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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.

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.