Bayesian Multilevel Compositional Data Analysis with the R Package multilevelcoda Article Swipe

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Flora Le , Dorothea Dumuid , Ty Stanford , Joshua F. Wiley ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1080/00273171.2025.2565598

Multilevel compositional data, such as data sampled over time that are non-negative and sum to a constant value, are common in various fields. However, there is currently no software specifically built to model compositional data in a multilevel framework. The R package multilevelcoda implements a collection of tools for modeling compositional data in a Bayesian multivariate, multilevel pipeline. The user-friendly setup only requires the data, model formula, and minimal specification of the analysis. This article outlines the statistical theory underlying the Bayesian compositional multilevel modeling approach and details the implementation of the functions available in multilevelcoda, using an example dataset of compositional daily sleep-wake behaviors. This innovative method can be used to robustly answer scientific questions from the increasingly available multilevel compositional data from intensive, longitudinal studies.

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Type: article
Language: en
Landing Page: https://doi.org/10.1080/00273171.2025.2565598
OA Status: hybrid
References: 35
OpenAlex ID: https://openalex.org/W4416300353

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DOI: https://doi.org/10.1080/00273171.2025.2565598

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Title: Bayesian Multilevel Compositional Data Analysis with the R Package multilevelcoda

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Type: article

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Language: en

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Publication year: 2025

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Publication date: 2025-11-17

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Authors: Flora Le, Dorothea Dumuid, Ty Stanford, Joshua F. Wiley

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Landing page: https://doi.org/10.1080/00273171.2025.2565598

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OA status: hybrid

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OA URL: https://doi.org/10.1080/00273171.2025.2565598

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References (count): 35

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