths=c(naive_lengths, result$naive_lengths)
-  FDR_sample=c(FDR_sample, result$FDR_sample)
-  power_sample=c(power_sample, result$power_sample)
-  n=result$n
-  p=result$p
-  s=result$s
-  rho=result$rho
-  snr=result$snr
-  i=i+1
-}
-
-print(i)
-
-setwd("/Users/Jelena/GitHub Jelena/R-selective/tests/comparisons/")
-
-saveRDS(list(pvalues=pvalues, 
-             sel_intervals=sel_intervals, sel_coverages=sel_coverages, sel_lengths=sel_lengths,
-             naive_pvalues=naive_pvalues, 
-             naive_intervals=naive_intervals, naive_coverages=naive_coverages, naive_lengths=naive_lengths,
-             n=n, p=p, s=s, rho=rho, snr=snr,
-             FDR_sample=FDR_sample, power_sample=power_sample), file=paste(method,".rds", sep=""))
-
-
-
-
diff --git a/tests/liu_high_dim_full/test_liu_full.R b/tests/liu_high_dim_full/test_liu_full.R
index dd062ad..234fa6a 100644
--- a/tests/liu_high_dim_full/test_liu_full.R
+++ b/tests/liu_high_dim_full/test_liu_full.R
@@ -96,5 +96,5 @@ test_liu_full = function(seed=1, outfile=NULL, nrep=1, n=4000, p=70000, s=20, rh
   return(list(pvalues=pvalues, naive_pvalues=naive_pvalues))
 }
 
-test_liu_full()
+#test_liu_full()
 

From 7268dcd5b41ae793afc2174513473e56a0c6fb9e Mon Sep 17 00:00:00 2001
From: Jelena Markovic <jelenam@stanford.edu>
Date: Mon, 2 Apr 2018 17:26:02 -0700
Subject: [PATCH 485/493] moved comparisons to the dropbox

---
 tests/comparisons/big uncorr/params.txt   | 3 ---
 tests/comparisons/small corr/params.txt   | 0
 tests/comparisons/small uncorr/params.txt | 0
 3 files changed, 3 deletions(-)
 delete mode 100644 tests/comparisons/big uncorr/params.txt
 delete mode 100644 tests/comparisons/small corr/params.txt
 delete mode 100644 tests/comparisons/small uncorr/params.txt

diff --git a/tests/comparisons/big uncorr/params.txt b/tests/comparisons/big uncorr/params.txt
deleted file mode 100644
index dca935c..0000000
--- a/tests/comparisons/big uncorr/params.txt	
+++ /dev/null
@@ -1,3 +0,0 @@
-n=1000, p=2000, rho=0,
-snr = sqrt(2*log(p)/n)
-nrep=100
\ No newline at end of file
diff --git a/tests/comparisons/small corr/params.txt b/tests/comparisons/small corr/params.txt
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/comparisons/small uncorr/params.txt b/tests/comparisons/small uncorr/params.txt
deleted file mode 100644
index e69de29..0000000

From b3bc535ff4f8a214fc5c49c2194ef0dd600dc053 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 4 Apr 2018 21:36:52 -0700
Subject: [PATCH 486/493] fixing indentation

---
 selectiveInference/R/funs.fixed.R | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 64dd026..39e2db5 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -318,20 +318,20 @@ debiasingMatrix = function(Xinfo,               # could be X or t(X) %*% X / n d
                            is_wide,
                            nsample, 
                            rows, 
- 		                       verbose=FALSE, 
-		                       bound=NULL,             # starting value of bound
-   			                   linesearch=TRUE,     # do a linesearch?
-   		                     scaling_factor=1.5,  # multiplicative factor for linesearch
-			                     max_active=NULL,     # how big can active set get?
-			                     max_try=10,          # how many steps in linesearch?
-			                     warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
-			                     max_iter=50,         # how many iterations for each optimization problem
+                           verbose=FALSE, 
+                           bound=NULL,             # starting value of bound
+                           linesearch=TRUE,     # do a linesearch?
+                           scaling_factor=1.5,  # multiplicative factor for linesearch
+                           max_active=NULL,     # how big can active set get?
+                           max_try=10,          # how many steps in linesearch?
+                           warn_kkt=FALSE,      # warn if KKT does not seem to be satisfied?
+                           max_iter=50,         # how many iterations for each optimization problem
                            kkt_stop=TRUE,       # stop based on KKT conditions?
                            parameter_stop=TRUE, # stop based on relative convergence of parameter?
-			                     objective_stop=TRUE, # stop based on relative decrease in objective?
+                           objective_stop=TRUE, # stop based on relative decrease in objective?
                            kkt_tol=1.e-4,       # tolerance for the KKT conditions
                            parameter_tol=1.e-4, # tolerance for relative convergence of parameter
-			                     objective_tol=1.e-4  # tolerance for relative decrease in objective
+                           objective_tol=1.e-4  # tolerance for relative decrease in objective
                            ) {
 
