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  "Date": "2025-04-16",
  "Title": "Firth's Bias-Reduced Logistic Regression",
  "Authors@R": "c(person(\"Georg\", \"Heinze\", role=c(\"aut\", \"cre\"), email=\"georg.heinze@meduniwien.ac.at\"),\nperson(\"Meinhard\", \"Ploner\", role=c(\"aut\")),\nperson(\"Daniela\",\"Dunkler\", role=c(\"ctb\")),\nperson(\"Harry\", \"Southworth\", role=c(\"ctb\")),\nperson(\"Lena\", \"Jiricka\", role=c(\"aut\")),\nperson(\"Gregor\", \"Steiner\", role=c(\"aut\")))",
  "Description": "Fit a logistic regression model using Firth's bias\nreduction method, equivalent to penalization of the\nlog-likelihood by the Jeffreys prior. Confidence intervals for\nregression coefficients can be computed by penalized profile\nlikelihood. Firth's method was proposed as ideal solution to\nthe problem of separation in logistic regression, see Heinze\nand Schemper (2002) <doi:10.1002/sim.1047>. If needed, the bias\nreduction can be turned off such that ordinary maximum\nlikelihood logistic regression is obtained. Two new\nmodifications of Firth's method, FLIC and FLAC, lead to\nunbiased predictions and are now available in the package as\nwell, see Puhr et al (2017) <doi:10.1002/sim.7273>.",
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