Methods for fitting mixture distributions to univariate data using expectation maximization, HWHM and other methods. Supports Gaussian, Cauchy, Student's t, skew-normal and von Mises mixtures. For more details see Merkys (2018) <https://www.lvb.lt/permalink/370LABT_NETWORK/1m6ui06/alma9910036312108451>.

Implementations of various robust and flexible model-based clustering methods for data sets with missing values at random (Tong and Tortora, 2025, <doi:10.18637/jss.v115.i03>). 
    Two main models are: Multivariate Contaminated Normal Mixture (MCNM, Tong and Tortora, 2022, <doi:10.1007/s11634-021-00476-1>) and 
    Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, <doi:10.1016/j.csda.2018.08.016>). Mixtures via some special or limiting
    cases of the multivariate generalized hyperbolic distribution are also included: Normal-Inverse Gaussian, Symmetric Normal-Inverse Gaussian, 
    Skew-Cauchy, Cauchy, Skew-t, Student's t, Normal, Symmetric Generalized Hyperbolic, Hyperbolic Univariate Marginals, 
    Hyperbolic, and Symmetric Hyperbolic. Funding: This work was partially supported by the National Science foundation NSF Grant NO. 2209974.

Implements large-scale hypothesis testing by variance mixing. It takes two statistics per testing unit -- an estimated effect and its associated squared standard error -- and fits a nonparametric, shape-constrained mixture separately on two latent parameters. It reports local false discovery rates (lfdr) and local false sign rates (lfsr). Manuscript describing algorithm of MixTwice: Zheng et al(2021) <doi: 10.1093/bioinformatics/btab162>.

Estimates Variable Length Markov Chains (VLMC) models and VLMC with
    covariates models from discrete sequences. Supports model selection via
    information criteria and simulation of new sequences from an estimated
    model. See Bühlmann, P. and Wyner, A. J. (1999) <doi:10.1214/aos/1018031204>
    for VLMC and Zanin Zambom, A., Kim, S. and Lopes Garcia, N. (2022)
    <doi:10.1111/jtsa.12615> for VLMC with covariates.