The Bayesian analysis of contingency table data using the bayesloglin R package

TL;DR

This paper introduces the bayesloglin R package, enabling Bayesian log-linear analysis of contingency tables using hyper Dirichlet priors, model exploration via MC3, and posterior sampling with Gibbs samplers.

Contribution

The paper presents a new R package that implements Bayesian methods for contingency table analysis, integrating model exploration and posterior sampling tools.

Findings

Provides functions for Bayesian model exploration with MC3.

Includes tools for posterior sampling using Gibbs samplers.

Facilitates Bayesian analysis of contingency tables in R.

Abstract

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.