| eyeprocess: Harmonize Eye-Tracking and Psychometric Process Data | eyeprocess-package eyeprocess |
| Create formal channel-ablation datasets | ablate_multimodal_channels |
| Compute aDDM/GLAM-inspired gaze-evidence proxy features | addm_glam_proxy_features |
| Extract confound-adjusted pupil values | adjust_pupil_confounds |
| Specify evidence required to promote advanced model interfaces | advanced_model_evidence_spec |
| Construct the advanced-model validation grid | advanced_validation_grid |
| Extract algorithm facet effects | algorithm_facet_effects |
| Entropy of an explicitly enumerated analysis-decision space | analysis_decision_entropy |
| Snapshot an eyeprocess analysis environment | analysis_environment_snapshot |
| Audit compatibility between measurement resolution and an analysis target | analysis_resolution_guard |
| AOI membership probabilities from uncertainty draws | aoi_membership_probability |
| Extract AOI growth-curve/trajectory features | aoi_trajectory_features |
| Map exported APIs to conceptual families | api_family_map |
| Compare two API lifecycle registries | api_lifecycle_diff |
| Summarise API surface by family and lifecycle status | api_surface_summary |
| Apply a pre-flight decision to data explicitly | apply_preflight_decision |
| Apply a synthetic corruption plan | apply_synthetic_corruption |
| Canonicalise parameter-recovery results | as_irt_recovery_results |
| Probabilistic AOI assignment and uncertainty propagation | 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 |
| Assign features to conservative process-feature families | assign_process_feature_family |
| Identify items for descriptive 3PL/process review | audit_3pl_process_signatures |
| Audit advanced-model scientific evidence | audit_advanced_model_evidence |
| Audit whether benchmark assets are ready for public release | audit_benchmark_release |
| Audit bias | audit_bias |
| Alias emphasizing sensitivity rather than automatic replacement | audit_biometric_imputation |
| Audit incoming biometric/process data before modelling | audit_biometric_preflight |
| Audit a candidate item bank against a seed model | audit_candidate_item_bank |
| Audit out-of-sample incremental information from a process channel | audit_channel_incremental_information |
| Audit convergence and classified failures | audit_convergence |
| Audit coverage | audit_coverage |
| Audit decision provenance and completeness | audit_decision_provenance |
| Audit distractor attention patterns | audit_distractor_attention |
| Audit API lifecycle completeness and replacement contracts | audit_eye_api |
| Audit a pipeline definition or completed run | audit_eye_pipeline |
| Audit whether a gated frontier model has a minimum evidence contract | audit_frontier_model_contract |
| Audit empirical identifiability from replicate estimates | audit_identifiability |
| Audit interval width | audit_interval_width |
| Audit item response-function shape departures | audit_irf_shape |
| Run stepwise Rasch item-reduction as a sensitivity analysis | audit_item_reduction_sensitivity |
| Audit the empirical latent-trait distribution | audit_latent_distribution |
| Summarise measurement transportability across held-out groups | audit_measurement_transportability |
| Audit promotion readiness for advanced model families | audit_model_promotion |
| Audit basic multimodal design identifiability | audit_multimodal_identifiability |
| Audit structural and data identifiability for M0-M2 | audit_multimodal_m2_identifiability |
| Audit structural and measurement support for M3 | audit_multimodal_m3_identifiability |
| Audit M4 structural and posterior identifiability | audit_multimodal_m4_identifiability |
| Audit a multimodal measurement object | audit_multimodal_measurement |
| Alias emphasizing data-quality interpretation of process anomaly auditing | audit_multivariate_process_quality |
| Run nonparametric Rasch diagnostics with eRm | audit_nonparametric_rasch |
| Audit presentation/accessibility sensitivity without clinical inference | audit_presentation_accessibility |
| Audit DIF before and after process-data adjustment | audit_process_adjusted_dif |
| Audit multivariate process/data-quality anomalies | audit_process_anomalies |
| Audit post-deployment psychometric and biometric drift | audit_process_drift |
| Audit external/structural validity of process traits | audit_process_external_validity |
| Audit inter-option/process local dependence | audit_process_local_dependence |
| Audit process measurement invariance across facets | audit_process_measurement_invariance |
| Audit sensitivity of process summaries to temporal window choices | audit_process_window_sensitivity |
| Audit within-person pupil fatigue/trial-order drift | audit_pupil_fatigue_drift |
| Audit stability of pupil frequency features across window lengths | audit_pupil_frequency_stability |
| Audit declared order of pupil preprocessing steps | audit_pupil_preprocessing_order |
| Audit rmse | audit_rmse |
| Audit semantic and numerical loss after a round trip | audit_roundtrip_loss |
| Audit sampling irregularity | audit_sampling_irregularity |
| Audit SBC rank uniformity | audit_sbc |
| Summarize a signal-filter audit | audit_signal_filter |
| Audit temporal leakage in a feature provenance table | audit_temporal_leakage |
| Audit whether a validation programme is complete | audit_validation_completion |
| Audit vendor field coverage against canonical semantics | audit_vendor_field_coverage |
| Audit a multi-vendor validation corpus | audit_vendor_validation |
| Audit visual-context dependence | audit_visual_context_dependence |
| Extract compact Bayesian process-model diagnostic flags | bayesian_process_diagnostic_flags |
| Summarize Bayesian process-model diagnostics | bayesian_process_diagnostics_dashboard |
| Return expected benchmark outputs | benchmark_expected_outputs |
| Benchmark storage formats and query operations | benchmark_eye_storage |
| Benchmark an eyeprocess operation | benchmark_eyeprocess |
| Memory estimate for an R object or generated problem size | benchmark_memory_estimate |
| Estimate scaling exponent from benchmark results | benchmark_scaling_curve |
| Bind compatible process-window objects | bind_process_windows |
| Run biometric-feature imputation as a sensitivity analysis | biometric_imputation_sensitivity |
| Bootstrap ICC reliability by resampling participants | bootstrap_process_reliability |
| Build the declared/fixture/empirical compatibility matrix | build_compatibility_matrix |
| Evidence and decision provenance graphs | 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 |
| Reconstruct media presentations as analysis trials | build_gazepoint_media_trials |
| Calibration drift profile across sessions/batches | calibration_drift_profile |
| Build an empirical bivariate calibration-error model | calibration_error_model |
| Sensitivity grid for deterministic calibration offsets | calibration_sensitivity_grid |
| Audit transfer of calibration across devices/sessions/sites | calibration_transfer_audit |
| Canonical API mapping | canonical_eye_api |
| Classify item missingness using exposure and response evidence | classify_item_missingness |
| Collect a disk-backed eye dataset | collect_eye_storage |
| Collect validation evidence into a common bundle | collect_validation_evidence |
| Collect validation checkpoints from one or more directories | collect_validation_jobs |
| Compare explicit AOI assignment methods | compare_aoi_methods |
| Compare AOI trajectory feature objects | compare_aoi_trajectories |
| Compare Bayesian process models by LOO or Bayes factor | compare_bayesian_process_models |
| Compare two research decision manifests | compare_decision_manifests |
| Compare two deployment batches descriptively | compare_deployment_batches |
| Compare diffusion and conventional accuracy-RT models | compare_diffusion_accuracy_rt |
| Compare dynamic transition models | compare_dynamic_transition_models |
| Compare multiple external-engine adapter results | compare_engine_adapters |
| Compare explicit fixation-detection methods | compare_fixation_methods |
| Compare functional and scalar pupil summaries | compare_functional_scalar_models |
| Compare hard and probabilistic AOI assignments | compare_hard_probabilistic_aoi |
| Compare multimodal IRT model objects | compare_irt_models |
| Compare simple latent-distribution reference models | compare_latent_distribution_models |
| Compare equivalent model engines | compare_model_engines |
| Compare conventional logistic and flexible IRF shapes | compare_parametric_nonparametric_irf |
| Compare outcomes across presentation variants | compare_presentation_fairness |
| Compare process external-validity models | compare_process_criterion_models |
| Compare explicit process-model specifications | compare_process_models |
| Compare candidate process-profile solutions | compare_process_profile_solutions |
| Compare pupil deconvolution kernels | compare_pupil_kernels |
| Compare explicit pupil-preprocessing methods | compare_pupil_preprocessing |
| Compare raw and confound-adjusted pupil values | compare_raw_adjusted_pupil |
| Compare two reproducibility fingerprints | compare_reproducibility_fingerprints |
| Compare multiple signal filters | compare_signal_filters |
| Compare mixture and continuous heterogeneity descriptions | compare_strategy_heterogeneity |
| Compare validation engines on common recovery output | compare_validation_engines |
| Compare semantic mappings between vendors | compare_vendor_semantics |
| Compare base and visual-context IRT models | compare_visual_context_irt |
