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type: software
license: GPL-3.0-or-later
title: 'shrinkem: Approximate Bayesian Regularization for Parsimonious Estimates'
version: 0.2.0
doi: 10.32614/CRAN.package.shrinkem
abstract: 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>).
authors:
- family-names: Mulder
  given-names: Joris
  email: j.mulder3@tilburguniversity.edu
- family-names: Karimova
  given-names: Diana
  email: dbkarimova@gmail.com
repository: https://CRAN.R-project.org/package=shrinkem
date-released: '2024-10-01'
contact:
- family-names: Mulder
  given-names: Joris
  email: j.mulder3@tilburguniversity.edu