{ "_id": "6ac9e9d2fe5cc25786e82948", "Package": "bbqr", "Type": "Package", "Title": "Bayesian Quantile Regression with Lasso and Adaptive Lasso", "Version": "0.2.0", "Authors@R": "c(\nperson(\"Fernando\", \"Rubio Garcia\", email = \"j332v755@wichita.edu\",\nrole = c(\"aut\", \"cre\"),\ncomment = \"Wichita State University\"),\nperson(\"Mai\", \"Dao\", role = \"ths\",\ncomment = \"Wichita State University\"),\nperson(\"Dries F.\", \"Benoit\", role = c(\"ctb\", \"cph\"),\ncomment = \"Author of 'bayesQR', from which the Fortran RNG wrapper and package layout are derived\"),\nperson(\"Rahim\", \"Al-Hamzawi\", role = \"ctb\"),\nperson(\"Keming\", \"Yu\", role = \"ctb\"),\nperson(\"Dirk\", \"Van den Poel\", role = \"ctb\"))", "Description": "Markov chain Monte Carlo samplers for Bayesian quantile\nregression, based on the asymmetric Laplace distribution and\nthe location-scale mixture representation of Kozumi and\nKobayashi (2011) . A binary\nresponse and an observed continuous response are both\nsupported, each with three penalty layers behind one interface:\nno penalty, following Benoit and Van den Poel (2012)\n; the Bayesian lasso, following Benoit,\nAl-Hamzawi and Yu (2013) ; and\nthe Bayesian adaptive lasso of Rubio Garcia (2023)\n.\nIn the binary family each is available as published and in a\ncorrected form, the default, in which every improper prior\ncomponent is replaced by a proper one so that the posterior\nexists unconditionally; the continuous family ships the\ncorrected form only. The continuous adaptive-lasso layer at its\ndefault reproduces the penalty of Alhamzawi, Yu and Benoit\n(2012) . A binary threshold\nmodel identifies the coefficient vector only up to a positive\nscale, so the binary samplers expose the identification anchor\nas an explicit argument, allowing fixing the scale of the error\ndistribution, fixing a single coefficient, and constraining the\nnorm of the coefficient vector to be compared directly; an\nobserved response identifies the scale, so the continuous\nsamplers have no anchor and draw it every sweep. The MCMC cores\nare written in Fortran and called from R.", "License": "GPL (>= 2)", "Copyright": "See inst/COPYRIGHTS for the attribution of third-party\ncomponents inherited from the 'bayesQR' package.", "Encoding": "UTF-8", "Config/testthat/edition": "3", "VignetteBuilder": "knitr", "NeedsCompilation": "yes", "RoxygenNote": "7.3.3", "URL": "https://github.com/fernandorubiogarcia/bbqr", "BugReports": "https://github.com/fernandorubiogarcia/bbqr/issues", "Packaged": { "Date": "2026-10-10 07:25:16 UTC", "User": "root" }, "Author": "Fernando Rubio Garcia [aut, cre] (Wichita State University),\nMai Dao [ths] (Wichita State University), Dries F. 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