 

From ba5a68d7380433999a94309d6609ff079aa130cd Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 4 Apr 2018 22:20:02 -0700
Subject: [PATCH 487/493] cleanup? of approximate

---
 selectiveInference/R/funs.full.main.R | 19 ++++---------------
 1 file changed, 4 insertions(+), 15 deletions(-)

diff --git a/selectiveInference/R/funs.full.main.R b/selectiveInference/R/funs.full.main.R
index deeb758..fc9ebf8 100644
--- a/selectiveInference/R/funs.full.main.R
+++ b/selectiveInference/R/funs.full.main.R
@@ -299,26 +299,16 @@ approximate = function(X, active_set){
   p=ncol(X)
   nactive=length(active_set)
   inactive_set=setdiff(1:p, active_set)
-  X_active = X[, active_set]
-  X_inactive = X[, inactive_set]
   
-  Xordered = X[,c(active_set,inactive_set,recursive=T)]
-  hsigmaS = 1/n*(t(X_active)%*%X_active) # hsigma[S,S]
-  hsigmaSinv =  solve(hsigmaS) # inverse of hsigma[S,S]
-  FS = rbind(diag(nactive),matrix(0,p-nactive,nactive))
-  GS = cbind(diag(nactive),matrix(0,nactive,p-nactive))
-  hsigma = 1/n*(t(Xordered)%*%Xordered)
   is_wide = n < (2 * p)
   
   if (!is_wide) {
-    hsigma = 1/n*(t(Xordered)%*%Xordered)
-    htheta = debiasingMatrix(hsigma, is_wide, n, 1:nactive)
-    ithetasigma = (GS-(htheta%*%hsigma))
+    hsigma = 1/n*(t(X) %*% X)
+    htheta = debiasingMatrix(hsigma, is_wide, n, active_set)
   } else {
-    htheta = debiasingMatrix(Xordered, is_wide, n, 1:nactive)
-    ithetasigma = (GS-((htheta%*%t(Xordered)) %*% Xordered)/n)
+    htheta = debiasingMatrix(X, is_wide, n, active_set)
   }
-  M_active <- ((htheta%*%t(Xordered))+ithetasigma%*%FS%*%hsigmaSinv%*%t(X_active))/n
+  M_active <- htheta%*%t(X) / n # the n is so that we approximate the inverse of (X^TX)^{-1} instead of (X^TX/n)^{-1}.
   return(M_active)
 }
 
@@ -361,7 +351,6 @@ get_QB = function(X, y, soln, active_set, loss){
     M_active = approximate(W_root %*% X, active_set) ## this should be the active rows of \Sigma_i (W^{1/2} X)^T
     QiE = M_active %*% t(M_active)
     beta_barE = soln[active_set] + M_active %*% diag(as.vector(1/diagonal)) %*% residuals
-    
     QE = hessian_active(X, soln, loss, active_set)
     Q_sq = W_root %*% X
     Qbeta_bar = t(QE)%*%soln[active_set]-gradient(X,y,soln,loss=loss)

From e2ebc9928021f479f274bc74596d70e6b7531f6c Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 5 Apr 2018 05:57:38 -0700
Subject: [PATCH 488/493] rewrote approximate a bit more using only rows of
 inverse, not multiplied by X

---
 selectiveInference/R/funs.full.main.R | 27 +++++++++++----------------
 1 file changed, 11 insertions(+), 16 deletions(-)

diff --git a/selectiveInference/R/funs.full.main.R b/selectiveInference/R/funs.full.main.R
index fc9ebf8..f6f1272 100644
--- a/selectiveInference/R/funs.full.main.R
+++ b/selectiveInference/R/funs.full.main.R
@@ -308,7 +308,7 @@ approximate = function(X, active_set){
   } else {
     htheta = debiasingMatrix(X, is_wide, n, active_set)
   }
-  M_active <- htheta%*%t(X) / n # the n is so that we approximate the inverse of (X^TX)^{-1} instead of (X^TX/n)^{-1}.
+  M_active <- htheta / n # the n is so that we approximate the inverse of (X^TX)^{-1} instead of (X^TX/n)^{-1}.
   return(M_active)
 }
 
