diff --git a/.Rbuildignore b/.Rbuildignore
index f0d5c2b..969595d 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -1,2 +1,4 @@
 ^\.travis\.yml$
 cran-comments.md
+^.*\.Rproj$
+^\.Rproj\.user$
diff --git a/.gitignore b/.gitignore
index a257aa0..5f74598 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,7 @@
 *.class
 *.jar
 *.zip
+*.Rproj
 *~
 semantic.cache
 html/
@@ -47,6 +48,7 @@ $RECYCLE.BIN/
 .TemporaryItems
 .Trashes
 .VolumeIcon.icns
+.Rhistory
 
 # Directories potentially created on remote AFP share
 .AppleDB
@@ -55,3 +57,4 @@ Network Trash Folder
 Temporary Items
 .apdisk
 inst/doc
+.Rproj.user
diff --git a/DESCRIPTION b/DESCRIPTION
index 2404bf1..1338882 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: maxnet
 Type: Package
 Title: Fitting 'Maxent' Species Distribution Models with 'glmnet'
-Version: 0.1.4
-Date: 2021-07-08
+Version: 0.1.5
+Date: 2026-16-03
 Author: Steven Phillips
 Maintainer: Steven Phillips <mrmaxent@gmail.com>
 Imports:
@@ -17,6 +17,6 @@ Description: Procedures to fit species distributions models from occurrence reco
 License: MIT + file LICENSE
 URL: https://github.com/mrmaxent/maxnet
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.1
+RoxygenNote: 7.3.3
 LazyData: true
 Encoding: UTF-8
diff --git a/R/hinge.R b/R/hinge.R
index e18182c..e85513e 100644
--- a/R/hinge.R
+++ b/R/hinge.R
@@ -33,15 +33,16 @@
 #'   hinge(bradypus$tmp6190_ann,nknots=10)
 #'   categorical(bradypus$ecoreg)
 #'  }
-hinge <-
-function(x, nknots=50)
+hinge <- function (x, nknots = 50) 
 {
-   min <- min(x)
-   max <- max(x)
-   k <- seq(min, max, length=nknots)
-   lh <- outer(x, utils::head(k,-1), function(w,h) hingeval(w, h, max))
-   rh <- outer(x, k[-1], function(w,h) hingeval(w, min, h))
-   colnames(lh) <- paste("", utils::head(k,-1), max, sep=":")
-   colnames(rh) <- paste("", min, k[-1], sep=":")
-   cbind(lh, rh)
+  #  if(length(unique(x)) < nknots) nknots <- length(unique(x)) - 1
+  Min <- min(x)
+  Max <- max(x)
+  k <- seq(Min, Max, length = nknots)
+  lh <- outer(x, utils::head(k, -1), function(w, h) hingeval(w, 
+                                                             h, Max))
+  rh <- outer(x, k[-1], function(w, h) hingeval(w, Min, h))
+  colnames(lh) <- paste("", utils::head(k, -1), Max, sep = ":")
+  colnames(rh) <- paste("", Min, k[-1], sep = ":")
+  cbind(lh, rh[,!colnames(rh)%in%colnames(lh)])
 }
diff --git a/R/maxnet.R b/R/maxnet.R
index 544ad8e..76df71b 100644
--- a/R/maxnet.R
+++ b/R/maxnet.R
@@ -21,12 +21,14 @@
 #' @param f formula, determines the features to be used
 #' @param regmult numeric, a constant to adjust regularization
 #' @param regfun function, computes regularization constant for each feature
+#' @param standardize logical, should glmnet use internal standardization? Defaults to FALSE for backwards compatability
 #' @param addsamplestobackground logical, if TRUE then add to the background any
 #'   presence sample that is not already there
 #' @param m a matrix of feature values
-#' @param classes charcater, continuous feature classes desired, either
+#' @param classes character, continuous feature classes desired, either
 #'   "default" or any subset of "lqpht" (for example, "lh")
-#' @param ... not used
+#' @param wt weight of absence points. Default is 100, for backwards compatibility. Can be of same length as p, to allow it to vary: note that when p=0 the weight is irrelevant.
+#' @param ... other arguments passed on to glmnet
 #'
 #' @return Maxnet returns an object of class \code{maxnet}, which is a list
 #'   consisting of a glmnet model with the following elements added:
@@ -55,12 +57,17 @@
 #'   plot(mod, "tmp6190_ann")
 #' }
 maxnet <-
-function(p, data, f=maxnet.formula(p, data), regmult=1.0, 
-         regfun=maxnet.default.regularization, addsamplestobackground=T, ...)
+function(p, data, f=maxnet.formula(p, data), regmult=1.0, standardize = FALSE, 
+         regfun=maxnet.default.regularization, addsamplestobackground=T, wt=100, ...)
 {
+   if(!length(wt)%in%c(1, length(p))) stop("wt should either be a scalar or the same length as p.")
    if (anyNA(data)) stop("NA values in data table. Please remove them and rerun.")
    if (!is.vector(p))
        stop("p must be a vector.")
+  Nvals <- apply(data, 2, function(x) length(unique(x)))
+  if(any(Nvals == 1)) stop("These columns of data only have a single value: ", 
+                           paste(names(Nvals)[Nvals==1], sep=", "), 
+                           ". They should be removed")
    if (addsamplestobackground) {
        pdata <- data[p==1, , drop = FALSE]
        ndata <- data[p==0, , drop = FALSE]
@@ -71,9 +78,9 @@ function(p, data, f=maxnet.formula(p, data), regmult=1.0,
    }   
    mm <- model.matrix(f, data)
    reg <- regfun(p,mm) * regmult
-   weights <- p+(1-p)*100
+   weights <- p+(1-p)*wt
    glmnet::glmnet.control(pmin=1.0e-8, fdev=0)  
-   model <- glmnet::glmnet(x=mm, y=as.factor(p), family="binomial", standardize=F, penalty.factor=reg, lambda=10^(seq(4,0,length.out=200))*sum(reg)/length(reg)*sum(p)/sum(weights), weights=weights, ...)
+   model <- glmnet::glmnet(x=mm, y=as.factor(p), family="binomial", standardize=standardize, penalty.factor=reg, lambda=10^(seq(4,0,length.out=200))*sum(reg)/length(reg)*sum(p)/sum(weights), weights=weights, ...)
    class(model) <- c("maxnet", class(model))
    if (length(model$lambda) < 200) {
         msg <- "Error: glmnet failed to complete regularization path.  Model may be infeasible."
@@ -86,7 +93,8 @@ function(p, data, f=maxnet.formula(p, data), regmult=1.0,
    model$alpha <- 0
    rr <- predict.maxnet(model, data[p==0, , drop = FALSE], type="exponent", clamp=F)
    raw <- rr / sum(rr)
-   model$entropy <- -sum(raw * log(raw))
+   if(!all(raw>0)) warning("Some fitted probabilities are not positive")
+   model$entropy <- -sum(raw[raw>0] * log(raw[raw>0]))
    model$alpha <- -log(sum(rr))
    model$penalty.factor <- reg
    model$featuremins <- apply(mm, 2, min)
diff --git a/R/predict.maxnet.R b/R/predict.maxnet.R
index 470b337..fdcd0fb 100644
--- a/R/predict.maxnet.R
+++ b/R/predict.maxnet.R
@@ -10,7 +10,7 @@
 #' @param type character, type of response required. Using \code{lp} for the linear predictor 
 #' and \code{entropy} for the entropy of the exponential model over the background data, 
 #' the values returned are determined by the value of \code{type}.
-#' \itemize{
+#' \describe{
 #'   \item{"link"}{yields \code{lp}}
 #'   \item{"exponential"}{yields \code{exp(lp)}}
 #'   \item{"cloglog"}{yields \code{1-exp(-exp(entropy+lp))}}
diff --git a/man/maxnet-package.Rd b/man/maxnet-package.Rd
index 70d2976..8cd89cd 100644
--- a/man/maxnet-package.Rd
+++ b/man/maxnet-package.Rd
@@ -3,7 +3,6 @@
 \docType{package}
 \name{maxnet-package}
 \alias{maxnet-package}
-\alias{_PACKAGE}
 \title{Maxent over glmnet}
 \description{
 Procedures to fit species distributions models from occurrence records and environmental variables, using 'glmnet' for model fitting. Model structure is the same as for the 'Maxent' Java package, version 3.4.0, with the same feature types and regularization options.  See the 'Maxent' website \url{http://biodiversityinformatics.amnh.org/open_source/maxent} for more details.
diff --git a/man/maxnet.Rd b/man/maxnet.Rd
index 5ff224a..cba604c 100644
--- a/man/maxnet.Rd
+++ b/man/maxnet.Rd
@@ -12,8 +12,10 @@ maxnet(
   data,
   f = maxnet.formula(p, data),
   regmult = 1,
+  standardize = FALSE,
   regfun = maxnet.default.regularization,
   addsamplestobackground = T,
+  wt = 100,
   ...
 )
 
