Package: footBayes 0.2.0
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, diagonal-inflated bivariate Poisson, and zero-inflated Skellam. 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>.
Authors:
footBayes_0.2.0.tar.gz
footBayes_0.2.0.zip(r-4.5)footBayes_0.2.0.zip(r-4.4)footBayes_0.2.0.zip(r-4.3)
footBayes_0.2.0.tgz(r-4.4-any)footBayes_0.2.0.tgz(r-4.3-any)
footBayes_0.2.0.tar.gz(r-4.5-noble)footBayes_0.2.0.tar.gz(r-4.4-noble)
footBayes_0.2.0.tgz(r-4.4-emscripten)footBayes_0.2.0.tgz(r-4.3-emscripten)
footBayes.pdf |footBayes.html✨
footBayes/json (API)
NEWS
# Install 'footBayes' in R: |
install.packages('footBayes', repos = c('https://leoegidi.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/leoegidi/footbayes/issues
Last updated 12 days agofrom:e50cec331c. Checks:OK: 1 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 25 2024 |
R-4.5-win | NOTE | Oct 25 2024 |
R-4.5-linux | NOTE | Oct 25 2024 |
R-4.4-win | NOTE | Oct 25 2024 |
R-4.4-mac | NOTE | Oct 25 2024 |
R-4.3-win | NOTE | Oct 25 2024 |
R-4.3-mac | NOTE | Oct 25 2024 |
Exports:cauchyfoot_abilitiesfoot_probfoot_rankfoot_round_robinlaplacemle_footnormalpp_footstan_footstudent_t
Dependencies:abindarmaskpassbackportsbase64encbayesplotBHbitbit64blobbootbroombslibcachemcallrcellrangercheckmateclicliprcodacolorspaceconflictedcpp11crayoncurldata.tableDBIdbplyrDEoptimRdescdigestdistributionaldplyrdtplyrevaluateextraDistrfansifarverfastmapfontawesomeforcatsfsgarglegenericsggplot2ggridgesgluegoogledrivegooglesheets4gridExtragtablehavenhighrhmshtmltoolshttridsinlineisobandjquerylibjsonliteknitrlabelinglatticelifecyclelme4loolubridatemagrittrMASSMatrixmatrixStatsmemoisemetRologymgcvmimeminqamodelrmunsellnlmenloptrnumDerivopensslpillarpkgbuildpkgconfigplyrposteriorprettyunitsprocessxprogresspspurrrQuickJSRR6raggrappdirsRColorBrewerRcppRcppEigenRcppParallelreadrreadxlrematchrematch2reprexreshape2rlangrmarkdownrobustbaserstanrstudioapirvestsassscalesselectrStanHeadersstringistringrsyssystemfontstensorAtextshapingtibbletidyrtidyselecttidyversetimechangetinytextzdbutf8uuidvctrsviridisLitevroomwithrxfunxml2yaml
Readme and manuals
Help Manual
Help page | Topics |
---|---|
English league results 1888-2022 | england |
Plot football abilities from Stan and MLE models | foot_abilities |
Plot football matches probabilities for out-of-sample football matches. | foot_prob |
Rank and points predictions | foot_rank |
Round-robin for football leagues | foot_round_robin |
Italy league results 1934-2022 | italy |
Fit football models with Maximum Likelihood | mle_foot |
Posterior predictive checks for football models | pp_foot |
Football priors distributions and options | cauchy laplace normal priors student_t |
Fit football models with Stan | stan_foot |