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) , Pungpapong et. al. (2017) ).


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.1 by Vitara Pungpapong, 4 years ago

Browse source code at https://github.com/cran/icmm

Authors: Vitara Pungpapong [aut, cre] , Min Zhang [aut] , Dabao Zhang [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports EbayesThresh

Suggests MASS, stats

See at CRAN