R/RcppExports.R

In mig: Multivariate Inverse Gaussian Distribution

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#' Likelihood cross-validation for truncated normal kernel density estimation
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#' Least squares cross-validation for MIG kernel density estimation
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#' Likelihood cross-validation for MIG kernel density estimation
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#' MIG kernel density estimator
#'
#' Given a data matrix over a half-space defined by \code{beta},
#' compute the log density taking in turn an observation in  \code{newdata}
#' as location vector and computing the kernel density estimate.
#' @inheritParams dmig
#' @param newdata matrix of new observations at which to evaluated the kernel density
#' @return the value of the likelihood cross-validation criterion
#' @keywords internal
#' @export
mig_kdens_arma <- function(x, newdata, Omega, beta, logd) {
    .Call(`_mig_mig_kdens_arma`, x, newdata, Omega, beta, logd)
}

#' Truncated Gaussian kernel density estimator
#'
#' Given a data matrix over a half-space defined by \code{beta},
#' compute the log density of the asymmetric truncated Gaussian kernel density estimator,
#' taking in turn an observation as location vector.
#' @inheritParams dmig
#' @param newdata matrix of new observations at which to evaluated the kernel density
#' @return the value of the likelihood cross-validation criterion
#' @keywords internal
#' @export
tnorm_kdens_arma <- function(x, newdata, Omega, beta, logd) {
    .Call(`_mig_tnorm_kdens_arma`, x, newdata, Omega, beta, logd)
}

#' Gaussian kernel density estimator
#'
#' Given a data matrix over a half-space defined by \code{beta},
#' compute the log density of the asymmetric truncated Gaussian kernel density estimator,
#' taking in turn an observation as location vector.
#' @param x matrix of observations
#' @param newdata matrix of new observations at which to evaluated the kernel density
#' @param Sigma covariance matrix
#' @param logd logical; if \code{TRUE}, return the log density
#' @return log density estimator
#' @export
#' @keywords internal
gauss_kdens_arma <- function(x, newdata, Sigma, logweights, logd) {
    .Call(`_mig_gauss_kdens_arma`, x, newdata, Sigma, logweights, logd)
}

#' Leave-one-out cross-validation for kernel density estimation with MIG
#'
#' Given a data matrix over a half-space defined by \code{beta},
#' compute the log density using leave-one-out cross validation,
#' taking in turn an observation as location vector and computing the
#' density of the resulting mixture.
#' @inheritParams dmig
#' @return the value of the likelihood cross-validation criterion
#' @export
#' @keywords internal
mig_loo <- function(x, Omega, beta) {
    .Call(`_mig_mig_loo`, x, Omega, beta)
}

#' Robust likelihood cross-validation for kernel density estimation for MIG
#'
#' Given a data matrix over a half-space defined by \code{beta},
#' compute the log density using leave-one-out cross validation,
#' taking in turn an observation as location vector and computing the
#' density of the resulting mixture.
#' @inheritParams dmig
#' @param xsamp matrix of points at which to evaluate the integral
#' @param dxsamp density of points
#' @return the value of the likelihood cross-validation criterion
#' @export
#' @keywords internal
mig_rlcv <- function(x, beta, Omega, an, xsamp, dxsamp, mckern = TRUE) {
    .Call(`_mig_mig_rlcv`, x, beta, Omega, an, xsamp, dxsamp, mckern)
}

#' Least squares cross-validation for MIG density estimation
#'
#' Given a data matrix over a half-space defined by \code{beta},
#' compute the average using leave-one-out cross validation density minus
#' half the squared density.
#' @inheritParams dmig
#' @inheritParams mig_rlcv
#' @return the value of the least square cross-validation criterion
#' @export
#' @keywords internal
mig_lscv <- function(x, beta, Omega, xsamp, dxsamp, mckern = TRUE) {
    .Call(`_mig_mig_lscv`, x, beta, Omega, xsamp, dxsamp, mckern)
}

#' Leave-one-out cross-validation for Gaussian kernel density estimation
#'
#' Given a data matrix, compute the log density using leave-one-out
#' cross validation, taking in turn an observation as location vector
#' and computing the density of the resulting mixture.
#' @param x \code{n} by \code{d} matrix of observations
#' @param Sigma smoothing positive-definite matrix
#' @param logweights vector of log weights
#' @return a vector of values for the weighted leave-one-out likelihood
#' @keywords internal
#' @export
gauss_loo <- function(x, Sigma, logweights) {
    .Call(`_mig_gauss_loo`, x, Sigma, logweights)
}

