Package: brglm2
Title: Bias Reduction in Generalized Linear Models
Version: 0.1.3
Authors@R: person("Ioannis", "Kosmidis", email = "i.kosmidis@ucl.ac.uk", role = c("aut", "cre"))
Description: Estimation and inference from generalized linear models
    based on various methods for bias reduction. The brglmFit fitting
    method can achieve reduction of estimation bias either through the
    adjusted score equations approach in Firth (1993)
    <https://doi.org/10.1093/biomet/80.1.27> and Kosmidis and Firth
    (2009) <https://doi.org/10.1093/biomet/asp055>, 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)
    <http://www.jstor.org/stable/2345592>. In the special case of
    generalized linear models for binomial and multinomial responses,
    the adjusted score equations approach returns 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). 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.
Depends: R (>= 3.3.0)
License: GPL-2 | GPL-3
Encoding: UTF-8
LazyData: true
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
RoxygenNote: 5.0.1
Imports: MASS, stats, Matrix, graphics, nnet, enrichwith
NeedsCompilation: yes
Packaged: 2017-03-31 18:54:53 UTC; yiannis
Author: Ioannis Kosmidis [aut, cre]
Maintainer: Ioannis Kosmidis <i.kosmidis@ucl.ac.uk>
Repository: CRAN
Date/Publication: 2017-04-04 06:12:59 UTC