@@ -320,15 +320,14 @@ get_QB = function(X, y, soln, active_set, loss){
   
   fit = X%*%soln
   if (loss=="ls"){
-    diagonal = rep(1,n)
-    W_root=diag(as.vector(diagonal))
+    W = rep(1,n)
     residuals = y-fit
   }
   if (loss=="logit"){
-    diagonal = exp(fit/2)/(1+exp(fit))  ## sqrt(pi*(1-pi))
-    W_root = diag(as.vector(diagonal))
+    W = exp(fit/2)/(1+exp(fit))  ## sqrt(pi*(1-pi))
     residuals = y-(exp(fit)/(1+exp(fit)))
   } 
+  W_root=diag(as.vector(W))
   
   if (n>p){
     Q=hessian(X, soln, loss=loss)
@@ -336,24 +335,20 @@ get_QB = function(X, y, soln, active_set, loss){
     Qi=solve(Q)
     QiE=Qi[active_set, active_set]
     Q_sq =  W_root %*% X
-    #beta_bar = mle(X,y,loss=loss)
-    #print("mle")
-    #print(mle(X,y,loss=loss))
     beta_bar = soln - Qi %*% gradient(X,y,soln, loss=loss)
-    #beta_bar = (soln - Qi %*% gradient(X,y,soln, loss=loss)+mle(X,y,loss))/2
-    #print("one step")
-    #print(beta_bar)
     Qbeta_bar = Q%*%soln - gradient(X,y,soln, loss=loss)
     beta_barE = beta_bar[active_set]
     
   } else{
     
-    M_active = approximate(W_root %*% X, active_set) ## this should be the active rows of \Sigma_i (W^{1/2} X)^T
-    QiE = M_active %*% t(M_active)
-    beta_barE = soln[active_set] + M_active %*% diag(as.vector(1/diagonal)) %*% residuals
-    QE = hessian_active(X, soln, loss, active_set)
     Q_sq = W_root %*% X
-    Qbeta_bar = t(QE)%*%soln[active_set]-gradient(X,y,soln,loss=loss)
+    M_active = approximate(Q_sq, active_set) ## this should be the active rows of \Sigma_i (W^{1/2} X)^T
+    G = gradient(X,y,soln,loss=loss)
+    M2 = M_active %*% t(X)
+    QiE = M2 %*% (diag(W) %*% t(M2))  # is there a more efficient way to do this diag multiplication? also above
+    beta_barE = soln[active_set] - M_active %*% G
+    QE = hessian_active(X, soln, loss, active_set)
+    Qbeta_bar = t(QE)%*%soln[active_set] - G
   }
   
   return(list(QE=QE, Q_sq=Q_sq, Qbeta_bar=Qbeta_bar, QiE=QiE, beta_barE=beta_barE))  

From 6b50cdba9e9575502e7d72d803d8d2a6b64e4843 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Thu, 5 Apr 2018 07:03:29 -0700
Subject: [PATCH 489/493] adding verbose argument

---
 selectiveInference/R/funs.full.main.R | 46 +++++++++++----------------
 1 file changed, 18 insertions(+), 28 deletions(-)

diff --git a/selectiveInference/R/funs.full.main.R b/selectiveInference/R/funs.full.main.R
index f6f1272..b4de063 100644
--- a/selectiveInference/R/funs.full.main.R
+++ b/selectiveInference/R/funs.full.main.R
@@ -343,7 +343,7 @@ get_QB = function(X, y, soln, active_set, loss){
     
     Q_sq = W_root %*% X
     M_active = approximate(Q_sq, active_set) ## this should be the active rows of \Sigma_i (W^{1/2} X)^T
-    G = gradient(X,y,soln,loss=loss)
+    G = gradient(X, y, soln, loss=loss)
     M2 = M_active %*% t(X)
     QiE = M2 %*% (diag(W) %*% t(M2))  # is there a more efficient way to do this diag multiplication? also above
     beta_barE = soln[active_set] - M_active %*% G
@@ -357,7 +357,7 @@ get_QB = function(X, y, soln, active_set, loss){
 
 
 inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sigma_est,
-                                 loss, algo, construct_ci){
+                                 loss, algo, construct_ci, verbose=FALSE){
   
   active_vars = which(soln!=0)
   
@@ -367,8 +367,10 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
   active_groups = unique(groups[active_vars])
   nactive_groups = length(active_groups)
   
