R/RcppExports.R

In MR.RGM: Multivariate Bidirectional Mendelian Randomization Networks

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

Sample_Rho <- function(Gamma, a_rho, b_rho) {
    .Call(`_MR_RGM_Sample_Rho`, Gamma, a_rho, b_rho)
}

Sample_Psi <- function(Phi, a_psi, b_psi) {
    .Call(`_MR_RGM_Sample_Psi`, Phi, a_psi, b_psi)
}

Sample_Tau <- function(a, gamma, tau, nu_1) {
    .Call(`_MR_RGM_Sample_Tau`, a, gamma, tau, nu_1)
}

Sample_Eta <- function(b, phi, eta, nu_2) {
    .Call(`_MR_RGM_Sample_Eta`, b, phi, eta, nu_2)
}

Sample_Gamma <- function(a, tau, rho, nu_1) {
    .Call(`_MR_RGM_Sample_Gamma`, a, tau, rho, nu_1)
}

Sample_Phi <- function(b, eta, psi, nu_2) {
    .Call(`_MR_RGM_Sample_Phi`, b, eta, psi, nu_2)
}

Sample_Sigma <- function(n, z_sum, a_sigma, b_sigma) {
    .Call(`_MR_RGM_Sample_Sigma`, n, z_sum, a_sigma, b_sigma)
}

Target_A <- function(a, N, gamma, tau, nu_1, Trace3, Trace4, Trace5, Trace6, logdet) {
    .Call(`_MR_RGM_Target_A`, a, N, gamma, tau, nu_1, Trace3, Trace4, Trace5, Trace6, logdet)
}

Sample_A <- function(S_YY, S_YX, A, A_Pseudo, i, j, Sigma_Inv, N, p, B, gamma, tau, nu_1, prop_var1, tA, Trace3, Trace4, Trace5, Trace6, InvMat, logdet) {
    .Call(`_MR_RGM_Sample_A`, S_YY, S_YX, A, A_Pseudo, i, j, Sigma_Inv, N, p, B, gamma, tau, nu_1, prop_var1, tA, Trace3, Trace4, Trace5, Trace6, InvMat, logdet)
}

Target_B <- function(b, phi, eta, nu_2, Trace1, Trace2) {
    .Call(`_MR_RGM_Target_B`, b, phi, eta, nu_2, Trace1, Trace2)
}

Sample_B <- function(S_YX, S_XX, B, B_Pseudo, i, j, Sigma_Inv, MultMat, N, phi, eta, nu_2, prop_var2, tB, Trace1, Trace2) {
    .Call(`_MR_RGM_Sample_B`, S_YX, S_XX, B, B_Pseudo, i, j, Sigma_Inv, MultMat, N, phi, eta, nu_2, prop_var2, tB, Trace1, Trace2)
}

Target_A_Star <- function(a, N, gamma, tau, nu_1, Trace3, Trace4, Trace5, logdet) {
    .Call(`_MR_RGM_Target_A_Star`, a, N, gamma, tau, nu_1, Trace3, Trace4, Trace5, logdet)
}

Sample_A_Star <- function(S_YY, A, A_Pseudo, i, j, Sigma_Inv, N, p, gamma, tau, nu_1, prop_var1, tA, Trace3, Trace4, Trace5, InvMat, logdet) {
    .Call(`_MR_RGM_Sample_A_Star`, S_YY, A, A_Pseudo, i, j, Sigma_Inv, N, p, gamma, tau, nu_1, prop_var1, tA, Trace3, Trace4, Trace5, InvMat, logdet)
}

LL_Star <- function(A, S_YY, Sigma_Inv, p, N) {
    .Call(`_MR_RGM_LL_Star`, A, S_YY, Sigma_Inv, p, N)
}

Sample_tn <- function(mu, sigma, a, b) {
    .Call(`_MR_RGM_Sample_tn`, mu, sigma, a, b)
}

tn_pdf <- function(x, mu, sigma, a, b) {
    .Call(`_MR_RGM_tn_pdf`, x, mu, sigma, a, b)
}

LL <- function(A, B, S_YY, S_YX, S_XX, Sigma_Inv, p, N) {
    .Call(`_MR_RGM_LL`, A, B, S_YY, S_YX, S_XX, Sigma_Inv, p, N)
}

RGM_Threshold2 <- function(S_YY, S_YX, S_XX, D, n, nIter, nBurnin, Thin, nu_1 = 0.0001, nu_2 = 0.0001, a_sigma = 0.01, b_sigma = 0.01, Prop_VarA = 0.01, Prop_VarB = 0.01) {
    .Call(`_MR_RGM_RGM_Threshold2`, S_YY, S_YX, S_XX, D, n, nIter, nBurnin, Thin, nu_1, nu_2, a_sigma, b_sigma, Prop_VarA, Prop_VarB)
}

RGM_SpikeSlab2 <- function(S_YY, S_YX, S_XX, D, n, nIter, nBurnin, Thin, a_rho = 3.0, b_rho = 1.0, nu_1 = 0.001, a_psi = 0.5, b_psi = 0.5, nu_2 = 0.0001, a_sigma = 0.01, b_sigma = 0.01, Prop_VarA = 0.01, Prop_VarB = 0.01) {
    .Call(`_MR_RGM_RGM_SpikeSlab2`, S_YY, S_YX, S_XX, D, n, nIter, nBurnin, Thin, a_rho, b_rho, nu_1, a_psi, b_psi, nu_2, a_sigma, b_sigma, Prop_VarA, Prop_VarB)
}

RGM_Threshold1 <- function(S_YY, n, nIter, nBurnin, Thin, nu_1 = 0.0001, a_sigma = 0.01, b_sigma = 0.01, Prop_VarA = 0.01) {
    .Call(`_MR_RGM_RGM_Threshold1`, S_YY, n, nIter, nBurnin, Thin, nu_1, a_sigma, b_sigma, Prop_VarA)
}

RGM_SpikeSlab1 <- function(S_YY, n, nIter, nBurnin, Thin, a_rho = 3.0, b_rho = 1.0, nu_1 = 0.001, a_sigma = 0.01, b_sigma = 0.01, Prop_VarA = 0.01) {
    .Call(`_MR_RGM_RGM_SpikeSlab1`, S_YY, n, nIter, nBurnin, Thin, a_rho, b_rho, nu_1, a_sigma, b_sigma, Prop_VarA)
}

NetworkMotif_cpp <- function(Gamma, Gamma_Pst) {
    .Call(`_MR_RGM_NetworkMotif_cpp`, Gamma, Gamma_Pst)
}