Title: Bias reduction in binomial-response generalized linear models.
Description: Fit binomial-response generalized linear models using either a modified-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. Fitting takes place by iteratively fitting a local generalized linear models on a pseudo-data representation. 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 bias-reduced estimates.

Description: 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.

Title: Bias Reduction in Binomial-Response Generalized Linear Models

Authors@R: person(given = "Ioannis", family = "Kosmidis", role = c("aut", "cre"), email = "ioannis.kosmidis@warwick.ac.uk", comment = c(ORCID = "0000-0003-1556-0302"))
Author: Ioannis Kosmidis [aut, cre] (<https://orcid.org/0000-0003-1556-0302>)

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