footBayes: Fitting Bayesian and MLE Football Models

This is the first package allowing for the estimation, visualization and prediction of the most well-known football models: double Poisson, bivariate Poisson, Skellam, student_t. The package allows Hamiltonian Monte Carlo (HMC) estimation through the underlying Stan environment and Maximum Likelihood estimation (MLE, for 'static' models only). The model construction relies on the most well-known football references, such as Dixon and Coles (1997) <doi:10.1111/1467-9876.00065>, Karlis and Ntzoufras (2003) <doi:10.1111/1467-9884.00366> and Egidi, Pauli and Torelli (2018) <doi:10.1177/1471082X18798414>.

Version: 0.1.0
Depends: R (≥ 3.1.0)
Imports: rstan (≥ 2.18.1), arm, reshape2, ggplot2, bayesplot, matrixStats, extraDistr, parallel, metRology, dplyr, numDeriv, tidyverse, magrittr
Suggests: testthat, knitr (≥ 1.37), rmarkdown (≥ 2.10), engsoccerdata, loo
Published: 2022-02-21
Author: Leonardo Egidi[aut, cre]
Maintainer: Leonardo Egidi <legidi at>
License: GPL-2
NeedsCompilation: no
SystemRequirements: pandoc (>= 1.12.3), pandoc-citeproc
Materials: NEWS
In views: SportsAnalytics
CRAN checks: footBayes results


Reference manual: footBayes.pdf
Vignettes: Fitting football models and visualizing predictions with the “'footBayes“' package


Package source: footBayes_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): footBayes_0.1.0.tgz, r-oldrel (arm64): footBayes_0.1.0.tgz, r-release (x86_64): footBayes_0.1.0.tgz, r-oldrel (x86_64): footBayes_0.1.0.tgz


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