BayesBinMix: an R Package for Model Based Clustering of Multivariate Binary Data

TL;DR

BayesBinMix is an R package that uses Bayesian methods and MCMC sampling to cluster multivariate binary data, effectively handling missing values and estimating the number of clusters.

Contribution

It introduces a Bayesian clustering approach with MCMC for multivariate Bernoulli data, including methods for model selection and label switching.

Findings

Outperforms EM algorithm in simulation studies

Effectively handles missing data

Accurately estimates number of clusters

Abstract

The BayesBinMix package offers a Bayesian framework for clustering binary data with or without missing values by fitting mixtures of multivariate Bernoulli distributions with an unknown number of components. It allows the joint estimation of the number of clusters and model parameters using Markov chain Monte Carlo sampling. Heated chains are run in parallel and accelerate the convergence to the target posterior distribution. Identifiability issues are addressed by implementing label switching algorithms. The package is demonstrated and benchmarked against the Expectation-Maximization algorithm using a simulation study as well as a real dataset.