{ "_id": "6a2270e2cd65a98ecbd48c02", "Package": "brglm2", "Title": "Bias Reduction in Generalized Linear Models", "Version": "1.1.0", "Authors@R": "c(person(given = \"Ioannis\", family = \"Kosmidis\", role = c(\"aut\", \"cre\"), email = \"ioannis.kosmidis@warwick.ac.uk\", comment = c(ORCID = \"0000-0003-1556-0302\")),\nperson(given = c(\"Euloge\", \"Clovis\"), family = c(\"Kenne Pagui\"), role = \"aut\", email = \"kenne@stat.unipd.it\", comment = c(ORCID = \"0000-0002-8998-9251\")),\nperson(given = \"Federico\", family = \"Boiocchi\", role = \"ctb\", email = \"federico.boiocchi@gmail.com\"),\nperson(given = \"Philipp\", family = \"Sterzinger\", role = \"ctb\", email = \"P.Sterzinger@lse.ac.uk\", comment = c(ORCID = \"0009-0007-7348-5810\")),\nperson(given = \"Kjell\", family = \"Konis\", role = \"ctb\", email = \"kjell.konis@me.com\"),\nperson(given = \"Nicola\", family = \"Sartori\", role = \"ctb\", email = \"sartori@stat.unipd.it\"))", "Description": "Estimation and inference from generalized linear models\nbased on various methods for bias reduction and maximum\npenalized likelihood with powers of the Jeffreys prior as\npenalty. The 'brglmFit()' fitting method can achieve reduction\nof estimation bias by solving either the mean bias-reducing\nadjusted score equations in Firth (1993)\n and Kosmidis and Firth (2009)\n, or the median bias-reducing\nadjusted score equations in Kenne et al. (2017)\n, or through the direct subtraction\nof an estimate of the bias of the maximum likelihood estimator\nfrom the maximum likelihood estimates as in Cordeiro and\nMcCullagh (1991) . See\nKosmidis et al (2020) for more\ndetails. Estimation in all cases takes place via a quasi Fisher\nscoring algorithm, and S3 methods for the construction of of\nconfidence intervals for the reduced-bias estimates are\nprovided. In the special case of generalized linear models for\nbinomial and multinomial responses (both ordinal and nominal),\nthe adjusted score approaches to mean and media bias reduction\nhave been found to return estimates with improved frequentist\nproperties, that are also always finite, even in cases where\nthe maximum likelihood estimates are infinite (e.g. complete\nand quasi-complete separation; see Kosmidis and Firth, 2020\n, for a proof for mean bias\nreduction in logistic regression). The 'mdyplFit()' fitting\nmethod fits logistic regression models using maximum\nDiaconis-Ylvisaker prior penalized likelihood, which also\nguarantees finite estimates. High-dimensionality corrections\nunder proportional asymptotics can be applied to the resulting\nobjects; see Sterzinger and Kosmidis (2024)\n for details.", "URL": "https://github.com/ikosmidis/brglm2", "BugReports": "https://github.com/ikosmidis/brglm2/issues", "License": "GPL-3", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.3", "Roxygen": "list(markdown = TRUE)", "VignetteBuilder": "knitr", "Repository": "https://ikosmidis.r-universe.dev", "Date/Publication": "2026-04-29 15:51:11 UTC", "RemoteUrl": "https://github.com/ikosmidis/brglm2", "RemoteRef": "HEAD", "RemoteSha": "aac8a817d2d7e80c5189bfe92931a2b27c8bf955", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-05 06:42:21 UTC", "User": "root" }, "Author": "Ioannis Kosmidis [aut, cre] (ORCID:\n),\nEuloge Clovis Kenne Pagui [aut] (ORCID:\n),\nFederico Boiocchi [ctb],\nPhilipp Sterzinger [ctb] (ORCID:\n),\nKjell Konis [ctb],\nNicola Sartori [ctb]", "Maintainer": "Ioannis Kosmidis ", "MD5sum": 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