Package {eyeprocess}

Type:	Package
Title:	Harmonize Eye-Tracking, Pupillometry, Biometrics, and Psychometric Process Data
Version:	0.11.1
Description:	Provides an extensible, vendor-neutral framework for importing, validating, harmonizing, transforming, visualizing, and modelling eye-tracking, pupillometry, behavioural, and biometric process data. The package uses explicit timebase and coordinate-space registries, preserves native fields and provenance, and offers first-class adapters for Gazepoint Analysis and Gazepoint Biometrics exports alongside generic and vendor-specific importers. Downstream tools support trial and area of interest reconstruction, signal-quality auditing, feature derivation, scanpath analysis, response-time and item-response workflows, and optional psychometric modelling engines. An integrated Gazepoint workflow produces quality-control evidence, media-trial reconstruction, plots, analysis-ready process tables, item response theory (IRT)-ready response structures, and reproducible reports. Brain Imaging Data Structure (BIDS) interoperability for eye-tracking and validation-release infrastructure support disk-backed storage, independent multi-vendor evidence, grouped validation, simulation calibration, model-equivalence audits, and explicitly experimental advanced psychometric process models. Research-scale infrastructure adds deterministic resumable Monte Carlo execution, atomic validation checkpoints, explicit advanced-model promotion gates, independent multi-vendor evidence registries, stable object contracts, partitioned disk-backed storage, optional probabilistic engines, and a fully synthetic multimodal benchmark for reproducibility testing. The measurement-intelligence programme adds probabilistic and compositional area of interest (AOI) analysis, measurement-uncertainty propagation, calibration and device-transportability audits, process reliability, phase-amplitude pupil registration, informative-missingness sensitivity, temporal and spatial process models, item-bank decision optimization, fairness monitoring, conditional process reference distributions, and evidence-provenance graphs.
License:	MIT + file LICENSE
URL:	https://stefanosbalaskas.github.io/eyeprocess/, https://github.com/stefanosbalaskas/eyeprocess
BugReports:	https://github.com/stefanosbalaskas/eyeprocess/issues
Encoding:	UTF-8
Depends:	R (≥ 4.1.0)
Imports:	graphics, grDevices, methods, splines, stats, utils, withr
Suggests:	brms, ggplot2, jsonlite, knitr, lme4, LNIRT, mirt, rmarkdown, TAM, arrow, diffIRT, GDINA, OpenMx, TraMineR, testthat (≥ 3.0.0), callr, cmdstanr, eyetrackingR, future, future.apply, openssl, PupillometryR, rtdists, seqHMM, LSMjml, MASS, mgcv, nnet, survival, eRm, FactoMineR, mice, missForest, plm, psychotree, ranger, robfilter, tidyLPA, loo, posterior, targets, equateIRT, catR, mirtCAT
Additional_repositories:	https://stan-dev.r-universe.dev
VignetteBuilder:	knitr
Config/testthat/edition:	3
Config/roxygen2/version:	8.0.0
NeedsCompilation:	no
Packaged:	2026-09-13 10:23:23 UTC; Stefanos-PC
Author:	Stefanos Balaskas [aut, cre]
Maintainer:	Stefanos Balaskas <s.balaskas@ac.upatras.gr>
Repository:	CRAN
Date/Publication:	2026-09-28 09:30:08 UTC

eyeprocess: Harmonize Eye-Tracking and Psychometric Process Data

Description

An extensible, vendor-neutral framework for importing, validating, harmonizing, transforming, visualizing, and modelling eye-tracking, pupillometry, behavioural, and biometric process data, with first-class Gazepoint support.

Details

The package represents heterogeneous exports as linked canonical tables with explicit timebase, coordinate-space, processing-level, quality, and provenance information. It supports generic mappings and dedicated adapters, then provides common downstream workflows for preprocessing, AOIs, trials, features, plots, and optional psychometric modelling.

Author(s)

Stefanos Balaskas

References

Package documentation and the methodological references cited in the vignettes.

Create formal channel-ablation datasets

Description

Create formal channel-ablation datasets

Usage

ablate_multimodal_channels(
  x,
  include = c("response", "rt", "gaze", "pupil"),
  include_response = TRUE
)

Arguments

Value

An 'eye_multimodal_ablation' list.

Compute aDDM/GLAM-inspired gaze-evidence proxy features

Description

Compute aDDM/GLAM-inspired gaze-evidence proxy features

Usage

addm_glam_proxy_features(
  data,
  by = c("person_id", "trial_id"),
  time = "time_ms",
  aoi = "aoi",
  target_aoi = "target",
  distractor_aoi = "distractor",
  action_aoi = "button"
)

Arguments

Value

An object of class "eye_decision_process_proxy", stored as a named list, with components "features", "by", "status", "caveat". It contains aDDM/GLAM-inspired gaze-evidence proxy features and associated metadata or diagnostics needed to interpret the result.

Extract confound-adjusted pupil values

Description

Extract confound-adjusted pupil values

Usage

adjust_pupil_confounds(x)

Arguments

Value

A data frame containing the fitted model data, including 'pupil_confound_residual' and 'pupil_confound_adjusted' columns.

Specify evidence required to promote advanced model interfaces

Description

Specify evidence required to promote advanced model interfaces

Usage

advanced_model_evidence_spec(
  models = c("fit_joint_process_model", "fit_shared_process_factor",
    "fit_strategy_mixture", "fit_process_irt", "fit_pupil_informed_irt",
    "fit_multimodal_irt", "fit_dynamic_aoi_model", "fit_gaze_weighted_choice",
    "fit_dynamic_irtree", "fit_joint_functional_pupil_irt", "fit_theory_strategy_irt",
    "fit_gaze_diffusion_irt"),
  require_recovery = TRUE,
  require_calibration = TRUE,
  require_misspecification = TRUE,
  require_grouped_validation = TRUE,
  require_engine_equivalence = TRUE,
  require_empirical_reproduction = TRUE,
  require_sensitivity = TRUE
)

Arguments

Value

An 'eye_advanced_evidence_spec'.

Construct the advanced-model validation grid

Description

By default this returns a one-factor-at-a-time screening design around a declared reference scenario. This preserves every factor and level from the research programme without accidentally launching hundreds of thousands of Monte Carlo scenarios. Set 'full_factorial = TRUE' only when the computing plan explicitly supports the complete Cartesian design.

Usage

advanced_validation_grid(quick = FALSE, full_factorial = FALSE)

Arguments

Value

A scenario data frame for 'run_model_validation()' or custom Monte Carlo programmes.

Extract algorithm facet effects

Description

Extract algorithm facet effects

Usage

algorithm_facet_effects(object, channel = c("response", "process"))

Arguments

Value

An object of class "eye_process_facet_effects", stored as a named list, with components "facet", "column", "channel", "random_effects", "variance_component". It contains algorithm facet effects and associated metadata or diagnostics needed to interpret the result.

Entropy of an explicitly enumerated analysis-decision space

Description

Entropy of an explicitly enumerated analysis-decision space

Usage

analysis_decision_entropy(..., base = 2)

Arguments

Value

Data frame with option counts and maximum entropy under equal weighting.

Snapshot an eyeprocess analysis environment

Description

Snapshot an eyeprocess analysis environment

Usage

analysis_environment_snapshot(packages = loadedNamespaces())

Arguments

Value

A named list with components "r_version", "platform", "os", "locale", "timezone", "packages", containing snapshot an eyeprocess analysis environment and associated metadata or diagnostics.

Audit compatibility between measurement resolution and an analysis target

Description

Audit compatibility between measurement resolution and an analysis target

Usage

analysis_resolution_guard(
  event_duration_ms,
  effective_hz,
  spatial_feature_size = NA_real_,
  radial_error = NA_real_,
  min_samples = 3,
  max_error_fraction = 0.5
)

Arguments

Value

A named list with components "expected_samples", "temporal_ok", "spatial_error_fraction", "spatial_ok", "min_samples", "max_error_fraction", "overall", "caveat", containing compatibility between measurement resolution and an analysis target and associated metadata or diagnostics.

AOI membership probabilities from uncertainty draws

Description

AOI membership probabilities from uncertainty draws

Usage

aoi_membership_probability(draws, aois)

Arguments

Value

A tabular R object containing aOI membership probabilities from uncertainty draws; rows represent analysis units and columns contain the returned quantities.

Extract AOI growth-curve/trajectory features

Description

Converts AOI occupancy over binned time into orthogonal-polynomial trajectory coefficients. Coefficients summarize temporal shape and are not latent psychological traits by themselves.

Usage

aoi_trajectory_features(
  data,
  person = "person_id",
  trial = "trial_id",
  time = "time_ms",
  aoi = "aoi",
  bin_ms = 100,
  degree = 3L,
  aois = NULL
)

Arguments

Value

An object of class "eye_aoi_trajectory", stored as a named list, with components "features", "aois", "degree", "bin_ms", "person", "trial", "caveat". It contains aOI growth-curve/trajectory features and associated metadata or diagnostics needed to interpret the result.

Map exported APIs to conceptual families

Description

Map exported APIs to conceptual families

Usage

api_family_map(inventory)

Arguments

Value

A data frame containing exported APIs to conceptual families. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Compare two API lifecycle registries

Description

Compare two API lifecycle registries

Usage

api_lifecycle_diff(old, new)

Arguments

Value

A tabular R object containing two API lifecycle registries; rows represent analysis units and columns contain the returned quantities.

Summarise API surface by family and lifecycle status

Description

Summarise API surface by family and lifecycle status

Usage

api_surface_summary(inventory)

Arguments

Value

A data frame containing aPI surface by family and lifecycle status. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Apply a pre-flight decision to data explicitly

Description

Apply a pre-flight decision to data explicitly

Usage

apply_preflight_decision(
  data,
  audit,
  keep_decisions = c("pass_preflight", "use_with_caution")
)

Arguments

Value

Filtered data with an attached 'preflight_application' attribute.

Apply a synthetic corruption plan

Description

Apply a synthetic corruption plan

Usage

apply_synthetic_corruption(
  data,
  plan,
  gaze_columns = c("gaze_x", "gaze_y"),
  pupil = "pupil",
  time = "timestamp_ms",
  aoi = NULL,
  device_column = NULL
)

Arguments

Value

An R object containing a synthetic corruption plan. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Canonicalise parameter-recovery results

Description

Canonicalise parameter-recovery results

Usage

as_irt_recovery_results(results)

Arguments

Value

A data frame containing canonicalise parameter-recovery results. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Probabilistic AOI assignment and uncertainty propagation

Description

Probabilistic AOI assignment and uncertainty propagation. These functions form the eyeprocess 0.6.0.9000 measurement-intelligence programme and use dependency-free reference implementations with explicit evidence limits.

Usage

assign_aois_probabilistic(x, aois, error_model = c("empirical", "gaussian",
  "ellipse"), accuracy = NULL, precision = NULL, x_col = NULL, y_col = NULL,
  id_cols = NULL)
audit_aoi_separation(x = NULL, aois = NULL)
summarise_aoi_membership(x, by = NULL)
propagate_aoi_uncertainty(x, metrics = c("dwell", "ttff", "transitions", "entropy"),
  draws = 500, time_col = NULL, duration_col = NULL, seed = 20260807)
plot_aoi_probability_map(x, ...)
plot_aoi_boundary_risk(x, ...)
plot_probabilistic_scanpath(x, ...)
plot_fuzzy_transition_matrix(x, ...)
plot_aoi_metric_uncertainty(x, ...)

Arguments

Details

The APIs return auditable S3 objects. Plot wrappers call registered base-graphics methods. Experimental or approximate engines are labelled in object status fields and should be validated before confirmatory or operational use.

Value

An eyeprocess result object, data frame, model object, plot, or audit table as documented by the individual function.

See Also

plot_diagnostics(), plot_evidence(), and plot_sensitivity().

Assign features to conservative process-feature families

Description

Assign features to conservative process-feature families

Usage

assign_process_feature_family(
  feature_names,
  registry = process_feature_family_registry()
)

Arguments

Value

A vector or matrix containing assign features to conservative process-feature families, with shape determined by the supplied analysis units.

