diff --git a/R/Converter.R b/R/Converter.R
index a690a31..284cdf5 100644
--- a/R/Converter.R
+++ b/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
diff --git a/R/DeepLift.R b/R/DeepLift.R
index 005d70e..242fc30 100644
--- a/R/DeepLift.R
+++ b/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
diff --git a/R/GradienBased.R b/R/GradienBased.R
index 31a84cb..8f19c1b 100644
--- a/R/GradienBased.R
+++ b/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
diff --git a/R/innsight_ggplot2.R b/R/innsight_ggplot2.R
index e690b0a..93b9e1c 100644
--- a/R/innsight_ggplot2.R
+++ b/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
diff --git a/R/innsight_sugar.R b/R/innsight_sugar.R
index 783349f..ffd34ef 100644
--- a/R/innsight_sugar.R
+++ b/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
diff --git a/cran-comments.md b/cran-comments.md
index 0916e80..e34305b 100644
--- a/cran-comments.md
+++ b/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.
diff --git a/man-roxygen/field-dtype.R b/man-roxygen/field-dtype.R
index 13f2e98..2b7cb36 100644
--- a/man-roxygen/field-dtype.R
+++ b/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
diff --git a/man-roxygen/param-data-agnostic.R b/man-roxygen/param-data-agnostic.R
index e050e2e..36acf8b 100644
--- a/man-roxygen/param-data-agnostic.R
+++ b/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
diff --git a/man-roxygen/param-data-optional.R b/man-roxygen/param-data-optional.R
index 4009cbf..60ea929 100644
--- a/man-roxygen/param-data-optional.R
+++ b/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
diff --git a/man-roxygen/param-data.R b/man-roxygen/param-data.R
index 3e4467c..65bd9ef 100644
--- a/man-roxygen/param-data.R
+++ b/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
diff --git a/man-roxygen/param-data_ref-agnostic.R b/man-roxygen/param-data_ref-agnostic.R
index d8917cb..15734fc 100644
--- a/man-roxygen/param-data_ref-agnostic.R
+++ b/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
diff --git a/man-roxygen/param-dtype.R b/man-roxygen/param-dtype.R
index 8e65df9..7b2684b 100644
--- a/man-roxygen/param-dtype.R
+++ b/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
diff --git a/man-roxygen/param-x_ref.R b/man-roxygen/param-x_ref.R
index e54fe10..7c73c95 100644
--- a/man-roxygen/param-x_ref.R
+++ b/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
diff --git a/man/AgnosticWrapper.Rd b/man/AgnosticWrapper.Rd
index c549961..a53df20 100644
--- a/man/AgnosticWrapper.Rd
+++ b/man/AgnosticWrapper.Rd
@@ -84,16 +84,16 @@ specified.\cr}
 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 \code{\link{matrix}}, an \code{\link{array}}, a \code{\link{data.frame}} or a
-\code{\link{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 \code{data} will be explained.\cr
 \strong{Note:} For the model-agnostic methods, only models with a single
 input and output layer is allowed!\cr}
 
-\item{\code{data_ref}}{(\code{\link{array}}, \code{\link{data.frame}} or \code{\link{torch_tensor}})\cr
+\item{\code{data_ref}}{(\code{\link{array}}, \code{\link{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 \code{\link{matrix}}, an \code{\link{array}}, a \code{\link{data.frame}} or a
-\code{\link{torch_tensor}}.\cr
+\code{\link[torch]{torch_tensor}}.\cr
 \strong{Note:} For the model-agnostic methods, only models with a single
 input and output layer is allowed!\cr}
 
diff --git a/man/ConnectionWeights.Rd b/man/ConnectionWeights.Rd
index b6c1b44..775593f 100644
--- a/man/ConnectionWeights.Rd
+++ b/man/ConnectionWeights.Rd
@@ -215,7 +215,7 @@ An instance of the \code{Converter} class that includes the
 torch-converted model and some other model-specific attributes. See
 \code{\link{Converter}} for details.\cr}
 
-\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link{torch_tensor}} or \code{list})\cr
+\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link[torch]{torch_tensor}} or \code{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
@@ -301,8 +301,8 @@ the output of the primitive R function \code{\link[=interactive]{interactive()}}
 
