logistf: Firth's Bias-Reduced Logistic Regression

Fit a logistic regression model using Firth's bias reduction method, equivalent to penalization of the log-likelihood by the Jeffreys prior. Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firth's method was proposed as ideal solution to the problem of separation in logistic regression, see Heinze and Schemper (2002) <doi:10.1002/sim.1047>. If needed, the bias reduction can be turned off such that ordinary maximum likelihood logistic regression is obtained. Two new modifications of Firth's method, FLIC and FLAC, lead to unbiased predictions and are now available in the package as well, see Puhr et al (2017) <doi:10.1002/sim.7273>.

Version: 1.26.0
Depends: R (≥ 3.0.0)
Imports: mice, mgcv, formula.tools, Matrix
Suggests: emmeans (≥ 1.4), estimability
Published: 2023-08-18
DOI: 10.32614/CRAN.package.logistf
Author: Georg Heinze [aut, cre], Meinhard Ploner [aut], Daniela Dunkler [ctb], Harry Southworth [ctb], Lena Jiricka [aut], Gregor Steiner [aut]
Maintainer: Georg Heinze <georg.heinze at meduniwien.ac.at>
BugReports: https://github.com/georgheinze/logistf/issues/
License: GPL-2 | GPL-3 [expanded from: GPL]
URL: https://cemsiis.meduniwien.ac.at/en/kb/science-research/software/statistical-software/firth-correction/
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: logistf results


Reference manual: logistf.pdf


Package source: logistf_1.26.0.tar.gz
Windows binaries: r-devel: logistf_1.26.0.zip, r-release: logistf_1.26.0.zip, r-oldrel: logistf_1.26.0.zip
macOS binaries: r-release (arm64): logistf_1.26.0.tgz, r-oldrel (arm64): logistf_1.26.0.tgz, r-release (x86_64): logistf_1.26.0.tgz, r-oldrel (x86_64): logistf_1.26.0.tgz
Old sources: logistf archive

Reverse dependencies:

Reverse depends: mDAG
Reverse imports: AUtests, GWASTools, multisite.accuracy, PhenStat, pogit, rnaEditr, SeqVarTools, Surrogate
Reverse suggests: clarify, EHR, ggeffects, insight, marginaleffects, metamisc, nncc, parameters, phyr, WeightIt
Reverse enhances: MuMIn


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