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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 does not change gp3bayes, its Bayesian model families, or its release evidence. The gpbiometricspy implementation is a separate frequentist/Laplace-approximation path for crossed conditional location and residual-scale heterogeneity, with participant- and item-specific location slopes, explicit seen/unseen-level prediction semantics, fail-closed design guards, and deterministic certificates.

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 R-parity surface remains 406/406 and is unchanged.

This ecosystem note is informational: it does not imply Bayesian equivalence, coefficient identity, or replacement of gp3bayes workflows.