icmm: Empirical Bayes Variable Selection via ICM/M Algorithm

Carries out empirical Bayes variable selection via ICM/M algorithm. The basic problem is to fit high-dimensional regression which most 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 (Pungpapong et. al. (2015) <doi:10.1214/15-EJS1034>, Pungpapong et. al. (2017) <arXiv:1707.08298>).

Version: 1.0
Imports: EbayesThresh
Suggests: MASS, stats
Published: 2017-07-27
Author: Vitara Pungpapong [aut, cre], Min Zhang [aut], Dabao Zhang [aut]
Maintainer: Vitara Pungpapong <vitara at cbs.chula.ac.th>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: icmm results

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Reference manual: icmm.pdf
Package source: icmm_1.0.tar.gz
Windows binaries: r-devel: icmm_1.0.zip, r-release: icmm_1.0.zip, r-oldrel: icmm_1.0.zip
OS X El Capitan binaries: r-release: icmm_1.0.tgz
OS X Mavericks binaries: r-oldrel: icmm_1.0.tgz

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