Package: r-cran-brglm2
Version: 1.1.0+dfsg-1
Architecture: arm64
Maintainer: Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
Original-Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Installed-Size: 3150
Depends: r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-nnet, r-cran-enrichwith, r-cran-numderiv, r-cran-statmod, r-cran-nleqslv
Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-tinytest, r-cran-vgam, r-cran-brglm
Section: gnu-r
Priority: optional
Homepage: https://cran.r-project.org/package=brglm2
Description: 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-reducing
 adjusted score equations in Kenne et al. (2017) <doi:10.1093/biomet/asx046>,
 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) <https://www.jstor.org/stable/2345592>. See Kosmidis
 et al (2020) <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 to mean and media bias reduction have been found
 to 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; see Kosmidis and Firth,
 2020 <doi:10.1093/biomet/asaa052>, for a proof for mean bias reduction
 in logistic regression). The 'mdyplFit()' fitting method fits logistic
 regression models using maximum Diaconis-Ylvisaker prior penalized likelihood,
 which also guarantees finite estimates. High-dimensionality corrections
 under proportional asymptotics can be applied to the resulting objects;
 see Sterzinger and Kosmidis (2024) <doi:10.48550/arXiv.2311.07419> for details.