| Build a detailed compatibility evidence matrix | compatibility_evidence_matrix |
| Extract visual-context factor effects/loadings | context_factor_effects |
| Coordinate semantic-fidelity audit | coordinate_fidelity_audit |
| Interval coverage calibration curve | coverage_calibration_curve |
| Create a public, de-identified benchmark bundle | create_public_benchmark |
| Cross-device process-scale equating audit | cross_device_process_equating_audit |
| Compare adapter output across software/format versions | cross_version_adapter_regression |
| Cross-classified grouped cross-validation | crossed_grouped_cv |
| Create cross-classified grouped folds | crossed_grouped_folds |
| Compact reporting table for eye-tracking data quality | data_quality_reporting_table |
| Alias for manifest comparison emphasizing changed decision paths | decision_manifest_diff |
| Stable hash of decision content | decision_manifest_hash |
| Flatten a decision manifest to a table | decision_manifest_table |
| Coverage of a declared decision space by evaluated specifications | decision_space_coverage |
| Overall decision-stability summary | decision_stability |
| Decode latent or fitted transition states | decode_dynamic_states |
| Compositional analysis of AOI attention | 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 |
| Derive the complete Gazepoint workflow feature set | derive_gazepoint_workflow_features |
| Calibration drift and offline recalibration | 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 |
| Detect missing, truncated, or modified partitions | detect_corrupt_partitions |
| Detect IRT/process change points using an SIC-inspired multichannel score | detect_irt_changepoints |
| Detect a response-process change point | detect_process_changepoint |
| Cognitive-episode change-point detection | 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 |
| Extract device facet effects | device_facet_effects |
| Construct a simulation-based identification study | diffusion_identification_study |
| Diagnose diffusion-parameter trade-offs and sampling | diffusion_parameter_diagnostics |
| Posterior predictive summaries for accuracy and RT | diffusion_posterior_predictive |
| Build a distractor process map | distractor_process_map |
| Drift audit stratified by device | drift_by_device |
| Drift audit stratified by study site | drift_by_site |
| Drift audit stratified by stimulus version | drift_by_stimulus_version |
| Drift audit stratified by vendor | drift_by_vendor |
| Evaluate dynamic-state recovery under misclassification | dynamic_irtree_recovery |
| Specify a hardened dynamic gaze-state IRTree | dynamic_irtree_spec |
| Posterior predictive checks for dynamic state models | dynamic_posterior_predictive_check |
| Build an explicit dynamic-transition design matrix | dynamic_transition_design |
| Estimate effective sampling frequency from timestamps | effective_sampling_frequency |
| Encode multiple-response item response combinations | encode_response_combinations |
| Report an adapter's availability and contract | engine_adapter_status |
| Equate IRT scales using anchor item parameters | equate_irt_scales |
| Estimate empirical calibration/validation error | estimate_calibration_error |
| Estimate visual exposure probability | estimate_visual_exposure_probability |
| Evaluate a validation acceptance rule | evaluate_validation_acceptance |
| Event-marker plausibility audit | event_marker_qc |
| Audit event survival across an interchange round trip | event_roundtrip_audit |
| Audit event semantic preservation | event_semantics_audit |
| Expand a stress evidence plan into one-factor-at-a-time scenarios | expand_eyeprocess_stress_evidence_plan |
| Expand a validation-evidence plan to a scenario table | expand_eyeprocess_validation_plan |
| Expand a process-validation design into explicit conditions | expand_process_validation_design |
| Expected process-aware item utility under a theta distribution | expected_process_information |
| Explain local person-item latent-space interactions | explain_latent_interaction |
| Export Eye-Tracking-BIDS physiological recordings | export_eye_bids |
| Export a pipeline manifest and optional run status | export_eye_pipeline |
| Export lightweight PROV-oriented JSON | export_prov_json |
| Export minimal RO-Crate 1.3 metadata | export_ro_crate_metadata |
| Export a validation evidence bundle | export_validation_bundle |
| Convert common external eye-tracking objects | as_eyeprocess_eyeris as_eyeprocess_eyetools as_eyeprocess_eyetrackingr as_eyeprocess_gazer as_eyeprocess_pupillometryr external_eye_adapters |
| List external engine adapters | external_model_engines |
| External validation on a completely held-out dataset | external_validate_irt |
| Extract diffusion parameter summaries | extract_diffusion_parameters |
| Extract functional pupil parameters for validation | extract_functional_pupil_parameters |
| Extract canonical parameter truth from simulated data | extract_parameter_truth |
| Extract standardized sliding-window process features | extract_process_windows |
| Construct a governed analysis pipeline | eye_analysis_pipeline |
| Define explicit analysis decisions for an eyeprocess workflow | eye_analysis_spec |
| Inventory the public eyeprocess API | eye_api_inventory |
| Normalize or create an API lifecycle registry | eye_api_lifecycle |
| Lifecycle recommendation for API review | eye_api_recommendation |
| Lookup lifecycle status for one or more APIs | eye_api_status |
| Return superseded/deprecated compatibility interfaces | eye_api_superseded |
| Define a computational benchmark design | eye_benchmark_design |
| Create a machine-readable research decision manifest | eye_decision_manifest |
| Render pipeline dependencies as Graphviz DOT | eye_pipeline_dot |
| Return pipeline vertices and dependency edges | eye_pipeline_graph |
| Machine-readable pipeline manifest | eye_pipeline_manifest |
| Render pipeline dependencies as Mermaid flowchart text | eye_pipeline_mermaid |
| Define a governed pipeline step | eye_pipeline_step |
| Measurement-intelligence plotting and result infrastructure | 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 |
| Construct a lightweight provenance graph | eye_prov_graph |
| Construct a reproducibility fingerprint | eye_reproducibility_fingerprint |
| Create a session-level provenance manifest | eye_session_manifest |
| Specify disk-backed eyeprocess storage | eye_storage_spec |
| Audit preservation of monocular/binocular stream semantics | eye_stream_fidelity_audit |
| Create a targets-compatible dependency manifest | eye_targets_manifest |
| Return the public eyeprocess API version | eyeprocess_api_version |
| Locate the bundled public benchmark study | eyeprocess_benchmark_study |
| Enumerate latent attribute profiles | eyeprocess_cdm_attribute_profiles |
| Summarise CDM classification uncertainty from profile probabilities | eyeprocess_cdm_classification_uncertainty |
| Compute deterministic DINA ideal responses from a Q-matrix | eyeprocess_cdm_dina_ideal_response |
| DINA response probabilities from slip and guess parameters | eyeprocess_cdm_dina_probability |
| Audit a cognitive-diagnosis Q-matrix | eyeprocess_cdm_qmatrix_audit |
| Declare a deprecation in a structured form | eyeprocess_deprecation |
| 2PL item-response probability | eyeprocess_irt_2pl_probability |
| 3PL item-response probability | eyeprocess_irt_3pl_probability |
| 4PL item-response probability | eyeprocess_irt_4pl_probability |
| Create an auditable adaptive-testing trace | eyeprocess_irt_adaptive_trace |
| Audit candidate anchor items using supplied DIF evidence | eyeprocess_irt_anchor_audit |
| Iteratively remove anchors exceeding a supplied effect threshold | eyeprocess_irt_anchor_purification |
| Apply linear IRT scale-linking coefficients | eyeprocess_irt_apply_link |
| Audit item-bank information coverage across a theta region | eyeprocess_irt_bank_coverage |
| Audit category probability functions | eyeprocess_irt_category_function_audit |
| Summarise decision precision at one or more theta cut scores | eyeprocess_irt_classification_precision |
| Conditional standard error from information | eyeprocess_irt_conditional_sem |
| Audit content balance in an administered adaptive form | eyeprocess_irt_content_balance_audit |
| Summarise parameter drift across acquisition devices | eyeprocess_irt_device_drift |
| Differential item functioning effect curve from two parameter sets | eyeprocess_irt_dif_effect_curve |
| Differential test functioning effect curve | eyeprocess_irt_dtf_curve |
| EAP score for dichotomous IRT item parameters | eyeprocess_irt_eap_score |
| Build an external-engine capability and availability table | eyeprocess_irt_engine_evidence_table |
| Registry of specialized external IRT engines | eyeprocess_irt_engine_registry |
| Query an external IRT engine | eyeprocess_irt_engine_status |
| Expected item score | eyeprocess_irt_expected_score |
| Summarise item exposure rates | eyeprocess_irt_exposure_summary |
| Audit extreme response scores without assigning behavioral labels | eyeprocess_irt_extreme_score_audit |
| Build an integrated IRT diagnostic dashboard object | eyeprocess_irt_fit_dashboard |
| Summarise DIF/DTF curve magnitude | eyeprocess_irt_functioning_effect_summary |
| Generalized partial-credit category probabilities | eyeprocess_irt_gpcm_probability |
| Graded-response category probabilities | eyeprocess_irt_grm_probability |
| Haebara item-characteristic-curve linking | eyeprocess_irt_haebara_link |
| Audit IRT scale/location identification | eyeprocess_irt_identification_audit |
