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]

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mvglmmRank.pdf |mvglmmRank.html
mvglmmRank/json (API)
NEWS

# Install 'mvglmmRank' in R:
install.packages('mvglmmRank', repos = c('https://akarl46556.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • f2008 - 2008 FBS College Football Regular Season Data
  • f2009 - 2009 FBS College Football Regular Season Data
  • f2010 - 2010 FBS College Football Regular Season Data
  • f2011 - 2011 FBS College Football Regular Season Data
  • f2012 - 2012 FBS College Football Regular Season Data
  • nba2013 - 2013 NBA Data
  • ncaab2012 - 2012 NCAA Division I Basketball Results
  • nfl2012 - 2012 NFL Regular Season Data

On CRAN:

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

1.34 score 2 stars 11 scripts 234 downloads 2 exports 4 dependencies

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

TargetResultDate
Doc / VignettesOKNov 12 2024
R-4.5-winOKNov 12 2024
R-4.5-linuxOKNov 12 2024
R-4.4-winOKNov 12 2024
R-4.4-macOKNov 12 2024
R-4.3-winOKNov 12 2024
R-4.3-macOKNov 12 2024

Exports:game.predmvglmmRank

Dependencies:latticeMASSMatrixnumDeriv