Bayesian Estimation of Dynamic VAR Models using STAN.

bvarnet

Bayesian Estimation of Multilevel Vector Autoregressive Networks using STAN

The bvarnet package allows user to estimate Bayesian multilevel Vector Auto Regressive (VAR) models for binary, ordinal and continuous outcome variables. Missing data is handled through listwise deletion and a skip-lag mechanism, which skips the estimation of the temporal structure when there is a gap between two timepoints. Further, we provide functionality to conduct hypothesis tests.

Installation

To install bvarnet from CRAN, you need to have CmdStanR installed which is not available on CRAN. To do this, make sure you have RTools (Windows) or Xcode (Mac) installed and the run the following code

install.packages("cmdstanr", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))
cmdstanr::check_cmdstan_toolchain(fix = TRUE)
cmdstanr::install_cmdstan(cores = 2)

If you run into any problems, you can look at the Getting started with CmdStanR guide.

After setting up cmdstanr you can install the newest version of the package from CRAN:

install.packages("bvarnet", type = "source")

Or you can install the development version of bvarnet from GitHub with:

if(!requireNamespace("remotes")) {
  install.packages("remotes")
}
remotes::install_github("flo1met/bvarnet")

Getting Started

The best place to start learning how to use this package to estimate Bayesian (multilevel) Vector Autoregression is the Getting Started Vignette. This vignette covers the basic model syntax, how to specify priors and how to extract the relevant parameters.

Feature Requests and Contributions

• Predictions
• Cross-sectional Networks
• Correlated Random Effects
• Hierarchical Prior Distributions
• Performance Optimisation

Roadmap

bvarnet is actively being developed. While the core functionality is stable, we have several features planned for future releases. For bug reports or feature request, please visit our Issue Tracker.