-  print(c("nactive", nactive_groups))
-  
+  if (verbose) {
+      print(c("nactive", nactive_groups))
+  }
+
   begin_setup = Sys.time()
   setup_params = get_QB(X=X, y=y, soln=soln, active_set=active_vars, loss=loss)
   QE=as.matrix(setup_params$QE)
@@ -378,8 +380,9 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
   Qbeta_bar = setup_params$Qbeta_bar
   QiE = QiE * sigma_est^2
   end_setup = Sys.time()
-  cat("setup time", end_setup-begin_setup, "\n")
-  
+  if (verbose) {
+     cat("setup time", end_setup-begin_setup, "\n")
+  }
   pvalues = NULL
   naive_pvalues = NULL
   selected_vars=NULL
@@ -402,8 +405,9 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
                          group=group, groups=groups, active_vars=active_vars,
                          lambda=lambda, penalty_factor=penalty_factor, loss=loss, algo=algo)
     end_TS = Sys.time()
-    cat("TS time", end_TS-begin_TS, "\n")
-    
+    if (verbose) {
+       cat("TS time", end_TS-begin_TS, "\n")
+    }
     center = TS$center
     radius = TS$radius
     
@@ -438,11 +442,11 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
             sel_intervals = cbind(sel_intervals, sel_int)
             naive_intervals = cbind(naive_intervals, naive_int)
           }
-        } else{
-          print("observation not within the truncation limits!")
+        } else {
+             warning("observation not within the truncation limits!")
         }
       }
-    } else{
+    } else {
       
       lower = target_cov*(-center-radius)/(sigma_est^2)
       upper = target_cov*(-center+radius)/(sigma_est^2)
@@ -453,9 +457,7 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
         intervals[1,] = c(-Inf, lower)
         intervals[2,] = c(upper, Inf)
         pval = tnorm.union.surv(target_stat, mean=0, sd=sqrt(target_cov), intervals)
-        #pval = test_TG(0, target_stat, target_cov, sigma_est, center, radius, alt="two-sided")
         pval = 2*min(pval, 1-pval)
-        #print(c("pval", pval))
         pvalues = c(pvalues, pval)
         
         selected_vars = c(selected_vars, group_vars)
@@ -463,29 +465,17 @@ inference_group_lasso = function(X, y, soln, groups, lambda, penalty_factor, sig
         naive_pval =  pvalue_naive_linear(target_stat, target_cov)
         naive_pvalues = c(naive_pvalues, naive_pval)
         
-        if (construct_ci){
+        if (construct_ci) {
           sel_int = create_tnorm_interval(z=target_stat, sd=sqrt(target_cov), alpha=0.1, intervals=intervals)
-          #print(c("tg intervals", sel_int))
-          #sel_int = selective_CI(target_stat, target_cov, sigma_est, center, radius)
-          #print(c("jelena int", sel_int))
           naive_int = naive_CI(target_stat, target_cov)
-          #cat("sel interval", sel_int, "\n")
-          #cat("naive interval", naive_int, "\n")
           sel_intervals = cbind(sel_intervals, sel_int)
           naive_intervals = cbind(naive_intervals, naive_int)
         }
-      } else{
-        print("observation not within the truncation limits!")
+      } else {
+           warning("observation not within the truncation limits!")
       }
       
     }
-    
-    #reference = get_reference(center, radius, target_stat, solve(target_cov))
-    #samples = sampling(target_cov, reference, center, radius, n=nrow(X), nsample=nsample)
-    #tilting_weights = compute_tilting_weights(reference, solve(target_cov), samples)
-    #pvalues[i] = weighted_pvalue(samples, tilting_weights, target_stat)
-    #naive_pvalues[i] = p_value(mvrnorm(n=nsample, mu=rep(0, length(target_stat)), Sigma = target_cov), target_stat)
-    #print(c("sampling", pvalues[i]))
   }
   
   return(list(pvalues=pvalues, naive_pvalues=naive_pvalues, active_vars=selected_vars,

From 8a2a30a5f14b080e6dea476cfb0dc21d6316afdb Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Sat, 7 Apr 2018 12:01:01 -0700
Subject: [PATCH 490/493] indentation fix

---
 selectiveInference/R/funs.fixed.R | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 39e2db5..26131db 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -3,11 +3,19 @@
 # min 1/2 || y - \beta_0 - X \beta ||_2^2 + \lambda || \beta ||_1
 
 fixedLassoInf <- function(x, y, beta, 
-                          lambda, family=c("gaussian","binomial","cox"),
-                          intercept=TRUE, add.targets=NULL, status=NULL,
-                          sigma=NULL, alpha=0.1,
-                          type=c("partial", "full"), tol.beta=1e-5, tol.kkt=0.1,
-                          gridrange=c(-100,100), bits=NULL, verbose=FALSE, 
+                          lambda, 
+                          family=c("gaussian","binomial","cox"),
+                          intercept=TRUE, 
+                          add.targets=NULL, 
+                          status=NULL,
+                          sigma=NULL, 
+                          alpha=0.1,
+                          type=c("partial", "full"), 
+                          tol.beta=1e-5, 
+                          tol.kkt=0.1,
+                          gridrange=c(-100,100), 
+			  bits=NULL, 
+			  verbose=FALSE, 
                           linesearch.try=10) {
 
   family = match.arg(family)

