brglm2: Bias Reduction in Generalized Linear Models

Estimation and inference from generalized linear models based on various methods for bias reduction and maximum penalized likelihood with powers of the Jeffreys prior as penalty. The 'brglmFit' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2016) <arXiv:1604.04768>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <>. See Kosmidis et al (2019) <doi:10.1007/s11222-019-09860-6> for more details. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses (both ordinal and nominal), the adjusted score approaches return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi-complete separation). 'brglm2' also provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in binomial response generalized linear models.

Version: 0.6.2
Depends: R (≥ 3.3.0)
Imports: MASS, stats, Matrix, graphics, nnet, enrichwith, lpSolveAPI, numDeriv
Suggests: testthat, knitr, rmarkdown, covr
Published: 2020-03-19
Author: Ioannis Kosmidis ORCID iD [aut, cre], Kjell Konis [ctb], Euloge Clovis Kenne Pagui [ctb], Nicola Sartori [ctb]
Maintainer: Ioannis Kosmidis <ioannis.kosmidis at>
License: GPL-3
NeedsCompilation: yes
Citation: brglm2 citation info
Materials: README NEWS
CRAN checks: brglm2 results


Reference manual: brglm2.pdf
Vignettes: Adjacent category logit models using brglm2
Bias reduction in generalized linear models
Multinomial logistic regression using brglm2
Detecting separation and infinite estimates in binomial response GLMs
Package source: brglm2_0.6.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: brglm2_0.6.2.tgz, r-oldrel: brglm2_0.6.2.tgz
Old sources: brglm2 archive

Reverse dependencies:

Reverse imports: SOIL
Reverse suggests: detectseparation, parameters, WeightIt


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