shrinkem

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).

Last 30 days

This package has been downloaded 152 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 2 times.

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Last 365 days

This package has been downloaded 1,027 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Oct 08, 2024 with 30 downloads.

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