Functions to Efficiently Simulate and Evaluate NFL Seasons
c(person(given = "Lee",
family = "Sharpe",
role = c("aut", "cph")),
person(given = "Sebastian",
family = "Carl",
role = c("cre", "aut"),
email = "mrcaseb@gmail.com"))
A set of functions to simulate National Football
League seasons including the sophisticated tie-breaking procedures.
cli, crayon, curl, dplyr, furrr, future, glue, gsubfn,
magrittr, progressr, purrr, readr, rlang, tibble, tidyr
knitr, rmarkdown, testthat (>= 3.0.0), tidyverse
Lee Sharpe [aut, cph],
Sebastian Carl [cre, aut]

cli, crayon, curl, dplyr, furrr, future, glue, gsubfn,
magrittr, nflreadr (>= 1.1.3), progressr, purrr, readr, rlang,
tibble, tidyr
gt, knitr, rmarkdown, scales, testthat (>= 3.0.0), tidyverse

c(
person("Sebastian", "Carl", , "mrcaseb@gmail.com", role = c("aut", "cre")),
person("Ben", "Baldwin", , "bbaldwin206@gmail.com", role = "aut"),
person("Lee", "Sharpe", role = "aut"),
person("Tan", "Ho", , "tan@tanho.ca", role = "aut",
comment = c(ORCID = "0000-0001-8388-5155"))
)
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/>.
Sebastian Carl [aut, cre],
Ben Baldwin [aut],
Lee Sharpe [aut],
Tan Ho [aut] (<https://orcid.org/0000-0001-8388-5155>)

Nonparametric Failure Time Bayesian Additive Regression Trees
c(person('Rodney', 'Sparapani',
role=c('aut', 'cre'), email='rsparapa@mcw.edu'),
person('Robert', 'McCulloch', role='aut'),
person('Matthew', 'Pratola', role='ctb'),
person('Hugh', 'Chipman', role='ctb'))
Rodney Sparapani [aut, cre],
Robert McCulloch [aut],
Matthew Pratola [ctb],
Hugh Chipman [ctb]
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) + s(x) E where functions f and s 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 technical description of the model <https://www.mcw.edu/-/media/MCW/Departments/Biostatistics/tr72.pdf?la=en>.