https://github.com/florale/multilevelcoda

multilevelcoda R package for Bayesian multilevel compositional data analysis
https://github.com/florale/multilevelcoda

bayesian-inference compositional-data-analysis multilevel-models multilevelcoda r r-package

Last synced: 21 days ago
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multilevelcoda R package for Bayesian multilevel compositional data analysis

• Host: GitHub
• URL: https://github.com/florale/multilevelcoda
• Owner: florale
• License: gpl-3.0
• Created: 2022-02-04T05:47:39.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2025-03-27T08:01:42.000Z (about 2 months ago)
• Last Synced: 2025-04-28T13:13:41.808Z (21 days ago)
• Topics: bayesian-inference, compositional-data-analysis, multilevel-models, multilevelcoda, r, r-package
• Language: R
• Homepage: https://florale.github.io/multilevelcoda/
• Size: 227 MB
• Stars: 17
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: NEWS.md
  • License: LICENSE.md

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README

        


# multilevelcoda

[![R-CMD-check](https://github.com/florale/multilevelcoda/workflows/R-CMD-check/badge.svg)](https://github.com/florale/multilevelcoda/actions)

[![CRAN Version](https://www.r-pkg.org/badges/version/multilevelcoda)](https://cran.r-project.org/package=multilevelcoda)

[![lifecycle](https://lifecycle.r-lib.org/articles/figures/lifecycle-experimental.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)

## Overview

This package provides functions to model compositional data in 

a multilevel framework using full Bayesian inference.

It integrates the principes of Compositional Data Analysis (CoDA) 

and Multilevel Modelling and supports both compositional data as 

an outcome and predictors in a wide range of 

generalized (non-)linear multivariate multilevel models.

## Installation

To install the latest release version from CRAN, run

```r 

install.packages("multilevelcoda")

```

The current developmental version can be downloaded from github via

```r

if (!requireNamespace("remotes")) {

  install.packages("remotes")

}

remotes::install_github("florale/multilevelcoda")

```

Because multilevelcoda is built on brms, which is based on Stan, a C++ compiler is required. 

The program Rtools (available on https://cran.r-project.org/bin/windows/Rtools/) comes with a C++ compiler for Windows. On Mac, Xcode is required. For further instructions on how to get the compilers running, see the prerequisites section on https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started.

## Resources

You can learn about the package from these vignettes:

- [Introduction to Compositional Multilevel Modelling](https://florale.github.io/multilevelcoda/articles/A-introduction.html)

- [Multilevel Models with Compositional Predictors](https://florale.github.io/multilevelcoda/articles/B-composition-MLM.html)

- [Multilevel Models with Compositional Outcome](https://florale.github.io/multilevelcoda/articles/C-composition-MMLM.html)

- [Compositional Substitution Multilevel Analysis](https://florale.github.io/multilevelcoda/articles/D-substitution.html)

## Citing `multilevelcoda` and related software 

When using multilevelcoda, please cite one or more of the following publications:

-   Le F., Dumuid D., Stanford T. E., Wiley J. F. (2024). 

    Bayesian multilevel compositional data analysis with the R package multilevelcoda.

    *arXiv preprint arXiv:2411.12407*.

-   Le, F., Stanford, T. E., Dumuid, D., & Wiley, J. F. (2024). 

    Bayesian Multilevel Compositional Data Analysis: 

    Introduction, Evaluation, and Application. 

    *arXiv preprint arXiv:2405.03985*.

As multilevelcoda depends on brms and Stan, please also consider citing:

-   Bürkner P. C. (2017). brms: An R Package for Bayesian Multilevel

    Models using Stan. *Journal of Statistical Software*. 80(1), 1-28.

    doi.org/10.18637/jss.v080.i01

-   Bürkner P. C. (2018). Advanced Bayesian Multilevel Modeling with the

    R Package brms. *The R Journal*. 10(1), 395-411.

    doi.org/10.32614/RJ-2018-017

-   Bürkner P. C. (2021). Bayesian Item Response Modeling in R with brms

    and Stan. *Journal of Statistical Software*, 100(5), 1-54.

    doi.org/10.18637/jss.v100.i05

-   Stan Development Team. YEAR. Stan Modeling Language Users Guide and

    Reference Manual, VERSION. 

-   Carpenter B., Gelman A., Hoffman M. D., Lee D., Goodrich B.,

    Betancourt M., Brubaker M., Guo J., Li P., and Riddell A. (2017).

    Stan: A probabilistic programming language. *Journal of Statistical

    Software*. 76(1). doi.org/10.18637/jss.v076.i01