Implements an efficient and powerful Bayesian approach for sparse highdimensional linear regression. It uses minimal prior assumptions on the parameters through plugin empirical Bayes estimates of hyperparameters. An efficient ParameterExpanded ExpectationConditionalMaximization (PXECM) algorithm estimates maximum a posteriori (MAP) values of regression parameters and variable selection probabilities. The PXECM results in a robust computationally efficient coordinatewise optimization, which adjusts for the impact of other predictor variables. The Estep is motivated by the popular twogroup approach to multiple testing. The result is a PaRtitiOned empirical Bayes Ecm (PROBE) algorithm applied to sparse highdimensional linear regression, implemented using oneatatime or allatonce type optimization. More information can be found in McLain, Zgodic, and Bondell (2022) <arXiv:2209.08139>.
Package details 


Author  Alexander McLain [aut, cre] (<https://orcid.org/0000000254750670>), Anja Zodiac [aut, ctb] 
Maintainer  Alexander McLain <mclaina@mailbox.sc.edu> 
License  GPL (>= 2) 
Version  1.1 
Package repository  View on CRAN 
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