From 781c516b54e39dfe3eb1403f369c56afe42d8835 Mon Sep 17 00:00:00 2001
From: kevinbfry <kfry@stanford.edu>
Date: Sun, 8 Apr 2018 01:40:15 -0700
Subject: [PATCH 491/493] Added type='full' for logistic and removed Xordered
 from gaussian and logistic

---
 selectiveInference/R/funs.fixed.R      |  14 +-
 selectiveInference/R/funs.fixedLogit.R | 191 ++++++++++++++++---------
 2 files changed, 130 insertions(+), 75 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 26131db..3aae749 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -23,7 +23,7 @@ fixedLassoInf <- function(x, y, beta,
   type = match.arg(type)
   
   if(family=="binomial")  {
-    if(type!="partial") stop("Only type= partial allowed with binomial family")
+    # if(type!="partial") stop("Only type= partial allowed with binomial family")
     out=fixedLogitLassoInf(x,y,beta,lambda,alpha=alpha, type="partial", tol.beta=tol.beta, tol.kkt=tol.kkt,
                            gridrange=gridrange, bits=bits, verbose=verbose,this.call=this.call)
     return(out)
@@ -177,7 +177,7 @@ fixedLassoInf <- function(x, y, beta,
       hbetaS = hbeta[S]
       
       # Reorder so that active set S is first
-      Xordered = Xint[,c(S,notS,recursive=T)]
+      # Xordered = Xint[,c(S,notS,recursive=T)]
       hsigmaS = 1/n*(t(XS)%*%XS) # hsigma[S,S]
       hsigmaSinv = solve(hsigmaS) # pinv(hsigmaS)
       
@@ -188,15 +188,15 @@ fixedLassoInf <- function(x, y, beta,
 
       # Approximate inverse covariance matrix for when (n < p) from lasso_Inference.R
       if (!is_wide) {
-           hsigma = 1/n*(t(Xordered)%*%Xordered)
-           htheta = debiasingMatrix(hsigma, is_wide, n, 1:length(S), verbose=FALSE, max_try=linesearch.try, warn_kkt=TRUE)
+           hsigma = 1/n*(t(Xint)%*%Xint)
+           htheta = debiasingMatrix(hsigma, is_wide, n, S, verbose=FALSE, max_try=linesearch.try, warn_kkt=TRUE)
            ithetasigma = (GS-(htheta%*%hsigma))
       } else {
-           htheta = debiasingMatrix(Xordered, is_wide, n, 1:length(S), verbose=FALSE, max_try=linesearch.try, warn_kkt=TRUE)
-           ithetasigma = (GS-((htheta%*%t(Xordered)) %*% Xordered)/n)
+           htheta = debiasingMatrix(Xint, is_wide, n, S, verbose=FALSE, max_try=linesearch.try, warn_kkt=TRUE)
+           ithetasigma = (GS-((htheta%*%t(Xint)) %*% Xint)/n)
       }
 
-      M <- (((htheta%*%t(Xordered))+ithetasigma%*%FS%*%hsigmaSinv%*%t(XS))/n)
+      M <- (((htheta%*%t(Xint))+ithetasigma%*%FS%*%hsigmaSinv%*%t(XS))/n)
 
       # vector which is offset for testing debiased beta's
       null_value <- (((ithetasigma%*%FS%*%hsigmaSinv)%*%sign(hbetaS))*lambda/n)
diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index 60b6451..8d810ce 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -19,7 +19,8 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
      p=ncol(x)
  # I assume that intcpt was used
      if(length(beta)!=p+1) stop("Since family='binomial', beta must be of length ncol(x)+1, that is, it should include an intercept")
-     nvar=sum(beta[-1]!=0)
+     vars = which(abs(beta[-1]) > tol.beta / sqrt(colSums(x^2)))
+     nvar=length(vars)
      pv=vlo=vup=sd=rep(NA, nvar)
      ci=tailarea=matrix(NA,nvar,2)
     
@@ -29,51 +30,97 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
   #  x = obj$x
   #  y = obj$y
   
- m=beta[-1]!=0  #active set
-         
-    bhat=c(beta[1],beta[-1][beta[-1]!=0]) # intcpt plus active vars
-     sign_bhat=sign(bhat)
-     lam2m=diag(c(0,rep(lambda,sum(m))))
- 
+     bhat=c(beta[1],beta[-1][vars]) # intcpt plus active vars
+     s2=sign(bhat)
 