| Compute residual-based Infit and Outfit summaries | eyeprocess_irt_infit_outfit |
| Area under an information curve | eyeprocess_irt_information_area |
| Information gain between two conditional standard errors | eyeprocess_irt_information_gain |
| Audit how well item information targets a theta distribution | eyeprocess_irt_information_targeting |
| Combine invariance evidence without converting it to a binary validity claim | eyeprocess_irt_invariance_evidence |
| Item bank object for adaptive design | eyeprocess_irt_item_bank |
| Compute item residual fit summaries from observed and predicted probabilities | eyeprocess_irt_item_fit_residuals |
| Compute item information for transparent IRT families | eyeprocess_irt_item_information |
| Select the most informative eligible item at a theta estimate | eyeprocess_irt_item_selection |
| Build a latent-regression design matrix with explicit centering metadata | eyeprocess_irt_latent_regression_design |
| Compare linking estimates across anchor subsets | eyeprocess_irt_link_stability |
| Extract high residual-dependence item pairs | eyeprocess_irt_local_dependence_pairs |
| MAP score for dichotomous IRT item parameters | eyeprocess_irt_map_score |
| Marginal reliability from latent-score variance and conditional error variance | eyeprocess_irt_marginal_reliability |
| Mean-mean IRT linking coefficients | eyeprocess_irt_mean_mean_link |
| Mean-sigma IRT linking coefficients | eyeprocess_irt_mean_sigma_link |
| Summarise measurement precision across a theta region | eyeprocess_irt_measurement_precision_profile |
| Audit missing-by-design structure in an IRT response matrix | eyeprocess_irt_missing_by_design_audit |
| Compare recovery under reference and misspecified scenarios | eyeprocess_irt_misspecification_metrics |
| Create a model-misspecification suite | eyeprocess_irt_misspecification_suite |
| Bounded ML score for dichotomous IRT item parameters | eyeprocess_irt_mle_score |
| Create a governed IRT model card | eyeprocess_irt_model_card |
| Audit completeness of an IRT model card | eyeprocess_irt_model_card_audit |
| Declare an eyeprocess IRT model specification | eyeprocess_irt_model_spec |
| Audit monotonicity of an item response curve | eyeprocess_irt_monotonicity_audit |
| Nominal-response category probabilities | eyeprocess_irt_nominal_probability |
| Audit basic plausibility of dichotomous item parameters | eyeprocess_irt_parameter_plausibility_audit |
| Standardized log-likelihood person-fit diagnostic | eyeprocess_irt_person_fit_lz |
| Compute person residual fit summaries | eyeprocess_irt_person_fit_residuals |
| Draw plausible values from a discrete posterior grid | eyeprocess_irt_plausible_values |
| Compare observed and replicated IRT discrepancy statistics | eyeprocess_irt_ppc_discrepancy |
| Build a paper-ready IRT information/precision table | eyeprocess_irt_precision_evidence_table |
| Construct a prior-sensitivity grid for Bayesian IRT analyses | eyeprocess_irt_prior_sensitivity_grid |
| Summarise sensitivity of an estimand across declared prior specifications | eyeprocess_irt_prior_sensitivity_summary |
| Declare prior families for Bayesian IRT engine adapters | eyeprocess_irt_prior_spec |
| Align item parameters with process-channel summaries | eyeprocess_irt_process_alignment |
| Process-aware selection penalty without mental-state inference | eyeprocess_irt_process_aware_selection_penalty |
| Compare psychometric DIF effect sizes with process-channel contrasts | eyeprocess_irt_process_dif_concordance |
| Compute Yen-style Q3 residual correlations | eyeprocess_irt_q3 |
| Create an IRT recovery design | eyeprocess_irt_recovery_design |
| Summarise recovery failure rates | eyeprocess_irt_recovery_failures |
| Summarise IRT parameter recovery | eyeprocess_irt_recovery_summary |
| Construct SBC ranks from scalar truths and posterior draws | eyeprocess_irt_sbc_ranks |
| Summarise IRT SBC ranks with the package SBC diagnostics | eyeprocess_irt_sbc_summary |
| Score a response matrix with EAP, MAP, or ML | eyeprocess_irt_score_table |
| Summarise score uncertainty | eyeprocess_irt_score_uncertainty |
| Summarise item-parameter drift over sessions | eyeprocess_irt_session_drift |
| Audit sparse person-item response coverage | eyeprocess_irt_sparse_design_audit |
| Stocking-Lord characteristic-curve linking | eyeprocess_irt_stocking_lord_link |
| Evaluate a simple adaptive stopping rule | eyeprocess_irt_stopping_rule |
| Compare an examinee distribution with item-bank targeting | eyeprocess_irt_targeting_gap |
| Test characteristic curve for dichotomous item parameters | eyeprocess_irt_test_characteristic_curve |
| Compute a test information curve from item parameters | eyeprocess_irt_test_information |
| Audit testlet sizes and singleton structures | eyeprocess_irt_testlet_audit |
| Declare a testlet structure for bifactor/two-tier IRT engines | eyeprocess_irt_testlet_spec |
| Audit ordered category thresholds | eyeprocess_irt_threshold_order_audit |
| Declare a response/process joint IRT specification | eyeprocess_joint_process_irt_spec |
| Directional multidimensional 2PL information | eyeprocess_mirt_directional_information |
| Multidimensional 2PL item information matrix | eyeprocess_mirt_information_matrix |
| Audit multidimensional IRT loading coverage | eyeprocess_mirt_loading_audit |
| Declare a multidimensional IRT loading structure | eyeprocess_mirt_loading_spec |
| Construct a multichannel measurement map | eyeprocess_multichannel_measurement_map |
| Declare negative-control evidence targets | eyeprocess_negative_control_evidence_plan |
| Build a paper-ready negative-control table | eyeprocess_negative_control_evidence_table |
| Prepare a sparse response/process bundle for external joint engines | eyeprocess_process_irt_data_bundle |
| Aggregate process channels by item | eyeprocess_process_item_profile |
| Summarise missingness patterns across response and process channels | eyeprocess_process_missingness_pattern |
| Aggregate process channels by person | eyeprocess_process_person_profile |
| Build a paper-ready parameter-recovery table | eyeprocess_recovery_evidence_table |
| Declare reliability evidence targets | eyeprocess_reliability_evidence_plan |
| Build a paper-ready reliability table | eyeprocess_reliability_evidence_table |
| Summarise response-time structure for joint IRT work | eyeprocess_response_time_profile |
| Build a paper-ready SBC table | eyeprocess_sbc_evidence_table |
| Describe speed-accuracy association without causal interpretation | eyeprocess_speed_accuracy_profile |
| Declare the Milestone #2 measurement-quality stress evidence plan | eyeprocess_stress_evidence_plan |
| Build a paper-ready stress-test table | eyeprocess_stress_evidence_table |
| Summarise gaps in a validation evidence atlas | eyeprocess_validation_atlas_gaps |
| Build a machine-readable validation claim/evidence matrix | eyeprocess_validation_claim_matrix |
| Assemble a validation evidence atlas | eyeprocess_validation_evidence_atlas |
| Grade the completeness of validation evidence | eyeprocess_validation_evidence_grade |
| Create an index over frozen validation evidence artifacts | eyeprocess_validation_evidence_index |
| Create an evidence manifest from files and in-memory objects | eyeprocess_validation_evidence_manifest |
| Declare an eyeprocess validation-evidence plan | eyeprocess_validation_plan |
| Evaluate readiness of a Milestone #2 validation evidence bundle | eyeprocess_validation_readiness |
| Apply a conservative software-release evidence gate | eyeprocess_validation_release_gate |
| Derive a deterministic bounded validation seed | eyeprocess_validation_seed |
| Mappings and adapter registry | 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 |
| Create and manage eyeprocess datasets | 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 |
| Coordinate and timebase management | 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 |
| Experimental psychometric process models | 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 |
| Export, reporting, and package bridges | 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 |
| Derive gaze, pupil, response-time, and biometric features | 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 |
| Validate real eye-tracking exports and compatibility corpora | 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 |
| Import Gazepoint and Gazepoint Biometrics exports | 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 |
| Import generic delimited eye-tracking data | read_eye_generic |
| Import Tobii, Pupil Labs, EyeLink, and SMI exports | 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 |
| Psychometric and process-data models | 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 |
| Visualize eye-tracking and multimodal process data | 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 |
| Gaze and pupil preprocessing | 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 |
| Quality control, sensitivity, and governance | 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 |
| Canonical schemas and coordinate spaces | canonical_table_names empty_eye_table eye_schema new_coordinate_space schema_table standardize_eye_table validate_eye_table |
| Simulate and validate eyeprocess workflows | parameter_recovery plot.eye_parameter_recovery power_process_simulation simulate_eye_dataset simulate_process_irt summary.eye_parameter_recovery |
| Trials, responses, stimuli, and areas of interest | add_responses assign_aois assign_trials build_aoi_visits build_item_responses build_stimulus_intervals build_trials new_aoi print.eye_aoi register_aois |
