HDMAADMM: ADMM for High-Dimensional Mediation Models

We use the Alternating Direction Method of Multipliers (ADMM) for parameter estimation in high-dimensional, single-modality mediation models. To improve the sensitivity and specificity of estimated mediation effects, 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. The methods employed in the package are based on Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). <doi:10.1561/2200000016>, Fan, J., & Lv, J. (2008) <doi:10.1111/j.1467-9868.2008.00674.x>, Li, C., & Li, H. (2008) <doi:10.1093/bioinformatics/btn081>, Tibshirani, R. (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, Zhao, Y., & Luo, X. (2022) <doi:10.4310/21-sii673>, and Zou, H., & Hastie, T. (2005) <doi:10.1111/j.1467-9868.2005.00503.x>.

Version:	0.0.1
Depends:	R (≥ 4.0.0)
Imports:	Rcpp (≥ 1.0.0), dqrng, RcppEigen
LinkingTo:	Rcpp, RcppEigen
Suggests:	roxygen2
Published:	2023-11-29
DOI:	10.32614/CRAN.package.HDMAADMM
Author:	Pei-Shan Yen [aut, cre], Ching-Chuan Chen [aut]
Maintainer:	Pei-Shan Yen <peishan0824 at gmail.com>
BugReports:	https://github.com/psyen0824/HDMAADMM/issues
License:	MIT + file LICENSE
URL:	https://github.com/psyen0824/HDMAADMM
NeedsCompilation:	yes
Materials:	README
CRAN checks:	HDMAADMM results

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