Functions to fit finite mixture of scale mixture of skew-normal (FM-SMSN) distributions, details in Prates, Lachos and Cabral (2013) <doi: 10.18637/jss.v054.i12>, Cabral, Lachos and Prates (2012) <doi:10.1016/j.csda.2011.06.026> and Basso, Lachos, Cabral and Ghosh (2010) <doi:10.1016/j.csda.2009.09.031>.

Mixtures of skewed and elliptical distributions are implemented using mixtures of multivariate skew 
    power exponential and power exponential distributions, respectively. A generalized expectation-maximization 
    framework is used for parameter estimation. See citation() for how to cite.

Provides an optimization method based on sequential
    quadratic programming (SQP) for maximum likelihood estimation of
    the mixture proportions in a finite mixture model where the
    component densities are known. The algorithm is expected to obtain
    solutions that are at least as accurate as the state-of-the-art
    MOSEK interior-point solver (called by function "KWDual" in the
    'REBayes' package), and they are expected to arrive at solutions
    more quickly when the number of samples is large and the number of
    mixture components is not too large. This implements the "mix-SQP"
    algorithm, with some improvements, described in Y. Kim,
    P. Carbonetto, M. Stephens & M. Anitescu (2020)
    <DOI:10.1080/10618600.2019.1689985>.

Developed for model-based clustering using the finite mixtures of skewed sub-Gaussian
              stable distributions developed by Teimouri (2022) <arXiv:2205.14067> and estimating
              parameters of the symmetric stable distribution within the Bayesian framework.

Analyzes finite mixture models for various parametric and semiparametric settings.  This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic, mixturegrams, and model selection criteria).  Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands.  This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772 and the Chan Zuckerberg Initiative: Essential Open Source Software for Science (Grant No. 2020-255193).