{ "_id": "6a1ac2221d7bb097a09d9a20", "Package": "pqrBayes", "Type": "Package", "Title": "Bayesian Penalized Quantile Regression", "Version": "1.2.2", "Date": "2026-04-28", "Authors@R": "c( \nperson(\"Kun\", \"Fan\", role = \"aut\"),\nperson(\"Cen\", \"Wu\", role = c(\"aut\", \"cre\"),email = \"wucen@ksu.edu\"),\nperson(\"Jie\", \"Ren\", role = \"aut\"),\nperson(\"Xiaoxi\", \"Li\", role = \"aut\"),\nperson(\"Fei\", \"Zhou\", role = \"aut\"))", "Description": "Bayesian regularized quantile regression utilizing two\nmajor classes of shrinkage priors (the spike-and-slab priors\nand the horseshoe family of priors) leads to efficient Bayesian\nshrinkage estimation, variable selection and valid statistical\ninference. In this package, we have implemented robust Bayesian\nvariable selection with spike-and-slab priors under\nhigh-dimensional linear regression models (Fan et al. (2024)\n and Ren et al. (2023)\n), and regularized quantile varying\ncoefficient models (Zhou et al.(2023)\n). In particular, valid robust\nBayesian inferences under both models in the presence of\nheavy-tailed errors can be validated on finite samples.\nAdditional models with spike-and-slab priors include robust\nBayesian group LASSO and robust binary Bayesian LASSO (Fan and\nWu (2025) ). Besides, robust sparse\nBayesian regression with the horseshoe family of (horseshoe,\nhorseshoe+ and regularized horseshoe) priors has also been\nimplemented and yielded valid inference results under\nheavy-tailed model errors (Fan et al.(2026)\n). The Markov chain Monte Carlo\n(MCMC) algorithms of the proposed and alternative models are\nimplemented in C++.", "License": "GPL-2", "Encoding": "UTF-8", "URL": "https://github.com/cenwu/pqrBayes", "BugReports": "https://github.com/cenwu/pqrBayes/issues", "LazyData": "true", "RoxygenNote": "7.3.3", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-30 10:14:39 UTC", "User": "root" }, "Author": "Kun Fan [aut], Cen Wu [aut, cre], Jie Ren [aut], Xiaoxi Li\n[aut], Fei Zhou [aut]", "Maintainer": "Cen Wu ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-04-30 09:32:29 UTC", "RemoteUrl": "https://github.com/cran/pqrBayes", "RemoteRef": "HEAD", "RemoteSha": "d74bf447d1ff69032a497022deaf87d860202f2b", "MD5sum": "5c5b4057aa970d7fa21a2eb24f8a6d16", "_user": "cran", "_type": "src", "_file": "pqrBayes_1.2.2.tar.gz", "_fileid": "02d7fbee1c3dbd4487fbb67827d97790327a5b363cfa6c85dd992c11bf60b43d", "_filesize": 293493, "_sha256": 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