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Harmonize Eye-Tracking, Pupillometry, Biometrics, and Psychometric Process Data

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fit_item_parameter_seed_model: Fit an experimental pre-pilot item-parameter seeding model

fit_item_parameter_seed_model: Fit an experimental pre-pilot item-parameter seeding model
In eyeprocess: Harmonize Eye-Tracking, Pupillometry, Biometrics, and Psychometric Process Data

View source: R/062-context-process-structure-0-8.R

fit_item_parameter_seed_modelR Documentation

Fit an experimental pre-pilot item-parameter seeding model

Description

Estimates screening predictions for item difficulty/discrimination from item design/process features. Predictions are not calibrated operational parameters.

Usage

fit_item_parameter_seed_model(
  item_data,
  difficulty = "irt_difficulty",
  discrimination = "irt_discrimination",
  predictors,
  engine = c("auto", "ranger", "lm"),
  seed = 2221
)

Arguments

item_data

Calibrated item-level training data.

difficulty, discrimination

Target columns.

predictors

Design/process predictors.

engine

'auto', 'ranger', or 'lm'.

seed

Seed.

Value

An object of class "eye_item_parameter_seed", stored as a named list, with components "difficulty_model", "discrimination_model", "difficulty", "discrimination", "predictors", "engine", "training_data", "status", "caveat". It contains an experimental pre-pilot item-parameter seeding model and associated metadata or diagnostics needed to interpret the result.


eyeprocess documentation built on Sept. 28, 2026, 5:08 p.m.

Related to fit_item_parameter_seed_model in eyeprocess...

eyeprocess index
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

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