@@ -6,6 +6,8 @@

 #' @param distMat A distance matrix

 #' @param minClusterSize The minimum cluster size

 #' @param alpha a value between 0 and 1 specifying the desired level of cutoff

+#' @return Optimal number of clusters 

+#'

 #' @importFrom stats kmeans

 .runDiscriminant <- function(distMat, minClusterSize, alpha = 0.001) {

@@ -8,6 +8,9 @@

 #'                      instead of the prcomp for computing the principal

 #'                      components when no weights are given (see details)

 #' @param ... not used

+#' @return A list containing: `prototype`, a matrix containing appropriate

+#'         initial prototypes, and `data.pca` the results of the PCA conducted

+#'         on the data

 #'

 #' @importFrom stats princomp prcomp cov.wt

 somInitPca.default <- function(data, somGrid, weights, with.princomp = FALSE, ...) {

@@ -2,6 +2,8 @@

 #' Function to do percentile normalizaton

 #'

 #' @param x Matrix to percentile normilse.

+#' @return percentile normalized version of `x`

+#'

 #' @importFrom stats quantile

 .percentileNorm <- function(x) {

   x <- as.matrix(x)

@@ -15,8 +17,10 @@

 #' Function to do min max normalization

 #'

-#' @param x Matrix to min max nomalize.

 #' @importFrom stats quantile

+#'

+#' @param x Matrix to min max nomalize.

+#' @return Max normalized version of `x`

 .minmaxNorm <- function(x) {

   x <- as.matrix(x)

   percentiles <- quantile(x, probs = c(0.01, 0.99))

@@ -30,6 +34,7 @@

 #'

 #' @param x A numeric or complex vector

 #' @param cofactor Cofactor of the vector. Default is 5.

+#' @return Arsinh normalized vector.

 .arsinhNnorm <- function(x, cofactor = 5) {

   x <- asinh(x / cofactor)

   return(x)

@@ -39,6 +44,7 @@

 #' this function was obtained from the Stab package

 #'

 #' @param data A data matrix.

+#' @return Uniform random noise with `dim(data)`

 .uniformData <- function(data) {

   # get dimensions of data

@@ -56,6 +62,7 @@

 #' A function to compute the elbow point given a set of points

 #'

 #' @param vals Values to compute the elbow point of.

+#' @return A integer indicating the elbow point of `vals`.

 .computeElbow <- function(vals) {

   diffs <- diff(vals)

   diffs <- diffs[-1]