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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.
Getting started
Browse package contents
Package details |
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| Author | Vitara Pungpapong [aut, cre], Min Zhang [ctb], Dabao Zhang [ctb] |
| Maintainer | Vitara Pungpapong <vitara@cbs.chula.ac.th> |
| License | GPL (>= 2) |
| Version | 1.2 |
| URL | https://www.researchgate.net/publication/279279744_Selecting_massive_variables_using_an_iterated_conditional_modesmedians_algorithm https://doi.org/10.1089/cmb.2019.0319 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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