Package: icmm
Title: Empirical Bayes Variable Selection via ICM/M Algorithm
Version: 1.2
Authors@R: c(person("Vitara", "Pungpapong", role = c("aut", "cre"),
		     email = "vitara@cbs.chula.ac.th"),
	      person("Min", "Zhang", role = "ctb", email="minzhang@purdue.edu"),
	      person("Dabao", "Zhang", role = "ctb", email = "zhangdb@purdue.edu"))
Author: Vitara Pungpapong [aut, cre], Min Zhang [ctb], Dabao Zhang
        [ctb]
Maintainer: Vitara Pungpapong <vitara@cbs.chula.ac.th>
Description: 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.
License: 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
Encoding: UTF-8
Imports: EbayesThresh
Suggests: MASS, stats
LazyData: true
RoxygenNote: 7.1.1
Packaged: 2026-06-19 08:30:43 UTC; root
NeedsCompilation: no
Repository: https://vitara-p.r-universe.dev
Date/Publication: 2021-05-26 04:20:02 UTC
RemoteUrl: https://github.com/cran/icmm
RemoteRef: HEAD
RemoteSha: f9f25daed5d0ce12ff343dce7920e6a86bb317e5