brglm: Bias Reduction in Binomial-Response Generalized Linear Models

Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'. More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.

Version: 0.6.2
Depends: R (≥ 2.6.0), profileModel
Suggests: MASS
Published: 2019-04-02
Author: Ioannis Kosmidis ORCID iD [aut, cre]
Maintainer: Ioannis Kosmidis <ioannis.kosmidis at warwick.ac.uk>
BugReports: https://github.com/ikosmidis/brglm/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/ikosmidis/brglm
NeedsCompilation: yes
Citation: brglm citation info
In views: Econometrics, SocialSciences
CRAN checks: brglm results

Downloads:

Reference manual: brglm.pdf
Package source: brglm_0.6.2.tar.gz
Windows binaries: r-devel: brglm_0.6.2.zip, r-release: brglm_0.6.2.zip, r-oldrel: brglm_0.6.2.zip
macOS binaries: r-release: brglm_0.6.2.tgz, r-oldrel: brglm_0.6.2.tgz
Old sources: brglm archive

Reverse dependencies:

Reverse depends: cnvGSA, glmvsd
Reverse imports: analogue, BradleyTerry2, brlrmr, MixedPsy, PrecisionTrialDrawer
Reverse suggests: enrichwith, mbrglm, optmatch, picante
Reverse enhances: prediction, stargazer

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