Fit generalized linear models with binomial responses using either an adjustedscore 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 pseudodata. The interface is essentially the same as 'glm'. More flexibility is provided by the fact that custom pseudodata representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reducedbias estimates.
Package details 


Author  Ioannis Kosmidis <[email protected]> 
Date of publication  20170628 21:44:33 
Maintainer  Ioannis Kosmidis <[email protected]> 
License  GPL (>= 2) 
Version  0.6.1 
URL  http://www.ucl.ac.uk/~ucakiko/index.html 
Package repository  View on RForge 
Installation 
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