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 (2024) <doi:10.48550/arXiv.2411.12407>.
Version: 1.3.2 Depends: R (≥ 4.0.0) Imports: stats, data.table (≥ 1.12.0), compositions, brms, bayestestR, extraoperators, ggplot2, foreach, future, doFuture, abind, graphics, shiny, shinystan, loo, bayesplot, emmeans, posterior, plotly, hrbrthemes, htmltools, bslib, DT, fs Suggests: testthat (≥ 3.0.0), covr, withr, knitr, rmarkdown, lme4, cmdstanr (≥ 0.5.0) Published: 2025-05-25 DOI: 10.32614/CRAN.package.multilevelcoda Author: Flora Le [aut, cre], Joshua F. Wiley [aut] Maintainer: Flora Le <floralebui at gmail.com> BugReports: https://github.com/florale/multilevelcoda/issues License: GPL (≥ 3) URL: https://florale.github.io/multilevelcoda/, https://github.com/florale/multilevelcoda NeedsCompilation: no Additional_repositories: https://mc-stan.org/r-packages/ Citation: multilevelcoda citation info Materials: README NEWS In views: CompositionalData CRAN checks: multilevelcoda results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=multilevelcoda to link to this page.
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