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