Estimate Bayesian Multilevel Models for Compositional Data R package multilevelcoda version 1.3.3 CRAN Task View: CompositionalData (https://CRAN.R-project.org/view=CompositionalData) 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>. Software Depends: R (>= 4.0.0) Imports: stats, data.table (>= 1.12.0), compositions, brms, extraoperators, ggplot2, foreach, future, doFuture, abind, graphics, shiny, shinystan, loo, bayesplot, emmeans, plotly, htmltools, bslib, DT, fs Suggests: testthat (>= 3.0.0), covr, withr, knitr, rmarkdown, lme4, cmdstanr (>= 0.5.0) Flora Le <floralebui@gmail.com> Comprehensive R Archive Network (CRAN) Flora Le [aut, cre] (ORCID: <https://orcid.org/0000-0003-0089-8167>), Joshua F. Wiley [aut] (ORCID: <https://orcid.org/0000-0002-0271-6702>) GPL (>= 3) 2025-11-11 application/tgz https://CRAN.R-project.org/package=multilevelcoda doi:10.32614/CRAN.package.multilevelcoda