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. If needed, the bias reduction can be turned off such that ordinary maximum likelihood logistic regression is obtained.

Version: 1.23
Depends: R (≥ 3.0.0)
Imports: mice, mgcv
Published: 2018-07-19
Author: Georg Heinze [aut, cre], Meinhard Ploner [aut], Daniela Dunkler [ctb], Harry Southworth [ctb]
Maintainer: Georg Heinze <georg.heinze at meduniwien.ac.at>
License: GPL-2 | GPL-3 [expanded from: GPL]
URL: http://cemsiis.meduniwien.ac.at/en/kb/science-research/software/statistical-software/fllogistf/
NeedsCompilation: yes
In views: SocialSciences
CRAN checks: logistf results


Reference manual: logistf.pdf
Package source: logistf_1.23.tar.gz
Windows binaries: r-devel: logistf_1.23.zip, r-devel-gcc8: logistf_1.23.zip, r-release: logistf_1.23.zip, r-oldrel: logistf_1.23.zip
OS X binaries: r-release: logistf_1.23.tgz, r-oldrel: logistf_1.23.tgz
Old sources: logistf archive

Reverse dependencies:

Reverse depends: mbmdr, mDAG
Reverse imports: apricom, AUtests, EHR, pogit, Surrogate
Reverse suggests: ggeffects, insight, metamisc, phyr
Reverse enhances: MuMIn


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