R/RcppExports.R

In lukesonnet/KRLS: Kernel-based Regularized Least squares

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

krls_gr_trunc <- function(U, D, y, w, fitted, dhat, lambda) {
    .Call(`_KRLS2_krls_gr_trunc`, U, D, y, w, fitted, dhat, lambda)
}

krls_hess_trunc_inv <- function(U, D, w, lambda) {
    .Call(`_KRLS2_krls_hess_trunc_inv`, U, D, w, lambda)
}

krlogit_fn_trunc <- function(par, U, D, y, w, lambda) {
    .Call(`_KRLS2_krlogit_fn_trunc`, par, U, D, y, w, lambda)
}

krlogit_gr_trunc <- function(par, U, D, y, w, lambda) {
    .Call(`_KRLS2_krlogit_gr_trunc`, par, U, D, y, w, lambda)
}

partial_logit <- function(K, coef, beta0) {
    .Call(`_KRLS2_partial_logit`, K, coef, beta0)
}

krlogit_hess_trunc_inv <- function(par, U, D, y, w, lambda) {
    .Call(`_KRLS2_krlogit_hess_trunc_inv`, par, U, D, y, w, lambda)
}

mult_diag <- function(x, d) {
    .Call(`_KRLS2_mult_diag`, x, d)
}

trace_mat <- function(x) {
    .Call(`_KRLS2_trace_mat`, x)
}

euc_dist <- function(x1, x2) {
    .Call(`_KRLS2_euc_dist`, x1, x2)
}

kern_gauss_1d <- function(x1, x2, b) {
    .Call(`_KRLS2_kern_gauss_1d`, x1, x2, b)
}

kern_gauss <- function(x, b) {
    .Call(`_KRLS2_kern_gauss`, x, b)
}

new_gauss_kern <- function(newx, oldx, b) {
    .Call(`_KRLS2_new_gauss_kern`, newx, oldx, b)
}

pwmfx <- function(k, x, coefhat, vcovc_mat, p, p2, b) {
    .Call(`_KRLS2_pwmfx`, k, x, coefhat, vcovc_mat, p, p2, b)
}

solve_for_d_ls <- function(y, U, D, lambda) {
    .Call(`_KRLS2_solve_for_d_ls`, y, U, D, lambda)
}

solve_for_d_ls_w <- function(y, U, D, w, lambda) {
    .Call(`_KRLS2_solve_for_d_ls_w`, y, U, D, w, lambda)
}