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This workflow places a data-quality gate before biometric/process modelling. It is designed to protect calibration, DIF, scoring, process-IRT, and deployment analyses from poor signal quality. It does not classify motivation, misconduct, diagnosis, or ability.
spec <- process_preflight_spec(
min_gaze_validity = 0.80,
min_pupil_validity = 0.70,
max_gaze_missingness = 0.25,
max_pupil_missingness = 0.30,
min_valid_trial_fraction = 0.70
)
audit <- audit_biometric_preflight(
trial_data,
by = c("person_id", "recording_id"),
spec = spec
)
preflight_decisions(audit)
preflight_failures(audit)
preflight_exclusion_manifest(audit)
plot(audit, type = "heatmap")
plot(audit, type = "decision_counts")No rows are removed automatically.
apply_preflight_decision() performs filtering only when
explicitly requested and records what decision levels were retained.
anomaly <- audit_process_anomalies(
person_process_data,
person = "person_id",
metrics = c("rt_ms", "dwell_ms", "pupil_peak", "valid_gaze_prop")
)
process_anomaly_distance(anomaly)
plot(anomaly)The Mahalanobis distance is a review statistic. A large distance can reflect calibration problems, glasses, lighting, tracker loss, atypical viewing, or other benign causes.
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.