#' Leave-one-out cross-validation for truncated Gaussian kernel density estimation
#'
#' Given a data matrix, compute the log density using leave-one-out
#' cross validation, taking in turn an observation as location vector
#' and computing the density of the resulting mixture.
#' @param x \code{n} by \code{d} matrix of observations
#' @param Omega smoothing positive-definite matrix
#' @param beta vector of constraints for the half-space
#' @return a vector of values for the weighted leave-one-out likelihood
#' @keywords internal
#' @export
tnorm_loo <- function(x, Omega, beta) {
    .Call(`_mig_tnorm_loo`, x, Omega, beta)
}

#' Likelihood cross-validation for MIG density estimation
#'
#' Likelihood cross-validation criterion function.
#' @param x \code{n} by \code{d} matrix of observations
#' @inheritParams dmig
#' @return LCV criterion value
#' @keywords internal
#' @export
mig_lcv <- function(x, Omega, beta) {
    .Call(`_mig_mig_lcv`, x, Omega, beta)
}

#' Likelihood cross-validation for Gaussian kernel density estimation
#'
#' Likelihood cross-validation criterion function.
#' @param x \code{n} by \code{d} matrix of observations
#' @param Sigma smoothing positive-definite matrix
#' @param logweights log vector of weights
#' @return LCV criterion value
#' @export
#' @keywords internal
gauss_lcv <- function(x, Sigma, logweights) {
    .Call(`_mig_gauss_lcv`, x, Sigma, logweights)
}

#' Likelihood cross-validation for truncated normal kernel density estimation
#'
#' Likelihood cross-validation criterion function.
#' @param x \code{n} by \code{d} matrix of observations
#' @param Omega smoothing positive-definite matrix
#' @param beta vector of constraints for the half-space
#' @return LCV criterion value
#' @export
#' @keywords internal
tnorm_lcv <- function(x, Omega, beta) {
    .Call(`_mig_tnorm_lcv`, x, Omega, beta)
}

#' Least squares cross-validation for Gaussian kernel density estimation
#'
#' Least squares cross-validation for weighted Gaussian samples.
#' @param xsamp \code{n} by \code{d} random sample for Monte Carlo estimation of bias
#' @param dxsamp \code{n} vector of density for the points from \code{xsamp}
#' @param mckern logical; if \code{TRUE}, uses the kernel as sampler for Monte Carlo estimation
#' @inheritParams gauss_lcv
#' @return least square criterion value
#' @export
#' @keywords internal
gauss_lscv <- function(x, Sigma, logweights, xsamp, dxsamp, mckern = TRUE) {
    .Call(`_mig_gauss_lscv`, x, Sigma, logweights, xsamp, dxsamp, mckern)
}

#' Least squares cross-validation for truncated Gaussian kernel density estimation
#'
#' Least squares cross-validation for truncated Gaussian samples.
#' @inheritParams gauss_lscv
#' @inheritParams tnorm_lcv
#' @return least square criterion value
#' @export
#' @keywords internal
tnorm_lscv <- function(x, Omega, beta, xsamp, dxsamp, mckern = TRUE) {
    .Call(`_mig_tnorm_lscv`, x, Omega, beta, xsamp, dxsamp, mckern)
}

#' Robust likelihood cross-validation for Gaussian kernel density estimation
#'
#' Robust likelihood cross-validation criterion function of Wu.
#' @inheritParams gauss_lscv
#' @inheritParams gauss_lcv
#' @param an threshold for linear approximation
#' @return RLCV criterion value
#' @export
#' @keywords internal
gauss_rlcv <- function(x, Sigma, logweights, an, xsamp, dxsamp, mckern = TRUE) {
    .Call(`_mig_gauss_rlcv`, x, Sigma, logweights, an, xsamp, dxsamp, mckern)
}

#' Likelihood cross-validation for truncated normal kernel density estimation
#'
#' Robust likelihood cross-validation criterion function.
#' @inheritParams gauss_lscv
#' @inheritParams tnorm_lcv
#' @param an threshold for linear approximation
#' @return RLCV criterion value
#' @export
#' @keywords internal
tnorm_rlcv <- function(x, Omega, beta, an, xsamp, dxsamp, mckern = TRUE) {
    .Call(`_mig_tnorm_rlcv`, x, Omega, beta, an, xsamp, dxsamp, mckern)
}

mig2_rlcv <- function(x, Omega, beta, an, xsamp, dxsamp, mckern = TRUE) {
    .Call(`_mig_mig2_rlcv`, x, Omega, beta, an, xsamp, dxsamp, mckern)
}

mig2_lscv <- function(x, Omega, beta, xsamp, dxsamp, mckern = TRUE) {
    .Call(`_mig_mig2_lscv`, x, Omega, beta, xsamp, dxsamp, mckern)
}

mig2_lcv <- function(x, Omega, beta) {
    .Call(`_mig_mig2_lcv`, x, Omega, beta)
}