Interactive 'R' tutorials and datasets for the textbook Field
    (2026), "Discovering Statistics Using R and RStudio",
    <https://www.discovr.rocks/>.  Interactive tutorials cover general
    workflow in 'R' and 'RStudio', summarizing data, visualizing data,
    fitting models and bias, correlation, the general linear model (GLM),
    moderation, mediation, missing values, comparing means using the GLM
    (analysis of variance), comparing adjusted means (analysis of covariance), factorial
    designs, repeated measures designs, exploratory factor analysis (EFA).
    There are no functions, only datasets and interactive tutorials.

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.