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Advanced model programme and validation

The advanced functions are model families with explicit validation obligations. They are not automatically confirmatory because they execute.

Dynamic gaze-state IRTree baseline

dynamic <- fit_dynamic_irtree(
  dataset,
  dynamic_irtree_spec(source = "samples", include_response = TRUE)
)
plot(dynamic)

Functional pupil-informed IRT

pupil_fit <- fit_joint_functional_pupil_irt(
  dataset,
  functional_pupil_irt_spec(df = 5, engine = "two_stage_lme4")
)
plot(pupil_fit)

Theory-defined strategies

prototypes <- rbind(
  constructive = c(matrix_dwell = 0.8, toggling = -0.5),
  elimination = c(matrix_dwell = -0.4, toggling = 0.9)
)
strategy_fit <- fit_theory_strategy_irt(
  dataset,
  theory_strategy_spec(prototypes)
)
plot(strategy_fit)

Gaze-informed diffusion

diffusion <- fit_gaze_diffusion_irt(
  dataset,
  gaze_diffusion_spec(
    engine = "ez_regression",
    gaze_features = c("dwell_time_ms", "first_fixation_latency_ms")
  )
)
plot(diffusion)

Each model should undergo parameter recovery, coverage, misspecification, grouped validation, preprocessing sensitivity, and empirical reproduction before confirmatory use.

Monte Carlo design

The package supplies a declared design grid rather than hiding validation conditions inside scripts. The screening grid varies sample size, item count, ability–speed correlation, process effects, feature reliability, process missingness, AOI-state error, pupil autocorrelation, luminance confounding, DIF, and local dependence.

grid <- advanced_validation_grid(quick = TRUE)
head(grid)

simulation <- do.call(
  simulate_advanced_process_data,
  c(as.list(grid[1, ]), list(seed = 20260804L))
)
str(simulation, max.level = 1)

A production validation run should use the full grid or a preregistered subset, sufficient replications, confidence intervals, explicit expected-failure scenarios, and grouped person/item validation. A fitted object without interval coverage or a reproduction object without published targets cannot satisfy the promotion gate.

Evidence promotion gate

evidence <- list(
  fit_process_irt = list(
    recovery = recovery_result,
    calibration = sbc_result,
    misspecification = misspecification_result,
    grouped_validation = grouped_result,
    engine_equivalence = engine_result,
    empirical_reproduction = reproduction_result,
    sensitivity = multiverse_result
  )
)
model_audit <- audit_advanced_model_evidence(evidence)
plot(model_audit)
write_advanced_model_evidence_report(model_audit, "validation/advanced-model-evidence.md")

A model is promoted only when all evidence gates declared in advanced_model_evidence_spec() are satisfied.

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.