R/RcppExports.R

In scalablebayesm: Distributed Markov Chain Monte Carlo for Bayesian Inference in Marketing

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

drawMixture_rcpp_loop <- function(predcompdraws, preddeltadraws, Z, drawdelta, olddelta, deltabar, Ad, mubar, Amu, nu, V, a, oldprob, ind, R, keep, nprint, N, verbose) {
    .Call(`_scalablebayesm_drawMixture_rcpp_loop`, predcompdraws, preddeltadraws, Z, drawdelta, olddelta, deltabar, Ad, mubar, Amu, nu, V, a, oldprob, ind, R, keep, nprint, N, verbose)
}

drawPosteriorParallel_cpp <- function(compdraw, probdraw, Deltadraw, V, R, s, post_burn_in, keep, Z, drawdelta) {
    .Call(`_scalablebayesm_drawPosteriorParallel_cpp`, compdraw, probdraw, Deltadraw, V, R, s, post_burn_in, keep, Z, drawdelta)
}

rcpparma_hello_world <- function() {
    .Call(`_scalablebayesm_rcpparma_hello_world`)
}

rcpparma_outerproduct <- function(x) {
    .Call(`_scalablebayesm_rcpparma_outerproduct`, x)
}

rcpparma_innerproduct <- function(x) {
    .Call(`_scalablebayesm_rcpparma_innerproduct`, x)
}

rcpparma_bothproducts <- function(x) {
    .Call(`_scalablebayesm_rcpparma_bothproducts`, x)
}

rheteroLinearIndepMetrop_rcpp_loop <- function(Data, betadraws, Mcmc, nu, ssq) {
    .Call(`_scalablebayesm_rheteroLinearIndepMetrop_rcpp_loop`, Data, betadraws, Mcmc, nu, ssq)
}

rheteroMnlIndepMetrop_rcpp_loop <- function(Data, draws, Mcmc) {
    .Call(`_scalablebayesm_rheteroMnlIndepMetrop_rcpp_loop`, Data, draws, Mcmc)
}

rhierLinearDPParallel_rcpp_loop <- function(regdata, Z, deltabar, Ad, Prioralphalist, lambda_hyper, mubar, Amu, nu, V, nu_e, maxuniq, gridsize, ssq, R, keep, nprint, olddelta, a, tau, drawdelta, BayesmConstantA, BayesmConstantnuInc, BayesmConstantDPalpha, verbose) {
    .Call(`_scalablebayesm_rhierLinearDPParallel_rcpp_loop`, regdata, Z, deltabar, Ad, Prioralphalist, lambda_hyper, mubar, Amu, nu, V, nu_e, maxuniq, gridsize, ssq, R, keep, nprint, olddelta, a, tau, drawdelta, BayesmConstantA, BayesmConstantnuInc, BayesmConstantDPalpha, verbose)
}

rhierLinearMixtureParallel_rcpp_loop <- function(regdata, Z, deltabar, Ad, mubar, Amu, nu, V, nu_e, ssq, R, keep, nprint, olddelta, a, oldprob, ind, tau, drawdelta, verbose) {
    .Call(`_scalablebayesm_rhierLinearMixtureParallel_rcpp_loop`, regdata, Z, deltabar, Ad, mubar, Amu, nu, V, nu_e, ssq, R, keep, nprint, olddelta, a, oldprob, ind, tau, drawdelta, verbose)
}

rhierMnlDPParallel_rcpp_loop <- function(R, keep, nprint, lgtdata, Z, deltabar, Ad, PrioralphaList, lambda_hyper, drawdelta, nvar, oldbetas, s, maxuniq, gridsize, BayesmConstantA, BayesmConstantnuInc, BayesmConstantDPalpha, verbose) {
    .Call(`_scalablebayesm_rhierMnlDPParallel_rcpp_loop`, R, keep, nprint, lgtdata, Z, deltabar, Ad, PrioralphaList, lambda_hyper, drawdelta, nvar, oldbetas, s, maxuniq, gridsize, BayesmConstantA, BayesmConstantnuInc, BayesmConstantDPalpha, verbose)
}

rhierMnlRwMixtureParallel_rcpp_loop <- function(lgtdata, Z, deltabar, Ad, mubar, Amu, nu, V, s, R, keep, nprint, drawdelta, olddelta, a, oldprob, oldbetas, ind, verbose) {
    .Call(`_scalablebayesm_rhierMnlRwMixtureParallel_rcpp_loop`, lgtdata, Z, deltabar, Ad, mubar, Amu, nu, V, s, R, keep, nprint, drawdelta, olddelta, a, oldprob, oldbetas, ind, verbose)
}