R/RcppExports.R

In MR.RGM: Fitting Multivariate Bidirectional Mendelian Randomization Networks Using Bayesian Directed Cyclic Graphical Models

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

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

RGM_SpikeSlab_Diag <- 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_SpikeSlab_Diag`, 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_SpikeSlab_Diag_Covariates <- function(S_YY, S_YX, S_XX, S_YU, S_XU, S_UU, 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, TAU = 2) {
    .Call(`_MR_RGM_RGM_SpikeSlab_Diag_Covariates`, S_YY, S_YX, S_XX, S_YU, S_XU, S_UU, 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, TAU)
}

RGM_SpikeSlab_Diag_Star <- 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_SpikeSlab_Diag_Star`, S_YY, n, nIter, nBurnin, Thin, a_rho, b_rho, nu_1, a_sigma, b_sigma, Prop_VarA)
}

RGM_SpikeSlab_Diag_Star_Covariates <- function(S_YY, S_YU, S_UU, 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, TAU = 2) {
    .Call(`_MR_RGM_RGM_SpikeSlab_Diag_Star_Covariates`, S_YY, S_YU, S_UU, n, nIter, nBurnin, Thin, a_rho, b_rho, nu_1, a_sigma, b_sigma, Prop_VarA, TAU)
}

RGM_SpikeSlab_IW <- 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, Prop_VarA = 0.01, Prop_VarB = 0.01) {
    .Call(`_MR_RGM_RGM_SpikeSlab_IW`, S_YY, S_YX, S_XX, D, n, nIter, nBurnin, Thin, a_rho, b_rho, nu_1, a_psi, b_psi, nu_2, Prop_VarA, Prop_VarB)
}

RGM_SpikeSlab_IW_Covariates <- function(S_YY, S_YX, S_XX, S_YU, S_XU, S_UU, 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, Prop_VarA = 0.01, Prop_VarB = 0.01, TAU = 2) {
    .Call(`_MR_RGM_RGM_SpikeSlab_IW_Covariates`, S_YY, S_YX, S_XX, S_YU, S_XU, S_UU, D, n, nIter, nBurnin, Thin, a_rho, b_rho, nu_1, a_psi, b_psi, nu_2, Prop_VarA, Prop_VarB, TAU)
}

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

RGM_SpikeSlab_IW_Star_Covariates <- function(S_YY, S_YU, S_UU, n, nIter, nBurnin, Thin, a_rho = 3.0, b_rho = 1.0, nu_1 = 0.001, Prop_VarA = 0.01, TAU = 2) {
    .Call(`_MR_RGM_RGM_SpikeSlab_IW_Star_Covariates`, S_YY, S_YU, S_UU, n, nIter, nBurnin, Thin, a_rho, b_rho, nu_1, Prop_VarA, TAU)
}

RGM_SpikeSlab_SSSL <- 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, Prop_VarA = 0.01, Prop_VarB = 0.01) {
    .Call(`_MR_RGM_RGM_SpikeSlab_SSSL`, S_YY, S_YX, S_XX, D, n, nIter, nBurnin, Thin, a_rho, b_rho, nu_1, a_psi, b_psi, nu_2, Prop_VarA, Prop_VarB)
}

RGM_SpikeSlab_SSSL_Covariates <- function(S_YY, S_YX, S_XX, S_YU, S_XU, S_UU, 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, Prop_VarA = 0.01, Prop_VarB = 0.01, TAU = 2) {
    .Call(`_MR_RGM_RGM_SpikeSlab_SSSL_Covariates`, S_YY, S_YX, S_XX, S_YU, S_XU, S_UU, D, n, nIter, nBurnin, Thin, a_rho, b_rho, nu_1, a_psi, b_psi, nu_2, Prop_VarA, Prop_VarB, TAU)
}

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

RGM_SpikeSlab_SSSL_Star_Covariates <- function(S_YY, S_YU, S_UU, n, nIter, nBurnin, Thin, a_rho = 3.0, b_rho = 1.0, nu_1 = 0.001, Prop_VarA = 0.01, TAU = 2) {
    .Call(`_MR_RGM_RGM_SpikeSlab_SSSL_Star_Covariates`, S_YY, S_YU, S_UU, n, nIter, nBurnin, Thin, a_rho, b_rho, nu_1, Prop_VarA, TAU)
}

