MultiModalR - Bayesian Mixture Modeling for Stratified and Hierarchical Multimodal Data

Fast Bayesian probability estimation for multimodal continuous data using speed-optimized Markov chain Monte Carlo (MCMC) implementation. The package provides two complementary approaches: (1) stratified mixture modeling with independent fits per category (fuss_PARALLEL_mcmc), and (2) a unified hierarchical mixture model with category-specific parameters and shared number of components (fuss_COVARIATE_mcmc). Both approaches detect subpopulations, estimate mixture components, and assign observations to subgroups with probability estimates. The methods are described in Dioszegi, G. et al. (2026) "Automatic Bayesian Mixture Modeling for Multimodal Categorical Data via Integrated Mode Detection and Metropolis-Hastings-within-Gibbs Sampling" (submitted).