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.

Version: 1.2-4
Depends: R (≥ 3.2.0), Matrix
Imports: numDeriv, methods, stats, utils, MASS
Published: 2023-01-08
DOI: 10.32614/CRAN.package.mvglmmRank
Author: Andrew T. Karl ORCID iD [cre, aut], Jennifer Broatch [aut]
Maintainer: Andrew T. Karl <akarl at>
License: GPL-2
NeedsCompilation: no
Materials: NEWS
In views: MixedModels, SportsAnalytics
CRAN checks: mvglmmRank results


Reference manual: mvglmmRank.pdf


Package source: mvglmmRank_1.2-4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mvglmmRank_1.2-4.tgz, r-oldrel (arm64): mvglmmRank_1.2-4.tgz, r-release (x86_64): mvglmmRank_1.2-4.tgz, r-oldrel (x86_64): mvglmmRank_1.2-4.tgz
Old sources: mvglmmRank archive


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