Identify items for descriptive 3PL/process review

Description

Identify items for descriptive 3PL/process review

Usage

audit_3pl_process_signatures(
  x,
  lower_asymptote_quantile = 0.8,
  fast_rt_quantile = 0.2,
  fast_ttff_quantile = 0.2
)

Arguments

Value

Item-level review table. These flags are not behavioral classifications.

Audit advanced-model scientific evidence

Description

Audit advanced-model scientific evidence

Usage

audit_advanced_model_evidence(evidence, spec = advanced_model_evidence_spec())

Arguments

Value

An 'eye_advanced_evidence_audit' data frame.

Audit whether benchmark assets are ready for public release

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

audit_benchmark_release(study = eyeprocess_benchmark_study())

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Audit bias

Description

Audit bias

Usage

audit_bias(results, threshold = 0.1, by = c("scenario", "engine", "parameter"))

Arguments

Value

An R object containing bias. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Alias emphasizing sensitivity rather than automatic replacement

Description

Alias emphasizing sensitivity rather than automatic replacement

Usage

audit_biometric_imputation(...)

Arguments

Value

An R object containing alias emphasizing sensitivity rather than automatic replacement. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Audit incoming biometric/process data before modelling

Description

Aggregates trial-level signal-quality indicators by person/recording (or other supplied grouping columns) and returns review-oriented flags. No rows are automatically deleted.

Usage

audit_biometric_preflight(
  data,
  by = c("person_id"),
  spec = process_preflight_spec(),
  valid_gaze_prop = "valid_gaze_prop",
  valid_pupil_prop = "valid_pupil_prop",
  missing_gaze = "missing_gaze",
  missing_pupil = "missing_pupil",
  rt_ms = "rt_ms",
  blink_cluster_count = "blink_cluster_count",
  sampling_rate_hz = "sampling_rate_hz"
)

Arguments

Value

An 'eye_biometric_preflight' object.

Audit a candidate item bank against a seed model

Description

Audit a candidate item bank against a seed model

Usage

audit_candidate_item_bank(
  object,
  candidate_data,
  difficulty_range = c(-3, 3),
  discrimination_min = 0.3
)

Arguments

Value

An object of class "eye_candidate_item_bank_audit", stored as a named list, with components "table", "seed_model", "status", "caveat". It contains a candidate item bank against a seed model and associated metadata or diagnostics needed to interpret the result.

Audit out-of-sample incremental information from a process channel

Description

Audit out-of-sample incremental information from a process channel

Usage

audit_channel_incremental_information(
  data,
  fold,
  baseline_fitter,
  process_fitter,
  predictor,
  scorer,
  higher_is_better = TRUE
)

Arguments

Value

A tabular R object containing out-of-sample incremental information from a process channel; rows represent analysis units and columns contain the returned quantities.

Audit convergence and classified failures

Description

Audit convergence and classified failures

Usage

audit_convergence(results, minimum = 0.95, by = c("scenario", "engine"))

Arguments

Value

An object of class "eye_irt_convergence_audit", "data.frame", stored as a data frame, containing convergence and classified failures and associated metadata needed to interpret the result.

Audit coverage

Description

Audit coverage

Usage

audit_coverage(
  results,
  minimum = 0.9,
  maximum = 1,
  by = c("scenario", "engine", "parameter")
)

Arguments

Value

An R object containing coverage. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Audit decision provenance and completeness

Description

Audit decision provenance and completeness

Usage

audit_decision_provenance(
  x,
  required_domains = c("sampling", "validity", "fixation", "pupil", "aoi", "model",
    "sensitivity", "exclusions"),
  required_provenance = c("data_source", "software_version", "analysis_commit")
)

Arguments

Value

An object of class "eye_decision_provenance_audit", stored as a named list, with components "missing_domains", "empty_domains", "missing_provenance", "complete", "manifest_hash". It contains decision provenance and completeness and associated metadata or diagnostics needed to interpret the result.

Audit distractor attention patterns

Description

Audit distractor attention patterns

Usage

audit_distractor_attention(
  data,
  response_option = "response_option",
  option_gaze,
  chosen_suffix = NULL
)

Arguments

Value

A data frame containing distractor attention patterns. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Audit API lifecycle completeness and replacement contracts

Description

Audit API lifecycle completeness and replacement contracts

Usage

audit_eye_api(inventory = eye_api_inventory(), registry = eye_api_lifecycle())

Arguments

Value

An object of class "eye_api_audit", stored as a named list, with components "table", "unreviewed", "invalid_replacements", "invalid_canonical", "reviewed_fraction", "valid". It contains aPI lifecycle completeness and replacement contracts and associated metadata or diagnostics needed to interpret the result.

Audit a pipeline definition or completed run

Description

Audit a pipeline definition or completed run

Usage

audit_eye_pipeline(x)

Arguments

Value

An object of class "eye_pipeline_audit", stored as a named list, with components "table", "undeclared_decisions", "valid", "pipeline_hash". It contains a pipeline definition or completed run and associated metadata or diagnostics needed to interpret the result.

Audit whether a gated frontier model has a minimum evidence contract

Description

Audit whether a gated frontier model has a minimum evidence contract

Usage

audit_frontier_model_contract(x, evidence = list())

Arguments

Value

A data frame containing whether a gated frontier model has a minimum evidence contract. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Audit empirical identifiability from replicate estimates

Description

Flags parameters with near-zero estimate variance, explosive dispersion, excessive missingness, or strongly correlated estimates when a covariance matrix is supplied.

Usage

audit_identifiability(
  results,
  max_missing = 0.05,
  max_sd_ratio = 10,
  correlation_matrix = NULL,
  max_abs_correlation = 0.995
)

Arguments

Value

An object of class "eye_irt_identifiability_audit", "data.frame", stored as a data frame, containing empirical identifiability from replicate estimates and associated metadata needed to interpret the result.

Audit interval width

Description

Audit interval width

Usage

audit_interval_width(
  results,
  maximum = Inf,
  by = c("scenario", "engine", "parameter")
)

Arguments

Value

An R object containing interval width. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Audit item response-function shape departures

Description

Audit item response-function shape departures

Usage

audit_irf_shape(
  comparison,
  mean_absolute_threshold = 0.05,
  max_absolute_threshold = 0.15
)

Arguments

Value

A tabular R object containing item response-function shape departures; rows represent analysis units and columns contain the returned quantities.

Run stepwise Rasch item-reduction as a sensitivity analysis

Description

Run stepwise Rasch item-reduction as a sensitivity analysis

Usage

audit_item_reduction_sensitivity(
  erm_model,
  criterion = list("itemfit"),
  alpha = 0.05,
  maxstep = 5L
)

Arguments

Value

An object of class "eye_item_reduction_sensitivity", stored as a named list, with components "model", "eliminated_items", "alpha", "maxstep", "status", "caveat". It contains stepwise Rasch item-reduction as a sensitivity analysis and associated metadata or diagnostics needed to interpret the result.

Audit the empirical latent-trait distribution

Description

Audit the empirical latent-trait distribution

Usage

audit_latent_distribution(theta, tail_z = 3)

Arguments

Value

A data frame containing the empirical latent-trait distribution. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Summarise measurement transportability across held-out groups

Description

Summarise measurement transportability across held-out groups

Usage

audit_measurement_transportability(
  validation,
  metric,
  higher_is_better = TRUE,
  max_range = NULL,
  minimum = NULL,
  maximum = NULL
)

Arguments

Value

An object of class "eye_measurement_transportability_audit", "data.frame", stored as a data frame, containing measurement transportability across held-out groups and associated metadata needed to interpret the result.

Audit promotion readiness for advanced model families

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

audit_model_promotion(evidence, spec = model_promotion_spec())

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Audit basic multimodal design identifiability

Description

This is a structural pre-flight screen, not a proof of statistical identifiability.

Usage

audit_multimodal_identifiability(x, min_person = 30L, min_item = 5L)

Arguments

Value

An 'eye_multimodal_identifiability_audit'.

Audit structural and data identifiability for M0-M2

Description

This is a conservative pre-fit audit. It does not prove global identifiability. It verifies support, design connectivity, channel coverage, response variation, and the explicit scale constraints used by the reference likelihoods.

Usage

audit_multimodal_m2_identifiability(
  x,
  person = "person_id",
  item = "item_id",
  response = "response",
  rt = "rt",
  gaze = "gaze",
  model = c("M2", "M1", "M0"),
  min_persons = 20L,
  min_items = 5L
)

Arguments

Value

An 'eye_multimodal_m2_identifiability' object.

Audit structural and measurement support for M3

Description

Performs a conservative pre-fit audit for the four-channel response, RT, fixation-count and pupil reference model. The audit checks design connectivity, channel coverage and variation, pupil nuisance availability, blink/interpolation burden, device/session representation and explicit M3 identification constraints. It is a support screen, not proof of global identifiability or construct validity.

Usage

audit_multimodal_m3_identifiability(
  x,
  pupil_scale = c("z", "raw"),
  min_persons = 20L,
  min_items = 5L,
  max_pupil_missing = 0.5,
  max_blink_rate = 0.3,
  max_interpolation_rate = 0.3
)

Arguments

Value

An 'eye_multimodal_m3_identifiability' object.

Audit M4 structural and posterior identifiability

Description

Evaluates sequence information, channel support, state occupancy, assignment uncertainty, state separation, transition degeneracy, and HMC diagnostics. The result is deliberately multi-criterion; M4 does not collapse validity to a single undocumented boolean.

Usage

audit_multimodal_m4_identifiability(
  x,
  spec = NULL,
  include_posterior = TRUE,
  rhat_max = 1.05,
  ess_min = 100,
  ebfmi_min = 0.3,
  occupancy_min = 0.03,
  entropy_fraction_review = 0.8
)

Arguments

Value

An 'eye_multimodal_m4_identifiability' with machine-readable checks.

Audit a multimodal measurement object

Description

Audit a multimodal measurement object

Usage

audit_multimodal_measurement(x)

Arguments

Value

An 'eye_multimodal_audit'.

Alias emphasizing data-quality interpretation of process anomaly auditing

Description

Alias emphasizing data-quality interpretation of process anomaly auditing

Usage

audit_multivariate_process_quality(...)

Arguments

Value

An R object containing alias emphasizing data-quality interpretation of process anomaly auditing. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Run nonparametric Rasch diagnostics with eRm

Description

Run nonparametric Rasch diagnostics with eRm

Usage

audit_nonparametric_rasch(
  response_matrix,
  methods = c("T1", "T10"),
  n = 100L,
  splitcr = "median",
  seed = 321
)

Arguments

Value

An object of class "eye_nonparametric_rasch_audit", stored as a named list, with components "tests", "status", "n", "splitcr", "caveat". It contains nonparametric Rasch diagnostics with eRm and associated metadata or diagnostics needed to interpret the result.

Audit presentation/accessibility sensitivity without clinical inference

Description

Creates a transparent presentation-review score from response time/dwell, revisit/entropy, pupil-effort proxies, and gaze quality. It is an experimental design/fairness audit only.

Usage

audit_presentation_accessibility(
  data,
  person = "person_id",
  rt = "rt_ms",
  dwell = "dwell_ms",
  revisits = "revisits",
  entropy = "aoi_entropy",
  pupil = "pupil_peak",
  gaze_validity = "valid_gaze_prop",
  review_quantile = 0.9
)

Arguments

Value

An object of class "eye_presentation_accessibility", stored as a named list, with components "table", "threshold", "review_quantile", "status", "caveat". It contains presentation/accessibility sensitivity without clinical inference and associated metadata or diagnostics needed to interpret the result.

Audit DIF before and after process-data adjustment

Description

Audit DIF before and after process-data adjustment

Usage

audit_process_adjusted_dif(
  data,
  response = "response",
  ability,
  group,
  item = "item_id",
  process_features,
  person = "participant_id"
)

Arguments

Value

An object of class "eye_process_adjusted_dif", stored as a named list, with components "unadjusted_model", "adjusted_model", "coefficients", "surrogate", "note". It contains dIF before and after process-data adjustment and associated metadata or diagnostics needed to interpret the result.

