Structure pkgdown site and address parsing error

NAMESPACE

@@ -14,7 +14,6 @@ S3method(summary,warped_model)
 S3method(unwarp,data.frame)
 S3method(unwarp,formula)
 S3method(unwarp,warped_df)
-S3method(warp,"function")
 S3method(warp,data.frame)
 S3method(warp,formula)
 S3method(warp,warped_df)
@@ -26,6 +25,7 @@ export(strucpca_warper)
 export(unwarp)
 export(warp)
 export(warp_fitted_model)
+export(warp_function)
 importFrom(MASS,lda)
 importFrom(magrittr,"%>%")
 importFrom(purrr,flatten)

R/warp-unwarp.R

@@ -5,29 +5,19 @@
 #' representation of the data, if a [pca_warper] is used.
 #'
 #' @param x The object to be transformed, normally a data frame.
-#' @param ... Additional arguments to be passed to the `warp` method.
+#' @param warper A `warper` object as, for example, created by [pca_warper()] for principal components transformations.
+#' @param ... Additional arguments to be passed to the \code{warp} method.
+#' @describeIn warp.data.frame Does nothing - `warped_df` has already been transformed
 #' @return A data frame with features in the transformed (e.g., PCA) space.
 #' @seealso [unwarp()] for the inverse transformation, and
 #'   [pca_warper()] for an example of a simple warper function based
 #'   on the principal component transformation.
 #' @export
 warp <- function(x, ...) {
-  UseMethod("warp", x)
+  UseMethod("warp")
 }
 
-#' Transform data from feature space into a transformed space
-#'
-#' `warp` methods apply the transformation \eqn{T} to data
-#' in feature space - the result is, for example, the principal components
-#' representation of the data, if a [pca_warper] is used.
-#'
-#' @param x The object to be transformed, normally a data frame.
 #' @param warper A `warper` object as, for example, created by [pca_warper()] for principal components transformations.
-#' @param ... Additional arguments to be passed to the \code{warp} method.
-#' @return A data frame with features in the transformed (e.g., PCA) space.
-#' @seealso [unwarp()] for the inverse transformation, and
-#'   [pca_warper()] for an example of a simple warper function based
-#'   on the principal component transformation.
 #' @export
 warp.data.frame <- function(x, warper, ...) {
   chkDots(...)
@@ -36,7 +26,6 @@ warp.data.frame <- function(x, warper, ...) {
   x
 }
 
-#' @describeIn warp.data.frame Does nothing - `warped_df` has already been transformed
 #' @export
 warp.warped_df <- function(x, warper, ...) {
   chkDots(...)
@@ -118,7 +107,7 @@ unwarp.data.frame <- function(x, warper = NULL, force_unwarp = FALSE, ...) {
 #'   what happens with `sperrorest`.
 #'
 #' @export
-warp.function <- function (x, warper, ...)
+warp_function <- function(x, warper, ...)
 {
   function(formula, data, ...) {
     data <- unwarp(data, warper = warper)

_pkgdown.yml

@@ -3,3 +3,35 @@ url: https://alexanderbrenning.github.io/wiml
 template:
   bootstrap: 5
 
+authors:
+  Alexander Brenning:
+    href: https://geods.netlify.app/
+
+reference:
+  - title: Warp
+    description: Transform data from feature space into a transformed space
+    contents:
+      - starts_with("warp")
+      - ends_with("warp")
+      - contains("warped")
+  - title: Unwarp
+    description: Backtransform from transformed ('warped') to original feature space
+    contents:
+      - contains("unwarp")
+  - title: Inbound/Outbound
+    contents:
+      - starts_with("inbound")
+      - starts_with("outbound")
+  - title: Principal components transformation of feature space
+    contents:
+      - contains("pca")
+  - title: Partial least squares transformation of feature space
+    contents:
+      - contains("pls")
+  - title: Datasets
+    contents:
+      - gabor
+      - maipofields
+  - title: Package metadata
+    contents:
+      - wiml

vignettes/gabor_rf.Rmd

@@ -4,7 +4,7 @@ subtitle: "A Remote-Sensing Case Study Using the Random Forest Classifier"
 author: Alexander Brenning
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{"The wiml Package: Transforming Feature Space to Interpret Machine Learning Models"}
+  %\VignetteIndexEntry{The `wiml` Package: Transforming Feature Space to Interpret Machine Learning Models}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 keywords:
@@ -268,8 +268,6 @@ As you can see, our small sample of four points actually consisted of PC data (f
 
 This means that we're ready to fool *any* model-agnostic interpretation tool - just don't tell'em that 'wfit' is not a real model!
 
-### Permutation variable importance
-
 We'll start with the permutation variable importance:
 
 ```{r strucpcaimp, cache = TRUE, cach.extra = do_cache()}
@@ -349,7 +347,7 @@ plot(weffs_2d_pdp)
 
 
 
-### References
+## References
 
 <div id="refs"></div>