-    xxm=cbind(1,x[,m])
-    
-   etahat = xxm %*% bhat
-   prhat = as.vector(exp(etahat) / (1 + exp(etahat)))
-   ww=prhat*(1-prhat)
- #  w=diag(ww)
-      
-#check KKT
-   z=etahat+(y-prhat)/ww
-  # g=  t(x)%*%w%*%(z-etahat)/lambda # negative gradient scaled by lambda
-  g=scale(t(x),FALSE,1/ww)%*%(z-etahat)/lambda # negative gradient scaled by lambda
+     xm=cbind(1,x[,vars])
+     xnotm=x[,-vars]
+     
+     etahat = xm %*% bhat
+     prhat = as.vector(exp(etahat) / (1 + exp(etahat)))
+     ww=prhat*(1-prhat)
+     w=diag(ww)
+     
+     #check KKT
+     z=etahat+(y-prhat)/ww
+     g=scale(t(x),FALSE,1/ww)%*%(z-etahat)/lambda # negative gradient scaled by lambda
      if (any(abs(g) > 1+tol.kkt) )
-    warning(paste("Solution beta does not satisfy the KKT conditions",
-                  "(to within specified tolerances)"))
-
- vars = which(abs(beta[-1]) > tol.beta / sqrt(colSums(x^2)))
-  if(length(vars)==0){
-      cat("Empty model",fill=T)
-      return()
-  }
-  if (any(sign(g[vars]) != sign(beta[-1][vars])))
-    warning(paste("Solution beta does not satisfy the KKT conditions",
-                  "(to within specified tolerances). You might try rerunning",
-                  "glmnet with a lower setting of the",
-                  "'thresh' parameter, for a more accurate convergence."))
-  
- #constraints for active variables             
- # MM=solve(t(xxm)%*%w%*%xxm)
-   MM=solve(scale(t(xxm),F,1/ww)%*%xxm)
-  gm = c(0,-g[vars]*lambda) # gradient at LASSO solution, first entry is 0 because intercept is unpenalized
-                            # at exact LASSO solution it should be sign_bhat[-1]
-  dbeta = MM %*% gm
-
-  # bbar=(bhat+lam2m%*%MM%*%sign_bhat)  # JT: this is wrong, shouldn't use sign of intercept anywhere...
-  bbar = bhat - dbeta
-
-  A1=-(mydiag(sign_bhat))[-1,]
-  b1= (sign_bhat * dbeta)[-1]
+       warning(paste("Solution beta does not satisfy the KKT conditions",
+                     "(to within specified tolerances)"))
+     
+     if(length(vars)==0){
+       cat("Empty model",fill=T)
+       return()
+     }
+     if (any(sign(g[vars]) != sign(beta[-1][vars])))
+       warning(paste("Solution beta does not satisfy the KKT conditions",
+                     "(to within specified tolerances). You might try rerunning",
+                     "glmnet with a lower setting of the",
+                     "'thresh' parameter, for a more accurate convergence."))
+     
+     #constraints for active variables             
+     MM=solve(scale(t(xm),F,1/ww)%*%xm)
+     gm = c(0,-g[vars]*lambda) # gradient at LASSO solution, first entry is 0 because intercept is unpenalized
+     # at exact LASSO solution it should be s2[-1]
+     dbeta = MM %*% gm
+     
+     MM = MM*n
+     
+     # bbar=(bhat+lam2m%*%MM%*%s2)  # JT: this is wrong, shouldn't use sign of intercept anywhere...
+     bbar = (bhat - dbeta)*sqrt(n)
+     
+     A1= matrix(-(mydiag(s2))[-1,],nrow=length(s2)-1)
+     b1= ((s2 * dbeta)[-1])*sqrt(n)
+     V = (diag(length(bbar))[-1,])/sqrt(n)
+     null_value = rep(0,nvar)
+     
+     if (type=='full') {
+       
+       is_wide = n < (2 * p) # somewhat arbitrary decision -- it is really for when we don't want to form with pxp matrices
+       
+       # Approximate inverse covariance matrix for when (n < p) from lasso_Inference.R
+       if (!is_wide) {
+         M = debiasingMatrix(1/n*(scale(t(x),FALSE,1/ww)%*%x), is_wide, n, vars, verbose=FALSE, max_try=linesearch.try, warn_kkt=TRUE)
+       } else {
+         M = debiasingMatrix(t(scale(t(x),1/sqrt(ww))), is_wide, n, vars, verbose=FALSE, max_try=linesearch.try, warn_kkt=TRUE)
+       }
+
+       M <- matrix(InverseLinfty(hsigma,n,dim(xm)[2],verbose=F,max.try=10),ncol=p+1)[-1,] # remove intercept row
+       I <- matrix(diag(dim(xm)[2])[-1,],nrow=dim(xm)[2]-1)
+       if (is.null(dim(M))) {
+         M_notE <- M[-vars]
+       } else {
+         M_notE <- M[,-vars]
+       }
+       M_notE = matrix(M_notE,nrow=nvar)
+       V <- matrix(cbind(I/sqrt(n),M_notE[,-1]/n),nrow=dim(xm)[2]-1)
+       
+       xnotm_w = scale(t(xnotm),FALSE,1/ww)
+       xnotm_w_xm = xnotm_w%*%xm
+       c <- matrix(c(gm[-1],xnotm_w_xm%*%(-dbeta)),ncol=1)
+       d <- -dbeta[-1]
+       null_value = -(M[,-1]%*%c/n - d)
+       
+       A0 = matrix(0,ncol(xnotm),length(bbar))
+       A0 = cbind(A0,diag(nrow(A0)))
+       fill = matrix(0,nrow(A1),ncol(xnotm))
+       A1 = cbind(A1,fill)
+       A1 = rbind(A1,A0,-A0)
+       
+       b1 = matrix(c(b1,rep(lambda,2*nrow(A0))),ncol=1)
+       
+       # full covariance
+       MMbr = (xnotm_w%*%xnotm - xnotm_w_xm%*%(MM/n)%*%t(xnotm_w_xm))
+       MM = cbind(MM,matrix(0,nrow(MM),ncol(MMbr)))
+       MMbr = cbind(matrix(0,nrow(MMbr),nrow(MM)),MMbr)
+       MM = rbind(MM,MMbr)
+       
+       etahat_bbar = xm %*% (bbar/sqrt(n))
+       gnotm = (scale(t(xnotm),FALSE,1/ww)%*%(z-etahat_bbar))
+       bbar = matrix(c(bbar,gnotm),ncol=1)
+     }
+     
+     if (is.null(dim(V))) V=matrix(V,nrow=1)
 