| Extract facet effects from a many-facet process model | facet_effects |
| Field-level semantic fidelity report | field_fidelity_report |
| Build a file hash manifest | file_hash_manifest |
| Filter a one-dimensional eye signal robustly | filter_eye_signal |
| Filter pupil signal robustly | filter_pupil_signal |
| Find process measures by channel, level, status, or text | find_process_measures |
| Fingerprint every file in a validation case | fingerprint_validation_case |
| Fit a single AOI growth curve | fit_aoi_growth_curve |
| fit brms adapter | fit_brms_adapter |
| Conditional censored-normal calibration for bounded process measurements | fit_censored_normal_process_irt |
| Fit a multimodal change-point IRT workflow | fit_changepoint_multimodal_irt |
| Fit a change-point RT IRT workflow | fit_changepoint_rt_irt |
| Cognitive-diagnosis model with process indicators | fit_cognitive_diagnosis_process |
| Continuous-time IRT external-engine gate | fit_continuous_time_irt |
| Cross-classified process IRT reference model | fit_crossclassified_process_irt |
| Create a gated scalable cross-classified MH-RM process IRT interface | fit_crossclassified_process_irt_mhrm |
| Cross-device and cross-vendor metric linking | 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 |
| Fit a diffusion IRT adapter | fit_diffirt_adapter |
| fit diffirt engine adapter | fit_diffirt_engine_adapter |
| Dynamic GPIRT external-engine gate | fit_dynamic_gpirt |
| Fit a dynamic gaze-state response-tree model | fit_dynamic_irtree |
| Fit an optional CmdStan dynamic-transition model | fit_dynamic_irtree_stan |
| Fit an event-time IRT reference workflow | fit_event_time_irt |
| Fit an external model engine through a stable adapter | fit_external_engine |
| Fit an eRm Rasch-family model without fallback substitution | fit_eyeprocess_erm |
| Fit a G-DINA cognitive-diagnosis model without fallback substitution | fit_eyeprocess_gdina |
| Fit a joint response/response-time LNIRT model without fallback substitution | fit_eyeprocess_lnirt |
| Fit a model with mirt without substituting another estimator | fit_eyeprocess_mirt |
| Fit a TAM model without substituting another estimator | fit_eyeprocess_tam |
| fit eyetrackingr adapter | fit_eyetrackingr_adapter |
| Spatio-temporal fixation point-process models | 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 |
| Flow-MIRT external-engine gate | fit_flow_mirt |
| Fit the bundled joint functional pupil-IRT Stan model | fit_functional_pupil_stan |
| Fit a standard 3PL and audit lower-asymptote alignment with process evidence | fit_gaze_anchored_3pl_audit |
| Fit a gaze-informed diffusion model | fit_gaze_diffusion_irt |
| Fit the CmdStan Wiener diffusion model | fit_gaze_diffusion_stan |
| Fit a gaze-informed missingness IRT diagnostic | fit_gaze_informed_missingness_irt |
| fit gdina adapter | fit_gdina_adapter |
| GPIRT model-criticism interface | fit_gpirt |
| Fit a registered multimodal IRT model | fit_irt_model |
| Fit an experimental pre-pilot item-parameter seeding model | fit_item_parameter_seed_model |
| Fit a functional pupil-informed IRT workflow | fit_joint_functional_pupil_irt |
| Joint response, response-time, and gaze-process IRT | fit_joint_gaze_rt_irt |
| Joint graded-response, RT, and process reference model | fit_joint_graded_rt_process_irt |
| Create a gated KDE latent-distribution IRT interface | fit_kde_latent_distribution_irt |
| Latent process-class IRT reference model | fit_latent_class_process_irt |
| Fit a latent-space IRT model using LSMjml | fit_latent_space_irt |
| fit lnirt adapter | fit_lnirt_adapter |
| Many-facet process IRT reference model | fit_manyfacet_process_irt |
| fit mirt adapter | fit_mirt_adapter |
| Fit a true mirt mixture-IRT response model | fit_mixture_irt_process_classes |
| Fit a multiblock psychometric/gaze/pupil/quality structure map | fit_multiblock_process_map |
| Fit the M2 response + RT + gaze reference model | fit_multimodal_m2 |
| Fit the M3 response + RT + gaze + pupil reference model | fit_multimodal_m3 |
| Fit the M4 latent response-process state model | fit_multimodal_m4 |
| Multimodal trait-model convenience wrapper | fit_multimodal_trait_irt |
| Fit a penalized multinomial transition model | fit_multinomial_transition |
| Fit a multiple-response process-IRT reference model | fit_multiple_response_process_irt |
| Nominal/distractor IRT with option-level gaze | fit_nominal_gaze_irt |
| Create a gated Bayesian nonignorable-missing IRT interface | fit_nonignorable_missing_irt |
| Response/RT/omission survival IRT reference model | fit_omission_survival_irt |
| fit openmx adapter | fit_openmx_adapter |
| Fit a user-defined OpenMx process model | fit_openmx_process_model |
| Create a gated persistence-augmented gaze-diffusion IRT interface | fit_persistence_gaze_diffusion_irt |
| Dynamic process-DIF and fairness drift | 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 |
| Process reliability and Generalizability Theory | 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 |
| Process-state HMM with an IRT response layer | fit_process_hmm_irt |
| Measurement-intelligence compatibility adapters | crossmodal_recurrence_model fit_process_missingness_model plot.eye_crossmodal_recurrence_model |
| Conditional process reference centiles | 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 |
| Informative missingness and MNAR sensitivity | 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 |
| Fit exploratory process profiles | fit_process_profile_mixture |
| Fit a process-informed Rasch tree | fit_process_rasch_tree |
| Fit a luminance/fatigue/process confound model for pupil response | fit_pupil_confound_model |
| Fit transparent event-related pupil deconvolution models | fit_pupil_event_deconvolution |
| fit pupillometryr adapter | fit_pupillometryr_adapter |
| Fit an IRT response model augmented by sequence embeddings | fit_response_process_embedding_irt |
| Fit a revisiting-aware cognitive-diagnosis process workflow | fit_revisit_process_cdm |
| fit seqhmm adapter | fit_seqhmm_adapter |
| Speed-accuracy-engagement IRT convenience wrapper | fit_speed_accuracy_engagement_irt |
| Fit the deterministic multi-start EM baseline | fit_strategy_mixture_em |
| Fit the probabilistic strategy-mixture engine | fit_strategy_mixture_stan |
| fit tam adapter | fit_tam_adapter |
| Fit a theory-constrained strategy mixture | fit_theory_strategy_irt |
| fit traminer adapter | fit_traminer_adapter |
| Fit one model-validation replicate | fit_validation_replicate |
| Variational IRT external-engine gate | fit_variational_irt |
| Fit an explicit visual-context/testlet IRT model | fit_visual_context_irt |
| Distance to nearest rectangular AOI boundary | fixation_boundary_uncertainty |
| Freeze IRT validation reference summaries | freeze_eyeprocess_irt_reference |
| Freeze a validation atlas with a reproducibility fingerprint | freeze_eyeprocess_validation_atlas |
| Freeze a complete Milestone #2 evidence bundle | freeze_eyeprocess_validation_evidence |
| Freeze software-paper evidence to an RDS with a hash | freeze_software_paper_evidence |
| Freeze a compact validation reference for regression testing | freeze_validation_reference |
| Construct a functional basis for pupil trajectories | functional_pupil_basis |
| Diagnose functional pupil model and preprocessing quality | functional_pupil_diagnostics |
| Specify a functional pupil-IRT model | functional_pupil_irt_spec |
| Return the process-alignment table from a gaze-anchored 3PL audit | gaze_anchored_3pl_alignment |
| Empirical gaze data-quality profile | gaze_data_quality_profile |
| Define a gaze-informed diffusion model | gaze_diffusion_spec |
| Estimate RMS successive-sample gaze imprecision | gaze_precision_rms_s2s |
| Recurrence and cross-recurrence analysis | 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 |
| Uncertainty ellipse implied by an empirical calibration-error model | gaze_uncertainty_ellipse |
| Build person-by-item-by-trial analysis tables | gazepoint_analysis_tables |
| Create IRT-ready response and process tables | gazepoint_irt_tables |
| Specify an integrated Gazepoint downstream workflow | gazepoint_workflow_spec |
| Generalizability-style variance decomposition for a process measure | generalizability_process_study |
| Retrieve a registered multimodal IRT model | get_irt_model |
| Grade model evidence against an explicit validation contract | grade_model_evidence |
| Evaluate a model with grouped cross-validation | grouped_cv |
| Create grouped cross-validation folds | grouped_folds |
| Build an eye dataset from the public benchmark | import_benchmark_study |
| Import Eye-Tracking-BIDS physiological recordings | import_eye_bids |
| Extract incremental process validity | incremental_process_validity |
| Create or validate a multi-vendor corpus directory | init_vendor_corpus |
| Inject AOI label noise | inject_aoi_label_noise |
| Inject additive gaze calibration offset in coordinate units | inject_calibration_offset |
| Inject an additive device/site shift in a numeric feature | inject_device_shift |
| Inject generic missingness into selected columns | inject_eye_missingness |
| Inject pupil dropout | inject_pupil_dropout |
| Inject timestamp jitter | inject_sampling_jitter |
| Inject trial/row imbalance by dropping observations | inject_trial_imbalance |
| Compositional AOI channel | irt_compositional_channel |
| Continuous/bounded process channel for multimodal IRT | irt_continuous_channel |
| Count-valued process channel for multimodal IRT | irt_count_channel |
| Functional trajectory channel | irt_functional_channel |
