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Theory-constrained strategy mixtures

Prespecification before fitting

A strategy mixture is appropriate only when theory defines distinguishable process signatures before estimation. Data-derived classes must not be named after cognition merely because their means differ.

spec <- theory_strategy_spec(
  strategies = list(
    analytic = c(prompt_dwell = 1, evidence_dwell = 1, option_switches = 0.5),
    heuristic = c(prompt_dwell = -0.5, evidence_dwell = -0.8, option_switches = -0.2)
  ),
  response = "score",
  participant = "participant_id",
  item = "item_id",
  condition = "condition",
  item_availability = availability,
  engine = "stan",
  anchor_strength = 3
)

fit <- fit_theory_strategy_irt(trials, spec, seed = 42)

Classification uncertainty

probability <- strategy_posterior_probabilities(fit)
strategy_classification_uncertainty(fit, threshold = 0.70)
strategy_label_switching_diagnostics(fit)

Posterior probabilities and entropy are primary outputs. Modal assignment alone conceals uncertainty.

Sensitivity and competing heterogeneity

sensitivity <- strategy_aoi_sensitivity(
  list(primary_aoi = trials_primary, expanded_aoi = trials_expanded),
  spec,
  seed = 42
)
plot(sensitivity)
compare_strategy_heterogeneity(fit)

The package compares the discrete mixture with a continuous process-heterogeneity model and requires external strategy manipulations before substantive class labels can be promoted.

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.