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)