| Define a multimodal IRT model specification | irt_model_spec |
| Nominal response/process channel | irt_nominal_channel |
| Binary/ordinal response channel for multimodal IRT | irt_response_channel |
| Response-time channel for multimodal IRT | irt_rt_channel |
| Sequence/process-state channel | irt_sequence_channel |
| Survival/event-time channel for multimodal IRT | irt_survival_channel |
| Specify a validation programme for a process-IRT model | irt_validation_spec |
| Multi-objective item-bank optimization | 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 |
| Stress-test IRT estimators across latent distributions | latent_distribution_stress_test |
| Estimate a descriptive continuous-time latent trajectory | latent_trait_trajectory |
| Leave device out validation | leave_device_out_validation |
| Leave item out validation | leave_item_out_validation |
| Leave session out validation | leave_session_out_validation |
| Leave site out validation | leave_site_out_validation |
| List registered multimodal IRT models | list_irt_models |
| Lock a decision manifest by content hash | lock_decision_manifest |
| Map supplied latent-class memberships to process summaries | map_latent_classes_to_process_profiles |
| Build a non-collapsed measurement-error budget | measurement_error_budget |
| Migrate a storage schema through an atomic rewrite | migrate_eye_storage_schema |
| Specify evidence gates for model promotion | model_promotion_spec |
| Specify a model-validation programme | model_validation_spec |
| Summarize model validation | model_validation_summary |
| Extract multiblock block contributions/coordinates | multiblock_contributions |
| Extract multiblock person coordinates | multiblock_person_coordinates |
| Extract multiblock variable coordinates | multiblock_variable_coordinates |
| Report multimodal backend availability | multimodal_backend_status |
| Consolidated multimodal IRT specification | multimodal_irt_spec |
| Fit M0, M1, and M2 as a response-target ablation sequence | multimodal_m2_ablation |
| Generate M2 alignment negative controls | multimodal_m2_negative_controls |
| Posterior predictive checks for the M2 three-way model | multimodal_m2_ppc |
| Quantify response-target process information in the M0-M2 sequence | multimodal_m2_process_information |
| Run repeated M2 estimator recovery | multimodal_m2_recovery |
| M2 response + RT + gaze reference specification | multimodal_m2_spec |
| Fit the complete M3 response-anchored channel-ablation lattice | multimodal_m3_ablation |
| Bridge existing functional pupil outputs into the scalar M3 reference layer | multimodal_m3_functional_bridge |
| Generate M3 multimodal falsification controls | multimodal_m3_negative_controls |
| Posterior predictive checks for the M3 four-channel model | multimodal_m3_ppc |
| Quantify M3 process information, pupil increment, redundancy and sensor value | multimodal_m3_process_information |
| Run M3 parameter-recovery and stress evidence | multimodal_m3_recovery |
| M3 response + RT + gaze + pupil specification | multimodal_m3_spec |
| Plan or fit the focused M3-to-M4 ablation set | multimodal_m4_ablation |
| Construct M4 temporal, nuisance, device, and overfitting negative controls | multimodal_m4_negative_controls |
| Posterior predictive checks for M4 measurement and sequential behavior | multimodal_m4_ppc |
| Quantify incremental response-target information supplied by M4 state structure | multimodal_m4_process_information |
| Evaluate deterministic M4 parameter and state recovery | multimodal_m4_recovery |
| Plan or run M4 state-count and modelling sensitivity analyses | multimodal_m4_sensitivity |
| Specify M4 trait-conditioned latent response-process states | multimodal_m4_spec |
| Summarize M4 latent-state uncertainty and dynamics | multimodal_m4_state_diagnostics |
| Posterior predictive checks for multimodal development fits | multimodal_ppc |
| Concordance of multiple negative-control families | negative_control_concordance |
| Negative-control test for an allegedly informative process channel | negative_control_process_test |
| Hash an R object reproducibly within an R serialization version | object_hash |
| Describe a stable object schema | object_schema |
| Open an eyeprocess storage handle | open_eye_storage |
| Open partitioned eye storage | open_partitioned_eye_storage |
| Quantify option-process information from a nominal gaze model | option_process_information |
| Audit whether candidate predictors can be constructed without an outcome column | outcome_blind_feature_audit |
| Create an outcome-blind data snapshot | outcome_blind_snapshot |
| Create a reproducibility manifest for files and software | package_reproducibility_manifest |
| Create a compact paper reproducibility manifest | paper_reproducibility_manifest |
| Create a partition specification | partition_eye_storage |
| Return failed pipeline steps | pipeline_failures |
| Extract a pipeline result by step name | pipeline_result |
| Pipeline step status table | pipeline_step_status |
| Audit a placebo/pre-event window | placebo_window_audit |
| Plot an AOI transition matrix | plot_aoi_transition_matrix |
| Plot top AOI transitions by probability/count | plot_aoi_transition_rank |
| Plot option-level distractor information | plot_distractor_information |
| Generate the complete Gazepoint workflow plot suite | plot_gazepoint_workflow |
| Plot interval coverage | plot_interval_coverage |
| Plot uncertainty for flexible item response functions | plot_irf_uncertainty |
| Plot parameter recovery | plot_parameter_recovery |
| Plot person/item latent-space coordinates | plot_person_item_space |
| Plot detected process change points | plot_process_changepoint |
| Plot channel-ablation delta from a full/reference model | plot_process_channel_ablation_delta |
| Plot process-feature stability across resamples/splits | plot_process_feature_stability |
| Explicit wrapper for process-window sensitivity plotting | plot_process_window_sensitivity |
| Plot pupil activity sensitivity to window length | plot_pupil_activity_sensitivity |
| Plot pupil activity features across windows/groups | plot_pupil_activity_windows |
| Plot pupil low/high-band power summaries | plot_pupil_band_power |
| Plot tonic/phasic pupil components | plot_pupil_components |
| Plot raw-to-processed pupil preprocessing stages | plot_pupil_preprocessing_audit |
| Plot a pupil-signal power spectrum | plot_pupil_spectrum |
| Plot SBC rank histograms | plot_sbc_rank |
| Plot validation failure rates | plot_validation_failures |
| Plot validation runtime | plot_validation_runtime |
| Plot eye adapter regression audit | plot.eye_adapter_regression_audit |
| Plot aoi growth curve diagnostics | plot.eye_aoi_growth_curve |
| Plot aoi trajectory diagnostics | plot.eye_aoi_trajectory |
| Plot bayesian process dashboard diagnostics | plot.eye_bayesian_process_dashboard |
| Plot eye bids roundtrip | plot.eye_bids_roundtrip |
| Plot biometric imputation sensitivity diagnostics | plot.eye_biometric_imputation_sensitivity |
| Plot biometric preflight diagnostics | plot.eye_biometric_preflight |
| Plot candidate item bank audit diagnostics | plot.eye_candidate_item_bank_audit |
| Plot detailed compatibility evidence | plot.eye_compatibility_evidence_matrix |
| Plot decision process proxy diagnostics | plot.eye_decision_process_proxy |
| plot eye dynamic irtree | plot.eye_dynamic_irtree |
| plot eye dynamic ppc | plot.eye_dynamic_ppc |
| Plot eye event roundtrip audit | plot.eye_event_roundtrip_audit |
| Plot eye event time irt | plot.eye_event_time_irt |
| plot eye functional pupil diagnostics | plot.eye_functional_pupil_diagnostics |
| plot eye functional pupil irt | plot.eye_functional_pupil_irt |
| plot eye functional pupil sensitivity | plot.eye_functional_pupil_sensitivity |
| Plot gated process model diagnostics | plot.eye_gated_process_model |
| Plot gaze anchored 3pl audit diagnostics | plot.eye_gaze_anchored_3pl_audit |
| Plot eye gaze informed missingness irt | plot.eye_gaze_informed_missingness_irt |
| Plot flexible IRF shape diagnostics | plot.eye_gpirt |
| Plot incremental process-channel information by fold | plot.eye_incremental_information_audit |
| Plot detected process changepoints | plot.eye_irt_changepoints |
| Plot an IRT linking/equating transformation | plot.eye_irt_equating |
| Plot posterior predictive discrepancy tail probabilities | plot.eye_irt_ppc |
| Plot parameter-recovery bias or RMSE | plot.eye_irt_recovery_summary |
| Plot SBC rank histograms by parameter | plot.eye_irt_sbc |
| Plot item parameter seed diagnostics | plot.eye_item_parameter_seed |
| Plot item reduction sensitivity diagnostics | plot.eye_item_reduction_sensitivity |
| Plot a joint gaze-response-time IRT fit | plot.eye_joint_gaze_rt_irt |
| Plot a graded response + RT/process fit | plot.eye_joint_graded_rt_process_irt |
| Plot eye latent distribution comparison | plot.eye_latent_distribution_comparison |
| Plot latent process alignment diagnostics | plot.eye_latent_process_alignment |
| Plot a latent-space IRT adapter fit | plot.eye_latent_space_irt |
| Plot many-facet process IRT effects | plot.eye_manyfacet_process_irt |
| Plot mixture irt process diagnostics | plot.eye_mixture_irt_process |
| plot eye model promotion audit | plot.eye_model_promotion_audit |
| Plot multiblock process map diagnostics | plot.eye_multiblock_process_map |
| Plot nominal-response gaze results | plot.eye_nominal_gaze_irt |
| Plot nonparametric rasch audit diagnostics | plot.eye_nonparametric_rasch_audit |
| Plot omission/not-reached survival IRT diagnostics | plot.eye_omission_survival_irt |
