Estimate Bayesian Multilevel Models for Compositional Data.

multilevelcoda

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

install.packages("multilevelcoda")

The current developmental version can be downloaded from github via

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
• Multilevel Models with Compositional Predictors
• Multilevel Models with Compositional Outcome
• Compositional Substitution Multilevel Analysis

Citing multilevelcoda and related software

TBA.