{ "_id": "6a1ea295b25058d4daad7418", "Type": "Package", "Package": "SeBR", "Title": "Semiparametric Bayesian Regression Analysis", "Version": "1.1.0", "Authors@R": "person(\"Dan\", \"Kowal\", email = \"daniel.r.kowal@gmail.com\", role = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0003-0917-3007\"))", "Description": "Monte Carlo sampling algorithms for semiparametric\nBayesian regression analysis. These models feature a\nnonparametric (unknown) transformation of the data paired with\nwidely-used regression models including linear regression,\nspline regression, quantile regression, and Gaussian processes.\nThe transformation enables broader applicability of these key\nmodels, including for real-valued, positive, and\ncompactly-supported data with challenging distributional\nfeatures. The samplers prioritize computational scalability\nand, for most cases, Monte Carlo (not MCMC) sampling for\ngreater efficiency. 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