icmm-package {icmm}R Documentation

Empirical Bayes Variable Selection via ICM/M

Description

Carries out empirical Bayes variable selection via ICM/M algorithm. The basic problem is to fit a high-dimensional regression which most of the coefficients are assumed to be zero. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. The current version of this package can handle the normal, binary logistic, and Cox's regression.

Details

Package: icmm
Type: Package
Version: 1.2
Date: 2021-5-12
License: GPL-2
LazyLoad: yes

Author(s)

Vitara Pungpapong, Min Zhang, Dabao Zhang

Maintainer: Vitara Pungpapong <vitara@cbs.chula.ac.th>

References

Pungpapong, V., Zhang, M. and Zhang, D. (2015). Selecting massive variables using an iterated conditional modes/medians algorithm. Electronic Journal of Statistics. 9:1243-1266. <doi:10.1214/15-EJS1034>.
Pungpapong, V., Zhang, M. and Zhang, D. (2020). Integrating Biological Knowledge Into Case-Control Analysis Through Iterated Conditional Modes/Medians Algorithm. Journal of Computational Biology. 27(7): 1171-1179. <doi:10.1089/cmb.2019.0319>.


[Package icmm version 1.2 Index]