# Sources

Official references used to align this case study with the PyMC-Marketing 1.x stack:

1. **PyMC-Marketing 1.0.0 release**  
   https://github.com/pymc-labs/pymc-marketing/releases/tag/1.0.0

2. **PyMC-Marketing 1.0 MMM API**  
   Public `pymc_marketing.mmm.MMM` interface used by the case-study model.  
   https://www.pymc-marketing.io/en/1.0.0/api/generated/pymc_marketing.mmm.mmm.MMM.html

3. **PyMC-Marketing 1.0 model save/load implementation**  
   The 1.0 model builder stores fitted model state in an `xarray.DataTree`, saves it to NetCDF/Zarr and reconstructs model configuration during `load`.  
   https://github.com/pymc-labs/pymc-marketing/blob/1.0.0/pymc_marketing/model_builder.py

4. **PyMC-Marketing 1.0 special priors**  
   `SpecialPrior`, `LogNormalPrior`, `LaplacePrior` and related serialisation behaviour.  
   https://github.com/pymc-labs/pymc-marketing/blob/1.0.0/pymc_marketing/special_priors.py

5. **PyMC-Marketing MMM documentation and examples**  
   Hierarchical priors, multidimensional modelling, partial pooling, adstock, saturation and MMM workflow examples.  
   https://www.pymc-marketing.io/en/1.0.0/

6. **ArviZ diagnostics documentation**  
   Rank-normalised R-hat and effective sample-size diagnostics used to discuss convergence and sampling efficiency.  
   https://python.arviz.org/en/stable/api/diagnostics.html

7. **ArviZ-Stats 1.2 PSIS implementation**  
   Pareto-smoothed importance-sampling behaviour used by the sweep package.  
   https://github.com/arviz-devs/arviz-stats/tree/v1.2.0

The example business data and posterior fixtures in this repository are synthetic and do not represent a real company, customer or retailer.