| Plot preaction process features diagnostics | plot.eye_preaction_process_features |
| Plot presentation accessibility diagnostics | plot.eye_presentation_accessibility |
| Plot presentation fairness comparison diagnostics | plot.eye_presentation_fairness_comparison |
| Plot process anomaly audit diagnostics | plot.eye_process_anomaly_audit |
| Plot CAT simulation information accumulation | plot.eye_process_cat_simulation |
| Plot process-channel ablation | plot.eye_process_channel_ablation |
| Plot process-dependent discrimination | plot.eye_process_dependent_discrimination |
| Plot process drift audit diagnostics | plot.eye_process_drift_audit |
| Plot process external validity diagnostics | plot.eye_process_external_validity |
| Plot eye process facet effects | plot.eye_process_facet_effects |
| Plot process feature blocks diagnostics | plot.eye_process_feature_blocks |
| Plot process-measure variance components | plot.eye_process_g_study |
| Plot a process-HMM IRT fit | plot.eye_process_hmm_irt |
| Plot process/local-dependence diagnostics | plot.eye_process_local_dependence_audit |
| Plot a process-channel negative-control distribution | plot.eye_process_negative_control |
| Plot process person-fit discrepancies | plot.eye_process_person_fit |
| Plot process profile mixture diagnostics | plot.eye_process_profile_mixture |
| Plot process rasch tree diagnostics | plot.eye_process_rasch_tree |
| Plot process window sensitivity diagnostics | plot.eye_process_window_sensitivity |
| Plot process windows diagnostics | plot.eye_process_windows |
| Plot pupil confound model diagnostics | plot.eye_pupil_confound_model |
| Plot pupil deconvolution diagnostics | plot.eye_pupil_deconvolution |
| Plot pupil fatigue drift diagnostics | plot.eye_pupil_fatigue_drift |
| Plot pupil frequency features diagnostics | plot.eye_pupil_frequency_features |
| Plot pupil frequency stability diagnostics | plot.eye_pupil_frequency_stability |
| plot eye roundtrip loss audit | plot.eye_roundtrip_loss_audit |
| Plot SBC audit summaries | plot.eye_sbc_audit |
| Plot semantic round-trip fidelity | plot.eye_semantic_roundtrip |
| Plot signal filter audit diagnostics | plot.eye_signal_filter_audit |
| plot eye storage benchmark | plot.eye_storage_benchmark |
| plot eye strategy aoi sensitivity | plot.eye_strategy_aoi_sensitivity |
| Plot streaming score diagnostics | plot.eye_streaming_score |
| plot eye transition diagnostics | plot.eye_transition_diagnostics |
| Plot validation bundle diagnostics | plot.eye_validation_bundle |
| plot eye vendor compatibility matrix | plot.eye_vendor_compatibility_matrix |
| Plot eye vendor semantic validation | plot.eye_vendor_semantic_validation |
| Plot visual context irt diagnostics | plot.eye_visual_context_irt |
| Posterior predictive discrepancy table | posterior_predictive_discrepancies |
| Define a posterior-SBC replication contract | posterior_sbc_contract |
| Build pre-action process features | preaction_process_features |
| Predict from an AOI growth curve | predict_aoi_trajectory |
| Predict pre-pilot item-parameter priors | predict_item_parameter_priors |
| Predict a latent trait at arbitrary times | predict_theta_at_time |
| Predict expected bounded response from a censored-normal process IRT fit | predict.eye_censored_normal_process_irt |
| Predict destination-state probabilities | predict.eye_multinomial_transition |
| Extract pre-flight decisions | preflight_decisions |
| Create an explicit pre-flight exclusion/review manifest | preflight_exclusion_manifest |
| Extract pre-flight failures/review cases | preflight_failures |
| Extract pre-flight passes | preflight_passed |
| Prepare ordered transition data for dynamic IRTree models | prepare_dynamic_irtree_data |
| Prepare aligned, corrected functional pupil data | prepare_functional_pupil_data |
| Prepare joint accuracy-response-time data | prepare_gaze_diffusion_data |
| Prepare a canonical multimodal person-item-trial measurement object | prepare_multimodal_irt_data |
| Prepare data for a strategy-mixture model | prepare_strategy_mixture_data |
| Prepare leakage-safe structured/unstructured process representations | prepare_structured_unstructured_process_features |
| Run a preprocessing or AOI multiverse | preprocessing_multiverse |
| print eye benchmark reproduction | print.eye_benchmark_reproduction |
| print eye benchmark study | print.eye_benchmark_study |
| print eye benchmark validation | print.eye_benchmark_validation |
| print eye diffusion diagnostics | print.eye_diffusion_diagnostics |
| print eye diffusion identification study | print.eye_diffusion_identification_study |
| print eye dynamic irtree | print.eye_dynamic_irtree |
| print eye dynamic ppc | print.eye_dynamic_ppc |
| print eye dynamic recovery | print.eye_dynamic_recovery |
| print eye engine adapter result | print.eye_engine_adapter_result |
| print eye functional pupil data | print.eye_functional_pupil_data |
| print eye functional pupil diagnostics | print.eye_functional_pupil_diagnostics |
| print eye functional pupil irt | print.eye_functional_pupil_irt |
| print eye functional pupil sensitivity | print.eye_functional_pupil_sensitivity |
| print eye functional scalar comparison | print.eye_functional_scalar_comparison |
| Print a gated process model object | print.eye_gated_process_model |
| print eye gaze diffusion data | print.eye_gaze_diffusion_data |
| print eye gaze diffusion irt | print.eye_gaze_diffusion_irt |
| print eye gaze diffusion spec | print.eye_gaze_diffusion_spec |
| Print eye irt evidence grade | print.eye_irt_evidence_grade |
| Print a multimodal IRT model specification | print.eye_irt_model_spec |
| Print eye irt validation spec | print.eye_irt_validation_spec |
| print eye model contract validation | print.eye_model_contract_validation |
| print eye model promotion audit | print.eye_model_promotion_audit |
| print eye multinomial transition | print.eye_multinomial_transition |
| print eye partition spec | print.eye_partition_spec |
| print eye partitioned storage | print.eye_partitioned_storage |
| Print a process drift spec object | print.eye_process_drift_spec |
| Print eye process negative control | print.eye_process_negative_control |
| Print a process preflight spec object | print.eye_process_preflight_spec |
| Print a process window spec object | print.eye_process_window_spec |
| print eye redaction result | print.eye_redaction_result |
| print eye roundtrip loss audit | print.eye_roundtrip_loss_audit |
| print eye strategy data | print.eye_strategy_data |
| print eye strategy manipulation validation | print.eye_strategy_manipulation_validation |
| print eye theory strategy irt | print.eye_theory_strategy_irt |
| print eye theory strategy spec | print.eye_theory_strategy_spec |
| print eye transition design | print.eye_transition_design |
| print eye transition diagnostics | print.eye_transition_diagnostics |
| Print a validation bundle object | print.eye_validation_bundle |
| print eye validation case fingerprint | print.eye_validation_case_fingerprint |
| print eye validation collection | print.eye_validation_collection |
| print eye validation completion audit | print.eye_validation_completion_audit |
| print eye validation job plan | print.eye_validation_job_plan |
| print eye validation run | print.eye_validation_run |
| print eye vendor case | print.eye_vendor_case |
| print eye vendor compatibility matrix | print.eye_vendor_compatibility_matrix |
| Print eye vendor schema contract | print.eye_vendor_schema_contract |
| print eye vendor semantic comparison | print.eye_vendor_semantic_comparison |
| Print a visual context registry object | print.eye_visual_context_registry |
| Probabilistic AOI assignment under empirical calibration uncertainty | probabilistic_aoi_assignment |
| Extract multivariate process anomaly distances | process_anomaly_distance |
| Bland-Altman repeatability summary for two sessions | process_bland_altman |
| Ablate process channels under a common out-of-sample evaluator | process_channel_ablation |
| Extract process-criterion associations | process_criterion_associations |
| Audit process-dependent item discrimination | process_dependent_discrimination_audit |
| Construct a process-data nuisance surrogate for DIF analysis | process_dif_nuisance_surrogate |
| Extract drift alerts | process_drift_alerts |
| Specify a process-deployment drift audit | process_drift_spec |
| Define conceptual process-feature blocks | process_feature_blocks |
| Registry of process-feature families and interpretation guardrails | process_feature_family_registry |
| Summarize process-feature stability across repeated analyses | process_feature_stability |
| Declare temporal provenance for process features | process_feature_time_provenance |
| Absolute-agreement ICC(A,1) for repeated process measures | process_icc |
| Quantify incremental process information | process_information |
| 2PL response item information | process_item_information |
| Return a one-measure process card | process_measure_card |
| Process-measure coverage for an observed dataset | process_measure_coverage |
| Process-measure guardrail table | process_measure_guardrails |
| Process-measure lineage table | process_measure_lineage |
| Unified process-measure registry | process_measure_registry |
| List units used by registered process measures | process_measure_units |
| Permutation negative control | process_negative_control_permute |
| Temporal-shift negative control | process_negative_control_shift |
| N-gram features from process sequences | process_ngram_features |
| Compare an observed effect against a negative-control null distribution | process_null_benchmark |
