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.