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.

Version:	1.0.0
Depends:	R (≥ 4.0.0)
Imports:	stats, data.table (≥ 1.12.0), compositions, zCompositions, bayestestR, brms, extraoperators, ggplot2, emmeans, insight, ggsci, foreach
Suggests:	testthat (≥ 3.0.0), covr, withr, knitr, rmarkdown, doFuture, lme4
Published:	2023-01-13
Author:	Flora Le [aut, cre], Joshua F. Wiley [aut]
Maintainer:	Flora Le <13florale 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
Materials:	README NEWS
CRAN checks:	multilevelcoda results

Documentation:

Reference manual:	multilevelcoda.pdf
Vignettes:	Multilevel Model with Compositional Outcomes Multilevel Models with Compositional Predictors Introduction to Compositional Multilevel Modelling Compositional Multilevel Substitution Models

Downloads:

Package source:	multilevelcoda_1.0.0.tar.gz
Windows binaries:	r-devel: multilevelcoda_1.0.0.zip, r-release: multilevelcoda_1.0.0.zip, r-oldrel: multilevelcoda_1.0.0.zip
macOS binaries:	r-release (arm64): multilevelcoda_1.0.0.tgz, r-oldrel (arm64): multilevelcoda_1.0.0.tgz, r-release (x86_64): multilevelcoda_1.0.0.tgz, r-oldrel (x86_64): multilevelcoda_1.0.0.tgz

Linking:

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