multilevelcoda: Estimate Bayesian Multilevel Models for Compositional Data

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.

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, insight
Suggests: testthat (≥ 3.0.0), covr, withr, knitr, rmarkdown, lme4, cmdstanr (≥ 0.5.0)
Published: 2024-07-09
DOI: 10.32614/CRAN.package.multilevelcoda
Author: Flora Le ORCID iD [aut, cre], Joshua F. Wiley ORCID iD [aut]
Maintainer: Flora Le <13florale at>
License: GPL (≥ 3)
NeedsCompilation: no
Materials: README NEWS
CRAN checks: multilevelcoda results


Reference manual: multilevelcoda.pdf
Vignettes: Introduction to Bayesian Compositional Multilevel Modelling
Multilevel Models with Compositional Predictors
Multilevel Model with Compositional Outcomes
Compositional Substitution Multilevel Models
Improving MCMC Sampling for Bayesian Compositional Multilevel Models


Package source: multilevelcoda_1.3.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): multilevelcoda_1.3.0.2.tgz, r-oldrel (arm64): multilevelcoda_1.3.0.2.tgz, r-release (x86_64): multilevelcoda_1.3.0.2.tgz, r-oldrel (x86_64): multilevelcoda_1.3.0.2.tgz
Old sources: multilevelcoda archive


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