Package: probe
Type: Package
Title: Sparse High-Dimensional Linear Regression with PROBE
Version: 1.1
Date: 2023-10-01
Authors@R: c(person("Alexander", "McLain", email = "mclaina@mailbox.sc.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-5475-0670")), person("Anja", "Zodiac", role = c("aut", "ctb")))
Description: 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) <arXiv:2209.08139>.
BugReports: https://github.com/alexmclain/PROBE/issues
License: GPL (>= 2)
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: Rcpp, glmnet
LinkingTo: Rcpp, RcppArmadillo
NeedsCompilation: yes
Packaged: 2023-10-30 18:02:52 UTC; guestaccount
Depends: R (>= 4.00)
Author: Alexander McLain [aut, cre] (<https://orcid.org/0000-0002-5475-0670>),
  Anja Zodiac [aut, ctb]
Maintainer: Alexander McLain <mclaina@mailbox.sc.edu>
Repository: CRAN
Date/Publication: 2023-10-31 16:30:02 UTC
Built: R 4.6.0; aarch64-apple-darwin23; 2026-04-26 09:35:29 UTC; unix
Archs: probe.so.dSYM
