Model-readiness and sensitivity analysis

This article is the Python counterpart of the corresponding gp3tools workflow. It uses bundled synthetic data so it can be run without private participant exports.

import gp3tools as gp3

data = gp3.load_example_master()
# prepare_gazepoint_pupil_window_model_data is available as gp3.prepare_gazepoint_pupil_window_model_data(...)
# fit_gazepoint_pupil_window_lmm is available as gp3.fit_gazepoint_pupil_window_lmm(...)
# tidy_gazepoint_model_summary is available as gp3.tidy_gazepoint_model_summary(...)
# compare_gazepoint_nested_models is available as gp3.compare_gazepoint_nested_models(...)

Recommended workflow

1. Inspect column availability and create/validate a master table.
2. Run the relevant quality gates before transformation or modelling.
3. Keep preprocessing and exclusion decisions explicit.
4. Save derived tables/plots with package/version metadata.
5. For backend-adapted statistical functions, report the Python backend and validate the inferential target against the R reference when confirmatory equivalence matters.

Backend note

The Python migration preserves the workflow and estimand intent but does not assert numerical identity with R-specific engines. Validate confirmatory results against the frozen R implementation until a dedicated parity fixture exists.

Reproducibility checkpoint

import gp3tools as gp3

print(gp3.__version__)
print(gp3.api_status().query("r_export in @funcs"))