HDMAADMM-package {HDMAADMM}R Documentation

HDMAADMM Package

Description

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

  1. Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1), 267–288.

  2. 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.

  3. Li, C., Li, H. (2008). Network-constrained regularization and variable selection for analysis of genomic data, Bioinformatics, 24(9), 1175–1182,

  4. Zhao, Y., & Luo, X. (2022). Pathway Lasso: pathway estimation and selection with high-dimensional mediators. Statistics and its interface, 15(1), 39.

[Package HDMAADMM version 0.0.1 Index]