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icmm
View on CRAN: Click
here
Download and install icmm package within the R console
Install from CRAN:
install.packages("icmm")
Install from Github:
library("remotes")
install_github("cran/icmm") Install by package version:
library("remotes")
install_version("icmm", "1.2") Attach the package and use:
library("icmm")
Maintained by
Vitara Pungpapong
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-07-27
Latest Update: 2021-05-26
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.
How to cite:
Vitara Pungpapong (2017). icmm: Empirical Bayes Variable Selection via ICM/M Algorithm. R package version 1.2, https://cran.r-project.org/web/packages/icmm. Accessed 27 Jun. 2026.
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Complete documentation for icmm
Functions, R codes and Examples using
the icmm R package
Some associated functions: get.ab . get.alpha . get.beta.ising . get.beta . get.pseudodata.binomial . get.pseudodata.cox . get.sigma . get.wpost . get.wprior . get.zeta.ising . get.zeta . icmm-package . icmm . initbetaBinomial . initbetaCox . initbetaGaussian . linearrelation . simBinomial . simCox . simGaussian .
Some associated R codes: get.ab.R . get.alpha.R . get.beta.R . get.beta.ising.R . get.pseudodata.binomial.R . get.pseudodata.cox.R . get.sigma.R . get.wpost.R . get.wprior.R . get.zeta.R . get.zeta.ising.R . icmm.R . Full icmm package functions and examples
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