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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>).
| Version: | 0.2.0 |
| Imports: | stats, mvtnorm, extraDistr, brms, CholWishart, matrixcalc |
| Suggests: | testthat |
| Published: | 2024-10-05 |
| DOI: | 10.32614/CRAN.package.shrinkem |
| Author: | Joris Mulder [aut, cre], Diana Karimova [aut, ctb], Sara van Erp [ctb] |
| Maintainer: | Joris Mulder <j.mulder3 at tilburguniversity.edu> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | shrinkem results |
| Reference manual: | shrinkem.pdf |
| Package source: | shrinkem_0.2.0.tar.gz |
| Windows binaries: | r-devel: shrinkem_0.2.0.zip, r-release: shrinkem_0.2.0.zip, r-oldrel: shrinkem_0.2.0.zip |
| macOS binaries: | r-release (arm64): shrinkem_0.2.0.tgz, r-oldrel (arm64): shrinkem_0.2.0.tgz, r-release (x86_64): shrinkem_0.2.0.tgz, r-oldrel (x86_64): shrinkem_0.2.0.tgz |
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These binaries (installable software) and packages are in development.
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