 \item{\code{dtype}}{(\code{character(1)})\cr
 The data type for the calculations. Use
-either \code{'float'} for \link{torch_float} or \code{'double'} for
-\link{torch_double}.\cr}
+either \code{'float'} for \code{\link[torch]{torch_float}} or \code{'double'} for
+\code{\link[torch]{torch_double}}.\cr}
 }
 \if{html}{\out{</div>}}
 }
diff --git a/man/Converter.Rd b/man/Converter.Rd
index 5f977a3..4d8ca5a 100644
--- a/man/Converter.Rd
+++ b/man/Converter.Rd
@@ -275,7 +275,8 @@ the field \code{model}.
 \subsection{Arguments}{
 \if{html}{\out{<div class="arguments">}}
 \describe{
-\item{\code{model}}{(\code{\link{nn_sequential}}, \code{\link[keras]{keras_model}},
+\item{\code{model}}{(\code{\link[torch]{nn_sequential}},
+\code{\link[keras]{keras_model}},
 \code{\link[neuralnet]{neuralnet}} or \code{list})\cr
 A trained neural network for classification or regression
 tasks to be interpreted. Only models from the following types or
diff --git a/man/DeepLift.Rd b/man/DeepLift.Rd
index dc17012..b096dbf 100644
--- a/man/DeepLift.Rd
+++ b/man/DeepLift.Rd
@@ -220,7 +220,7 @@ An instance of the \code{Converter} class that includes the
 torch-converted model and some other model-specific attributes. See
 \code{\link{Converter}} for details.\cr}
 
-\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link{torch_tensor}} or \code{list})\cr
+\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link[torch]{torch_tensor}} or \code{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
@@ -300,7 +300,7 @@ often omitted.\cr}
 Name of the applied rule to calculate the
 contributions. Use either \code{'rescale'} or \code{'reveal_cancel'}. \cr}
 
-\item{\code{x_ref}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link{torch_tensor}} or \code{list})\cr
+\item{\code{x_ref}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link[torch]{torch_tensor}} or \code{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
@@ -330,8 +330,8 @@ the output of the primitive R function \code{\link[=interactive]{interactive()}}
 
 \item{\code{dtype}}{(\code{character(1)})\cr
 The data type for the calculations. Use
-either \code{'float'} for \link{torch_float} or \code{'double'} for
-\link{torch_double}.\cr}
+either \code{'float'} for \code{\link[torch]{torch_float}} or \code{'double'} for
+\code{\link[torch]{torch_double}}.\cr}
 }
 \if{html}{\out{</div>}}
 }
diff --git a/man/DeepSHAP.Rd b/man/DeepSHAP.Rd
index 3be1342..b667c53 100644
--- a/man/DeepSHAP.Rd
+++ b/man/DeepSHAP.Rd
@@ -220,7 +220,7 @@ An instance of the \code{Converter} class that includes the
 torch-converted model and some other model-specific attributes. See
 \code{\link{Converter}} for details.\cr}
 
-\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link{torch_tensor}} or \code{list})\cr
+\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link[torch]{torch_tensor}} or \code{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
@@ -300,7 +300,8 @@ often omitted.\cr}
 Name of the applied rule to calculate the
 contributions. Use either \code{'rescale'} or \code{'reveal_cancel'}. \cr}
 
-\item{\code{data_ref}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link{torch_tensor}} or \code{list})\cr
+\item{\code{data_ref}}{(\code{\link{array}}, \code{\link{data.frame}},
+\code{\link[torch]{torch_tensor}} or \code{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
@@ -335,8 +336,8 @@ the output of the primitive R function \code{\link[=interactive]{interactive()}}
 
 \item{\code{dtype}}{(\code{character(1)})\cr
 The data type for the calculations. Use
-either \code{'float'} for \link{torch_float} or \code{'double'} for
-\link{torch_double}.\cr}
+either \code{'float'} for \code{\link[torch]{torch_float}} or \code{'double'} for
+\code{\link[torch]{torch_double}}.\cr}
 }
 \if{html}{\out{</div>}}
 }
diff --git a/man/ExpectedGradient.Rd b/man/ExpectedGradient.Rd
index b8c044b..20f7ac1 100644
--- a/man/ExpectedGradient.Rd
+++ b/man/ExpectedGradient.Rd
@@ -219,7 +219,7 @@ An instance of the \code{Converter} class that includes the
 torch-converted model and some other model-specific attributes. See
 \code{\link{Converter}} for details.\cr}
 
-\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link{torch_tensor}} or \code{list})\cr
+\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link[torch]{torch_tensor}} or \code{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
@@ -230,7 +230,7 @@ size \emph{(batch_size, dim_in)}, if e.g., the model has only one input layer, o
 upper point) for each of the input layers.\cr
 }}
 
-\item{\code{data_ref}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link{torch_tensor}} or \code{list})\cr
+\item{\code{data_ref}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link[torch]{torch_tensor}} or \code{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
@@ -320,8 +320,8 @@ the output of the primitive R function \code{\link[=interactive]{interactive()}}
 
