This repository implements a Gibbs sampling algorithm for Bayesian inference of Dirichlet process mixture models with Hamming distributed kernels. The approach provides model-based clustering for categorical data that lacks natural ordering. Our implementation incorporates split-and-merge Markov chain Monte Carlo techniques to efficiently navigate