Provides several data sets for use with discrete statistical tests
  and discrete multiple testing procedures. Some of them are also available as a
  four-column version, so that each row represents a 2x2 table.

Implementation of new discrete statistical distributions. Each distribution includes the traditional functions as well as an additional function called the family function, which can be used to estimate parameters within the 'gamlss' framework.

Tools for fitting Bayesian Distributed Lag Models (DLMs) to longitudinal response data that is a count or binary. Count data is fit using negative binomial regression and binary is fit using quantile regression. The contribution of the lags are fit via b-splines. In addition, infers the predictor inclusion uncertainty. Multimomial models are not supported. Based on Dempsey and Wyse (2025) <doi:10.48550/arXiv.2403.03646>.

Implementations of the multiple testing procedures for discrete
  tests described in the paper Döhler, Durand and Roquain (2018) "New FDR
  bounds for discrete and heterogeneous tests" <doi:10.1214/18-EJS1441>. The
  main procedures of the paper (HSU and HSD), their adaptive counterparts
  (AHSU and AHSD), and the HBR variant are available and are coded to take as
  input the results of a test procedure from package 'DiscreteTests', or a set
  of observed p-values and their discrete support under their nulls. A
  shortcut function to obtain such p-values and supports is also provided,
  along with a wrapper allowing to apply discrete procedures directly to data.

Implements fast Monte Carlo simulations for 
    goodness-of-fit (GOF) tests for discrete distributions. This 
    includes tests based on the Chi-squared statistic, the 
    log-likelihood-ratio (G^2) statistic, the Freeman-Tukey 
    (Hellinger-distance) statistic, the Kolmogorov-Smirnov 
    statistic, the Cramer-von Mises statistic as described in 
    Choulakian, Lockhart and Stephens (1994) <doi:10.2307/3315828>, 
    and the root-mean-square statistic, see Perkins, 
    Tygert, and Ward (2011) <doi:10.1016/j.amc.2011.03.124>.