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 gmx.at>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	binomlogit results

Documentation:

Reference manual:

binomlogit.pdf

Downloads:

Package source:	binomlogit_1.2.tar.gz
Windows binaries:	r-devel: binomlogit_1.2.zip, r-release: binomlogit_1.2.zip, r-oldrel: binomlogit_1.2.zip
macOS binaries:	r-release (arm64): binomlogit_1.2.tgz, r-oldrel (arm64): binomlogit_1.2.tgz, r-release (x86_64): binomlogit_1.2.tgz, r-oldrel (x86_64): binomlogit_1.2.tgz
Old sources:	binomlogit archive

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