   tol.poly = 0.01 
   if (max((A1 %*% bbar) - b1) > tol.poly)
@@ -83,31 +130,39 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
                "(beyond the grid of values visited by glmnet).",
                "You might also try rerunning glmnet with a lower setting of the",
                "'thresh' parameter, for a more accurate convergence."))
-
-
   
-    for(jj in 1:sum(m)){
-       vj=c(rep(0,sum(m)+1));vj[jj+1]=sign_bhat[jj+1]
-      # compute p-values
-      junk=TG.pvalue(bbar, A1, b1, vj, MM)
-      pv[jj] = junk$pv
- 
-   vlo[jj]=junk$vlo
-   vup[jj]=junk$vup
-       sd[jj]=junk$sd
-
-     junk2=TG.interval(bbar, A1, b1, vj, MM,alpha=alpha, flip=(sign_bhat[jj+1]==-1))
-
-     ci[jj,]=junk2$int
-     tailarea[jj,] = junk2$tailarea
-   }
-
-  # JT: these are not the one step estimators but they are close
-  fit0=glm(y~x[,m],family="binomial")
-  sfit0=summary(fit0)
-      coef0=bbar[-1]        #fit0$coef[-1]
-      se0=sqrt(diag(MM)[-1]) # sfit0$cov.scaled)[-1])
-      zscore0=coef0/se0
+  sign=numeric(nvar)
+  coef0=numeric(nvar)
+  
+  for(j in 1:nvar){
+    if (verbose) cat(sprintf("Inference for variable %i ...\n",vars[j]))
+    
+    if (is.null(dim(V))) vj = V
+    else vj = matrix(V[j,],nrow=1)
+    coef0[j] = vj%*%bbar # sum(vj * bbar)
+    sign[j] = sign(coef0[j])
+    vj = vj * sign[j]
+    
+    # compute p-values
+    limits.info = TG.limits(bbar, A1, b1, vj, Sigma=MM)
+    # if(is.null(limits.info)) return(list(pv=NULL,MM=MM,eta=vj))
+    a = TG.pvalue.base(limits.info, null_value=null_value[j], bits=bits)
+    pv[j] = a$pv
+    vlo[j] = a$vlo # * mj # Unstandardize (mult by norm of vj)
+    vup[j] = a$vup # * mj # Unstandardize (mult by norm of vj)
+    sd[j] = a$sd # * mj # Unstandardize (mult by norm of vj)
+    
+    a = TG.interval.base(limits.info, 
+                         alpha=alpha,
+                         gridrange=gridrange,
+                         flip=(sign[j]==-1),
+                         bits=bits)
+    ci[j,] = (a$int-null_value[j]) # * mj # Unstandardize (mult by norm of vj)
+    tailarea[j,] = a$tailarea
+  }
+  
+  se0 = sqrt(diag(V%*%MM%*%t(V)))
+  zscore0 = (coef0+null_value)/se0
       
   out = list(type=type,lambda=lambda,pv=pv,ci=ci,
     tailarea=tailarea,vlo=vlo,vup=vup,sd=sd,

