useR! 2024

In this talk, I will present dynamite: an R package for Bayesian inference of intensive panel data comprising of multiple measurements per multiple individuals measured in time. The package supports joint modeling of multiple response variables, time-varying and time invariant effects, a wide range of discrete and continuous distributions, group-specific random effects, and latent factors via the dynamic multivariate panel model (DMPM) framework. Models in the package are defined via a user-friendly formula interface, and estimation of the posterior distribution of the model parameters takes advantage of state of-the-art Markov chain Monte Carlo methods. The package enables efficient computation of both individual-level and summarized predictions and offers a comprehensive suite of tools for visualization and model diagnostics. I will demonstrate how the package can be used to estimate long-term causal effects of interventions with proper accounting for the uncertainties in these estimates.