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>).

Version: 0.2.0
Imports: stats, mvtnorm, extraDistr, brms, CholWishart, matrixcalc
Suggests: testthat
Published: 2024-10-05
DOI: 10.32614/CRAN.package.shrinkem
Author: Joris Mulder [aut, cre], Diana Karimova [aut, ctb], Sara van Erp [ctb]
Maintainer: Joris Mulder <j.mulder3 at tilburguniversity.edu>
License: GPL (≥ 3)
NeedsCompilation: no
Materials: README
CRAN checks: shrinkem results

Documentation:

Reference manual: shrinkem.pdf

Downloads:

Package source: shrinkem_0.2.0.tar.gz
Windows binaries: r-devel: shrinkem_0.2.0.zip, r-release: shrinkem_0.2.0.zip, r-oldrel: shrinkem_0.2.0.zip
macOS binaries: r-release (arm64): shrinkem_0.2.0.tgz, r-oldrel (arm64): shrinkem_0.2.0.tgz, r-release (x86_64): shrinkem_0.2.0.tgz, r-oldrel (x86_64): shrinkem_0.2.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=shrinkem to link to this page.