Wrapper for the 'ggplot2' package that creates a variety of common
  charts (e.g. bar, line, area, ROC, waterfall, pie) while aiming to reduce
  typing.

Runs a Shiny App in the local machine for basic
    statistical and graphical analyses. The point-and-click interface 
    of Shiny App enables obtaining the same analysis outputs (e.g., plots and
    tables) more quickly, as compared with typing the required code in R,
    especially for users without much experience or expertise with coding.
    Examples of possible analyses include tabulating descriptive
    statistics for a variable, creating histograms by experimental groups,
    and creating a scatter plot and calculating the correlation between
    two variables.

Streamlines common steps for working with animal tracking data, 
    from raw telemetry points to summaries, interactive maps, and home range 
    estimates. Designed to be beginner-friendly, it enables rapid exploration 
    of spatial and movement data with minimal wrangling, providing a unified 
    workflow for importing, summarizing, and visualizing, and analyzing animal 
    movement datasets.

Contains two functions that are intended to make
    tuning supervised learning methods easy. The eztune function uses a
    genetic algorithm or Hooke-Jeeves optimizer to find the best 
    set of tuning parameters. The user can choose the optimizer, the 
    learning method, and if optimization will be based on accuracy 
    obtained through validation error, cross validation, or resubstitution. 
    The function eztune.cv will compute a cross validated error rate. The purpose 
    of eztune_cv is to provide a cross validated accuracy or MSE when 
    resubstitution or validation data are used for optimization because 
    error measures from both approaches can be misleading.

Obtain Formula 1 data via the 'Jolpica API' <https://jolpi.ca> and the unofficial API <https://www.formula1.com/en/timing/f1-live> via the 'fastf1' 'Python' library <https://docs.fastf1.dev/>.