doc: Fix external package links

R/SMD_example_data.R

@@ -5,7 +5,7 @@
 #' with constant effect sizes of 1 and X7 to X9 are noncausal with effect size of 0. The outcome y is a linear combination of the causal
 #' predictor variables and a normally distributed error term. All basic variables were sampled from a normal distribution
 #' (N(0,1)) just like the noise (N(0,0.2)). For each of the six basic variables X1, X2, X3, X7, X8, and X9 ten variables
-#' with predefined correlations of 0.9 for X1 and X7, 0.6 for X2 and X8, and 0.3 for X3 and X9 were obtained by \link[WGCNA]{simulateModule} function of
+#' with predefined correlations of 0.9 for X1 and X7, 0.6 for X2 and X8, and 0.3 for X3 and X9 were obtained by [WGCNA::simulateModule()] of
 #' the R package WGCNA. The ten variables of each basis variable are labeled: Cp_basicvariable_number. Additional non-correlated and
 #' independent predictor variables (cgn) were simulated using the standard normal distribution to reach a total number of 200 variables.
 #'

R/build_clusters.R

 #' Apply cluster analysis to build variable groups
 #'
-#' This function generates variables groups of relation information that was obtained by \link[SurrogateMinimalDepth]{var.relations} function applying
-#' \link[linkcomm]{getLinkCommunities}.
+#' This function generates variables groups of relation information that was obtained by [var.relations] applying
+#' [linkcomm::getLinkCommunities()].
 #'
-#' @param rel a list containing variables, surr.res, threshold, and var. This is the output of \link[SurrogateMinimalDepth]{var.relations} function.
-#' @param hcmethod the hierarchical clustering method that is used. (see \link[linkcomm]{getLinkCommunities})
+#' @param rel a list containing variables, surr.res, threshold, and var. This is the output of [var.relations()].
+#' @param hcmethod the hierarchical clustering method that is used. (see [linkcomm::getLinkCommunities()])
 #'
 #' @return a data frame containing the variable names and their associated clusters.
 #'

man/SMD_example_data.Rd

@@ -16,7 +16,7 @@ For the simulation of the 200 additional variables nine variables X1,…,X9 call
 with constant effect sizes of 1 and X7 to X9 are noncausal with effect size of 0. The outcome y is a linear combination of the causal
 predictor variables and a normally distributed error term. All basic variables were sampled from a normal distribution
 (N(0,1)) just like the noise (N(0,0.2)). For each of the six basic variables X1, X2, X3, X7, X8, and X9 ten variables
-with predefined correlations of 0.9 for X1 and X7, 0.6 for X2 and X8, and 0.3 for X3 and X9 were obtained by \link[WGCNA]{simulateModule} function of
+with predefined correlations of 0.9 for X1 and X7, 0.6 for X2 and X8, and 0.3 for X3 and X9 were obtained by \code{\link[WGCNA:simulateModule]{WGCNA::simulateModule()}} of
 the R package WGCNA. The ten variables of each basis variable are labeled: Cp_basicvariable_number. Additional non-correlated and
 independent predictor variables (cgn) were simulated using the standard normal distribution to reach a total number of 200 variables.
 }

man/build.clusters.Rd

@@ -7,16 +7,16 @@
 build.clusters(rel, hcmethod = "ward.D")
 }
 \arguments{
-\item{rel}{a list containing variables, surr.res, threshold, and var. This is the output of \link[SurrogateMinimalDepth]{var.relations} function.}
+\item{rel}{a list containing variables, surr.res, threshold, and var. This is the output of \code{\link[=var.relations]{var.relations()}}.}
 
-\item{hcmethod}{the hierarchical clustering method that is used. (see \link[linkcomm]{getLinkCommunities})}
+\item{hcmethod}{the hierarchical clustering method that is used. (see \code{\link[linkcomm:getLinkCommunities]{linkcomm::getLinkCommunities()}})}
 }
 \value{
 a data frame containing the variable names and their associated clusters.
 }
 \description{
-This function generates variables groups of relation information that was obtained by \link[SurrogateMinimalDepth]{var.relations} function applying
-\link[linkcomm]{getLinkCommunities}.
+This function generates variables groups of relation information that was obtained by \link{var.relations} applying
+\code{\link[linkcomm:getLinkCommunities]{linkcomm::getLinkCommunities()}}.
 }
 \examples{
 # read data