R/RcppExports.R

In gmwmx2: Estimate Functional and Stochastic Parameters of Linear Models with Correlated Residuals and Missing Data

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

powerlaw_autocovariance <- function(kappa, sigma2, n) {
    .Call(`_gmwmx2_powerlaw_autocovariance`, kappa, sigma2, n)
}

fast_toeplitz_matrix_from_vector_cpp <- function(v) {
    .Call(`_gmwmx2_fast_toeplitz_matrix_from_vector_cpp`, v)
}

estimate_p1_p2_mle_cpp <- function(omega) {
    .Call(`_gmwmx2_estimate_p1_p2_mle_cpp`, omega)
}

create_vec_theo_autocov_omega_cpp <- function(p1, p2, n) {
    .Call(`_gmwmx2_create_vec_theo_autocov_omega_cpp`, p1, p2, n)
}

gen_flicker <- function(N, sigma) {
    .Call(`_gmwmx2_gen_flicker`, N, sigma)
}

compute_h_cpp <- function(kappa, N) {
    .Call(`_gmwmx2_compute_h_cpp`, kappa, N)
}

vec_mean_autocov_powerlaw <- function(kappa, N) {
    .Call(`_gmwmx2_vec_mean_autocov_powerlaw`, kappa, N)
}

var_cov_powerlaw_cpp <- function(sigma2, kappa, n) {
    .Call(`_gmwmx2_var_cov_powerlaw_cpp`, sigma2, kappa, n)
}

compute_power_of_a_base <- function(x, J) {
    .Call(`_gmwmx2_compute_power_of_a_base`, x, J)
}

autocovariance_to_wv <- function(acf, tau) {
    .Call(`_gmwmx2_autocovariance_to_wv`, acf, tau)
}

compute_cov_W_scale_1_cpp <- function(Sigma_X) {
    .Call(`_gmwmx2_compute_cov_W_scale_1_cpp`, Sigma_X)
}

compute_cov_W_all_scales_cpp <- function(Sigma_X) {
    .Call(`_gmwmx2_compute_cov_W_all_scales_cpp`, Sigma_X)
}

compute_index_pairs_scales_cpp <- function(max_j) {
    .Call(`_gmwmx2_compute_index_pairs_scales_cpp`, max_j)
}

compute_cov_W_between_scales_all_scales_cpp <- function(mat_index, n, lst_cov_W) {
    .Call(`_gmwmx2_compute_cov_W_between_scales_all_scales_cpp`, mat_index, n, lst_cov_W)
}

f_jk_cpp <- function(h, cov_Wj_Wjk, Mjk, Mj) {
    .Call(`_gmwmx2_f_jk_cpp`, h, cov_Wj_Wjk, Mjk, Mj)
}

f_jk_approx_cpp <- function(h, cov_Wj_Wjk, Mjk, Mj, approx_type = "3") {
    .Call(`_gmwmx2_f_jk_approx_cpp`, h, cov_Wj_Wjk, Mjk, Mj, approx_type)
}

compute_cov_wv_cpp_1 <- function(cov_W_j, J, n, mat_index) {
    .Call(`_gmwmx2_compute_cov_wv_cpp_1`, cov_W_j, J, n, mat_index)
}

compute_cov_wv_cpp <- function(Sigma_X) {
    .Call(`_gmwmx2_compute_cov_wv_cpp`, Sigma_X)
}

compute_l_index_to_compute_cpp <- function(Mjk, approx_type) {
    .Call(`_gmwmx2_compute_l_index_to_compute_cpp`, Mjk, approx_type)
}

compute_indices <- function(all_l_values, lst_index_row) {
    .Call(`_gmwmx2_compute_indices`, all_l_values, lst_index_row)
}

compute_cov_wv_cpp_approx_faster <- function(Sigma_X, approx_type = "1") {
    .Call(`_gmwmx2_compute_cov_wv_cpp_approx_faster`, Sigma_X, approx_type)
}

sum_of_powers_of_2 <- function(from, to) {
    .Call(`_gmwmx2_sum_of_powers_of_2`, from, to)
}

get_cov_W_scale_1_from_autocov_cpp_with_treshold <- function(h, autocov_vec, lag_treshold = -1L) {
    .Call(`_gmwmx2_get_cov_W_scale_1_from_autocov_cpp_with_treshold`, h, autocov_vec, lag_treshold)
}

compute_all_cov_wv_recursive_2_cpp_with_mat <- function(n, autocov_vec, lag_treshold = -1L) {
    .Call(`_gmwmx2_compute_all_cov_wv_recursive_2_cpp_with_mat`, n, autocov_vec, lag_treshold)
}

compute_autocov_W_j_equal_1_from_autocov_X <- function(autocov_vec, n, lag_treshold = -1L) {
    .Call(`_gmwmx2_compute_autocov_W_j_equal_1_from_autocov_X`, autocov_vec, n, lag_treshold)
}

compute_all_cov_W_recursive_from_j_2 <- function(n, autocov_W_j_equal_1) {
    .Call(`_gmwmx2_compute_all_cov_W_recursive_from_j_2`, n, autocov_W_j_equal_1)
}

get_cov_wvar_cpp <- function(j, k, n, mat_autocov_W) {
    .Call(`_gmwmx2_get_cov_wvar_cpp`, j, k, n, mat_autocov_W)
}

get_var_wvar_j_from_autcov_W_j_cpp <- function(j, n, autocov_W) {
    .Call(`_gmwmx2_get_var_wvar_j_from_autcov_W_j_cpp`, j, n, autocov_W)
}

get_theo_cov_matrix_wvar_cpp <- function(n, autocov_vec_X = NULL, autocov_vec_W = NULL, num_off_diagonal = -1L, lag_treshold = -1L) {
    .Call(`_gmwmx2_get_theo_cov_matrix_wvar_cpp`, n, autocov_vec_X, autocov_vec_W, num_off_diagonal, lag_treshold)
}

sum_all_upper_diagonals <- function(matrix) {
    .Call(`_gmwmx2_sum_all_upper_diagonals`, matrix)
}

linear_interp_cpp <- function(x, y, xout) {
    .Call(`_gmwmx2_linear_interp_cpp`, x, y, xout)
}

pre_compute_quantities_on_D_only_required_smarter_cpp <- function(D, approx_type = "3") {
    .Call(`_gmwmx2_pre_compute_quantities_on_D_only_required_smarter_cpp`, D, approx_type)
}

compute_all_mean_diag_fast_w_linear_interp_only_required_cpp <- function(mat_D_q_term_1, mat_D_q_term_2, sum_on_sub_diag_of_D, vec_autocov, approx_type = "3") {
    .Call(`_gmwmx2_compute_all_mean_diag_fast_w_linear_interp_only_required_cpp`, mat_D_q_term_1, mat_D_q_term_2, sum_on_sub_diag_of_D, vec_autocov, approx_type)
}