Changes in version 0.1.0 (2026-07-04) 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.