The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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 |
Please use the canonical form https://CRAN.R-project.org/package=icmm to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.