Audit multivariate process/data-quality anomalies

Description

Computes a regularized Mahalanobis distance over selected person-level process metrics. Flags indicate review needs only; they are not cheating, identity, diagnosis, or intent classifications.

Usage

audit_process_anomalies(
  data,
  person = "person_id",
  metrics = NULL,
  alpha = 0.975,
  aggregate = TRUE,
  ridge = 1e-06
)

Arguments

Value

An object of class "eye_process_anomaly_audit", stored as a named list, with components "table", "metrics", "alpha", "threshold", "center", "covariance", "caveat". It contains multivariate process/data-quality anomalies and associated metadata or diagnostics needed to interpret the result.

Audit post-deployment psychometric and biometric drift

Description

Audit post-deployment psychometric and biometric drift

Usage

audit_process_drift(
  data,
  item = "item_id",
  batch = "deployment_batch",
  metrics = c("irt_difficulty", "irt_discrimination", "rt_ms", "dwell_ms", "pupil_bc",
    "valid_gaze_prop", "screen_luminance"),
  spec = process_drift_spec(),
  reference_batch = NULL,
  aggregate_fun = .ep08_mean
)

Arguments

Value

An object of class "eye_process_drift_audit", stored as a named list, with components "table", "trajectories", "item", "batch", "metrics", "spec", "reference_batch", "flag_columns", "caveat". It contains post-deployment psychometric and biometric drift and associated metadata or diagnostics needed to interpret the result.

Audit external/structural validity of process traits

Description

Audit external/structural validity of process traits

Usage

audit_process_external_validity(
  data,
  criterion,
  predictors,
  baseline_predictors = NULL
)

Arguments

Value

An object of class "eye_process_external_validity", stored as a named list, with components "full_model", "baseline_model", "comparison", "associations", "criterion", "predictors", "baseline_predictors", "data", "incremental_r2", "status", "caveat". It contains external/structural validity of process traits and associated metadata or diagnostics needed to interpret the result.

Audit inter-option/process local dependence

Description

Computes pairwise residual correlations (a Q3-style diagnostic) across item or option columns and, when supplied, analogous process-residual correlations. This is a diagnostic for local dependence, not a formal test with universal cutoffs.

Usage

audit_process_local_dependence(
  response_residuals,
  process_residuals = NULL,
  threshold = 0.2
)

Arguments

Value

An object of class "eye_process_local_dependence_audit", stored as a named list, with components "pairs", "threshold", "max_absolute_response", "note". It contains inter-option/process local dependence and associated metadata or diagnostics needed to interpret the result.

Audit process measurement invariance across facets

Description

Audit process measurement invariance across facets

Usage

audit_process_measurement_invariance(
  object,
  channel = c("process", "response"),
  relative_sd_threshold = 0.25
)

Arguments

Value

An object of class "eye_process_measurement_invariance", stored as a named list, with components "pass", "threshold", "components", "channel", "note". It contains process measurement invariance across facets and associated metadata or diagnostics needed to interpret the result.

Audit sensitivity of process summaries to temporal window choices

Description

Audit sensitivity of process summaries to temporal window choices

Usage

audit_process_window_sensitivity(
  data,
  widths_ms = c(250, 500, 1000, 1500),
  steps_ms = c(100, 250, 500),
  metric = "pupil_mean",
  grid = TRUE,
  ...
)

Arguments

Value

An object of class "eye_process_window_sensitivity", stored as a named list, with components "table", "metric", "settings", "caveat". It contains sensitivity of process summaries to temporal window choices and associated metadata or diagnostics needed to interpret the result.

Audit within-person pupil fatigue/trial-order drift

Description

Audit within-person pupil fatigue/trial-order drift

Usage

audit_pupil_fatigue_drift(
  data,
  pupil = "pupil_peak",
  trial_order = "trial_sequence",
  person = "person_id",
  luminance = NULL,
  difficulty = NULL,
  engine = c("auto", "plm", "lm_fixed_effects")
)

Arguments

Value

An object of class "eye_pupil_fatigue_drift", stored as a named list, with components "model", "coefficients", "data", "engine", "status", "caveat". It contains within-person pupil fatigue/trial-order drift and associated metadata or diagnostics needed to interpret the result.

Audit stability of pupil frequency features across window lengths

Description

Audit stability of pupil frequency features across window lengths

Usage

audit_pupil_frequency_stability(
  data,
  windows_ms = c(500, 1000, 2000),
  by = c("person_id", "trial_id"),
  time = "time_ms",
  pupil = "pupil_bc",
  sampling_rate_hz = 60
)

Arguments

Value

An object of class "eye_pupil_frequency_stability", stored as a named list, with components "table", "windows_ms", "caveat". It contains stability of pupil frequency features across window lengths and associated metadata or diagnostics needed to interpret the result.

Audit declared order of pupil preprocessing steps

Description

Audit declared order of pupil preprocessing steps

Usage

audit_pupil_preprocessing_order(
  steps,
  cleaning_patterns = c("blink", "missing", "interpol", "artifact", "smooth", "filter"),
  baseline_pattern = "baseline"
)

Arguments

Value

A named list with components "steps", "baseline_positions", "cleaning_positions", "cleaning_after_baseline", "status", "caveat", containing declared order of pupil preprocessing steps and associated metadata or diagnostics.

Audit rmse

Description

Audit rmse

Usage

audit_rmse(results, threshold = 0.3, by = c("scenario", "engine", "parameter"))

Arguments

Value

An R object containing rmse. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Audit semantic and numerical loss after a round trip

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

audit_roundtrip_loss(source, roundtrip, tables = canonical_table_names(),
  tolerance = 1e-8)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Audit sampling irregularity

Description

Audit sampling irregularity

Usage

audit_sampling_irregularity(
  data,
  time = "timestamp_ms",
  unit = c("ms", "s", "us"),
  by = NULL,
  cv_threshold = 0.05
)

Arguments

Value

An object of class "eye_sampling_irregularity_audit", stored as a named list, with components "table", "cv_threshold", "caveat". It contains sampling irregularity and associated metadata or diagnostics needed to interpret the result.

Audit SBC rank uniformity

Description

Uses binned chi-square diagnostics as a coarse screening diagnostic and also reports the mean/variance of normalized ranks. It is not a replacement for rank-histogram inspection.

Usage

audit_sbc(x, bins = 10L, alpha = 0.01)

Arguments

Value

An object of class "eye_sbc_audit", "data.frame", stored as a data frame, containing sBC rank uniformity and associated metadata needed to interpret the result.

Summarize a signal-filter audit

Description

Summarize a signal-filter audit

Usage

audit_signal_filter(x)

Arguments

Value

A data frame containing a signal-filter audit. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Audit temporal leakage in a feature provenance table

Description

Leakage here means information becoming available after the declared outcome boundary. It is a data/analysis property and is not an allegation of misconduct.

Usage

audit_temporal_leakage(provenance, allow_equal = TRUE, tolerance = 0)

Arguments

Value

A named list with components "status", "n_features", "n_flagged", "flagged_fraction", "detail", "interpretation", containing temporal leakage in a feature provenance table and associated metadata or diagnostics.

Audit whether a validation programme is complete

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

audit_validation_completion(x, thresholds = validation_thresholds(),
  empirical_reproduction = NULL)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Audit vendor field coverage against canonical semantics

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

audit_vendor_field_coverage(semantics, required_fields)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Audit a multi-vendor validation corpus

Description

Audit a multi-vendor validation corpus

Usage

audit_vendor_validation(x, spec = vendor_validation_spec())

Arguments

Value

An 'eye_vendor_validation' data frame.

Audit visual-context dependence

Description

Audit visual-context dependence

Usage

audit_visual_context_dependence(x)

Arguments

Value

A data frame containing visual-context dependence. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Extract compact Bayesian process-model diagnostic flags

Description

Extract compact Bayesian process-model diagnostic flags

Usage

bayesian_process_diagnostic_flags(
  x,
  rhat_threshold = 1.01,
  ess_threshold = 400
)

Arguments

Value

An R object containing compact Bayesian process-model diagnostic flags. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Summarize Bayesian process-model diagnostics

Description

Collects model availability, optional approximate leave-one-out summaries, posterior diagnostics, and (only when explicitly requested) a Bayes-factor comparison. The function does not treat any one diagnostic as proof of the substantive process interpretation.

Usage

bayesian_process_diagnostics_dashboard(
  ...,
  model_names = NULL,
  compute_loo = TRUE,
  compute_bayes_factor = FALSE,
  posterior_summary = TRUE
)

Arguments

Value

An 'eye_bayesian_process_dashboard' object.

Return expected benchmark outputs

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

benchmark_expected_outputs(study = eyeprocess_benchmark_study())

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Benchmark storage formats and query operations

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

benchmark_eye_storage(x, formats = c("rds", "csv", "parquet"),
  partition_by = c("participant_id", "recording_id"), repetitions = 3L,
  directory = tempdir())

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Benchmark an eyeprocess operation

Description

Benchmark an eyeprocess operation

Usage

benchmark_eyeprocess(expr, iterations = 5L, label = "operation")

Arguments

Value

An 'eye_benchmark' data frame.

Memory estimate for an R object or generated problem size

Description

Memory estimate for an R object or generated problem size

Usage

benchmark_memory_estimate(x, generator = NULL)

Arguments

Value

A data frame containing memory estimate for an R object or generated problem size. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Estimate scaling exponent from benchmark results

Description

Estimate scaling exponent from benchmark results

Usage

benchmark_scaling_curve(x)

Arguments

Value

A data frame containing scaling exponent from benchmark results. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Bind compatible process-window objects

Description

Bind compatible process-window objects

Usage

bind_process_windows(...)

Arguments

Value

An object of class "eye_process_windows", stored as a named list, with components "data", "spec", "source_n", "status". It contains bind compatible process-window objects and associated metadata or diagnostics needed to interpret the result.

Run biometric-feature imputation as a sensitivity analysis

Description

Run biometric-feature imputation as a sensitivity analysis

Usage

biometric_imputation_sensitivity(
  data,
  variables,
  methods = c("mice", "missForest"),
  m = 3L,
  maxit = 3L,
  seed = 521
)

Arguments

Value

An 'eye_biometric_imputation_sensitivity' object. Complete-case analysis is not replaced automatically.

Bootstrap ICC reliability by resampling participants

Description

Bootstrap ICC reliability by resampling participants

Usage

bootstrap_process_reliability(
  data,
  person,
  session,
  measure,
  replications = 500L,
  seed = 1L
)

Arguments

Value

A data frame containing bootstrap ICC reliability by resampling participants. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Build the declared/fixture/empirical compatibility matrix

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

build_compatibility_matrix(x, required_vendors = c("gazepoint", "tobii", "pupillabs",
  "eyelink", "smi"), min_empirical_cases = 2L)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Evidence and decision provenance graphs

Description

Evidence and decision provenance graphs. These functions form the eyeprocess 0.6.0.9000 measurement-intelligence programme and use dependency-free reference implementations with explicit evidence limits.

Usage

build_evidence_graph(raw_data, transformations = NULL, metrics = NULL,
  models = NULL, diagnostics = NULL, decisions = NULL, edges = NULL)
trace_item_decision(graph, item_id)
compare_decision_provenance(graph_a, graph_b)
audit_evidence_dependencies(graph)
plot_evidence_graph(x, ...)
plot_item_decision_path(x, ...)
plot_metric_dependency_graph(x, ...)
plot_model_decision_impact(x, ...)

Arguments

Details

The APIs return auditable S3 objects. Plot wrappers call registered base-graphics methods. Experimental or approximate engines are labelled in object status fields and should be validated before confirmatory or operational use.

Value

An eyeprocess result object, data frame, model object, plot, or audit table as documented by the individual function.

See Also

plot_diagnostics(), plot_evidence(), and plot_sensitivity().

Reconstruct media presentations as analysis trials

Description

Converts contiguous Gazepoint media runs into explicit trial intervals. This provides stable person-by-item-by-trial keys even when the original export contains no behavioural response file.

Usage

build_gazepoint_media_trials(x, item_map = NULL, overwrite = TRUE)

Arguments

Value

The updated 'eye_dataset'.

