An implementation of the TEAM algorithm to identify local differences 
    between two (e.g. case and control) independent, univariate distributions, as 
    described in J Pura, C Chan, and J Xie (2019) <arXiv:1906.07757>. The algorithm 
    is based on embedding a multiple-testing procedure on a hierarchical structure 
    to identify high-resolution differences between two distributions. The 
    hierarchical structure is designed to identify strong, short-range differences 
    at lower layers and weaker, but long-range differences at increasing layers. 
    TEAM yields consistent layer-specific and overall false discovery rate control.

Provides color palettes corresponding to professional and amateur, 
    sports teams. These can be useful in creating data graphics that are themed 
    for particular teams.

Package of wrapper functions using R6 class to send requests to 
  Microsoft 'Teams' <https://products.office.com/en-us/microsoft-teams/group-chat-software> through webhooks.
  When you need to share information or data from R to 'Teams', rather than copying/pasting, you
  can use this package to send well-formatted output from multiple R objects.

Models the direction of the maximum horizontal stress using
    relative plate motion parameters. Statistical algorithms to evaluate
    the modeling results compared with the observed data. Provides plots
    to visualize the results. Methods described in Stephan et al. (2023)
    <doi:10.1038/s41598-023-42433-2> and Wdowinski (1998)
    <doi:10.1016/S0079-1946(98)00091-3>.

The typicality and eccentricity data analysis (TEDA) framework was
    put forward by Angelov (2013) <DOI:10.14313/JAMRIS_2-2014/16>. It has been further developed into multiple
    different techniques since, and provides a non-parametric way of determining how
    similar an observation, from a process that is not purely random, is to other
    observations generated by the process. This package provides code to use the
    batch and recursive TEDA methods that have been published.