gpbiometrics provides reproducible Gazepoint-native infrastructure for importing, validating, quality-checking, preprocessing, synchronising, summarising, modelling, plotting, and reporting physiological and eye-tracking exports. It covers EDA/GSR/SCR, PPG, HR, IBI/HRV, pupil, gaze, fixations, AOIs, TTL/event timing, multimodal alignment, interoperability, reporting, and privacy-safe validation.
Current GitHub release: 2.1.1 (v2.1.1, 22 October 2026) · CRAN: 0.1.0 (verified October 2026)
Website · Reference · Articles · Release · Signals paper
The published GitHub 2.1.0 release added four conservative diagnostics motivated by the September 2026 methods-surveillance tranche:
compare_hrv_prv_devices() reports ICC(A,1), Lin's CCC, Bland–Altman bias, and 95% limits for one derived metric at a time, while retaining signal-source and sensor-site provenance. It does not create a global ECG/PPG interchangeability decision.scr_responsivity_sensitivity() preserves the conventional SCR non-responder flag, estimates a Beta-Binomial posterior response probability, and retains low-reactive participants for model-based sensitivity analysis instead of imposing irreversible preprocessing deletion.physiology_validation_ladder() separates acquisition QC, analytical QC, construct evidence, within-person evidence, and held-out-person generalization. Population/generalization claims require held-out-person validation to pass.ppg_topology_features() is opt-in and experimental. It summarizes H0 persistence lifetimes from a delay embedding as a structural waveform descriptor, not a direct physiological surrogate, and carries a participant-grouped validation guardrail.Those diagnostics originated in the immutable GitHub v2.1.0 release and the historical v2.0.0 archive is unchanged. GitHub v2.1.1 additionally integrates the synthetic acquisition-availability article with two original R plots; it does not establish clinical or empirical validation.
See the Measurement accountability for Gazepoint physiology article.
The acquisition-availability article includes two original R-generated synthetic figures describing available versus missing physiological observation windows. These demonstrations are synthetic and are not empirical device-calibration, causal, or clinical evidence.
Channels: GitHub 2.1.1 (22 October 2026); CRAN 0.1.0 (latest independently checked). The GitHub and CRAN source versions are not interchangeable.
Install the exact published GitHub 2.1.1 release:
install.packages("remotes")
remotes::install_github("stefanosbalaskas/gpbiometrics", ref = "v2.1.1")
For current development sources, including October documentation changes:
# remotes::install_github("stefanosbalaskas/gpbiometrics")
install.packages("gpbiometrics", repos = c('https://stefanosbalaskas.r-universe.dev', 'https://cloud.r-project.org'))
library(gpbiometrics)
folder <- "path/to/gazepoint_exports"
workflow <- run_gazepoint_biometrics_workflow(
path = folder,
include_all_gaze = TRUE,
include_fixations = FALSE,
include_other_csv = FALSE,
expected_sampling_rate_hz = 60
)
summarise_gazepoint_biometrics_workflow(workflow)
For private real data, keep source exports and generated smoke-test outputs outside the package repository.
Gazepoint / external exports
-> schema + signal availability audit
-> timing / TTL / missingness / quality checks
-> EDA / PPG / IBI / pupil / gaze preprocessing
-> fixation / AOI / event / multimodal alignment
-> physiological and process feature extraction
-> model-ready tables
-> plots + reports + reproducibility evidence
-> optional external-toolbox cross-checks
The package is intentionally modular: researchers can run a complete workflow or call individual helpers while keeping preprocessing and interpretation decisions explicit.
The v2.0.0 release record reports:
R CMD check: 0 errors, 0 warnings, 0 notes; andThe September 2026 additions are development functionality layered on top of this qualified release baseline.
gpbiometrics treats biometric outputs as physiological descriptors, quality-control evidence, or analysis-ready measurements. It does not infer emotion, stress, cognition, preference, attention quality, health status, diagnosis, or another psychological/clinical state directly from EDA, HRV/PRV, PPG, pupil, gaze, or multimodal signals.
Metric agreement does not imply sensor interchangeability; retention-first responsivity does not diagnose participant state; held-out prediction does not establish construct validity; and experimental topology features are not physiological surrogates without independent validation.
For the installed package citation:
citation("gpbiometrics")
packageVersion("gpbiometrics")
The package paper is “gpbiometrics: An R Package for Reproducible Analysis and Reporting of Gazepoint Biometrics Exports”, Signals (2026), DOI 10.3390/signals7050086.
MIT License.