Calibration drift profile across sessions/batches

Description

Calibration drift profile across sessions/batches

Usage

calibration_drift_profile(
  data,
  by,
  gaze_x = "gaze_x",
  gaze_y = "gaze_y",
  target_x = "target_x",
  target_y = "target_y"
)

Arguments

Value

An object of class "eye_calibration_drift_profile", stored as a named list, with components "table", "by", "caveat". It contains calibration drift profile across sessions/batches and associated metadata or diagnostics needed to interpret the result.

Build an empirical bivariate calibration-error model

Description

Build an empirical bivariate calibration-error model

Usage

calibration_error_model(
  data,
  gaze_x = "gaze_x",
  gaze_y = "gaze_y",
  target_x = "target_x",
  target_y = "target_y"
)

Arguments

Value

An object of class "eye_calibration_error_model", stored as a named list, with components "mean_error", "covariance", "errors", "n", "metrics", "coordinate_units", "status", "caveat". It contains an empirical bivariate calibration-error model and associated metadata or diagnostics needed to interpret the result.

Sensitivity grid for deterministic calibration offsets

Description

Sensitivity grid for deterministic calibration offsets

Usage

calibration_sensitivity_grid(
  offset_x = c(-0.02, 0, 0.02),
  offset_y = c(-0.02, 0, 0.02)
)

Arguments

Value

A tabular R object containing sensitivity grid for deterministic calibration offsets; rows represent analysis units and columns contain the returned quantities.

Audit transfer of calibration across devices/sessions/sites

Description

Audit transfer of calibration across devices/sessions/sites

Usage

calibration_transfer_audit(data, group, observed, predicted)

Arguments

Value

An object of class "eye_calibration_transfer_audit", "data.frame", stored as a data frame, containing transfer of calibration across devices/sessions/sites and associated metadata needed to interpret the result.

Canonical API mapping

Description

Canonical API mapping

Usage

canonical_eye_api(registry = eye_api_lifecycle())

Arguments

Value

A tabular R object containing canonical API mapping; rows represent analysis units and columns contain the returned quantities.

Classify item missingness using exposure and response evidence

Description

Classify item missingness using exposure and response evidence

Usage

classify_item_missingness(
  data,
  response = "response",
  reached = "reached",
  inspected = NULL,
  started = NULL
)

Arguments

Value

An object of class "factor", stored as an R object, containing classify item missingness using exposure and response evidence and associated metadata needed to interpret the result.

Collect a disk-backed eye dataset

Description

Collect a disk-backed eye dataset

Usage

collect_eye_storage(x, tables = NULL)

Arguments

Value

An 'eye_dataset'.

Collect validation evidence into a common bundle

Description

Collect validation evidence into a common bundle

Usage

collect_validation_evidence(..., model_name = NULL, notes = NULL)

Arguments

Value

An 'eye_validation_bundle' object.

Collect validation checkpoints from one or more directories

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

collect_validation_jobs(path, plan = NULL, strict = TRUE)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Compare explicit AOI assignment methods

Description

Compare explicit AOI assignment methods

Usage

compare_aoi_methods(
  data,
  methods,
  analysis_fun,
  extract_fun = .ep09_default_sensitivity_extract
)

Arguments

Value

An object of class "eye_process_sensitivity", stored as a named list, with components "grid", "results", "failures", "warnings", "grid_hash", "created_at", "status", "caveat". It contains explicit AOI assignment methods and associated metadata or diagnostics needed to interpret the result.

Compare AOI trajectory feature objects

Description

Compare AOI trajectory feature objects

Usage

compare_aoi_trajectories(...)

Arguments

Value

A data frame containing aOI trajectory feature objects. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Compare Bayesian process models by LOO or Bayes factor

Description

Compare Bayesian process models by LOO or Bayes factor

Usage

compare_bayesian_process_models(..., method = c("loo", "bayes_factor"))

Arguments

Value

An R object containing bayesian process models by LOO or Bayes factor. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Compare two research decision manifests

Description

Compare two research decision manifests

Usage

compare_decision_manifests(old, new)

Arguments

Value

An object of class "eye_decision_manifest_diff", "data.frame", stored as a data frame, containing two research decision manifests and associated metadata needed to interpret the result.

Compare two deployment batches descriptively

Description

Compare two deployment batches descriptively

Usage

compare_deployment_batches(
  data,
  batch = "deployment_batch",
  batch_a,
  batch_b,
  metrics = NULL,
  item = "item_id"
)

Arguments

Value

A data frame containing two deployment batches descriptively. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Compare diffusion and conventional accuracy-RT models

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

compare_diffusion_accuracy_rt(object)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Compare dynamic transition models

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

compare_dynamic_transition_models(..., criterion = c("AIC", "BIC", "log_score",
  "accuracy"))

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Compare multiple external-engine adapter results

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

compare_engine_adapters(...)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Compare explicit fixation-detection methods

Description

Compare explicit fixation-detection methods

Usage

compare_fixation_methods(
  data,
  methods,
  analysis_fun,
  extract_fun = .ep09_default_sensitivity_extract
)

Arguments

Value

An object of class "eye_process_sensitivity", stored as a named list, with components "grid", "results", "failures", "warnings", "grid_hash", "created_at", "status", "caveat". It contains explicit fixation-detection methods and associated metadata or diagnostics needed to interpret the result.

Compare functional and scalar pupil summaries

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

compare_functional_scalar_models(x, scalar_features = c("pupil_peak", "pupil_auc",
  "pupil_mean"), criterion = c("AIC", "log_loss"), folds = 5L, seed = 1L)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Compare hard and probabilistic AOI assignments

Description

Compare hard and probabilistic AOI assignments

Usage

compare_hard_probabilistic_aoi(
  data,
  aois,
  probabilistic,
  x = "gaze_x",
  y = "gaze_y"
)

Arguments

Value

An R object containing hard and probabilistic AOI assignments. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Compare multimodal IRT model objects

Description

Uses supplied scoring functions when models expose different engines. The default extracts AIC/BIC/logLik when available and never treats in-sample fit as sufficient evidence for model promotion.

Usage

compare_irt_models(..., names = NULL)

Arguments

Value

An R object containing multimodal IRT model objects. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Compare simple latent-distribution reference models

Description

Compares Gaussian, location/scale Student-t, and two-normal-mixture reference densities by AIC/BIC. This is a stress-test diagnostic; it does not change the latent distribution inside an already fitted IRT model.

Usage

compare_latent_distribution_models(theta)

Arguments

Value

An object of class "eye_latent_distribution_comparison", stored as a named list, with components "comparison", "audit", "student_t", "mixture", "status". It contains simple latent-distribution reference models and associated metadata or diagnostics needed to interpret the result.

Compare equivalent model engines

Description

Compare equivalent model engines

Usage

compare_model_engines(
  data,
  engines,
  extractors,
  reference = names(engines)[1L],
  tolerance = 0.05
)

Arguments

Value

An 'eye_engine_comparison' object.

Compare conventional logistic and flexible IRF shapes

Description

Compare conventional logistic and flexible IRF shapes

Usage

compare_parametric_nonparametric_irf(
  response_matrix,
  gpirt_object = NULL,
  theta_grid = seq(-4, 4, length.out = 101)
)

Arguments

Value

An object of class "eye_irf_comparison", "data.frame", stored as a data frame, containing conventional logistic and flexible IRF shapes and associated metadata needed to interpret the result.

Compare outcomes across presentation variants

Description

Compare outcomes across presentation variants

Usage

compare_presentation_fairness(data, variant, outcome, person = NULL)

Arguments

Value

An object of class "eye_presentation_fairness_comparison", stored as a named list, with components "model", "summary", "status", "caveat". It contains outcomes across presentation variants and associated metadata or diagnostics needed to interpret the result.

Compare process external-validity models

Description

Compare process external-validity models

Usage

compare_process_criterion_models(x)

Arguments

Value

A data frame containing process external-validity models. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Compare explicit process-model specifications

Description

Compare explicit process-model specifications

Usage

compare_process_models(
  data,
  methods,
  analysis_fun,
  extract_fun = .ep09_default_sensitivity_extract
)

Arguments

Value

An object of class "eye_process_sensitivity", stored as a named list, with components "grid", "results", "failures", "warnings", "grid_hash", "created_at", "status", "caveat". It contains explicit process-model specifications and associated metadata or diagnostics needed to interpret the result.

Compare candidate process-profile solutions

Description

Compare candidate process-profile solutions

Usage

compare_process_profile_solutions(data, variables, k_values = 2:6, seed = 777)

Arguments

Value

A tabular R object containing candidate process-profile solutions; rows represent analysis units and columns contain the returned quantities.

Compare pupil deconvolution kernels

Description

Compare pupil deconvolution kernels

Usage

compare_pupil_kernels(data, tmax_values = c(512, 930), ...)

Arguments

Value

A tabular R object containing pupil deconvolution kernels; rows represent analysis units and columns contain the returned quantities.

Compare explicit pupil-preprocessing methods

Description

Compare explicit pupil-preprocessing methods

Usage

compare_pupil_preprocessing(
  data,
  methods,
  analysis_fun,
  extract_fun = .ep09_default_sensitivity_extract
)

Arguments

Value

An object of class "eye_process_sensitivity", stored as a named list, with components "grid", "results", "failures", "warnings", "grid_hash", "created_at", "status", "caveat". It contains explicit pupil-preprocessing methods and associated metadata or diagnostics needed to interpret the result.

Compare raw and confound-adjusted pupil values

Description

Compare raw and confound-adjusted pupil values

Usage

compare_raw_adjusted_pupil(x)

Arguments

Value

A data frame containing raw and confound-adjusted pupil values. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Compare two reproducibility fingerprints

Description

Compare two reproducibility fingerprints

Usage

compare_reproducibility_fingerprints(old, new)

Arguments

Value

A named list with components "detail", "identical", "old_hash", "new_hash", containing two reproducibility fingerprints and associated metadata or diagnostics.

Compare multiple signal filters

Description

Compare multiple signal filters

Usage

compare_signal_filters(
  signal,
  widths = c(5L, 9L, 15L),
  methods = c("runmed", "robfilter")
)

Arguments

Value

A tabular R object containing multiple signal filters; rows represent analysis units and columns contain the returned quantities.

Compare mixture and continuous heterogeneity descriptions

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

compare_strategy_heterogeneity(object)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Compare validation engines on common recovery output

Description

Compare validation engines on common recovery output

Usage

compare_validation_engines(results)

Arguments

Value

An object of class "eye_validation_engine_comparison", "data.frame", stored as a data frame, containing validation engines on common recovery output and associated metadata needed to interpret the result.

Compare semantic mappings between vendors

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

compare_vendor_semantics(x, vendors = NULL)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Compare base and visual-context IRT models

Description

Compare base and visual-context IRT models

Usage

compare_visual_context_irt(x)

Arguments

Value

A data frame containing base and visual-context IRT models. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Build a detailed compatibility evidence matrix

Description

Build a detailed compatibility evidence matrix

Usage

compatibility_evidence_matrix(compatibility, evidence = NULL)

Arguments

Value

An 'eye_compatibility_evidence_matrix' object.

Extract visual-context factor effects/loadings

Description

Extract visual-context factor effects/loadings

Usage

context_factor_effects(x, IRTpars = FALSE)

Arguments

Value

An R object containing visual-context factor effects/loadings. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Coordinate semantic-fidelity audit

Description

Detects lossless preservation and stable affine coordinate transformations.

Usage

coordinate_fidelity_audit(
  source,
  roundtrip,
  source_x = "x",
  source_y = "y",
  roundtrip_x = source_x,
  roundtrip_y = source_y,
  key = NULL,
  tolerance = 1e-06,
  correlation_floor = 0.999
)

Arguments

Value

An object of class "eye_coordinate_fidelity", stored as a named list, with components "status", "x", "y", "matched_n", "tolerance", "correlation_floor". It contains coordinate semantic-fidelity audit and associated metadata or diagnostics needed to interpret the result.

