# gp3ml 0.3.0

## CRAN availability and metadata

* Added the canonical CRAN package page, CRAN installation instructions, package-status badge, and CRAN package DOI following publication of gp3ml 0.1.0 on CRAN.

## Next-generation governed research-ML infrastructure

* Added explicit decision-rule contracts, threshold evaluation, deterministic threshold selection, and abstention auditing without hidden threshold defaults.
* Added target-aware split-conformal calibration for regression intervals and binary prediction sets, with explicit calibration-unit and generalization-target semantics.
* Added predictor-distribution and missingness-shift audits that remain distinct from prevalence, calibration, and performance drift.
* Added frozen analysis-plan contracts with SHA-256 locking and post hoc deviation auditing.
* Added portable model artifacts with optional `bundle` support, serialization validation, and optional fresh-process checks through `callr`.
* Added robustness and stability diagnostics across seeds, features, thresholds, and missingness scenarios.
* Added environment capture/comparison, minimal RO-Crate-oriented research-object export, SHA-256 release manifests, and a manual GitHub release-attestation workflow.
* Added gp3ml-native, NIST AI RMF 1.0, ISO/IEC 23894-oriented, and ISO/IEC 42001-oriented governance evidence profiles. Standards-oriented outputs explicitly do not claim certification or conformity.
* Added six new development articles covering decision governance, conformal prediction, dataset shift and robustness, frozen analysis plans, portable research artifacts, and governance standards crosswalks.


* Added an explicit API-stability registry, public-object schema audit, and contract validation for the established 0.2.0 surface.
* Added lightweight cross-package handoff contracts for gp3tools, gpbiometrics, gp3sequences, study-design data, and custom prepared data without duplicating upstream preprocessing.
* Added deterministic documentation-output controls plus audits for volatile timestamps, temporary paths, and runtime addresses.
* Added engine capability auditing and a Windows/macOS/Linux optional-engine CI matrix.
* Added a realistic synthetic three-package research handoff and governed participant-generalization validation workflow.
* Added five package-maturity vignettes covering API contracts, interoperability, reproducibility, portability, and integrated workflow validation.

# gp3ml 0.2.0

## Grouped evaluation and governed tuning

* Added fold-local evaluation across materialized `gazepoint_group_folds`, with retained predictions, metrics, exclusions, leakage audits, warnings, and failures.
* Added explicit deterministic tuning grids, candidate comparison, and human-reviewed selection requiring a predeclared metric and direction.
* Added nested grouped resampling with inner tuning isolated inside every outer analysis partition.

## Target-aligned uncertainty

* Added observation, participant-cluster, stimulus-cluster, and participant-and-stimulus bootstrap uncertainty with the resampling unit recorded explicitly.
* Added fold- and repeat-distribution summaries without relabelling row-level results as cluster-level estimates.

## External validation and reporting

* Added independent-dataset declarations, schema checks, predictor availability, prevalence shift, calibration drift, participant/stimulus coverage, and transportability limitations.
* Added explicit `not_externally_validated` status when no independent dataset is available.
* Added release-ready model cards recording selection procedures, uncertainty units, transportability status, limitations, and human oversight.

## Vignettes and validation

* Added nine deterministic synthetic vignettes covering recording quality, assigned conditions, observed outcomes, participant and stimulus generalization, simultaneous participant/stimulus generalization, contaminated manifests, nested resampling, and external validation.
* Added focused tests, an end-to-end smoke test, a full validation driver, and a read-only 0.2.0 release audit.

# gp3ml 0.1.0

## First formal release

* Established `gp3ml` as a governance-first package for
  leakage-resistant predictive modelling and validation using
  Gazepoint-derived research data.
* Restricted supported tasks to explicitly observed, non-sensitive
  outcomes and clearly declared scientific purposes.
* Added explicit prohibited-use documentation covering identification,
  authentication, diagnostic, protected-attribute, emotion, stress,
  personality, deception, cognition, comprehension, intent, and other
  mental-state inference.

## Governance, provenance, and leakage protection

* Added task declaration, use-case assertion, variable-role validation,
  and machine-readable prohibited-use helpers.
* Added feature-provenance manifests covering predictor origins,
  transformations, availability stages, roles, and preprocessing scope.
* Added structured leakage audits for row, participant, participant-trial,
  stimulus, identifier, target-derived, and post-outcome risks.

## Group-aware validation

* Added deterministic group-aware holdout splitting and repeated grouped
  resampling for new trials among known participants, new participants,
  new stimuli, and simultaneous new-participant and new-stimulus
  generalization.
* Materialized analysis, assessment, and explicitly excluded partitions
  with complete source-row accounting and embedded leakage audits.
* Added fold-balance, coverage, exclusion, and outcome-representation
  diagnostics with structured pass, review, and fail findings.

## Governed modelling core

* Added fold-local preprocessing objects with separate fitting and baking
  interfaces.
* Added governed model engines, explicit black-box integration,
  classification and regression metrics, calibration assessment, and
  explicitly labelled bootstrap metric uncertainty.
* Added external-validation evaluation and reporting without treating an
  internal holdout as external validation.
* Added model cards and reproducibility reports that record task purpose,
  governance decisions, model settings, performance, calibration,
  uncertainty, limitations, and reproducibility information.
* Model selection remains explicit and reviewable; the package does not
  perform autonomous black-box winner selection.

## Documentation and quality assurance

* Added machine-readable CSV, Markdown, and JSON reporting interfaces
  where supported by the relevant object.
* Added deterministic synthetic tests across supported generalization
  targets, governance failures, leakage cases, model engines, metrics,
  calibration, reporting, and serialization.
* Added complete pkgdown reference organization for the first formal
  release.
