docs: fix CRAN note (#154)

R/generics.R

@@ -499,7 +499,7 @@ setGeneric('metric', function(
 #' @title Plot the fitted trajectories
 #' @description Plot the fitted trajectories as represented by the given model
 #' @inheritParams fittedTrajectories
-#' @inheritDotParams fittedTrajectories
+#' @param ... Additional arguments passed to [fittedTrajectories].
 #' @seealso [fittedTrajectories]
 setGeneric('plotFittedTrajectories',
   function(object, ...) standardGeneric('plotFittedTrajectories')
@@ -512,6 +512,7 @@ setGeneric('plotFittedTrajectories',
 #' @title Plot cluster trajectories
 #' @description Plot the cluster trajectories associated with the given model.
 #' @inheritParams clusterTrajectories
+#' @param ... Additional arguments passed to [clusterTrajectories].
 #' @seealso [clusterTrajectories]
 setGeneric('plotClusterTrajectories',
   function(object, ...) standardGeneric('plotClusterTrajectories')
@@ -523,7 +524,7 @@ setGeneric('plotClusterTrajectories',
 #' @name plotTrajectories
 #' @title Plot the data trajectories
 #' @description Plots the output of [trajectories] for the given object.
-#' @param ... Additional arguments.
+#' @param ... Additional arguments passed to [trajectories].
 #' @seealso [trajectories]
 setGeneric('plotTrajectories', function(object, ...) standardGeneric('plotTrajectories'))
 
@@ -718,6 +719,7 @@ setGeneric('predictForCluster', function(object, newdata = NULL, cluster, ...) {
 #' @title Posterior probability for new data
 #' @description Returns the observation-specific posterior probabilities for the given data.
 #' @param newdata Optional `data.frame` for which to compute the posterior probability. If omitted, the model training data is used.
+#' @param ... Additional arguments passed to [postprob].
 #' @inheritParams predictForCluster
 #' @return A N-by-K `matrix` indicating the posterior probability per trajectory per measurement on each row, for each cluster (the columns).
 #' Here, `N = nrow(newdata)` and `K = nClusters(object)`.

R/model.R

@@ -1160,7 +1160,6 @@ setMethod('predictForCluster', 'lcModel',
 #' @rdname predictPostprob
 #' @aliases predictPostprob,lcModel-method
 #' @description For `lcModel`: The default implementation returns a uniform probability matrix.
-#' @inheritDotParams postprob
 #' @section Implementation:
 #' Classes extending `lcModel` should override this method to enable posterior probability predictions for new data.
 #' \preformatted{
@@ -1267,7 +1266,6 @@ setMethod('plot', c('lcModel', 'ANY'), function(x, y, ...) {
 #' @rdname plotFittedTrajectories
 #' @aliases plotFittedTrajectories,lcModel-method
 #' @param ... Arguments passed to [fittedTrajectories()] and [plotTrajectories].
-#' @inheritDotParams plotTrajectories
 #' @return A `ggplot` object.
 #' @seealso [plotClusterTrajectories] [plotTrajectories] [plot]
 #' @examples
@@ -1301,8 +1299,6 @@ setMethod('plotFittedTrajectories', 'lcModel', function(object, ...) {
 #' @param clusterOrder Specify which clusters to plot and the order.
 #' Can be the cluster names or index.
 #' By default, all clusters are shown.
-#' @inheritDotParams clusterTrajectories
-#' @param ... Arguments passed to [clusterTrajectories()], and [ggplot2::geom_line()] for plotting the cluster trajectory lines.
 #' @return A `ggplot` object.
 #' @seealso [plotTrajectories] [plot]
 #' @examples
@@ -1377,7 +1373,6 @@ setMethod('plotClusterTrajectories', 'lcModel',
 #' @export
 #' @rdname plotTrajectories
 #' @aliases plotTrajectories,lcModel-method
-#' @inheritDotParams trajectories
 #' @examples
 #' data(latrendData)
 #' method <- lcMethodLMKM(Y ~ Time, id = "Id", time = "Time")