fix wrong Rd cross-reference

bips-hb · Nov 25, 2024 · f9f9b80 · f9f9b80
1 parent 15c89af
commit f9f9b80
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Showing 16 changed files with 24 additions and 20 deletions.
diff --git 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
@@ -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
@@ -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/man/AgnosticWrapper.Rd b/man/AgnosticWrapper.Rd
diff --git a/man/ConnectionWeights.Rd b/man/ConnectionWeights.Rd
diff --git a/man/Converter.Rd b/man/Converter.Rd
diff --git a/man/DeepLift.Rd b/man/DeepLift.Rd
diff --git a/man/DeepSHAP.Rd b/man/DeepSHAP.Rd
diff --git a/man/ExpectedGradient.Rd b/man/ExpectedGradient.Rd
diff --git a/man/Gradient.Rd b/man/Gradient.Rd
diff --git a/man/GradientBased.Rd b/man/GradientBased.Rd
diff --git a/man/IntegratedGradient.Rd b/man/IntegratedGradient.Rd
diff --git a/man/LIME.Rd b/man/LIME.Rd
diff --git a/man/LRP.Rd b/man/LRP.Rd
diff --git a/man/SHAP.Rd b/man/SHAP.Rd
diff --git a/man/SmoothGrad.Rd b/man/SmoothGrad.Rd