betula-cluster v0.1.3

Added

• betula_cluster.tune — memory-aware hyperparameter search over the CF knobs, scored by an internal
  metric (Calinski-Harabasz / Davies-Bouldin) or ARI, with a multi-objective quality / memory /
  speed Pareto mode. NumPy-only by default; an optional Optuna backend (TPE / NSGA-II) via
  pip install 'betula-cluster[tune]'.
• Property-based tests (proptest, dev-only) for the CF-tree invariants: the clustering feature is a
  commutative monoid (merge is associative/commutative and equals a sequential build), folding a
  tree's leaf features reconstructs the whole-dataset feature, the full-covariance upper-triangular
  index is a bijection (incl. dim ≥ 4), and the Frequent-Directions sketch is lossless on low-rank
  data and never overshoots the exact scatter.
• Sparse-text benchmark (20 newsgroups, TF-IDF): the O(nnz) fit_predict_sparse path and the
  standard reduce-then-cluster pipeline (TruncatedSVD / NMF → k-means) vs scikit-learn, written up
  honestly in bench/RESULTS.md (raw high-d TF-IDF concentrates for every fast clusterer; on NMF
  topics betula matches sklearn).
• MapperGraph.edge_overlap — a Bhattacharyya coefficient in (0, 1] per Mapper edge, from the pooled
  diagonal-Gaussian summaries of the two nodes' member microclusters. Surfaced on to_networkx() edges
  as overlap=…, so a bridge between well-separated regions reads as a lower-weight edge than one
  inside a dense blob.
• Documentation site (MkDocs Material + mkdocstrings API autodoc, MathJax-rendered math) built from
  docs/, with a GitHub Pages deploy workflow; pip install 'betula-cluster[docs]' for the toolchain.

Changed

• Coverage floor (cargo llvm-cov, ≥95 % lines) now also measures the persistence and cli feature
  sets, not just the default core.
• Declared rust-version = "1.82" (MSRV) and lowered the real floor to it — the streaming heads had an
  implicit 1.87 dependency (u64::is_multiple_of), now rewritten. Added Documentation / Changelog
  project URLs.
• Docs reconciled to the current suite: 167-case Python suite, 141 Rust tests (137 unit + 4
  integration), and five end-to-end use cases (README, DESIGN.md).
• Repository hardening: macOS / Windows CI test legs, an sdist install smoke test, a nightly
  cargo audit cron, Dependabot, and SECURITY.md / CONTRIBUTING.md / issue templates.