Provides governance-first infrastructure for leakage-resistant
    predictive modelling and validation using 'Gazepoint'-derived research
    data. Supports explicit task and role declarations, feature-provenance
    manifests, group-aware holdout splitting and repeated resampling,
    fold-local preprocessing, governed model engines, discrimination and
    calibration metrics, bootstrap uncertainty, external-validation
    reports, model cards, and reproducibility reports. Intended only for
    explicitly observed, non-sensitive outcomes and declared scientific
    purposes. Use is prohibited for person identification, biometric
    authentication, health or protected-attribute inference, and direct or
    indirect inference of emotion, stress, personality, deception,
    cognition, comprehension, intent, or other mental states.

Provides transparent, reproducible, and auditable tools for
    validating, preparing, encoding, and summarising ordered categorical
    sequence data. Supports configurable long-format inputs, explicit
    preprocessing policies, machine-readable diagnostics, structural motif
    analysis, and focused visualisation for scanpaths, navigation paths,
    behavioural states, and other ordered state data.

Tools for importing, inspecting, cleaning, summarising,
    modelling, and reporting Gazepoint GP3 and Gazepoint Analysis CSV
    exports. The package supports offline workflows for all-gaze,
    fixation, pupil, area-of-interest, transition, time-course, quality-audit, and
    manuscript-reporting analyses.
    The package methodology is described in the peer-reviewed
    software paper <doi:10.3390/jemr19040076>.

Aligning multiple visualisations by utilising generalised orthogonal Procrustes analysis (GPA) before combining coordinates into a single biplot display as described in Nienkemper-Swanepoel, le Roux and Lubbe (2023)<doi:10.1080/03610918.2021.1914089>. This is mainly suitable to combine visualisations constructed from multiple imputations, however, it can be generalised to combine variations of visualisations from the same datasets (i.e. resamples).