brglm2

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.

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Examples that run

68%13 run in a plain check · 4 only when donttest examples are enabled (R CMD check --as-cran) · 2 hold no code

Exported functions with examples

59%

Documented parameters

98%

Return-value docs

47%

Help pages citing references

50%

Installing brglm2 adds 4 packages (2 with compiled code, 365 KB of source tarballs). Heaviest: enrichwith 103 KB, nleqslv 97 KB, statmod 91 KB.

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First published

2017-04-04

Total releases

23 / 9 yrs

License

GPL-3 OSI

Minimum R

≥ 3.5

Bundled data

1.9 MB / 9 files

Download size

2.3 MB

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