A set of functions to estimate outcomes of fourth down plays
    in the National Football League and obtain fourth down plays from
    <https://www.nfl.com/> and <https://www.espn.com/>.

A set of functions to access National Football League
    play-by-play data from <https://www.nfl.com/>.

A set of functions to visualize National Football League
    analysis in 'ggplot2' plots and 'gt' tables.

A minimal package for downloading data from 'GitHub'
    repositories of the 'nflverse' project.

A set of functions to simulate National Football League
    seasons including the sophisticated tie-breaking procedures.

The intent here is to enable the simulation of plays/drives and
    evaluate game-play strategies in the National Football League (NFL).
    Built-in strategies include going for it on fourth down and varying the 
    proportion of passing/rushing plays during a drive. The user should be
    familiar with nflscrapR data before trying to write his/her own 
    strategies. This work is inspired by a blog post by Mike Lopez, 
    currently the  Director of Data and Analytics at the NFL, Lopez (2019) <https://statsbylopez.netlify.app/post/resampling-nfl-drives/>.

The 'nflverse' is a set of packages dedicated to data of the
    National Football League. This package is designed to make it easy to
    install and load multiple 'nflverse' packages in a single step. Learn
    more about the 'nflverse' at <https://nflverse.nflverse.com/>.

Nonparametric Failure Time (NFT) Bayesian Additive Regression Trees (BART): Time-to-event Machine Learning with Heteroskedastic Bayesian Additive Regression Trees (HBART) and Low Information Omnibus (LIO) Dirichlet Process Mixtures (DPM). An NFT BART model is of the form Y = mu + f(x) + sd(x) E where functions f and sd have BART and HBART priors, respectively, while E is a nonparametric error distribution due to a DPM LIO prior hierarchy. See the following for a description of the model at <doi:10.1111/biom.13857>.

Density, distribution function, quantile function and random generation for the 3D Navarro, Frenk & White (NFW) profile. For details see Robotham & Howlett (2018) <arXiv:1805.09550>.