inject_aoi_label_noise: Inject AOI label noise
In eyeprocess: Harmonize Eye-Tracking, Pupillometry, Biometrics, and Psychometric Process Data
View source: R/075-benchmark-stress-0-9.R
inject_aoi_label_noise R Documentation
Inject AOI label noise
Description
Inject AOI label noise
Usage
inject_aoi_label_noise(data, aoi = "aoi", proportion, seed = 1L)
Arguments
data
Data.
aoi
AOI label column.
proportion
Proportion reassigned to another observed label.
seed
Seed.
Value
An R object containing inject AOI label noise. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.
eyeprocess documentation built on Sept. 28, 2026, 5:08 p.m.
Related to inject_aoi_label_noise in eyeprocess...
Package overview
README.md
Adaptive testing design and governance
Advanced model programme and validation
Advanced pupillometry representations and confound control
Advanced Rasch, mixture-IRT, and imputation sensitivity
API lifecycle and canonical interfaces
Bayesian and 3PL Process Diagnostics
Building a software-paper evidence bundle
Calibration uncertainty and eye-tracking data quality
Cognitive diagnosis and Q-matrix governance
Complete Gazepoint Downstream Workflow
Computational benchmarking and synthetic stress testing
Does Latent State Structure Add Measurement Information?
Dynamic IRTree and transition-model hardening
Empirical validation programmes in eyeprocess
Evidence and Decision Provenance
Experimental Process-IRT Methods and Evidence Gates
External IRT engines and exact-method gating
Frozen validation evidence programme
Functional pupil-IRT modelling
Gaze-informed diffusion-IRT modelling
Gazepoint and Gazepoint Biometrics Workflows
Getting Started with eyeprocess
Governed end-to-end analysis pipelines
Identifiability, State Separation, and Label Uncertainty
Importing and Harmonizing Eye-Tracking Exports
Independent multi-vendor validation corpus
Independent Vendor Validation and Semantic Fidelity
Interoperability, Eye-Tracking-BIDS, and storage
Interoperability with targets-style workflows
IRT information, scoring, and diagnostics
IRT linking, DIF, DTF, and invariance evidence
IRT recovery, SBC, and misspecification evidence
Item-Bank Decisions, Fairness Drift, and Reference Centiles
Item seeding, accessibility review, and presentation fairness
M2 Posterior Predictive Checks and Negative Controls
M2 Process Information and Channel Ablation
M2 Recovery and Identifiability
M2 Three-Way Reference Model: Response, RT, and Gaze
M3 device transport, falsification controls, and sensor value
M3: Four-channel response, RT, gaze, and pupil measurement
M3 functional pupil bridge: from trajectories to joint measurement
M3 process information: ablation, redundancy, and sensor value
M3 pupil measurement: confounds, quality, and missingness
M3 recovery, identifiability, and failure-case validation
Manual Installation of Multimodal Backends
Measurement accountability: pupil latency, event plausibility, and validation
Measurement-quality stress tests and negative controls
Measurement Uncertainty and Recalibration
Multiblock process structure, profiles, and external validity
Multidimensional, testlet, and latent-regression IRT
Multimodal Process IRT: Responses, Time, Gaze, and Missingness
Multiple-Response Items, Revisiting, and Local Dependence
Multi-vendor empirical validation
Negative Controls and State-Count Sensitivity
Negative controls, placebo windows, and temporal leakage
Post-deployment psychometric-biometric drift monitoring
Preprocessing, AOIs, and Feature Engineering
Probabilistic AOIs and Compositional Attention
Process-decision proxies and research-frontier gates
Process Information and Channel Ablation
Process-IRT Model Atlas: What to Fit, What to Validate, What Not to Claim
Process-measure registry, repeatability, and reliability
Process pre-flight and anomaly governance
Process Reliability and Device Transportability
Psychometric Process-Data Models
Public benchmark and software-paper reproduction
Pupil Measurement Boundaries in Multimodal IRT
Pupil Phase-Amplitude Registration and Missingness
Quality, Provenance, and Responsible Interpretation
Recovery and Validation of Latent Response-Process States
Representative Scanpaths and Cognitive Episodes
Reproducibility fingerprints, provenance, and RO-Crate
Research-scale validation execution
Research validation and software-paper programme
Response-time and process-aware IRT
Sensitivity, specification curves, and decision stability
Simulation-based calibration and measurement-resolution guards
Specifying and Fitting the M4 Reference Model
Stable APIs, scalable storage, and external adapters
Streaming scoring and validation evidence bundles
Temporal and Spatial Process Science
Temporal process windows and AOI trajectories
Theory-constrained strategy mixtures
Trait-Conditioned Latent Response-Process States
Unified Multimodal Process-IRT
Validating Real Eye-Tracking Exports
Validation Before Promotion: Recovery, SBC, PPC, and Transportability
Validation evidence atlas and software-paper reporting
Visual-context and testlet IRT
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
View source: R/075-benchmark-stress-0-9.R
| inject_aoi_label_noise | R Documentation |
Inject AOI label noise
Description
Inject AOI label noise
Usage
inject_aoi_label_noise(data, aoi = "aoi", proportion, seed = 1L)
Arguments
data |
Data. |
aoi |
AOI label column. |
proportion |
Proportion reassigned to another observed label. |
seed |
Seed. |
Value
An R object containing inject AOI label noise. The concrete class and structure follow the selected method, engine, or input object and are preserved as documented by that workflow.
