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.


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Reference manual

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install.packages("icmm")

1.2 by Vitara Pungpapong, 4 months ago


https://www.researchgate.net/publication/279279744_Selecting_massive_variables_using_an_iterated_conditional_modesmedians_algorithm, https://doi.org/10.1089/cmb.2019.0319


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


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


Documentation:   PDF Manual  


GPL (>= 2) license


Imports EbayesThresh

Suggests MASS, stats


See at CRAN