R/RcppExports.R

In mnodzenski/epistasisGA: An R package to identify multi-snp effects in nuclear family studies using the GADGETS method

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

as_integer <- function(x) {
    .Call('_epistasisGA_as_integer', PACKAGE = 'epistasisGA', x)
}

scalar_min <- function(x, y) {
    .Call('_epistasisGA_scalar_min', PACKAGE = 'epistasisGA', x, y)
}

scalar_max <- function(x, y) {
    .Call('_epistasisGA_scalar_max', PACKAGE = 'epistasisGA', x, y)
}

concat <- function(x, y) {
    .Call('_epistasisGA_concat', PACKAGE = 'epistasisGA', x, y)
}

concat_list <- function(x, y) {
    .Call('_epistasisGA_concat_list', PACKAGE = 'epistasisGA', x, y)
}

sort_by_order <- function(x, y, sort_type) {
    .Call('_epistasisGA_sort_by_order', PACKAGE = 'epistasisGA', x, y, sort_type)
}

seq_by2 <- function(l) {
    .Call('_epistasisGA_seq_by2', PACKAGE = 'epistasisGA', l)
}

unique_chrom_list <- function(chromosome_list, chrom_size) {
    .Call('_epistasisGA_unique_chrom_list', PACKAGE = 'epistasisGA', chromosome_list, chrom_size)
}

subset_matrix_cols <- function(in_matrix, cols) {
    .Call('_epistasisGA_subset_matrix_cols', PACKAGE = 'epistasisGA', in_matrix, cols)
}

subset_lmatrix_cols <- function(in_matrix, cols) {
    .Call('_epistasisGA_subset_lmatrix_cols', PACKAGE = 'epistasisGA', in_matrix, cols)
}

subset_matrix_rows <- function(in_matrix, rows) {
    .Call('_epistasisGA_subset_matrix_rows', PACKAGE = 'epistasisGA', in_matrix, rows)
}

subset_lmatrix_rows <- function(in_matrix, rows) {
    .Call('_epistasisGA_subset_lmatrix_rows', PACKAGE = 'epistasisGA', in_matrix, rows)
}

sign_scalar <- function(x) {
    .Call('_epistasisGA_sign_scalar', PACKAGE = 'epistasisGA', x)
}

subset_matrix <- function(in_matrix, rows, cols) {
    .Call('_epistasisGA_subset_matrix', PACKAGE = 'epistasisGA', in_matrix, rows, cols)
}

subset_lmatrix <- function(in_matrix, rows, cols) {
    .Call('_epistasisGA_subset_lmatrix', PACKAGE = 'epistasisGA', in_matrix, rows, cols)
}

weighted_sub_colsums <- function(x, y, target_rows, target_cols, row_weights) {
    .Call('_epistasisGA_weighted_sub_colsums', PACKAGE = 'epistasisGA', x, y, target_rows, target_cols, row_weights)
}

sub_rowsums_start <- function(x, y, target_cols) {
    .Call('_epistasisGA_sub_rowsums_start', PACKAGE = 'epistasisGA', x, y, target_cols)
}

sub_rowsums_parent_weights <- function(x, y, target_cols) {
    .Call('_epistasisGA_sub_rowsums_parent_weights', PACKAGE = 'epistasisGA', x, y, target_cols)
}

sub_colsums <- function(in_mat, target_rows, target_cols, target_val) {
    .Call('_epistasisGA_sub_colsums', PACKAGE = 'epistasisGA', in_mat, target_rows, target_cols, target_val)
}

sub_rowsums_both_one <- function(x, y, target_rows, target_cols) {
    .Call('_epistasisGA_sub_rowsums_both_one', PACKAGE = 'epistasisGA', x, y, target_rows, target_cols)
}

n_pos_high_risk <- function(in_mat, target_rows, target_cols) {
    .Call('_epistasisGA_n_pos_high_risk', PACKAGE = 'epistasisGA', in_mat, target_rows, target_cols)
}

n_neg_high_risk <- function(in_mat, target_rows, target_cols) {
    .Call('_epistasisGA_n_neg_high_risk', PACKAGE = 'epistasisGA', in_mat, target_rows, target_cols)
}

sub_colmeans <- function(in_mat, target_rows, target_cols, target_val) {
    .Call('_epistasisGA_sub_colmeans', PACKAGE = 'epistasisGA', in_mat, target_rows, target_cols, target_val)
}

split_int_mat <- function(in_mat, in_vec, uni_in_vec) {
    .Call('_epistasisGA_split_int_mat', PACKAGE = 'epistasisGA', in_mat, in_vec, uni_in_vec)
}

split_logical_mat <- function(in_mat, in_vec, uni_in_vec) {
    .Call('_epistasisGA_split_logical_mat', PACKAGE = 'epistasisGA', in_mat, in_vec, uni_in_vec)
}

get_target_snps_ld_blocks <- function(target_snps_in, ld_block_vec) {
    .Call('_epistasisGA_get_target_snps_ld_blocks', PACKAGE = 'epistasisGA', target_snps_in, ld_block_vec)
}

