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>.

Tools to create and manipulate probability distributions
    using S3.  Generics pdf(), cdf(), quantile(), and random() provide
    replacements for base R's d/p/q/r style functions.  Functions and
    arguments have been named carefully to minimize confusion for students
    in intro stats courses. The documentation for each distribution
    contains detailed mathematical notes.

A library of density, distribution function, quantile function, (bounded) raw moments and random generation for a collection of distributions relevant for the firm size literature. Additionally, the package contains tools to fit these distributions using maximum likelihood and evaluate these distributions based on (i) log-likelihood ratio and (ii) deviations between the empirical and parametrically implied moments of the distributions. We add flexibility by allowing the considered distributions to be combined into piecewise composite or finite mixture distributions, as well as to be used when truncated. See Dewitte (2020) <https://hdl.handle.net/1854/LU-8644700> for a description and application of methods available in this package.