{ "_id": "6a6019a214e95619b75d7ec0", "Package": "rregm", "Type": "Package", "Title": "Reparameterized Regression Models", "Version": "1.3", "Authors@R": "c(person(\"Diego\", \"Gallardo\", role = c(\"aut\",\"cre\"), email = \"dgallardo@ubiobio.cl\"),\nperson(\"Marcelo\", \"Bourguignon\", role = \"aut\", email = \"m.p.bourguignon@gmail.com\"),\nperson(\"Marcia\",\"Brandao\",role=\"aut\", email=\"mbrandao07@gmail.com\"),\nperson(\"Tiago\",\"Magalhaes\",role=\"ctb\", email=\"tiago.magalhaes@ufjf.br\"),\nperson(\"Rafael\",\"Izbicki\",role=\"ctb\", email=\"rizbicki@ufscar.br\"))", "Maintainer": "Diego Gallardo ", "Description": "Provides estimation and data generation tools for several\nnew regression models, including the gamma, beta, inverse\ngamma, beta prime, log-normal and log-logistic distributions.\nThese models can be parameterized based on the mean, median,\nmode, geometric mean and harmonic mean, except for the\nlog-logistic model which is based on alternative\nparametrizations. For details, see Bourguignon and Gallardo\n(2025a) and Bourguignon\nand Gallardo (2025b) . The package also\nimplements higher-order likelihood inference through\nSkovgaard-adjusted likelihood ratio statistics and predictive\nshrinkage estimators reparameterized beta regression models.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "NeedsCompilation": "no", "Author": "Diego Gallardo [aut, cre], Marcelo Bourguignon [aut], Marcia\nBrandao [aut], Tiago Magalhaes [ctb], Rafael Izbicki [ctb]", "Packaged": { "Date": "2026-07-22 01:12:00 UTC", "User": "root" }, "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-07-22 00:12:33 UTC", "RemoteUrl": "https://github.com/cran/rregm", "RemoteRef": "HEAD", "RemoteSha": "387a00fd234dbd14df786add83111f83bee1639c", "_user": "cran", "_type": "src", "_file": "rregm_1.3.tar.gz", "_fileid": "https://r2.ropensci.org/ab9700442914dcda9406e6799c776f359d7a6f968b3f3c65156abb1e6d4968d9", "_filesize": 357972, "_sha256": "ab9700442914dcda9406e6799c776f359d7a6f968b3f3c65156abb1e6d4968d9", "_expires": "2026-10-30T01:15:12.000Z", 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