A set of functions which use the Expectation Maximisation (EM) 
    algorithm (Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977) 
    <doi:10.1111/j.2517-6161.1977.tb01600.x> Maximum likelihood from 
    incomplete data via the EM algorithm, Journal of the Royal Statistical 
    Society, 39(1), 1--22) to take a finite mixture model approach to 
    clustering. The package is designed to cluster multivariate data that have 
    categorical and continuous variables and that possibly contain missing 
    values. The method is described in Hunt, L. and Jorgensen, M. (1999) 
    <doi:10.1111/1467-842X.00071> Australian & New Zealand Journal of Statistics 
    41(2), 153--171 and Hunt, L. and Jorgensen, M. (2003) 
    <doi:10.1016/S0167-9473(02)00190-1> Mixture model clustering for mixed data 
    with missing information, Computational Statistics & Data Analysis, 41(3-4), 
    429--440.

Fast Bayesian probability estimation for multimodal categorical 
    data using speed-optimized Markov chain Monte Carlo (MCMC) implementation 
    (Metropolis-Hastings-within-partial-Gibbs).
    The package provides efficient algorithms for detecting subpopulations, estimating
    mixture components, and assigning 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 to Journal of Statistical Software).

Different examples and methods for testing (including different proposals described in Ameijeiras-Alonso et al., 2019 <DOI:10.1007/s11749-018-0611-5>) and exploring (including the mode tree, mode forest and SiZer) the number of modes using nonparametric techniques <DOI:10.18637/jss.v097.i09>.

Provides methods for quantifying the information gain contributed by individual 
	modalities in multimodal regression models. Information gain is measured using Expected 
	Relative Entropy (ERE) or pseudo-R² metrics, with corresponding confidence 
	intervals. Currently supports linear regression, logistic regression, and the Cox proportional
	hazards model. A robust Median-of-Means based estimator is also provided
	for heavy-tailed responses under the Gaussian and Negative-Binomial families, with basic
	bootstrap confidence intervals.