 \item{\code{dtype}}{(\code{character(1)})\cr
 The data type for the calculations. Use
-either \code{'float'} for \link{torch_float} or \code{'double'} for
-\link{torch_double}.\cr}
+either \code{'float'} for \code{\link[torch]{torch_float}} or \code{'double'} for
+\code{\link[torch]{torch_double}}.\cr}
 }
 \if{html}{\out{</div>}}
 }
diff --git a/man/Gradient.Rd b/man/Gradient.Rd
index 2609dda..5e8fbab 100644
--- a/man/Gradient.Rd
+++ b/man/Gradient.Rd
@@ -202,7 +202,7 @@ An instance of the \code{Converter} class that includes the
 torch-converted model and some other model-specific attributes. See
 \code{\link{Converter}} for details.\cr}
 
-\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link{torch_tensor}} or \code{list})\cr
+\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link[torch]{torch_tensor}} or \code{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
@@ -289,8 +289,8 @@ the output of the primitive R function \code{\link[=interactive]{interactive()}}
 
 \item{\code{dtype}}{(\code{character(1)})\cr
 The data type for the calculations. Use
-either \code{'float'} for \link{torch_float} or \code{'double'} for
-\link{torch_double}.\cr}
+either \code{'float'} for \code{\link[torch]{torch_float}} or \code{'double'} for
+\code{\link[torch]{torch_double}}.\cr}
 }
 \if{html}{\out{</div>}}
 }
diff --git a/man/GradientBased.Rd b/man/GradientBased.Rd
index 9ba4494..995e640 100644
--- a/man/GradientBased.Rd
+++ b/man/GradientBased.Rd
@@ -73,7 +73,7 @@ An instance of the \code{Converter} class that includes the
 torch-converted model and some other model-specific attributes. See
 \code{\link{Converter}} for details.\cr}
 
-\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link{torch_tensor}} or \code{list})\cr
+\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link[torch]{torch_tensor}} or \code{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
@@ -160,8 +160,8 @@ the output of the primitive R function \code{\link[=interactive]{interactive()}}
 
 \item{\code{dtype}}{(\code{character(1)})\cr
 The data type for the calculations. Use
-either \code{'float'} for \link{torch_float} or \code{'double'} for
-\link{torch_double}.\cr}
+either \code{'float'} for \code{\link[torch]{torch_float}} or \code{'double'} for
+\code{\link[torch]{torch_double}}.\cr}
 }
 \if{html}{\out{</div>}}
 }
diff --git a/man/IntegratedGradient.Rd b/man/IntegratedGradient.Rd
index bd0712e..322a6bb 100644
--- a/man/IntegratedGradient.Rd
+++ b/man/IntegratedGradient.Rd
@@ -223,7 +223,7 @@ An instance of the \code{Converter} class that includes the
 torch-converted model and some other model-specific attributes. See
 \code{\link{Converter}} for details.\cr}
 
-\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link{torch_tensor}} or \code{list})\cr
+\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link[torch]{torch_tensor}} or \code{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
@@ -234,7 +234,7 @@ size \emph{(batch_size, dim_in)}, if e.g., the model has only one input layer, o
 upper point) for each of the input layers.\cr
 }}
 
-\item{\code{x_ref}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link{torch_tensor}} or \code{list})\cr
+\item{\code{x_ref}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link[torch]{torch_tensor}} or \code{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
@@ -330,8 +330,8 @@ the output of the primitive R function \code{\link[=interactive]{interactive()}}
 
 \item{\code{dtype}}{(\code{character(1)})\cr
 The data type for the calculations. Use
-either \code{'float'} for \link{torch_float} or \code{'double'} for
-\link{torch_double}.\cr}
+either \code{'float'} for \code{\link[torch]{torch_float}} or \code{'double'} for
+\code{\link[torch]{torch_double}}.\cr}
 }
 \if{html}{\out{</div>}}
 }
diff --git a/man/InterpretingMethod.Rd b/man/InterpretingMethod.Rd
index 068698a..86faeaf 100644
--- a/man/InterpretingMethod.Rd
+++ b/man/InterpretingMethod.Rd
@@ -53,7 +53,8 @@ is calculated.\cr}
 
 \item{\code{dtype}}{(\code{character(1)})\cr
 The data type for the calculations. Either \code{'float'} for
-\link{torch_float} or \code{'double'} for \link{torch_double}.\cr}
+\code{\link[torch]{torch_float}} or \code{'double'} for
+\code{\link[torch]{torch_double}}.\cr}
 
