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 modelling 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 paper outlines the statistical theory underlying the Bayesian compositional multilevel modelling approach and details the implementation of the functions available in multilevelcoda, using an example dataset of compositional daily sleep-wake behaviours. This innovative method can be used to gain robust answers to scientific questions using the increasingly available multilevel compositional data from intensive, longitudinal studies.

翻译：多水平成分数据（例如随时间采样、非负且总和为常数的数据）在多个领域中普遍存在。然而，目前尚无专门用于在多水平框架下建模成分数据的软件。R包multilevelcoda实现了一套在贝叶斯多元多水平流程中建模成分数据的工具。其用户友好的设置仅需提供数据、模型公式及最少量的分析规范。本文概述了贝叶斯成分多水平建模方法背后的统计理论，并以每日睡眠-觉醒行为的成分数据集为例，详细说明了multilevelcoda中可用函数的实现。这一创新方法可用于利用日益增多的、来自密集纵向研究的多水平成分数据，为科学问题提供稳健的解答。