Interval coverage calibration curve

Description

Interval coverage calibration curve

Usage

coverage_calibration_curve(truth, lower, upper, nominal = NULL)

Arguments

Value

A data frame containing interval coverage calibration curve. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Create a public, de-identified benchmark bundle

Description

Create a public, de-identified benchmark bundle

Usage

create_public_benchmark(
  x,
  path,
  max_participants = 50L,
  include_samples = FALSE,
  overwrite = FALSE
)

Arguments

Value

Output directory.

Cross-device process-scale equating audit

Description

Fits a simple affine linking map on anchor observations and reports residual bias/RMSE by device. It complements, rather than replaces, IRT anchor linking.

Usage

cross_device_process_equating_audit(
  data,
  value,
  reference_value,
  device,
  anchor = NULL
)

Arguments

Value

An object of class "eye_cross_device_equating_audit", "data.frame", stored as a data frame, containing cross-device process-scale equating audit and associated metadata needed to interpret the result.

Compare adapter output across software/format versions

Description

Compare adapter output across software/format versions

Usage

cross_version_adapter_regression(
  input,
  baseline_adapter,
  candidate_adapter,
  baseline_version = "baseline",
  candidate_version = "candidate",
  extract_samples = .ep07_adapter_extract_samples,
  audit_args = list()
)

Arguments

Value

An object of class "eye_adapter_regression_audit", stored as a named list, with components "status", "baseline_version", "candidate_version", "fidelity", "baseline", "candidate". It contains adapter output across software/format versions and associated metadata or diagnostics needed to interpret the result.

Cross-classified grouped cross-validation

Description

Cross-classified grouped cross-validation

Usage

crossed_grouped_cv(
  data,
  formula,
  family = stats::binomial(),
  groups = c("participant_id", "item_id"),
  v = 5L,
  metric = c("log_loss", "brier", "accuracy"),
  seed = 1L
)

Arguments

Value

An 'eye_crossed_grouped_cv' object.

Create cross-classified grouped folds

Description

Holds out levels from every declared grouping dimension simultaneously. The assessment set is the intersection of held-out levels; the analysis set excludes every held-out level. Rows combining held-out and retained levels form a buffer and are deliberately used in neither set.

Usage

crossed_grouped_folds(
  data,
  groups = c("participant_id", "item_id"),
  v = 5L,
  seed = 1L
)

Arguments

Value

An 'eye_crossed_grouped_folds' object.

Compact reporting table for eye-tracking data quality

Description

Compact reporting table for eye-tracking data quality

Usage

data_quality_reporting_table(x)

Arguments

Value

An R object containing compact reporting table for eye-tracking data quality. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Alias for manifest comparison emphasizing changed decision paths

Description

Alias for manifest comparison emphasizing changed decision paths

Usage

decision_manifest_diff(old, new)

Arguments

Value

An R object containing alias for manifest comparison emphasizing changed decision paths. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Stable hash of decision content

Description

Stable hash of decision content

Usage

decision_manifest_hash(x)

Arguments

Value

An R object containing stable hash of decision content. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Flatten a decision manifest to a table

Description

Flatten a decision manifest to a table

Usage

decision_manifest_table(x)

Arguments

Value

A logical value or vector indicating flatten a decision manifest to a table.

Coverage of a declared decision space by evaluated specifications

Description

Coverage of a declared decision space by evaluated specifications

Usage

decision_space_coverage(grid, evaluated)

Arguments

Value

A data frame containing coverage of a declared decision space by evaluated specifications. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Overall decision-stability summary

Description

Overall decision-stability summary

Usage

decision_stability(
  x,
  effect = "effect",
  p_value = NULL,
  alpha = 0.05,
  threshold = 0
)

Arguments

Value

An object of class "eye_decision_stability", stored as a named list, with components "summary", "stable_sign", "stable_threshold", "stable_significance", "thresholds", "caveat". It contains overall decision-stability summary and associated metadata or diagnostics needed to interpret the result.

Decode latent or fitted transition states

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

decode_dynamic_states(object, method = c("mode", "probability", "draw"))

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Compositional analysis of AOI attention

Description

Compositional analysis of AOI attention. These functions form the eyeprocess 0.6.0.9000 measurement-intelligence programme and use dependency-free reference implementations with explicit evidence limits.

Usage

derive_aoi_composition(x, aois, denominator = c("total_aoi_dwell",
  "trial_duration"), zero_method = c("multiplicative", "bayesian"), id_cols = NULL,
  aoi_col = "aoi", value_col = "dwell_ms", trial_duration_col = NULL)
transform_aoi_composition(x, method = c("ilr", "clr", "alr"), reference = NULL)
fit_aoi_compositional_model(composition, formula, random = NULL, data = NULL,
  method = "ilr")
compare_aoi_compositions(x, group, method = c("permanova", "compositional_manova"),
  permutations = 499, seed = 20260807)
aoi_balance_coordinates(x, balances)
plot_aoi_ternary(x, ...)
plot_aoi_balance_biplot(x, ...)
plot_aoi_variation_matrix(x, ...)
plot_compositional_group_difference(x, ...)
plot_aoi_composition_trajectory(x, ...)

Arguments

Details

The APIs return auditable S3 objects. Plot wrappers call registered base-graphics methods. Experimental or approximate engines are labelled in object status fields and should be validated before confirmatory or operational use.

Value

An eyeprocess result object, data frame, model object, plot, or audit table as documented by the individual function.

See Also

plot_diagnostics(), plot_evidence(), and plot_sensitivity().

Derive the complete Gazepoint workflow feature set

Description

Derive the complete Gazepoint workflow feature set

Usage

derive_gazepoint_workflow_features(x, reset_workflow_features = TRUE)

Arguments

Value

The updated 'eye_dataset'.

Calibration drift and offline recalibration

Description

Calibration drift and offline recalibration. These functions form the eyeprocess 0.6.0.9000 measurement-intelligence programme and use dependency-free reference implementations with explicit evidence limits.

Usage

detect_calibration_drift(x, references = NULL, window = "30 sec",
  method = c("targets", "known_aois", "fixation_density"), x_col = NULL,
  y_col = NULL, time_col = NULL)
fit_offline_recalibration(x, method = c("translation", "affine", "polynomial"),
  robust = TRUE, x_col = NULL, y_col = NULL, reference_x_col = NULL,
  reference_y_col = NULL)
apply_offline_recalibration(x, model, x_col = NULL, y_col = NULL,
  suffix = "_recalibrated")
audit_recalibration(before, after, minimum_improvement = NULL)
plot_calibration_vector_field(x, ...)
plot_calibration_error_ellipses(x, ...)
plot_drift_over_time(x, ...)
plot_recalibration_before_after(x, ...)
plot_screen_coverage(x, ...)

Arguments

Details

The APIs return auditable S3 objects. Plot wrappers call registered base-graphics methods. Experimental or approximate engines are labelled in object status fields and should be validated before confirmatory or operational use.

Value

An eyeprocess result object, data frame, model object, plot, or audit table as documented by the individual function.

See Also

plot_diagnostics(), plot_evidence(), and plot_sensitivity().

Detect missing, truncated, or modified partitions

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

detect_corrupt_partitions(storage)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Detect IRT/process change points using an SIC-inspired multichannel score

Description

This implementation is a transparent package reference inspired by the 2026 SIC-CPA literature. It combines Bernoulli response likelihood with normal log-RT and standardized gaze likelihoods. It is not a line-for-line reproduction of the article's estimator.

Usage

detect_irt_changepoints(
  data,
  person = "participant_id",
  order = "item_order",
  response = "response",
  rt = "rt",
  gaze = NULL,
  min_segment = 5L,
  min_delta_sic = 2,
  max_changes = 2L
)

Arguments

Value

An object of class "eye_irt_changepoints", stored as a named list, with components "results", "channels", "method", "min_segment", "min_delta_sic", "max_changes". It contains iRT/process change points using an SIC-inspired multichannel score and associated metadata or diagnostics needed to interpret the result.

Detect a response-process change point

Description

Public roadmap alias for 'detect_irt_changepoints()'.

Usage

detect_process_changepoint(...)

Arguments

Value

An R object containing a response-process change point. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Cognitive-episode change-point detection

Description

Cognitive-episode change-point detection. These functions form the eyeprocess 0.6.0.9000 measurement-intelligence programme and use dependency-free reference implementations with explicit evidence limits.

Usage

detect_process_changepoints(x, channels = c("gaze_velocity", "aoi", "pupil", "eda"),
  time_col = NULL, window = 10, threshold_quantile = 0.9, min_segment = 5)
segment_process_episodes(x, ...)
label_process_episodes(x, rules = NULL, model = NULL)
compare_episode_structure(x, group)
plot_process_episodes(x, ...)
plot_changepoint_ribbons(x, ...)
plot_episode_waterfall(x, ...)
plot_episode_transition_graph(x, ...)
plot_episode_duration_distribution(x, ...)

Arguments

Details

The APIs return auditable S3 objects. Plot wrappers call registered base-graphics methods. Experimental or approximate engines are labelled in object status fields and should be validated before confirmatory or operational use.

Value

An eyeprocess result object, data frame, model object, plot, or audit table as documented by the individual function.

See Also

plot_diagnostics(), plot_evidence(), and plot_sensitivity().

Extract device facet effects

Description

Extract device facet effects

Usage

device_facet_effects(object, channel = c("response", "process"))

Arguments

Value

An object of class "eye_process_facet_effects", stored as a named list, with components "facet", "column", "channel", "random_effects", "variance_component". It contains device facet effects and associated metadata or diagnostics needed to interpret the result.

Construct a simulation-based identification study

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

diffusion_identification_study(conditions = list(n_person = c(50L, 150L),
  n_item = c(10L, 30L), gaze_effect = c(0, 0.35), contaminant_fraction = c(0, 0.05)),
  replications = 20L, base_seed = 20260805L,
  spec = gaze_diffusion_spec(drift_features = "gaze_balance", engine = "stan"))

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Diagnose diffusion-parameter trade-offs and sampling

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

diffusion_parameter_diagnostics(object, correlation_threshold = 0.85)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Posterior predictive summaries for accuracy and RT

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

diffusion_posterior_predictive(object, draws = 200L, method = c("rtdists",
  "stan_proxy"), seed = 1L)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Build a distractor process map

Description

Build a distractor process map

Usage

distractor_process_map(object)

Arguments

Value

A data frame containing a distractor process map. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Drift audit stratified by device

Description

Drift audit stratified by device

Usage

drift_by_device(data, device = "device_id", ...)

Arguments

Value

An R object containing drift audit stratified by device. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Drift audit stratified by study site

Description

Drift audit stratified by study site

Usage

drift_by_site(data, site = "site_id", ...)

Arguments

Value

An R object containing drift audit stratified by study site. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Drift audit stratified by stimulus version

Description

Drift audit stratified by stimulus version

Usage

drift_by_stimulus_version(data, stimulus_version = "stimulus_version", ...)

Arguments

Value

An R object containing drift audit stratified by stimulus version. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Drift audit stratified by vendor

Description

Drift audit stratified by vendor

Usage

drift_by_vendor(data, vendor = "vendor", ...)

Arguments

Value

An R object containing drift audit stratified by vendor. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Evaluate dynamic-state recovery under misclassification

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

dynamic_irtree_recovery(grid = expand.grid(state_misclassification = c(0, 0.05, 0.15),
  missing_state = c(0, 0.10), stringsAsFactors = FALSE), replications = 20L,
  spec = dynamic_irtree_spec(engine = "multinomial"), base_seed = 1L)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Specify a hardened dynamic gaze-state IRTree

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

dynamic_irtree_spec(source = c("samples", "visits", "fixations"),
  collapse_consecutive = TRUE, engine = c("baseline", "multinomial", "stan"),
  hidden_states = 0L, include_response = TRUE, include_person = FALSE,
  include_item = TRUE, condition_columns = character(),
  transition_predictors = character(), interactions = character(),
  include_time_gap = TRUE, person_effect = c("none", "fixed", "random"),
  item_effect = c("none", "fixed", "random"), structural_zeros = NULL,
  allowed_transitions = NULL, hidden_structural_zeros = NULL,
  hidden_allowed_transitions = NULL, missing_state = c("drop", "unknown",
  "marginalize"), uncertain_state_probability = NULL, misclassification_matrix = NULL,
  ridge = 1e-4, standardize = TRUE, reference_state = NULL, chains = 4L,
  parallel_chains = chains, iter_warmup = 1000L, iter_sampling = 1000L,
  adapt_delta = 0.95, max_treedepth = 12L)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Posterior predictive checks for dynamic state models

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

dynamic_posterior_predictive_check(object, draws = 200L, seed = 1L)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Build an explicit dynamic-transition design matrix

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

dynamic_transition_design(data, spec = dynamic_irtree_spec(), formula = NULL)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Estimate effective sampling frequency from timestamps

Description

Estimate effective sampling frequency from timestamps

Usage

effective_sampling_frequency(
  data,
  time = "timestamp_ms",
  unit = c("ms", "s", "us"),
  by = NULL
)

Arguments

Value

A logical value or vector indicating effective sampling frequency from timestamps.