RGM_Threshold_Diag <- 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_Threshold_Diag`, S_YY, S_YX, S_XX, D, n, nIter, nBurnin, Thin, nu_1, nu_2, a_sigma, b_sigma, Prop_VarA, Prop_VarB)
}

RGM_Threshold_Diag_Covariates <- function(S_YY, S_YX, S_XX, S_YU, S_XU, S_UU, 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, TAU = 2) {
    .Call(`_MR_RGM_RGM_Threshold_Diag_Covariates`, S_YY, S_YX, S_XX, S_YU, S_XU, S_UU, D, n, nIter, nBurnin, Thin, nu_1, nu_2, a_sigma, b_sigma, Prop_VarA, Prop_VarB, TAU)
}

RGM_Threshold_Diag_Star <- 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_Threshold_Diag_Star`, S_YY, n, nIter, nBurnin, Thin, nu_1, a_sigma, b_sigma, Prop_VarA)
}

RGM_Threshold_Diag_Star_Covariates <- function(S_YY, S_YU, S_UU, n, nIter, nBurnin, Thin, nu_1 = 0.0001, a_sigma = 0.01, b_sigma = 0.01, Prop_VarA = 0.01, TAU = 2) {
    .Call(`_MR_RGM_RGM_Threshold_Diag_Star_Covariates`, S_YY, S_YU, S_UU, n, nIter, nBurnin, Thin, nu_1, a_sigma, b_sigma, Prop_VarA, TAU)
}

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

RGM_Threshold_IW_Covariates <- function(S_YY, S_YX, S_XX, S_YU, S_XU, S_UU, D, n, nIter, nBurnin, Thin, nu_1 = 0.0001, nu_2 = 0.0001, Prop_VarA = 0.01, Prop_VarB = 0.01, TAU = 2) {
    .Call(`_MR_RGM_RGM_Threshold_IW_Covariates`, S_YY, S_YX, S_XX, S_YU, S_XU, S_UU, D, n, nIter, nBurnin, Thin, nu_1, nu_2, Prop_VarA, Prop_VarB, TAU)
}

RGM_Threshold_IW_Star <- function(S_YY, n, nIter, nBurnin, Thin, nu_1 = 0.0001, Prop_VarA = 0.01) {
    .Call(`_MR_RGM_RGM_Threshold_IW_Star`, S_YY, n, nIter, nBurnin, Thin, nu_1, Prop_VarA)
}

RGM_Threshold_IW_Star_Covariates <- function(S_YY, S_YU, S_UU, n, nIter, nBurnin, Thin, nu_1 = 0.0001, Prop_VarA = 0.01, TAU = 2) {
    .Call(`_MR_RGM_RGM_Threshold_IW_Star_Covariates`, S_YY, S_YU, S_UU, n, nIter, nBurnin, Thin, nu_1, Prop_VarA, TAU)
}

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

RGM_Threshold_SSSL_Covariates <- function(S_YY, S_YX, S_XX, S_YU, S_XU, S_UU, D, n, nIter, nBurnin, Thin, nu_1 = 0.0001, nu_2 = 0.0001, Prop_VarA = 0.01, Prop_VarB = 0.01, TAU = 2) {
    .Call(`_MR_RGM_RGM_Threshold_SSSL_Covariates`, S_YY, S_YX, S_XX, S_YU, S_XU, S_UU, D, n, nIter, nBurnin, Thin, nu_1, nu_2, Prop_VarA, Prop_VarB, TAU)
}

RGM_Threshold_SSSL_Star <- function(S_YY, n, nIter, nBurnin, Thin, nu_1 = 0.0001, Prop_VarA = 0.01) {
    .Call(`_MR_RGM_RGM_Threshold_SSSL_Star`, S_YY, n, nIter, nBurnin, Thin, nu_1, Prop_VarA)
}

RGM_Threshold_SSSL_Star_Covariates <- function(S_YY, S_YU, S_UU, n, nIter, nBurnin, Thin, nu_1 = 0.0001, Prop_VarA = 0.01, TAU = 2) {
    .Call(`_MR_RGM_RGM_Threshold_SSSL_Star_Covariates`, S_YY, S_YU, S_UU, n, nIter, nBurnin, Thin, nu_1, Prop_VarA, TAU)
}