@@ -30,16 +32,20 @@ maxnet.formula(p, data, classes = "default")
 
 \item{regmult}{numeric, a constant to adjust regularization}
 
+\item{standardize}{logical, should glmnet use internal standardization? Defaults to FALSE for backwards compatability}
+
 \item{regfun}{function, computes regularization constant for each feature}
 
 \item{addsamplestobackground}{logical, if TRUE then add to the background any
 presence sample that is not already there}
 
-\item{...}{not used}
+\item{wt}{weight of absence points. Default is 100, for backwards compatibility. Can be of same length as p, to allow it to vary: note that when p=0 the weight is irrelevant.}
+
+\item{...}{other arguments passed on to glmnet}
 
 \item{m}{a matrix of feature values}
 
-\item{classes}{charcater, continuous feature classes desired, either
+\item{classes}{character, continuous feature classes desired, either
 "default" or any subset of "lqpht" (for example, "lh")}
 }
 \value{
diff --git a/man/predict.maxnet.Rd b/man/predict.maxnet.Rd
index 0dad803..440aee2 100644
--- a/man/predict.maxnet.Rd
+++ b/man/predict.maxnet.Rd
@@ -23,7 +23,7 @@ matrix, data.frame, \code{SpatRaster} or \code{stars} object.}
 \item{type}{character, type of response required. Using \code{lp} for the linear predictor
 and \code{entropy} for the entropy of the exponential model over the background data,
 the values returned are determined by the value of \code{type}.
-\itemize{
+\describe{
 \item{"link"}{yields \code{lp}}
 \item{"exponential"}{yields \code{exp(lp)}}
 \item{"cloglog"}{yields \code{1-exp(-exp(entropy+lp))}}