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brglm2
View on CRAN: Click
here
Download and install brglm2 package within the R console
Install from CRAN:
install.packages("brglm2")
Install from Github:
library("remotes")
install_github("cran/brglm2")
Install by package version:
library("remotes")
install_version("brglm2", "0.9.2")
Attach the package and use:
library("brglm2")
Maintained by
Ioannis Kosmidis
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.brglm2 . https://github.com/ikosmidis/brglm2/issues . https://github.com/ikosmidis/brglm2 . brglm2 citation info . brglm2 results . brglm2.pdf . Adjacent category logit models using **brglm2** . Estimating the exponential of regression parameters using **brglm2** . Multinomial logistic regression using **brglm2** . Negative binomial regression using **brglm2** . brglm2_0.9.2.tar.gz . brglm2_0.9.2.zip . brglm2_0.9.2.zip . brglm2_0.9.2.zip . brglm2_0.9.2.tgz . brglm2_0.9.2.tgz . brglm2_0.9.2.tgz . brglm2_0.9.2.tgz . brglm2 archive . https://CRAN.R-project.org/package=brglm2 .
First Published: 2017-04-04
Latest Update: 2023-02-06
Description:
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) and Kosmidis and Firth (2009) , or the median bias-reduction adjusted score equations in Kenne et al. (2017) , 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 (2020) 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 , for a proof for mean bias reduction in logistic regression).
How to cite:
Ioannis Kosmidis (2017). brglm2: Bias Reduction in Generalized Linear Models. R package version 0.9.2, https://cran.r-project.org/web/packages/brglm2. Accessed 08 May. 2025.
Previous versions and publish date:
0.1.3 (2017-04-04 08:13), 0.1.4 (2017-05-23 05:43), 0.1.5 (2017-10-13 23:21), 0.1.6 (2018-02-09 19:44), 0.1.7 (2018-02-23 18:32), 0.1.8 (2018-04-16 15:00), 0.5.0 (2019-02-06 16:13), 0.5.1 (2019-02-14 18:02), 0.5.2 (2019-10-12 10:10), 0.6.0 (2019-11-08 13:00), 0.6.1 (2020-02-06 15:10), 0.6.2 (2020-03-19 15:50), 0.7.0 (2020-10-19 19:20), 0.7.1 (2021-01-22 14:10), 0.8.0 (2021-07-17 17:20), 0.8.1 (2021-09-27 12:10), 0.8.2 (2021-11-21 17:20), 0.9 (2023-02-06 22:32)
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Other R packages that brglm2 depends,
imports, suggests or enhances
Complete documentation for brglm2
Functions, R codes and Examples using
the brglm2 R package
Some associated functions: aids . alligators . bracl . brglm2-defunct . brglm2 . brglmControl . brglmFit . brmultinom . brnb . coalition . coef.brglmFit . coef.brglmFit_expo . coef.brnb . confint.brglmFit . confint.brmultinom . confint.brnb . endometrial . enzymes . expo.brglmFit . hepatitis . lizards . mis . ordinal_superiority.bracl . predict.bracl . predict.brmultinom . residuals.brmultinom . simulate.brmultinom . simulate.brnb . stemcell . summary.brglmFit . summary.brnb . vcov.brglmFit . vcov.brnb .
Some associated R codes: bracl.R . brglm2-defunct.R . brglm2-package.R . brglmControl.R . brglmFit.R . brmultinom.R . brnb.R . data.R . expo.R . mis-link.R . ordinal_superiority.R . utils.R . warnings.R . zzz_conventions.R . Full brglm2 package functions and examples
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