A Tutorial on Bayesian Multilevel Latent Time Series Models using Stan with the mlts R-package

This tutorial introduces and illustrates the estimation of Bayesian multilevel latent time series models with the R package `mlts`. We provide a conceptual overview of Dynamic Structural Equation Modeling (DSEM) for intensive longitudinal data, highlighting how it combines time series analysis, multilevel modeling, and latent variable approaches. Step-by-step guidance is given on specifying, estimating, and interpreting two-level vector autoregressive models using `mlts`. We illustrate modeling extensions provided in `mlts`, including multiple-indicator measurement models, between-person covariates, latent interaction effects, the handling of censored variables, and multiple-group models. Practical considerations such as data preparation, model diagnostics, and handling of varying measurement intervals are discussed. Assessment of model fit is provided via posterior predictive checks. By use of working examples with empricial data, we showcase how `mlts` enables flexible Bayesian DSEM analyses while avoiding the complexities of custom Stan programming. This tutorial aims to make advanced dynamic modeling of intensive longitudinal data more accessible to applied researchers across the behavioral sciences.