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probe: Sparse High-Dimensional Linear Regression with PROBE

Implements an efficient and powerful Bayesian approach for sparse high-dimensional linear regression. It uses minimal prior assumptions on the parameters through plug-in empirical Bayes estimates of hyperparameters. An efficient Parameter-Expanded Expectation-Conditional-Maximization (PX-ECM) algorithm estimates maximum a posteriori (MAP) values of regression parameters and variable selection probabilities. The PX-ECM results in a robust computationally efficient coordinate-wise optimization, which adjusts for the impact of other predictor variables. The E-step is motivated by the popular two-group approach to multiple testing. The result is a PaRtitiOned empirical Bayes Ecm (PROBE) algorithm applied to sparse high-dimensional linear regression, implemented using one-at-a-time or all-at-once type optimization. More information can be found in McLain, Zgodic, and Bondell (2022) <doi:10.48550/arXiv.2209.08139>.

Version:	1.1
Depends:	R (≥ 4.00)
Imports:	Rcpp,glmnet
LinkingTo:	Rcpp,RcppArmadillo
Published:	2023-10-31
DOI:	10.32614/CRAN.package.probe
Author:	Alexander McLain [aut, cre], Anja Zodiac [aut, ctb]
Maintainer:	Alexander McLain <mclaina at mailbox.sc.edu>
BugReports:	https://github.com/alexmclain/PROBE/issues
License:	GPL-2 |GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
CRAN checks:	probe results

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