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

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.

• Publication year

  2016

• Venue

  The R Journal

• Publication date

  2016-09-22

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.32614/RJ-2017-022arXiv 1609.06960
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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