Download data from the 'StatsWales' public API
    (<https://api.stats.gov.wales/v2>) into R. Provides functions to list,
    search, and browse datasets by topic, retrieve data with optional
    filtering, sorting, and pivoting, and download datasets as CSV or Excel
    files.

Includes an interactive application designed to support
    educators in wide-ranging disciplines, with a particular focus on
    those teaching introductory statistical methods (descriptive and/or
    inferential) for data analysis. Users are able to randomly generate
    data, make new versions of existing data through common adjustments
    (e.g., add random normal noise and perform transformations), and check
    the suitability of the resulting data for statistical analyses.

Computes confidence intervals for variance using the Chi-Square distribution, without requiring raw data. Wikipedia (2025) <https://en.wikipedia.org/wiki/Chi-squared_distribution>. 'All-in-One Chi Distribution CI' provides functions to calculate confidence intervals for the population variance based on a chi-squared distribution, utilizing a sample variance and sample size. It offers only a simple all-in-one method for quick calculations to find the CI for Chi Distribution.

The statistical tools in this package do one of four things:
    1) Enhance basic statistical functions with more 
    flexible inputs, smarter defaults, and richer, clearer, and ready-to-use 
    output (e.g., t.test2())
    2) Produce publication-ready commonly needed figures with one line of code (e.g., plot_cdf())
    3) Implement novel analytical tools developed by the authors (e.g., twolines())
    4) Deliver niche functions of high value to the authors that are not easily
    available elsewhere (e.g., clear(), convert_to_sql(), resize_images()).

Provides model-aware behavioral validation and audit
    certificates for statistical analyses. Controlled transformations and
    model-specific diagnostic checks are organized across five domains:
    computational integrity, numerical stability, design adequacy, assumption
    screening, and influence stability. Supported workflows include linear
    models, generalized linear models, classical and repeated-measures analyses
    of variance, mixed-effects models fitted using 'lme4' or 'glmmTMB', and
    survival models fitted using 'survival'. Checks are selected according to
    registered applicability conditions for each model class. The resulting
    certificates describe computational behavior and selected diagnostic
    findings; they do not establish causal validity, model correctness, or
    scientific appropriateness.