brglm2
1.1.0Bias Reduction in Generalized Linear Models
Overview
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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- NOTE2026-07-1611 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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- WARNING2026-06-0812 OK · 0 NOTE · 1 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 84%
- Documented parameters
- 98%
- Return-value docs
- 50%
- References docs
- 50%
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People & History
22 releases. Pick two to compare their code metrics. R releases are shown for context.
- 1.1.0Latest
- RR 4.6.0 released · 2026-04-24
- 1.0.12025-11-22 · diff ↗
- 1.0.02025-08-29 · diff ↗
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 0.9.22023-10-11 · diff ↗
- RR 4.3.0 released · 2023-04-21
- 0.92023-02-06 · diff ↗
- RR 4.2.0 released · 2022-04-22
- 0.8.22021-11-21 · diff ↗
- 0.8.12021-09-27 · diff ↗
- 0.8.02021-07-17 · diff ↗
- RR 4.1.0 released · 2021-05-18
- 0.7.12021-01-22 · diff ↗
- 0.7.02020-10-19 · diff ↗
Show 17 earlier events
- RR 4.0.0 released · 2020-04-24
- 0.6.22020-03-19 · diff ↗
- 0.6.12020-02-06 · diff ↗
- 0.6.02019-11-08 · diff ↗
- 0.5.22019-10-12 · diff ↗
- RR 3.6.0 released · 2019-04-26
- 0.5.12019-02-14 · diff ↗
- 0.5.02019-02-06 · diff ↗
- RR 3.5.0 released · 2018-04-23
- 0.1.82018-04-16 · diff ↗
- 0.1.72018-02-23 · diff ↗
- 0.1.62018-02-09 · diff ↗
- 0.1.52017-10-13 · diff ↗
- 0.1.42017-05-23 · diff ↗
- RR 3.4.0 released · 2017-04-21
- 0.1.32017-04-04
- RR 3.3.0 released · 2016-05-03
Package metadata
- First published
- 2017-04-04
- Total releases
- 22 / 9 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.5
- Bundled data
- 1.9 MB / 9 files
- Download size
- 2.3 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet