---
title: "Item-Bank Decisions, Fairness Drift, and Reference Centiles"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Item-Bank Decisions, Fairness Drift, and Reference Centiles}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval = FALSE)
```


Item selection can be treated as a constrained multi-objective decision rather than a sequence of isolated cutoffs.

```{r}
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.

```{r}
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.

```{r}
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)
```