v0.0.11

New tutorial: Checking a VAR Before You Trust It

A full model-checking walkthrough on real U.S. monetary policy data (1965–2007): stationarity and cointegration pretests (adf_test, kpss_test, integration_order, johansen_test), prior predictive checks, MCMC diagnostics in ArviZ (divergences, rank-normalised R-hat, bulk/tail ESS, trace, rank, and energy plots — including a real slow-mixing failure and its low-rank mass-matrix fix), and posterior predictive checks with quantile coverage. Fully cited, and rendered as part of the docs.

Bug fix

FittedVAR.posterior_predictive with named DataFrame indexes. Data built via VARData.from_df from a DataFrame whose index carries a name (e.g. "date") crashed with CoordinateValidationError: xarray adopted the index name as the coordinate's dimension, clashing with the explicit time dim. The time coordinate is now pinned with the explicit-dimension form, matching identified.py. Regression test included.