Ecosystem update — September 2026

Related methodological addition

Within the wider Gazepoint research-software ecosystem, the related Python package gpbiometricspy now includes a fully exact-main-certified crossed participant–item Gaussian hierarchical location–scale model with one location random slope for each crossed factor.

The method models conditional-association heterogeneity across both participants and items/stimuli while retaining crossed residual-scale heterogeneity, explicit population-versus-conditional prediction semantics, fail-closed design checks, and deterministic reproducibility certificates. It is an additive Python-native method and does not change the API, release record, or scientific claims of gp3tools.

Certification for gpbiometricspy PR #129 is pinned to merge SHA d078e0366ace49c3ebeb2f6800bad6394d70631e: 14/14 exact-main push workflow families, 12/12 OS/Python matrix lanes, 782/782 tests, 14,015/14,015 statements, and 6,757/6,776 raw branches (99.7196%). The remaining 19 branch arcs are the unchanged audited structural-debt set, with zero unexpected, stale, or unaudited debt.

The frozen gpbiometrics 2.0.0 R-parity surface in gpbiometricspy remains 406/406; the new method sits outside that frozen contract.

• Crossed participant–item random-slope guide
• gpbiometricspy PR #129

For gp3tools users, this is a downstream modelling option after eye-tracking import, QC, pupil/gaze preparation, AOI construction, and other measurement workflows have been completed under an appropriate study design.