# R/RcppExports.R In zrmacc/BinaryModels: Binary Regression Models

#### Documented in fastInvInfopAlphapBetarSigmawls

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

#' Fast Inverse
#'
#' Matrix pseudo-inverse.
#' @param A Numeric matrix.
fastInv <- function(A) {
.Call(_BinaryModels_fastInv, A)
}

#' Weight Least Squares Estimator
#'
#' Estimates the coefficient of a WLS model
#' @param z Outcome
#' @param X Design matrix
#' @param W Weight matrix
#'
wls <- function(z, X, W) {
.Call(_BinaryModels_wls, z, X, W)
}

#' Probit Estimate Beta
#'
#' PX-E Estimate of beta
#' @param z1 Expectation of z given y
#' @param X Design matrix
pBeta <- function(z1, X) {
.Call(_BinaryModels_pBeta, z1, X)
}

#' Estimate Alpha
#'
#' PX-E Estimate of alpha
#' @param z1 Expectation of z given y
#' @param z2 Expectation of \eqn{z^2} given y
#' @param eta Linear predictor
pAlpha <- function(z1, z2, eta) {
.Call(_BinaryModels_pAlpha, z1, z2, eta)
}

#' Probit Information Matrix
#'
#' Calculate information matrix for probit coefficient
#' @param X Design matrix
#' @param W Weight matrix
Info <- function(X, W) {
.Call(_BinaryModels_Info, X, W)
}

#' Robit Estimate of Sigma
#'
#' PX-E Estimate of sigma
#' @param nu Degrees of freedom
#' @param z1 Expectation of z given y
#' @param eta Linear predictor
#' @param tau1 Expectation of \eqn{\tau} given y
#' @param X Design matrix
rSigma <- function(nu, z1, eta, T1, X) {
.Call(_BinaryModels_rSigma, nu, z1, eta, T1, X)
}

zrmacc/BinaryModels documentation built on Jan. 14, 2018, 9:09 a.m.