BTdecayLasso: Bradley-Terry Model with Exponential Time Decayed Log-Likelihood and Adaptive Lasso

We apply Bradley-Terry Model to estimate teams' ability in paired comparison data. Exponential Decayed Log-likelihood function is applied for dynamic approximation of current rankings and Lasso penalty is applied 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: 0.1.0
Imports: optimr, ggplot2, stats
Published: 2018-06-27
Author: Yunpeng Zhou [aut, cre], Jinfeng Xu [aut]
Maintainer: Yunpeng Zhou <michael.zhou.hku at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README NEWS
CRAN checks: BTdecayLasso results

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Reference manual: BTdecayLasso.pdf
Package source: BTdecayLasso_0.1.0.tar.gz
Windows binaries: r-devel: BTdecayLasso_0.1.0.zip, r-release: BTdecayLasso_0.1.0.zip, r-oldrel: BTdecayLasso_0.1.0.zip
OS X binaries: r-release: BTdecayLasso_0.1.0.tgz, r-oldrel: BTdecayLasso_0.1.0.tgz

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