Encode multiple-response item response combinations

Description

Encode multiple-response item response combinations

Usage

encode_response_combinations(
  data,
  person = "participant_id",
  item = "item_id",
  option = "option_id",
  selected = "selected",
  sort_options = TRUE,
  empty_code = "<none>"
)

Arguments

Value

A tabular R object containing encode multiple-response item response combinations; rows represent analysis units and columns contain the returned quantities.

Report an adapter's availability and contract

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

engine_adapter_status(engine)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Equate IRT scales using anchor item parameters

Description

Implements mean-sigma, mean-mean, Stocking-Lord, and Haebara linking for dichotomous 2PL-style item parameters. New-form parameters are transformed onto the reference scale using theta_ref = A * theta_new + B.

Usage

equate_irt_scales(
  reference,
  new,
  method = c("stocking-lord", "haebara", "mean-sigma", "mean-mean"),
  theta_grid = seq(-4, 4, length.out = 81)
)

Arguments

Value

An object of class "eye_irt_equating", stored as a named list, with components "A", "B", "method", "transformed", "reference", "new", "equation". It contains equate IRT scales using anchor item parameters and associated metadata or diagnostics needed to interpret the result.

Estimate empirical calibration/validation error

Description

Estimate empirical calibration/validation error

Usage

estimate_calibration_error(
  data,
  gaze_x = "gaze_x",
  gaze_y = "gaze_y",
  target_x = "target_x",
  target_y = "target_y",
  by = NULL
)

Arguments

Value

A tabular R object containing empirical calibration/validation error; rows represent analysis units and columns contain the returned quantities.

Estimate visual exposure probability

Description

Estimate visual exposure probability

Usage

estimate_visual_exposure_probability(
  data,
  exposed = "reached",
  predictors,
  family = stats::binomial()
)

Arguments

Value

An object of class "eye_visual_exposure_model", stored as a named list, with components "model", "fitted_probability", "exposed", "predictors". It contains visual exposure probability and associated metadata or diagnostics needed to interpret the result.

Evaluate a validation acceptance rule

Description

Evaluate a validation acceptance rule

Usage

evaluate_validation_acceptance(value, rule)

Arguments

Value

A logical value or vector indicating a validation acceptance rule.

Event-marker plausibility audit

Description

Evaluates whether independent channel offsets corroborate a nominal event. This is annotation/event plausibility QC, not clock synchronization, and it never modifies timestamps.

Usage

event_marker_qc(offsets, tolerance, min_corroborating = 2L)

Arguments

Value

A list containing status, consensus offset, uncertainty, and counts.

Audit event survival across an interchange round trip

Description

Audit event survival across an interchange round trip

Usage

event_roundtrip_audit(source_events, roundtrip_events, hed_column = NULL, ...)

Arguments

Value

An object of class "eye_event_roundtrip_audit", stored as a named list, with components "status", "event_semantics", "hed". It contains event survival across an interchange round trip and associated metadata or diagnostics needed to interpret the result.

Audit event semantic preservation

Description

Audit event semantic preservation

Usage

event_semantics_audit(
  source_events,
  roundtrip_events,
  label = "event",
  time = "timestamp",
  key = NULL,
  tolerance = 1e-06
)

Arguments

Value

An object of class "eye_event_semantics", stored as a named list, with components "status", "source_n", "roundtrip_n", "matched_n", "exact_label_fraction", "max_time_error". It contains event semantic preservation and associated metadata or diagnostics needed to interpret the result.

Expand a stress evidence plan into one-factor-at-a-time scenarios

Description

Expand a stress evidence plan into one-factor-at-a-time scenarios

Usage

expand_eyeprocess_stress_evidence_plan(plan)

Arguments

Value

A tabular R object containing expand a stress evidence plan into one-factor-at-a-time scenarios; rows represent analysis units and columns contain the returned quantities.

Expand a validation-evidence plan to a scenario table

Description

Expand a validation-evidence plan to a scenario table

Usage

expand_eyeprocess_validation_plan(x)

Arguments

Value

An R object containing expand a validation-evidence plan to a scenario table. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Expand a process-validation design into explicit conditions

Description

Expand a process-validation design into explicit conditions

Usage

expand_process_validation_design(x, max_conditions = 250000L)

Arguments

Value

A tabular R object containing expand a process-validation design into explicit conditions; rows represent analysis units and columns contain the returned quantities.

Expected process-aware item utility under a theta distribution

Description

Expected process-aware item utility under a theta distribution

Usage

expected_process_information(info, theta_weights = NULL)

Arguments

Value

An R object containing expected process-aware item utility under a theta distribution. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Explain local person-item latent-space interactions

Description

Returns the closest person-item pairs in the fitted residual latent space. Closeness is descriptive residual structure, not a causal explanation.

Usage

explain_latent_interaction(object, person = NULL, item = NULL, top = 10L)

Arguments

Value

An R object containing explain local person-item latent-space interactions. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Export Eye-Tracking-BIDS physiological recordings

Description

Writes BIDS-compatible '_physio.tsv.gz' and JSON pairs with 'PhysioType = "eyetrack"'. Continuous files contain no header; 'Columns' is stored in the sidecar. Each recorded eye is written separately.

Usage

export_eye_bids(
  x,
  path,
  task = "task",
  dataset_name = "eyeprocess eye-tracking dataset",
  overwrite = FALSE,
  screen_distance_m = NULL,
  screen_size_m = NULL
)

Arguments

Value

A manifest of written files.

Export a pipeline manifest and optional run status

Description

Export a pipeline manifest and optional run status

Usage

export_eye_pipeline(x, path)

Arguments

Value

An R object containing a pipeline manifest and optional run status. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Export lightweight PROV-oriented JSON

Description

This is a compact interoperability representation inspired by W3C PROV. It is not asserted to be a complete PROV-O serialization; consumers requiring full conformance should validate/transform it externally.

Usage

export_prov_json(x, path)

Arguments

Value

An R object containing lightweight PROV-oriented JSON. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Export minimal RO-Crate 1.3 metadata

Description

Export minimal RO-Crate 1.3 metadata

Usage

export_ro_crate_metadata(
  path = "ro-crate-metadata.json",
  name = "eyeprocess analysis",
  description = "Reproducible eyeprocess analysis crate",
  files = NULL,
  creator = NULL,
  license = NULL,
  doi = NULL
)

Arguments

Value

An R object containing minimal RO-Crate 1.3 metadata. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Export a validation evidence bundle

Description

Export a validation evidence bundle

Usage

export_validation_bundle(x, directory, overwrite = FALSE, include_rds = TRUE)

Arguments

Value

An object of class "eye_validation_export", stored as a named list, with components "directory", "files", "manifest". It contains a validation evidence bundle and associated metadata or diagnostics needed to interpret the result.

Convert common external eye-tracking objects

Description

Convert common external eye-tracking objects

Usage

as_eyeprocess_eyetools(x, mapping = NULL, ...)

as_eyeprocess_eyetrackingr(x, mapping = NULL, ...)

as_eyeprocess_gazer(x, mapping = NULL, ...)

as_eyeprocess_eyeris(x, mapping = NULL, ...)

as_eyeprocess_pupillometryr(x, mapping = NULL, ...)

Arguments

Value

An 'eye_dataset'.

List external engine adapters

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

external_model_engines()

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

External validation on a completely held-out dataset

Description

External validation on a completely held-out dataset

Usage

external_validate_irt(
  train_data,
  external_data,
  fitter,
  predictor,
  scorer,
  label = "external"
)

Arguments

Value

A logical value or vector indicating external validation on a completely held-out dataset.

Extract diffusion parameter summaries

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

extract_diffusion_parameters(object, variables = c("beta_drift", "beta_boundary",
  "beta_nondecision", "beta_starting", "person_drift", "item_difficulty", "boundary",
  "nondecision", "starting", "contaminant_probability"))

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Extract functional pupil parameters for validation

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

extract_functional_pupil_parameters(x, pattern = NULL, confidence = 0.95)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Extract canonical parameter truth from simulated data

Description

Extract canonical parameter truth from simulated data

Usage

extract_parameter_truth(simulation)

Arguments

Value

Data frame with 'parameter' and 'truth'.

Extract standardized sliding-window process features

Description

Extract standardized sliding-window process features

Usage

extract_process_windows(
  data,
  person = "person_id",
  trial = "trial_id",
  time = "time_ms",
  spec = process_window_spec(),
  align_time = NULL,
  pupil = "pupil_bc",
  pupil_tonic = "pupil_tonic",
  pupil_phasic = "pupil_phasic",
  gaze_x = "x",
  gaze_y = "y",
  aoi = "aoi",
  valid_gaze = "valid_gaze_prop",
  valid_pupil = "valid_pupil_prop",
  blink = "blink",
  trackloss = "trackloss"
)

Arguments

Value

An 'eye_process_windows' object.

Construct a governed analysis pipeline

Description

Construct a governed analysis pipeline

Usage

eye_analysis_pipeline(
  ...,
  spec = eye_analysis_spec(),
  name = "eye_analysis",
  strict = TRUE
)

Arguments

Value

An object of class "eye_analysis_pipeline", stored as a named list, with components "name", "steps", "spec", "strict", "created_at", "status". It contains a governed analysis pipeline and associated metadata or diagnostics needed to interpret the result.

Define explicit analysis decisions for an eyeprocess workflow

Description

Define explicit analysis decisions for an eyeprocess workflow

Usage

eye_analysis_spec(
  blink_correction = NULL,
  pupil_baseline = NULL,
  fixation_algorithm = NULL,
  aoi_rule = NULL,
  exclusions = NULL,
  model = NULL,
  sensitivity = NULL,
  ...
)

Arguments

Value

An 'eye_analysis_spec' object.

Inventory the public eyeprocess API

Description

Inventory the public eyeprocess API

Usage

eye_api_inventory(package = "eyeprocess", lifecycle = eye_api_lifecycle())

Arguments

Value

Data frame of exported symbols and lifecycle metadata.

Normalize or create an API lifecycle registry

Description

Normalize or create an API lifecycle registry

Usage

eye_api_lifecycle(registry = NULL)

Arguments

Value

'eye_api_lifecycle' data frame.

Lifecycle recommendation for API review

Description

Lifecycle recommendation for API review

Usage

eye_api_recommendation(audit)

Arguments

Value

A data frame containing lifecycle recommendation for API review. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Lookup lifecycle status for one or more APIs

Description

Lookup lifecycle status for one or more APIs

Usage

eye_api_status(name, registry = eye_api_lifecycle())

Arguments

Value

A data frame containing lookup lifecycle status for one or more APIs. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Return superseded/deprecated compatibility interfaces

Description

Return superseded/deprecated compatibility interfaces

Usage

eye_api_superseded(registry = eye_api_lifecycle())

Arguments

Value

A tabular R object containing return superseded/deprecated compatibility interfaces; rows represent analysis units and columns contain the returned quantities.

Define a computational benchmark design

Description

Define a computational benchmark design

Usage

eye_benchmark_design(
  n_obs = c(10000, 1e+05, 1e+06),
  repetitions = 3L,
  label = "eyeprocess_scaling"
)

Arguments

Value

A tabular R object containing define a computational benchmark design; rows represent analysis units and columns contain the returned quantities.