find_high_risk <- function(n_target, n_pos, n_neg, neg_risk_int, pos_risk_int, case_data, comp, informative_families, target_snps) {
    .Call('_epistasisGA_find_high_risk', PACKAGE = 'epistasisGA', n_target, n_pos, n_neg, neg_risk_int, pos_risk_int, case_data, comp, informative_families, target_snps)
}

compute_dif_vecs <- function(case_genetic_data, comp_genetic_data, target_snps, both_one_snps, weight_lookup, n_different_snps_weight = 2L, n_both_one_weight = 1L) {
    .Call('_epistasisGA_compute_dif_vecs', PACKAGE = 'epistasisGA', case_genetic_data, comp_genetic_data, target_snps, both_one_snps, weight_lookup, n_different_snps_weight, n_both_one_weight)
}

chrom_fitness_score <- function(case_genetic_data_in, complement_genetic_data_in, target_snps_in, ld_block_vec, weight_lookup, n_different_snps_weight = 2L, n_both_one_weight = 1L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, epi_test = FALSE) {
    .Call('_epistasisGA_chrom_fitness_score', PACKAGE = 'epistasisGA', case_genetic_data_in, complement_genetic_data_in, target_snps_in, ld_block_vec, weight_lookup, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, epi_test)
}

GxE_fitness_score_mvlm <- function(case_genetic_data_, mom_genetic_data_, dad_genetic_data_, exposure_mat_, target_snps, weight_lookup, null_means, null_se, n_different_snps_weight = 2L, n_both_one_weight = 1L) {
    .Call('_epistasisGA_GxE_fitness_score_mvlm', PACKAGE = 'epistasisGA', case_genetic_data_, mom_genetic_data_, dad_genetic_data_, exposure_mat_, target_snps, weight_lookup, null_means, null_se, n_different_snps_weight, n_both_one_weight)
}

chrom_fitness_list <- function(case_genetic_data, complement_genetic_data, chromosome_list, ld_block_vec, weight_lookup, n_different_snps_weight = 2L, n_both_one_weight = 1L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, epi_test = FALSE) {
    .Call('_epistasisGA_chrom_fitness_list', PACKAGE = 'epistasisGA', case_genetic_data, complement_genetic_data, chromosome_list, ld_block_vec, weight_lookup, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, epi_test)
}

GxE_fitness_score_mvlm_list <- function(case_genetic_data_, mom_genetic_data_, dad_genetic_data_, exposure_mat_, chromosome_list, weight_lookup, null_means, null_se, n_different_snps_weight = 2L, n_both_one_weight = 1L) {
    .Call('_epistasisGA_GxE_fitness_score_mvlm_list', PACKAGE = 'epistasisGA', case_genetic_data_, mom_genetic_data_, dad_genetic_data_, exposure_mat_, chromosome_list, weight_lookup, null_means, null_se, n_different_snps_weight, n_both_one_weight)
}

GxE_mvlm_fitness_vec_mat <- function(case_genetic_data_, mom_genetic_data_, dad_genetic_data_, exposure_mat_, chromosome_list, weight_lookup, null_means, null_se, n_different_snps_weight = 2L, n_both_one_weight = 1L) {
    .Call('_epistasisGA_GxE_mvlm_fitness_vec_mat', PACKAGE = 'epistasisGA', case_genetic_data_, mom_genetic_data_, dad_genetic_data_, exposure_mat_, chromosome_list, weight_lookup, null_means, null_se, n_different_snps_weight, n_both_one_weight)
}

compute_population_fitness <- function(case_genetic_data, complement_genetic_data, ld_block_vec, chromosome_list, weight_lookup, case_genetic_data_n, mom_genetic_data, dad_genetic_data, exposure_mat_n, weight_lookup_n, null_mean_vec, null_se_vec, n_different_snps_weight = 2L, n_both_one_weight = 1L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, E_GADGETS = FALSE) {
    .Call('_epistasisGA_compute_population_fitness', PACKAGE = 'epistasisGA', case_genetic_data, complement_genetic_data, ld_block_vec, chromosome_list, weight_lookup, case_genetic_data_n, mom_genetic_data, dad_genetic_data, exposure_mat_n, weight_lookup_n, null_mean_vec, null_se_vec, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, E_GADGETS)
}

find_top_chrom <- function(fitness_scores, chromosome_list, chromosome_size) {
    .Call('_epistasisGA_find_top_chrom', PACKAGE = 'epistasisGA', fitness_scores, chromosome_list, chromosome_size)
}

