Changelog
=========

1.3.0rc1 (2026-03-20)
---------------------

**New features**

- Empirical Bayes Dirichlet classifier with automatic prior tuning via
  Minka's fixed-point iteration for the Dirichlet-Multinomial marginal
  likelihood, with stabilized forgetting
- Empirical Bayes normal regressor with automatic hyperparameter tuning via
  MacKay's evidence maximization (#200)
- Kulhavy-Zarrop stabilized forgetting to prevent prior collapse under decay
  (#202)
- Takahashi recursion for efficient trace computation in sparse precision
  matrices (#204), with Cython implementation (#206)
- Sparse factor caching to avoid redundant factorizations (#198)
- ``rng`` property with setter for reseeding agents and pipelines after
  deserialization (#224)

**Performance**

- BLAS-level optimizations for NormalRegressor (#219), BayesianGLM IRLS (#218),
  and EmpiricalBayesNormalRegressor (#220)
- Refactored sparse factor classes for better performance and reuse (#213, #214)
- Benchmark suite with pytest-benchmark (#217, #219, #220, #221)
- Modernized Cython code with typed memoryviews (#212)

**Documentation**

- Complete documentation overhaul following Diataxis framework
- How-to guides: pipelines, decay, reward functions, delayed rewards,
  production deployment, sparse features
- Mathematical reference: NormalRegressor, NIG, empirical Bayes, Dirichlet EB,
  intercept-only models, GLM, exploration policies
- Explanation pages: "Knowledge Is Prediction" (worldview), "Separating
  Inference from Decisions" (decision theory)
- Comprehensive docstrings for all estimators, policies, agents, and arms
- Quick-start guide (#223)

**Infrastructure**

- Cross-platform wheel builds via cibuildwheel (Linux x86_64/aarch64, macOS
  arm64, Windows x86_64)
- Migrated from black + flake8 to ruff (#215)
- NumPy 2.0 dependency, scikit-sparse 0.5.0 (#188, #205)
- Pickling support fix for BayesianGLM (#196)