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Item seeding, accessibility review, and presentation fairness

Experimental item-parameter seeding

seed <- fit_item_parameter_seed_model(
  calibrated_items,
  predictors = c("visual_density", "text_complexity", "word_count", "screen_luminance")
)

candidate_predictions <- predict_item_parameter_priors(seed, candidate_items)
audit_candidate_item_bank(seed, candidate_items)
plot(seed, candidate_data = candidate_items)

These predictions are screening priors/cold-start estimates only. They do not replace content review, accessibility/bias review, pilot testing, or IRT calibration.

Presentation/accessibility sensitivity

a <- audit_presentation_accessibility(person_process_data)
sim <- simulate_presentation_variants(a)
plot(a)

This audit must not be used to infer dyslexia, ADHD, neurodivergence, visual impairment, or another diagnosis. It identifies presentation patterns worth evaluating with calibrated alternative versions.

compare_presentation_fairness(
  experiment_data,
  variant = "presentation_version",
  outcome = "accuracy"
)

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.
Health stats visible at Monitor.