Ecosystem update — September 2026

Related hierarchical modelling addition

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 a complementary modelling path rather than an addition to gp3ml. gp3ml remains focused on governed predictive modelling and its own frozen API/release evidence. The gpbiometricspy method instead targets repeated continuous outcomes with crossed participant/item heterogeneity in both location and residual scale, plus factor-specific location slopes.

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 not altered by the new Python-native method.

• Crossed participant–item random-slope guide
• gpbiometricspy PR #129

The two packages therefore remain methodologically distinct: use gp3ml when the scientific claim is predictive and its validation/generalization contract is primary; use the gpbiometricspy location–scale family when the target is explicitly modelled conditional distributional heterogeneity under the stated assumptions.