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We utilize the Bradley-Terry Model to estimate the abilities of teams using paired comparison data. For dynamic approximation of current rankings, we employ the Exponential Decayed Log-likelihood function, and we also apply the Lasso penalty for variance reduction and grouping. The main algorithm applies the Augmented Lagrangian Method described by Masarotto and Varin (2012) <doi:10.1214/12-AOAS581>.
| Version: | 0.1.1 |
| Imports: | optimx, ggplot2, stats |
| Published: | 2023-12-07 |
| Author: | Yunpeng Zhou [aut, cre], Jinfeng Xu [aut] |
| Maintainer: | Yunpeng Zhou <u3514104 at connect.hku.hk> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | BTdecayLasso results |
| Reference manual: | BTdecayLasso.pdf |
| Package source: | BTdecayLasso_0.1.1.tar.gz |
| Windows binaries: | r-devel: BTdecayLasso_0.1.1.zip, r-release: BTdecayLasso_0.1.1.zip, r-oldrel: BTdecayLasso_0.1.1.zip |
| macOS binaries: | r-release (arm64): BTdecayLasso_0.1.1.tgz, r-oldrel (arm64): BTdecayLasso_0.1.1.tgz, r-release (x86_64): BTdecayLasso_0.1.1.tgz, r-oldrel (x86_64): BTdecayLasso_0.1.1.tgz |
| Old sources: | BTdecayLasso archive |
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