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>.

Version:	1.3.3
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)
Published:	2025-11-11
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

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