Cran v0.3.1

R/Converter.R

@@ -81,7 +81,8 @@ Converter <- R6Class("Converter",
     #' torch-converted model ([ConvertedModel]) is created and stored in
     #' the field `model`.
     #'
-    #' @param model ([`nn_sequential`], \code{\link[keras]{keras_model}},
+    #' @param model (\code{\link[torch]{nn_sequential}},
+    #' \code{\link[keras]{keras_model}},
     #' \code{\link[neuralnet]{neuralnet}} or `list`)\cr
     #' A trained neural network for classification or regression
     #' tasks to be interpreted. Only models from the following types or

R/DeepLift.R

@@ -212,7 +212,8 @@ DeepSHAP <- R6Class(
     #' another variant is implemented, which treats a MaxPooling layer as an
     #' AveragePooling layer in the backward pass only, leading to an uniform
     #' distribution of the upper-layer contribution to the lower layer.\cr
-    #' @param data_ref ([`array`], [`data.frame`], [`torch_tensor`] or `list`)\cr
+    #' @param data_ref ([`array`], [`data.frame`],
+    #' \code{\link[torch]{torch_tensor}} or `list`)\cr
     #' The reference data which is used to estimate the conditional expectation.
     #' These must have the same format as the input data of the passed model to
     #' the converter object. This means either

R/GradienBased.R

@@ -319,7 +319,7 @@ IntegratedGradient <- R6Class(
     #' @param n (`integer(1)`)\cr
     #' Number of steps for the approximation of the integration path along
     #' \eqn{\alpha} (default: \eqn{50}).\cr
-    #' @param x_ref ([`array`], [`data.frame`], [`torch_tensor`] or `list`)\cr
+    #' @param x_ref ([`array`], [`data.frame`], \code{\link[torch]{torch_tensor}} or `list`)\cr
     #' The reference input for the IntegratedGradient method. This value
     #' must have the same format as the input data of the passed model to the
     #' converter object. This means either
@@ -723,7 +723,7 @@ ExpectedGradient <- R6Class(
     #' Number of samples from the distribution of reference values and number
     #' of samples for the approximation of the integration path along
     #' \eqn{\alpha} (default: \eqn{50}).\cr
-    #' @param data_ref ([`array`], [`data.frame`], [`torch_tensor`] or `list`)\cr
+    #' @param data_ref ([`array`], [`data.frame`], \code{\link[torch]{torch_tensor}} or `list`)\cr
     #' The reference inputs for the ExpectedGradient method. This value
     #' must have the same format as the input data of the passed model to the
     #' converter object. This means either

R/innsight_ggplot2.R

@@ -4,8 +4,8 @@ NULL
 #' S4 class for ggplot2-based plots
 #'
 #' The S4 class `innsight_ggplot2` visualizes the results of the methods
-#' provided from the package `innsight` using [ggplot2]. In addition, it
-#' allows easier analysis of the results and modification of the
+#' provided from the package `innsight` using [ggplot2][ggplot2::ggplot2]. In
+#' addition, it allows easier analysis of the results and modification of the
 #' visualization by basic generic functions. The individual slots are for
 #' internal use only and should not be modified.
 #'
@@ -23,11 +23,12 @@ NULL
 #'
 #' @details
 #'
-#' This S4 class is a simple extension of a [ggplot2] object that enables
-#' a more detailed analysis of the results and a way to visualize the results
-#' of models with multiple input layers (e.g., images and tabular data).
-#' The distinction between one and multiple input layers decides the behavior
-#' of this class, and this information is stored in the slot `multiplot`.
+#' This S4 class is a simple extension of a [ggplot2][ggplot2::ggplot2] object
+#' that enables a more detailed analysis of the results and a way to visualize
+#' the results of models with multiple input layers (e.g., images and tabular
+#' data). The distinction between one and multiple input layers decides the
+#' behavior of this class, and this information is stored in the slot
+#' `multiplot`.
 #'
 #' ## One input layer (`multiplot = FALSE`)
 #'
@@ -187,8 +188,9 @@ setMethod(
 #' Generic add function for `innsight_ggplot2`
 #'
 #' This generic add function allows to treat an instance of [`innsight_ggplot2`]
-#' as an ordinary plot object of [`ggplot2`]. For example geoms, themes and
-#' scales can be added as usual (see [`ggplot2::+.gg`] for more information).\cr \cr
+#' as an ordinary plot object of [ggplot2][ggplot2::ggplot2]. For example geoms,
+#' themes and scales can be added as usual (see [`ggplot2::+.gg`] for more
+#' information).\cr \cr
 #' **Note:** If `e1` represents a multiplot (i.e., `e1@mulitplot = TRUE`),
 #' `e2` is added to each individual plot. If only specific plots need to be
 #' changed, the generic assignment function should be used (see

R/innsight_sugar.R

@@ -23,7 +23,7 @@
 #' @template param-converter
 #' @template param-data_ref-agnostic
 #'
-#' @param model ([`nn_sequential`], \code{\link[keras]{keras_model}},
+#' @param model (\code{\link[torch]{nn_sequential}}, \code{\link[keras]{keras_model}},
 #' \code{\link[neuralnet]{neuralnet}} or `list`)\cr
 #' A trained neural network for classification or regression
 #' tasks to be interpreted. Only models from the following types or
@@ -40,7 +40,7 @@
 #' \code{\link[neuralnet]{neuralnet}} or [`Converter`],
 #' these have already been pre-implemented and do not need to be
 #' specified.\cr
-#' @param data ([`array`], [`data.frame`], [`torch_tensor`] or `list`)\cr
+#' @param data ([`array`], [`data.frame`], \code{\link[torch]{torch_tensor}} or `list`)\cr
 #' The data to which the method is to be applied. These must
 #' have the same format as the input data of the passed model to the
 #' converter object. This means either

cran-comments.md

@@ -8,6 +8,11 @@ after publication on CRAN.
 * GitHub Actions (windows): release
 * Github Actions (macOS): release
 
