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.
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.