probe: Sparse High-Dimensional Linear Regression with PROBE (original) (raw)
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 |
| 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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