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).

Version: 1.2
Published: 2014-03-12
Author: Agnes Fussl
Maintainer: Agnes Fussl <avf at>
License: GPL-3
NeedsCompilation: no
CRAN checks: binomlogit results


Reference manual: binomlogit.pdf
Package source: binomlogit_1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: binomlogit_1.2.tgz
OS X Mavericks binaries: r-oldrel: binomlogit_1.2.tgz
Old sources: binomlogit archive


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