This package provides functions to model compositional data in a multilevel framework using full Bayesian inference. It integrates the principes of Compositional Data Analysis (CoDA) and Multilevel Modelling and supports both compositional data as an outcome and predictors in a wide range of generalized (non-)linear multivariate multilevel models.
To install the latest release version from CRAN, run
install.packages("multilevelcoda")
The current developmental version can be downloaded from github via
if (!requireNamespace("remotes")) {
install.packages("remotes")
}
remotes::install_github("florale/multilevelcoda")Because multilevelcoda is built on brms, which is based on Stan, a C++ compiler is required. The program Rtools (available on https://cran.r-project.org/bin/windows/Rtools/) comes with a C++ compiler for Windows. On Mac, Xcode is required. For further instructions on how to get the compilers running, see the prerequisites section on https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started.
You can learn about the package from these vignettes:
- Introduction to Compositional Multilevel Modelling
- Multilevel Models with Compositional Predictors
- Multilevel Models with Compositional Outcome
- Compositional Substitution Multilevel Analysis
When using multilevelcoda, please cite one or more of the following publications:
- Le, F., Stanford, T. E., Dumuid, D., & Wiley, J. F. (2025). Bayesian multilevel compositional data analysis: introduction, evaluation, and application. Psychological Methods. https://doi.org/10.1037/met0000750
- Le F., Dumuid D., Stanford T. E., Wiley J. F. (2024). Bayesian multilevel compositional data analysis with the R package multilevelcoda. arXiv preprint arXiv:2411.12407.
As multilevelcoda depends on brms and Stan, please also consider citing:
- Bürkner P. C. (2017). brms: An R Package for Bayesian Multilevel Models using Stan. Journal of Statistical Software. 80(1), 1-28. doi.org/10.18637/jss.v080.i01
- Bürkner P. C. (2018). Advanced Bayesian Multilevel Modeling with the R Package brms. The R Journal. 10(1), 395-411. doi.org/10.32614/RJ-2018-017
- Bürkner P. C. (2021). Bayesian Item Response Modeling in R with brms and Stan. Journal of Statistical Software, 100(5), 1-54. doi.org/10.18637/jss.v100.i05
- Stan Development Team. YEAR. Stan Modeling Language Users Guide and Reference Manual, VERSION. https://mc-stan.org
- Carpenter B., Gelman A., Hoffman M. D., Lee D., Goodrich B., Betancourt M., Brubaker M., Guo J., Li P., and Riddell A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software. 76(1). doi.org/10.18637/jss.v076.i01