  Bayesian supervised predictive classifiers, hypothesis testing, and
parametric estimation under Partition Exchangeability are implemented. The two
classifiers presented are the marginal classifier (that assumes test data is
i.i.d.) next to a more computationally costly but accurate simultaneous
classifier (that finds a labelling for the entire test dataset at once based on
simultanous use of all the test data to predict each label). We also provide
the Maximum Likelihood Estimation (MLE) of the only underlying parameter of the
partition exchangeability generative model as well as hypothesis testing
statistics for equality of this parameter with a single value, alternative, or
multiple samples. We present functions to simulate the sequences from Ewens
Sampling Formula as the realisation of the Poisson-Dirichlet distribution and
their respective probabilities.
