CRAN/E | brglm2

brglm2

Bias Reduction in Generalized Linear Models

Installation

About

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-reduction 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) . 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).

Citation brglm2 citation info
github.com/ikosmidis/brglm2
Bug report File report

Key Metrics

Version 0.9.2
R ≥ 3.3.0
Published 2023-10-11 170 days ago
Needs compilation? yes
License GPL-3
CRAN checks brglm2 results

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Maintainer

Maintainer

Ioannis Kosmidis

ioannis.kosmidis@warwick.ac.uk

Authors

Ioannis Kosmidis

aut / cre

Euloge Clovis Kenne Pagui

aut

Kjell Konis

ctb

Nicola Sartori

ctb

Material

README
NEWS
Reference manual
Package source

Vignettes

Adjacent category logit models using **brglm2**
Estimating the exponential of regression parameters using **brglm2**
Bias reduction in generalized linear models
Multinomial logistic regression using **brglm2**
Negative binomial regression using **brglm2**

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

brglm2 archive

Depends

R ≥ 3.3.0

Imports

MASS
stats
Matrix
graphics
nnet
enrichwith
numDeriv

Suggests

detectseparation
knitr
rmarkdown
covr
tinytest
VGAM
brglm
mbrglm

Reverse Depends

pawscore
ProSGPV

Reverse Imports

ExactMed
PPSFS
smcfcs
SOIL

Reverse Suggests

cobalt
detectseparation
ggeffects
marginaleffects
parameters
WeightIt