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  "Package": "pqrBayes",
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  "Title": "Bayesian Penalized Quantile Regression",
  "Version": "1.2.2",
  "Date": "2026-04-29",
  "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<doi:10.3390/e26090794> and Ren et al. (2023)\n<doi:10.1111/biom.13670>), and regularized quantile varying\ncoefficient models (Zhou et al.(2023)\n<doi:10.1016/j.csda.2023.107808>). 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) <doi:10.1002/sta4.70078>). 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<doi:10.1016/j.csda.2026.108358>). The Markov chain Monte Carlo\n(MCMC) algorithms of the proposed and alternative models are\nimplemented in C++.",
  "License": "GPL-2",
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  "Date/Publication": "2026-04-30 03:46:04 UTC",
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  "Author": "Kun Fan [aut],\nCen Wu [aut, cre],\nJie Ren [aut],\nXiaoxi Li [aut],\nFei Zhou [aut]",
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      "commit": "4ced7491efb82005e9599968772b4dba8deef859",
      "fileid": "https://r2.ropensci.org/f1ed81f91e517ae387e3196001e3920c5be0d5a5f330f9c21dfd17a0377b244f",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cenwu/actions/runs/36686128952"
    }
  ]
}