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 |
| 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 |
| Package source: | multilevelcoda_1.3.2.tar.gz |
| Windows binaries: | r-devel: multilevelcoda_1.3.2.zip, r-release: multilevelcoda_1.3.2.zip, r-oldrel: multilevelcoda_1.3.2.zip |
| macOS binaries: | r-release (arm64): multilevelcoda_1.3.2.tgz, r-oldrel (arm64): multilevelcoda_1.3.2.tgz, r-release (x86_64): multilevelcoda_1.3.2.tgz, r-oldrel (x86_64): multilevelcoda_1.3.2.tgz |
| Old sources: | multilevelcoda archive |
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