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.


Reference manual

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


1.2 by Vitara Pungpapong, 8 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