binomlogit: Efficient MCMC for Binomial Logit Models

The R package contains different MCMC schemes to estimate the regression coefficients of a binomial (or binary) logit model within a Bayesian framework: a data-augmented independence MH-sampler, an auxiliary mixture sampler and a hybrid auxiliary mixture (HAM) sampler. All sampling procedures are based on algorithms using data augmentation, where the regression coefficients are estimated by rewriting the logit model as a latent variable model called difference random utility model (dRUM).

AuthorAgnes Fussl
Date of publication2014-03-12 18:11:36
MaintainerAgnes Fussl <avf@gmx.at>
LicenseGPL-3
Version1.2

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Files in this package

binomlogit
binomlogit/NAMESPACE
binomlogit/data
binomlogit/data/caesarean.rda
binomlogit/data/caesarean_binary.rda
binomlogit/data/simul.rda
binomlogit/data/simul_binary.rda
binomlogit/data/caesarean_aux.rda
binomlogit/R
binomlogit/R/dRUMIndMH.R binomlogit/R/print.binomlogit.R binomlogit/R/compmix.R binomlogit/R/summary.binomlogit.R
binomlogit/R/dRUMHAM.R
binomlogit/R/IndivdRUMIndMH.R binomlogit/R/dRUMAuxMix.R binomlogit/R/plot.binomlogit.R
binomlogit/MD5
binomlogit/DESCRIPTION
binomlogit/man
binomlogit/man/caesarean.Rd binomlogit/man/simul.Rd binomlogit/man/dRUMAuxMix.Rd binomlogit/man/dRUMHAM.Rd binomlogit/man/IndivdRUMIndMH.Rd binomlogit/man/binomlogit-package.Rd binomlogit/man/dRUMIndMH.Rd

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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