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.