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) <arXiv:2209.08139>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.1 |
rolling linux/jammy R-4.5 | probe_1.1.tar.gz |
466.6 KiB |
1.1 |
rolling linux/noble R-4.5 | probe_1.1.tar.gz |
474.2 KiB |
1.1 |
rolling source/ R- | probe_1.1.tar.gz |
283.1 KiB |
1.1 |
latest linux/jammy R-4.5 | probe_1.1.tar.gz |
466.6 KiB |
1.1 |
latest linux/noble R-4.5 | probe_1.1.tar.gz |
474.2 KiB |
1.1 |
latest source/ R- | probe_1.1.tar.gz |
283.1 KiB |
1.1 |
2026-04-26 source/ R- | probe_1.1.tar.gz |
283.1 KiB |
1.1 |
2026-04-23 source/ R- | probe_1.1.tar.gz |
283.1 KiB |
1.1 |
2026-04-09 windows/windows R-4.5 | probe_1.1.zip |
874.0 KiB |
1.1 |
2025-04-20 source/ R- | probe_1.1.tar.gz |
283.1 KiB |