sport: Sequential Pairwise Online Rating Techniques

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>.

Version: 0.2.0
Depends: R (≥ 3.0)
Imports: Rcpp, data.table, ggplot2
LinkingTo: Rcpp
Suggests: dplyr, knitr, lobstr, magrittr, pkgdown, rmarkdown, spelling, testthat
Published: 2020-01-07
Author: Dawid Kałędkowski ORCID iD [aut, cre]
Maintainer: Dawid Kałędkowski <dawid.kaledkowski at gmail.com>
BugReports: https://github.com/gogonzo/sport/issues
License: GPL-2
URL: https://github.com/gogonzo/sport
NeedsCompilation: yes
Language: en-US
Materials: NEWS
CRAN checks: sport results

Downloads:

Reference manual: sport.pdf
Vignettes: sport an R package for online update algorithms
The theory of the online update algorithms
Package source: sport_0.2.0.tar.gz
Windows binaries: r-prerelease: sport_0.2.0.zip, r-release: sport_0.2.0.zip, r-oldrel: sport_0.2.0.zip
macOS binaries: r-prerelease: sport_0.2.0.tgz, r-release: sport_0.2.0.tgz, r-oldrel: sport_0.2.0.tgz
Old sources: sport archive

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