Create a machine-readable research decision manifest

Description

Create a machine-readable research decision manifest

Usage

eye_decision_manifest(
  sampling = list(),
  validity = list(),
  fixation = list(),
  pupil = list(),
  aoi = list(),
  model = list(),
  sensitivity = list(),
  exclusions = list(),
  provenance = list(),
  notes = NULL,
  ...
)

Arguments

Value

An object of class "eye_decision_manifest", stored as a named list, with components "domains", "notes", "created_at", "schema_version", "status", "caveat". It contains a machine-readable research decision manifest and associated metadata or diagnostics needed to interpret the result.

Render pipeline dependencies as Graphviz DOT

Description

Render pipeline dependencies as Graphviz DOT

Usage

eye_pipeline_dot(x)

Arguments

Value

A character value or vector containing render pipeline dependencies as Graphviz DOT.

Return pipeline vertices and dependency edges

Description

Return pipeline vertices and dependency edges

Usage

eye_pipeline_graph(x)

Arguments

Value

A named list with components "vertices", "edges", containing return pipeline vertices and dependency edges and associated metadata or diagnostics.

Machine-readable pipeline manifest

Description

Machine-readable pipeline manifest

Usage

eye_pipeline_manifest(x)

Arguments

Value

A data frame containing machine-readable pipeline manifest. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Render pipeline dependencies as Mermaid flowchart text

Description

Render pipeline dependencies as Mermaid flowchart text

Usage

eye_pipeline_mermaid(x)

Arguments

Value

A character value or vector containing render pipeline dependencies as Mermaid flowchart text.

Define a governed pipeline step

Description

Define a governed pipeline step

Usage

eye_pipeline_step(
  name,
  fun,
  requires = character(),
  optional = FALSE,
  description = NULL,
  decision = NULL
)

Arguments

Value

An 'eye_pipeline_step' object.

Measurement-intelligence plotting and result infrastructure

Description

Measurement-intelligence plotting and result infrastructure. These functions form the eyeprocess 0.6.0.9000 measurement-intelligence programme and use dependency-free reference implementations with explicit evidence limits.

Usage

eye_plot_spec(type = "default", title = NULL, xlab = NULL, ylab = NULL,
  caption = NULL, show_uncertainty = TRUE, show_raw = TRUE, facet_by = NULL,
  label_items = FALSE, interactive = FALSE)
plot_diagnostics(x, ...)
plot_evidence(x, ...)
plot_sensitivity(x, ...)
autoplot_eyeprocess(object, ...)

Arguments

Details

The APIs return auditable S3 objects. Plot wrappers call registered base-graphics methods. Experimental or approximate engines are labelled in object status fields and should be validated before confirmatory or operational use.

Value

An eyeprocess result object, data frame, model object, plot, or audit table as documented by the individual function.

See Also

plot_diagnostics(), plot_evidence(), and plot_sensitivity().

Construct a lightweight provenance graph

Description

Construct a lightweight provenance graph

Usage

eye_prov_graph(
  nodes,
  edges = data.frame(from = character(), to = character(), relation = character()),
  metadata = list()
)

Arguments

Value

A named list with components "nodes", "edges", "metadata", containing a lightweight provenance graph and associated metadata or diagnostics.

Construct a reproducibility fingerprint

Description

Construct a reproducibility fingerprint

Usage

eye_reproducibility_fingerprint(
  data = NULL,
  analysis_spec = NULL,
  model_spec = NULL,
  decisions = NULL,
  result = NULL,
  files = NULL,
  seeds = NULL,
  label = "eyeprocess_analysis"
)

Arguments

Value

A named list with components "schema_version", "label", "eyeprocess_version", "data_hash", "analysis_spec_hash", "model_spec_hash", "decisions_hash", "result_hash", "file_manifest", "seeds", "environment", containing a reproducibility fingerprint and associated metadata or diagnostics.

Create a session-level provenance manifest

Description

Create a session-level provenance manifest

Usage

eye_session_manifest(
  data = NULL,
  files = NULL,
  adapter = NA_character_,
  decisions = NULL,
  pipeline = NULL,
  seeds = NULL,
  notes = NULL
)

Arguments

Value

A named list with components "created_utc", "eyeprocess_version", "data_hash", "files", "adapter", "decisions_hash", "pipeline_hash", "seeds", "environment", "notes", containing a session-level provenance manifest and associated metadata or diagnostics.

Specify disk-backed eyeprocess storage

Description

Specify disk-backed eyeprocess storage

Usage

eye_storage_spec(
  path,
  format = c("rds", "parquet", "arrow_dataset"),
  tables = canonical_table_names(),
  partitioning = NULL,
  compression = "zstd"
)

Arguments

Value

An 'eye_storage_spec' object.

Audit preservation of monocular/binocular stream semantics

Description

Audit preservation of monocular/binocular stream semantics

Usage

eye_stream_fidelity_audit(
  source,
  roundtrip,
  source_eye = "eye",
  roundtrip_eye = source_eye,
  key = NULL
)

Arguments

Value

An object of class "eye_stream_fidelity", stored as a named list, with components "status", "matched_n", "source_streams", "roundtrip_streams", "confusion". It contains preservation of monocular/binocular stream semantics and associated metadata or diagnostics needed to interpret the result.

Create a targets-compatible dependency manifest

Description

This function does not execute or silently translate arbitrary closures into a targets pipeline. It provides the dependency contract required to build an explicit '_targets.R' file.

Usage

eye_targets_manifest(x)

Arguments

Value

A data frame containing a targets-compatible dependency manifest. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Mappings and adapter registry

Description

Map heterogeneous exports, register import adapters, detect formats, and combine canonical datasets.

Details

These functions operate on the canonical relational representation used by eyeprocess. Native fields and timestamps are retained whenever possible, and transformations should be recorded in the provenance table. Optional modelling engines are used only when their packages are installed.

Value

The returned value depends on the function. Import and transformation functions generally return an eye_dataset; audit and feature functions return data frames or enriched datasets; plotting functions return their plotted data invisibly; modelling functions return engine-specific or eyeprocess_model objects.

See Also

new_eye_dataset(), read_eye_export(), validate_eye_dataset(), provenance_manifest()

Create and manage eyeprocess datasets

Description

Construct, inspect, validate, modify, and summarize canonical eye-tracking datasets.

Details

These functions operate on the canonical relational representation used by eyeprocess. Native fields and timestamps are retained whenever possible, and transformations should be recorded in the provenance table. Optional modelling engines are used only when their packages are installed.

Value

The returned value depends on the function. Import and transformation functions generally return an eye_dataset; audit and feature functions return data frames or enriched datasets; plotting functions return their plotted data invisibly; modelling functions return engine-specific or eyeprocess_model objects.

See Also

new_eye_dataset(), read_eye_export(), validate_eye_dataset(), provenance_manifest()

Coordinate and timebase management

Description

Register and convert coordinate spaces, normalize native clocks, estimate sampling rates, and synchronize streams.

Details

These functions operate on the canonical relational representation used by eyeprocess. Native fields and timestamps are retained whenever possible, and transformations should be recorded in the provenance table. Optional modelling engines are used only when their packages are installed.

Value

The returned value depends on the function. Import and transformation functions generally return an eye_dataset; audit and feature functions return data frames or enriched datasets; plotting functions return their plotted data invisibly; modelling functions return engine-specific or eyeprocess_model objects.

See Also

new_eye_dataset(), read_eye_export(), validate_eye_dataset(), provenance_manifest()

Experimental psychometric process models

Description

Experimental interfaces for multimodal IRT, functional pupil summaries, strategy mixtures, diffusion summaries, and missing-process sensitivity.

Details

These functions operate on the canonical relational representation used by eyeprocess. Native fields and timestamps are retained whenever possible, and transformations should be recorded in the provenance table. Optional modelling engines are used only when their packages are installed.

Value

The returned value depends on the function. Import and transformation functions generally return an eye_dataset; audit and feature functions return data frames or enriched datasets; plotting functions return their plotted data invisibly; modelling functions return engine-specific or eyeprocess_model objects.

See Also

new_eye_dataset(), read_eye_export(), validate_eye_dataset(), provenance_manifest()

Export, reporting, and package bridges

Description

Persist canonical datasets, produce provenance-aware reports, and bridge gp3tools or gpbiometrics objects.

Details

These functions operate on the canonical relational representation used by eyeprocess. Native fields and timestamps are retained whenever possible, and transformations should be recorded in the provenance table. Optional modelling engines are used only when their packages are installed.

Value

The returned value depends on the function. Import and transformation functions generally return an eye_dataset; audit and feature functions return data frames or enriched datasets; plotting functions return their plotted data invisibly; modelling functions return engine-specific or eyeprocess_model objects.

See Also

new_eye_dataset(), read_eye_export(), validate_eye_dataset(), provenance_manifest()

Derive gaze, pupil, response-time, and biometric features

Description

Generate declared person-item-trial features, scanpaths, transitions, entropy measures, and feature dictionaries.

Details

These functions operate on the canonical relational representation used by eyeprocess. Native fields and timestamps are retained whenever possible, and transformations should be recorded in the provenance table. Optional modelling engines are used only when their packages are installed.

Value

The returned value depends on the function. Import and transformation functions generally return an eye_dataset; audit and feature functions return data frames or enriched datasets; plotting functions return their plotted data invisibly; modelling functions return engine-specific or eyeprocess_model objects.

See Also

new_eye_dataset(), read_eye_export(), validate_eye_dataset(), provenance_manifest()

Validate real eye-tracking exports and compatibility corpora

Description

Build reproducible evidence for source-format compatibility. The validation framework distinguishes declared support, synthetic-fixture testing, and empirical validation with real exports. It audits source structure, adapter detection, canonical import, field coverage, native timestamp preservation, coordinate semantics, provenance, and canonical round-trip fidelity.

Details

validate_eye_source() validates one file or folder. Use validation_manifest() and validate_eye_corpus() to evaluate a versioned collection of exports from multiple vendors, devices, and software versions. init_validation_corpus() is idempotent and preserves an existing manifest and case files unless overwrite = TRUE is supplied. A source can be retained temporarily in the result for creation of an anonymized validation bundle; raw vendor data are never included in bundles by default.

anonymize_eye_dataset() replaces participant, recording, and session identifiers, removes raw tables and source paths by default, and records the operation in provenance. Automated anonymization cannot guarantee removal of all study-specific free text, so exported bundles still require human review.

Value

Depending on the function, an eye_format_validation_spec, source-file manifest, schema-coverage table, source-preservation audit, canonical round-trip result, eye_format_validation, eye_corpus_validation, anonymized eye_dataset, report path, or validation-bundle path.

See Also

read_eye_export(), validate_eye_dataset(), supported_eye_formats(), write_eye_dataset(), provenance_manifest()

Import Gazepoint and Gazepoint Biometrics exports

Description

Detect, profile, import, pair, validate, and reconstruct Gazepoint Analysis and Biometrics exports, including Gazepoint Analysis 7.x *_all_gaze.csv, *_fixations.csv, and multi-section Data_Summary_export_*.csv files.

Details

These functions operate on the canonical relational representation used by eyeprocess. For Gazepoint Analysis 7.x exports, TIMETICK(f=...) is retained as the native clock and normalized to seconds from recording start, while media-relative TIME(...) values are retained as source fields. Fixation identifiers that restart for each media item are namespaced by stimulus. Multi-section Data Summary reports are parsed into AOI definitions, user-AOI features, raw report tables, and vendor metadata. Native fields and transformations remain represented in provenance and vendor-specific metadata.

Value

The returned value depends on the function. Import and transformation functions generally return an eye_dataset; audit and feature functions return data frames or enriched datasets; plotting functions return their plotted data invisibly; modelling functions return engine-specific or eyeprocess_model objects.

See Also

new_eye_dataset(), read_eye_export(), validate_eye_dataset(), provenance_manifest()

Import generic delimited eye-tracking data

Description

Transform explicitly mapped data frames, CSV files, and TSV files into canonical eyeprocess datasets.

Details

These functions operate on the canonical relational representation used by eyeprocess. Native fields and timestamps are retained whenever possible, and transformations should be recorded in the provenance table. Optional modelling engines are used only when their packages are installed.

