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Item selection can be treated as a constrained multi-objective decision rather than a sequence of isolated cutoffs.
objectives <- item_objective_spec(
information = "test_information",
process_burden = "pupil_effort",
fairness = "absolute_dif",
exposure = "exposure_rate",
content_constraints = list(content_domain = c(1, 5))
)
pareto <- item_pareto_front(item_bank, objectives)
plot_item_pareto(pareto)
selected <- optimize_item_bank(
pareto,
n_items = 20,
objectives = objectives,
method = "evolutionary"
)
plot_selected_bank_profile(selected)
stability <- audit_bank_decision_stability(selected)
plot_decision_stability(stability)Process-DIF is reported separately from psychometric DIF and can be monitored over deployment batches.
dif <- fit_process_dif(
person_item_data,
response = "accuracy",
process = "dwell_ms",
group = "group",
item = "item_id",
ability = "theta"
)
plot_process_dif_forest(dif)
drift <- monitor_dif_drift(
monitoring_data,
time = "deployment_batch",
group = "group",
metrics = c("difficulty", "dwell_ms", "pupil_auc"),
item = "item_id"
)
plot_dif_drift_heatmap(drift)Conditional centiles are reference distributions, not clinical classifications.
norms <- fit_process_norms(reference_sample, "dwell_ms", c("age", "item_difficulty"))
predict_process_centiles(norms, new_people)
score_process_deviation(norms, new_people, type = "centile")
plot_process_centiles(norms)
audit_norm_transportability(norms, external_sample)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.