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 (2025) <doi:10.1016/j.jmp.2025.102925>. 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>).
| Version: |
0.3.0 |
| Imports: |
Rcpp, stats, extraDistr, CholWishart, matrixcalc, logspline |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
tinytest |
| Published: |
2026-06-10 |
| DOI: |
10.32614/CRAN.package.shrinkem |
| Author: |
Joris Mulder [aut, cre],
Diana Karimova [aut, ctb],
Sara van Erp [ctb],
Roger Leenders [ctb] |
| Maintainer: |
Joris Mulder <j.mulder3 at tilburguniversity.edu> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
yes |
| Materials: |
README |
| CRAN checks: |
shrinkem results |
Documentation:
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