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

## Related crossed location–scale development

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**.

This does not change `gp3bayes`, its Bayesian model families, or its release evidence. The gpbiometricspy implementation is a separate frequentist/Laplace-approximation path for crossed conditional location and residual-scale heterogeneity, with participant- and item-specific location slopes, explicit seen/unseen-level prediction semantics, fail-closed design guards, and deterministic certificates.

Certified 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 frozen `gpbiometrics 2.0.0` R-parity surface remains 406/406 and is unchanged.

- [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)

This ecosystem note is informational: it does not imply Bayesian equivalence, coefficient identity, or replacement of `gp3bayes` workflows.