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).
|Date of publication||2014-03-12 18:11:36|
|Maintainer||Agnes Fussl <email@example.com>|
binomlogit-package: Efficient MCMC for Binomial Logit Models
caesarean: Caesarean Birth Data
dRUMAuxMix: Auxiliary mixture sampling for the binomial logit model
dRUMHAM: Hybrid auxiliary mixture sampling for the binomial logit...
dRUMIndMH: Data-augmented independence Metropolis-Hastings sampling for...
IndivdRUMIndMH: Data-augmented independence Metropolis-Hastings sampling for...
simul: Simulated data set