The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
The companion 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 addition is intentionally outside the frozen
gpbiometrics 2.0.0 semantic-parity contract. It does
not add a new R export, modify the
gpbiometrics API, or change the scientific/release record
of this R package. The frozen R surface remains the reference against
which gpbiometricspy preserves 406/406 semantic parity;
the crossed random-slope model is later additive Python-native
development.
The new method estimates participant- and item-specific heterogeneity in conditional associations while retaining crossed residual-scale heterogeneity, explicit population-versus-conditional prediction semantics, fail-closed design checks, and deterministic reproducibility certificates. It is not a causal model, artifact detector, sensor-validity score, or latent-state inference engine.
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
19 remaining branch arcs are the unchanged audited structural-debt set,
with zero unexpected, stale, or unaudited debt.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.