Univariate and multivariate normal data simulation. They also supply a brief summary of the analysis for each experiment/design:
    - Independent samples.
    - One-way and two-way Anova.
    - Paired samples (T-Test & Regression).
    - Repeated measures (Anova & Multiple Regression).
    - Clinical Assay.

Provides interactive 'Shiny' applications for building intuition in 
    statistics and data science. The apps run locally and use
    visualization, simulation, and repeated sampling to illustrate topics
    such as probability distributions, permutation tests, analysis of
    variance, linear regression, and sums of squares.

Pull data from the 'STAT Search Analytics' API <https://help.getstat.com/knowledgebase/api-services/>. It was developed by the Search Discovery team to help analyze keyword ranking data.

Utilities for producing dataframes with rich details for the
    most common types of statistical approaches and tests: parametric,
    nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation
    analyses, contingency table analyses, and meta-analyses. The functions
    are pipe-friendly and provide a consistent syntax to work with tidy
    data. These dataframes additionally contain expressions with
    statistical details, and can be used in graphing packages. This
    package also forms the statistical processing backend for
    'ggstatsplot'. References: Patil (2021) <doi:10.21105/joss.03236>.

Data and functions to support Bayesian and frequentist inference and decision making 
            for the Coursera Specialization "Statistics with R".
            See <https://github.com/StatsWithR/statsr> for more information.

Provides utility functions for validation and quality control of
    clinical trial datasets and outputs across 'SDTM', 'ADaM' and 'TFL' workflows.
    The package supports dataset loading, metadata inspection, frequency and
    summary calculations, table-ready aggregations, and compare-style dataset
    review similar to 'SAS' 'PROC COMPARE'. Functions are designed to support
    reproducible execution, transparent review, and independent verification of
    statistical programming results. Dataset comparisons may leverage 'arsenal'
    <https://cran.r-project.org/package=arsenal>.