 \item{\code{ignore_last_act}}{(\code{logical(1)})\cr
 A logical value to include the last activation
@@ -167,7 +168,7 @@ An instance of the \code{Converter} class that includes the
 torch-converted model and some other model-specific attributes. See
 \code{\link{Converter}} for details.\cr}
 
-\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link{torch_tensor}} or \code{list})\cr
+\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link[torch]{torch_tensor}} or \code{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
@@ -261,8 +262,8 @@ the output of the primitive R function \code{\link[=interactive]{interactive()}}
 
 \item{\code{dtype}}{(\code{character(1)})\cr
 The data type for the calculations. Use
-either \code{'float'} for \link{torch_float} or \code{'double'} for
-\link{torch_double}.\cr}
+either \code{'float'} for \code{\link[torch]{torch_float}} or \code{'double'} for
+\code{\link[torch]{torch_double}}.\cr}
 }
 \if{html}{\out{</div>}}
 }
diff --git a/man/LIME.Rd b/man/LIME.Rd
index f331d71..3d912ae 100644
--- a/man/LIME.Rd
+++ b/man/LIME.Rd
@@ -192,16 +192,16 @@ specified.\cr}
 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 \code{\link{matrix}}, an \code{\link{array}}, a \code{\link{data.frame}} or a
-\code{\link{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 \code{data} will be explained.\cr
 \strong{Note:} For the model-agnostic methods, only models with a single
 input and output layer is allowed!\cr}
 
-\item{\code{data_ref}}{(\code{\link{array}}, \code{\link{data.frame}} or \code{\link{torch_tensor}})\cr
+\item{\code{data_ref}}{(\code{\link{array}}, \code{\link{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 \code{\link{matrix}}, an \code{\link{array}}, a \code{\link{data.frame}} or a
-\code{\link{torch_tensor}}.\cr
+\code{\link[torch]{torch_tensor}}.\cr
 \strong{Note:} For the model-agnostic methods, only models with a single
 input and output layer is allowed!\cr}
 
diff --git a/man/LRP.Rd b/man/LRP.Rd
index 78ff91d..f7f9093 100644
--- a/man/LRP.Rd
+++ b/man/LRP.Rd
@@ -245,7 +245,7 @@ An instance of the \code{Converter} class that includes the
 torch-converted model and some other model-specific attributes. See
 \code{\link{Converter}} for details.\cr}
 
-\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link{torch_tensor}} or \code{list})\cr
+\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link[torch]{torch_tensor}} or \code{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
@@ -367,8 +367,8 @@ the output of the primitive R function \code{\link[=interactive]{interactive()}}
 
 \item{\code{dtype}}{(\code{character(1)})\cr
 The data type for the calculations. Use
-either \code{'float'} for \link{torch_float} or \code{'double'} for
-\link{torch_double}.\cr}
+either \code{'float'} for \code{\link[torch]{torch_float}} or \code{'double'} for
+\code{\link[torch]{torch_double}}.\cr}
 }
 \if{html}{\out{</div>}}
 }
diff --git a/man/SHAP.Rd b/man/SHAP.Rd
index 67dbb3d..a5cd2b3 100644
--- a/man/SHAP.Rd
+++ b/man/SHAP.Rd
@@ -186,16 +186,16 @@ specified.\cr}
 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 \code{\link{matrix}}, an \code{\link{array}}, a \code{\link{data.frame}} or a
-\code{\link{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 \code{data} will be explained.\cr
 \strong{Note:} For the model-agnostic methods, only models with a single
 input and output layer is allowed!\cr}
 
-\item{\code{data_ref}}{(\code{\link{array}}, \code{\link{data.frame}} or \code{\link{torch_tensor}})\cr
+\item{\code{data_ref}}{(\code{\link{array}}, \code{\link{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 \code{\link{matrix}}, an \code{\link{array}}, a \code{\link{data.frame}} or a
-\code{\link{torch_tensor}}.\cr
+\code{\link[torch]{torch_tensor}}.\cr
 \strong{Note:} For the model-agnostic methods, only models with a single
 input and output layer is allowed!\cr}
 
diff --git a/man/SmoothGrad.Rd b/man/SmoothGrad.Rd
index 782fb64..8ca6d48 100644
--- a/man/SmoothGrad.Rd
+++ b/man/SmoothGrad.Rd
@@ -212,7 +212,7 @@ An instance of the \code{Converter} class that includes the
 torch-converted model and some other model-specific attributes. See
 \code{\link{Converter}} for details.\cr}
 
