Tests whether a set of counts fit a given expected ratio. For
    example, a genetic cross might be expected to produce four types in the
    relative frequencies of 9:3:3:1. To see whether a set of observed counts
    fits this expectation, one can examine all possible outcomes with xmulti() or a
    random sample of them with xmonte() and find the probability of an observation
    deviating from the expectation by at least as much as the observed. As a
    measure of deviation from the expected, one can use the log-likelihood
    ratio, the multinomial probability, or the classic chi-square statistic. A
    histogram of the test statistic can also be plotted and compared with the
    asymptotic curve.

Analysis of crossover interference in experimental crosses,
    particularly regarding the gamma model. See, for example,
    Broman and Weber (2000) <doi:10.1086/302923>.

Cross platform solution to open files, directories or 'URLs'
    with their associated programs.

Implements a probabilistic approach to time series forecasting combining XGBoost regression with conformal inference methods. The package provides functionality for generating predictive distributions, evaluating uncertainty, and optimizing hyperparameters using Bayesian, coarse-to-fine, or random search strategies.

Helps systematize and ease the process of 
    building unit tests with the 'testthat' package by providing 
    tools for generating expectations.

Allows to provide live interpretations and explanations of statistical 
    functions in R. These interpretations and explanations are shown when the explained function 
    is called by the user. They can interact with the values of the explained function's actual 
    results to offer relevant, meaningful insights. The 'xplain' interpretations and explanations 
    are based on an easy-to-use XML format that allows to include R code to interact with the 
    returns of the explained function.

Tools for interactive data exploration built using 'shiny'. Includes apps for descriptive 
    statistics, visualizing probability distributions, inferential statistics, linear regression, 
    logistic regression and RFM analysis.