Calculates expected values, variance, different moments (kth 
    moment, truncated mean), stop-loss, mean excess loss, Value-at-Risk (VaR)
    and Tail Value-at-Risk (TVaR) as well as some density and cumulative 
    (survival) functions of continuous, discrete and compound distributions. 
    This package also includes a visual 'Shiny' component to enable students 
    to visualize distributions and understand the impact of their parameters.
    This package is intended to expand the 'stats' package so as
    to enable students to develop an intuition for probability.

Vectorised distribution objects with tools for manipulating, 
    visualising, and using probability distributions. Designed to allow model
    prediction outputs to return distributions rather than their parameters, 
    allowing users to directly interact with predictive distributions in a
    data-oriented workflow. In addition to providing generic replacements for
    p/d/q/r functions, other useful statistics can be computed including means,
    variances, intervals, and highest density regions.

Distributional instrumental variable (DIV) model
    for estimation of the interventional distribution of the outcome Y under a
    do intervention on the treatment X. Instruments, predictors and targets can
    be univariate or multivariate. Functionality includes estimation of the (conditional)
    interventional mean and quantiles, as well as sampling from the fitted (conditional)
    interventional distribution.

Fits Gaussian Mixtures by applying evolution. As fitness function a mixture of the chi square test for distributions and a novel measure for approximating the common area under curves between multiple Gaussians is used. The package presents an alternative to the commonly used Likelihood Maximization as is used in Expectation Maximization. The algorithm and applications of this package are published under: Lerch, F., Ultsch, A., Lotsch, J. (2020) <doi:10.1038/s41598-020-57432-w>. The evolution is based on the 'GA' package: Scrucca, L. (2013) <doi:10.18637/jss.v053.i04> while the Gaussian Mixture Logic stems from 'AdaptGauss': Ultsch, A, et al. (2015) <doi:10.3390/ijms161025897>.