{ "_id": "6ac95f77de46216d5eddabad", "Package": "eyeprocess", "Type": "Package", "Title": "Harmonize Eye-Tracking, Pupillometry, Biometrics, and\nPsychometric Process Data", "Version": "0.12.0.9000", "Authors@R": "c( person(\"Stefanos\", \"Balaskas\", role = c(\"aut\", \"cre\"), email = \"s.balaskas@ac.upatras.gr\", comment =\nc(ORCID = \"0000-0003-2444-9796\")) )", "Description": "Provides an extensible, vendor-neutral framework for\nimporting, validating, harmonizing, transforming, visualizing,\nand modelling eye-tracking, pupillometry, behavioural, and\nbiometric process data. The package uses explicit timebase and\ncoordinate-space registries, preserves native fields and\nprovenance, and offers first-class adapters for Gazepoint\nAnalysis and Gazepoint Biometrics exports alongside generic and\nvendor-specific importers. Downstream tools support trial and\narea of interest reconstruction, signal-quality auditing,\nfeature derivation, scanpath analysis, response-time and\nitem-response workflows, and optional psychometric modelling\nengines. An integrated Gazepoint workflow produces\nquality-control evidence, media-trial reconstruction, plots,\nanalysis-ready process tables, item response theory (IRT)-ready\nresponse structures, and reproducible reports. Brain Imaging\nData Structure (BIDS) interoperability for eye-tracking and\nvalidation-release infrastructure support disk-backed storage,\nindependent multi-vendor evidence, grouped validation,\nsimulation calibration, model-equivalence audits, and\nexplicitly experimental advanced psychometric process models.\nResearch-scale infrastructure adds deterministic resumable\nMonte Carlo execution, atomic validation checkpoints, explicit\nadvanced-model promotion gates, independent multi-vendor\nevidence registries, stable object contracts, partitioned\ndisk-backed storage, optional probabilistic engines, and a\nfully synthetic multimodal benchmark for reproducibility\ntesting. The measurement-intelligence programme adds\nprobabilistic and compositional area of interest (AOI)\nanalysis, measurement-uncertainty propagation, calibration and\ndevice-transportability audits, process reliability,\nphase-amplitude pupil registration, informative-missingness\nsensitivity, right-censored gaze-latency survival analysis,\ntemporal and spatial process models, item-bank decision\noptimization, fairness monitoring, conditional process\nreference distributions, and evidence-provenance graphs.", "License": "MIT + file LICENSE", "URL": "https://stefanosbalaskas.github.io/eyeprocess/,\nhttps://github.com/stefanosbalaskas/eyeprocess", "BugReports": "https://github.com/stefanosbalaskas/eyeprocess/issues", "Encoding": "UTF-8", "Additional_repositories": "https://stan-dev.r-universe.dev", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Config/roxygen2/version": "8.0.0", "Repository": "https://stefanosbalaskas.r-universe.dev", "Date/Publication": "2026-10-09 20:31:30 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"eyeprocess_irt_invariance_evidence", "eyeprocess_irt_item_bank", "eyeprocess_irt_item_fit_residuals", "eyeprocess_irt_item_information", "eyeprocess_irt_item_selection", "eyeprocess_irt_latent_regression_design", "eyeprocess_irt_link_stability", "eyeprocess_irt_local_dependence_pairs", "eyeprocess_irt_map_score", "eyeprocess_irt_marginal_reliability", "eyeprocess_irt_mean_mean_link", "eyeprocess_irt_mean_sigma_link", "eyeprocess_irt_measurement_precision_profile", "eyeprocess_irt_missing_by_design_audit", "eyeprocess_irt_misspecification_metrics", "eyeprocess_irt_misspecification_suite", "eyeprocess_irt_mle_score", "eyeprocess_irt_model_card", "eyeprocess_irt_model_card_audit", "eyeprocess_irt_model_spec", "eyeprocess_irt_monotonicity_audit", "eyeprocess_irt_nominal_probability", "eyeprocess_irt_parameter_plausibility_audit", "eyeprocess_irt_person_fit_lz", "eyeprocess_irt_person_fit_residuals", "eyeprocess_irt_plausible_values", "eyeprocess_irt_ppc_discrepancy", "eyeprocess_irt_precision_evidence_table", "eyeprocess_irt_prior_sensitivity_grid", "eyeprocess_irt_prior_sensitivity_summary", "eyeprocess_irt_prior_spec", "eyeprocess_irt_process_alignment", "eyeprocess_irt_process_aware_selection_penalty", "eyeprocess_irt_process_dif_concordance", "eyeprocess_irt_q3", "eyeprocess_irt_recovery_design", "eyeprocess_irt_recovery_failures", "eyeprocess_irt_recovery_summary", "eyeprocess_irt_sbc_ranks", "eyeprocess_irt_sbc_summary", "eyeprocess_irt_score_table", "eyeprocess_irt_score_uncertainty", "eyeprocess_irt_session_drift", "eyeprocess_irt_sparse_design_audit", "eyeprocess_irt_stocking_lord_link", "eyeprocess_irt_stopping_rule", "eyeprocess_irt_targeting_gap", "eyeprocess_irt_test_characteristic_curve", "eyeprocess_irt_test_information", "eyeprocess_irt_testlet_audit", "eyeprocess_irt_testlet_spec", "eyeprocess_irt_threshold_order_audit", "eyeprocess_joint_process_irt_spec", "eyeprocess_mirt_directional_information", "eyeprocess_mirt_information_matrix", "eyeprocess_mirt_loading_audit", "eyeprocess_mirt_loading_spec", "eyeprocess_multichannel_measurement_map", "eyeprocess_negative_control_evidence_plan", "eyeprocess_negative_control_evidence_table", "eyeprocess_process_irt_data_bundle", "eyeprocess_process_item_profile", "eyeprocess_process_missingness_pattern", "eyeprocess_process_person_profile", "eyeprocess_recovery_evidence_table", "eyeprocess_reliability_evidence_plan", "eyeprocess_reliability_evidence_table", "eyeprocess_response_time_profile", "eyeprocess_sbc_evidence_table", "eyeprocess_speed_accuracy_profile", "eyeprocess_stress_evidence_plan", "eyeprocess_stress_evidence_table", "eyeprocess_validation_atlas_gaps", "eyeprocess_validation_claim_matrix", "eyeprocess_validation_evidence_atlas", "eyeprocess_validation_evidence_grade", "eyeprocess_validation_evidence_index", "eyeprocess_validation_evidence_manifest", "eyeprocess_validation_plan", "eyeprocess_validation_readiness", "eyeprocess_validation_release_gate", "eyeprocess_validation_seed", "facet_effects", "feature_dictionary", "feature_spec", "features_wide", "field_fidelity_report", "file_hash_manifest", "filter_eye_signal", "filter_gaze", "filter_pupil", "filter_pupil_signal", "find_process_measures", "fingerprint_eye_dataset", "fingerprint_validation_case", "fit_accuracy_rt", "fit_aoi_compositional_model", "fit_aoi_growth_curve", "fit_brms_adapter", "fit_censored_normal_process_irt", "fit_changepoint_multimodal_irt", "fit_changepoint_rt_irt", "fit_cognitive_diagnosis_process", "fit_continuous_time_irt", "fit_crossclassified_process_irt", "fit_crossclassified_process_irt_mhrm", "fit_device_linking", "fit_dif", "fit_diffirt_adapter", "fit_diffirt_engine_adapter", "fit_dynamic_aoi_model", "fit_dynamic_gpirt", "fit_dynamic_irtree", "fit_dynamic_irtree_stan", "fit_event_time_irt", "fit_explanatory_irt", "fit_external_engine", "fit_eyeprocess_erm", "fit_eyeprocess_gdina", "fit_eyeprocess_lnirt", "fit_eyeprocess_mirt", "fit_eyeprocess_tam", "fit_eyetrackingr_adapter", "fit_fixation_point_process", "fit_flow_mirt", "fit_functional_pupil_stan", "fit_gaze_aft_model", "fit_gaze_anchored_3pl_audit", "fit_gaze_cox_model", "fit_gaze_diffusion_irt", "fit_gaze_diffusion_stan", "fit_gaze_informed_irt", "fit_gaze_informed_missingness_irt", "fit_gaze_mixed_cox_model", "fit_gaze_weighted_choice", "fit_gdina_adapter", "fit_gpirt", "fit_irt", "fit_irt_model", "fit_item_parameter_seed_model", "fit_joint_functional_pupil_irt", "fit_joint_gaze_rt_irt", "fit_joint_graded_rt_process_irt", "fit_joint_process_model", "fit_joint_signal_missingness", "fit_kde_latent_distribution_irt", "fit_latent_class_process_irt", "fit_latent_space_irt", "fit_lnirt_adapter", "fit_manyfacet_process_irt", "fit_marked_gaze_process", "fit_mirt_adapter", "fit_mixture_irt_process_classes", "fit_multiblock_process_map", "fit_multimodal_irt", "fit_multimodal_m2", "fit_multimodal_m3", "fit_multimodal_m4", "fit_multimodal_trait_irt", "fit_multinomial_transition", "fit_multiple_response_process_irt", "fit_nominal_gaze_irt", "fit_nonignorable_missing_irt", "fit_offline_recalibration", "fit_omission_survival_irt", "fit_openmx_adapter", "fit_openmx_process_model", "fit_persistence_gaze_diffusion_irt", "fit_phase_amplitude_irt", "fit_process_dif", "fit_process_gstudy", "fit_process_hmm_irt", "fit_process_irt", "fit_process_missingness_model", "fit_process_norms", "fit_process_observation_model", "fit_process_profile_mixture", "fit_process_rasch_tree", "fit_pupil_confound_model", "fit_pupil_event_deconvolution", "fit_pupil_informed_irt", "fit_pupillometryr_adapter", "fit_response_process_embedding_irt", "fit_revisit_process_cdm", "fit_seqhmm_adapter", "fit_shared_process_factor", "fit_speed_accuracy_engagement_irt", "fit_strategy_mixture", "fit_strategy_mixture_em", "fit_strategy_mixture_stan", "fit_tam_adapter", "fit_theory_strategy_irt", "fit_traminer_adapter", "fit_validation_replicate", "fit_variational_irt", "fit_visual_context_irt", "fixation_boundary_uncertainty", "flag_gaze_outliers", "format_compatibility_matrix", "format_validation_spec", "freeze_eyeprocess_irt_reference", "freeze_eyeprocess_validation_atlas", "freeze_eyeprocess_validation_evidence", "freeze_software_paper_evidence", "freeze_validation_reference", "functional_pupil_basis", "functional_pupil_diagnostics", "functional_pupil_features", "functional_pupil_irt_spec", "gaze_anchored_3pl_alignment", "gaze_data_quality_profile", "gaze_diffusion_spec", "gaze_entropy", "gaze_precision_rms_s2s", "gaze_recurrence", "gaze_uncertainty_ellipse", "gaze_velocity", "gazepoint_analysis_tables", "gazepoint_irt_tables", "gazepoint_workflow_spec", "generalizability_process_study", "get_eye_table", "get_irt_model", "gp_align_media_ids", "gp_audit_file_pairs", "gp_check_biometrics_sync", "gp_check_fixation_ids", "gp_check_media_timing", "gp_check_pupil_channels", "gp_check_sampling_rate", "gp_check_validity_fields", "gp_identify_export_type", "gp_list_export_fields", "gp_match_biometrics", "gp_match_recordings", "gp_pair_exports", "gp_parse_markers", "gp_parse_media_events", "gp_parse_user_events", "gp_profile_export", "gp_reconstruct_stimuli", "gp_reconstruct_trials", "gp_validate_export", "grade_model_evidence", "grouped_cv", "grouped_folds", "identify_mediation_levels", "import_benchmark_study", "import_canonical", "import_external_detector_events", "import_eye_bids", "incremental_process_validity", "infer_eye_mapping", "init_validation_corpus", "init_vendor_corpus", "inject_aoi_label_noise", "inject_calibration_offset", "inject_device_shift", "inject_eye_missingness", "inject_pupil_dropout", "inject_sampling_jitter", "inject_trial_imbalance", "inspect_eye_source", "interpolate_pupil", "interpretive_warnings", "irt_compositional_channel", "irt_continuous_channel", "irt_count_channel", "irt_functional_channel", "irt_model_spec", "irt_nominal_channel", "irt_response_channel", "irt_rt_channel", "irt_sequence_channel", "irt_survival_channel", "irt_validation_spec", "is_eye_dataset", "is_eyelink_export", "is_gazepoint_export", "is_pupil_labs_export", "is_smi_export", "is_tobii_export", "item_objective_spec", "item_parameters", "item_pareto_front", "jitter_aoi", "label_process_episodes", "latent_distribution_stress_test", "latent_trait_trajectory", "leave_device_out_validation", "leave_item_out_validation", "leave_session_out_validation", "leave_site_out_validation", "list_irt_models", "lock_decision_manifest", "map_latent_classes_to_process_profiles", "match_detected_events", "measurement_error_budget", "migrate_eye_storage_schema", "model_data", "model_fit_statistics", "model_missing_process", "model_promotion_spec", "model_validation_spec", "model_validation_summary", "monitor_dif_drift", "multiblock_contributions", "multiblock_person_coordinates", "multiblock_variable_coordinates", "multimodal_backend_status", "multimodal_irt_spec", "multimodal_m2_ablation", "multimodal_m2_negative_controls", "multimodal_m2_ppc", "multimodal_m2_process_information", "multimodal_m2_recovery", "multimodal_m2_spec", "multimodal_m3_ablation", "multimodal_m3_functional_bridge", "multimodal_m3_negative_controls", "multimodal_m3_ppc", "multimodal_m3_process_information", "multimodal_m3_recovery", "multimodal_m3_spec", "multimodal_m4_ablation", "multimodal_m4_negative_controls", "multimodal_m4_ppc", "multimodal_m4_process_information", "multimodal_m4_recovery", "multimodal_m4_sensitivity", "multimodal_m4_spec", "multimodal_m4_state_diagnostics", "multimodal_ppc", "negative_control_concordance", "negative_control_process_test", "new_aoi", "new_coordinate_space", "new_eye_dataset", "normalize_timebase", "object_hash", "object_schema", "open_eye_storage", "open_partitioned_eye_storage", "optimize_item_bank", "option_process_information", "outcome_blind_feature_audit", "outcome_blind_snapshot", "package_reproducibility_manifest", "paper_reproducibility_manifest", "parameter_recovery", "partition_eye_storage", "person_scores", "perturb_aoi_geometry", "pipeline_failures", "pipeline_result", "pipeline_step_status", "placebo_window_audit", "plot_aoi_assignment_stability", "plot_aoi_balance_biplot", "plot_aoi_boundary_risk", "plot_aoi_coefficient_stability", "plot_aoi_composition_trajectory", "plot_aoi_dwell", "plot_aoi_metric_uncertainty", "plot_aoi_perturbations", "plot_aoi_probability_map", "plot_aoi_robustness_surface", "plot_aoi_ternary", "plot_aoi_transition_matrix", "plot_aoi_transition_rank", "plot_aoi_variation_matrix", "plot_bank_information_coverage", "plot_bcea", "plot_biometrics", "plot_calibration_error_ellipses", "plot_calibration_vector_field", "plot_changepoint_ribbons", "plot_clock_alignment", "plot_complete_case_sensitivity", "plot_compositional_group_difference", "plot_coordinate_spaces", "plot_covariate_effect_surface", "plot_cross_vendor_metric_matrix", "plot_crossmodal_recurrence", "plot_decision_stability", "plot_dependability_surface", "plot_detector_agreement", "plot_detector_coefficient_stability", "plot_detector_event_timeline", "plot_detector_feature_distributions", "plot_detector_multiverse", "plot_device_agreement", "plot_device_bias_by_magnitude", "plot_device_equivalence_intervals", "plot_device_transfer_curve", "plot_diagnostics", "plot_diagonal_recurrence_profile", "plot_dif_drift_heatmap", "plot_distractor_information", "plot_drift_over_time", "plot_episode_duration_distribution", "plot_episode_transition_graph", "plot_episode_waterfall", "plot_evidence", "plot_evidence_graph", "plot_eye_overview", "plot_eye_trace", "plot_fairness_transport_matrix", "plot_feature_correlation", "plot_feature_distribution", "plot_fixation_intensity", "plot_fixations", "plot_fuzzy_transition_matrix", "plot_gaze_accuracy", "plot_gaze_cox_diagnostics", "plot_gaze_cumulative_incidence", "plot_gaze_hazard", "plot_gaze_heatmap", "plot_gaze_precision", "plot_gaze_quality_dashboard", "plot_gaze_survival_curve", "plot_gazepoint_workflow", "plot_group_icc_process_overlay", "plot_group_scanpath_transport", "plot_interval_coverage", "plot_irf_uncertainty", "plot_item_decision_path", "plot_item_difficulty", "plot_item_group_process_curves", "plot_item_normative_deviation", "plot_item_pareto", "plot_item_phase_delay", "plot_item_sampling_reliability", "plot_metric_dependency_graph", "plot_missingness", "plot_missingness_by_aoi", "plot_missingness_by_time", "plot_mnar_tipping_point", "plot_model_decision_impact", "plot_model_diagnostics", "plot_normative_fan", "plot_objective_tradeoffs", "plot_observation_probability", "plot_observed_expected_fixations", "plot_parameter_recovery", "plot_person_item_space", "plot_person_normative_profile", "plot_phase_amplitude_scores", "plot_probabilistic_scanpath", "plot_process_centiles", "plot_process_changepoint", "plot_process_channel_ablation_delta", "plot_process_dif_forest", "plot_process_episodes", "plot_process_feature_stability", "plot_process_window_sensitivity", "plot_pupil_activity_sensitivity", "plot_pupil_activity_windows", "plot_pupil_band_power", "plot_pupil_components", "plot_pupil_preprocessing_audit", "plot_pupil_registration", "plot_pupil_spectrum", "plot_pupil_timeseries", "plot_recalibration_before_after", "plot_recurrence_matrix", "plot_recurrence_network", "plot_registered_pupil_effects", "plot_reliability_by_metric", "plot_representative_scanpath", "plot_sampling_intervals", "plot_sampling_rate", "plot_sbc_rank", "plot_scanpath", "plot_scanpath_atlas", "plot_scanpath_dispersion", "plot_scanpath_similarity_matrix", "plot_screen_coverage", "plot_selected_bank_profile", "plot_sensitivity", "plot_session_stability", "plot_signal_quality", "plot_spatial_residuals", "plot_temporal_excitation_kernel", "plot_transition_matrix", "plot_trial_timeline", "plot_uncertainty_by_item", "plot_uncertainty_by_stage", "plot_uncertainty_tornado", "plot_uncertainty_waterfall", "plot_validation_failures", "plot_validation_runtime", "plot_variance_components", "plot_warping_functions", "plot_windowed_recurrence", "posterior_predictive_discrepancies", "posterior_sbc_contract", "power_process_simulation", "preaction_process_features", "predict_aoi_trajectory", "predict_fixation_intensity", "predict_gaze_survival", "predict_item_parameter_priors", "predict_process_centiles", "predict_theta_at_time", "preflight_decisions", "preflight_exclusion_manifest", "preflight_failures", "preflight_passed", "prepare_dynamic_irtree_data", "prepare_functional_pupil_data", "prepare_gaze_diffusion_data", "prepare_gaze_survival_data", "prepare_multilevel_mediation_data", "prepare_multimodal_irt_data", "prepare_strategy_mixture_data", "prepare_structured_unstructured_process_features", "preprocess_eye", "preprocess_spec", "preprocessing_multiverse", "probabilistic_aoi_assignment", "process_anomaly_distance", "process_bland_altman", "process_channel_ablation", "process_criterion_associations", "process_dependent_discrimination_audit", "process_dif_nuisance_surrogate", "process_drift_alerts", "process_drift_spec", "process_feature_blocks", "process_feature_family_registry", "process_feature_stability", "process_feature_time_provenance", "process_icc", "process_information", "process_irt_diagnostics", "process_irt_spec", "process_item_information", "process_measure_card", "process_measure_coverage", "process_measure_guardrails", "process_measure_lineage", "process_measure_registry", "process_measure_units", "process_negative_control_permute", "process_negative_control_shift", "process_ngram_features", "process_null_benchmark", "process_pattern_mixture", "process_person_fit", "process_preflight_spec", "process_profile_probabilities", "process_profile_summary", "process_reliability_profile", "process_residual_map", "process_sensitivity_grid", "process_sequence_embedding", "process_state_occupancy", "process_state_transition_summary", "process_temporal_stability", "process_uncertainty_spec", "process_validation_design", "process_variance_components", "process_window_spec", "promote_irt_model", "promote_vendor_support", "propagate_aoi_uncertainty", "propagate_calibration_uncertainty", "propagate_detector_to_aoi", "propagate_detector_to_features", "propagate_process_uncertainty", "provenance_edge_table", "provenance_lineage_table", "provenance_manifest", "prune_validation_checkpoints", "public_validation_corpus", "pupil_activity_index", "pupil_band_power", "pupil_baseline_sensitivity", "pupil_confound_effects", "pupil_deconvolve", "pupil_event_effects", "pupil_event_regressor", "pupil_frequency_features", "pupil_labs_format", "pupil_latency_sensitivity", "pupil_preprocessing_grid", "pupil_preprocessing_sensitivity", "pupil_response_kernel", "pupil_unit_fidelity_audit", "pupil_velocity_activity", "quantify_process_leakage", "query_eye_storage", "raven_reproduction_spec", "read_api_lifecycle_registry", "read_benchmark_table", "read_decision_manifest", "read_eye_dataset", "read_eye_export", "read_eye_folder", "read_eye_generic", "read_eyelink_asc", "read_eyelink_edf", "read_eyelink_report", "read_eyeprocess_validation_evidence", "read_gazepoint", "read_gazepoint_aoi_statistics", "read_gazepoint_biometrics", "read_gazepoint_combined", "read_gazepoint_events", "read_gazepoint_fixations", "read_gazepoint_folder", "read_gazepoint_gaze", "read_gazepoint_summary", "read_pupil_core", "read_pupil_neon", "read_pupillabs", "read_reproducibility_fingerprint", "read_smi", "read_smi_aoi_export", "read_smi_event_export", "read_smi_raw_export", "read_tobii", "read_validation_job_manifest", "read_validation_manifest", "read_validation_scenario_manifest", "read_vendor_registry", "recalibrate_after_changepoint", "recommended_validation_replications", "recompute_aoi_features", "recurrence_features", "redact_validation_case", "register_aois", "register_coordinate_space", "register_eye_adapter", "register_eye_api_status", "register_irt_model", "register_process_measure", "register_pupil_curves", "register_validation_case", "register_vendor_semantics", "remap_recording_ids", "report_aoi_sensitivity", "report_detector_multiverse", "report_eye_dataset", "report_gaze_quality", "report_gaze_survival_model", "report_processirt", "reporting_guideline_audit", "representative_scanpath", "response_matrix", "response_time_matrix", "resume_eye_pipeline", "resume_validation_jobs", "rolling_apply", "roundtrip_eye_bids", "roundtrip_eye_dataset", "run_aoi_sensitivity_analysis", "run_benchmark_reproduction", "run_detector_inference_multiverse", "run_detector_multiverse", "run_eye_benchmark", "run_eye_pipeline", "run_eyeprocess_equateirt", "run_eyeprocess_irt_ability_sbc", "run_eyeprocess_irt_recovery", "run_eyeprocess_mirtcat", "run_eyeprocess_stress_evidence", "run_eyeprocess_validation_program", "run_gazepoint_workflow", "run_model_validation", "run_posterior_sbc", "run_process_negative_controls", "run_process_sensitivity", "run_process_validation", "run_raven_reproduction", "run_sbc", "run_validation_jobs", "sbc_ecdf_deviation", "sbc_rank_diagnostics", "sbc_summary", "scanpath_dispersion", "scanpath_sequence", "schema_coverage", "schema_coverage_summary", "schema_table", "score_partial_response_pattern", "score_process_deviation", "score_response_stream", "segment_process_episodes", "select_next_item_process", "semantic_fidelity_spec", "semantic_loss_map", "semantic_roundtrip_audit", "sensitivity_branch_fingerprint", "sensitivity_decision_leverage", "sensitivity_fragility_index", "sensitivity_missing_process", "sensitivity_mnar_process", "sensitivity_multiverse_manifest", "sensitivity_process", "sensitivity_rank_stability", "sensitivity_sign_stability", "sensitivity_significance_stability", "sensitivity_threshold_stability", "session_facet_effects", "set_eye_table", "simulate_advanced_process_data", "simulate_detector_multiverse_data", "simulate_dynamic_irtree_data", "simulate_eye_dataset", "simulate_eyeprocess_catr", "simulate_eyeprocess_irt_binary", "simulate_from_model", "simulate_gaze_diffusion_data", "simulate_gaze_quality_calibration", "simulate_gaze_survival_example", "simulate_gaze_survival_inputs", "simulate_irt_model", "simulate_multimodal_irt", "simulate_multimodal_m2", "simulate_multimodal_m3", "simulate_multimodal_m4", "simulate_presentation_variants", "simulate_process_cat", "simulate_process_irt", "simulate_process_validation_data", "simulate_strategy_mixture_data", "simulation_based_calibration", "simulation_rank_statistic", "software_paper_claim_matrix", "software_paper_coverage", "software_paper_evidence_bundle", "software_paper_gap_analysis", "software_paper_readiness", "software_paper_validation_table", "source_preservation_audit", "specification_coverage", "specification_curve_data", "split_half_process_reliability", "split_validation_plan", "standardize_eye_table", "storage_transaction_manifest", "store_quality", "strategy_aoi_sensitivity", "strategy_classification_uncertainty", "strategy_label_switching_diagnostics", "strategy_posterior_probabilities", "streaming_score_history", "stress_test_latent_distribution", "stress_test_local_dependence", "stress_test_missingness", "stress_test_misspecification", "stress_test_preprocessing", "stress_test_process_pipeline", "stress_test_speededness", "stress_test_summary", "stress_tolerance_frontier", "structural_transition_mask", "summarise_aoi_membership", "summarise_aoi_sensitivity", "summarise_detector_disagreement", "summarise_detector_events", "summarise_detector_robustness", "summarise_eye_benchmark", "summarise_eyeprocess_stress_evidence", "summarise_gaze_censoring", "summarise_process_negative_controls", "summarise_process_sensitivity", "summarise_process_validation", "summarise_sampling_quality", "summarise_spatial_quality", "summarise_validation_acceptance", "summarise_within_between_variance", "summarize_fixations", "summarize_parameter_recovery", "summarize_process_windows", "supported_eye_formats", "synchronize_eye_biometrics", "synthetic_corruption_plan", "theory_strategy_spec", "tidy_gaze_survival_model", "timestamp_fidelity_audit", "trace_item_decision", "transform_aoi_composition", "transition_entropy", "transition_matrix", "transition_residual_diagnostics", "translate_aoi", "trial_table", "uncertainty_budget", "unregister_eye_adapter", "update_person_score", "upgrade_eye_dataset", "upgrade_eyeprocess_model", "validate_against_reference", "validate_aoi_geometry", "validate_benchmark_study", "validate_bids_eye_semantics", "validate_decision_manifest", "validate_engine_adapter", "validate_event_detector_spec", "validate_eye_corpus", "validate_eye_dataset", "validate_eye_mapping", "validate_eye_pipeline", "validate_eye_prov_graph", "validate_eye_source", "validate_eye_storage_metadata", "validate_eye_table", "validate_eyelink_export", "validate_eyeprocess_external_irt_fit", "validate_eyeprocess_irt_item_bank", "validate_eyeprocess_irt_model_spec", "validate_eyeprocess_joint_process_irt_spec", "validate_eyeprocess_validation_plan", "validate_feature_availability", "validate_gaze_quality_inputs", "validate_gaze_survival_data", "validate_gazepoint_workflow", "validate_generic_export", "validate_hed_event_semantics", "validate_irt_model", "validate_latent_space_process_similarity", "validate_model_object", "validate_multilevel_mediation_data", "validate_multimodal_irt", "validate_multimodal_m2", "validate_multimodal_m3", "validate_multimodal_m4", "validate_process_measure_registry", "validate_process_validation_design", "validate_process_windows", "validate_pupillabs_export", "validate_smi_export", "validate_strategy_manipulation", "validate_tobii_export", "validate_vendor_semantics", "validate_vendor_timestamp_semantics", "validation_acceptance_matrix", "validation_acceptance_rule", "validation_bundle_manifest", "validation_calibration_summary", "validation_condition_id", "validation_condition_ranking", "validation_coverage_table", "validation_evidence_levels", "validation_evidence_matrix", "validation_failure_profile", "validation_failure_summary", "validation_failure_taxonomy", "validation_job_plan", "validation_ladder", "validation_manifest", "validation_mcse", "validation_mcse_profile", "validation_recovery_summary", "validation_recovery_table", "validation_replication_budget", "validation_report", "validation_robustness_score", "validation_runtime_summary", "validation_sbc_summary", "validation_scenario_manifest", "validation_seed", "validation_summary_mcse", "validation_thresholds", "vendor_schema_contract", "vendor_validation_spec", "verify_decision_manifest_lock", "verify_eyeprocess_validation_atlas", "verify_eyeprocess_validation_evidence", "verify_outcome_blind_snapshot", "verify_reproducibility_fingerprint", "verify_reproducibility_manifest", "visual_context_registry", "windowed_recurrence", "write_advanced_model_evidence_report", "write_api_lifecycle_registry", "write_benchmark_data_dictionary", "write_decision_manifest", "write_eye_dataset", "write_eye_pipeline_report", "write_eye_storage", "write_eye_targets_template", "write_eyeprocess_validation_evidence", "write_eyeprocess_validation_report", "write_format_validation_report", "write_gazepoint_workflow_report", "write_model_promotion_report", "write_partitioned_eye_storage", "write_prov_dot", "write_provenance", "write_reporting_guideline_report", "write_reproducibility_fingerprint", "write_software_paper_evidence", "write_software_paper_reproduction", "write_software_paper_scaffold", "write_validation_job_manifest", "write_validation_manifest", "write_validation_release_report", "write_validation_report", "write_validation_scenario_manifest", "write_vendor_case_report", "write_vendor_registry", "write_vendor_validation_report" ], "_help": [ { "page": "eyeprocess-package", "title": "eyeprocess: Harmonize Eye-Tracking and Psychometric Process Data", "topics": [ "eyeprocess-package", "eyeprocess" ] }, { "page": "ablate_multimodal_channels", "title": "Create formal channel-ablation datasets", "topics": [ "ablate_multimodal_channels" ] }, { "page": "addm_glam_proxy_features", "title": "Compute aDDM/GLAM-inspired gaze-evidence proxy features", "topics": [ "addm_glam_proxy_features" ] }, { "page": "adjust_pupil_confounds", "title": "Extract confound-adjusted pupil values", "topics": [ "adjust_pupil_confounds" ] }, { "page": "advanced_model_evidence_spec", "title": "Specify evidence required to promote advanced model interfaces", "topics": [ "advanced_model_evidence_spec" ] }, { "page": "advanced_validation_grid", "title": "Construct the advanced-model validation grid", "topics": [ "advanced_validation_grid" ] }, { "page": "algorithm_facet_effects", "title": "Extract algorithm facet effects", "topics": [ "algorithm_facet_effects" ] }, { "page": "analysis_decision_entropy", "title": "Entropy of an explicitly enumerated analysis-decision space", "topics": [ "analysis_decision_entropy" ] }, { "page": "analysis_environment_snapshot", "title": "Snapshot an eyeprocess analysis environment", "topics": [ "analysis_environment_snapshot" ] }, { "page": "analysis_resolution_guard", "title": "Audit compatibility between measurement resolution and an analysis target", "topics": [ "analysis_resolution_guard" ] }, { "page": "aoi_membership_probability", "title": "AOI membership probabilities from uncertainty draws", "topics": [ "aoi_membership_probability" ] }, { "page": "aoi_perturbation_uncertainty", "title": "AOI Perturbation and Uncertainty Analysis", "topics": [ "aoi_perturbation_spec", "aoi_perturbation_uncertainty", "apply_aoi_perturbation_grid", "assess_aoi_inference_stability", "compare_aoi_assignments", "convert_aoi_margin_to_degrees", "convert_aoi_margin_to_pixels", "create_aoi_perturbation_grid", "dilate_aoi", "erode_aoi", "estimate_aoi_assignment_stability", "estimate_fixation_assignment_probability", "jitter_aoi", "perturb_aoi_geometry", "plot_aoi_assignment_stability", "plot_aoi_coefficient_stability", "plot_aoi_perturbations", "plot_aoi_robustness_surface", "recompute_aoi_features", "report_aoi_sensitivity", "run_aoi_sensitivity_analysis", "summarise_aoi_sensitivity", "translate_aoi", "validate_aoi_geometry" ] }, { "page": "aoi_trajectory_features", "title": "Extract AOI growth-curve/trajectory features", "topics": [ "aoi_trajectory_features" ] }, { "page": "api_family_map", "title": "Map exported APIs to conceptual families", "topics": [ "api_family_map" ] }, { "page": "api_lifecycle_diff", "title": "Compare two API lifecycle registries", "topics": [ "api_lifecycle_diff" ] }, { "page": "api_surface_summary", "title": "Summarise API surface by family and lifecycle status", "topics": [ "api_surface_summary" ] }, { "page": "apply_preflight_decision", "title": "Apply a pre-flight decision to data explicitly", "topics": [ "apply_preflight_decision" ] }, { "page": "apply_synthetic_corruption", "title": "Apply a synthetic corruption plan", "topics": [ "apply_synthetic_corruption" ] }, { "page": "as_irt_recovery_results", "title": "Canonicalise parameter-recovery results", "topics": [ "as_irt_recovery_results" ] }, { "page": "031-probabilistic-aoi", "title": "Probabilistic AOI assignment and uncertainty propagation", "topics": [ "assign_aois_probabilistic", "audit_aoi_separation", "plot.eye_aoi_separation_audit", "plot.eye_aoi_uncertainty", "plot.eye_probabilistic_aoi", "plot_aoi_boundary_risk", "plot_aoi_metric_uncertainty", "plot_aoi_probability_map", "plot_fuzzy_transition_matrix", "plot_probabilistic_scanpath", "propagate_aoi_uncertainty", "summarise_aoi_membership" ] }, { "page": "assign_process_feature_family", "title": "Assign features to conservative process-feature families", "topics": [ "assign_process_feature_family" ] }, { "page": "audit_3pl_process_signatures", "title": "Identify items for descriptive 3PL/process review", "topics": [ "audit_3pl_process_signatures" ] }, { "page": "audit_advanced_model_evidence", "title": "Audit advanced-model scientific evidence", "topics": [ "audit_advanced_model_evidence" ] }, { "page": "audit_benchmark_release", "title": "Audit whether benchmark assets are ready for public release", "topics": [ "audit_benchmark_release" ] }, { "page": "audit_bias", "title": "Audit bias", "topics": [ "audit_bias" ] }, { "page": "audit_biometric_imputation", "title": "Alias emphasizing sensitivity rather than automatic replacement", "topics": [ "audit_biometric_imputation" ] }, { "page": "audit_biometric_preflight", "title": "Audit incoming biometric/process data before modelling", "topics": [ "audit_biometric_preflight" ] }, { "page": "audit_candidate_item_bank", "title": "Audit a candidate item bank against a seed model", "topics": [ "audit_candidate_item_bank" ] }, { "page": "audit_channel_incremental_information", "title": "Audit out-of-sample incremental information from a process channel", "topics": [ "audit_channel_incremental_information" ] }, { "page": "audit_convergence", "title": "Audit convergence and classified failures", "topics": [ "audit_convergence" ] }, { "page": "audit_coverage", "title": "Audit coverage", "topics": [ "audit_coverage" ] }, { "page": "audit_decision_provenance", "title": "Audit decision provenance and completeness", "topics": [ "audit_decision_provenance" ] }, { "page": "audit_distractor_attention", "title": "Audit distractor attention patterns", "topics": [ "audit_distractor_attention" ] }, { "page": "audit_eye_api", "title": "Audit API lifecycle completeness and replacement contracts", "topics": [ "audit_eye_api" ] }, { "page": "audit_eye_pipeline", "title": "Audit a pipeline definition or completed run", "topics": [ "audit_eye_pipeline" ] }, { "page": "audit_frontier_model_contract", "title": "Audit whether a gated frontier model has a minimum evidence contract", "topics": [ "audit_frontier_model_contract" ] }, { "page": "audit_identifiability", "title": "Audit empirical identifiability from replicate estimates", "topics": [ "audit_identifiability" ] }, { "page": "audit_interval_width", "title": "Audit interval width", "topics": [ "audit_interval_width" ] }, { "page": "audit_irf_shape", "title": "Audit item response-function shape departures", "topics": [ "audit_irf_shape" ] }, { "page": "audit_item_reduction_sensitivity", "title": "Run stepwise Rasch item-reduction as a sensitivity analysis", "topics": [ "audit_item_reduction_sensitivity" ] }, { "page": "audit_latent_distribution", "title": "Audit the empirical latent-trait distribution", "topics": [ "audit_latent_distribution" ] }, { "page": "audit_measurement_transportability", "title": "Summarise measurement transportability across held-out groups", "topics": [ "audit_measurement_transportability" ] }, { "page": "audit_model_promotion", "title": "Audit promotion readiness for advanced model families", "topics": [ "audit_model_promotion" ] }, { "page": "audit_multimodal_identifiability", "title": "Audit basic multimodal design identifiability", "topics": [ "audit_multimodal_identifiability" ] }, { "page": "audit_multimodal_m2_identifiability", "title": "Audit structural and data identifiability for M0-M2", "topics": [ "audit_multimodal_m2_identifiability" ] }, { "page": "audit_multimodal_m3_identifiability", "title": "Audit structural and measurement support for M3", "topics": [ "audit_multimodal_m3_identifiability" ] }, { "page": "audit_multimodal_m4_identifiability", "title": "Audit M4 structural and posterior identifiability", "topics": [ "audit_multimodal_m4_identifiability" ] }, { "page": "audit_multimodal_measurement", "title": "Audit a multimodal measurement object", "topics": [ "audit_multimodal_measurement" ] }, { "page": "audit_multivariate_process_quality", "title": "Alias emphasizing data-quality interpretation of process anomaly auditing", "topics": [ "audit_multivariate_process_quality" ] }, { "page": "audit_nonparametric_rasch", "title": "Run nonparametric Rasch diagnostics with eRm", "topics": [ "audit_nonparametric_rasch" ] }, { "page": "audit_presentation_accessibility", "title": "Audit presentation/accessibility sensitivity without clinical inference", "topics": [ "audit_presentation_accessibility" ] }, { "page": "audit_process_adjusted_dif", "title": "Audit DIF before and after process-data adjustment", "topics": [ "audit_process_adjusted_dif" ] }, { "page": "audit_process_anomalies", "title": "Audit multivariate process/data-quality anomalies", "topics": [ "audit_process_anomalies" ] }, { "page": "audit_process_drift", "title": "Audit post-deployment psychometric and biometric drift", "topics": [ "audit_process_drift" ] }, { "page": "audit_process_external_validity", "title": "Audit external/structural validity of process traits", "topics": [ "audit_process_external_validity" ] }, { "page": "audit_process_local_dependence", "title": "Audit inter-option/process local dependence", "topics": [ "audit_process_local_dependence" ] }, { "page": "audit_process_measurement_invariance", "title": "Audit process measurement invariance across facets", "topics": [ "audit_process_measurement_invariance" ] }, { "page": "audit_process_window_sensitivity", "title": "Audit sensitivity of process summaries to temporal window choices", "topics": [ "audit_process_window_sensitivity" ] }, { "page": "audit_pupil_fatigue_drift", "title": "Audit within-person pupil fatigue/trial-order drift", "topics": [ "audit_pupil_fatigue_drift" ] }, { "page": "audit_pupil_frequency_stability", "title": "Audit stability of pupil frequency features across window lengths", "topics": [ "audit_pupil_frequency_stability" ] }, { "page": "audit_pupil_preprocessing_order", "title": "Audit declared order of pupil preprocessing steps", "topics": [ "audit_pupil_preprocessing_order" ] }, { "page": "audit_rmse", "title": "Audit rmse", "topics": [ "audit_rmse" ] }, { "page": "audit_roundtrip_loss", "title": "Audit semantic and numerical loss after a round trip", "topics": [ "audit_roundtrip_loss" ] }, { "page": "audit_sampling_irregularity", "title": "Audit sampling irregularity", "topics": [ "audit_sampling_irregularity" ] }, { "page": "audit_sbc", "title": "Audit SBC rank uniformity", "topics": [ "audit_sbc" ] }, { "page": "audit_signal_filter", "title": "Summarize a signal-filter audit", "topics": [ "audit_signal_filter" ] }, { "page": "audit_temporal_leakage", "title": "Audit temporal leakage in a feature provenance table", "topics": [ "audit_temporal_leakage" ] }, { "page": "audit_validation_completion", "title": "Audit whether a validation programme is complete", "topics": [ "audit_validation_completion" ] }, { "page": "audit_vendor_field_coverage", "title": "Audit vendor field coverage against canonical semantics", "topics": [ "audit_vendor_field_coverage" ] }, { "page": "audit_vendor_validation", "title": "Audit a multi-vendor validation corpus", "topics": [ "audit_vendor_validation" ] }, { "page": "audit_visual_context_dependence", "title": "Audit visual-context dependence", "topics": [ "audit_visual_context_dependence" ] }, { "page": "bayesian_process_diagnostic_flags", "title": "Extract compact Bayesian process-model diagnostic flags", "topics": [ "bayesian_process_diagnostic_flags" ] }, { "page": "bayesian_process_diagnostics_dashboard", "title": "Summarize Bayesian process-model diagnostics", "topics": [ "bayesian_process_diagnostics_dashboard" ] }, { "page": "benchmark_expected_outputs", "title": "Return expected benchmark outputs", "topics": [ "benchmark_expected_outputs" ] }, { "page": "benchmark_eye_storage", "title": "Benchmark storage formats and query operations", "topics": [ "benchmark_eye_storage" ] }, { "page": "benchmark_eyeprocess", "title": "Benchmark an eyeprocess operation", "topics": [ "benchmark_eyeprocess" ] }, { "page": "benchmark_memory_estimate", "title": "Memory estimate for an R object or generated problem size", "topics": [ "benchmark_memory_estimate" ] }, { "page": "benchmark_scaling_curve", "title": "Estimate scaling exponent from benchmark results", "topics": [ "benchmark_scaling_curve" ] }, { "page": "bind_process_windows", "title": "Bind compatible process-window objects", "topics": [ "bind_process_windows" ] }, { "page": "biometric_imputation_sensitivity", "title": "Run biometric-feature imputation as a sensitivity analysis", "topics": [ "biometric_imputation_sensitivity" ] }, { "page": "bootstrap_process_reliability", "title": "Bootstrap ICC reliability by resampling participants", "topics": [ "bootstrap_process_reliability" ] }, { "page": "build_compatibility_matrix", "title": "Build the declared/fixture/empirical compatibility matrix", "topics": [ "build_compatibility_matrix" ] }, { "page": "046-evidence-provenance-graph", "title": "Evidence and decision provenance graphs", "topics": [ "audit_evidence_dependencies", "build_evidence_graph", "compare_decision_provenance", "plot.eye_decision_trace", "plot.eye_evidence_graph", "plot.eye_provenance_comparison", "plot_evidence_graph", "plot_item_decision_path", "plot_metric_dependency_graph", "plot_model_decision_impact", "trace_item_decision" ] }, { "page": "build_gazepoint_media_trials", "title": "Reconstruct media presentations as analysis trials", "topics": [ "build_gazepoint_media_trials" ] }, { "page": "calibration_drift_profile", "title": "Calibration drift profile across sessions/batches", "topics": [ "calibration_drift_profile" ] }, { "page": "calibration_error_model", "title": "Build an empirical bivariate calibration-error model", "topics": [ "calibration_error_model" ] }, { "page": "calibration_sensitivity_grid", "title": "Sensitivity grid for deterministic calibration offsets", "topics": [ "calibration_sensitivity_grid" ] }, { "page": "calibration_transfer_audit", "title": "Audit transfer of calibration across devices/sessions/sites", "topics": [ "calibration_transfer_audit" ] }, { "page": "canonical_eye_api", "title": "Canonical API mapping", "topics": [ "canonical_eye_api" ] }, { "page": "classify_item_missingness", "title": "Classify item missingness using exposure and response evidence", "topics": [ "classify_item_missingness" ] }, { "page": "collect_eye_storage", "title": "Collect a disk-backed eye dataset", "topics": [ "collect_eye_storage" ] }, { "page": "collect_validation_evidence", "title": "Collect validation evidence into a common bundle", "topics": [ "collect_validation_evidence" ] }, { "page": "collect_validation_jobs", "title": "Collect validation checkpoints from one or more directories", "topics": [ "collect_validation_jobs" ] }, { "page": "compare_aoi_methods", "title": "Compare explicit AOI assignment methods", "topics": [ "compare_aoi_methods" ] }, { "page": "compare_aoi_trajectories", "title": "Compare AOI trajectory feature objects", "topics": [ "compare_aoi_trajectories" ] }, { "page": "compare_bayesian_process_models", "title": "Compare Bayesian process models by LOO or Bayes factor", "topics": [ "compare_bayesian_process_models" ] }, { "page": "compare_decision_manifests", "title": "Compare two research decision manifests", "topics": [ "compare_decision_manifests" ] }, { "page": "compare_deployment_batches", "title": "Compare two deployment batches descriptively", "topics": [ "compare_deployment_batches" ] }, { "page": "compare_diffusion_accuracy_rt", "title": "Compare diffusion and conventional accuracy-RT models", "topics": [ "compare_diffusion_accuracy_rt" ] }, { "page": "compare_dynamic_transition_models", "title": "Compare dynamic transition models", "topics": [ "compare_dynamic_transition_models" ] }, { "page": "compare_engine_adapters", "title": "Compare multiple external-engine adapter results", "topics": [ "compare_engine_adapters" ] }, { "page": "compare_fixation_methods", "title": "Compare explicit fixation-detection methods", "topics": [ "compare_fixation_methods" ] }, { "page": "compare_functional_scalar_models", "title": "Compare functional and scalar pupil summaries", "topics": [ "compare_functional_scalar_models" ] }, { "page": "compare_hard_probabilistic_aoi", "title": "Compare hard and probabilistic AOI assignments", "topics": [ "compare_hard_probabilistic_aoi" ] }, { "page": "compare_irt_models", "title": "Compare multimodal IRT model objects", "topics": [ "compare_irt_models" ] }, { "page": "compare_latent_distribution_models", "title": "Compare simple latent-distribution reference models", "topics": [ "compare_latent_distribution_models" ] }, { "page": "compare_model_engines", "title": "Compare equivalent model engines", "topics": [ "compare_model_engines" ] }, { "page": "compare_parametric_nonparametric_irf", "title": "Compare conventional logistic and flexible IRF shapes", "topics": [ "compare_parametric_nonparametric_irf" ] }, { "page": "compare_presentation_fairness", "title": "Compare outcomes across presentation variants", "topics": [ "compare_presentation_fairness" ] }, { "page": "compare_process_criterion_models", "title": "Compare process external-validity models", "topics": [ "compare_process_criterion_models" ] }, { "page": "compare_process_models", "title": "Compare explicit process-model specifications", "topics": [ "compare_process_models" ] }, { "page": "compare_process_profile_solutions", "title": "Compare candidate process-profile solutions", "topics": [ "compare_process_profile_solutions" ] }, { "page": "compare_pupil_kernels", "title": "Compare pupil deconvolution kernels", "topics": [ "compare_pupil_kernels" ] }, { "page": "compare_pupil_preprocessing", "title": "Compare explicit pupil-preprocessing methods", "topics": [ "compare_pupil_preprocessing" ] }, { "page": "compare_raw_adjusted_pupil", "title": "Compare raw and confound-adjusted pupil values", "topics": [ "compare_raw_adjusted_pupil" ] }, { "page": "compare_reproducibility_fingerprints", "title": "Compare two reproducibility fingerprints", "topics": [ "compare_reproducibility_fingerprints" ] }, { "page": "compare_signal_filters", "title": "Compare multiple signal filters", "topics": [ "compare_signal_filters" ] }, { "page": "compare_strategy_heterogeneity", "title": "Compare mixture and continuous heterogeneity descriptions", "topics": [ "compare_strategy_heterogeneity" ] }, { "page": "compare_validation_engines", "title": "Compare validation engines on common recovery output", "topics": [ "compare_validation_engines" ] }, { "page": "compare_vendor_semantics", "title": "Compare semantic mappings between vendors", "topics": [ "compare_vendor_semantics" ] }, { "page": "compare_visual_context_irt", "title": "Compare base and visual-context IRT models", "topics": [ "compare_visual_context_irt" ] }, { "page": "compatibility_evidence_matrix", "title": "Build a detailed compatibility evidence matrix", "topics": [ "compatibility_evidence_matrix" ] }, { "page": "context_factor_effects", "title": "Extract visual-context factor effects/loadings", "topics": [ "context_factor_effects" ] }, { "page": "coordinate_fidelity_audit", "title": "Coordinate semantic-fidelity audit", "topics": [ "coordinate_fidelity_audit" ] }, { "page": "coverage_calibration_curve", "title": "Interval coverage calibration curve", "topics": [ "coverage_calibration_curve" ] }, { "page": "create_public_benchmark", "title": "Create a public, de-identified benchmark bundle", "topics": [ "create_public_benchmark" ] }, { "page": "cross_device_process_equating_audit", "title": "Cross-device process-scale equating audit", "topics": [ "cross_device_process_equating_audit" ] }, { "page": "cross_version_adapter_regression", "title": "Compare adapter output across software/format versions", "topics": [ "cross_version_adapter_regression" ] }, { "page": "crossed_grouped_cv", "title": "Cross-classified grouped cross-validation", "topics": [ "crossed_grouped_cv" ] }, { "page": "crossed_grouped_folds", "title": "Create cross-classified grouped folds", "topics": [ "crossed_grouped_folds" ] }, { "page": "data_quality_reporting_table", "title": "Compact reporting table for eye-tracking data quality", "topics": [ "data_quality_reporting_table" ] }, { "page": "decision_manifest_diff", "title": "Alias for manifest comparison emphasizing changed decision paths", "topics": [ "decision_manifest_diff" ] }, { "page": "decision_manifest_hash", "title": "Stable hash of decision content", "topics": [ "decision_manifest_hash" ] }, { "page": "decision_manifest_table", "title": "Flatten a decision manifest to a table", "topics": [ "decision_manifest_table" ] }, { "page": "decision_space_coverage", "title": "Coverage of a declared decision space by evaluated specifications", "topics": [ "decision_space_coverage" ] }, { "page": "decision_stability", "title": "Overall decision-stability summary", "topics": [ "decision_stability" ] }, { "page": "decode_dynamic_states", "title": "Decode latent or fitted transition states", "topics": [ "decode_dynamic_states" ] }, { "page": "032-compositional-aoi", "title": "Compositional analysis of AOI attention", "topics": [ "aoi_balance_coordinates", "compare_aoi_compositions", "derive_aoi_composition", "fit_aoi_compositional_model", "plot.eye_aoi_composition", "plot.eye_aoi_composition_comparison", "plot.eye_aoi_composition_model", "plot_aoi_balance_biplot", "plot_aoi_composition_trajectory", "plot_aoi_ternary", "plot_aoi_variation_matrix", "plot_compositional_group_difference", "transform_aoi_composition" ] }, { "page": "derive_gazepoint_workflow_features", "title": "Derive the complete Gazepoint workflow feature set", "topics": [ "derive_gazepoint_workflow_features" ] }, { "page": "034-calibration-recalibration", "title": "Calibration drift and offline recalibration", "topics": [ "apply_offline_recalibration", "audit_recalibration", "detect_calibration_drift", "fit_offline_recalibration", "plot.eye_calibration_drift", "plot.eye_recalibration_audit", "plot_calibration_error_ellipses", "plot_calibration_vector_field", "plot_drift_over_time", "plot_recalibration_before_after", "plot_screen_coverage" ] }, { "page": "detect_corrupt_partitions", "title": "Detect missing, truncated, or modified partitions", "topics": [ "detect_corrupt_partitions" ] }, { "page": "detect_irt_changepoints", "title": "Detect IRT/process change points using an SIC-inspired multichannel score", "topics": [ "detect_irt_changepoints" ] }, { "page": "detect_process_changepoint", "title": "Detect a response-process change point", "topics": [ "detect_process_changepoint" ] }, { "page": "042-process-episodes", "title": "Cognitive-episode change-point detection", "topics": [ "compare_episode_structure", "detect_process_changepoints", "label_process_episodes", "plot.eye_episode_comparison", "plot.eye_process_changepoints", "plot.eye_process_episodes", "plot_changepoint_ribbons", "plot_episode_duration_distribution", "plot_episode_transition_graph", "plot_episode_waterfall", "plot_process_episodes", "segment_process_episodes" ] }, { "page": "device_facet_effects", "title": "Extract device facet effects", "topics": [ "device_facet_effects" ] }, { "page": "diffusion_identification_study", "title": "Construct a simulation-based identification study", "topics": [ "diffusion_identification_study" ] }, { "page": "diffusion_parameter_diagnostics", "title": "Diagnose diffusion-parameter trade-offs and sampling", "topics": [ "diffusion_parameter_diagnostics" ] }, { "page": "diffusion_posterior_predictive", "title": "Posterior predictive summaries for accuracy and RT", "topics": [ "diffusion_posterior_predictive" ] }, { "page": "distractor_process_map", "title": "Build a distractor process map", "topics": [ "distractor_process_map" ] }, { "page": "drift_by_device", "title": "Drift audit stratified by device", "topics": [ "drift_by_device" ] }, { "page": "drift_by_site", "title": "Drift audit stratified by study site", "topics": [ "drift_by_site" ] }, { "page": "drift_by_stimulus_version", "title": "Drift audit stratified by stimulus version", "topics": [ "drift_by_stimulus_version" ] }, { "page": "drift_by_vendor", "title": "Drift audit stratified by vendor", "topics": [ "drift_by_vendor" ] }, { "page": "dynamic_irtree_recovery", "title": "Evaluate dynamic-state recovery under misclassification", "topics": [ "dynamic_irtree_recovery" ] }, { "page": "dynamic_irtree_spec", "title": "Specify a hardened dynamic gaze-state IRTree", "topics": [ "dynamic_irtree_spec" ] }, { "page": "dynamic_posterior_predictive_check", "title": "Posterior predictive checks for dynamic state models", "topics": [ "dynamic_posterior_predictive_check" ] }, { "page": "dynamic_transition_design", "title": "Build an explicit dynamic-transition design matrix", "topics": [ "dynamic_transition_design" ] }, { "page": "effective_sampling_frequency", "title": "Estimate effective sampling frequency from timestamps", "topics": [ "effective_sampling_frequency" ] }, { "page": "encode_response_combinations", "title": "Encode multiple-response item response combinations", "topics": [ "encode_response_combinations" ] }, { "page": "engine_adapter_status", "title": "Report an adapter's availability and contract", "topics": [ "engine_adapter_status" ] }, { "page": "equate_irt_scales", "title": "Equate IRT scales using anchor item parameters", "topics": [ "equate_irt_scales" ] }, { "page": "estimate_calibration_error", "title": "Estimate empirical calibration/validation error", "topics": [ "estimate_calibration_error" ] }, { "page": "estimate_visual_exposure_probability", "title": "Estimate visual exposure probability", "topics": [ "estimate_visual_exposure_probability" ] }, { "page": "evaluate_validation_acceptance", "title": "Evaluate a validation acceptance rule", "topics": [ "evaluate_validation_acceptance" ] }, { "page": "event_detector_multiverse", "title": "Event-detector multiverse and inference robustness", "topics": [ "assess_detector_inference_stability", "compare_event_catalogues", "create_detector_multiverse", "define_event_detector_spec", "detect_events_with_spec", "estimate_detector_agreement", "import_external_detector_events", "match_detected_events", "plot_detector_agreement", "plot_detector_coefficient_stability", "plot_detector_event_timeline", "plot_detector_feature_distributions", "plot_detector_multiverse", "propagate_detector_to_aoi", "propagate_detector_to_features", "report_detector_multiverse", "run_detector_inference_multiverse", "run_detector_multiverse", "simulate_detector_multiverse_data", "summarise_detector_disagreement", "summarise_detector_events", "summarise_detector_robustness", "validate_event_detector_spec" ] }, { "page": "event_marker_qc", "title": "Event-marker plausibility audit", "topics": [ "event_marker_qc" ] }, { "page": "event_roundtrip_audit", "title": "Audit event survival across an interchange round trip", "topics": [ "event_roundtrip_audit" ] }, { "page": "event_semantics_audit", "title": "Audit event semantic preservation", "topics": [ "event_semantics_audit" ] }, { "page": "expand_eyeprocess_stress_evidence_plan", "title": "Expand a stress evidence plan into one-factor-at-a-time scenarios", "topics": [ "expand_eyeprocess_stress_evidence_plan" ] }, { "page": "expand_eyeprocess_validation_plan", "title": "Expand a validation-evidence plan to a scenario table", "topics": [ "expand_eyeprocess_validation_plan" ] }, { "page": "expand_process_validation_design", "title": "Expand a process-validation design into explicit conditions", "topics": [ "expand_process_validation_design" ] }, { "page": "expected_process_information", "title": "Expected process-aware item utility under a theta distribution", "topics": [ "expected_process_information" ] }, { "page": "explain_latent_interaction", "title": "Explain local person-item latent-space interactions", "topics": [ "explain_latent_interaction" ] }, { "page": "export_eye_bids", "title": "Export Eye-Tracking-BIDS physiological recordings", "topics": [ "export_eye_bids" ] }, { "page": "export_eye_pipeline", "title": "Export a pipeline manifest and optional run status", "topics": [ "export_eye_pipeline" ] }, { "page": "export_prov_json", "title": "Export lightweight PROV-oriented JSON", "topics": [ "export_prov_json" ] }, { "page": "export_ro_crate_metadata", "title": "Export minimal RO-Crate 1.3 metadata", "topics": [ "export_ro_crate_metadata" ] }, { "page": "export_validation_bundle", "title": "Export a validation evidence bundle", "topics": [ "export_validation_bundle" ] }, { "page": "external_eye_adapters", "title": "Convert common external eye-tracking objects", "topics": [ "as_eyeprocess_eyeris", "as_eyeprocess_eyetools", "as_eyeprocess_eyetrackingr", "as_eyeprocess_gazer", "as_eyeprocess_pupillometryr", "external_eye_adapters" ] }, { "page": "external_model_engines", "title": "List external engine adapters", "topics": [ "external_model_engines" ] }, { "page": "external_validate_irt", "title": "External validation on a completely held-out dataset", "topics": [ "external_validate_irt" ] }, { "page": "extract_diffusion_parameters", "title": "Extract diffusion parameter summaries", "topics": [ "extract_diffusion_parameters" ] }, { "page": "extract_functional_pupil_parameters", "title": "Extract functional pupil parameters for validation", "topics": [ "extract_functional_pupil_parameters" ] }, { "page": "extract_parameter_truth", "title": "Extract canonical parameter truth from simulated data", "topics": [ "extract_parameter_truth" ] }, { "page": "extract_process_windows", "title": "Extract standardized sliding-window process features", "topics": [ "extract_process_windows" ] }, { "page": "eye_analysis_pipeline", "title": "Construct a governed analysis pipeline", "topics": [ "eye_analysis_pipeline" ] }, { "page": "eye_analysis_spec", "title": "Define explicit analysis decisions for an eyeprocess workflow", "topics": [ "eye_analysis_spec" ] }, { "page": "eye_api_inventory", "title": "Inventory the public eyeprocess API", "topics": [ "eye_api_inventory" ] }, { "page": "eye_api_lifecycle", "title": "Normalize or create an API lifecycle registry", "topics": [ "eye_api_lifecycle" ] }, { "page": "eye_api_recommendation", "title": "Lifecycle recommendation for API review", "topics": [ "eye_api_recommendation" ] }, { "page": "eye_api_status", "title": "Lookup lifecycle status for one or more APIs", "topics": [ "eye_api_status" ] }, { "page": "eye_api_superseded", "title": "Return superseded/deprecated compatibility interfaces", "topics": [ "eye_api_superseded" ] }, { "page": "eye_benchmark_design", "title": "Define a computational benchmark design", "topics": [ "eye_benchmark_design" ] }, { "page": "eye_decision_manifest", "title": "Create a machine-readable research decision manifest", "topics": [ "eye_decision_manifest" ] }, { "page": "eye_pipeline_dot", "title": "Render pipeline dependencies as Graphviz DOT", "topics": [ "eye_pipeline_dot" ] }, { "page": "eye_pipeline_graph", "title": "Return pipeline vertices and dependency edges", "topics": [ "eye_pipeline_graph" ] }, { "page": "eye_pipeline_manifest", "title": "Machine-readable pipeline manifest", "topics": [ "eye_pipeline_manifest" ] }, { "page": "eye_pipeline_mermaid", "title": "Render pipeline dependencies as Mermaid flowchart text", "topics": [ "eye_pipeline_mermaid" ] }, { "page": "eye_pipeline_step", "title": "Define a governed pipeline step", "topics": [ "eye_pipeline_step" ] }, { "page": "030-measurement-intelligence-utils", "title": "Measurement-intelligence plotting and result infrastructure", "topics": [ "autoplot_eyeprocess", "eye_plot_spec", "plot_diagnostics", "plot_diagnostics.default", "plot_diagnostics.eye_mi_result", "plot_evidence", "plot_evidence.default", "plot_evidence.eye_mi_result", "plot_sensitivity", "plot_sensitivity.default", "plot_sensitivity.eye_mi_result", "print.eye_mi_result", "print.eye_plot_spec" ] }, { "page": "eye_prov_graph", "title": "Construct a lightweight provenance graph", "topics": [ "eye_prov_graph" ] }, { "page": "eye_reproducibility_fingerprint", "title": "Construct a reproducibility fingerprint", "topics": [ "eye_reproducibility_fingerprint" ] }, { "page": "eye_session_manifest", "title": "Create a session-level provenance manifest", "topics": [ "eye_session_manifest" ] }, { "page": "eye_storage_spec", "title": "Specify disk-backed eyeprocess storage", "topics": [ "eye_storage_spec" ] }, { "page": "eye_stream_fidelity_audit", "title": "Audit preservation of monocular/binocular stream semantics", "topics": [ "eye_stream_fidelity_audit" ] }, { "page": "eye_targets_manifest", "title": "Create a targets-compatible dependency manifest", "topics": [ "eye_targets_manifest" ] }, { "page": "eyeprocess_api_version", "title": "Return the public eyeprocess API version", "topics": [ "eyeprocess_api_version" ] }, { "page": "eyeprocess_benchmark_study", "title": "Locate the bundled public benchmark study", "topics": [ "eyeprocess_benchmark_study" ] }, { "page": "eyeprocess_cdm_attribute_profiles", "title": "Enumerate latent attribute profiles", "topics": [ "eyeprocess_cdm_attribute_profiles" ] }, { "page": "eyeprocess_cdm_classification_uncertainty", "title": "Summarise CDM classification uncertainty from profile probabilities", "topics": [ "eyeprocess_cdm_classification_uncertainty" ] }, { "page": "eyeprocess_cdm_dina_ideal_response", "title": "Compute deterministic DINA ideal responses from a Q-matrix", "topics": [ "eyeprocess_cdm_dina_ideal_response" ] }, { "page": "eyeprocess_cdm_dina_probability", "title": "DINA response probabilities from slip and guess parameters", "topics": [ "eyeprocess_cdm_dina_probability" ] }, { "page": "eyeprocess_cdm_qmatrix_audit", "title": "Audit a cognitive-diagnosis Q-matrix", "topics": [ "eyeprocess_cdm_qmatrix_audit" ] }, { "page": "eyeprocess_deprecation", "title": "Declare a deprecation in a structured form", "topics": [ "eyeprocess_deprecation" ] }, { "page": "eyeprocess_irt_2pl_probability", "title": "2PL item-response probability", "topics": [ "eyeprocess_irt_2pl_probability" ] }, { "page": "eyeprocess_irt_3pl_probability", "title": "3PL item-response probability", "topics": [ "eyeprocess_irt_3pl_probability" ] }, { "page": "eyeprocess_irt_4pl_probability", "title": "4PL item-response probability", "topics": [ "eyeprocess_irt_4pl_probability" ] }, { "page": "eyeprocess_irt_adaptive_trace", "title": "Create an auditable adaptive-testing trace", "topics": [ "eyeprocess_irt_adaptive_trace" ] }, { "page": "eyeprocess_irt_anchor_audit", "title": "Audit candidate anchor items using supplied DIF evidence", "topics": [ "eyeprocess_irt_anchor_audit" ] }, { "page": "eyeprocess_irt_anchor_purification", "title": "Iteratively remove anchors exceeding a supplied effect threshold", "topics": [ "eyeprocess_irt_anchor_purification" ] }, { "page": "eyeprocess_irt_apply_link", "title": "Apply linear IRT scale-linking coefficients", "topics": [ "eyeprocess_irt_apply_link" ] }, { "page": "eyeprocess_irt_bank_coverage", "title": "Audit item-bank information coverage across a theta region", "topics": [ "eyeprocess_irt_bank_coverage" ] }, { "page": "eyeprocess_irt_category_function_audit", "title": "Audit category probability functions", "topics": [ "eyeprocess_irt_category_function_audit" ] }, { "page": "eyeprocess_irt_classification_precision", "title": "Summarise decision precision at one or more theta cut scores", "topics": [ "eyeprocess_irt_classification_precision" ] }, { "page": "eyeprocess_irt_conditional_sem", "title": "Conditional standard error from information", "topics": [ "eyeprocess_irt_conditional_sem" ] }, { "page": "eyeprocess_irt_content_balance_audit", "title": "Audit content balance in an administered adaptive form", "topics": [ "eyeprocess_irt_content_balance_audit" ] }, { "page": "eyeprocess_irt_device_drift", "title": "Summarise parameter drift across acquisition devices", "topics": [ "eyeprocess_irt_device_drift" ] }, { "page": "eyeprocess_irt_dif_effect_curve", "title": "Differential item functioning effect curve from two parameter sets", "topics": [ "eyeprocess_irt_dif_effect_curve" ] }, { "page": "eyeprocess_irt_dtf_curve", "title": "Differential test functioning effect curve", "topics": [ "eyeprocess_irt_dtf_curve" ] }, { "page": "eyeprocess_irt_eap_score", "title": "EAP score for dichotomous IRT item parameters", "topics": [ "eyeprocess_irt_eap_score" ] }, { "page": "eyeprocess_irt_engine_evidence_table", "title": "Build an external-engine capability and availability table", "topics": [ "eyeprocess_irt_engine_evidence_table" ] }, { "page": "eyeprocess_irt_engine_registry", "title": "Registry of specialized external IRT engines", "topics": [ "eyeprocess_irt_engine_registry" ] }, { "page": "eyeprocess_irt_engine_status", "title": "Query an external IRT engine", "topics": [ "eyeprocess_irt_engine_status" ] }, { "page": "eyeprocess_irt_expected_score", "title": "Expected item score", "topics": [ "eyeprocess_irt_expected_score" ] }, { "page": "eyeprocess_irt_exposure_summary", "title": "Summarise item exposure rates", "topics": [ "eyeprocess_irt_exposure_summary" ] }, { "page": "eyeprocess_irt_extreme_score_audit", "title": "Audit extreme response scores without assigning behavioral labels", "topics": [ "eyeprocess_irt_extreme_score_audit" ] }, { "page": "eyeprocess_irt_fit_dashboard", "title": "Build an integrated IRT diagnostic dashboard object", "topics": [ "eyeprocess_irt_fit_dashboard" ] }, { "page": "eyeprocess_irt_functioning_effect_summary", "title": "Summarise DIF/DTF curve magnitude", "topics": [ "eyeprocess_irt_functioning_effect_summary" ] }, { "page": "eyeprocess_irt_gpcm_probability", "title": "Generalized partial-credit category probabilities", "topics": [ "eyeprocess_irt_gpcm_probability" ] }, { "page": "eyeprocess_irt_grm_probability", "title": "Graded-response category probabilities", "topics": [ "eyeprocess_irt_grm_probability" ] }, { "page": "eyeprocess_irt_haebara_link", "title": "Haebara item-characteristic-curve linking", "topics": [ "eyeprocess_irt_haebara_link" ] }, { "page": "eyeprocess_irt_identification_audit", "title": "Audit IRT scale/location identification", "topics": [ "eyeprocess_irt_identification_audit" ] }, { "page": "eyeprocess_irt_infit_outfit", "title": "Compute residual-based Infit and Outfit summaries", "topics": [ "eyeprocess_irt_infit_outfit" ] }, { "page": "eyeprocess_irt_information_area", "title": "Area under an information curve", "topics": [ "eyeprocess_irt_information_area" ] }, { "page": "eyeprocess_irt_information_gain", "title": "Information gain between two conditional standard errors", "topics": [ "eyeprocess_irt_information_gain" ] }, { "page": "eyeprocess_irt_information_targeting", "title": "Audit how well item information targets a theta distribution", "topics": [ "eyeprocess_irt_information_targeting" ] }, { "page": "eyeprocess_irt_invariance_evidence", "title": "Combine invariance evidence without converting it to a binary validity claim", "topics": [ "eyeprocess_irt_invariance_evidence" ] }, { "page": "eyeprocess_irt_item_bank", "title": "Item bank object for adaptive design", "topics": [ "eyeprocess_irt_item_bank" ] }, { "page": "eyeprocess_irt_item_fit_residuals", "title": "Compute item residual fit summaries from observed and predicted probabilities", "topics": [ "eyeprocess_irt_item_fit_residuals" ] }, { "page": "eyeprocess_irt_item_information", "title": "Compute item information for transparent IRT families", "topics": [ "eyeprocess_irt_item_information" ] }, { "page": "eyeprocess_irt_item_selection", "title": "Select the most informative eligible item at a theta estimate", "topics": [ "eyeprocess_irt_item_selection" ] }, { "page": "eyeprocess_irt_latent_regression_design", "title": "Build a latent-regression design matrix with explicit centering metadata", "topics": [ "eyeprocess_irt_latent_regression_design" ] }, { "page": "eyeprocess_irt_link_stability", "title": "Compare linking estimates across anchor subsets", "topics": [ "eyeprocess_irt_link_stability" ] }, { "page": "eyeprocess_irt_local_dependence_pairs", "title": "Extract high residual-dependence item pairs", "topics": [ "eyeprocess_irt_local_dependence_pairs" ] }, { "page": "eyeprocess_irt_map_score", "title": "MAP score for dichotomous IRT item parameters", "topics": [ "eyeprocess_irt_map_score" ] }, { "page": "eyeprocess_irt_marginal_reliability", "title": "Marginal reliability from latent-score variance and conditional error variance", "topics": [ "eyeprocess_irt_marginal_reliability" ] }, { "page": "eyeprocess_irt_mean_mean_link", "title": "Mean-mean IRT linking coefficients", "topics": [ "eyeprocess_irt_mean_mean_link" ] }, { "page": "eyeprocess_irt_mean_sigma_link", "title": "Mean-sigma IRT linking coefficients", "topics": [ "eyeprocess_irt_mean_sigma_link" ] }, { "page": "eyeprocess_irt_measurement_precision_profile", "title": "Summarise measurement precision across a theta region", "topics": [ "eyeprocess_irt_measurement_precision_profile" ] }, { "page": "eyeprocess_irt_missing_by_design_audit", "title": "Audit missing-by-design structure in an IRT response matrix", "topics": [ "eyeprocess_irt_missing_by_design_audit" ] }, { "page": "eyeprocess_irt_misspecification_metrics", "title": "Compare recovery under reference and misspecified scenarios", "topics": [ "eyeprocess_irt_misspecification_metrics" ] }, { "page": "eyeprocess_irt_misspecification_suite", "title": "Create a model-misspecification suite", "topics": [ "eyeprocess_irt_misspecification_suite" ] }, { "page": "eyeprocess_irt_mle_score", "title": "Bounded ML score for dichotomous IRT item parameters", "topics": [ "eyeprocess_irt_mle_score" ] }, { "page": "eyeprocess_irt_model_card", "title": "Create a governed IRT model card", "topics": [ "eyeprocess_irt_model_card" ] }, { "page": "eyeprocess_irt_model_card_audit", "title": "Audit completeness of an IRT model card", "topics": [ "eyeprocess_irt_model_card_audit" ] }, { "page": "eyeprocess_irt_model_spec", "title": "Declare an eyeprocess IRT model specification", "topics": [ "eyeprocess_irt_model_spec" ] }, { "page": "eyeprocess_irt_monotonicity_audit", "title": "Audit monotonicity of an item response curve", "topics": [ "eyeprocess_irt_monotonicity_audit" ] }, { "page": "eyeprocess_irt_nominal_probability", "title": "Nominal-response category probabilities", "topics": [ "eyeprocess_irt_nominal_probability" ] }, { "page": "eyeprocess_irt_parameter_plausibility_audit", "title": "Audit basic plausibility of dichotomous item parameters", "topics": [ "eyeprocess_irt_parameter_plausibility_audit" ] }, { "page": "eyeprocess_irt_person_fit_lz", "title": "Standardized log-likelihood person-fit diagnostic", "topics": [ "eyeprocess_irt_person_fit_lz" ] }, { "page": "eyeprocess_irt_person_fit_residuals", "title": "Compute person residual fit summaries", "topics": [ "eyeprocess_irt_person_fit_residuals" ] }, { "page": "eyeprocess_irt_plausible_values", "title": "Draw plausible values from a discrete posterior grid", "topics": [ "eyeprocess_irt_plausible_values" ] }, { "page": "eyeprocess_irt_ppc_discrepancy", "title": "Compare observed and replicated IRT discrepancy statistics", "topics": [ "eyeprocess_irt_ppc_discrepancy" ] }, { "page": "eyeprocess_irt_precision_evidence_table", "title": "Build a paper-ready IRT information/precision table", "topics": [ "eyeprocess_irt_precision_evidence_table" ] }, { "page": "eyeprocess_irt_prior_sensitivity_grid", "title": "Construct a prior-sensitivity grid for Bayesian IRT analyses", "topics": [ "eyeprocess_irt_prior_sensitivity_grid" ] }, { "page": "eyeprocess_irt_prior_sensitivity_summary", "title": "Summarise sensitivity of an estimand across declared prior specifications", "topics": [ "eyeprocess_irt_prior_sensitivity_summary" ] }, { "page": "eyeprocess_irt_prior_spec", "title": "Declare prior families for Bayesian IRT engine adapters", "topics": [ "eyeprocess_irt_prior_spec" ] }, { "page": "eyeprocess_irt_process_alignment", "title": "Align item parameters with process-channel summaries", "topics": [ "eyeprocess_irt_process_alignment" ] }, { "page": "eyeprocess_irt_process_aware_selection_penalty", "title": "Process-aware selection penalty without mental-state inference", "topics": [ "eyeprocess_irt_process_aware_selection_penalty" ] }, { "page": "eyeprocess_irt_process_dif_concordance", "title": "Compare psychometric DIF effect sizes with process-channel contrasts", "topics": [ "eyeprocess_irt_process_dif_concordance" ] }, { "page": "eyeprocess_irt_q3", "title": "Compute Yen-style Q3 residual correlations", "topics": [ "eyeprocess_irt_q3" ] }, { "page": "eyeprocess_irt_recovery_design", "title": "Create an IRT recovery design", "topics": [ "eyeprocess_irt_recovery_design" ] }, { "page": "eyeprocess_irt_recovery_failures", "title": "Summarise recovery failure rates", "topics": [ "eyeprocess_irt_recovery_failures" ] }, { "page": "eyeprocess_irt_recovery_summary", "title": "Summarise IRT parameter recovery", "topics": [ "eyeprocess_irt_recovery_summary" ] }, { "page": "eyeprocess_irt_sbc_ranks", "title": "Construct SBC ranks from scalar truths and posterior draws", "topics": [ "eyeprocess_irt_sbc_ranks" ] }, { "page": "eyeprocess_irt_sbc_summary", "title": "Summarise IRT SBC ranks with the package SBC diagnostics", "topics": [ "eyeprocess_irt_sbc_summary" ] }, { "page": "eyeprocess_irt_score_table", "title": "Score a response matrix with EAP, MAP, or ML", "topics": [ "eyeprocess_irt_score_table" ] }, { "page": "eyeprocess_irt_score_uncertainty", "title": "Summarise score uncertainty", "topics": [ "eyeprocess_irt_score_uncertainty" ] }, { "page": "eyeprocess_irt_session_drift", "title": "Summarise item-parameter drift over sessions", "topics": [ "eyeprocess_irt_session_drift" ] }, { "page": "eyeprocess_irt_sparse_design_audit", "title": "Audit sparse person-item response coverage", "topics": [ "eyeprocess_irt_sparse_design_audit" ] }, { "page": "eyeprocess_irt_stocking_lord_link", "title": "Stocking-Lord characteristic-curve linking", "topics": [ "eyeprocess_irt_stocking_lord_link" ] }, { "page": "eyeprocess_irt_stopping_rule", "title": "Evaluate a simple adaptive stopping rule", "topics": [ "eyeprocess_irt_stopping_rule" ] }, { "page": "eyeprocess_irt_targeting_gap", "title": "Compare an examinee distribution with item-bank targeting", "topics": [ "eyeprocess_irt_targeting_gap" ] }, { "page": "eyeprocess_irt_test_characteristic_curve", "title": "Test characteristic curve for dichotomous item parameters", "topics": [ "eyeprocess_irt_test_characteristic_curve" ] }, { "page": "eyeprocess_irt_test_information", "title": "Compute a test information curve from item parameters", "topics": [ "eyeprocess_irt_test_information" ] }, { "page": "eyeprocess_irt_testlet_audit", "title": "Audit testlet sizes and singleton structures", "topics": [ "eyeprocess_irt_testlet_audit" ] }, { "page": "eyeprocess_irt_testlet_spec", "title": "Declare a testlet structure for bifactor/two-tier IRT engines", "topics": [ "eyeprocess_irt_testlet_spec" ] }, { "page": "eyeprocess_irt_threshold_order_audit", "title": "Audit ordered category thresholds", "topics": [ "eyeprocess_irt_threshold_order_audit" ] }, { "page": "eyeprocess_joint_process_irt_spec", "title": "Declare a response/process joint IRT specification", "topics": [ "eyeprocess_joint_process_irt_spec" ] }, { "page": "eyeprocess_mirt_directional_information", "title": "Directional multidimensional 2PL information", "topics": [ "eyeprocess_mirt_directional_information" ] }, { "page": "eyeprocess_mirt_information_matrix", "title": "Multidimensional 2PL item information matrix", "topics": [ "eyeprocess_mirt_information_matrix" ] }, { "page": "eyeprocess_mirt_loading_audit", "title": "Audit multidimensional IRT loading coverage", "topics": [ "eyeprocess_mirt_loading_audit" ] }, { "page": "eyeprocess_mirt_loading_spec", "title": "Declare a multidimensional IRT loading structure", "topics": [ "eyeprocess_mirt_loading_spec" ] }, { "page": "eyeprocess_multichannel_measurement_map", "title": "Construct a multichannel measurement map", "topics": [ "eyeprocess_multichannel_measurement_map" ] }, { "page": "eyeprocess_negative_control_evidence_plan", "title": "Declare negative-control evidence targets", "topics": [ "eyeprocess_negative_control_evidence_plan" ] }, { "page": "eyeprocess_negative_control_evidence_table", "title": "Build a paper-ready negative-control table", "topics": [ "eyeprocess_negative_control_evidence_table" ] }, { "page": "eyeprocess_process_irt_data_bundle", "title": "Prepare a sparse response/process bundle for external joint engines", "topics": [ "eyeprocess_process_irt_data_bundle" ] }, { "page": "eyeprocess_process_item_profile", "title": "Aggregate process channels by item", "topics": [ "eyeprocess_process_item_profile" ] }, { "page": "eyeprocess_process_missingness_pattern", "title": "Summarise missingness patterns across response and process channels", "topics": [ "eyeprocess_process_missingness_pattern" ] }, { "page": "eyeprocess_process_person_profile", "title": "Aggregate process channels by person", "topics": [ "eyeprocess_process_person_profile" ] }, { "page": "eyeprocess_recovery_evidence_table", "title": "Build a paper-ready parameter-recovery table", "topics": [ "eyeprocess_recovery_evidence_table" ] }, { "page": "eyeprocess_reliability_evidence_plan", "title": "Declare reliability evidence targets", "topics": [ "eyeprocess_reliability_evidence_plan" ] }, { "page": "eyeprocess_reliability_evidence_table", "title": "Build a paper-ready reliability table", "topics": [ "eyeprocess_reliability_evidence_table" ] }, { "page": "eyeprocess_response_time_profile", "title": "Summarise response-time structure for joint IRT work", "topics": [ "eyeprocess_response_time_profile" ] }, { "page": "eyeprocess_sbc_evidence_table", "title": "Build a paper-ready SBC table", "topics": [ "eyeprocess_sbc_evidence_table" ] }, { "page": "eyeprocess_speed_accuracy_profile", "title": "Describe speed-accuracy association without causal interpretation", "topics": [ "eyeprocess_speed_accuracy_profile" ] }, { "page": "eyeprocess_stress_evidence_plan", "title": "Declare the Milestone #2 measurement-quality stress evidence plan", "topics": [ "eyeprocess_stress_evidence_plan" ] }, { "page": "eyeprocess_stress_evidence_table", "title": "Build a paper-ready stress-test table", "topics": [ "eyeprocess_stress_evidence_table" ] }, { "page": "eyeprocess_validation_atlas_gaps", "title": "Summarise gaps in a validation evidence atlas", "topics": [ "eyeprocess_validation_atlas_gaps" ] }, { "page": "eyeprocess_validation_claim_matrix", "title": "Build a machine-readable validation claim/evidence matrix", "topics": [ "eyeprocess_validation_claim_matrix" ] }, { "page": "eyeprocess_validation_evidence_atlas", "title": "Assemble a validation evidence atlas", "topics": [ "eyeprocess_validation_evidence_atlas" ] }, { "page": "eyeprocess_validation_evidence_grade", "title": "Grade the completeness of validation evidence", "topics": [ "eyeprocess_validation_evidence_grade" ] }, { "page": "eyeprocess_validation_evidence_index", "title": "Create an index over frozen validation evidence artifacts", "topics": [ "eyeprocess_validation_evidence_index" ] }, { "page": "eyeprocess_validation_evidence_manifest", "title": "Create an evidence manifest from files and in-memory objects", "topics": [ "eyeprocess_validation_evidence_manifest" ] }, { "page": "eyeprocess_validation_plan", "title": "Declare an eyeprocess validation-evidence plan", "topics": [ "eyeprocess_validation_plan" ] }, { "page": "eyeprocess_validation_readiness", "title": "Evaluate readiness of a Milestone #2 validation evidence bundle", "topics": [ "eyeprocess_validation_readiness" ] }, { "page": "eyeprocess_validation_release_gate", "title": "Apply a conservative software-release evidence gate", "topics": [ "eyeprocess_validation_release_gate" ] }, { "page": "eyeprocess_validation_seed", "title": "Derive a deterministic bounded validation seed", "topics": [ "eyeprocess_validation_seed" ] }, { "page": "eyeprocess-adapters", "title": "Mappings and adapter registry", "topics": [ "combine_eye_datasets", "detect_eye_format", "eye_mapping", "infer_eye_mapping", "print.eye_format_detection", "print.eye_mapping", "read_eye_export", "read_eye_folder", "register_eye_adapter", "remap_recording_ids", "supported_eye_formats", "unregister_eye_adapter", "validate_eye_mapping" ] }, { "page": "eyeprocess-class", "title": "Create and manage eyeprocess datasets", "topics": [ "add_provenance", "append_eye_table", "as_eye_dataset", "as_eye_dataset.data.frame", "as_eye_dataset.default", "as_eye_dataset.eye_dataset", "compact_eye_dataset", "get_eye_table", "is_eye_dataset", "new_eye_dataset", "print.eye_dataset", "print.eye_validation", "print.summary.eye_dataset", "provenance_manifest", "set_eye_table", "summary.eye_dataset", "validate_eye_dataset" ] }, { "page": "eyeprocess-coordinates-time", "title": "Coordinate and timebase management", "topics": [ "align_clock", "apply_clock_transform", "audit_clock_sync", "audit_coordinate_spaces", "audit_timebase", "convert_coordinates", "convert_xy", "coordinate_space", "estimate_clock_transform", "estimate_sampling_rate", "normalize_timebase", "print.eye_clock_transform", "register_coordinate_space", "synchronize_eye_biometrics" ] }, { "page": "eyeprocess-experimental", "title": "Experimental psychometric process models", "topics": [ "estimate_ez_diffusion", "fit_gaze_informed_irt", "fit_gaze_weighted_choice", "fit_multimodal_irt", "fit_process_irt", "fit_pupil_informed_irt", "fit_strategy_mixture", "functional_pupil_features", "model_missing_process", "print.eye_missing_sensitivity", "print.eye_strategy_mixture", "print.process_irt_spec", "process_irt_diagnostics", "process_irt_spec", "sensitivity_missing_process" ] }, { "page": "eyeprocess-export-report", "title": "Export, reporting, and package bridges", "topics": [ "as_eye_biometrics", "as_eye_biometrics.data.frame", "as_eye_biometrics.eye_dataset", "as_eye_dataset.gp3_analysis", "as_eye_dataset.gp3_data", "as_eye_dataset.gp3_recording", "as_eye_dataset.gpbiometrics_data", "export_canonical", "import_canonical", "read_eye_dataset", "report_eye_dataset", "report_processirt", "write_eye_dataset", "write_provenance" ] }, { "page": "eyeprocess-features", "title": "Derive gaze, pupil, response-time, and biometric features", "topics": [ "derive_all_features", "derive_biometric_features", "derive_gaze_features", "derive_pupil_features", "derive_rt_features", "features_wide", "feature_dictionary", "feature_spec", "gaze_entropy", "print.eye_feature_spec", "scanpath_sequence", "summarize_fixations", "transition_entropy", "transition_matrix", "trial_table" ] }, { "page": "eyeprocess-format-validation", "title": "Validate real eye-tracking exports and compatibility corpora", "topics": [ "anonymize_eye_dataset", "compare_eye_datasets", "create_validation_bundle", "discover_validation_cases", "eye_format_profiles", "fingerprint_eye_dataset", "format_compatibility_matrix", "format_validation_spec", "init_validation_corpus", "inspect_eye_source", "plot.eye_corpus_validation", "plot.eye_format_validation", "print.eye_corpus_validation", "print.eye_format_validation", "print.eye_format_validation_spec", "print.eye_roundtrip_validation", "read_validation_manifest", "roundtrip_eye_dataset", "schema_coverage", "schema_coverage_summary", "source_preservation_audit", "summary.eye_format_validation", "validate_eyelink_export", "validate_eye_corpus", "validate_eye_source", "validate_generic_export", "validate_pupillabs_export", "validate_smi_export", "validate_tobii_export", "validation_manifest", "write_format_validation_report", "write_validation_manifest" ] }, { "page": "eyeprocess-import-gazepoint", "title": "Import Gazepoint and Gazepoint Biometrics exports", "topics": [ "gp_align_media_ids", "gp_audit_file_pairs", "gp_check_biometrics_sync", "gp_check_fixation_ids", "gp_check_media_timing", "gp_check_pupil_channels", "gp_check_sampling_rate", "gp_check_validity_fields", "gp_identify_export_type", "gp_list_export_fields", "gp_match_biometrics", "gp_match_recordings", "gp_pair_exports", "gp_parse_markers", "gp_parse_media_events", "gp_parse_user_events", "gp_profile_export", "gp_reconstruct_stimuli", "gp_reconstruct_trials", "gp_validate_export", "is_gazepoint_export", "read_gazepoint", "read_gazepoint_aoi_statistics", "read_gazepoint_biometrics", "read_gazepoint_combined", "read_gazepoint_events", "read_gazepoint_fixations", "read_gazepoint_folder", "read_gazepoint_gaze", "read_gazepoint_summary" ] }, { "page": "eyeprocess-import-generic", "title": "Import generic delimited eye-tracking data", "topics": [ "read_eye_generic" ] }, { "page": "eyeprocess-import-vendors", "title": "Import Tobii, Pupil Labs, EyeLink, and SMI exports", "topics": [ "is_eyelink_export", "is_pupil_labs_export", "is_smi_export", "is_tobii_export", "pupil_labs_format", "read_eyelink_asc", "read_eyelink_edf", "read_eyelink_report", "read_pupillabs", "read_pupil_core", "read_pupil_neon", "read_smi", "read_smi_aoi_export", "read_smi_event_export", "read_smi_raw_export", "read_tobii" ] }, { "page": "eyeprocess-models", "title": "Psychometric and process-data models", "topics": [ "align_response_matrices", "check_local_dependence", "fit_accuracy_rt", "fit_dif", "fit_dynamic_aoi_model", "fit_explanatory_irt", "fit_irt", "fit_joint_process_model", "fit_shared_process_factor", "item_parameters", "model_data", "model_fit_statistics", "person_scores", "predict.eyeprocess_model", "print.eyeprocess_model", "print.eye_dynamic_aoi", "print.eye_two_stage_rt", "print.summary.eyeprocess_model", "response_matrix", "response_time_matrix", "summary.eyeprocess_model" ] }, { "page": "eyeprocess-plots", "title": "Visualize eye-tracking and multimodal process data", "topics": [ "plot.eye_dataset", "plot_aoi_dwell", "plot_biometrics", "plot_clock_alignment", "plot_coordinate_spaces", "plot_eye_overview", "plot_eye_trace", "plot_feature_correlation", "plot_feature_distribution", "plot_fixations", "plot_gaze_heatmap", "plot_item_difficulty", "plot_missingness", "plot_model_diagnostics", "plot_pupil_timeseries", "plot_sampling_rate", "plot_scanpath", "plot_signal_quality", "plot_transition_matrix", "plot_trial_timeline" ] }, { "page": "eyeprocess-preprocessing", "title": "Gaze and pupil preprocessing", "topics": [ "baseline_pupil", "detect_blinks", "detect_fixations_idt", "detect_fixations_ivt", "detect_saccades", "filter_gaze", "filter_pupil", "flag_gaze_outliers", "gaze_velocity", "interpolate_pupil", "preprocess_eye", "preprocess_spec", "print.eye_preprocess_spec", "pupil_deconvolve", "rolling_apply" ] }, { "page": "eyeprocess-quality", "title": "Quality control, sensitivity, and governance", "topics": [ "analysis_readiness", "audit_aois", "audit_episodes", "audit_event_order", "audit_missingness", "audit_pupil_quality", "audit_sampling_rate", "audit_signal_quality", "audit_trial_coverage", "check_feature_level", "check_process_leakage", "compare_aoi_definitions", "compare_preprocessing", "interpretive_warnings", "print.eye_readiness", "print.eye_sensitivity", "sensitivity_process", "store_quality" ] }, { "page": "eyeprocess-schema", "title": "Canonical schemas and coordinate spaces", "topics": [ "canonical_table_names", "empty_eye_table", "eye_schema", "new_coordinate_space", "schema_table", "standardize_eye_table", "validate_eye_table" ] }, { "page": "eyeprocess-simulation", "title": "Simulate and validate eyeprocess workflows", "topics": [ "parameter_recovery", "plot.eye_parameter_recovery", "power_process_simulation", "simulate_eye_dataset", "simulate_process_irt", "summary.eye_parameter_recovery" ] }, { "page": "eyeprocess-trials-aoi", "title": "Trials, responses, stimuli, and areas of interest", "topics": [ "add_responses", "assign_aois", "assign_trials", "build_aoi_visits", "build_item_responses", "build_stimulus_intervals", "build_trials", "new_aoi", "print.eye_aoi", "register_aois" ] }, { "page": "facet_effects", "title": "Extract facet effects from a many-facet process model", "topics": [ "facet_effects" ] }, { "page": "field_fidelity_report", "title": "Field-level semantic fidelity report", "topics": [ "field_fidelity_report" ] }, { "page": "file_hash_manifest", "title": "Build a file hash manifest", "topics": [ "file_hash_manifest" ] }, { "page": "filter_eye_signal", "title": "Filter a one-dimensional eye signal robustly", "topics": [ "filter_eye_signal" ] }, { "page": "filter_pupil_signal", "title": "Filter pupil signal robustly", "topics": [ "filter_pupil_signal" ] }, { "page": "find_process_measures", "title": "Find process measures by channel, level, status, or text", "topics": [ "find_process_measures" ] }, { "page": "fingerprint_validation_case", "title": "Fingerprint every file in a validation case", "topics": [ "fingerprint_validation_case" ] }, { "page": "fit_aoi_growth_curve", "title": "Fit a single AOI growth curve", "topics": [ "fit_aoi_growth_curve" ] }, { "page": "fit_brms_adapter", "title": "fit brms adapter", "topics": [ "fit_brms_adapter" ] }, { "page": "fit_censored_normal_process_irt", "title": "Conditional censored-normal calibration for bounded process measurements", "topics": [ "fit_censored_normal_process_irt" ] }, { "page": "fit_changepoint_multimodal_irt", "title": "Fit a multimodal change-point IRT workflow", "topics": [ "fit_changepoint_multimodal_irt" ] }, { "page": "fit_changepoint_rt_irt", "title": "Fit a change-point RT IRT workflow", "topics": [ "fit_changepoint_rt_irt" ] }, { "page": "fit_cognitive_diagnosis_process", "title": "Cognitive-diagnosis model with process indicators", "topics": [ "fit_cognitive_diagnosis_process" ] }, { "page": "fit_continuous_time_irt", "title": "Continuous-time IRT external-engine gate", "topics": [ "fit_continuous_time_irt" ] }, { "page": "fit_crossclassified_process_irt", "title": "Cross-classified process IRT reference model", "topics": [ "fit_crossclassified_process_irt" ] }, { "page": "fit_crossclassified_process_irt_mhrm", "title": "Create a gated scalable cross-classified MH-RM process IRT interface", "topics": [ "fit_crossclassified_process_irt_mhrm" ] }, { "page": "036-device-linking", "title": "Cross-device and cross-vendor metric linking", "topics": [ "apply_device_linking", "audit_device_equivalence", "estimate_device_specific_error", "fit_device_linking", "plot.eye_device_equivalence", "plot.eye_device_linking", "plot_cross_vendor_metric_matrix", "plot_device_agreement", "plot_device_bias_by_magnitude", "plot_device_equivalence_intervals", "plot_device_transfer_curve" ] }, { "page": "fit_diffirt_adapter", "title": "Fit a diffusion IRT adapter", "topics": [ "fit_diffirt_adapter" ] }, { "page": "fit_diffirt_engine_adapter", "title": "fit diffirt engine adapter", "topics": [ "fit_diffirt_engine_adapter" ] }, { "page": "fit_dynamic_gpirt", "title": "Dynamic GPIRT external-engine gate", "topics": [ "fit_dynamic_gpirt" ] }, { "page": "fit_dynamic_irtree", "title": "Fit a dynamic gaze-state response-tree model", "topics": [ "fit_dynamic_irtree" ] }, { "page": "fit_dynamic_irtree_stan", "title": "Fit an optional CmdStan dynamic-transition model", "topics": [ "fit_dynamic_irtree_stan" ] }, { "page": "fit_event_time_irt", "title": "Fit an event-time IRT reference workflow", "topics": [ "fit_event_time_irt" ] }, { "page": "fit_external_engine", "title": "Fit an external model engine through a stable adapter", "topics": [ "fit_external_engine" ] }, { "page": "fit_eyeprocess_erm", "title": "Fit an eRm Rasch-family model without fallback substitution", "topics": [ "fit_eyeprocess_erm" ] }, { "page": "fit_eyeprocess_gdina", "title": "Fit a G-DINA cognitive-diagnosis model without fallback substitution", "topics": [ "fit_eyeprocess_gdina" ] }, { "page": "fit_eyeprocess_lnirt", "title": "Fit a joint response/response-time LNIRT model without fallback substitution", "topics": [ "fit_eyeprocess_lnirt" ] }, { "page": "fit_eyeprocess_mirt", "title": "Fit a model with mirt without substituting another estimator", "topics": [ "fit_eyeprocess_mirt" ] }, { "page": "fit_eyeprocess_tam", "title": "Fit a TAM model without substituting another estimator", "topics": [ "fit_eyeprocess_tam" ] }, { "page": "fit_eyetrackingr_adapter", "title": "fit eyetrackingr adapter", "topics": [ "fit_eyetrackingr_adapter" ] }, { "page": "040-fixation-point-process", "title": "Spatio-temporal fixation point-process models", "topics": [ "diagnose_gaze_point_process", "fit_fixation_point_process", "fit_marked_gaze_process", "plot.eye_fixation_point_process", "plot.eye_gaze_point_process_diagnostics", "plot_covariate_effect_surface", "plot_fixation_intensity", "plot_observed_expected_fixations", "plot_spatial_residuals", "plot_temporal_excitation_kernel", "predict_fixation_intensity" ] }, { "page": "fit_flow_mirt", "title": "Flow-MIRT external-engine gate", "topics": [ "fit_flow_mirt" ] }, { "page": "fit_functional_pupil_stan", "title": "Fit the bundled joint functional pupil-IRT Stan model", "topics": [ "fit_functional_pupil_stan" ] }, { "page": "fit_gaze_anchored_3pl_audit", "title": "Fit a standard 3PL and audit lower-asymptote alignment with process evidence", "topics": [ "fit_gaze_anchored_3pl_audit" ] }, { "page": "fit_gaze_diffusion_irt", "title": "Fit a gaze-informed diffusion model", "topics": [ "fit_gaze_diffusion_irt" ] }, { "page": "fit_gaze_diffusion_stan", "title": "Fit the CmdStan Wiener diffusion model", "topics": [ "fit_gaze_diffusion_stan" ] }, { "page": "fit_gaze_informed_missingness_irt", "title": "Fit a gaze-informed missingness IRT diagnostic", "topics": [ "fit_gaze_informed_missingness_irt" ] }, { "page": "fit_gdina_adapter", "title": "fit gdina adapter", "topics": [ "fit_gdina_adapter" ] }, { "page": "fit_gpirt", "title": "GPIRT model-criticism interface", "topics": [ "fit_gpirt" ] }, { "page": "fit_irt_model", "title": "Fit a registered multimodal IRT model", "topics": [ "fit_irt_model" ] }, { "page": "fit_item_parameter_seed_model", "title": "Fit an experimental pre-pilot item-parameter seeding model", "topics": [ "fit_item_parameter_seed_model" ] }, { "page": "fit_joint_functional_pupil_irt", "title": "Fit a functional pupil-informed IRT workflow", "topics": [ "fit_joint_functional_pupil_irt" ] }, { "page": "fit_joint_gaze_rt_irt", "title": "Joint response, response-time, and gaze-process IRT", "topics": [ "fit_joint_gaze_rt_irt" ] }, { "page": "fit_joint_graded_rt_process_irt", "title": "Joint graded-response, RT, and process reference model", "topics": [ "fit_joint_graded_rt_process_irt" ] }, { "page": "fit_kde_latent_distribution_irt", "title": "Create a gated KDE latent-distribution IRT interface", "topics": [ "fit_kde_latent_distribution_irt" ] }, { "page": "fit_latent_class_process_irt", "title": "Latent process-class IRT reference model", "topics": [ "fit_latent_class_process_irt" ] }, { "page": "fit_latent_space_irt", "title": "Fit a latent-space IRT model using LSMjml", "topics": [ "fit_latent_space_irt" ] }, { "page": "fit_lnirt_adapter", "title": "fit lnirt adapter", "topics": [ "fit_lnirt_adapter" ] }, { "page": "fit_manyfacet_process_irt", "title": "Many-facet process IRT reference model", "topics": [ "fit_manyfacet_process_irt" ] }, { "page": "fit_mirt_adapter", "title": "fit mirt adapter", "topics": [ "fit_mirt_adapter" ] }, { "page": "fit_mixture_irt_process_classes", "title": "Fit a true mirt mixture-IRT response model", "topics": [ "fit_mixture_irt_process_classes" ] }, { "page": "fit_multiblock_process_map", "title": "Fit a multiblock psychometric/gaze/pupil/quality structure map", "topics": [ "fit_multiblock_process_map" ] }, { "page": "fit_multimodal_m2", "title": "Fit the M2 response + RT + gaze reference model", "topics": [ "fit_multimodal_m2" ] }, { "page": "fit_multimodal_m3", "title": "Fit the M3 response + RT + gaze + pupil reference model", "topics": [ "fit_multimodal_m3" ] }, { "page": "fit_multimodal_m4", "title": "Fit the M4 latent response-process state model", "topics": [ "fit_multimodal_m4" ] }, { "page": "fit_multimodal_trait_irt", "title": "Multimodal trait-model convenience wrapper", "topics": [ "fit_multimodal_trait_irt" ] }, { "page": "fit_multinomial_transition", "title": "Fit a penalized multinomial transition model", "topics": [ "fit_multinomial_transition" ] }, { "page": "fit_multiple_response_process_irt", "title": "Fit a multiple-response process-IRT reference model", "topics": [ "fit_multiple_response_process_irt" ] }, { "page": "fit_nominal_gaze_irt", "title": "Nominal/distractor IRT with option-level gaze", "topics": [ "fit_nominal_gaze_irt" ] }, { "page": "fit_nonignorable_missing_irt", "title": "Create a gated Bayesian nonignorable-missing IRT interface", "topics": [ "fit_nonignorable_missing_irt" ] }, { "page": "fit_omission_survival_irt", "title": "Response/RT/omission survival IRT reference model", "topics": [ "fit_omission_survival_irt" ] }, { "page": "fit_openmx_adapter", "title": "fit openmx adapter", "topics": [ "fit_openmx_adapter" ] }, { "page": "fit_openmx_process_model", "title": "Fit a user-defined OpenMx process model", "topics": [ "fit_openmx_process_model" ] }, { "page": "fit_persistence_gaze_diffusion_irt", "title": "Create a gated persistence-augmented gaze-diffusion IRT interface", "topics": [ "fit_persistence_gaze_diffusion_irt" ] }, { "page": "044-process-dif-fairness", "title": "Dynamic process-DIF and fairness drift", "topics": [ "audit_fairness_transportability", "decompose_dif_evidence", "fit_process_dif", "monitor_dif_drift", "plot.eye_dif_drift", "plot.eye_fairness_transportability", "plot.eye_process_dif", "plot_dif_drift_heatmap", "plot_fairness_transport_matrix", "plot_group_icc_process_overlay", "plot_item_group_process_curves", "plot_process_dif_forest" ] }, { "page": "035-process-reliability", "title": "Process reliability and Generalizability Theory", "topics": [ "audit_process_reliability", "design_process_dstudy", "fit_process_gstudy", "plot.eye_process_dstudy", "plot.eye_process_gstudy", "plot.eye_process_reliability_audit", "plot_dependability_surface", "plot_item_sampling_reliability", "plot_reliability_by_metric", "plot_session_stability", "plot_variance_components", "process_variance_components" ] }, { "page": "fit_process_hmm_irt", "title": "Process-state HMM with an IRT response layer", "topics": [ "fit_process_hmm_irt" ] }, { "page": "047-measurement-intelligence-adapters", "title": "Measurement-intelligence compatibility adapters", "topics": [ "crossmodal_recurrence_model", "fit_process_missingness_model", "plot.eye_crossmodal_recurrence_model" ] }, { "page": "045-process-norms", "title": "Conditional process reference centiles", "topics": [ "audit_norm_transportability", "fit_process_norms", "plot.eye_norm_transportability", "plot.eye_process_norms", "plot_item_normative_deviation", "plot_normative_fan", "plot_person_normative_profile", "plot_process_centiles", "predict_process_centiles", "score_process_deviation" ] }, { "page": "038-informative-missingness", "title": "Informative missingness and MNAR sensitivity", "topics": [ "fit_joint_signal_missingness", "fit_process_observation_model", "plot.eye_mnar_sensitivity", "plot.eye_mnar_tipping_point", "plot.eye_process_observation_model", "plot_complete_case_sensitivity", "plot_missingness_by_aoi", "plot_missingness_by_time", "plot_mnar_tipping_point", "plot_observation_probability", "process_pattern_mixture", "sensitivity_mnar_process" ] }, { "page": "fit_process_profile_mixture", "title": "Fit exploratory process profiles", "topics": [ "fit_process_profile_mixture" ] }, { "page": "fit_process_rasch_tree", "title": "Fit a process-informed Rasch tree", "topics": [ "fit_process_rasch_tree" ] }, { "page": "fit_pupil_confound_model", "title": "Fit a luminance/fatigue/process confound model for pupil response", "topics": [ "fit_pupil_confound_model" ] }, { "page": "fit_pupil_event_deconvolution", "title": "Fit transparent event-related pupil deconvolution models", "topics": [ "fit_pupil_event_deconvolution" ] }, { "page": "fit_pupillometryr_adapter", "title": "fit pupillometryr adapter", "topics": [ "fit_pupillometryr_adapter" ] }, { "page": "fit_response_process_embedding_irt", "title": "Fit an IRT response model augmented by sequence embeddings", "topics": [ "fit_response_process_embedding_irt" ] }, { "page": "fit_revisit_process_cdm", "title": "Fit a revisiting-aware cognitive-diagnosis process workflow", "topics": [ "fit_revisit_process_cdm" ] }, { "page": "fit_seqhmm_adapter", "title": "fit seqhmm adapter", "topics": [ "fit_seqhmm_adapter" ] }, { "page": "fit_speed_accuracy_engagement_irt", "title": "Speed-accuracy-engagement IRT convenience wrapper", "topics": [ "fit_speed_accuracy_engagement_irt" ] }, { "page": "fit_strategy_mixture_em", "title": "Fit the deterministic multi-start EM baseline", "topics": [ "fit_strategy_mixture_em" ] }, { "page": "fit_strategy_mixture_stan", "title": "Fit the probabilistic strategy-mixture engine", "topics": [ "fit_strategy_mixture_stan" ] }, { "page": "fit_tam_adapter", "title": "fit tam adapter", "topics": [ "fit_tam_adapter" ] }, { "page": "fit_theory_strategy_irt", "title": "Fit a theory-constrained strategy mixture", "topics": [ "fit_theory_strategy_irt" ] }, { "page": "fit_traminer_adapter", "title": "fit traminer adapter", "topics": [ "fit_traminer_adapter" ] }, { "page": "fit_validation_replicate", "title": "Fit one model-validation replicate", "topics": [ "fit_validation_replicate" ] }, { "page": "fit_variational_irt", "title": "Variational IRT external-engine gate", "topics": [ "fit_variational_irt" ] }, { "page": "fit_visual_context_irt", "title": "Fit an explicit visual-context/testlet IRT model", "topics": [ "fit_visual_context_irt" ] }, { "page": "fixation_boundary_uncertainty", "title": "Distance to nearest rectangular AOI boundary", "topics": [ "fixation_boundary_uncertainty" ] }, { "page": "freeze_eyeprocess_irt_reference", "title": "Freeze IRT validation reference summaries", "topics": [ "freeze_eyeprocess_irt_reference" ] }, { "page": "freeze_eyeprocess_validation_atlas", "title": "Freeze a validation atlas with a reproducibility fingerprint", "topics": [ "freeze_eyeprocess_validation_atlas" ] }, { "page": "freeze_eyeprocess_validation_evidence", "title": "Freeze a complete Milestone #2 evidence bundle", "topics": [ "freeze_eyeprocess_validation_evidence" ] }, { "page": "freeze_software_paper_evidence", "title": "Freeze software-paper evidence to an RDS with a hash", "topics": [ "freeze_software_paper_evidence" ] }, { "page": "freeze_validation_reference", "title": "Freeze a compact validation reference for regression testing", "topics": [ "freeze_validation_reference" ] }, { "page": "functional_pupil_basis", "title": "Construct a functional basis for pupil trajectories", "topics": [ "functional_pupil_basis" ] }, { "page": "functional_pupil_diagnostics", "title": "Diagnose functional pupil model and preprocessing quality", "topics": [ "functional_pupil_diagnostics" ] }, { "page": "functional_pupil_irt_spec", "title": "Specify a functional pupil-IRT model", "topics": [ "functional_pupil_irt_spec" ] }, { "page": "gaze_anchored_3pl_alignment", "title": "Return the process-alignment table from a gaze-anchored 3PL audit", "topics": [ "gaze_anchored_3pl_alignment" ] }, { "page": "gaze_data_quality_profile", "title": "Empirical gaze data-quality profile", "topics": [ "gaze_data_quality_profile" ] }, { "page": "gaze_diffusion_spec", "title": "Define a gaze-informed diffusion model", "topics": [ "gaze_diffusion_spec" ] }, { "page": "gaze_precision_rms_s2s", "title": "Estimate RMS successive-sample gaze imprecision", "topics": [ "gaze_precision_rms_s2s" ] }, { "page": "039-recurrence-analysis", "title": "Recurrence and cross-recurrence analysis", "topics": [ "cross_recurrence", "gaze_recurrence", "plot.eye_cross_recurrence", "plot.eye_recurrence", "plot.eye_windowed_recurrence", "plot_crossmodal_recurrence", "plot_diagonal_recurrence_profile", "plot_recurrence_matrix", "plot_recurrence_network", "plot_windowed_recurrence", "recurrence_features", "windowed_recurrence" ] }, { "page": "gaze_uncertainty_ellipse", "title": "Uncertainty