The R Journal: accepted article

This article will be copy edited and may be changed before publication.

BayesBinMix: an R Package for Model Based Clustering of Multivariate Binary Data
Panagiotis Papastamoulis and Magnus Rattray

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.

Received: 2016-09-30; online 2017-04-04
CRAN packages: BayesBinMix, label.switching, foreach, doParallel, coda, FlexMix, flexclust , CRAN Task Views implied by cited CRAN packages: Bayesian, Cluster, gR, HighPerformanceComputing


CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2017-022,
  author = {Panagiotis Papastamoulis and Magnus Rattray},
  title = {{BayesBinMix: an R Package for Model Based Clustering of
          Multivariate Binary Data}},
  year = {2017},
  journal = {{The R Journal}},
  url = {https://journal.r-project.org/archive/2017/RJ-2017-022/index.html}
}