Anomalyzer v0.2.1

Anomalyzer 0.2.1 — explicit sparse-data semantics

Applications can preserve unknown calendar periods, declare confirmed empty
additive volume, or use comparable event-index measurements without inventing
regular timestamps. Schema 1.0 remains unchanged; schema 1.1 gains per-source
policies, coverage, availability/revision inputs and inspectable transformations.

• Frozen sparse references retain actual calendar phase, count eligible
  training/calibration observations and report unsupported phases.
• Missing/incomplete/excluded positions and resets break patterns and change
  comparisons. A measured range rule can operate before statistical learning.
• Relationships preserve unavailable reasons, zero-denominator semantics,
  original source lineage and model exclusions.
• Sparse schema-1.1 multi-resolution retains native evidence and completed UTC
  day views. Daily ratios divide sums; incomplete child periods cannot create a
  completed total. Daily rules are explicit and cases survive policy replay.
• README progression chapters 14–22 include reproducible fixtures, checked
  results and rendered figures, published with the existing documentation site.

See the sparse-data API guide at docs/sparse-data.md and the release/consumer
guide at docs/releases/README.md. The installed-artifact checks cover the full
public API and an offline Linux x86_64/Python 3.12 Lambda-compatible bundle.
CI also records bounded 1,344-hour and event-index benchmark results and result
sizes. These are synthetic mechanics/performance checks, not abuse accuracy or
production cost claims.

Explicit limits: event-index timestamps must be unique; adaptive/explicit trend
recipes use complete contiguous spans; multi-resolution children are frozen and
use fixed UTC days. Daily lagged/normalized/joint relationships, local-calendar
DST aggregation, simultaneous multi-seasonality, episode quarantine and durable
incremental state/diffing remain separate work.

Guard continues to own collection contracts, profile compatibility, persistence,
scheduling, business impact/severity, incident lifecycle and opt-in notifications.
This release does not modify or deploy UsageTap Guard. Install the exact release
wheel and hash-pinned dependencies during the application's Linux build and
bundle them for offline runtime; no service, checkout, CLI or MCP process is
required. PyPI publication is not part of this release.

Release automation now finds unpublished drafts through the authenticated release
list, resumes existing drafts without duplicate creation, and checks publication
by release ID. Version 0.2.0 remains an unpublished candidate; its original tag
is retained. Version 0.2.1 is the first published release of these features.

Anomalyzer 0.2.1 — local Python analysis library.

Source revision: 9920e427d8f25caa628fc21ae469959f9f005100.

Wheel and source distribution tested on Python 3.11–3.13; Linux/Python 3.12 Lambda consumer verified offline. See SHA256SUMS and release-manifest.json for artifact identities.

Release and consumption guide.