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gpbiometrics 0.1.0

Overview

• Initial validated development release of gpbiometrics, an R package for importing, validating, quality-checking, preprocessing, synchronising, summarising, modelling, plotting, and reporting Gazepoint Biometrics and Gazepoint GP3 biometric exports.
• The package focuses on Gazepoint-specific biometric channels, including GSR/EDA, heart rate, interbeat intervals, pulse signal, engagement dial, TTL markers, pupil-related columns, AOI fields, and synchronisation variables.
• The current feature inventory contains 155 available user-facing helpers across 11 complete workflow domains.
• Interpretation is intentionally conservative: biometric features are treated as physiological descriptors, quality-control outputs, or analysis-ready signals, not direct labels for emotion, stress, cognition, preference, health status, or diagnosis.

Import, schema, and workflow infrastructure

• Added import helpers for single files and export folders, including import_gazepoint_biometrics(), import_gazepoint_biometric_folder(), import_gazepoint_data_summary(), and import_gazepoint_lsl_xdf().
• Added Gazepoint schema and channel-detection helpers, including check_gazepoint_biometric_columns(), detect_gazepoint_biometric_schema(), detect_gazepoint_time_columns(), detect_active_biometric_channels(), and standardise_gazepoint_biometric_names().
• Added the main workflow wrapper run_gazepoint_biometrics_workflow() and summary/diagnostic helpers, including summarise_gazepoint_biometrics_workflow() and diagnose_gazepoint_biometrics_workflow().
• Added synthetic data generation with simulate_gazepoint_biometrics() for examples, teaching, and controlled validation.

Quality control and readiness

• Added validation, missingness, sampling, signal-activity, time-reset, dropout, distributional-drift, and real-data readiness checks.
• Added run_gazepoint_biometrics_real_data_readiness() as a final readiness gate for real Gazepoint exports.
• Added exclusion-recommendation helpers for participant-level and window-level biometric quality decisions.
• Added artifact-detection helpers, including MAD-based, Kleckner-style, and SVM-feature workflows.
• Added audit_gazepoint_gsr_units() to help distinguish conductance-like and resistance-like GSR columns before downstream EDA/SCR processing.
• Added audit_gazepoint_stabilization_period() for flagging or trimming the initial electrode-stabilisation period.

Preprocessing and signal correction

• Added baseline correction, smoothing, within-unit standardisation, z-score/range correction, adaptive EMA smoothing, wavelet denoising, quantisation-noise handling, and optional autoencoder-denoising bridges.
• Added EDA/GSR unit auditing and conductance-conversion helpers.
• Added environmental and stimulus-confound controls, including correct_gazepoint_eda_temperature(), audit_gazepoint_stabilization_period(), and regress_gazepoint_pupil_luminance().
• Added both British and American spelling aliases where useful, including standardise/standardize variants.

EDA, GSR, and SCR analysis

• Added EDA/GSR quality audits, tonic/phasic summaries, SCR event and peak detection, SCR event-window summaries, nonresponder screening, threshold-sensitivity checks, and SCR multiverse workflows.
• Added SCR recovery-time extraction with extract_gazepoint_scr_recovery_times(), including half-recovery and 63 percent recovery-time summaries.
• Added advanced EDA helpers for spectral power, complexity, TVSymp-style analysis, bilateral EDA asymmetry, skin-potential analysis, AC admittance/susceptance, stochastic change-point screening, and EDA-gram-style visualisation.
• Added external EDA interoperability helpers for Ledalab, PsPM, cvxEDA, NeuroKit-style input, and DCM/CTSI-oriented bridges.
• Added run_gazepoint_automated_statistics() for exploratory group comparisons with normality screening, ANOVA/Kruskal-Wallis selection, post-hoc testing, and multiplicity correction.

Pulse, IBI, HR, HRV, and respiration

• Added HR, IBI, and HRV quality and consistency checks.
• Added HR/IBI window summaries and IBI-derived HRV feature extraction.
• Added nonlinear and geometric HRV descriptors, including RQA, fragmentation, asymmetry, FuzzyEn/CSI, RCMSE, surrogate nonlinearity testing, and IPFM-style impulse-train modelling.
• Added Gazepoint pulse beat-candidate extraction with extract_gazepoint_beats_kmeans().
• Added respiration-related helpers, including PPG-derived respiration, ECG-derived respiration PCA bridges, CEEMDAN-style respiration extraction, RSA proxy calculation, and Kalman fusion of respiration proxy streams.
• Added point-process and cardiorespiratory directionality helpers for advanced exploratory analysis.

