icmm: Empirical Bayes Variable Selection via ICM/M Algorithm

Empirical Bayes variable selection via ICM/M algorithm for normal, binary logistic, and Cox's regression. The basic problem is to fit high-dimensional regression which sparse coefficients. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. More information can be found in the papers listed in the URL below.

Version: 1.2
Imports: EbayesThresh
Suggests: MASS, stats
Published: 2021-05-26
DOI: 10.32614/CRAN.package.icmm
Author: Vitara Pungpapong [aut, cre], Min Zhang [ctb], Dabao Zhang [ctb]
Maintainer: Vitara Pungpapong <vitara at cbs.chula.ac.th>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://www.researchgate.net/publication/279279744_Selecting_massive_variables_using_an_iterated_conditional_modesmedians_algorithm, https://doi.org/10.1089/cmb.2019.0319
NeedsCompilation: no
Materials: NEWS
CRAN checks: icmm results


Reference manual: icmm.pdf


Package source: icmm_1.2.tar.gz
Windows binaries: r-devel: icmm_1.2.zip, r-release: icmm_1.2.zip, r-oldrel: icmm_1.2.zip
macOS binaries: r-release (arm64): icmm_1.2.tgz, r-oldrel (arm64): icmm_1.2.tgz, r-release (x86_64): icmm_1.2.tgz, r-oldrel (x86_64): icmm_1.2.tgz
Old sources: icmm archive


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