Skip to content

eyetrajectoriespy 0.9.0rc1

Release candidate — 27 September 2026

This is the first coordinated public release candidate for eyetrajectoriespy. It marks the transition from active method expansion to a scientifically qualified pre-1.0 platform.

What is in the release candidate

The package exposes five canonical scientific workflows:

  1. continuous gaze exploration and FPCA;
  2. experimental functional regression;
  3. repeated-trial functional mixed effects;
  4. generalized binary/count functional responses;
  5. nonlinear and recurrence trajectory analysis.

The release candidate includes the 0.55–0.57 stabilization work:

  • machine-checked canonical workflow hierarchy;
  • compatibility-first API/deprecation policy;
  • analytical, independent-implementation and simulation-recovery validation ledgers;
  • explicit numerical-tolerance policy;
  • repeated runtime/peak-memory performance qualification;
  • portable JSON + NPZ scientific-result snapshots;
  • package/Python/dependency/platform environment capture;
  • realistic executable examples for all five canonical workflows;
  • build/twine/fresh-wheel/fresh-sdist/install smoke qualification;
  • coordinated GitHub Release + PyPI automation using one build artifact and PyPI Trusted Publishing.

Scientific scope

The generalized functional-response layer supports Bernoulli/grouped-binomial logit and Poisson expected-count/rate marginal GEE contracts. Additional families such as negative-binomial, zero-inflated, hurdle and Tweedie are not part of this release candidate.

The Gaussian functional mixed-effects layer supports participant functional random effects, guarded participant random slopes, nested trial functional effects, residual covariance/whitening, full-refit bootstrap and covariance sensitivity.

The nonlinear layer includes recurrence/RQA, recurrence networks, Lyapunov diagnostics, surrogate testing, transfer entropy and return-map analysis with method-specific interpretation boundaries.

Validation

Release-candidate qualification requires:

  • Python 3.11–3.13 core CI on Ubuntu, Windows and macOS;
  • package build;
  • documentation build;
  • canonical examples;
  • scikit-fda qualification;
  • FDApy sparse qualification on Python 3.11–3.12;
  • performance-envelope qualification;
  • release-readiness build/twine/wheel/sdist/install qualification.

The configured coverage floor remains 90%; independent scientific reference validation is treated as more important than increasing line coverage for its own sake.

Reproducibility

Portable scientific-result snapshots retain scientific arrays, identifiers, specifications, units, diagnostics and provenance while explicitly marking backend-native objects as nonportable. Environment capture records package, Python, dependency/backend and platform metadata plus Git commit when available.

Installation

Because this is a prerelease:

pip install --pre eyetrajectoriespy==0.9.0rc1

Release-candidate expectations

The 0.9.x release-candidate phase is for defect correction, installation feedback, documentation clarity and reproducibility verification. It is not a signal to resume broad estimator expansion before 1.0 readiness is assessed.