README

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

bvar() fits its models with Stan, so it needs a compiled Stan executable for each of the three outcome families. Installing is a two-step flow, and works the same way whether or not you have a C++ toolchain:

# Step 1: install bvarnet from CRAN
install.packages("bvarnet")

# Step 2: set up the Stan models
bvarnet::bvarnet_setup_models()

bvarnet_setup_models() offers to either download precompiled model binaries for your platform (recommended) or compile them locally if you already have a working CmdStan installation. You only need to set this up once; re-run it only after updating bvarnet to a new version.

No toolchain? (most users, recommended)

Just run bvarnet::bvarnet_setup_models() and choose the download option when prompted. This fetches a small, platform-specific set of precompiled Stan executables.

Have a C++ toolchain? (advanced)

For this option you need to have RTools (Windows) or Xcode (Mac) installed. Further, you need to have cmdstanr installed and the C++ toolchain set up. If you don’t have CmdStan yet, after installing RTools/Xcode install it using:

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

Then you can use bvarnet::bvarnet_setup_models() to compile the models locally.

Alternatively, install.packages("bvarnet", type = "source") compiles the models at install time (requires CmdStan to already be set up). This works equivalent to the two-step flow above, just compiled into the package’s install tree instead of a user cache directory.

Development version

You can install the development version of bvarnet from GitHub. If you use the development version, you will have to compile the models yourself!

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

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

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.

bvarnet