GNU R package for bias reduction in binomial-response GLMs
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.
