icmm

Empirical Bayes Variable Selection via ICM/M Algorithm

Carries out empirical Bayes variable selection via ICM/M algorithm. The basic problem is to fit high-dimensional regression which most coefficients are assumed to be zero. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. The current version of this package can handle the normal, binary logistic, and Cox's regression (Pungpapong et. al. (2015) <doi:10.1214/15-EJS1034>, Pungpapong et. al. (2017) <arXiv:1707.08298>).

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Description file content

Package
icmm
Type
Package
Title
Empirical Bayes Variable Selection via ICM/M Algorithm
Version
1.1
Author
Vitara Pungpapong [aut, cre], Min Zhang [aut], Dabao Zhang [aut]
Maintainer
Vitara Pungpapong
Description
Carries out empirical Bayes variable selection via ICM/M algorithm. The basic problem is to fit high-dimensional regression which most coefficients are assumed to be zero. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. The current version of this package can handle the normal, binary logistic, and Cox's regression (Pungpapong et. al. (2015) , Pungpapong et. al. (2017) ).
License
GPL (>= 2)
Imports
EbayesThresh
Suggests
MASS, stats
LazyData
TRUE
RoxygenNote
5.0.1
NeedsCompilation
no
Packaged
2017-10-12 02:05:18 UTC; vpungpap
Repository
CRAN
Date/Publication
2017-10-12 03:17:25 UTC

install.packages('icmm')

1.1

a month ago

Vitara Pungpapong

GPL (>= 2)

Imports

EbayesThresh

Suggests

MASS, stats

Discussions