+**Note:** There is currently something wrong with the image on MacOS, which is 
+why the GitHub Actions workflow failed, but this is unrelated to the package
+(see [here](https://forum.posit.co/t/r-cmd-check-fails-on-macos-latest-release-r-lib-actions-setup-r-v2/195084)
+for details).
+
 #### R CMD check results
 
 There were no errors or warnings only one note which is not related to our 
@@ -30,28 +35,12 @@ of the authors of torch (see torch
 and disabled their execution on CRAN.
 
 ### Test environments without LibTorch
-- winbuilder Windows Server 2022, R-devel, 64 bit
-- winbuilder Windows Server 2022, R-release, 64 bit
-- winbuilder Windows Server 2022, R-oldrel, 64 bit
-- R-hub Ubuntu Linux 20.04.1 LTS, R-release
-- R-hub Fedora Linux, R-devel, clang, gfortran
+- R-hub Ubuntu Linux 22.04 R-release
+- R-hub Ubuntu Linux, R-devel
+- R-hub Windows, R-devel
+- R-hub macOS, R-devel
 - macOS builder, R-release
 
 #### R CMD check results
 
-There were no errors or warnings, only some notes under R-Hub unrelated to the 
-package: (see issues [#548](https://github.com/r-hub/rhub/issues/548), 
-[#560](https://github.com/r-hub/rhub/issues/560),
-[#503](https://github.com/r-hub/rhub/issues/503)):
-
-```
-* checking HTML version of manual ... NOTE
-  Skipping checking HTML validation: no command 'tidy' found
-  Skipping checking math rendering: package 'V8' unavailable
-* checking for non-standard things in the check directory ... NOTE
-  Found the following files/directories:
-    ''NULL''
-* checking for detritus in the temp directory ... NOTE
-  Found the following files/directories:
-    'lastMiKTeXException'
-```
+There were no errors, warnings or notes.

man-roxygen/field-dtype.R

@@ -1,3 +1,4 @@
 #' @field dtype (`character(1)`)\cr
 #' The data type for the calculations. Either `'float'` for
-#' [torch_float] or `'double'` for [torch_double].\cr
+#' \code{\link[torch]{torch_float}} or `'double'` for
+#' \code{\link[torch]{torch_double}}.\cr

man-roxygen/param-data-agnostic.R

@@ -2,7 +2,7 @@
 #' The individual instances to be explained by the method.
 #' These must have the same format as the input data of the passed model
 #' and has to be either [`matrix`], an [`array`], a [`data.frame`] or a
-#' [`torch_tensor`]. If no value is specified, all instances in the
+#' \code{\link[torch]{torch_tensor}}. If no value is specified, all instances in the
 #' dataset `data` will be explained.\cr
 #' **Note:** For the model-agnostic methods, only models with a single
 #' input and output layer is allowed!\cr

man-roxygen/param-data-optional.R

@@ -1,4 +1,4 @@
-#' @param data ([`array`], [`data.frame`], [`torch_tensor`] or `list`)\cr
+#' @param data ([`array`], [`data.frame`], \code{\link[torch]{torch_tensor}} or `list`)\cr
 #' The data to which the method is to be applied. These must
 #' have the same format as the input data of the passed model to the
 #' converter object. This means either

man-roxygen/param-data.R

@@ -1,4 +1,4 @@
-#' @param data ([`array`], [`data.frame`], [`torch_tensor`] or `list`)\cr
+#' @param data ([`array`], [`data.frame`], \code{\link[torch]{torch_tensor}} or `list`)\cr
 #' The data to which the method is to be applied. These must
 #' have the same format as the input data of the passed model to the
 #' converter object. This means either

man-roxygen/param-data_ref-agnostic.R

@@ -1,7 +1,7 @@
-#' @param data_ref ([`array`], [`data.frame`] or [`torch_tensor`])\cr
+#' @param data_ref ([`array`], [`data.frame`] or \code{\link[torch]{torch_tensor}})\cr
 #' The dataset to which the method is to be applied. These must
 #' have the same format as the input data of the passed model and has to
 #' be either [`matrix`], an [`array`], a [`data.frame`] or a
-#' [`torch_tensor`].\cr
+#' \code{\link[torch]{torch_tensor}}.\cr
 #' **Note:** For the model-agnostic methods, only models with a single
 #' input and output layer is allowed!\cr

man-roxygen/param-dtype.R

@@ -1,4 +1,4 @@
 #' @param dtype (`character(1)`)\cr
 #' The data type for the calculations. Use
-#' either `'float'` for [torch_float] or `'double'` for
-#' [torch_double].\cr
+#' either `'float'` for \code{\link[torch]{torch_float}} or `'double'` for
+#' \code{\link[torch]{torch_double}}.\cr

man-roxygen/param-x_ref.R

@@ -1,4 +1,4 @@
-#' @param x_ref ([`array`], [`data.frame`], [`torch_tensor`] or `list`)\cr
+#' @param x_ref ([`array`], [`data.frame`], \code{\link[torch]{torch_tensor}} or `list`)\cr
 #' The reference input for the DeepLift method. This value
 #' must have the same format as the input data of the passed model to the
 #' converter object. This means either