ellipse implied by an empirical calibration-error model", "topics": [ "gaze_uncertainty_ellipse" ] }, { "page": "gaze-survival", "title": "Censored Gaze-Latency Survival Analysis", "topics": [ "check_gaze_proportional_hazards", "compare_gaze_survival_models", "compare_gaze_survival_specifications", "estimate_gaze_latency_quantiles", "estimate_gaze_survival", "fit_gaze_aft_model", "fit_gaze_cox_model", "fit_gaze_mixed_cox_model", "plot_gaze_cox_diagnostics", "plot_gaze_cumulative_incidence", "plot_gaze_hazard", "plot_gaze_survival_curve", "predict_gaze_survival", "prepare_gaze_survival_data", "report_gaze_survival_model", "simulate_gaze_survival_example", "simulate_gaze_survival_inputs", "summarise_gaze_censoring", "tidy_gaze_survival_model", "validate_gaze_survival_data" ] }, { "page": "gazepoint_analysis_tables", "title": "Build person-by-item-by-trial analysis tables", "topics": [ "gazepoint_analysis_tables" ] }, { "page": "gazepoint_irt_tables", "title": "Create IRT-ready response and process tables", "topics": [ "gazepoint_irt_tables" ] }, { "page": "gazepoint_workflow_spec", "title": "Specify an integrated Gazepoint downstream workflow", "topics": [ "gazepoint_workflow_spec" ] }, { "page": "generalizability_process_study", "title": "Generalizability-style variance decomposition for a process measure", "topics": [ "generalizability_process_study" ] }, { "page": "get_irt_model", "title": "Retrieve a registered multimodal IRT model", "topics": [ "get_irt_model" ] }, { "page": "grade_model_evidence", "title": "Grade model evidence against an explicit validation contract", "topics": [ "grade_model_evidence" ] }, { "page": "grouped_cv", "title": "Evaluate a model with grouped cross-validation", "topics": [ "grouped_cv" ] }, { "page": "grouped_folds", "title": "Create grouped cross-validation folds", "topics": [ "grouped_folds" ] }, { "page": "import_benchmark_study", "title": "Build an eye dataset from the public benchmark", "topics": [ "import_benchmark_study" ] }, { "page": "import_eye_bids", "title": "Import Eye-Tracking-BIDS physiological recordings", "topics": [ "import_eye_bids" ] }, { "page": "incremental_process_validity", "title": "Extract incremental process validity", "topics": [ "incremental_process_validity" ] }, { "page": "init_vendor_corpus", "title": "Create or validate a multi-vendor corpus directory", "topics": [ "init_vendor_corpus" ] }, { "page": "inject_aoi_label_noise", "title": "Inject AOI label noise", "topics": [ "inject_aoi_label_noise" ] }, { "page": "inject_calibration_offset", "title": "Inject additive gaze calibration offset in coordinate units", "topics": [ "inject_calibration_offset" ] }, { "page": "inject_device_shift", "title": "Inject an additive device/site shift in a numeric feature", "topics": [ "inject_device_shift" ] }, { "page": "inject_eye_missingness", "title": "Inject generic missingness into selected columns", "topics": [ "inject_eye_missingness" ] }, { "page": "inject_pupil_dropout", "title": "Inject pupil dropout", "topics": [ "inject_pupil_dropout" ] }, { "page": "inject_sampling_jitter", "title": "Inject timestamp jitter", "topics": [ "inject_sampling_jitter" ] }, { "page": "inject_trial_imbalance", "title": "Inject trial/row imbalance by dropping observations", "topics": [ "inject_trial_imbalance" ] }, { "page": "irt_compositional_channel", "title": "Compositional AOI channel", "topics": [ "irt_compositional_channel" ] }, { "page": "irt_continuous_channel", "title": "Continuous/bounded process channel for multimodal IRT", "topics": [ "irt_continuous_channel" ] }, { "page": "irt_count_channel", "title": "Count-valued process channel for multimodal IRT", "topics": [ "irt_count_channel" ] }, { "page": "irt_functional_channel", "title": "Functional trajectory channel", "topics": [ "irt_functional_channel" ] }, { "page": "irt_model_spec", "title": "Define a multimodal IRT model specification", "topics": [ "irt_model_spec" ] }, { "page": "irt_nominal_channel", "title": "Nominal response/process channel", "topics": [ "irt_nominal_channel" ] }, { "page": "irt_response_channel", "title": "Binary/ordinal response channel for multimodal IRT", "topics": [ "irt_response_channel" ] }, { "page": "irt_rt_channel", "title": "Response-time channel for multimodal IRT", "topics": [ "irt_rt_channel" ] }, { "page": "irt_sequence_channel", "title": "Sequence/process-state channel", "topics": [ "irt_sequence_channel" ] }, { "page": "irt_survival_channel", "title": "Survival/event-time channel for multimodal IRT", "topics": [ "irt_survival_channel" ] }, { "page": "irt_validation_spec", "title": "Specify a validation programme for a process-IRT model", "topics": [ "irt_validation_spec" ] }, { "page": "043-item-bank-optimization", "title": "Multi-objective item-bank optimization", "topics": [ "audit_bank_decision_stability", "item_objective_spec", "item_pareto_front", "optimize_item_bank", "plot.eye_bank_decision_stability", "plot.eye_item_bank_optimization", "plot.eye_item_pareto", "plot_bank_information_coverage", "plot_decision_stability", "plot_item_pareto", "plot_objective_tradeoffs", "plot_selected_bank_profile" ] }, { "page": "latent_distribution_stress_test", "title": "Stress-test IRT estimators across latent distributions", "topics": [ "latent_distribution_stress_test" ] }, { "page": "latent_trait_trajectory", "title": "Estimate a descriptive continuous-time latent trajectory", "topics": [ "latent_trait_trajectory" ] }, { "page": "leave_device_out_validation", "title": "Leave device out validation", "topics": [ "leave_device_out_validation" ] }, { "page": "leave_item_out_validation", "title": "Leave item out validation", "topics": [ "leave_item_out_validation" ] }, { "page": "leave_session_out_validation", "title": "Leave session out validation", "topics": [ "leave_session_out_validation" ] }, { "page": "leave_site_out_validation", "title": "Leave site out validation", "topics": [ "leave_site_out_validation" ] }, { "page": "list_irt_models", "title": "List registered multimodal IRT models", "topics": [ "list_irt_models" ] }, { "page": "lock_decision_manifest", "title": "Lock a decision manifest by content hash", "topics": [ "lock_decision_manifest" ] }, { "page": "map_latent_classes_to_process_profiles", "title": "Map supplied latent-class memberships to process summaries", "topics": [ "map_latent_classes_to_process_profiles" ] }, { "page": "measurement_error_budget", "title": "Build a non-collapsed measurement-error budget", "topics": [ "measurement_error_budget" ] }, { "page": "migrate_eye_storage_schema", "title": "Migrate a storage schema through an atomic rewrite", "topics": [ "migrate_eye_storage_schema" ] }, { "page": "model_promotion_spec", "title": "Specify evidence gates for model promotion", "topics": [ "model_promotion_spec" ] }, { "page": "model_validation_spec", "title": "Specify a model-validation programme", "topics": [ "model_validation_spec" ] }, { "page": "model_validation_summary", "title": "Summarize model validation", "topics": [ "model_validation_summary" ] }, { "page": "multiblock_contributions", "title": "Extract multiblock block contributions/coordinates", "topics": [ "multiblock_contributions" ] }, { "page": "multiblock_person_coordinates", "title": "Extract multiblock person coordinates", "topics": [ "multiblock_person_coordinates" ] }, { "page": "multiblock_variable_coordinates", "title": "Extract multiblock variable coordinates", "topics": [ "multiblock_variable_coordinates" ] }, { "page": "multilevel-mediation", "title": "Trial-level multilevel mediation preparation", "topics": [ "add_multilevel_mediation_component", "audit_mediation_missingness", "center_within_participant", "check_mediation_trial_counts", "decompose_within_between", "identify_mediation_levels", "multilevel-mediation", "prepare_multilevel_mediation_data", "summarise_within_between_variance", "validate_multilevel_mediation_data" ] }, { "page": "multimodal_backend_status", "title": "Report multimodal backend availability", "topics": [ "multimodal_backend_status" ] }, { "page": "multimodal_irt_spec", "title": "Consolidated multimodal IRT specification", "topics": [ "multimodal_irt_spec" ] }, { "page": "multimodal_m2_ablation", "title": "Fit M0, M1, and M2 as a response-target ablation sequence", "topics": [ "multimodal_m2_ablation" ] }, { "page": "multimodal_m2_negative_controls", "title": "Generate M2 alignment negative controls", "topics": [ "multimodal_m2_negative_controls" ] }, { "page": "multimodal_m2_ppc", "title": "Posterior predictive checks for the M2 three-way model", "topics": [ "multimodal_m2_ppc" ] }, { "page": "multimodal_m2_process_information", "title": "Quantify response-target process information in the M0-M2 sequence", "topics": [ "multimodal_m2_process_information" ] }, { "page": "multimodal_m2_recovery", "title": "Run repeated M2 estimator recovery", "topics": [ "multimodal_m2_recovery" ] }, { "page": "multimodal_m2_spec", "title": "M2 response + RT + gaze reference specification", "topics": [ "multimodal_m2_spec" ] }, { "page": "multimodal_m3_ablation", "title": "Fit the complete M3 response-anchored channel-ablation lattice", "topics": [ "multimodal_m3_ablation" ] }, { "page": "multimodal_m3_functional_bridge", "title": "Bridge existing functional pupil outputs into the scalar M3 reference layer", "topics": [ "multimodal_m3_functional_bridge" ] }, { "page": "multimodal_m3_negative_controls", "title": "Generate M3 multimodal falsification controls", "topics": [ "multimodal_m3_negative_controls" ] }, { "page": "multimodal_m3_ppc", "title": "Posterior predictive checks for the M3 four-channel model", "topics": [ "multimodal_m3_ppc" ] }, { "page": "multimodal_m3_process_information", "title": "Quantify M3 process information, pupil increment, redundancy and sensor value", "topics": [ "multimodal_m3_process_information" ] }, { "page": "multimodal_m3_recovery", "title": "Run M3 parameter-recovery and stress evidence", "topics": [ "multimodal_m3_recovery" ] }, { "page": "multimodal_m3_spec", "title": "M3 response + RT + gaze + pupil specification", "topics": [ "multimodal_m3_spec" ] }, { "page": "multimodal_m4_ablation", "title": "Plan or fit the focused M3-to-M4 ablation set", "topics": [ "multimodal_m4_ablation" ] }, { "page": "multimodal_m4_negative_controls", "title": "Construct M4 temporal, nuisance, device, and overfitting negative controls", "topics": [ "multimodal_m4_negative_controls" ] }, { "page": "multimodal_m4_ppc", "title": "Posterior predictive checks for M4 measurement and sequential behavior", "topics": [ "multimodal_m4_ppc" ] }, { "page": "multimodal_m4_process_information", "title": "Quantify incremental response-target information supplied by M4 state structure", "topics": [ "multimodal_m4_process_information" ] }, { "page": "multimodal_m4_recovery", "title": "Evaluate deterministic M4 parameter and state recovery", "topics": [ "multimodal_m4_recovery" ] }, { "page": "multimodal_m4_sensitivity", "title": "Plan or run M4 state-count and modelling sensitivity analyses", "topics": [ "multimodal_m4_sensitivity" ] }, { "page": "multimodal_m4_spec", "title": "Specify M4 trait-conditioned latent response-process states", "topics": [ "multimodal_m4_spec" ] }, { "page": "multimodal_m4_state_diagnostics", "title": "Summarize M4 latent-state uncertainty and dynamics", "topics": [ "multimodal_m4_state_diagnostics" ] }, { "page": "multimodal_ppc", "title": "Posterior predictive checks for multimodal development fits", "topics": [ "multimodal_ppc" ] }, { "page": "negative_control_concordance", "title": "Concordance of multiple negative-control families", "topics": [ "negative_control_concordance" ] }, { "page": "negative_control_process_test", "title": "Negative-control test for an allegedly informative process channel", "topics": [ "negative_control_process_test" ] }, { "page": "object_hash", "title": "Hash an R object reproducibly within an R serialization version", "topics": [ "object_hash" ] }, { "page": "object_schema", "title": "Describe a stable object schema", "topics": [ "object_schema" ] }, { "page": "open_eye_storage", "title": "Open an eyeprocess storage handle", "topics": [ "open_eye_storage" ] }, { "page": "open_partitioned_eye_storage", "title": "Open partitioned eye storage", "topics": [ "open_partitioned_eye_storage" ] }, { "page": "option_process_information", "title": "Quantify option-process information from a nominal gaze model", "topics": [ "option_process_information" ] }, { "page": "outcome_blind_feature_audit", "title": "Audit whether candidate predictors can be constructed without an outcome column", "topics": [ "outcome_blind_feature_audit" ] }, { "page": "outcome_blind_snapshot", "title": "Create an outcome-blind data snapshot", "topics": [ "outcome_blind_snapshot" ] }, { "page": "package_reproducibility_manifest", "title": "Create a reproducibility manifest for files and software", "topics": [ "package_reproducibility_manifest" ] }, { "page": "paper_reproducibility_manifest", "title": "Create a compact paper reproducibility manifest", "topics": [ "paper_reproducibility_manifest" ] }, { "page": "partition_eye_storage", "title": "Create a partition specification", "topics": [ "partition_eye_storage" ] }, { "page": "pipeline_failures", "title": "Return failed pipeline steps", "topics": [ "pipeline_failures" ] }, { "page": "pipeline_result", "title": "Extract a pipeline result by step name", "topics": [ "pipeline_result" ] }, { "page": "pipeline_step_status", "title": "Pipeline step status table", "topics": [ "pipeline_step_status" ] }, { "page": "placebo_window_audit", "title": "Audit a placebo/pre-event window", "topics": [ "placebo_window_audit" ] }, { "page": "plot_aoi_transition_matrix", "title": "Plot an AOI transition matrix", "topics": [ "plot_aoi_transition_matrix" ] }, { "page": "plot_aoi_transition_rank", "title": "Plot top AOI transitions by probability/count", "topics": [ "plot_aoi_transition_rank" ] }, { "page": "plot_distractor_information", "title": "Plot option-level distractor information", "topics": [ "plot_distractor_information" ] }, { "page": "plot_gazepoint_workflow", "title": "Generate the complete Gazepoint workflow plot suite", "topics": [ "plot_gazepoint_workflow" ] }, { "page": "plot_interval_coverage", "title": "Plot interval coverage", "topics": [ "plot_interval_coverage" ] }, { "page": "plot_irf_uncertainty", "title": "Plot uncertainty for flexible item response functions", "topics": [ "plot_irf_uncertainty" ] }, { "page": "plot_parameter_recovery", "title": "Plot parameter recovery", "topics": [ "plot_parameter_recovery" ] }, { "page": "plot_person_item_space", "title": "Plot person/item latent-space coordinates", "topics": [ "plot_person_item_space" ] }, { "page": "plot_process_changepoint", "title": "Plot detected process change points", "topics": [ "plot_process_changepoint" ] }, { "page": "plot_process_channel_ablation_delta", "title": "Plot channel-ablation delta from a full/reference model", "topics": [ "plot_process_channel_ablation_delta" ] }, { "page": "plot_process_feature_stability", "title": "Plot process-feature stability across resamples/splits", "topics": [ "plot_process_feature_stability" ] }, { "page": "plot_process_window_sensitivity", "title": "Explicit wrapper for process-window sensitivity plotting", "topics": [ "plot_process_window_sensitivity" ] }, { "page": "plot_pupil_activity_sensitivity", "title": "Plot pupil activity sensitivity to window length", "topics": [ "plot_pupil_activity_sensitivity" ] }, { "page": "plot_pupil_activity_windows", "title": "Plot pupil activity features across windows/groups", "topics": [ "plot_pupil_activity_windows" ] }, { "page": "plot_pupil_band_power", "title": "Plot pupil low/high-band power summaries", "topics": [ "plot_pupil_band_power" ] }, { "page": "plot_pupil_components", "title": "Plot tonic/phasic pupil components", "topics": [ "plot_pupil_components" ] }, { "page": "plot_pupil_preprocessing_audit", "title": "Plot raw-to-processed pupil preprocessing stages", "topics": [ "plot_pupil_preprocessing_audit" ] }, { "page": "plot_pupil_spectrum", "title": "Plot a pupil-signal power spectrum", "topics": [ "plot_pupil_spectrum" ] }, { "page": "plot_sbc_rank", "title": "Plot SBC rank histograms", "topics": [ "plot_sbc_rank" ] }, { "page": "plot_validation_failures", "title": "Plot validation failure rates", "topics": [ "plot_validation_failures" ] }, { "page": "plot_validation_runtime", "title": "Plot validation runtime", "topics": [ "plot_validation_runtime" ] }, { "page": "plot.eye_adapter_regression_audit", "title": "Plot eye adapter regression audit", "topics": [ "plot.eye_adapter_regression_audit" ] }, { "page": "plot.eye_aoi_growth_curve", "title": "Plot aoi growth curve diagnostics", "topics": [ "plot.eye_aoi_growth_curve" ] }, { "page": "plot.eye_aoi_trajectory", "title": "Plot aoi trajectory diagnostics", "topics": [ "plot.eye_aoi_trajectory" ] }, { "page": "plot.eye_bayesian_process_dashboard", "title": "Plot bayesian process dashboard diagnostics", "topics": [ "plot.eye_bayesian_process_dashboard" ] }, { "page": "plot.eye_bids_roundtrip", "title": "Plot eye bids roundtrip", "topics": [ "plot.eye_bids_roundtrip" ] }, { "page": "plot.eye_biometric_imputation_sensitivity", "title": "Plot biometric imputation sensitivity diagnostics", "topics": [ "plot.eye_biometric_imputation_sensitivity" ] }, { "page": "plot.eye_biometric_preflight", "title": "Plot biometric preflight diagnostics", "topics": [ "plot.eye_biometric_preflight" ] }, { "page": "plot.eye_candidate_item_bank_audit", "title": "Plot candidate item bank audit diagnostics", "topics": [ "plot.eye_candidate_item_bank_audit" ] }, { "page": "plot.eye_compatibility_evidence_matrix", "title": "Plot detailed compatibility evidence", "topics": [ "plot.eye_compatibility_evidence_matrix" ] }, { "page": "plot.eye_decision_process_proxy", "title": "Plot decision process proxy diagnostics", "topics": [ "plot.eye_decision_process_proxy" ] }, { "page": "plot.eye_dynamic_irtree", "title": "plot eye dynamic irtree", "topics": [ "plot.eye_dynamic_irtree" ] }, { "page": "plot.eye_dynamic_ppc", "title": "plot eye dynamic ppc", "topics": [ "plot.eye_dynamic_ppc" ] }, { "page": "plot.eye_event_roundtrip_audit", "title": "Plot eye event roundtrip audit", "topics": [ "plot.eye_event_roundtrip_audit" ] }, { "page": "plot.eye_event_time_irt", "title": "Plot eye event time irt", "topics": [ "plot.eye_event_time_irt" ] }, { "page": "plot.eye_functional_pupil_diagnostics", "title": "plot eye functional pupil diagnostics", "topics": [ "plot.eye_functional_pupil_diagnostics" ] }, { "page": "plot.eye_functional_pupil_irt", "title": "plot eye functional pupil irt", "topics": [ "plot.eye_functional_pupil_irt" ] }, { "page": "plot.eye_functional_pupil_sensitivity", "title": "plot eye functional pupil sensitivity", "topics": [ "plot.eye_functional_pupil_sensitivity" ] }, { "page": "plot.eye_gated_process_model", "title": "Plot gated process model diagnostics", "topics": [ "plot.eye_gated_process_model" ] }, { "page": "plot.eye_gaze_anchored_3pl_audit", "title": "Plot gaze anchored 3pl audit diagnostics", "topics": [ "plot.eye_gaze_anchored_3pl_audit" ] }, { "page": "plot.eye_gaze_informed_missingness_irt", "title": "Plot eye gaze informed missingness irt", "topics": [ "plot.eye_gaze_informed_missingness_irt" ] }, { "page": "plot.eye_gpirt", "title": "Plot flexible IRF shape diagnostics", "topics": [ "plot.eye_gpirt" ] }, { "page": "plot.eye_incremental_information_audit", "title": "Plot incremental process-channel information by fold", "topics": [ "plot.eye_incremental_information_audit" ] }, { "page": "plot.eye_irt_changepoints", "title": "Plot detected process changepoints", "topics": [ "plot.eye_irt_changepoints" ] }, { "page": "plot.eye_irt_equating", "title": "Plot an IRT linking/equating transformation", "topics": [ "plot.eye_irt_equating" ] }, { "page": "plot.eye_irt_ppc", "title": "Plot posterior predictive discrepancy tail probabilities", "topics": [ "plot.eye_irt_ppc" ] }, { "page": "plot.eye_irt_recovery_summary", "title": "Plot parameter-recovery bias or RMSE", "topics": [ "plot.eye_irt_recovery_summary" ] }, { "page": "plot.eye_irt_sbc", "title": "Plot SBC rank histograms by parameter", "topics": [ "plot.eye_irt_sbc" ] }, { "page": "plot.eye_item_parameter_seed", "title": "Plot item parameter seed diagnostics", "topics": [ "plot.eye_item_parameter_seed" ] }, { "page": "plot.eye_item_reduction_sensitivity", "title": "Plot item reduction sensitivity diagnostics", "topics": [ "plot.eye_item_reduction_sensitivity" ] }, { "page": "plot.eye_joint_gaze_rt_irt", "title": "Plot a joint gaze-response-time IRT fit", "topics": [ "plot.eye_joint_gaze_rt_irt" ] }, { "page": "plot.eye_joint_graded_rt_process_irt", "title": "Plot a graded response + RT/process fit", "topics": [ "plot.eye_joint_graded_rt_process_irt" ] }, { "page": "plot.eye_latent_distribution_comparison", "title": "Plot