Provides tools for exploring animal tracking data, from raw
    telemetry points to summaries, interactive maps, and home range
    estimates. It is designed to be beginner-friendly and requires minimal
    data wrangling.

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/>.

Formula 1 pit stop data. The package provides information on teams and drivers across seasons (2019 or higher). It also includes a function to visualize pit stop performance.

Provides a local execution environment for testing and developing the 'FaaSr' workflows
    without requiring cloud infrastructure. The 'FaaSr' package enables R developers to
    validate and test workflows locally before deploying to Function-as-a-Service (FaaS) platforms.
    Key features include:
    1) Parsing and validating JSON workflow configurations compliant with the 'FaaSr' schema
    2) Simulated S3 storage operations using local file system with local logging
    3) Support for conditional branching
    4) Support for parallel rank functions execution
    5) Workflow cycle detection and validation
    6) No cloud credentials or infrastructure required for testing
    This package is designed for development and testing purposes. For production deployment
    to cloud FaaS platforms, use the main 'FaaSr' package available at
    <https://faasr.io/>.