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.

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>.

Shiny apps for automated data analysis, annotated outputs and human-readable interpretation in natural language. Designed especially for learners and applied researchers. Currently available methods: EDA, EDA with Python, Correlation Analysis, Principal Components Analysis, Confirmatory Factor Analysis.

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.

Download data from 'StatsWales' into R. Removes the need for the user to
    write their own loops when parsing data from the 'StatsWales' API. Provides functions
    for datasets (<http://open.statswales.gov.wales/en-gb/dataset>) and metadata
    (<http://open.statswales.gov.wales/en-gb/discover/metadata>) endpoints.

Visualization functions in the applications of translational medicine (TM) and biomarker (BM) development to compare groups by statistically visualizing data and/or results of analyses, such as visualizing data by displaying in one figure different groups' histograms, boxplots, densities, scatter plots, error-bar plots, or trajectory plots, by displaying scatter plots of top principal components or dendrograms with data points colored based on group information, or visualizing volcano plots to check the results of whole genome analyses for gene differential expression.