{ "_id": "6a57be31ff0138dec5d71be3", "Package": "shrinkem", "Type": "Package", "Title": "Approximate Bayesian Regularization for Parsimonious Estimates", "Date": "2026-07-15", "Version": "0.4.0", "Authors@R": "c(person(given = c(\"Joris\"),\nfamily = \"Mulder\",\nrole = c(\"aut\", \"cre\"),\nemail = \"j.mulder3@tilburguniversity.edu\"),\nperson(given = \"Diana\",\nfamily = \"Karimova\",\nrole = c(\"aut\", \"ctb\"),\nemail = \"dbkarimova@gmail.com\"),\nperson(given = \"Sara\",\nfamily = \"van Erp\",\nrole = c(\"ctb\"),\nemail = \"s.j.vanerp@uu.nl\"),\nperson(given = \"Roger\",\nfamily = \"Leenders\",\nrole = c(\"ctb\"),\nemail = \"r.t.a.j.leenders@tilburguniversity.edu\")\n)", "Author": "Joris Mulder [aut, cre], Diana Karimova [aut, ctb], Sara van\nErp [ctb], Roger Leenders [ctb]", "Maintainer": "Joris Mulder ", "Description": "Approximate Bayesian regularization using Gaussian\napproximations. The input is a vector of estimates and a\nGaussian error covariance matrix of the key parameters.\nBayesian shrinkage is then applied to obtain parsimonious\nsolutions. The method is described on Karimova, van Erp,\nLeenders, and Mulder (2025) .\nGibbs samplers are used for model fitting. The shrinkage priors\nthat are supported are Gaussian (ridge) priors, Laplace (lasso)\npriors (Park and Casella, 2008\n), and horseshoe priors\n(Carvalho, et al., 2010; ). These\npriors include an option for grouped regularization of\ndifferent subsets of parameters (Meier et al., 2008;\n). F priors are used for\nthe penalty parameters lambda^2 (Mulder and Pericchi, 2018\n). 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