S4-classes and methods for distributions.

Provides documentation in form of a common vignette to packages 'distr',
            'distrEx', 'distrMod', 'distrSim', 'distrTEst', 'distrTeach', and 'distrEllipse'.

Functions for visualizing distributional regression models fitted using the 'gamlss', 'bamlss' or 'betareg' R package. The core of the package consists of a 'shiny' application, where the model results can be interactively explored and visualized.

Distribution (S4-)classes for elliptically contoured distributions (based on
            package 'distr').

Extends package 'distr' by functionals, distances, and conditional distributions.

A different way for calculating pdf/pmf, cdf, quantile and random data such that the user is able to consider the name of related distribution as an argument and so easily can changed by a changing argument by user. It must be mentioned that the core and computation base of package 'DISTRIB' is package 'stats'. Although similar functions are introduced previously in package 'stats', but the package 'DISTRIB' has some special applications in some special computational programs.

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.