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>.

Implementation for 'nFunNN' method, which is a novel nonlinear functional principal component analysis method using neural networks. The crucial function of this package is nFunNNmodel().

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>.