Value

The returned value depends on the function. Import and transformation functions generally return an eye_dataset; audit and feature functions return data frames or enriched datasets; plotting functions return their plotted data invisibly; modelling functions return engine-specific or eyeprocess_model objects.

See Also

new_eye_dataset(), read_eye_export(), validate_eye_dataset(), provenance_manifest()

Import Tobii, Pupil Labs, EyeLink, and SMI exports

Description

Dedicated import adapters for common and legacy eye-tracking export ecosystems.

Details

These functions operate on the canonical relational representation used by eyeprocess. Native fields and timestamps are retained whenever possible, and transformations should be recorded in the provenance table. Optional modelling engines are used only when their packages are installed.

Value

The returned value depends on the function. Import and transformation functions generally return an eye_dataset; audit and feature functions return data frames or enriched datasets; plotting functions return their plotted data invisibly; modelling functions return engine-specific or eyeprocess_model objects.

See Also

new_eye_dataset(), read_eye_export(), validate_eye_dataset(), provenance_manifest()

Psychometric and process-data models

Description

Prepare response matrices and fit optional IRT, explanatory, response-time, DIF, shared-process, and dynamic models.

Details

These functions operate on the canonical relational representation used by eyeprocess. Native fields and timestamps are retained whenever possible, and transformations should be recorded in the provenance table. Optional modelling engines are used only when their packages are installed.

Value

The returned value depends on the function. Import and transformation functions generally return an eye_dataset; audit and feature functions return data frames or enriched datasets; plotting functions return their plotted data invisibly; modelling functions return engine-specific or eyeprocess_model objects.

See Also

new_eye_dataset(), read_eye_export(), validate_eye_dataset(), provenance_manifest()

Visualize eye-tracking and multimodal process data

Description

Base-R plots for traces, fixations, scanpaths, heatmaps, AOIs, pupil, biometrics, quality, timing, and models.

Details

These functions operate on the canonical relational representation used by eyeprocess. Native fields and timestamps are retained whenever possible, and transformations should be recorded in the provenance table. Optional modelling engines are used only when their packages are installed.

Value

The returned value depends on the function. Import and transformation functions generally return an eye_dataset; audit and feature functions return data frames or enriched datasets; plotting functions return their plotted data invisibly; modelling functions return engine-specific or eyeprocess_model objects.

See Also

new_eye_dataset(), read_eye_export(), validate_eye_dataset(), provenance_manifest()

Gaze and pupil preprocessing

Description

Declare preprocessing, filter signals, interpolate pupil gaps, detect ocular events, and retain a transformation record.

Details

These functions operate on the canonical relational representation used by eyeprocess. Native fields and timestamps are retained whenever possible, and transformations should be recorded in the provenance table. Optional modelling engines are used only when their packages are installed.

Value

The returned value depends on the function. Import and transformation functions generally return an eye_dataset; audit and feature functions return data frames or enriched datasets; plotting functions return their plotted data invisibly; modelling functions return engine-specific or eyeprocess_model objects.

See Also

new_eye_dataset(), read_eye_export(), validate_eye_dataset(), provenance_manifest()

Quality control, sensitivity, and governance

Description

Audit data quality, flag process leakage, assess readiness, and compare preprocessing or AOI specifications.

Details

These functions operate on the canonical relational representation used by eyeprocess. Native fields and timestamps are retained whenever possible, and transformations should be recorded in the provenance table. Optional modelling engines are used only when their packages are installed.

Value

The returned value depends on the function. Import and transformation functions generally return an eye_dataset; audit and feature functions return data frames or enriched datasets; plotting functions return their plotted data invisibly; modelling functions return engine-specific or eyeprocess_model objects.

See Also

new_eye_dataset(), read_eye_export(), validate_eye_dataset(), provenance_manifest()

Canonical schemas and coordinate spaces

Description

Define, inspect, standardize, and validate the relational tables used by eyeprocess.

Details

These functions operate on the canonical relational representation used by eyeprocess. Native fields and timestamps are retained whenever possible, and transformations should be recorded in the provenance table. Optional modelling engines are used only when their packages are installed.

Value

The returned value depends on the function. Import and transformation functions generally return an eye_dataset; audit and feature functions return data frames or enriched datasets; plotting functions return their plotted data invisibly; modelling functions return engine-specific or eyeprocess_model objects.

See Also

new_eye_dataset(), read_eye_export(), validate_eye_dataset(), provenance_manifest()

Simulate and validate eyeprocess workflows

Description

Simulate canonical multimodal datasets and conduct parameter-recovery or power experiments.

Details

These functions operate on the canonical relational representation used by eyeprocess. Native fields and timestamps are retained whenever possible, and transformations should be recorded in the provenance table. Optional modelling engines are used only when their packages are installed.

Value

The returned value depends on the function. Import and transformation functions generally return an eye_dataset; audit and feature functions return data frames or enriched datasets; plotting functions return their plotted data invisibly; modelling functions return engine-specific or eyeprocess_model objects.

See Also

new_eye_dataset(), read_eye_export(), validate_eye_dataset(), provenance_manifest()

Trials, responses, stimuli, and areas of interest

Description

Reconstruct trial intervals, attach responses, register static or dynamic AOIs, and derive AOI visits.

Details

These functions operate on the canonical relational representation used by eyeprocess. Native fields and timestamps are retained whenever possible, and transformations should be recorded in the provenance table. Optional modelling engines are used only when their packages are installed.

Value

The returned value depends on the function. Import and transformation functions generally return an eye_dataset; audit and feature functions return data frames or enriched datasets; plotting functions return their plotted data invisibly; modelling functions return engine-specific or eyeprocess_model objects.

See Also

new_eye_dataset(), read_eye_export(), validate_eye_dataset(), provenance_manifest()

Return the public eyeprocess API version

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

eyeprocess_api_version()

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Locate the bundled public benchmark study

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

eyeprocess_benchmark_study(path = NULL)

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

Enumerate latent attribute profiles

Description

Enumerate latent attribute profiles

Usage

eyeprocess_cdm_attribute_profiles(
  n_attributes,
  attribute_names = paste0("A", seq_len(n_attributes))
)

Arguments

Value

An R object containing enumerate latent attribute profiles. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Summarise CDM classification uncertainty from profile probabilities

Description

Summarise CDM classification uncertainty from profile probabilities

Usage

eyeprocess_cdm_classification_uncertainty(profile_probabilities)

Arguments

Value

A data frame containing cDM classification uncertainty from profile probabilities. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Compute deterministic DINA ideal responses from a Q-matrix

Description

Compute deterministic DINA ideal responses from a Q-matrix

Usage

eyeprocess_cdm_dina_ideal_response(Q, profiles)

Arguments

Value

A vector or matrix containing deterministic DINA ideal responses from a Q-matrix, with shape determined by the supplied analysis units.

DINA response probabilities from slip and guess parameters

Description

DINA response probabilities from slip and guess parameters

Usage

eyeprocess_cdm_dina_probability(ideal_response, slip = 0.1, guess = 0.2)

Arguments

Value

An object of class "matrix", stored as an R object, containing dINA response probabilities from slip and guess parameters and associated metadata needed to interpret the result.

Audit a cognitive-diagnosis Q-matrix

Description

Audit a cognitive-diagnosis Q-matrix

Usage

eyeprocess_cdm_qmatrix_audit(
  Q,
  item_ids = rownames(Q),
  attribute_names = colnames(Q)
)

Arguments

Value

An object of class "eye_cdm_qmatrix_audit", stored as a named list, with components "item", "attribute", "duplicate_rows", "complete_identity_block". It contains a cognitive-diagnosis Q-matrix and associated metadata or diagnostics needed to interpret the result.

Declare a deprecation in a structured form

Description

Part of the research-scale validation, advanced-model, interoperability, storage, adapter, or reproducibility programme. Experimental model functions remain subject to declared evidence gates.

Usage

eyeprocess_deprecation(old, replacement, since, remove_after, reason = "")

Arguments

Value

The documented eyeprocess object, data frame, plot, report path, or adapter result.

2PL item-response probability

Description

2PL item-response probability

Usage

eyeprocess_irt_2pl_probability(theta, a = 1, b = 0, D = 1)

Arguments

Value

A numeric value or vector containing 2PL item-response probability.

3PL item-response probability

Description

3PL item-response probability

Usage

eyeprocess_irt_3pl_probability(theta, a = 1, b = 0, c = 0.2, D = 1)

Arguments

Value

A numeric value or vector containing 3PL item-response probability.

4PL item-response probability

Description

4PL item-response probability

Usage

eyeprocess_irt_4pl_probability(theta, a = 1, b = 0, c = 0, d = 1, D = 1)

Arguments

Value

A numeric value or vector containing 4PL item-response probability.

Create an auditable adaptive-testing trace

Description

Create an auditable adaptive-testing trace

Usage

eyeprocess_irt_adaptive_trace(
  item_id,
  theta_before,
  theta_after,
  se_after,
  information,
  response = NA_real_
)

Arguments

Value

An object of class "eye_irt_adaptive_trace", "data.frame", stored as a data frame, containing an auditable adaptive-testing trace and associated metadata needed to interpret the result.

Audit candidate anchor items using supplied DIF evidence

Description

Audit candidate anchor items using supplied DIF evidence

Usage

eyeprocess_irt_anchor_audit(
  items,
  dif = NULL,
  max_abs_effect = 0.1,
  min_information = NULL
)

Arguments

Value

A data frame containing candidate anchor items using supplied DIF evidence. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Iteratively remove anchors exceeding a supplied effect threshold

Description

Iteratively remove anchors exceeding a supplied effect threshold

Usage

eyeprocess_irt_anchor_purification(
  items,
  effect_fun,
  initial = items$item_id,
  threshold = 0.1,
  max_iter = 10L
)

Arguments

Value

An object of class "eye_irt_anchor_purification", stored as a named list, with components "anchors", "history", "threshold". It contains iteratively remove anchors exceeding a supplied effect threshold and associated metadata or diagnostics needed to interpret the result.

Apply linear IRT scale-linking coefficients

Description

Apply linear IRT scale-linking coefficients

Usage

eyeprocess_irt_apply_link(items, link)

Arguments

Value

An R object containing linear IRT scale-linking coefficients. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.

Audit item-bank information coverage across a theta region

Description

Audit item-bank information coverage across a theta region

Usage

eyeprocess_irt_bank_coverage(
  items,
  theta = seq(-4, 4, length.out = 161),
  target_information = 5,
  target = c(-2, 2)
)

Arguments

Value

An object of class "eye_irt_bank_coverage", stored as a named list, with components "curve", "target", "target_information", "fraction_target_met", "minimum_information", "maximum_sem", "gaps". It contains item-bank information coverage across a theta region and associated metadata or diagnostics needed to interpret the result.

Audit category probability functions

Description

Audit category probability functions

Usage

eyeprocess_irt_category_function_audit(probabilities, tolerance = 1e-08)

Arguments

Value

An object of class "eye_irt_category_audit", stored as a named list, with components "valid_bounds", "rows_sum_to_one", "max_sum_error", "min_probability", "max_probability". It contains category probability functions and associated metadata or diagnostics needed to interpret the result.

Summarise decision precision at one or more theta cut scores

Description

Summarise decision precision at one or more theta cut scores

Usage

eyeprocess_irt_classification_precision(
  theta_estimate,
  standard_error,
  cut_score = 0,
  confidence = 0.95
)

Arguments

Value

A tabular R object containing decision precision at one or more theta cut scores; rows represent analysis units and columns contain the returned quantities.

Conditional standard error from information

Description

Conditional standard error from information

Usage

eyeprocess_irt_conditional_sem(information)

Arguments

Value

A logical value or vector indicating conditional standard error from information.

Audit content balance in an administered adaptive form

Description

Audit content balance in an administered adaptive form

Usage

eyeprocess_irt_content_balance_audit(administered, item_bank, target = NULL)

Arguments

Value

A data frame containing content balance in an administered adaptive form. Rows represent the analysis units and columns contain the identifiers, estimates, or diagnostics defined by the function.

Summarise parameter drift across acquisition devices

Description

Summarise parameter drift across acquisition devices