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 ORCID iD [aut, cre], Anja Zodiac [aut, ctb]
Maintainer: Alexander McLain <mclaina at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: probe results


Reference manual: probe.pdf


Package source: probe_1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): probe_1.1.tgz, r-oldrel (arm64): probe_1.1.tgz, r-release (x86_64): probe_1.1.tgz, r-oldrel (x86_64): probe_1.1.tgz


Please use the canonical form to link to this page.