-\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link{torch_tensor}} or \code{list})\cr
+\item{\code{data}}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link[torch]{torch_tensor}} or \code{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
@@ -306,8 +306,8 @@ the output of the primitive R function \code{\link[=interactive]{interactive()}}
 
 \item{\code{dtype}}{(\code{character(1)})\cr
 The data type for the calculations. Use
-either \code{'float'} for \link{torch_float} or \code{'double'} for
-\link{torch_double}.\cr}
+either \code{'float'} for \code{\link[torch]{torch_float}} or \code{'double'} for
+\code{\link[torch]{torch_double}}.\cr}
 }
 \if{html}{\out{</div>}}
 }
diff --git a/man/innsight_ggplot2-class.Rd b/man/innsight_ggplot2-class.Rd
index 8a02a3d..335de8d 100644
--- a/man/innsight_ggplot2-class.Rd
+++ b/man/innsight_ggplot2-class.Rd
@@ -6,17 +6,18 @@
 \title{S4 class for ggplot2-based plots}
 \description{
 The S4 class \code{innsight_ggplot2} visualizes the results of the methods
-provided from the package \code{innsight} using \link{ggplot2}. In addition, it
-allows easier analysis of the results and modification of the
+provided from the package \code{innsight} using \link[ggplot2:ggplot2-package]{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.
 }
 \details{
-This S4 class is a simple extension of a \link{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 \code{multiplot}.
+This S4 class is a simple extension of a \link[ggplot2:ggplot2-package]{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
+\code{multiplot}.
 \subsection{One input layer (\code{multiplot = FALSE})}{
 
 If the model passed to a method from the innsight package has only one
diff --git a/man/innsight_ggplot2-plus.Rd b/man/innsight_ggplot2-plus.Rd
index bf2692c..a3379a9 100644
--- a/man/innsight_ggplot2-plus.Rd
+++ b/man/innsight_ggplot2-plus.Rd
@@ -14,8 +14,9 @@
 }
 \description{
 This generic add function allows to treat an instance of \code{\link{innsight_ggplot2}}
-as an ordinary plot object of \code{\link{ggplot2}}. For example geoms, themes and
-scales can be added as usual (see \code{\link[ggplot2:gg-add]{ggplot2::+.gg}} for more information).\cr \cr
+as an ordinary plot object of \link[ggplot2:ggplot2-package]{ggplot2}. For example geoms,
+themes and scales can be added as usual (see \code{\link[ggplot2:gg-add]{ggplot2::+.gg}} for more
+information).\cr \cr
 \strong{Note:} If \code{e1} represents a multiplot (i.e., \code{e1@mulitplot = TRUE}),
 \code{e2} is added to each individual plot. If only specific plots need to be
 changed, the generic assignment function should be used (see
diff --git a/man/innsight_sugar.Rd b/man/innsight_sugar.Rd
index 942adce..1d8c784 100644
--- a/man/innsight_sugar.Rd
+++ b/man/innsight_sugar.Rd
@@ -49,7 +49,7 @@ run_lime(model, data, data_ref, ...)
 run_shap(model, data, data_ref, ...)
 }
 \arguments{
-\item{model}{(\code{\link{nn_sequential}}, \code{\link[keras]{keras_model}},
+\item{model}{(\code{\link[torch]{nn_sequential}}, \code{\link[keras]{keras_model}},
 \code{\link[neuralnet]{neuralnet}} or \code{list})\cr
 A trained neural network for classification or regression
 tasks to be interpreted. Only models from the following types or
@@ -75,7 +75,7 @@ An instance of the \code{Converter} class that includes the
 torch-converted model and some other model-specific attributes. See
 \code{\link{Converter}} for details.\cr}
 
-\item{data}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link{torch_tensor}} or \code{list})\cr
+\item{data}{(\code{\link{array}}, \code{\link{data.frame}}, \code{\link[torch]{torch_tensor}} or \code{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
@@ -88,11 +88,11 @@ upper point) for each of the input layers.
 \strong{Note:} For the model-agnostic methods, only models with a single
 input and output layer is allowed!\cr}
 
-\item{data_ref}{(\code{\link{array}}, \code{\link{data.frame}} or \code{\link{torch_tensor}})\cr
+\item{data_ref}{(\code{\link{array}}, \code{\link{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 \code{\link{matrix}}, an \code{\link{array}}, a \code{\link{data.frame}} or a
-\code{\link{torch_tensor}}.\cr
+\code{\link[torch]{torch_tensor}}.\cr
 \strong{Note:} For the model-agnostic methods, only models with a single
 input and output layer is allowed!\cr}
 }