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Changelog

All notable user-facing changes to GazeAudit are documented here.

The 0.1.0 entry below defines the first stable release candidate metadata. A repository state containing this entry is not, by itself, evidence that v0.1.0 has been tagged or that a GitHub Release, PyPI distribution, DOI, or archival deposit exists. Those are separate publication actions performed only after the release-candidate gate succeeds.

Unreleased

Documentation and website

  • Align public documentation, homepage installation guidance, and provenance surfaces with the verified v0.2.0 GitHub Release and PyPI publication.
  • Add a sensitivity-analysis learning path spanning conceptual rationale, protocol design, a worked sampling/missingness example, and a study-level workflow.
  • Improve plot and documentation discovery while preserving all frozen scientific validation outcomes and their original evidence boundaries.

0.2.0 — 2026-09-21

Scientific and software qualification

  • Reach 100% executable statement coverage across the GazeAudit Python package.
  • Qualify the complete test matrix on Python 3.10, 3.11, 3.12, and 3.13.
  • Expand deterministic fail-closed tests for source intake, provenance, archive verification, execution contracts, missingness, AOI uncertainty, freeze records, reporting, readiness, and publication infrastructure.
  • Preserve the frozen GazeBase, Korthals, and Pedrotti scientific outcomes without changing estimators, cohorts, thresholds, endpoints, protocols, or interpretation rules.
  • Normalize Korthals v2 companion paths for portable Windows/POSIX execution.

Release engineering

  • Generalize GitHub Release and PyPI workflows so new releases are version-driven rather than hard-coded to the historical 0.1.0 publication files.
  • Preserve exact-tag rebuilding, checksum verification, clean-wheel installation, GitHub Release round-trip verification, and PyPI Trusted Publishing.
  • Require one source distribution and one universal wheel for the qualified payload.

0.1.0 — 2026-09-14

Scientific MVP

  • Validate GazeAudit against known-truth tests and three canonical real-data cases.
  • Preserve the GazeBase multi-detector audit as incomplete after its frozen completeness gate failed rather than removing failing detectors post hoc.
  • Preserve the Korthals target-tracking AOI audit as robust_negative under the frozen measurement-error propagation protocol.
  • Preserve the Pedrotti/de Chambrier sampling/missingness audit as materially_fragile under the frozen recovery rule.
  • Bind protocols, source identities/source locks, controlled executions, fingerprints, checksums, and archived artifacts with archive-before-reveal provenance.

Reproducibility and publication support

  • Add an authoritative validation matrix linking all three canonical real-data cases.
  • Add the archived Pedrotti/de Chambrier sampling/missingness result record.
  • Add a publication-facing validation summary and machine-readable reproducibility index.
  • Add citation, reporting, interpretation-boundary, and reuse guidance.
  • Add repository citation metadata through CITATION.cff.
  • Add regression tests binding canonical scientific identities and indexed repository paths.

Packaging and release engineering

  • Mark the package as Alpha.
  • Qualify clean source and wheel distributions in CI with metadata/content validation.
  • Install the built wheel in a fresh environment and verify bundled scientific data.
  • Add a fail-closed stable metadata/tag/changelog release gate.
  • Add a release-candidate workflow that rebuilds and archives candidate distributions without automatically publishing them.
  • Require exact-head PR qualification followed by exact-main tests, live interoperability, distribution, and Pages certification before tagging.

Scientific MVP baseline — 2026-09-14

The scientific MVP was completed before stable-release preparation.

  • GazeBase multi-detector audit: canonical classification incomplete.
  • Korthals target-tracking AOI measurement-error audit: canonical classification robust_negative.
  • Pedrotti/de Chambrier sampling/missingness audit: canonical classification materially_fragile.

These scientific outcomes are post-outcome frozen. Later documentation, packaging, release, or publication work must not change the underlying protocols or reinterpret classifications by altering the predeclared decision rules.

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