Package: mvglmmRank 1.2-4

mvglmmRank: Multivariate Generalized Linear Mixed Models for Ranking Sports Teams

Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation as documented by Broatch and Karl (2018) <doi:10.48550/arXiv.1710.05284>, Karl, Yang, and Lohr (2014) <doi:10.1016/j.csda.2013.11.019>, and by Karl (2012) <doi:10.1515/1559-0410.1471>. Karl and Zimmerman <doi:10.1016/j.jspi.2020.06.004> use this package to illustrate how the home field effect estimator from a mixed model can be biased under nonrandom scheduling.

Authors:Andrew T. Karl [cre, aut], Jennifer Broatch [aut]

mvglmmRank_1.2-4.tar.gz
mvglmmRank_1.2-4.zip(r-4.5)mvglmmRank_1.2-4.zip(r-4.4)mvglmmRank_1.2-4.zip(r-4.3)
mvglmmRank_1.2-4.tgz(r-4.4-any)mvglmmRank_1.2-4.tgz(r-4.3-any)
mvglmmRank_1.2-4.tar.gz(r-4.5-noble)mvglmmRank_1.2-4.tar.gz(r-4.4-noble)
mvglmmRank_1.2-4.tgz(r-4.4-emscripten)mvglmmRank_1.2-4.tgz(r-4.3-emscripten)
mvglmmRank.pdf |mvglmmRank.html✨
mvglmmRank/json (API)
NEWS

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2 exports 2 stars 0.23 score 4 dependencies 11 scripts 242 downloads

Last updated 2 years agofrom:78cfef41cd. Checks:OK: 7. Indexed: yes.

Exports:game.predmvglmmRank

Dependencies:latticeMASSMatrixnumDeriv