Features at a Glance | Design options | Generator families | Advanced simulation options | Study Designs: Groups, Sizes, and Time | Distribution Families | Categorical Variables | Correlated and Compositional Predictors | Location-Scale Predictors | Custom Generators | A First Outcome Model and Parameter Recovery | Dynamic Outcome Simulation | To Center or Not to Center: Non-Centered Lagged Predictors | Simulating the Between-Within (brmcoda) Model | Non-Gaussian Outcomes | Poisson | Binomial | Negative Binomial | Gamma | Beta

Intro | Fitting main model | Substitution models | Basic Substitution Analysis | Average Marginal Substitution Effects

Multilevel model with compositional outcomes. | Computing compositions and isometric log ratio coordinates. | Fitting model | Bayes Factor for compositional multilevel modelling

Multilevel model with compositional predictors | Compositions and isometric log ratio (ILR) coordinates. | Fitting model | Bayes Factor for significance testing | Substitution model | Between-person substitution model | Within-person substitution model | More interesting substitution models

Introduction | Generating Data from Simulation Study | Example Models | Improving within-chain autocorrelation of MCMC sampling | Increased posterior draws | Centered Parameterisation | Computational Time | Conclusion | References

Compositional Data Analysis | Multilevel Modelling for Compositional Data