Calculates ratings for two-player or multi-player challenges. Methods included in package such as are able to estimate ratings (players strengths) and their evolution in time, also able to predict output of challenge. Algorithms are based on Bayesian Approximation Method, and they don't involve any matrix inversions nor likelihood estimation. Parameters are updated sequentially, and computation doesn't require any additional RAM to make estimation feasible. Additionally, base of the package is written in C++ what makes sport computation even faster. Methods used in the package refers to Mark E. Glickman (1999) <http://www.glicko.net/research/glicko.pdf>; Mark E. Glickman (2001) <doi:10.1080/02664760120059219>; Ruby C. Weng, Chih-Jen Lin (2011) <http://jmlr.csail.mit.edu/papers/volume12/weng11a/weng11a.pdf>; W. Penny, Stephen J. Roberts (1999) <doi:10.1109/IJCNN.1999.832603>.
|Depends:||R (≥ 3.0)|
|Imports:||Rcpp, data.table, ggplot2|
|Suggests:||dplyr, knitr, lobstr, magrittr, pkgdown, rmarkdown, spelling, testthat|
|Author:||Dawid Kałędkowski [aut, cre]|
|Maintainer:||Dawid Kałędkowski <dawid.kaledkowski at gmail.com>|
|CRAN checks:||sport results|
sport an R package for online update algorithms
The theory of the online update algorithms
|Windows binaries:||r-devel: sport_0.2.0.zip, r-release: sport_0.2.0.zip, r-oldrel: sport_0.2.0.zip|
|macOS binaries:||r-release (arm64): sport_0.2.0.tgz, r-oldrel (arm64): sport_0.2.0.tgz, r-release (x86_64): sport_0.2.0.tgz, r-oldrel (x86_64): sport_0.2.0.tgz|
|Old sources:||sport archive|
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