---
title: "Ecosystem update — September 2026"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Ecosystem update — September 2026}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## 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](https://stefanosbalaskas.github.io/gpbiometricspy/methods/crossed-random-slopes-location-scale/)
- [gpbiometricspy PR #129](https://github.com/stefanosbalaskas/gpbiometricspy/pull/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.