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).


Reference manual

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1.2 by Agnes Fussl, 5 years ago

Browse source code at https://github.com/cran/binomlogit

Authors: Agnes Fussl

Documentation:   PDF Manual  

GPL-3 license

See at CRAN