Gaussian finite mixture models fitted via EM algorithm for
  model-based clustering, classification, and density estimation, 
  including Bayesian regularization, dimension reduction for 
  visualisation, and resampling-based inference.

Extend the functionality of the 'mclust' package for 
  Gaussian finite mixture modeling by including: 
  density estimation for data with bounded support 
  (Scrucca, 2019 <doi:10.1002/bimj.201800174>);
  modal clustering using MEM (Modal EM) algorithm for Gaussian mixtures 
  (Scrucca, 2021 <doi:10.1002/sam.11527>);
  entropy estimation via Gaussian mixture modeling
  (Robin & Scrucca, 2023 <doi:10.1016/j.csda.2022.107582>).

Given a set of data points, a clustering is defined as a disjoint partition
    where each pair of sets in a partition has no overlapping elements. 
    This package provides 25 methods that play a role somewhat similar to 
    distance or metric that measures similarity of two clusterings - or partitions.
    For a more detailed description, see Meila, M. (2005) <doi:10.1145/1102351.1102424>.

Estimate and test inter-generational social mobility effect on an outcome with cross-sectional or longitudinal data.

Simulates continuous distributions of random vectors using
    Markov chain Monte Carlo (MCMC).  Users specify the distribution by an
    R function that evaluates the log unnormalized density.  Algorithms
    are random walk Metropolis algorithm (function metrop), simulated
    tempering (function temper), and morphometric random walk Metropolis
    (Johnson and Geyer, 2012, <doi:10.1214/12-AOS1048>,
    function morph.metrop),
    which achieves geometric ergodicity by change of variable.

Poisson-lognormal generalized linear mixed model analysis of multivariate counts data using MCMC, aiming to infer the changes in relative proportions of individual variables. The package was originally designed for sequence-based analysis of microbial communities ("metabarcoding", variables = operational taxonomic units, OTUs), but can be used for other types of multivariate counts, such as in ecological applications (variables = species). The results are summarized and plotted using 'ggplot2' functions. Includes functions to remove sample and variable outliers and reformat counts into normalized log-transformed values for correlation and principal component/coordinate analysis. Walkthrough and examples: http://www.bio.utexas.edu/research/matz_lab/matzlab/Methods_files/walkthroughExample_mcmcOTU_R.txt.