| Joint response-process person-fit diagnostic | process_person_fit |
| Specify a biometric process pre-flight gate | process_preflight_spec |
| Extract process-profile probabilities | process_profile_probabilities |
| Summarize process profiles | process_profile_summary |
| Test-retest process reliability profile | process_reliability_profile |
| Return person/item latent-space coordinates | process_residual_map |
| Construct an explicit process-analysis sensitivity grid | process_sensitivity_grid |
| Low-dimensional embedding of response-process sequences | process_sequence_embedding |
| Summarize HMM state occupancy | process_state_occupancy |
| Summarize HMM process-state transitions | process_state_transition_summary |
| Pairwise temporal stability across sessions | process_temporal_stability |
| Process measurement-uncertainty budgets | 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 |
| Define an empirical process-validation design | process_validation_design |
| Specify temporal process windows | process_window_spec |
| Promote an IRT model after evidence gates are met | promote_irt_model |
| Promote a case support level only when evidence is supplied | promote_vendor_support |
| Propagate empirical calibration uncertainty around gaze samples | propagate_calibration_uncertainty |
| Build a provenance edge table | provenance_edge_table |
| Build a provenance lineage node table | provenance_lineage_table |
| Remove obsolete or corrupt validation checkpoints | prune_validation_checkpoints |
| Public validation-corpus registry | public_validation_corpus |
| Compute a transparent pupil activity index | pupil_activity_index |
| Compute pupil signal power in a frequency band | pupil_band_power |
| Evaluate pupil baseline-window sensitivity | pupil_baseline_sensitivity |
| Extract pupil confound-model effects | pupil_confound_effects |
| Extract event effects from pupil deconvolution | pupil_event_effects |
| Build an event-locked pupil regressor | pupil_event_regressor |
| Extract pupil frequency-domain and activity features by group | pupil_frequency_features |
| Pupil latency estimator sensitivity and resolvability audit | pupil_latency_sensitivity |
| Create a preprocessing sensitivity grid for pupil analysis | pupil_preprocessing_grid |
| Run functional pupil preprocessing sensitivity analysis | pupil_preprocessing_sensitivity |
| Canonical gamma-shaped pupil response kernel | pupil_response_kernel |
| Pupil-unit semantic-fidelity audit | pupil_unit_fidelity_audit |
| Derivative-based pupil activity magnitude | pupil_velocity_activity |
| Quantify leakage from row-wise rather than grouped validation | quantify_process_leakage |
| Query partitioned eye storage lazily where possible | query_eye_storage |
| Specify a published Raven strategy-model reproduction | raven_reproduction_spec |
| Read API lifecycle registry from CSV | read_api_lifecycle_registry |
| Read a benchmark table | read_benchmark_table |
| Read a decision manifest written by eyeprocess | read_decision_manifest |
| Read and verify a frozen evidence bundle | read_eyeprocess_validation_evidence |
| Read a reproducibility fingerprint | read_reproducibility_fingerprint |
| Read a validation manifest | read_validation_job_manifest |
| Read a validation scenario manifest | read_validation_scenario_manifest |
| Read the multi-vendor case registry | read_vendor_registry |
| Iteratively detect, clean, and recalibrate after process change points | recalibrate_after_changepoint |
| Approximate simulation replications needed for a target Monte Carlo error | recommended_validation_replications |
| Redact a validation case without inventing replacement data | redact_validation_case |
| Add or update API lifecycle metadata without global mutation | register_eye_api_status |
| Register a multimodal IRT model | register_irt_model |
| Add a process measure to a registry without global mutation | register_process_measure |
| Pupil phase-amplitude registration | 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 |
| Register an independent validation case | register_validation_case |
| Register vendor-field semantics | register_vendor_semantics |
| Audit reporting-guideline coverage | reporting_guideline_audit |
| Representative scanpaths and scanpath distributions | 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 |
| Resume a governed pipeline from a prior run | resume_eye_pipeline |
| Resume incomplete or failed validation jobs | resume_validation_jobs |
| Execute and audit an Eye-Tracking-BIDS round trip | roundtrip_eye_bids |
| Derive reproducible benchmark summaries | run_benchmark_reproduction |
| Run a computational scaling benchmark | run_eye_benchmark |
| Run a governed eyeprocess pipeline | run_eye_pipeline |
| Run a named equateIRT linking/equating function without fallback substitution | run_eyeprocess_equateirt |
| Run simulation-based calibration for known-item IRT ability scoring | run_eyeprocess_irt_ability_sbc |
| Run IRT parameter recovery with the exact mirt engine | run_eyeprocess_irt_recovery |
| Run a mirtCAT adaptive-testing workflow without fallback substitution | run_eyeprocess_mirtcat |
| Execute a declared measurement-stress evidence plan | run_eyeprocess_stress_evidence |
| Run the complete validation-release programme | run_eyeprocess_validation_program |
| Run the complete Gazepoint downstream workflow | run_gazepoint_workflow |
| Run parameter-recovery, coverage, and misspecification validation | run_model_validation |
| Run posterior simulation-based calibration from an explicit contract | run_posterior_sbc |
| Run repeated process negative controls | run_process_negative_controls |
| Run an explicit process-analysis multiverse | run_process_sensitivity |
| Run an empirical process-validation programme | run_process_validation |
| Execute a licensed published-model reproduction | run_raven_reproduction |
| Run generic simulation-based calibration | run_sbc |
| Run validation jobs with checkpointing and deterministic seeds | run_validation_jobs |
| ECDF deviation summary for SBC ranks | sbc_ecdf_deviation |
| Build SBC rank diagnostics | sbc_rank_diagnostics |
| Summarize simulation-based calibration | sbc_summary |
| Score a partial response pattern from a calibrated mirt model | score_partial_response_pattern |
| Score a response stream cumulatively | score_response_stream |
| Select the next item using response/process utility | select_next_item_process |
| Semantic fidelity specification | semantic_fidelity_spec |
| Convert a semantic round-trip audit into a loss map | semantic_loss_map |
| Audit a complete semantic round trip | semantic_roundtrip_audit |
| Stable fingerprint of a sensitivity branch | sensitivity_branch_fingerprint |
| Decision leverage of each analytical choice | sensitivity_decision_leverage |
| Fragility index across analysis specifications | sensitivity_fragility_index |
| Machine-readable multiverse manifest | sensitivity_multiverse_manifest |
| Rank stability across specifications | sensitivity_rank_stability |
| Effect-sign stability across specifications | sensitivity_sign_stability |
| Significance-decision stability across specifications | sensitivity_significance_stability |
| Substantive-threshold stability across specifications | sensitivity_threshold_stability |
| Convert scanpaths to process/sequence package contracts | as_procdata_sequence as_seqhmm_data as_traminer_sequence sequence_interoperability |
| Extract session facet effects | session_facet_effects |
| Simulate advanced response-process data | simulate_advanced_process_data |
| Simulate observed dynamic-state transitions | simulate_dynamic_irtree_data |
| Run a catR adaptive-testing simulation without fallback substitution | simulate_eyeprocess_catr |
| Simulate dichotomous IRT responses with optional local dependence and missingness | simulate_eyeprocess_irt_binary |
| Simulate data from a model or registered model specification | simulate_from_model |
| Simulate a hierarchical gaze-diffusion study | simulate_gaze_diffusion_data |
| Simulate from a registered multimodal IRT model | simulate_irt_model |
| Simulate multimodal IRT process data | simulate_multimodal_irt |
| Simulate from the M2 response + RT + gaze generative model | simulate_multimodal_m2 |
| Simulate the M3 response + RT + gaze + pupil generative model | simulate_multimodal_m3 |
| Simulate M4 multimodal sequential measurement data | simulate_multimodal_m4 |
| Simulate pre-registered presentation variants for review | simulate_presentation_variants |
| Simulate a simple process-aware CAT policy | simulate_process_cat |
| Simulate a generic multimodal validation dataset with known truth | simulate_process_validation_data |
| Simulate a theory-defined strategy-mixture study | simulate_strategy_mixture_data |
| Run simulation-based calibration | simulation_based_calibration |
| Compute a simulation-based calibration rank statistic | simulation_rank_statistic |
| Create or normalize a software-paper claim matrix | software_paper_claim_matrix |
| Compute descriptive evidence coverage | software_paper_coverage |
| Construct a software-paper evidence bundle | software_paper_evidence_bundle |
| Identify gaps in a software-paper evidence bundle | software_paper_gap_analysis |
| Descriptive software-paper readiness audit | software_paper_readiness |
| Summarise validation evidence for a software paper | software_paper_validation_table |
| Fraction of planned specifications successfully evaluated | specification_coverage |
| Prepare ordered specification-curve data | specification_curve_data |
