R/RcppExports.R

In bvegetabile/gpbalancer: Estimates propensity scores using Gaussian processes

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

construct_sqexp <- function(X, M, sig, noise) {
    .Call(`_gpbalancer_construct_sqexp`, X, M, sig, noise)
}

sqexp_ard <- function(X, hyperparams, scale = 1.0, noise = 1e-6) {
    .Call(`_gpbalancer_sqexp_ard`, X, hyperparams, scale, noise)
}

sqexp_common <- function(X, lengthscale, scale = 1.0, noise = 1e-6) {
    .Call(`_gpbalancer_sqexp_common`, X, lengthscale, scale, noise)
}

polykernel <- function(X, sig_zero, pwr = 1L, scale = 1.0, noise = 1e-6) {
    .Call(`_gpbalancer_polykernel`, X, sig_zero, pwr, scale, noise)
}

par_ep <- function(y, cov_matrix, tol, max_iters, verbose) {
    .Call(`_gpbalancer_par_ep`, y, cov_matrix, tol, max_iters, verbose)
}

seq_ep <- function(y, cov_matrix, tol, max_iters, verbose) {
    .Call(`_gpbalancer_seq_ep`, y, cov_matrix, tol, max_iters, verbose)
}

gp_mcla <- function(covmat, targets, n_classes, tol = 1e-10, max_iters = 20L, verbose = FALSE) {
    .Call(`_gpbalancer_gp_mcla`, covmat, targets, n_classes, tol, max_iters, verbose)
}

la_probit <- function(targets, covmat, tol = 1e-2, max_iters = 20L, verbose = FALSE) {
    .Call(`_gpbalancer_la_probit`, targets, covmat, tol, max_iters, verbose)
}

mc_sqexp_common <- function(X, inv_ls_vec, scale = 1.0, noise = 1e-6) {
    .Call(`_gpbalancer_mc_sqexp_common`, X, inv_ls_vec, scale, noise)
}

mc_normpoly_common <- function(X, sig_shift, sig_scale, power = 1L, noise = 1e-6) {
    .Call(`_gpbalancer_mc_normpoly_common`, X, sig_shift, sig_scale, power, noise)
}

par_sqexp <- function(design_x, vec_theta, sig_noise = 1e-6) {
    .Call(`_gpbalancer_par_sqexp`, design_x, vec_theta, sig_noise)
}