Package: shrinkem 0.2.0
Joris Mulder
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>).
Authors:
shrinkem_0.2.0.tar.gz
shrinkem_0.2.0.tar.gz(r-4.5-noble)shrinkem_0.2.0.tar.gz(r-4.4-noble)
shrinkem_0.2.0.tgz(r-4.4-emscripten)shrinkem_0.2.0.tgz(r-4.3-emscripten)
shrinkem.pdf |shrinkem.html✨
shrinkem/json (API)
# Install 'shrinkem' in R: |
install.packages('shrinkem', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 months agofrom:46cd063f74. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
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Doc / Vignettes | OK | Dec 05 2024 |
R-4.5-linux | OK | Dec 05 2024 |
Dependencies:abindbackportsbayesplotBHbridgesamplingbrmsBrobdingnagcallrcheckmateCholWishartclicodacodetoolscolorspacedescdigestdistributionaldplyrextraDistrfansifarverfuturefuture.applygenericsggplot2ggridgesglobalsgluegridExtragtableinlineisobandlabelinglatticelifecyclelistenvloomagrittrMASSMatrixmatrixcalcmatrixStatsmgcvmunsellmvtnormnleqslvnlmenumDerivparallellypillarpkgbuildpkgconfigplyrposteriorprocessxpsQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelreshape2rlangrstanrstantoolsscalesStanHeadersstringistringrtensorAtibbletidyselectutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
Help page | Topics |
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The (scaled) F Distribution | dF F rF |
The matrix F Distribution | dmvF mvF rmvF |
Fast Bayesian regularization using Gaussian approximations | shrinkem |