Provides histograms, boxplots and dotplots as alternatives to scatterplots of data when plotting fitted logistic regressions.

Analysis of sparse contingency tables with penalization approaches

Firth's bias reduced logistic regression
Meinhard Ploner <meinhard.ploner@akh-wien.ac.at>, Daniela Dunkler, i
Harry Southworth, Georg Heinze
Firth's bias reduced logistic regression approach with
penalized profile likelihood based confidence intervals for
parameter estimates.

Meinhard Ploner, Daniela Dunkler,
Harry Southworth, Georg Heinze <georg.heinze@meduniwien.ac.at>

Meinhard Ploner, Daniela Dunkler, Harry Southworth, Georg
Heinze <georg.heinze@meduniwien.ac.at>

Georg Heinze <georg.heinze@meduniwien.ac.at>, Meinhard Ploner,
Daniela Dunkler (former versions), Harry Southworth (former
versions)

c(person("Georg", "Heinze", role=c("aut", "cre"), email="georg.heinze@meduniwien.ac.at"),
person("Meinhard", "Ploner", role=c("aut")),
person("Daniela","Dunkler", role=c("ctb")),
person("Harry", "Southworth", role=c("ctb")))
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.
Georg Heinze [aut, cre],
Meinhard Ploner [aut],
Daniela Dunkler [ctb],
Harry Southworth [ctb]

c(person("Georg", "Heinze", role=c("aut", "cre"), email="georg.heinze@meduniwien.ac.at"),
person("Meinhard", "Ploner", role=c("aut")),
person("Daniela","Dunkler", role=c("ctb")),
person("Harry", "Southworth", role=c("ctb")),
person("Lena", "Jiricka", role=c("aut")))
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>.
Georg Heinze [aut, cre],
Meinhard Ploner [aut],
Daniela Dunkler [ctb],
Harry Southworth [ctb],
Lena Jiricka [aut]