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shrinkem  

Approximate Bayesian Regularization for Parsimonious Estimates
View on CRAN: Click here


Download and install shrinkem package within the R console
Install from CRAN:
install.packages("shrinkem")

Install from Github:
library("remotes")
install_github("cran/shrinkem")

Install by package version:
library("remotes")
install_version("shrinkem", "0.2.0")



Attach the package and use:
library("shrinkem")
Maintained by
Joris Mulder
[Scholar Profile | Author Map]
First Published: 2024-10-05
Latest Update: 2024-10-05
Description:
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>).
How to cite:
Joris Mulder (2024). shrinkem: Approximate Bayesian Regularization for Parsimonious Estimates. R package version 0.2.0, https://cran.r-project.org/web/packages/shrinkem. Accessed 02 Apr. 2025.
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