eye latent distribution comparison", "topics": [ "plot.eye_latent_distribution_comparison" ] }, { "page": "plot.eye_latent_process_alignment", "title": "Plot latent process alignment diagnostics", "topics": [ "plot.eye_latent_process_alignment" ] }, { "page": "plot.eye_latent_space_irt", "title": "Plot a latent-space IRT adapter fit", "topics": [ "plot.eye_latent_space_irt" ] }, { "page": "plot.eye_manyfacet_process_irt", "title": "Plot many-facet process IRT effects", "topics": [ "plot.eye_manyfacet_process_irt" ] }, { "page": "plot.eye_mixture_irt_process", "title": "Plot mixture irt process diagnostics", "topics": [ "plot.eye_mixture_irt_process" ] }, { "page": "plot.eye_model_promotion_audit", "title": "plot eye model promotion audit", "topics": [ "plot.eye_model_promotion_audit" ] }, { "page": "plot.eye_multiblock_process_map", "title": "Plot multiblock process map diagnostics", "topics": [ "plot.eye_multiblock_process_map" ] }, { "page": "plot.eye_nominal_gaze_irt", "title": "Plot nominal-response gaze results", "topics": [ "plot.eye_nominal_gaze_irt" ] }, { "page": "plot.eye_nonparametric_rasch_audit", "title": "Plot nonparametric rasch audit diagnostics", "topics": [ "plot.eye_nonparametric_rasch_audit" ] }, { "page": "plot.eye_omission_survival_irt", "title": "Plot omission/not-reached survival IRT diagnostics", "topics": [ "plot.eye_omission_survival_irt" ] }, { "page": "plot.eye_preaction_process_features", "title": "Plot preaction process features diagnostics", "topics": [ "plot.eye_preaction_process_features" ] }, { "page": "plot.eye_presentation_accessibility", "title": "Plot presentation accessibility diagnostics", "topics": [ "plot.eye_presentation_accessibility" ] }, { "page": "plot.eye_presentation_fairness_comparison", "title": "Plot presentation fairness comparison diagnostics", "topics": [ "plot.eye_presentation_fairness_comparison" ] }, { "page": "plot.eye_process_anomaly_audit", "title": "Plot process anomaly audit diagnostics", "topics": [ "plot.eye_process_anomaly_audit" ] }, { "page": "plot.eye_process_cat_simulation", "title": "Plot CAT simulation information accumulation", "topics": [ "plot.eye_process_cat_simulation" ] }, { "page": "plot.eye_process_channel_ablation", "title": "Plot process-channel ablation", "topics": [ "plot.eye_process_channel_ablation" ] }, { "page": "plot.eye_process_dependent_discrimination", "title": "Plot process-dependent discrimination", "topics": [ "plot.eye_process_dependent_discrimination" ] }, { "page": "plot.eye_process_drift_audit", "title": "Plot process drift audit diagnostics", "topics": [ "plot.eye_process_drift_audit" ] }, { "page": "plot.eye_process_external_validity", "title": "Plot process external validity diagnostics", "topics": [ "plot.eye_process_external_validity" ] }, { "page": "plot.eye_process_facet_effects", "title": "Plot eye process facet effects", "topics": [ "plot.eye_process_facet_effects" ] }, { "page": "plot.eye_process_feature_blocks", "title": "Plot process feature blocks diagnostics", "topics": [ "plot.eye_process_feature_blocks" ] }, { "page": "plot.eye_process_g_study", "title": "Plot process-measure variance components", "topics": [ "plot.eye_process_g_study" ] }, { "page": "plot.eye_process_hmm_irt", "title": "Plot a process-HMM IRT fit", "topics": [ "plot.eye_process_hmm_irt" ] }, { "page": "plot.eye_process_local_dependence_audit", "title": "Plot process/local-dependence diagnostics", "topics": [ "plot.eye_process_local_dependence_audit" ] }, { "page": "plot.eye_process_negative_control", "title": "Plot a process-channel negative-control distribution", "topics": [ "plot.eye_process_negative_control" ] }, { "page": "plot.eye_process_person_fit", "title": "Plot process person-fit discrepancies", "topics": [ "plot.eye_process_person_fit" ] }, { "page": "plot.eye_process_profile_mixture", "title": "Plot process profile mixture diagnostics", "topics": [ "plot.eye_process_profile_mixture" ] }, { "page": "plot.eye_process_rasch_tree", "title": "Plot process rasch tree diagnostics", "topics": [ "plot.eye_process_rasch_tree" ] }, { "page": "plot.eye_process_window_sensitivity", "title": "Plot process window sensitivity diagnostics", "topics": [ "plot.eye_process_window_sensitivity" ] }, { "page": "plot.eye_process_windows", "title": "Plot process windows diagnostics", "topics": [ "plot.eye_process_windows" ] }, { "page": "plot.eye_pupil_confound_model", "title": "Plot pupil confound model diagnostics", "topics": [ "plot.eye_pupil_confound_model" ] }, { "page": "plot.eye_pupil_deconvolution", "title": "Plot pupil deconvolution diagnostics", "topics": [ "plot.eye_pupil_deconvolution" ] }, { "page": "plot.eye_pupil_fatigue_drift", "title": "Plot pupil fatigue drift diagnostics", "topics": [ "plot.eye_pupil_fatigue_drift" ] }, { "page": "plot.eye_pupil_frequency_features", "title": "Plot pupil frequency features diagnostics", "topics": [ "plot.eye_pupil_frequency_features" ] }, { "page": "plot.eye_pupil_frequency_stability", "title": "Plot pupil frequency stability diagnostics", "topics": [ "plot.eye_pupil_frequency_stability" ] }, { "page": "plot.eye_roundtrip_loss_audit", "title": "plot eye roundtrip loss audit", "topics": [ "plot.eye_roundtrip_loss_audit" ] }, { "page": "plot.eye_sbc_audit", "title": "Plot SBC audit summaries", "topics": [ "plot.eye_sbc_audit" ] }, { "page": "plot.eye_semantic_roundtrip", "title": "Plot semantic round-trip fidelity", "topics": [ "plot.eye_semantic_roundtrip" ] }, { "page": "plot.eye_signal_filter_audit", "title": "Plot signal filter audit diagnostics", "topics": [ "plot.eye_signal_filter_audit" ] }, { "page": "plot.eye_storage_benchmark", "title": "plot eye storage benchmark", "topics": [ "plot.eye_storage_benchmark" ] }, { "page": "plot.eye_strategy_aoi_sensitivity", "title": "plot eye strategy aoi sensitivity", "topics": [ "plot.eye_strategy_aoi_sensitivity" ] }, { "page": "plot.eye_streaming_score", "title": "Plot streaming score diagnostics", "topics": [ "plot.eye_streaming_score" ] }, { "page": "plot.eye_transition_diagnostics", "title": "plot eye transition diagnostics", "topics": [ "plot.eye_transition_diagnostics" ] }, { "page": "plot.eye_validation_bundle", "title": "Plot validation bundle diagnostics", "topics": [ "plot.eye_validation_bundle" ] }, { "page": "plot.eye_vendor_compatibility_matrix", "title": "plot eye vendor compatibility matrix", "topics": [ "plot.eye_vendor_compatibility_matrix" ] }, { "page": "plot.eye_vendor_semantic_validation", "title": "Plot eye vendor semantic validation", "topics": [ "plot.eye_vendor_semantic_validation" ] }, { "page": "plot.eye_visual_context_irt", "title": "Plot visual context irt diagnostics", "topics": [ "plot.eye_visual_context_irt" ] }, { "page": "posterior_predictive_discrepancies", "title": "Posterior predictive discrepancy table", "topics": [ "posterior_predictive_discrepancies" ] }, { "page": "posterior_sbc_contract", "title": "Define a posterior-SBC replication contract", "topics": [ "posterior_sbc_contract" ] }, { "page": "preaction_process_features", "title": "Build pre-action process features", "topics": [ "preaction_process_features" ] }, { "page": "predict_aoi_trajectory", "title": "Predict from an AOI growth curve", "topics": [ "predict_aoi_trajectory" ] }, { "page": "predict_item_parameter_priors", "title": "Predict pre-pilot item-parameter priors", "topics": [ "predict_item_parameter_priors" ] }, { "page": "predict_theta_at_time", "title": "Predict a latent trait at arbitrary times", "topics": [ "predict_theta_at_time" ] }, { "page": "predict.eye_censored_normal_process_irt", "title": "Predict expected bounded response from a censored-normal process IRT fit", "topics": [ "predict.eye_censored_normal_process_irt" ] }, { "page": "predict.eye_multinomial_transition", "title": "Predict destination-state probabilities", "topics": [ "predict.eye_multinomial_transition" ] }, { "page": "preflight_decisions", "title": "Extract pre-flight decisions", "topics": [ "preflight_decisions" ] }, { "page": "preflight_exclusion_manifest", "title": "Create an explicit pre-flight exclusion/review manifest", "topics": [ "preflight_exclusion_manifest" ] }, { "page": "preflight_failures", "title": "Extract pre-flight failures/review cases", "topics": [ "preflight_failures" ] }, { "page": "preflight_passed", "title": "Extract pre-flight passes", "topics": [ "preflight_passed" ] }, { "page": "prepare_dynamic_irtree_data", "title": "Prepare ordered transition data for dynamic IRTree models", "topics": [ "prepare_dynamic_irtree_data" ] }, { "page": "prepare_functional_pupil_data", "title": "Prepare aligned, corrected functional pupil data", "topics": [ "prepare_functional_pupil_data" ] }, { "page": "prepare_gaze_diffusion_data", "title": "Prepare joint accuracy-response-time data", "topics": [ "prepare_gaze_diffusion_data" ] }, { "page": "prepare_multimodal_irt_data", "title": "Prepare a canonical multimodal person-item-trial measurement object", "topics": [ "prepare_multimodal_irt_data" ] }, { "page": "prepare_strategy_mixture_data", "title": "Prepare data for a strategy-mixture model", "topics": [ "prepare_strategy_mixture_data" ] }, { "page": "prepare_structured_unstructured_process_features", "title": "Prepare leakage-safe structured/unstructured process representations", "topics": [ "prepare_structured_unstructured_process_features" ] }, { "page": "preprocessing_multiverse", "title": "Run a preprocessing or AOI multiverse", "topics": [ "preprocessing_multiverse" ] }, { "page": "print.eye_benchmark_reproduction", "title": "print eye benchmark reproduction", "topics": [ "print.eye_benchmark_reproduction" ] }, { "page": "print.eye_benchmark_study", "title": "print eye benchmark study", "topics": [ "print.eye_benchmark_study" ] }, { "page": "print.eye_benchmark_validation", "title": "print eye benchmark validation", "topics": [ "print.eye_benchmark_validation" ] }, { "page": "print.eye_diffusion_diagnostics", "title": "print eye diffusion diagnostics", "topics": [ "print.eye_diffusion_diagnostics" ] }, { "page": "print.eye_diffusion_identification_study", "title": "print eye diffusion identification study", "topics": [ "print.eye_diffusion_identification_study" ] }, { "page": "print.eye_dynamic_irtree", "title": "print eye dynamic irtree", "topics": [ "print.eye_dynamic_irtree" ] }, { "page": "print.eye_dynamic_ppc", "title": "print eye dynamic ppc", "topics": [ "print.eye_dynamic_ppc" ] }, { "page": "print.eye_dynamic_recovery", "title": "print eye dynamic recovery", "topics": [ "print.eye_dynamic_recovery" ] }, { "page": "print.eye_engine_adapter_result", "title": "print eye engine adapter result", "topics": [ "print.eye_engine_adapter_result" ] }, { "page": "print.eye_functional_pupil_data", "title": "print eye functional pupil data", "topics": [ "print.eye_functional_pupil_data" ] }, { "page": "print.eye_functional_pupil_diagnostics", "title": "print eye functional pupil diagnostics", "topics": [ "print.eye_functional_pupil_diagnostics" ] }, { "page": "print.eye_functional_pupil_irt", "title": "print eye functional pupil irt", "topics": [ "print.eye_functional_pupil_irt" ] }, { "page": "print.eye_functional_pupil_sensitivity", "title": "print eye functional pupil sensitivity", "topics": [ "print.eye_functional_pupil_sensitivity" ] }, { "page": "print.eye_functional_scalar_comparison", "title": "print eye functional scalar comparison", "topics": [ "print.eye_functional_scalar_comparison" ] }, { "page": "print.eye_gated_process_model", "title": "Print a gated process model object", "topics": [ "print.eye_gated_process_model" ] }, { "page": "print.eye_gaze_diffusion_data", "title": "print eye gaze diffusion data", "topics": [ "print.eye_gaze_diffusion_data" ] }, { "page": "print.eye_gaze_diffusion_irt", "title": "print eye gaze diffusion irt", "topics": [ "print.eye_gaze_diffusion_irt" ] }, { "page": "print.eye_gaze_diffusion_spec", "title": "print eye gaze diffusion spec", "topics": [ "print.eye_gaze_diffusion_spec" ] }, { "page": "print.eye_irt_evidence_grade", "title": "Print eye irt evidence grade", "topics": [ "print.eye_irt_evidence_grade" ] }, { "page": "print.eye_irt_model_spec", "title": "Print a multimodal IRT model specification", "topics": [ "print.eye_irt_model_spec" ] }, { "page": "print.eye_irt_validation_spec", "title": "Print eye irt validation spec", "topics": [ "print.eye_irt_validation_spec" ] }, { "page": "print.eye_model_contract_validation", "title": "print eye model contract validation", "topics": [ "print.eye_model_contract_validation" ] }, { "page": "print.eye_model_promotion_audit", "title": "print eye model promotion audit", "topics": [ "print.eye_model_promotion_audit" ] }, { "page": "print.eye_multinomial_transition", "title": "print eye multinomial transition", "topics": [ "print.eye_multinomial_transition" ] }, { "page": "print.eye_partition_spec", "title": "print eye partition spec", "topics": [ "print.eye_partition_spec" ] }, { "page": "print.eye_partitioned_storage", "title": "print eye partitioned storage", "topics": [ "print.eye_partitioned_storage" ] }, { "page": "print.eye_process_drift_spec", "title": "Print a process drift spec object", "topics": [ "print.eye_process_drift_spec" ] }, { "page": "print.eye_process_negative_control", "title": "Print eye process negative control", "topics": [ "print.eye_process_negative_control" ] }, { "page": "print.eye_process_preflight_spec", "title": "Print a process preflight spec object", "topics": [ "print.eye_process_preflight_spec" ] }, { "page": "print.eye_process_window_spec", "title": "Print a process window spec object", "topics": [ "print.eye_process_window_spec" ] }, { "page": "print.eye_redaction_result", "title": "print eye redaction result", "topics": [ "print.eye_redaction_result" ] }, { "page": "print.eye_roundtrip_loss_audit", "title": "print eye roundtrip loss audit", "topics": [ "print.eye_roundtrip_loss_audit" ] }, { "page": "print.eye_strategy_data", "title": "print eye strategy data", "topics": [ "print.eye_strategy_data" ] }, { "page": "print.eye_strategy_manipulation_validation", "title": "print eye strategy manipulation validation", "topics": [ "print.eye_strategy_manipulation_validation" ] }, { "page": "print.eye_theory_strategy_irt", "title": "print eye theory strategy irt", "topics": [ "print.eye_theory_strategy_irt" ] }, { "page": "print.eye_theory_strategy_spec", "title": "print eye theory strategy spec", "topics": [ "print.eye_theory_strategy_spec" ] }, { "page": "print.eye_transition_design", "title": "print eye transition design", "topics": [ "print.eye_transition_design" ] }, { "page": "print.eye_transition_diagnostics", "title": "print eye transition diagnostics", "topics": [ "print.eye_transition_diagnostics" ] }, { "page": "print.eye_validation_bundle", "title": "Print a validation bundle object", "topics": [ "print.eye_validation_bundle" ] }, { "page": "print.eye_validation_case_fingerprint", "title": "print eye validation case fingerprint", "topics": [ "print.eye_validation_case_fingerprint" ] }, { "page": "print.eye_validation_collection", "title": "print eye validation collection", "topics": [ "print.eye_validation_collection" ] }, { "page": "print.eye_validation_completion_audit", "title": "print eye validation completion audit", "topics": [ "print.eye_validation_completion_audit" ] }, { "page": "print.eye_validation_job_plan", "title": "print eye validation job plan", "topics": [ "print.eye_validation_job_plan" ] }, { "page": "print.eye_validation_run", "title": "print eye validation run", "topics": [ "print.eye_validation_run" ] }, { "page": "print.eye_vendor_case", "title": "print eye vendor case", "topics": [ "print.eye_vendor_case" ] }, { "page": "print.eye_vendor_compatibility_matrix", "title": "print eye vendor compatibility matrix", "topics": [ "print.eye_vendor_compatibility_matrix" ] }, { "page": "print.eye_vendor_schema_contract", "title": "Print eye vendor schema contract", "topics": [ "print.eye_vendor_schema_contract" ] }, { "page": "print.eye_vendor_semantic_comparison", "title": "print eye vendor semantic comparison", "topics": [ "print.eye_vendor_semantic_comparison" ] }, { "page": "print.eye_visual_context_registry", "title": "Print a visual context registry object", "topics": [ "print.eye_visual_context_registry" ] }, { "page": "probabilistic_aoi_assignment", "title": "Probabilistic AOI assignment under empirical calibration uncertainty", "topics": [ "probabilistic_aoi_assignment" ] }, { "page": "process_anomaly_distance", "title": "Extract multivariate process anomaly distances", "topics": [ "process_anomaly_distance" ] }, { "page": "process_bland_altman", "title": "Bland-Altman repeatability summary for two sessions", "topics": [ "process_bland_altman" ] }, { "page": "process_channel_ablation", "title": "Ablate process channels under a common out-of-sample evaluator", "topics": [ "process_channel_ablation" ] }, { "page": "process_criterion_associations", "title": "Extract process-criterion associations", "topics": [ "process_criterion_associations" ] }, { "page": "process_dependent_discrimination_audit", "title": "Audit process-dependent item discrimination", "topics": [ "process_dependent_discrimination_audit" ] }, { "page": "process_dif_nuisance_surrogate", "title": "Construct a process-data nuisance surrogate for DIF analysis", "topics": [ "process_dif_nuisance_surrogate" ] }, { "page": "process_drift_alerts", "title": "Extract drift alerts", "topics": [ "process_drift_alerts" ] }, { "page": "process_drift_spec", "title": "Specify a process-deployment drift audit", "topics": [ "process_drift_spec" ] }, { "page": "process_feature_blocks", "title": "Define conceptual process-feature blocks", "topics": [ "process_feature_blocks" ] }, { "page": "process_feature_family_registry", "title": "Registry of process-feature families and interpretation guardrails", "topics": [ "process_feature_family_registry" ] }, { "page": "process_feature_stability", "title": "Summarize process-feature stability across repeated analyses", "topics": [ "process_feature_stability" ] }, { "page": "process_feature_time_provenance", "title": "Declare temporal provenance for process features", "topics": [ "process_feature_time_provenance" ] }, { "page": "process_icc", "title": "Absolute-agreement ICC(A,1) for repeated process measures", "topics": [ "process_icc" ] }, { "page": "process_information", "title": "Quantify incremental process information", "topics": [ "process_information" ] }, { "page": "process_item_information", "title": "2PL response item information", "topics": [ "process_item_information" ] }, { "page": "process_measure_card", "title": "Return a one-measure process card", "topics": [ "process_measure_card" ] }, { "page": "process_measure_coverage", "title": "Process-measure coverage for an observed dataset", "topics": [ "process_measure_coverage" ] }, { "page": "process_measure_guardrails", "title": "Process-measure guardrail table", "topics": [ "process_measure_guardrails" ] }, { "page": "process_measure_lineage", "title": "Process-measure lineage table", "topics": [ "process_measure_lineage" ] }, { "page": "process_measure_registry", "title": "Unified process-measure registry", "topics": [ "process_measure_registry" ] }, { "page": "process_measure_units", "title": "List units used by registered process measures", "topics": [ "process_measure_units" ] }, { "page": "process_negative_control_permute", "title": "Permutation negative control", "topics": [ "process_negative_control_permute" ] }, { "page": "process_negative_control_shift", "title": "Temporal-shift negative control", "topics": [ "process_negative_control_shift" ] }, { "page": "process_ngram_features", "title": "N-gram features from process sequences", "topics": [ "process_ngram_features" ] }, { "page": "process_null_benchmark", "title": "Compare an observed effect against a negative-control null distribution", "topics": [ "process_null_benchmark" ] }, { "page": "process_person_fit", "title": "Joint response-process person-fit diagnostic", "topics": [ "process_person_fit" ] }, { "page": "process_preflight_spec", "title": "Specify a biometric process pre-flight gate", "topics": [ "process_preflight_spec" ] }, { "page": "process_profile_probabilities", "title": "Extract process-profile probabilities", "topics": [ "process_profile_probabilities" ] }, { "page": "process_profile_summary", "title": "Summarize process profiles", "topics": [ "process_profile_summary" ] }, { "page": "process_reliability_profile", "title": "Test-retest process reliability profile", "topics": [ "process_reliability_profile" ] }, { "page": "process_residual_map", "title": "Return person/item latent-space coordinates", "topics": [ "process_residual_map" ] }, { "page": "process_sensitivity_grid", "title": "Construct an explicit process-analysis sensitivity grid", "topics": [ "process_sensitivity_grid" ] }, { "page": "process_sequence_embedding", "title": "Low-dimensional embedding of response-process sequences", "topics": [ "process_sequence_embedding" ] }, { "page": "process_state_occupancy", "title": "Summarize HMM state occupancy", "topics": [ "process_state_occupancy" ] }, { "page": "process_state_transition_summary", "title": "Summarize HMM process-state transitions", "topics": [ "process_state_transition_summary" ] }, { "page": "process_temporal_stability", "title": "Pairwise temporal stability across sessions", "topics": [ "process_temporal_stability" ] }, { "page": "033-process-uncertainty", "title": "Process measurement-uncertainty budgets", "topics": [ "compare_uncertainty_budgets", "estimate_process_uncertainty", "plot.eye_process_uncertainty", "plot.eye_process_uncertainty_propagation", "plot.eye_uncertainty_budget_comparison", "plot_uncertainty_by_item", "plot_uncertainty_by_stage", "plot_uncertainty_tornado", "plot_uncertainty_waterfall", "print.eye_process_uncertainty_spec", "process_uncertainty_spec", "propagate_process_uncertainty", "uncertainty_budget" ] }, { "page": "process_validation_design", "title": "Define an empirical process-validation design", "topics": [ "process_validation_design" ] }, { "page": "process_window_spec", "title": "Specify temporal process windows", "topics": [ "process_window_spec" ] }, { "page": "promote_irt_model", "title": "Promote an IRT model after evidence gates are met", "topics": [ "promote_irt_model" ] }, { "page": "promote_vendor_support", "title": "Promote a case support level only when evidence is supplied", "topics": [ "promote_vendor_support" ] }, { "page": "propagate_calibration_uncertainty", "title": "Propagate empirical calibration uncertainty around gaze samples", "topics": [ "propagate_calibration_uncertainty" ] }, { "page": "provenance_edge_table", "title": "Build a provenance edge table", "topics": [ "provenance_edge_table" ] }, { "page": "provenance_lineage_table", "title": "Build a provenance lineage node table", "topics": [ "provenance_lineage_table" ] }, { "page": "prune_validation_checkpoints", "title": "Remove obsolete or corrupt validation checkpoints", "topics": [ "prune_validation_checkpoints" ] }, { "page": "public_validation_corpus", "title": "Public validation-corpus registry", "topics": [ "public_validation_corpus" ] }, { "page": "pupil_activity_index", "title": "Compute a transparent pupil activity index", "topics": [ "pupil_activity_index" ] }, { "page": "pupil_band_power", "title": "Compute pupil signal power in a frequency band", "topics": [ "pupil_band_power" ] }, { "page": "pupil_baseline_sensitivity", "title": "Evaluate pupil baseline-window sensitivity", "topics": [ "pupil_baseline_sensitivity" ] }, { "page": "pupil_confound_effects", "title": "Extract pupil confound-model effects", "topics": [ "pupil_confound_effects" ] }, { "page": "pupil_event_effects", "title": "Extract event effects from pupil deconvolution", "topics": [ "pupil_event_effects" ] }, { "page": "pupil_event_regressor", "title": "Build an event-locked pupil regressor", "topics": [ "pupil_event_regressor" ] }, { "page": "pupil_frequency_features", "title": "Extract pupil frequency-domain and activity features by group", "topics": [ "pupil_frequency_features" ] }, { "page": "pupil_latency_sensitivity", "title": "Pupil latency estimator sensitivity and resolvability audit", "topics": [ "pupil_latency_sensitivity" ] }, { "page": "pupil_preprocessing_grid", "title": "Create a preprocessing sensitivity grid for pupil analysis", "topics": [ "pupil_preprocessing_grid" ] }, { "page": "pupil_preprocessing_sensitivity", "title": "Run functional pupil preprocessing sensitivity analysis", "topics": [ "pupil_preprocessing_sensitivity" ] }, { "page": "pupil_response_kernel", "title": "Canonical gamma-shaped pupil response kernel", "topics": [ "pupil_response_kernel" ] }, { "page": "pupil_unit_fidelity_audit", "title": "Pupil-unit semantic-fidelity audit", "topics": [ "pupil_unit_fidelity_audit" ] }, { "page": "pupil_velocity_activity", "title": "Derivative-based pupil activity magnitude", "topics": [ "pupil_velocity_activity" ] }, { "page": "quantify_process_leakage", "title": "Quantify leakage from row-wise rather than grouped validation", "topics": [ "quantify_process_leakage" ] }, { "page": "query_eye_storage", "title": "Query partitioned eye storage lazily where possible", "topics": [ "query_eye_storage" ] }, { "page": "raven_reproduction_spec", "title": "Specify a published Raven strategy-model reproduction", "topics": [ "raven_reproduction_spec" ] }, { "page": "read_api_lifecycle_registry", "title": "Read API lifecycle registry from CSV", "topics": [ "read_api_lifecycle_registry" ] }, { "page": "read_benchmark_table", "title": "Read a benchmark table", "topics": [ "read_benchmark_table" ] }, { "page": "read_decision_manifest", "title": "Read a decision manifest written by eyeprocess", "topics": [ "read_decision_manifest" ] }, { "page": "read_eyeprocess_validation_evidence", "title": "Read and verify a frozen evidence bundle", "topics": [ "read_eyeprocess_validation_evidence" ] }, { "page": "read_reproducibility_fingerprint", "title": "Read a reproducibility fingerprint", "topics": [ "read_reproducibility_fingerprint" ] }, { "page": "read_validation_job_manifest", "title": "Read a validation manifest", "topics": [ "read_validation_job_manifest" ] }, { "page": "read_validation_scenario_manifest", "title": "Read a validation scenario manifest", "topics": [ "read_validation_scenario_manifest" ] }, { "page": "read_vendor_registry", "title": "Read the multi-vendor case registry", "topics": [ "read_vendor_registry" ] }, { "page": "recalibrate_after_changepoint", "title": "Iteratively detect, clean, and recalibrate after process change points", "topics": [ "recalibrate_after_changepoint" ] }, { "page": "recommended_validation_replications", "title": "Approximate simulation replications needed for a target Monte Carlo error", "topics": [ "recommended_validation_replications" ] }, { "page": "redact_validation_case", "title": "Redact a validation case without inventing replacement data", "topics": [ "redact_validation_case" ] }, { "page": "register_eye_api_status", "title": "Add or update API lifecycle metadata without global mutation", "topics": [ "register_eye_api_status" ] }, { "page": "register_irt_model", "title": "Register a multimodal IRT model", "topics": [ "register_irt_model" ] }, { "page": "register_process_measure", "title": "Add a process measure to a registry without global mutation", "topics": [ "register_process_measure" ] }, { "page": "037-pupil-registration", "title": "Pupil phase-amplitude registration", "topics": [ "audit_pupil_registration", "decompose_pupil_phase_amplitude", "fit_phase_amplitude_irt", "plot.eye_phase_amplitude_irt", "plot.eye_pupil_phase_amplitude", "plot.eye_pupil_registration", "plot_item_phase_delay", "plot_phase_amplitude_scores", "plot_pupil_registration", "plot_registered_pupil_effects", "plot_warping_functions", "register_pupil_curves" ] }, { "page": "register_validation_case", "title": "Register an independent validation case", "topics": [ "register_validation_case" ] }, { "page": "register_vendor_semantics", "title": "Register vendor-field semantics", "topics": [ "register_vendor_semantics" ] }, { "page": "reporting_guideline_audit", "title": "Audit reporting-guideline coverage", "topics": [ "reporting_guideline_audit" ] }, { "page": "041-representative-scanpaths", "title": "Representative scanpaths and scanpath distributions", "topics": [ "bootstrap_representative_scanpath", "compare_scanpath_distributions", "plot.eye_scanpath_bootstrap", "plot.eye_scanpath_comparison", "plot.eye_scanpath_representative", "plot_group_scanpath_transport", "plot_representative_scanpath", "plot_scanpath_atlas", "plot_scanpath_dispersion", "plot_scanpath_similarity_matrix", "representative_scanpath", "scanpath_dispersion" ] }, { "page": "resume_eye_pipeline", "title": "Resume a governed pipeline from a prior run", "topics": [ "resume_eye_pipeline" ] }, { "page": "resume_validation_jobs", "title": "Resume incomplete or failed validation jobs", "topics": [ "resume_validation_jobs" ] }, { "page": "roundtrip_eye_bids", "title": "Execute and audit an Eye-Tracking-BIDS round trip", "topics": [ "roundtrip_eye_bids" ] }, { "page": "run_benchmark_reproduction", "title": "Derive reproducible benchmark summaries", "topics": [ "run_benchmark_reproduction" ] }, { "page": "run_eye_benchmark", "title": "Run a computational scaling benchmark", "topics": [ "run_eye_benchmark" ] }, { "page": "run_eye_pipeline", "title": "Run a governed eyeprocess pipeline", "topics": [ "run_eye_pipeline" ] }, { "page": "run_eyeprocess_equateirt", "title": "Run a named equateIRT linking/equating function without fallback substitution", "topics": [ "run_eyeprocess_equateirt" ] }, { "page": "run_eyeprocess_irt_ability_sbc", "title": "Run simulation-based calibration for known-item IRT ability scoring", "topics": [ "run_eyeprocess_irt_ability_sbc" ] }, { "page": "run_eyeprocess_irt_recovery", "title": "Run IRT parameter recovery with the exact mirt engine", "topics": [ "run_eyeprocess_irt_recovery" ] }, { "page": "run_eyeprocess_mirtcat", "title": "Run a mirtCAT adaptive-testing workflow without fallback substitution", "topics": [ "run_eyeprocess_mirtcat" ] }, { "page": "run_eyeprocess_stress_evidence", "title": "Execute a declared measurement-stress evidence plan", "topics": [ "run_eyeprocess_stress_evidence" ] }, { "page": "run_eyeprocess_validation_program", "title": "Run the complete validation-release programme", "topics": [ "run_eyeprocess_validation_program" ] }, { "page": "run_gazepoint_workflow", "title": "Run the complete Gazepoint downstream workflow", "topics": [ "run_gazepoint_workflow" ] }, { "page": "run_model_validation", "title": "Run parameter-recovery, coverage, and misspecification validation", "topics": [ "run_model_validation" ] }, { "page": "run_posterior_sbc", "title": "Run posterior simulation-based calibration from an explicit contract", "topics": [ "run_posterior_sbc" ] }, { "page": "run_process_negative_controls", "title": "Run repeated process negative controls", "topics": [ "run_process_negative_controls" ] }, { "page": "run_process_sensitivity", "title": "Run an explicit process-analysis multiverse", "topics": [ "run_process_sensitivity" ] }, { "page": "run_process_validation", "title": "Run an empirical process-validation programme", "topics": [ "run_process_validation" ] }, { "page": "run_raven_reproduction", "title": "Execute a licensed published-model reproduction", "topics": [ "run_raven_reproduction" ] }, { "page": "run_sbc", "title": "Run generic simulation-based calibration", "topics": [ "run_sbc" ] }, { "page": "run_validation_jobs", "title": "Run validation jobs with checkpointing and deterministic seeds", "topics": [ "run_validation_jobs" ] }, { "page": "sbc_ecdf_deviation", "title": "ECDF deviation summary for SBC ranks", "topics": [ "sbc_ecdf_deviation" ] }, { "page": "sbc_rank_diagnostics", "title": "Build SBC rank diagnostics", "topics": [ "sbc_rank_diagnostics" ] }, { "page": "sbc_summary", "title": "Summarize simulation-based calibration", "topics": [ "sbc_summary" ] }, { "page": "score_partial_response_pattern", "title": "Score a partial response pattern from a calibrated mirt model", "topics": [ "score_partial_response_pattern" ] }, { "page": "score_response_stream", "title": "Score a response stream cumulatively", "topics": [ "score_response_stream" ] }, { "page": "select_next_item_process", "title": "Select the next item using response/process utility", "topics": [ "select_next_item_process" ] }, { "page": "semantic_fidelity_spec", "title": "Semantic fidelity specification", "topics": [ "semantic_fidelity_spec" ] }, { "page": "semantic_loss_map", "title": "Convert a semantic round-trip audit into a loss map", "topics": [ "semantic_loss_map" ] }, { "page": "semantic_roundtrip_audit", "title": "Audit a complete semantic round trip", "topics": [ "semantic_roundtrip_audit" ] }, { "page": "sensitivity_branch_fingerprint", "title": "Stable fingerprint of a sensitivity branch", "topics": [ "sensitivity_branch_fingerprint" ] }, { "page": "sensitivity_decision_leverage", "title": "Decision leverage of each analytical choice", "topics": [ "sensitivity_decision_leverage" ] }, { "page": "sensitivity_fragility_index", "title": "Fragility index across analysis specifications", "topics": [ "sensitivity_fragility_index" ] }, { "page": "sensitivity_multiverse_manifest", "title": "Machine-readable multiverse manifest", "topics": [ "sensitivity_multiverse_manifest" ] }, { "page": "sensitivity_rank_stability", "title": "Rank stability across specifications", "topics": [ "sensitivity_rank_stability" ] }, { "page": "sensitivity_sign_stability", "title": "Effect-sign stability across specifications", "topics": [ "sensitivity_sign_stability" ] }, { "page": "sensitivity_significance_stability", "title": "Significance-decision stability across specifications", "topics": [ "sensitivity_significance_stability" ] }, { "page": "sensitivity_threshold_stability", "title": "Substantive-threshold stability across specifications", "topics": [ "sensitivity_threshold_stability" ] }, { "page": "sequence_interoperability", "title": "Convert scanpaths to process/sequence package contracts", "topics": [ "as_procdata_sequence", "as_seqhmm_data", "as_traminer_sequence", "sequence_interoperability" ] }, { "page": "session_facet_effects", "title": "Extract session facet effects", "topics": [ "session_facet_effects" ] }, { "page": "simulate_advanced_process_data", "title": "Simulate advanced response-process data", "topics": [ "simulate_advanced_process_data" ] }, { "page": "simulate_dynamic_irtree_data", "title": "Simulate observed dynamic-state transitions", "topics": [ "simulate_dynamic_irtree_data" ] }, { "page": "simulate_eyeprocess_catr", "title": "Run a catR adaptive-testing simulation without fallback substitution", "topics": [ "simulate_eyeprocess_catr" ] }, { "page": "simulate_eyeprocess_irt_binary", "title": "Simulate dichotomous IRT responses with optional local dependence and missingness", "topics": [ "simulate_eyeprocess_irt_binary" ] }, { "page": "simulate_from_model", "title": "Simulate data from a model or registered model specification", "topics": [ "simulate_from_model" ] }, { "page": "simulate_gaze_diffusion_data", "title": "Simulate a hierarchical gaze-diffusion study", "topics": [ "simulate_gaze_diffusion_data" ] }, { "page": "simulate_irt_model", "title": "Simulate from a registered multimodal IRT model", "topics": [ "simulate_irt_model" ] }, { "page": "simulate_multimodal_irt", "title": "Simulate multimodal IRT process data", "topics": [ "simulate_multimodal_irt" ] }, { "page": "simulate_multimodal_m2", "title": "Simulate from the M2 response + RT + gaze generative model", "topics": [ "simulate_multimodal_m2" ] }, { "page": "simulate_multimodal_m3", "title": "Simulate the M3 response + RT + gaze + pupil generative model", "topics": [ "simulate_multimodal_m3" ] }, { "page": "simulate_multimodal_m4", "title": "Simulate M4 multimodal sequential measurement data", "topics": [ "simulate_multimodal_m4" ] }, { "page": "simulate_presentation_variants", "title": "Simulate pre-registered presentation variants for review", "topics": [ "simulate_presentation_variants" ] }, { "page": "simulate_process_cat", "title": "Simulate a simple process-aware CAT policy", "topics": [ "simulate_process_cat" ] }, { "page": "simulate_process_validation_data", "title": "Simulate a generic multimodal validation dataset with known truth", "topics": [ "simulate_process_validation_data" ] }, { "page": "simulate_strategy_mixture_data", "title": "Simulate a theory-defined strategy-mixture study", "topics": [ "simulate_strategy_mixture_data" ] }, { "page": "simulation_based_calibration", "title": "Run simulation-based calibration", "topics": [ "simulation_based_calibration" ] }, { "page": "simulation_rank_statistic", "title": "Compute a simulation-based calibration rank statistic", "topics": [ "simulation_rank_statistic" ] }, { "page": "software_paper_claim_matrix", "title": "Create or normalize a software-paper claim matrix", "topics": [ "software_paper_claim_matrix" ] }, { "page": "software_paper_coverage", "title": "Compute descriptive evidence coverage", "topics": [ "software_paper_coverage" ] }, { "page": "software_paper_evidence_bundle", "title": "Construct a software-paper evidence bundle", "topics": [ "software_paper_evidence_bundle" ] }, { "page": "software_paper_gap_analysis", "title": "Identify gaps in a software-paper evidence bundle", "topics": [ "software_paper_gap_analysis" ] }, { "page": "software_paper_readiness", "title": "Descriptive software-paper readiness audit", "topics": [ "software_paper_readiness" ] }, { "page": "software_paper_validation_table", "title": "Summarise validation evidence for a software paper", "topics": [ "software_paper_validation_table" ] }, { "page": "specification_coverage", "title": "Fraction of planned specifications successfully evaluated", "topics": [ "specification_coverage" ] }, { "page": "specification_curve_data", "title": "Prepare ordered specification-curve data", "topics": [ "specification_curve_data" ] }, { "page": "split_half_process_reliability", "title": "Split-half reliability for a trial-level process measure", "topics": [ "split_half_process_reliability" ] }, { "page": "split_validation_plan", "title": "Split a validation plan into independent chunks", "topics": [ "split_validation_plan" ] }, { "page": "standardized_spatial_quality", "title": "Standardized vendor-neutral gaze data-quality metrics", "topics": [ "compare_gaze_quality_conditions", "compare_gaze_quality_sessions", "compute_bcea", "compute_gaze_accuracy", "compute_gaze_data_loss", "compute_gaze_precision", "compute_gaze_sd_precision", "compute_rms_s2s", "compute_valid_sample_fraction", "create_gaze_quality_report", "estimate_effective_sampling_rate", "estimate_sampling_interval", "estimate_sampling_jitter", "plot_bcea", "plot_gaze_accuracy", "plot_gaze_precision", "plot_gaze_quality_dashboard", "plot_sampling_intervals", "report_gaze_quality", "simulate_gaze_quality_calibration", "standardized_spatial_quality", "summarise_sampling_quality", "summarise_spatial_quality", "validate_gaze_quality_inputs" ] }, { "page": "storage_transaction_manifest", "title": "Return the transaction manifest", "topics": [ "storage_transaction_manifest" ] }, { "page": "strategy_aoi_sensitivity", "title": "Assess sensitivity to alternative AOI feature definitions", "topics": [ "strategy_aoi_sensitivity" ] }, { "page": "strategy_classification_uncertainty", "title": "Quantify strategy-classification uncertainty", "topics": [ "strategy_classification_uncertainty" ] }, { "page": "strategy_label_switching_diagnostics", "title": "Diagnose label stability across multiple starts", "topics": [ "strategy_label_switching_diagnostics" ] }, { "page": "strategy_posterior_probabilities", "title": "Posterior strategy probabilities", "topics": [ "strategy_posterior_probabilities" ] }, { "page": "streaming_score_history", "title": "Extract streaming score history", "topics": [ "streaming_score_history" ] }, { "page": "stress_test_latent_distribution", "title": "Stress test latent distribution", "topics": [ "stress_test_latent_distribution" ] }, { "page": "stress_test_local_dependence", "title": "Stress test local dependence", "topics": [ "stress_test_local_dependence" ] }, { "page": "stress_test_missingness", "title": "Stress test missingness", "topics": [ "stress_test_missingness" ] }, { "page": "stress_test_misspecification", "title": "Run a generic misspecification stress-test grid", "topics": [ "stress_test_misspecification" ] }, { "page": "stress_test_preprocessing", "title": "Stress test preprocessing", "topics": [ "stress_test_preprocessing" ] }, { "page": "stress_test_process_pipeline", "title": "Stress-test an analysis under explicit synthetic corruptions", "topics": [ "stress_test_process_pipeline" ] }, { "page": "stress_test_speededness", "title": "Stress test speededness", "topics": [ "stress_test_speededness" ] }, { "page": "stress_test_summary", "title": "Summarise stress-test metrics", "topics": [ "stress_test_summary" ] }, { "page": "stress_tolerance_frontier", "title": "Identify the empirical stress frontier for a metric", "topics": [ "stress_tolerance_frontier" ] }, { "page": "structural_transition_mask", "title": "Define structural-zero and allowed transition masks", "topics": [ "structural_transition_mask" ] }, { "page": "summarise_eye_benchmark", "title": "Summarise benchmark timing and memory by problem size", "topics": [ "summarise_eye_benchmark" ] }, { "page": "summarise_eyeprocess_stress_evidence", "title": "Summarise executed 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