Package: probe
Type: Package
Title: Sparse High-Dimensional Linear Regression with a PaRtitiOned
        Empirical Bayes Ecm (PROBE) Algorithm
Version: 1.3
Date: 2026-02-27
Authors@R: c(person("Alexander", "McLain", email = "mclaina@mailbox.sc.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-5475-0670")), person("Anja", "Zgodic", 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. Simulation
        studies found the all-at-once variant to be superior.
BugReports: https://github.com/alexmclain/PROBE/issues
License: GPL (>= 2)
Encoding: UTF-8
RoxygenNote: 7.3.3
Imports: Rcpp, glmnet, RcppArmadillo
LinkingTo: Rcpp, RcppArmadillo
NeedsCompilation: yes
Packaged: 2026-07-02 09:22:59 UTC; root
Depends: R (>= 4.00)
Repository: https://alexmclain.r-universe.dev
Date/Publication: 2026-02-27 17:38:31 UTC
RemoteUrl: https://github.com/alexmclain/probe
RemoteRef: HEAD
RemoteSha: 79045968c6497f936e4f5c1b8fc75cab0e75c5a6
RemoteSubdir: probe
Author: Alexander McLain [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-5475-0670>),
  Anja Zgodic [aut, ctb]
Maintainer: Alexander McLain <mclaina@mailbox.sc.edu>