R/RcppExports.R

In mniw: The Matrix-Normal Inverse-Wishart Distribution

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

CrossProdVXX <- function(X, V, p, q, inverse = FALSE) {
    .Call(`_mniw_CrossProdVXX`, X, V, p, q, inverse)
}

CrossProdVXY <- function(X, Y, V, p, q, r, inverse = FALSE) {
    .Call(`_mniw_CrossProdVXY`, X, Y, V, p, q, r, inverse)
}

HierUneqVModelGibbs <- function(nSamples, nBurn, Y, X, V, Lambda, Omega, Psi, nu, Beta0, iSigma0, Mu0, updateBetaSigma, updateMu, storeBetaSigma, storeMu) {
    .Call(`_mniw_HierUneqVModelGibbs`, nSamples, nBurn, Y, X, V, Lambda, Omega, Psi, nu, Beta0, iSigma0, Mu0, updateBetaSigma, updateMu, storeBetaSigma, storeMu)
}

GenerateMatrixNIW <- function(N, Lambda, Sigma, Psi, nu, inverse = FALSE) {
    .Call(`_mniw_GenerateMatrixNIW`, N, Lambda, Sigma, Psi, nu, inverse)
}

LogDensityMatrixNormal <- function(X, Lambda, SigmaR, SigmaC) {
    .Call(`_mniw_LogDensityMatrixNormal`, X, Lambda, SigmaR, SigmaC)
}

GenerateMatrixNormal <- function(N, Lambda, SigmaR, SigmaC) {
    .Call(`_mniw_GenerateMatrixNormal`, N, Lambda, SigmaR, SigmaC)
}

LogDensityMatrixT <- function(X, Lambda, SigmaR, SigmaC, nu) {
    .Call(`_mniw_LogDensityMatrixT`, X, Lambda, SigmaR, SigmaC, nu)
}

GenerateMatrixT <- function(N, Lambda, SigmaR, SigmaC, nu, inverse = FALSE) {
    .Call(`_mniw_GenerateMatrixT`, N, Lambda, SigmaR, SigmaC, nu, inverse)
}

LogDensityMultivariateNormal <- function(X, mu, V) {
    .Call(`_mniw_LogDensityMultivariateNormal`, X, mu, V)
}

GenerateMultivariateNormal <- function(N, mu, Sigma) {
    .Call(`_mniw_GenerateMultivariateNormal`, N, mu, Sigma)
}

GenerateRandomEffectsNormal <- function(N, x, V, lambda, Sigma) {
    .Call(`_mniw_GenerateRandomEffectsNormal`, N, x, V, lambda, Sigma)
}

LogDensityWishart <- function(X, Psi, nu, inverse = FALSE) {
    .Call(`_mniw_LogDensityWishart`, X, Psi, nu, inverse)
}

GenerateWishart <- function(N, Psi, nu, inverse = FALSE) {
    .Call(`_mniw_GenerateWishart`, N, Psi, nu, inverse)
}