dynamite: An R Package for Dynamic Multivariate Panel Models

TL;DR

The paper tackles analysis of intensive panel data with multiple interdependent outcomes by introducing dynamic multivariate panel models (DMPMs) implemented in R via the dynamite package. It provides a Bayesian inference framework using Stan, supports time-varying effects via Bayesian P-splines, latent factors, and flexible distributions, and offers a formula-based interface for model construction, fitting, and prediction. The contributions include scalable MCMC-based estimation, comprehensive predictions, diagnostics, and visualization, plus demonstration on real and synthetic data including causal effect analysis. The work enables researchers to perform joint, time-evolving analyses and policy-relevant causal inferences on complex panel data with many variables.

Abstract

dynamite is an R package for Bayesian inference of intensive panel (time series) data comprising 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, latent factors, and customization of prior distributions of the model parameters. 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 aggregated predictions and offers a comprehensive suite of tools for visualization and model diagnostics.