shrinkem
Approximate Bayesian Regularization for Parsimonious Estimates
Approximate Bayesian regularization using Gaussian approximations. The input is a vector of estimates and a Gaussian error covariance matrix of the key parameters. Bayesian shrinkage is then applied to obtain parsimonious solutions. The method is described on Karimova, van Erp, Leenders, and Mulder (2024) <DOI:10.31234/osf.io/2g8qm>. Gibbs samplers are used for model fitting. The shrinkage priors that are supported are Gaussian (ridge) priors, Laplace (lasso) priors (Park and Casella, 2008 <DOI:10.1198/016214508000000337>), and horseshoe priors (Carvalho, et al., 2010; <DOI:10.1093/biomet/asq017>). These priors include an option for grouped regularization of different subsets of parameters (Meier et al., 2008; <DOI:10.1111/j.1467-9868.2007.00627.x>). F priors are used for the penalty parameters lambda^2 (Mulder and Pericchi, 2018 <DOI:10.1214/17-BA1092>). This correspond to half-Cauchy priors on lambda (Carvalho, Polson, Scott, 2010 <DOI:10.1093/biomet/asq017>).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.0 |
rolling linux/jammy R-4.5 | shrinkem_0.2.0.tar.gz |
42.0 KiB |
0.2.0 |
rolling linux/noble R-4.5 | shrinkem_0.2.0.tar.gz |
42.0 KiB |
0.2.0 |
rolling source/ R- | shrinkem_0.2.0.tar.gz |
8.8 KiB |
0.2.0 |
latest linux/jammy R-4.5 | shrinkem_0.2.0.tar.gz |
42.0 KiB |
0.2.0 |
latest linux/noble R-4.5 | shrinkem_0.2.0.tar.gz |
42.0 KiB |
0.2.0 |
latest source/ R- | shrinkem_0.2.0.tar.gz |
8.8 KiB |
0.2.0 |
2026-04-26 source/ R- | shrinkem_0.2.0.tar.gz |
8.8 KiB |
0.2.0 |
2026-04-23 source/ R- | shrinkem_0.2.0.tar.gz |
8.8 KiB |
0.2.0 |
2026-04-09 windows/windows R-4.5 | shrinkem_0.2.0.zip |
44.5 KiB |
0.2.0 |
2025-04-20 source/ R- | shrinkem_0.2.0.tar.gz |
8.8 KiB |