reg.dpm.R

#' Function to compute regularized DPM
#'
#' @param data is a matrix containing samples in rows and variables in columns
#'
#' @return a nvar*nvar matrix containing reg.DPM scores for all links (edges) where nvar is the number of variables
#'
#' @import corpcor
#'
#' @examples
#' example1:
#' ## Generate non-linear data with 100 samples (from the network discussed in the paper) where the gaussian noise with standard deviation of sigma=0.2 is added to the generated data
#' data <- nldata(nsamp=100,sigma=0.2)$data
#'
#'## run regularized DPM
#' reg.dpm(data)
#'
#' example2:
#' ##load DREAM4 data
#' data(dream4)
#'
#'## Run regularized DPM
#' res <- reg.dpm(data)
#'
#'
#' @export


###########################################
### Function to compute regularized DPM ###
###########################################

reg.dpm <- function(data) {
  # compute the double-centred vectors of each variable and concatenate them into the columns of matrix d
  d <- apply(as.matrix(data), 2, Dcenter)
  # compute the regularised precision matrix of d
  res <- corpcor::pcor.shrink(d)
  res <- matrix(res,nrow(res),ncol(res))
  return(res)
}