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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.