Related to inject_aoi_label_noise in eyeprocess...
Package overview
README.md
Adaptive testing design and governance
Advanced model programme and validation
Advanced pupillometry representations and confound control
Advanced Rasch, mixture-IRT, and imputation sensitivity
API lifecycle and canonical interfaces
Bayesian and 3PL Process Diagnostics
Building a software-paper evidence bundle
Calibration uncertainty and eye-tracking data quality
Cognitive diagnosis and Q-matrix governance
Complete Gazepoint Downstream Workflow
Computational benchmarking and synthetic stress testing
Does Latent State Structure Add Measurement Information?
Dynamic IRTree and transition-model hardening
Empirical validation programmes in eyeprocess
Evidence and Decision Provenance
Experimental Process-IRT Methods and Evidence Gates
External IRT engines and exact-method gating
Frozen validation evidence programme
Functional pupil-IRT modelling
Gaze-informed diffusion-IRT modelling
Gazepoint and Gazepoint Biometrics Workflows
Getting Started with eyeprocess
Governed end-to-end analysis pipelines
Identifiability, State Separation, and Label Uncertainty
Importing and Harmonizing Eye-Tracking Exports
Independent multi-vendor validation corpus
Independent Vendor Validation and Semantic Fidelity
Interoperability, Eye-Tracking-BIDS, and storage
Interoperability with targets-style workflows
IRT information, scoring, and diagnostics
IRT linking, DIF, DTF, and invariance evidence
IRT recovery, SBC, and misspecification evidence
Item-Bank Decisions, Fairness Drift, and Reference Centiles
Item seeding, accessibility review, and presentation fairness
M2 Posterior Predictive Checks and Negative Controls
M2 Process Information and Channel Ablation
M2 Recovery and Identifiability
M2 Three-Way Reference Model: Response, RT, and Gaze
M3 device transport, falsification controls, and sensor value
M3: Four-channel response, RT, gaze, and pupil measurement
M3 functional pupil bridge: from trajectories to joint measurement
M3 process information: ablation, redundancy, and sensor value
M3 pupil measurement: confounds, quality, and missingness
M3 recovery, identifiability, and failure-case validation
Manual Installation of Multimodal Backends
Measurement accountability: pupil latency, event plausibility, and validation
Measurement-quality stress tests and negative controls
Measurement Uncertainty and Recalibration
Multiblock process structure, profiles, and external validity
Multidimensional, testlet, and latent-regression IRT
Multimodal Process IRT: Responses, Time, Gaze, and Missingness
Multiple-Response Items, Revisiting, and Local Dependence
Multi-vendor empirical validation
Negative Controls and State-Count Sensitivity
Negative controls, placebo windows, and temporal leakage
Post-deployment psychometric-biometric drift monitoring
Preprocessing, AOIs, and Feature Engineering
Probabilistic AOIs and Compositional Attention
Process-decision proxies and research-frontier gates
Process Information and Channel Ablation
Process-IRT Model Atlas: What to Fit, What to Validate, What Not to Claim
Process-measure registry, repeatability, and reliability
Process pre-flight and anomaly governance
Process Reliability and Device Transportability
Psychometric Process-Data Models
Public benchmark and software-paper reproduction
Pupil Measurement Boundaries in Multimodal IRT
Pupil Phase-Amplitude Registration and Missingness
Quality, Provenance, and Responsible Interpretation
Recovery and Validation of Latent Response-Process States
Representative Scanpaths and Cognitive Episodes
Reproducibility fingerprints, provenance, and RO-Crate
Research-scale validation execution
Research validation and software-paper programme
Response-time and process-aware IRT
Sensitivity, specification curves, and decision stability
Simulation-based calibration and measurement-resolution guards
Specifying and Fitting the M4 Reference Model
Stable APIs, scalable storage, and external adapters
Streaming scoring and validation evidence bundles
Temporal and Spatial Process Science
Temporal process windows and AOI trajectories
Theory-constrained strategy mixtures
Trait-Conditioned Latent Response-Process States
Unified Multimodal Process-IRT
Validating Real Eye-Tracking Exports
Validation Before Promotion: Recovery, SBC, PPC, and Transportability
Validation evidence atlas and software-paper reporting
Visual-context and testlet IRT
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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