From 20ea9c715f0656cd8305dc5e391790d490a998e6 Mon Sep 17 00:00:00 2001
From: kevinbfry <kfry@stanford.edu>
Date: Fri, 20 Apr 2018 22:53:51 -0700
Subject: [PATCH 492/493] added scaling of vlo, vup, and sd to account for use
 of local alternatives; fixed issue where reporting p-values for wrong-sided
 test

---
 selectiveInference/R/funs.fixed.R      |  2 ++
 selectiveInference/R/funs.fixedLogit.R | 14 ++++++++++++--
 2 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/selectiveInference/R/funs.fixed.R b/selectiveInference/R/funs.fixed.R
index 3aae749..153e48a 100644
--- a/selectiveInference/R/funs.fixed.R
+++ b/selectiveInference/R/funs.fixed.R
@@ -231,6 +231,8 @@ fixedLassoInf <- function(x, y, beta,
     pv[j] = a$pv
     if (is.na(sign_vars[j])) { # for variables not in the active set, report 2-sided pvalue
        pv[j] = 2 * min(pv[j], 1 - pv[j])
+    }  else if (sign_vars[j]<0){
+      pv[j]=1-pv[j]  
     }
     vlo[j] = a$vlo * mj # Unstandardize (mult by norm of vj)
     vup[j] = a$vup * mj # Unstandardize (mult by norm of vj)
diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index 8d810ce..b056d5d 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -110,13 +110,13 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
        b1 = matrix(c(b1,rep(lambda,2*nrow(A0))),ncol=1)
        
        # full covariance
-       MMbr = (xnotm_w%*%xnotm - xnotm_w_xm%*%(MM/n)%*%t(xnotm_w_xm))
+       MMbr = (xnotm_w%*%xnotm - xnotm_w_xm%*%(MM/n)%*%t(xnotm_w_xm))*n
        MM = cbind(MM,matrix(0,nrow(MM),ncol(MMbr)))
        MMbr = cbind(matrix(0,nrow(MMbr),nrow(MM)),MMbr)
        MM = rbind(MM,MMbr)
        
        etahat_bbar = xm %*% (bbar/sqrt(n))
-       gnotm = (scale(t(xnotm),FALSE,1/ww)%*%(z-etahat_bbar))
+       gnotm = (scale(t(xnotm),FALSE,1/ww)%*%(z-etahat_bbar))*sqrt(n)
        bbar = matrix(c(bbar,gnotm),ncol=1)
      }
      
@@ -148,9 +148,19 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
     # if(is.null(limits.info)) return(list(pv=NULL,MM=MM,eta=vj))
     a = TG.pvalue.base(limits.info, null_value=null_value[j], bits=bits)
     pv[j] = a$pv
+    if (is.na(s2[j])) { # for variables not in the active set, report 2-sided pvalue
+      pv[j] = 2 * min(pv[j], 1 - pv[j])
+    }  else if (s2[j]<0){
+      pv[j]=1-pv[j]  
+    }
     vlo[j] = a$vlo # * mj # Unstandardize (mult by norm of vj)
     vup[j] = a$vup # * mj # Unstandardize (mult by norm of vj)
     sd[j] = a$sd # * mj # Unstandardize (mult by norm of vj)
+    if (type=='full') { # rescale because of local alternatives
+      vlo[j] = vlo[j]/sqrt(n)
+      vup[j] = vup[j]/sqrt(n)
+      sd[j] = sd[j]/sqrt(n)
+    }
     
     a = TG.interval.base(limits.info, 
                          alpha=alpha,

From b24bb31b2a19ee3fe4a0857394188976e8723b06 Mon Sep 17 00:00:00 2001
From: kevinbfry <kfry@stanford.edu>
Date: Fri, 20 Apr 2018 22:57:54 -0700
Subject: [PATCH 493/493] added 'full' as argument option in fixedLogitLassoInf

---
 selectiveInference/R/funs.fixedLogit.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/selectiveInference/R/funs.fixedLogit.R b/selectiveInference/R/funs.fixedLogit.R
index b056d5d..3a43f0f 100644
--- a/selectiveInference/R/funs.fixedLogit.R
+++ b/selectiveInference/R/funs.fixedLogit.R
@@ -1,5 +1,5 @@
 
-fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.beta=1e-5, tol.kkt=0.1,
+fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial,full"), tol.beta=1e-5, tol.kkt=0.1,
                      gridrange=c(-100,100), bits=NULL, verbose=FALSE,this.call=NULL){