multilevelcoda
Estimate Bayesian Multilevel Models for Compositional Data
Implement Bayesian multilevel modelling for compositional data. Compute multilevel compositional data and perform log-ratio transforms at between and within-person levels, fit Bayesian multilevel models for compositional predictors and outcomes, and run post-hoc analyses such as isotemporal substitution models. References: Le, Stanford, Dumuid, and Wiley (2025) doi:10.1037/met0000750, Le, Dumuid, Stanford, and Wiley (2025) doi:10.1080/00273171.2025.2565598.
- Version1.3.3
- R version≥ 4.0.0
- LicenseGPL (≥ 3)
- Needs compilation?No
- multilevelcoda citation info
- Last release11/11/2025
Documentation
- VignetteIntroduction to Bayesian Compositional Multilevel Modelling
- VignetteMultilevel Models with Compositional Predictors
- VignetteMultilevel Model with Compositional Outcomes
- VignetteCompositional Substitution Multilevel Models
- VignetteImproving MCMC Sampling for Bayesian Compositional Multilevel Models
- MaterialREADME
- MaterialNEWS
- In ViewsCompositionalData
Team
Flora Le
MaintainerShow author detailsJoshua F. Wiley
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