References

Agresti, Alan. 2018. An Introduction to Categorical Data Analysis. John Wiley & Sons.
Arel-Bundock, Vincent. 2024. Marginaleffects: Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests. https://marginaleffects.com/.
Bates, Douglas, Martin Maechler, Ben Bolker, and Steven Walker. 2023. Lme4: Linear Mixed-Effects Models Using Eigen and S4. https://github.com/lme4/lme4/.
Breslow, Norman E, and David G Clayton. 1993. “Approximate Inference in Generalized Linear Mixed Models.” Journal of the American Statistical Association 88 (421): 9–25.
Brooks, Mollie, Ben Bolker, Kasper Kristensen, Martin Maechler, Arni Magnusson, Hans Skaug, Anders Nielsen, Casper Berg, and Koen van Bentham. 2023. glmmTMB: Generalized Linear Mixed Models Using Template Model Builder. https://github.com/glmmTMB/glmmTMB.
Christensen, Rune Haubo Bojesen. 2023. Ordinal: Regression Models for Ordinal Data. https://github.com/runehaubo/ordinal.
Elff, Martin. 2022. Mclogit: Multinomial Logit Models, with or Without Random Effects or Overdispersion. http://mclogit.elff.eu.
Galecki, A., and T. Burzykowski. 2012. Linear Mixed-Effects Models Using r. 1st ed. New York: Springer.
Halekoh, Ulrich, and Søren Højsgaard. 2023. Pbkrtest: Parametric Bootstrap, Kenward-Roger and Satterthwaite Based Methods for Test in Mixed Models. https://people.math.aau.dk/~sorenh/software/pbkrtest/.
Hartig, Florian. 2022. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. http://florianhartig.github.io/DHARMa/.
Laird, Nan M., and James H. Ware. 1982. “Random-Effects Models for Longitudinal Data.” Biometrics 38 (4): 963–74. http://www.jstor.org/stable/2529876.
Lüdecke, Daniel. 2024. Ggeffects: Create Tidy Data Frames of Marginal Effects for Ggplot from Model Outputs. https://strengejacke.github.io/ggeffects/.
Nakagawa, Shinichi, Paul CD Johnson, and Holger Schielzeth. 2017. “The Coefficient of Determination r 2 and Intra-Class Correlation Coefficient from Generalized Linear Mixed-Effects Models Revisited and Expanded.” Journal of the Royal Society Interface 14 (134): 20170213.
Nakagawa, Shinichi, H Schielzeth, and Robert B O’Hara. 2013. “A General and Simple Method for Obtaining R2 from Generalized Linear Mixed-Effects Models. Methods Ecol Evol 4: 133–142.”
Pinheiro, J., and D. Bates. 2000. Mixed-Effects Models in s and s-PLUS. 1st ed. New York: Springer.
Pinheiro, José, Douglas Bates, and R Core Team. 2023. Nlme: Linear and Nonlinear Mixed Effects Models. https://svn.r-project.org/R-packages/trunk/nlme/.
Ripley, Brian. 2023. MASS: Support Functions and Datasets for Venables and Ripley’s MASS. http://www.stats.ox.ac.uk/pub/MASS4/.
Scheipl, Fabian. 2022. RLRsim: Exact (Restricted) Likelihood Ratio Tests for Mixed and Additive Models. https://github.com/fabian-s/RLRsim.
Stasinopoulos, Mikis, and Robert Rigby. 2023. Gamlss: Generalised Additive Models for Location Scale and Shape. https://www.gamlss.com/.
Verbeke, G., and G Molenberghs. 2000. Linear Mixed Models for Longitudinal Data. 1st ed. New York: Springer.
Wang, Xiaofeng, Yu Ryan Yue, and Julian J Faraway. 2018. Bayesian Regression Modeling with INLA. CRC Press.
West, Welch, B. T. 2014. Linear Mixed Models: A Practical Guide Using Statistical Software. 2nd ed. Chapman; Hall/CRC.
Xie, Yihui. 2015. Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC. http://yihui.name/knitr/.
———. 2023. Bookdown: Authoring Books and Technical Documents with r Markdown. https://github.com/rstudio/bookdown.