Implement Bayesian Multilevel Modelling for compositional data in a multilevel framework. Compute multilevel compositional data and Isometric log ratio (ILR) 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 (2024) <doi:10.48550/arXiv.2405.03985>, Le, Dumuid, Stanford, and Wiley (2024) <doi:10.48550/arXiv.2411.12407>.
Version: | 1.3.1 |
Depends: | R (≥ 4.0.0) |
Imports: | stats, data.table (≥ 1.12.0), compositions, brms, bayestestR, extraoperators, ggplot2, foreach, future, doFuture, abind, graphics, shiny, shinystan, plotly, hrbrthemes, bslib, DT, loo, bayesplot, emmeans, posterior |
Suggests: | testthat (≥ 3.0.0), covr, withr, knitr, rmarkdown, lme4, cmdstanr (≥ 0.5.0) |
Published: | 2024-11-23 |
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/ |
Materials: | README NEWS |
CRAN checks: | multilevelcoda results |
Package source: | multilevelcoda_1.3.1.tar.gz |
Windows binaries: | r-devel: multilevelcoda_1.3.0.2.zip, r-release: multilevelcoda_1.3.1.zip, r-oldrel: multilevelcoda_1.3.1.zip |
macOS binaries: | r-release (arm64): multilevelcoda_1.3.1.tgz, r-oldrel (arm64): multilevelcoda_1.3.1.tgz, r-release (x86_64): multilevelcoda_1.3.1.tgz, r-oldrel (x86_64): multilevelcoda_1.3.1.tgz |
Old sources: | multilevelcoda archive |
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