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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