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 [cre, aut], Jennifer Broatch [aut]
Maintainer:	Andrew T. Karl <akarl at asu.edu>
License:	GPL-2
NeedsCompilation:	no
Materials:	NEWS
In views:	MixedModels, SportsAnalytics
CRAN checks:	mvglmmRank results

Documentation:

Reference manual:

mvglmmRank.html , mvglmmRank.pdf

Downloads:

Package source:	mvglmmRank_1.2-4.tar.gz
Windows binaries:	r-devel: mvglmmRank_1.2-4.zip, r-release: mvglmmRank_1.2-4.zip, r-oldrel: mvglmmRank_1.2-4.zip
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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