#' Clustering
#'
#' A sample clustering framework (described in "Examples" section, used as
#' default in iterClust framework). Customized frameworks can be defined
#' following rules specified in "Usage", "Arguments" and "Value" sections.
#'
#' @param dset (numeric matrix) features in rows and observations in columns
#' @param iteration (positive integer) specifies current iteration
#'
#' @return a list, each element contains clustering vectors (named numeric
#' vector with observation names as name and corresponding cluster number as
#' element) under a specific clustering parameter
#'
#' @keywords coreClust
#' @examples
#' coreClust <- function(dset, iteration){
#' dist <- as.dist(1 - cor(dset))
#' range=seq(2, ncol(dset)-1, by = 1)
#' clust <- vector("list", length(range))
#' for (i in 1:length(range)) clust[[i]] <- pam(dist, range[i])$clustering
#' return(clust)}
#'
#' @author DING, HONGXU (hd2326@columbia.edu)
#'
#' @importFrom stats as.dist
#' @importFrom stats cor
#' @importFrom cluster pam
#'
#' @export
coreClust <- function(dset, iteration){
dist <- as.dist(1 - cor(dset))
range=seq(2, ncol(dset)-1, by = 1)
clust <- vector("list", length(range))
for (i in 1:length(range)) clust[[i]] <- pam(dist, range[i])$clustering
return(clust)}
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