dynamite: An R Package for Dynamic Multivariate Panel Models

By Santtu Tikka, Jouni Helske in Bayesian Infererence Causal Inference R Package

January 14, 2023



dynamite is an R package for Bayesian inference of intensive panel (time series) 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, 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 summarized predictions and offers a comprehensive suite of tools for visualization and model diagnostics.

Posted on:
January 14, 2023
1 minute read, 113 words
Bayesian Infererence Causal Inference R Package
Markov Chain Monte Carlo Panel Data
See Also:
Estimating Causal Effects from Panel Data with Dynamic Multivariate Panel Models
A Bayesian spatio-temporal analysis of markets during the Finnish 1860s famine
Efficient Bayesian generalized linear models with time-varying coefficients: The walker package in R