R/RcppExports.R

In stfit: Spatio-Temporal Functional Imputation Tool

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

nbr <- function(ii, nRow, nCol, dRow, dCol) {
    .Call(`_stfit_nbr`, ii, nRow, nCol, dRow, dCol)
}

lc_cov1_ <- function(X, W, ii, jj, nRow, nCol, pidx) {
    .Call(`_stfit_lc_cov1_`, X, W, ii, jj, nRow, nCol, pidx)
}

sparse_emp_cov_est <- function(X, nRow, nCol, nnr) {
    .Call(`_stfit_sparse_emp_cov_est`, X, nRow, nCol, nnr)
}

sparse_emp_cov_est1 <- function(X, nRow, nCol, nnr, pidx) {
    .Call(`_stfit_sparse_emp_cov_est1`, X, nRow, nCol, nnr, pidx)
}

sparse_lc_cov_est <- function(X, W, nRow, nCol, nnr) {
    .Call(`_stfit_sparse_lc_cov_est`, X, W, nRow, nCol, nnr)
}

sparse_lc_cov_est1 <- function(X, W, nRow, nCol, nnr, pidx) {
    .Call(`_stfit_sparse_lc_cov_est1`, X, W, nRow, nCol, nnr, pidx)
}

#' Local constant covariance estimation
#' @param ids a vector indicating subject/group ids
#' @param time integer vector of observed time points, the minimum time unit is 1
#' @param resid vector of residual values used for covariance calculation
#' @param W weight vector, it contains both kernel and bandwidth information in general 
#' local polynomial estimation setting up
#' @param t1 time point 1
#' @param t2 time point 2
#' @retrun covariance value between t1 and t2
#' @export
lc_cov_1d <- function(ids, time, resid, W, t1, t2) {
    .Call(`_stfit_lc_cov_1d`, ids, time, resid, W, t1, t2)
}

#' Local constant covariance estimation
#' @param ids a vector indicating subject/group ids
#' @param time integer vector of observed time points, the minimum time unit is 1
#' @param resid vector of residual values used for covariance calculation
#' @param W weight vector, it contains both kernel and bandwidth information in general 
#' local polynomial estimation setting up
#' @param tt time vector
#' @retrun a covariance matrix evaluated at time points \code{tt} on the covariance function 
#' @export
lc_cov_1d_est <- function(ids, time, resid, W, tt) {
    .Call(`_stfit_lc_cov_1d_est`, ids, time, resid, W, tt)
}

mean_est <- function(X, nRow, nCol, W) {
    .Call(`_stfit_mean_est`, X, nRow, nCol, W)
}