Package: logistf 1.26.0

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>.

Authors:Georg Heinze [aut, cre], Meinhard Ploner [aut], Daniela Dunkler [ctb], Harry Southworth [ctb], Lena Jiricka [aut], Gregor Steiner [aut]

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Bug tracker:https://github.com/georgheinze/logistf/issues

On CRAN:

12 exports 11 stars 5.90 score 63 dependencies 15 dependents 66 mentions 266 scripts 6.8k downloads

Last updated 1 years agofrom:e2213b4764. Checks:OK: 7 NOTE: 2. Indexed: yes.

Exports:backwardCLIP.confintCLIP.profileflacflicforwardlogistflogistf.controllogistf.mod.controllogistftestlogistpl.controlPVR.confint

Dependencies:backportsbitbit64bootbroomclicliprcodetoolscpp11crayondplyrfansiforcatsforeachformula.toolsgenericsglmnetgluehavenhmsiteratorsjomolatticelifecyclelme4magrittrMASSMatrixmgcvmiceminqamitmlnlmenloptrnnetnumDerivoperator.toolsordinalpanpillarpkgconfigprettyunitsprogresspurrrR6RcppRcppEigenreadrrlangrpartshapestringistringrsurvivaltibbletidyrtidyselecttzdbucminfutf8vctrsvroomwithr