Sparse High-Dimensional Linear Regression with PROBE R package probe version 1.1 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>. Software Depends: R (>= 4.00) Imports: Rcpp, glmnet LinkingTo: Rcpp, RcppArmadillo Alexander McLain <mclaina@mailbox.sc.edu> Comprehensive R Archive Network (CRAN) Alexander McLain [aut, cre] (ORCID: <https://orcid.org/0000-0002-5475-0670>), Anja Zodiac [aut, ctb] GPL (>= 2) 2023-10-31 application/tgz https://CRAN.R-project.org/package=probe doi:10.32614/CRAN.package.probe