Contains functions for analysis and summary of tidal datasets. Also provides access to tidal data collected by the National Oceanic and Atmospheric Administration's Center for Operational Oceanographic Products and Services and the Permanent Service for Mean Sea Level. For detailed description and application examples, see Hill, T.D. and S.C. Anisfeld (2021) <doi:10.6084/m9.figshare.14161202.v1> and Hill, T.D. and S.C. Anisfeld (2015) <doi:10.1016/j.ecss.2015.06.004>.

Calculates the volume under the ROC surface and its (co)variance for ordered multi-class ROC analysis as well as certain bivariate ordinal measures of association.

Offers a comprehensive set of assertion tests to help users validate the integrity of their data. 
    These tests can be used to check for specific conditions or properties within a dataset and 
    help ensure that data is accurate and reliable. 
    The package is designed to make it easy to add quality control checks to data analysis 
    workflows and to aid in identifying and correcting any errors or inconsistencies in data.

Allow R users to interact with the 'Canvas' Learning Management System (LMS) API (see
    <https://canvas.instructure.com/doc/api/all_resources.html> for details).
    It provides a set of functions to access and manipulate course data, assignments, grades, users, 
    and other resources available through the 'Canvas' API.

Provides a set of functions for data transformations.
    Transformations are performed on character and numeric data. As the scope of the package is
    within Student Analytics, there are functions focused around the academic year.

Provides a user-friendly R 'shiny' app for performing various statistical tests on datasets. 
  It allows users to upload data in numerous formats and perform statistical analyses. 
  The app dynamically adapts its options based on the selected columns and supports both single and multiple column comparisons.
  The app's user interface is designed to streamline the process of selecting datasets, columns, and test options, 
  making it easy for users to explore and interpret their data. The underlying functions for statistical tests are well-organized and can be used independently within other R scripts.