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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 ORCID iD [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

Documentation:

Reference manual: probe.pdf

Downloads:

Package source: probe_1.1.tar.gz
Windows binaries: r-devel: probe_1.1.zip, r-release: probe_1.1.zip, r-oldrel: probe_1.1.zip
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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=probe to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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