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Semi-supervised and unsupervised Bayesian mixture models that simultaneously infer the cluster/class structure and a batch correction. Densities available are the multivariate normal and the multivariate t. The model sampler is implemented in C++. This package is aimed at analysis of low-dimensional data generated across several batches. See Coleman et al. (2022) for details of the model.

Advice on using the package

The main functions a user should be aware of are runMCMCChains, plotLikelihoods, plotAcceptanceRates, continueChains and processChains.

Parameters are sampled using Metropolis-Hastings so checking that the acceptance rate is important. We recommend aiming for acceptance rates between 0.1 and 0.5 for the class and batch means and batch scales (