The project provides a Python implementation of a master's thesis on Bayesian lag selection for higher-order stationary vector autoregressions. It includes prior simulation, posterior inference via CmdStan, and visualization utilities, all wrapped in a CLI. Researchers and statisticians can run reproducible simulations and fit models without building the pipeline from scratch. Compared to generic Bayesian packages, it offers a focused, ready‑to‑use workflow for VAR lag selection.
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