R/HDMAADMM-package.R

In HDMAADMM: ADMM for High-Dimensional Mediation Models

## Copyright (C) 2023        Ching-Chuan Chen, Pei-Shan Yen
##
## This file is part of HDMAADMM.
##
## HDMAADMM is free software: you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 2 of the License, or
## (at your option) any later version.
##
## HDMAADMM is distributed in the hope that it will be useful, but
## WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
## GNU General Public License for more details.

#' `HDMAADMM` Package
#'
#' This package enables the estimation of single-modality high-dimensional mediation models.
#' We employ penalized maximum likelihood and solve the estimation using the
#' Alternating Direction Method of Multipliers (ADMM) to provide high-dimensional mediator estimates.
#' To improve the sensitivity and specificity of non-zero mediators,
#' we offer the sure independence screening (SIS) function for dimension reduction.
#' The available penalty options include Lasso, Elastic Net, Pathway Lasso, and Network-constrained Penalty.
#'
#' @references
#' \enumerate{
#'   \item Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1), 267–288.
#'   \item Zou, H., & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 67(2), 301–320.
#'   \item Li, C., Li, H. (2008). Network-constrained regularization and variable selection for analysis of genomic data, Bioinformatics, 24(9), 1175–1182,
#'   \item Zhao, Y., & Luo, X. (2022). Pathway Lasso: pathway estimation and selection with high-dimensional mediators. Statistics and its interface, 15(1), 39.
#' }
#'
#' @docType package
#' @name HDMAADMM-package
#' @useDynLib HDMAADMM
#' @importFrom Rcpp evalCpp
#' @importFrom RcppEigen RcppEigen.package.skeleton
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