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Computational benchmarking and synthetic stress testing

Scientific validity and computational feasibility are separate questions. eye_benchmark_design() measures runtime/scaling under declared dataset sizes, while synthetic corruption plans probe robustness to missingness, pupil dropout, calibration offsets, timestamp jitter, AOI label noise, device shifts, and trial imbalance.

plans <- list(
 synthetic_corruption_plan(missingness=.05),
 synthetic_corruption_plan(missingness=.20, sampling_jitter_sd=2),
 synthetic_corruption_plan(pupil_dropout=.30, gaze_offset_x=.02)
)
st <- stress_test_process_pipeline(data, plans, analysis_fun)
stress_test_summary(st)
plot(st, severity="missingness", metric="effect")

Stress tests describe sensitivity to the perturbations actually supplied. They do not replace validation on independent empirical data.

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.