TTL, synchronisation, windows, and model-ready data

• Added TTL event extraction and TTL alignment helpers.
• Added signal-lag estimation and synchronisation-drift diagnostics.
• Added multimodal time-window summaries and model-ready table preparation helpers for biometric, AOI-linked, and LME-style analyses.
• Added chunking and online design-optimisation decision-support helpers for advanced experimental workflows.
• Added helpers for synchronising Gazepoint Biometrics outputs with Gazepoint eye-tracking master tables.

AOI-linked biometrics and plotting

• Added AOI-linked biometric summaries, AOI-biometric model data preparation, and AOI-biometric plotting.
• Added biometric signal plots, quality plots, decomposition plots, SCR plots, multimodal timelines, activity/time-reset plots, report dashboards, SCR specification-curve plots, saccade main-sequence plots, and EDA-gram-style plots.
• Added plot-contract helpers to store plot data, settings, and interpretation metadata for reproducibility.

Reporting, feature inventory, and documentation

• Added checklist, methods-text, report-table, report-bundle, preregistration-template, and Shiny/annotator helpers.
• Added create_gazepoint_biometrics_feature_inventory() for programmatic workflow coverage checks.
• Added formatted inventory helpers, format_gazepoint_biometrics_feature_inventory() and summarise_gazepoint_biometrics_feature_inventory().
• Added a compact user-facing README and the first workflow vignette, vignettes/gpbiometrics-workflow.Rmd.
• Updated workflow documentation to use the current run_gazepoint_biometrics_workflow(path = ...) API and to export report bundles through export_gazepoint_biometrics_report_bundle().
• Added a public, fully synthetic Gazepoint-like kiosk demo dataset under inst/extdata/gazepoint_biometrics_kiosk_demo_exports/.
• The demo dataset contains 36 synthetic participants, four kiosk tasks per participant, 69,120 rows, 36 all-gaze CSV exports, task metadata, gaze/AOI fields, pupil columns, GSR/EDA, HR, IBI, pulse waveform, engagement dial, and TTL markers.
• Added data-raw/create_gazepoint_biometrics_kiosk_demo_exports.R to regenerate the synthetic demo exports reproducibly.
• Added package tests to ensure the synthetic kiosk demo remains available, importable, and schema-valid.

Interoperability and optional external methods

• Added RHRV, pyPPG, NeuroKit2, Ledalab, PsPM, cvxEDA, DCM, and CTSI-oriented preparation/export bridges.
• External-method bridges remain optional and do not make external software a hard dependency.
• Advanced bridge functions prepare or structure data for external workflows unless explicit cross-check execution is requested and available.

Validation

• Current local validation passed with:

devtools::test()
# FAIL 0 | WARN 0 | SKIP 0 | PASS 1662

devtools::check()
# 0 errors | 0 warnings | 0 notes

• The workflow vignette builds during devtools::check().
• A private real-data smoke test on a local Gazepoint export folder passed import, readiness, workflow, summary, and report-bundle export checks.
• The private workflow used 6 source files, 7340 imported all-gaze rows, 70 columns, 1323 TTL events, 0 validation issues, and 3 active signal groups.
• The private report-bundle export wrote 81 files with 0 skipped items.
• Private data and private smoke-test outputs remain outside the package repository.

Interpretation safeguards

• EDA/GSR/SCR features describe electrodermal dynamics and arousal-related physiology; they do not directly infer emotion, stress, cognition, health status, or diagnosis.
• HR, IBI, HRV, pulse, and respiration-proxy features describe cardiovascular or signal-derived dynamics; they are not clinical labels.
• Pupil outputs are affected by luminance and visual context; luminance-adjusted residuals are not proof of cognitive-load-only effects.
• AOI-linked biometric summaries describe signal values during AOI exposure and do not establish emotional valence, preference, or cognitive evaluation by themselves.
• Automated statistics and advanced models are exploratory/reporting aids unless matched to a preregistered design and reviewed analytically.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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