Approximate Bayesian Regularization for Parsimonious Estimates R package shrinkem version 0.4.0 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>). Software Imports: Rcpp, stats, extraDistr, CholWishart, matrixcalc, logspline LinkingTo: Rcpp, RcppArmadillo Suggests: tinytest Joris Mulder <j.mulder3@tilburguniversity.edu> Comprehensive R Archive Network (CRAN) Joris Mulder [aut, cre], Diana Karimova [aut, ctb], Sara van Erp [ctb], Roger Leenders [ctb] GPL (>= 3) 2026-07-15 application/tgz https://CRAN.R-project.org/package=shrinkem doi:10.32614/CRAN.package.shrinkem