| Split-half reliability for a trial-level process measure | split_half_process_reliability |
| Split a validation plan into independent chunks | split_validation_plan |
| Return the transaction manifest | storage_transaction_manifest |
| Assess sensitivity to alternative AOI feature definitions | strategy_aoi_sensitivity |
| Quantify strategy-classification uncertainty | strategy_classification_uncertainty |
| Diagnose label stability across multiple starts | strategy_label_switching_diagnostics |
| Posterior strategy probabilities | strategy_posterior_probabilities |
| Extract streaming score history | streaming_score_history |
| Stress test latent distribution | stress_test_latent_distribution |
| Stress test local dependence | stress_test_local_dependence |
| Stress test missingness | stress_test_missingness |
| Run a generic misspecification stress-test grid | stress_test_misspecification |
| Stress test preprocessing | stress_test_preprocessing |
| Stress-test an analysis under explicit synthetic corruptions | stress_test_process_pipeline |
| Stress test speededness | stress_test_speededness |
| Summarise stress-test metrics | stress_test_summary |
| Identify the empirical stress frontier for a metric | stress_tolerance_frontier |
| Define structural-zero and allowed transition masks | structural_transition_mask |
| Summarise benchmark timing and memory by problem size | summarise_eye_benchmark |
| Summarise executed measurement-stress evidence | summarise_eyeprocess_stress_evidence |
| Summarise process negative controls | summarise_process_negative_controls |
| Summarise process sensitivity results | summarise_process_sensitivity |
| Summarise a process-validation result | summarise_process_validation |
| Summarise an acceptance matrix | summarise_validation_acceptance |
| Summarise parameter recovery | summarize_parameter_recovery |
| Summarize extracted process windows | summarize_process_windows |
| Summarize a validation bundle object | summary.eye_validation_bundle |
| summary eye validation job plan | summary.eye_validation_job_plan |
| Define synthetic measurement corruptions for stress testing | synthetic_corruption_plan |
| Define a theory-constrained strategy-mixture model | theory_strategy_spec |
| Timestamp semantic-fidelity audit | timestamp_fidelity_audit |
| Compute transition residual diagnostics | transition_residual_diagnostics |
| Update a partial person score with one new response | update_person_score |
| Upgrade a legacy eye dataset | upgrade_eye_dataset |
| Upgrade a legacy eyeprocess model | upgrade_eyeprocess_model |
| Compare a validation result with a frozen reference | validate_against_reference |
| Validate benchmark integrity and relational constraints | validate_benchmark_study |
| Validate BIDS eye-tracking semantics | validate_bids_eye_semantics |
| Validate a research decision manifest | validate_decision_manifest |
| Validate an external-engine adapter contract | validate_engine_adapter |
| Validate a governed eyeprocess pipeline | validate_eye_pipeline |
| Validate a provenance graph | validate_eye_prov_graph |
| Validate storage metadata and partition fingerprints | validate_eye_storage_metadata |
| Validate that an external IRT fit used the requested engine | validate_eyeprocess_external_irt_fit |
| Validate an adaptive IRT item bank | validate_eyeprocess_irt_item_bank |
| Validate an eyeprocess IRT model specification | validate_eyeprocess_irt_model_spec |
| Validate a joint process IRT specification | validate_eyeprocess_joint_process_irt_spec |
| Validate a validation-evidence plan | validate_eyeprocess_validation_plan |
| Validate feature availability against an analysis cutoff | validate_feature_availability |
| Validate an integrated Gazepoint workflow result | validate_gazepoint_workflow |
| Minimal HED annotation audit for event tables | validate_hed_event_semantics |
| Validate a registered multimodal IRT model | validate_irt_model |
| Validate latent-space proximity against process similarity | validate_latent_space_process_similarity |
| Validate a fitted model against the stable model contract | validate_model_object |
| Validate a multimodal IRT development object | validate_multimodal_irt |
| Validate an M2 fit or simulation | validate_multimodal_m2 |
| Validate an M3 simulation or fitted four-channel model | validate_multimodal_m3 |
| Validate M4 data, computation, state behavior, and evidence | validate_multimodal_m4 |
| Validate a process-measure registry | validate_process_measure_registry |
| Validate a process-validation design | validate_process_validation_design |
| Validate a process-window representation | validate_process_windows |
| Validate strategy posteriors against an experimental manipulation | validate_strategy_manipulation |
| Validate imported data against a vendor semantic contract | validate_vendor_semantics |
| Validate vendor-specific timestamp semantics | validate_vendor_timestamp_semantics |
| Evaluate a table against named validation rules | validation_acceptance_matrix |
| Define a validation acceptance rule | validation_acceptance_rule |
| Create a machine-readable validation manifest | validation_bundle_manifest |
| Summarize prediction calibration | validation_calibration_summary |
| Return stable validation condition identifiers | validation_condition_id |
| Rank validation conditions by a transparent robustness score | validation_condition_ranking |
| Interval-coverage table | validation_coverage_table |
| Detailed validation evidence levels | validation_evidence_levels |
| Create a model-by-evidence validation matrix | validation_evidence_matrix |
| Failure profile for a validation programme | validation_failure_profile |
| Summarize convergence and execution failures | validation_failure_summary |
| Classify common estimator failures without hiding the original message | validation_failure_taxonomy |
| Create a deterministic validation job plan | validation_job_plan |
| Build a measurement-to-generalization validation ladder | validation_ladder |
| Monte Carlo standard errors for validation metrics | validation_mcse |
| Estimate Monte Carlo uncertainty for validation summaries | validation_mcse_profile |
| Summarize parameter recovery | validation_recovery_summary |
| Parameter-recovery table | validation_recovery_table |
| Compute a replication budget from a target MCSE | validation_replication_budget |
| Render a conservative validation report | validation_report |
| Overall validation robustness score | validation_robustness_score |
| Summarize validation runtime and checkpoint scale | validation_runtime_summary |
| Summarize simulation-based calibration ranks | validation_sbc_summary |
| Create a scenario manifest for frozen validation work | validation_scenario_manifest |
| Allocate a deterministic validation seed | validation_seed |
| Monte Carlo standard-error diagnostics for validation summaries | validation_summary_mcse |
| Specify completion and scientific-promotion thresholds | validation_thresholds |
| Declare a vendor semantic schema contract | vendor_schema_contract |
| Specify multi-vendor empirical validation requirements | vendor_validation_spec |
| Verify that a locked manifest has not changed | verify_decision_manifest_lock |
| Verify a frozen validation atlas | verify_eyeprocess_validation_atlas |
| Verify the integrity hash of a frozen evidence bundle | verify_eyeprocess_validation_evidence |
| Verify an outcome-blind snapshot has not changed | verify_outcome_blind_snapshot |
| Verify an internally stored fingerprint hash | verify_reproducibility_fingerprint |
| Verify a reproducibility manifest | verify_reproducibility_manifest |
| Build an item-to-visual-context registry | visual_context_registry |
| Write an advanced-model evidence report | write_advanced_model_evidence_report |
| Write API lifecycle registry to CSV | write_api_lifecycle_registry |
| Write the benchmark data dictionary | write_benchmark_data_dictionary |
| Write a decision manifest | write_decision_manifest |
| Write a conservative pipeline report | write_eye_pipeline_report |
| Write an eye dataset to RDS or Arrow/Parquet storage | write_eye_storage |
| Write an explicit `_targets.R` template from a governed pipeline | write_eye_targets_template |
| Write a frozen evidence bundle | write_eyeprocess_validation_evidence |
| Write a compact Markdown validation report | write_eyeprocess_validation_report |
| Write a reproducible Gazepoint workflow report | write_gazepoint_workflow_report |
| Write a model-promotion report | write_model_promotion_report |
| Write an eye dataset as atomic partitioned storage | write_partitioned_eye_storage |
| Return Graphviz DOT for a provenance graph | write_prov_dot |
| Write a reporting-guideline audit report | write_reporting_guideline_report |
| Write a reproducibility fingerprint | write_reproducibility_fingerprint |
| Write a human-readable software-paper evidence report | write_software_paper_evidence |
| Write a complete software-paper reproduction scaffold | write_software_paper_reproduction |
| Write a methodological software-paper scaffold | write_software_paper_scaffold |
| Write a machine-readable validation manifest | write_validation_job_manifest |
| Write a validation release report | write_validation_release_report |
| Write a validation report to disk | write_validation_report |
| Write a validation scenario manifest | write_validation_scenario_manifest |
| Write a vendor case evidence report | write_vendor_case_report |
| Write the multi-vendor case registry | write_vendor_registry |
| Write a multi-vendor validation report | write_vendor_validation_report |