R/RcppExports.R

In BClustLonG: A Dirichlet Process Mixture Model for Clustering Longitudinal Gene Expression Data

# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

#' Internal function for sorting
#' @noRd
stlSort <- function(x) {
    .Call('_BClustLonG_stlSort', PACKAGE = 'BClustLonG', x)
}

#' Internal function for concatenating
#' @noRd
myc <- function(x, y) {
    .Call('_BClustLonG_myc', PACKAGE = 'BClustLonG', x, y)
}

#' Internal function for density function of MVN
#' @noRd
dmvnrmArma <- function(x, mean, sigma, logd = FALSE) {
    .Call('_BClustLonG_dmvnrmArma', PACKAGE = 'BClustLonG', x, mean, sigma, logd)
}

#' Internal function for finding matched index
#' @noRd
myfind <- function(evec, e) {
    .Call('_BClustLonG_myfind', PACKAGE = 'BClustLonG', evec, e)
}

#' Internal function for generating MVN random variables
#' @noRd
mvrnormArma <- function(n, mu, sigma) {
    .Call('_BClustLonG_mvrnormArma', PACKAGE = 'BClustLonG', n, mu, sigma)
}

#' Internal function for sampling lambda in the factor analysis
#' @noRd
samLamV2Cpp <- function(A, eta, sig, lam, phi, tau) {
    .Call('_BClustLonG_samLamV2Cpp', PACKAGE = 'BClustLonG', A, eta, sig, lam, phi, tau)
}

#' Internal function for sampling memberhsip indicators in sparse both
#' @noRd
polyurncppBoth <- function(e, A, muA0, sigmaA, sigmaAInv, sigmaA0, sigmaA0Inv, B, muB0, sigmaB, sigmaBInv, sigmaB0, sigmaB0Inv, c) {
    .Call('_BClustLonG_polyurncppBoth', PACKAGE = 'BClustLonG', e, A, muA0, sigmaA, sigmaAInv, sigmaA0, sigmaA0Inv, B, muB0, sigmaB, sigmaBInv, sigmaB0, sigmaB0Inv, c)
}

#' Internal function for sampling memberhsip indicators in sparse int
#' @noRd
polyurncppInt <- function(e, muA0, sigma0, A, sigma, sigmaInv, sigma0Inv, c) {
    .Call('_BClustLonG_polyurncppInt', PACKAGE = 'BClustLonG', e, muA0, sigma0, A, sigma, sigmaInv, sigma0Inv, c)
}

#' Function to calculate the similarity matrix based on the
#' cluster membership indicator of each iteration.
#' @param mat Matrix of cluster membership indicator from all iterations
#' @examples
#' n = 90 ##number of subjects
#' iters = 200 ##number of iterations
#' ## matrix of cluster membership indicators
#' ## perfect clustering with three clusters
#' mat = matrix(rep(1:3,each=n/3),nrow=n,ncol=iters)
#' sim = calSim(t(mat))
calSim <- function(mat) {
    .Call('_BClustLonG_calSim', PACKAGE = 'BClustLonG', mat)
}