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

## Related crossed repeated-measures method

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 is an ecosystem-level addition, not a new `gp3sequences` sequence model. `gp3sequences` remains focused on ordered categorical sequences, scanpaths, motifs, transition structure, distances, clustering, randomization, and latent-state workflows. The gpbiometricspy method instead models continuous outcomes with crossed participant/item heterogeneity in conditional location and residual scale.

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` 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)

A study can therefore use sequence methods for ordered process structure and, where scientifically justified, a separate gpbiometricspy location–scale model for continuous repeated outcomes without conflating the two inferential targets.