Compile 'Typst' files using the 'typst-cli' (<https://typst.app>)
    command line tool. Automatically falls back to rendering via embedded 'Typst'
    from 'Quarto' (<https://quarto.org>) if 'Typst' is not installed. Includes
    utilities to check for 'typst-cli' availability and run 'Typst' commands.

Create 'Typst' table markup from data frames. Features a pipe-friendly interface for column, row, and cell styling with support for grouped headers, grouped rows, and data-driven formatting.

Provides an up-to-date copy of the Internet Assigned Numbers
    Authority (IANA) Time Zone Database. It is updated periodically to
    reflect changes made by political bodies to time zone boundaries, UTC
    offsets, and daylight saving time rules. Additionally, this package
    provides a C++ interface for working with the 'date' library. 'date'
    provides comprehensive support for working with dates and date-times,
    which this package exposes to make it easier for other R packages to
    utilize. Headers are provided for calendar specific calculations,
    along with a limited interface for time zone manipulations.

Download and compile any version of the
  IANA Time Zone Database (also known as Olson database)
  and make it current in your R session.
  Beware: on Windows 'Cygwin' is required!

An educational toolkit for learning statistical concepts through 
    interactive exploration. Provides functions for basic statistics (mean, 
    variance, etc.) and probability distributions with step-by-step explanations 
    and interactive learning modes. Each function can be used for simple 
    calculations, detailed learning with explanations, or interactive practice 
    with feedback.

A comprehensive educational package combining clustering algorithms with 
    detailed step-by-step explanations. Provides implementations of both traditional 
    (hierarchical, k-means) and modern (Density-Based Spatial Clustering of Applications with Noise (DBSCAN), 
    Gaussian Mixture Models (GMM), genetic k-means) clustering methods 
    as described in Ezugwu et. al., (2022) <doi:10.1016/j.engappai.2022.104743>. 
    Includes educational datasets highlighting different clustering challenges, based on 
    'scikit-learn' examples (Pedregosa et al., 2011) 
    <https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html>. Features detailed 
    algorithm explanations, visualizations, and weighted distance calculations for 
    enhanced learning.