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The uncertainty programme separates calibration, AOI assignment, preprocessing, sampling, and model components. It produces a source-by-metric budget and can propagate the combined uncertainty to a study estimand.
spec <- process_uncertainty_spec(
source_sd = c(calibration = 0.018, aoi_assignment = 0.025),
draws = 2000
)
uncertainty <- estimate_process_uncertainty(
trial_features, spec, metrics = c("dwell_ms", "pupil_auc"), cluster = "person_id"
)
uncertainty_budget(uncertainty)
plot_uncertainty_waterfall(uncertainty, metric = "pupil_auc")
plot_uncertainty_tornado(uncertainty, metric = "pupil_auc")
propagated <- propagate_process_uncertainty(
uncertainty,
estimand = function(data) mean(data$pupil_auc, na.rm = TRUE),
method = "simulation"
)Spatial drift should be reviewed before derived AOI metrics are interpreted.
drift <- detect_calibration_drift(
calibration_samples,
window = "30 sec",
x_col = "gaze_x",
y_col = "gaze_y",
time_col = "time"
)
plot_calibration_vector_field(drift)
plot_drift_over_time(drift)
model <- fit_offline_recalibration(drift, method = "affine", robust = TRUE)
corrected <- apply_offline_recalibration(samples, model, "gaze_x", "gaze_y")
audit <- audit_recalibration(calibration_samples, corrected)
plot_recalibration_before_after(audit)Recalibration must be estimated from defensible reference points and audited on held-out targets whenever possible.
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.