Provides supplemental functions for the 'mixRasch'
    package (Willse, 2014), <https://cran.r-project.org/package=mixRasch/mixRasch.pdf> including a 
    plotting function to compare item parameters for multiple
    class models and a function that provides average theta values for each class in
    a mixture model.

It offers random-forest-based functions to impute clustered
    incomplete data. The package is tailored for but not limited to imputing
    multitissue expression data, in which a gene's expression is measured on the
    collected tissues of an individual but missing on the uncollected tissues.

The current version of the 'MixSAL' package allows users to generate data from a multivariate SAL distribution or a mixture of multivariate SAL distributions, evaluate the probability density function of a multivariate SAL distribution or a mixture of multivariate SAL distributions, and fit a mixture of multivariate SAL distributions using the Expectation-Maximization (EM) algorithm (see Franczak et. al, 2014, <doi:10.1109/TPAMI.2013.216>, for details).

Various functions are provided to estimate parametric mixture models
    (with Gaussian, t, Laplace, log-concave distributions, etc.) and
    non-parametric mixture models. The package performs hypothesis tests
    and addresses label switching issues in mixture models.
    The package also allows for parameter estimation in mixture of regressions,
    proportion-varying mixture of regressions, and robust mixture of regressions.

Creates and runs Bayesian mixing models to analyze
    biological tracer data (i.e. stable isotopes, fatty acids), which estimate the
    proportions of source (prey) contributions to a mixture (consumer). 'MixSIAR'
    is not one model, but a framework that allows a user to create a mixing model
    based on their data structure and research questions, via options for fixed/
    random effects, source data types, priors, and error terms. 'MixSIAR' incorporates
    several years of advances since 'MixSIR' and 'SIAR'.