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.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Papastamoulis, Panagiotis: Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components (2020)
- Papastamoulis, Panagiotis: Overfitting Bayesian mixtures of factor analyzers with an unknown number of components (2018)
- Panagiotis Papastamoulis, Magnus Rattray: BayesBinMix: an R Package for Model Based Clustering of Multivariate Binary Data (2016) arXiv