A tutorial on Bayesian multilevel latent time series models using stan with the mlts R package.

Abstract

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 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. Using working examples with empirical data, we showcase how mlts enables flexible Bayesian dynamic structural equation modeling 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. (PsycInfo Database Record (c) 2026 APA, all rights reserved).