R/RcppExports.R

In SBMTrees: Sequential Imputation with Bayesian Trees Mixed-Effects Models for Longitudinal Data

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

DP <- function(parameters, M, N_truncated, N_sample, CDP = TRUE) {
    .Call(`_SBMTrees_DP`, parameters, M, N_truncated, N_sample, CDP)
}

update_DP_normal <- function(X, tau, L = -1, U = 2) {
    .Call(`_SBMTrees_update_DP_normal`, X, tau, L, U)
}

DP_sampler <- function(N, parameters) {
    .Call(`_SBMTrees_DP_sampler`, N, parameters)
}

bart_train <- function(X, Y, nburn = 100L, npost = 1000L, verbose = TRUE) {
    .Call(`_SBMTrees_bart_train`, X, Y, nburn, npost, verbose)
}

sequential_imputation_cpp <- function(X, Y, type, Z, subject_id, R, binary_outcome = FALSE, nburn = 0L, npost = 3L, skip = 1L, verbose = TRUE, CDP_residual = FALSE, CDP_re = FALSE, seed = NULL, tol = 1e-20, ncores = 0L, ntrees = 200L, fit_loss = FALSE, resample = 0L, pi_CDP = 0.99) {
    .Call(`_SBMTrees_sequential_imputation_cpp`, X, Y, type, Z, subject_id, R, binary_outcome, nburn, npost, skip, verbose, CDP_residual, CDP_re, seed, tol, ncores, ntrees, fit_loss, resample, pi_CDP)
}

BMTrees_mcmc <- function(X, Y, Z, subject_id, obs_ind, binary = FALSE, nburn = 0L, npost = 3L, verbose = TRUE, CDP_residual = FALSE, CDP_re = FALSE, seed = NULL, tol = 1e-40, ntrees = 200L, resample = 0L, pi_CDP = 0.99) {
    .Call(`_SBMTrees_BMTrees_mcmc`, X, Y, Z, subject_id, obs_ind, binary, nburn, npost, verbose, CDP_residual, CDP_re, seed, tol, ntrees, resample, pi_CDP)
}

update_Covariance <- function(B, Mu, inverse_wishart_matrix, df, N_subject) {
    .Call(`_SBMTrees_update_Covariance`, B, Mu, inverse_wishart_matrix, df, N_subject)
}

max_d <- function(x, y) {
    .Call(`_SBMTrees_max_d`, x, y)
}

seqD <- function(x, y, by = 1) {
    .Call(`_SBMTrees_seqD`, x, y, by)
}

seqC <- function(x, y, by = 1L) {
    .Call(`_SBMTrees_seqC`, x, y, by)
}

prodC <- function(x) {
    .Call(`_SBMTrees_prodC`, x)
}

sum_array_by_name <- function(X) {
    .Call(`_SBMTrees_sum_array_by_name`, X)
}

set_tol <- function(X, tol) {
    .Call(`_SBMTrees_set_tol`, X, tol)
}

set_value <- function(X, tol) {
    .Call(`_SBMTrees_set_value`, X, tol)
}

character_vector_equals <- function(X, Y) {
    .Call(`_SBMTrees_character_vector_equals`, X, Y)
}

row_matrix <- function(X, index) {
    .Call(`_SBMTrees_row_matrix`, X, index)
}

matrix_add <- function(X, Z) {
    .Call(`_SBMTrees_matrix_add`, X, Z)
}

contains <- function(s, L) {
    .Call(`_SBMTrees_contains`, s, L)
}

contains_index <- function(L, s) {
    .Call(`_SBMTrees_contains_index`, L, s)
}

row_matrix_unique_rowname <- function(X, rowname) {
    .Call(`_SBMTrees_row_matrix_unique_rowname`, X, rowname)
}

row_matrix_rowname <- function(X, rowname) {
    .Call(`_SBMTrees_row_matrix_rowname`, X, rowname)
}

matrix_mul_scalar <- function(X, scalar) {
    .Call(`_SBMTrees_matrix_mul_scalar`, X, scalar)
}

row_matrix_by_rowname <- function(X, rowname) {
    .Call(`_SBMTrees_row_matrix_by_rowname`, X, rowname)
}

count_if <- function(x) {
    .Call(`_SBMTrees_count_if`, x)
}

logic_and <- function(x, y) {
    .Call(`_SBMTrees_logic_and`, x, y)
}

any <- function(x) {
    .Call(`_SBMTrees_any`, x)
}

solve <- function(m) {
    .Call(`_SBMTrees_solve`, m)
}

solve_pos_def <- function(m) {
    .Call(`_SBMTrees_solve_pos_def`, m)
}

matrix_multiply <- function(mat1, mat2) {
    .Call(`_SBMTrees_matrix_multiply`, mat1, mat2)
}

rtgamma <- function(n, shape, scale, lower, upper) {
    .Call(`_SBMTrees_rtgamma`, n, shape, scale, lower, upper)
}

make_symmetric <- function(m) {
    .Call(`_SBMTrees_make_symmetric`, m)
}

vector_mul_generate_matrix <- function(v) {
    .Call(`_SBMTrees_vector_mul_generate_matrix`, v)
}

make_nonsingular <- function(s) {
    .Call(`_SBMTrees_make_nonsingular`, s)
}

create_subject_to_B <- function(subject_id) {
    .Call(`_SBMTrees_create_subject_to_B`, subject_id)
}

create_row_id_to_row <- function(row_id) {
    .Call(`_SBMTrees_create_row_id_to_row`, row_id)
}

innerProduct <- function(x, y) {
    .Call(`_SBMTrees_innerProduct`, x, y)
}

cov <- function(m, regularization = 1e-6) {
    .Call(`_SBMTrees_cov`, m, regularization)
}

isPositiveDefinite <- function(m) {
    .Call(`_SBMTrees_isPositiveDefinite`, m)
}

fix_riwish <- function(m, regularization = 1e-6) {
    .Call(`_SBMTrees_fix_riwish`, m, regularization)
}

matrix_slice_parallel <- function(A, i, row) {
    .Call(`_SBMTrees_matrix_slice_parallel`, A, i, row)
}

rmvnormArma <- function(n, mean, sigma) {
    .Call(`_SBMTrees_rmvnormArma`, n, mean, sigma)
}

cholArma <- function(sigma) {
    .Call(`_SBMTrees_cholArma`, sigma)
}

rwishart <- function(df, S) {
    .Call(`_SBMTrees_rwishart`, df, S)
}

riwishArma <- function(df, S) {
    .Call(`_SBMTrees_riwishArma`, df, S)
}

rinvgamma <- function(a, b) {
    .Call(`_SBMTrees_rinvgamma`, a, b)
}

qinvgamma <- function(p, shape, scale) {
    .Call(`_SBMTrees_qinvgamma`, p, shape, scale)
}

quadratic_form <- function(X, mu, Sigma) {
    .Call(`_SBMTrees_quadratic_form`, X, mu, Sigma)
}

rowSums_I <- function(mat) {
    .Call(`_SBMTrees_rowSums_I`, mat)
}