Hierarchicalforecast

New features
* [FEAT] Add sparse non-negative OLS and WLS via QP for ``MinTraceSparse`` by christophertitchen in https://github.com/Nixtla/hierarchicalforecast/pull/319
* [FEAT] Implement adjacency matrix by christophertitchen in https://github.com/Nixtla/hierarchicalforecast/pull/332
* [FEAT] Extremely fast forecast proportions by christophertitchen in https://github.com/Nixtla/hierarchicalforecast/pull/334

Bug fixes
* [FIX] Handle zero division in top down methods by mattbuot in https://github.com/Nixtla/hierarchicalforecast/pull/325
* [FIX] Raise warning on NaN values when using average proportions and proportion averages methods by janrth in https://github.com/Nixtla/hierarchicalforecast/pull/335
* [FIX] TopDown method failing on combinations with other methods by elephaint in https://github.com/Nixtla/hierarchicalforecast/pull/330
* [FIX] ERM-reg and ERM-reg-bu equations by elephaint in https://github.com/Nixtla/hierarchicalforecast/pull/331
* [FIX] Produce reproducable samples for PERMBU by elephaint in https://github.com/Nixtla/hierarchicalforecast/pull/337

Documentation
* [FIX] API reference links, removal of unnecessary headers elephaint in https://github.com/Nixtla/hierarchicalforecast/pull/318

New Contributors
* mattbuot made their first contribution in https://github.com/Nixtla/hierarchicalforecast/pull/325
* janrth made their first contribution in https://github.com/Nixtla/hierarchicalforecast/pull/335

**Full Changelog**: https://github.com/Nixtla/hierarchicalforecast/compare/v1.0.1...v1.1.0

Hotfix
* [FIX] Use Numpy bool_ instead of Python bool in eagerly compiled functions by elephaint in https://github.com/Nixtla/hierarchicalforecast/pull/315

**Full Changelog**: https://github.com/Nixtla/hierarchicalforecast/compare/v1.0.0...v1.0.1

New features
* [FEAT] Polars support in https://github.com/Nixtla/hierarchicalforecast/pull/305
* [FEAT] Evaluation to utils in https://github.com/Nixtla/hierarchicalforecast/pull/311

Breaking changes
As of v1.0.0, HierarchicalForecast no longer supports the unique_id as index. Users may have to perform a `.reset_index()` when using a Pandas DataFrame that has the unique_id still as index. The old behavior has been deprecated throughout the entire Nixtlaverse, so it may be wise to update all Nixtla packages to ensure the same consistent behavior is observed everywhere.

**Full Changelog**: https://github.com/Nixtla/hierarchicalforecast/compare/v0.4.3...v1.0.0

New Features
- [FEAT] Sparse middle-out reconciliation via ``MiddleOutSparse`` christophertitchen (281)
- [FEAT] Add support for exogenous variables in utils.aggregate KuriaMaingi (297)
- [FEAT] Efficient Schafer-Strimmer for MinT elephaint (280)
- [FEAT] Improve residuals-based reconciliation stability and faster ma.cov elephaint (295)

Dependencies
- As of v0.4.3, hierarchicalforecast no longer officially supports Python 3.8, which is EOL.

New Features
- Add sparse top-down reconciliation via ``TopDownSparse`` christopher-titchen (277)
- Decrease wall time of ``_get_PW_matrices`` for ``BottomUp`` and ``BottomUpSparse`` christopher-titchen (276)
- Efficient MinTrace (ols/wls_var/wls_struct/mint_cov/mint_shrink) elephaint (264)

Documentation
- Create CODE_OF_CONDUCT.md tracykteal (267)
- Fix evaluate argument in readme jmoralez (257)
- Update ml frameworks example jmoralez (254)
- Add step to trigger mintlify workflow rpmccarter (259)

Dependencies
- Remove numpy pin DManowitz (272)

Enhancement
- Warn when num_threads is not used in MinTrace jmoralez (273)

Bug Fixes

- keep only observed combinations in aggregate jmoralez (242)

Documentation

- Fix parsing errors and add mint.json hahnbeelee (243)

Enhancement

- support parallel MinTrace optimization jmoralez (249)