Small documentation improvements

R/method_clustering.R

@@ -56,6 +56,8 @@ clusteriness <- function(data, span = 500000, weight = 0.5) {
 #' @param id Unique ID for the method and its results.
 #' @param name Human readable name for the method.
 #' @param description Method description.
+#' @param span See [clusteriness()].
+#' @param weight See [clusteriness()].
 #'
 #' @return An object of class `geposan_method`.
 #'

R/preset.R

@@ -6,7 +6,7 @@
 #' filtered based on how many species have data for them. Afterwards, species
 #' that still have many missing genes will also be excluded. See the different
 #' method functions for the available methods: [clustering()], [correlation()],
-#' [neural()], [adjacency()] and [species_adjacency()].
+#' [distance()], [neural()] and [random_forest()].
 #'
 #' @param reference_gene_ids IDs of reference genes to compare to.
 #' @param methods List of methods to apply.

man/clustering.Rd

@@ -18,6 +18,10 @@ clustering(
 \item{name}{Human readable name for the method.}
 
 \item{description}{Method description.}
+
+\item{span}{See \code{\link[=clusteriness]{clusteriness()}}.}
+
+\item{weight}{See \code{\link[=clusteriness]{clusteriness()}}.}
 }
 \value{
 An object of class \code{geposan_method}.

man/preset.Rd

@@ -38,5 +38,5 @@ reference genes to be able to assess the results later. The genes will be
 filtered based on how many species have data for them. Afterwards, species
 that still have many missing genes will also be excluded. See the different
 method functions for the available methods: \code{\link[=clustering]{clustering()}}, \code{\link[=correlation]{correlation()}},
-\code{\link[=neural]{neural()}}, \code{\link[=adjacency]{adjacency()}} and \code{\link[=species_adjacency]{species_adjacency()}}.
+\code{\link[=distance]{distance()}}, \code{\link[=neural]{neural()}} and \code{\link[=random_forest]{random_forest()}}.
 }