initiate_population <- function(n_candidate_snps, case_genetic_data, complement_genetic_data, ld_block_vec, n_chromosomes, chromosome_size, weight_lookup, case_genetic_data_n, mom_genetic_data_n, dad_genetic_data_n, exposure_mat_n, weight_lookup_n, null_mean_vec, null_se_vec, n_different_snps_weight = 2L, n_both_one_weight = 1L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, max_generations = 500L, initial_sample_duplicates = FALSE, E_GADGETS = FALSE) {
    .Call('_epistasisGA_initiate_population', PACKAGE = 'epistasisGA', n_candidate_snps, case_genetic_data, complement_genetic_data, ld_block_vec, n_chromosomes, chromosome_size, weight_lookup, case_genetic_data_n, mom_genetic_data_n, dad_genetic_data_n, exposure_mat_n, weight_lookup_n, null_mean_vec, null_se_vec, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat, max_generations, initial_sample_duplicates, E_GADGETS)
}

check_convergence <- function(island_cluster_size, island_populations) {
    .Call('_epistasisGA_check_convergence', PACKAGE = 'epistasisGA', island_cluster_size, island_populations)
}

check_max_gens <- function(island_populations, max_generations) {
    .Call('_epistasisGA_check_max_gens', PACKAGE = 'epistasisGA', island_populations, max_generations)
}

run_GADGETS <- function(island_cluster_size, n_migrations, ld_block_vec, n_chromosomes, chromosome_size, weight_lookup, snp_chisq, case_genetic_data_in, complement_genetic_data_in, case_genetic_data_n, mom_genetic_data_n, dad_genetic_data_n, exposure_data_n, weight_lookup_n, null_mean_vec, null_se_vec, n_different_snps_weight = 2L, n_both_one_weight = 1L, migration_interval = 50L, gen_same_fitness = 50L, max_generations = 500L, initial_sample_duplicates = FALSE, crossover_prop = 0.8, recessive_ref_prop = 0.75, recode_test_stat = 1.64, E_GADGETS = FALSE) {
    .Call('_epistasisGA_run_GADGETS', PACKAGE = 'epistasisGA', island_cluster_size, n_migrations, ld_block_vec, n_chromosomes, chromosome_size, weight_lookup, snp_chisq, case_genetic_data_in, complement_genetic_data_in, case_genetic_data_n, mom_genetic_data_n, dad_genetic_data_n, exposure_data_n, weight_lookup_n, null_mean_vec, null_se_vec, n_different_snps_weight, n_both_one_weight, migration_interval, gen_same_fitness, max_generations, initial_sample_duplicates, crossover_prop, recessive_ref_prop, recode_test_stat, E_GADGETS)
}

epistasis_test_permute <- function(case_inf, comp_inf, target_snps_ld_blocks, uni_ld_blocks, n_families, weight_lookup, n_different_snps_weight = 2L, n_both_one_weight = 1L, recessive_ref_prop = 0.75, recode_test_stat = 1.64) {
    .Call('_epistasisGA_epistasis_test_permute', PACKAGE = 'epistasisGA', case_inf, comp_inf, target_snps_ld_blocks, uni_ld_blocks, n_families, weight_lookup, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat)
}

epistasis_test_null_scores <- function(n_permutes, case_inf, comp_inf, target_snps_ld_blocks, uni_ld_blocks, n_families, weight_lookup, n_different_snps_weight = 2L, n_both_one_weight = 1L, recessive_ref_prop = 0.75, recode_test_stat = 1.64) {
    .Call('_epistasisGA_epistasis_test_null_scores', PACKAGE = 'epistasisGA', n_permutes, case_inf, comp_inf, target_snps_ld_blocks, uni_ld_blocks, n_families, weight_lookup, n_different_snps_weight, n_both_one_weight, recessive_ref_prop, recode_test_stat)
}

epistasis_test <- function(snp_cols, preprocessed_list, n_permutes = 10000L, n_different_snps_weight = 2L, n_both_one_weight = 1L, weight_function_int = 2L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, warn = TRUE, maternal_fetal = FALSE) {
    .Call('_epistasisGA_epistasis_test', PACKAGE = 'epistasisGA', snp_cols, preprocessed_list, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int, recessive_ref_prop, recode_test_stat, warn, maternal_fetal)
}

GxE_test <- function(target_snps, preprocessed_list, null_mean_vec, null_se_vec, n_permutes = 10000L, n_different_snps_weight = 2L, n_both_one_weight = 1L, weight_function_int = 2L) {
    .Call('_epistasisGA_GxE_test', PACKAGE = 'epistasisGA', target_snps, preprocessed_list, null_mean_vec, null_se_vec, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int)
}

n2log_epistasis_pvals <- function(chromosome_list, preprocessed_list, n_permutes = 10000L, n_different_snps_weight = 2L, n_both_one_weight = 1L, weight_function_int = 2L, recessive_ref_prop = 0.75, recode_test_stat = 1.64, null_mean_vec_ = NULL, null_se_vec_ = NULL) {
    .Call('_epistasisGA_n2log_epistasis_pvals', PACKAGE = 'epistasisGA', chromosome_list, preprocessed_list, n_permutes, n_different_snps_weight, n_both_one_weight, weight_function_int, recessive_ref_prop, recode_test_stat, null_mean_vec_, null_se_vec_)
}