R/RcppExports.R

In privefl/bigsnpr: Analysis of Massive SNP Arrays

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

bedXPtr <- function(path, n, p) {
    .Call(`_bigsnpr_bedXPtr`, path, n, p)
}

bed_colstats <- function(obj_bed, ind_row, ind_col, ncores) {
    .Call(`_bigsnpr_bed_colstats`, obj_bed, ind_row, ind_col, ncores)
}

bed_col_counts_cpp <- function(obj_bed, ind_row, ind_col, ncores) {
    .Call(`_bigsnpr_bed_col_counts_cpp`, obj_bed, ind_row, ind_col, ncores)
}

bed_row_counts_cpp <- function(obj_bed, ind_row, ind_col, ncores) {
    .Call(`_bigsnpr_bed_row_counts_cpp`, obj_bed, ind_row, ind_col, ncores)
}

prod_and_rowSumsSq <- function(obj_bed, ind_row, ind_col, center, scale, V) {
    .Call(`_bigsnpr_prod_and_rowSumsSq`, obj_bed, ind_row, ind_col, center, scale, V)
}

read_bed <- function(obj_bed, ind_row, ind_col) {
    .Call(`_bigsnpr_read_bed`, obj_bed, ind_row, ind_col)
}

read_bed_scaled <- function(obj_bed, ind_row, ind_col, center, scale) {
    .Call(`_bigsnpr_read_bed_scaled`, obj_bed, ind_row, ind_col, center, scale)
}

bed_pMatVec4 <- function(obj_bed, ind_row, ind_col, center, scale, x, ncores) {
    .Call(`_bigsnpr_bed_pMatVec4`, obj_bed, ind_row, ind_col, center, scale, x, ncores)
}

bed_cpMatVec4 <- function(obj_bed, ind_row, ind_col, center, scale, x, ncores) {
    .Call(`_bigsnpr_bed_cpMatVec4`, obj_bed, ind_row, ind_col, center, scale, x, ncores)
}

bed_clumping_chr <- function(obj_bed, BM2, ind_row, ind_col, center, scale, ordInd, rankInd, pos, size, thr, ncores) {
    invisible(.Call(`_bigsnpr_bed_clumping_chr`, obj_bed, BM2, ind_row, ind_col, center, scale, ordInd, rankInd, pos, size, thr, ncores))
}

clumping_chr_cached <- function(BM, BM2, sqcor, spInd, rowInd, colInd, ordInd, rankInd, pos, sumX, denoX, size, thr, ncores) {
    .Call(`_bigsnpr_clumping_chr_cached`, BM, BM2, sqcor, spInd, rowInd, colInd, ordInd, rankInd, pos, sumX, denoX, size, thr, ncores)
}

clumping_chr <- function(BM, BM2, rowInd, colInd, ordInd, rankInd, pos, sumX, denoX, size, thr, ncores) {
    invisible(.Call(`_bigsnpr_clumping_chr`, BM, BM2, rowInd, colInd, ordInd, rankInd, pos, sumX, denoX, size, thr, ncores))
}

snp_colstats <- function(BM, rowInd, colInd, ncores) {
    .Call(`_bigsnpr_snp_colstats`, BM, rowInd, colInd, ncores)
}

replaceSNP <- function(BM, BM2, rowInd, colInd) {
    invisible(.Call(`_bigsnpr_replaceSNP`, BM, BM2, rowInd, colInd))
}

corMat <- function(obj, rowInd, colInd, size, thr, pos, fill_diag, ncores) {
    .Call(`_bigsnpr_corMat`, obj, rowInd, colInd, size, thr, pos, fill_diag, ncores)
}

impute <- function(BM, method, ncores) {
    invisible(.Call(`_bigsnpr_impute`, BM, method, ncores))
}

lassosum2 <- function(corr, beta_hat, lambda, delta_plus_one, ind_sub, dfmax, maxiter, tol) {
    .Call(`_bigsnpr_lassosum2`, corr, beta_hat, lambda, delta_plus_one, ind_sub, dfmax, maxiter, tol)
}

ld_scores_sfbm <- function(X, ind_sub, ncores) {
    .Call(`_bigsnpr_ld_scores_sfbm`, X, ind_sub, ncores)
}

ld_scores <- function(obj, rowInd, colInd, size, pos, ncores) {
    .Call(`_bigsnpr_ld_scores`, obj, rowInd, colInd, size, pos, ncores)
}

