A framework for predicting retention times in liquid
    chromatography. Users can train custom models for specific
    chromatography columns, predict retention times using existing models,
    or adjust existing models to account for altered experimental
    conditions. The provided functionalities can be accessed either via
    the R console or via a graphical user interface. Related work: Bonini
    et al. (2020) <doi:10.1021/acs.analchem.9b05765>.

Samples generalized random product graphs, a generalization of
    a broad class of network models. Given matrices X, S, and Y with with
    non-negative entries, samples a matrix with expectation X S Y^T and
    independent Poisson or Bernoulli entries using the fastRG algorithm of
    Rohe et al. (2017) <https://www.jmlr.org/papers/v19/17-128.html>. The
    algorithm first samples the number of edges and then puts them down
    one-by-one.  As a result it is O(m) where m is the number of edges, a
    dramatic improvement over element-wise algorithms that which require
    O(n^2) operations to sample a random graph, where n is the number of
    nodes.

A utility to scrape and load play-by-play data
    and statistics from the Premier Hockey Federation (PHF) <https://www.premierhockeyfederation.com/>, formerly
    known as the National Women's Hockey League (NWHL). Additionally, allows access to the National Hockey League's
    stats API <https://www.nhl.com/>.

A data package that hosts all models for the 'nflfastR'
    package.

Infrastrcture for creating rich, dynamic web content using R scripts while maintaining very fast response time.

Computes fast (relative to other implementations) approximate 
    Shapley values for any supervised learning model. Shapley values help to 
    explain the predictions from any black box model using ideas from game 
    theory; see Strumbel and Kononenko (2014) <doi:10.1007/s10115-013-0679-x> 
    for details.

Functions for computing spillover measures, especially spillover
    tables and spillover indices, as well as their average, minimal, and maximal
    values.