#' @title Sensitivity matrix
#' @author Wagner Hugo Bonat and Eduardo Elias Ribeiro Jr
#'
#' @description Compute the sensitivity matrix associated with the
#' Pearson estimating function.
#'
#' @param product A list of matrix.
#' @param W weights.
#' @return The sensitivity matrix associated with the Pearson estimating
#' function.
#' @keywords internal
#' @details This function implements the equation 7 of Bonat and
#' Jorgensen (2016).
#' @useDynLib mglm4twin
#' @importFrom Rcpp sourceCpp
ef_sensitivity <- function(product, W) {
#sourceCpp("src/mc_sensitivity_op.cpp")
Sensitivity <- ef_sensitivity_op(products = product, W = W)
Sensitivity <- forceSymmetric(Sensitivity, uplo = "L")
return(Sensitivity)
}
#ef_sensitivity <- function(product) {
# #sourceCpp("src/mc_sensitivity_op.cpp")
# Sensitivity <- ef_sensitivity_op(products = product)
# Sensitivity <- forceSymmetric(Sensitivity, uplo = FALSE)
# return(Sensitivity)
#}
#ef_sensitivity <- function(product) {
# n_par <- length(product)
# Sensitivity <- matrix(0, n_par, n_par)
# Sensitivity_temp <- matrix(0, n_par, n_par)
# Sensitivity1 <- matrix(0, n_par, n_par)
# for (i in 1:n_par) {
# for (j in 1:n_par) {
# Sensitivity[i, j] <- -sum(Matrix::t(product[[i]]) * product[[j]])
# }
# }
# return(Sensitivity)
#}
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