MLE_alpha <- function(par, ind_causal, log_var, curr_beta, alpha_bounds, boot = FALSE, verbose = FALSE) {
    .Call(`_bigsnpr_MLE_alpha`, par, ind_causal, log_var, curr_beta, alpha_bounds, boot, verbose)
}

ldpred2_gibbs_auto <- function(corr, beta_hat, n_vec, log_var, ind_sub, p_init, h2_init, burn_in, num_iter, report_step, no_jump_sign, shrink_corr, use_mle, p_bounds, alpha_bounds, mean_ld = 1, verbose = FALSE) {
    .Call(`_bigsnpr_ldpred2_gibbs_auto`, corr, beta_hat, n_vec, log_var, ind_sub, p_init, h2_init, burn_in, num_iter, report_step, no_jump_sign, shrink_corr, use_mle, p_bounds, alpha_bounds, mean_ld, verbose)
}

ldpred2_gibbs_one_sampling <- function(corr, beta_hat, n_vec, ind_sub, h2, p, sparse, burn_in, num_iter) {
    .Call(`_bigsnpr_ldpred2_gibbs_one_sampling`, corr, beta_hat, n_vec, ind_sub, h2, p, sparse, burn_in, num_iter)
}

ldpred2_gibbs_one <- function(corr, beta_hat, n_vec, ind_sub, h2, p, sparse, burn_in, num_iter) {
    .Call(`_bigsnpr_ldpred2_gibbs_one`, corr, beta_hat, n_vec, ind_sub, h2, p, sparse, burn_in, num_iter)
}

multLinReg <- function(obj, ind_row, ind_col, U, ncores = 1L) {
    .Call(`_bigsnpr_multLinReg`, obj, ind_row, ind_col, U, ncores)
}

extract_submat_bgen <- function(filename, offsets, X, ind_row, decode, dosage, N, ncores) {
    .Call(`_bigsnpr_extract_submat_bgen`, filename, offsets, X, ind_row, decode, dosage, N, ncores)
}

prod_bgen2 <- function(filename, offsets, XY, Y, ind_row, decode, dosage, N, max_size, ncores) {
    .Call(`_bigsnpr_prod_bgen2`, filename, offsets, XY, Y, ind_row, decode, dosage, N, max_size, ncores)
}

read_bgen <- function(filename, offsets, BM, ind_row, ind_col, decode, dosage, N, ncores) {
    .Call(`_bigsnpr_read_bgen`, filename, offsets, BM, ind_row, ind_col, decode, dosage, N, ncores)
}

readbina <- function(filename, BM, tab) {
    .Call(`_bigsnpr_readbina`, filename, BM, tab)
}

readbina2 <- function(BM, obj_bed, ind_row, ind_col, ncores) {
    invisible(.Call(`_bigsnpr_readbina2`, BM, obj_bed, ind_row, ind_col, ncores))
}

sp_colSumsSq_sym <- function(p, i, x) {
    .Call(`_bigsnpr_sp_colSumsSq_sym`, p, i, x)
}

get_L <- function(p, i, x, thr_r2, max_r2) {
    .Call(`_bigsnpr_get_L`, p, i, x, thr_r2, max_r2)
}

get_C <- function(L, min_size, max_size, max_K, max_cost, pos_scaled) {
    .Call(`_bigsnpr_get_C`, L, min_size, max_size, max_K, max_cost, pos_scaled)
}

get_perc <- function(p, i, all_last) {
    .Call(`_bigsnpr_get_perc`, p, i, all_last)
}

writebina <- function(filename, BM, tab, rowInd, colInd) {
    invisible(.Call(`_bigsnpr_writebina`, filename, BM, tab, rowInd, colInd))
}

testWrite <- function(v, filename) {
    invisible(.Call(`_bigsnpr_testWrite`, v, filename))
}