  For log-linear analysis, the hyper Dirichlet conjugate prior is available to
work in the Bayesian paradigm. With this prior, the MC3 algorithm allows for
exploration of the space of models to try to find those with the highest
posterior probability. Once top models have been identified, a block Gibbs
sampler can be constructed to sample from the posterior distribution and to
estimate parameters of interest. Our aim in this paper, is to introduce the
bayesloglin R package \citep{R} which contains functions to carry out these
tasks.
