gp3bayes: Contract-First Bayesian Workflows for Hierarchical Behavioural
Data
Provides transparent, contract-first infrastructure for Bayesian
analysis of repeated-measures and hierarchical behavioural data. It
supports approved Bernoulli-logit, positive lognormal duration, and
governed Gaussian dynamic-pupillometry workflows with strict readiness
auditing, deterministic simulation, explicit preparation and
transformation replay, inspectable scale-aware priors, prior and posterior
predictive checks, restricted optional fitting through 'brms' with either
'rstan' or 'cmdstanr', sampling and temporal diagnostics, explicit
posterior estimands, sensitivity analysis, target-specific predictive
validation, simulation-based calibration, and conservative reporting.
Core contracts and validation remain backend-independent.
Version 0.5 adds governed robust and distributional dynamic pupillometry, bounded ARMA residual structures, Gaussian-process trajectories, explicit measurement uncertainty and missing-data models, joint binocular analysis, predictive model comparison, functional posterior estimands, and experimental nonlinear response-shape models while preserving explicit scientific and computational governance boundaries.
Version 0.6 adds retention-first Bayesian SCR responsivity
accountability and governed trial-level multilevel gaze mediation with
explicit within- and between-participant estimands, serial and
moderated extensions, convergence gating, and observation-aligned
predictive model comparison.
| Version: |
0.6.1 |
| Imports: |
withr, stats |
| Suggests: |
ggplot2, SBC, bayesplot, brms, cmdstanr, detectseparation, knitr, loo, posterior, priorsense, rmarkdown, rstan, testthat (≥ 3.0.0) |
| Published: |
2026-10-22 |
| DOI: |
10.32614/CRAN.package.gp3bayes |
| Author: |
Stefanos Balaskas 
[aut, cre, cph] |
| Maintainer: |
Stefanos Balaskas <s.balaskas at ac.upatras.gr> |
| BugReports: |
https://github.com/stefanosbalaskas/gp3bayes/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://stefanosbalaskas.github.io/gp3bayes/,
https://github.com/stefanosbalaskas/gp3bayes |
| NeedsCompilation: |
no |
| Additional_repositories: |
https://stan-dev.r-universe.dev,
https://r-multiverse.r-universe.dev |
| Citation: |
gp3bayes citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
gp3bayes results |
Documentation:
| Reference manual: |
gp3bayes.html , gp3bayes.pdf
|
| Vignettes: |
Advanced Dynamic Pupillometry in gp3bayes 0.5 (source, R code)
Advanced Optional Bayesian Workflows (source, R code)
Advanced Predictive Diagnostics (source, R code)
Bounded ARMA and Temporal Diagnostics (source, R code)
Backend Portability and Installation (source, R code)
Backend Reliability, Parity and Object Schemas (source, R code)
Bayesian dynamic pupillometry: governed foundation (source, R code)
Bayesian Multilevel Gaze Mediation (source, R code)
End-to-End Hierarchical Binary Workflow (source, R code)
Joint Binocular Pupil Models (source, R code)
Computational Governance and Model Cards (source, R code)
Declared Priors versus Fitted Posteriors (source, R code)
End-to-End Hierarchical Lognormal Duration Workflow (source, R code)
Ecosystem update — September 2026 (source)
End-to-End Evidence and Publication Showcase (source, R code)
First-Class Estimands and Sensitivity Workflows (source, R code)
Evidence Graphics and Governance (source, R code)
Experimental Interpretable Pupil Response Shape (source, R code)
Fitting hierarchical pupil time-course models (source, R code)
Functional Dynamics and Predictive Calibration (source, R code)
Gaussian-Process Pupil Trajectories (source, R code)
Gazepoint pupil-data interoperability (source, R code)
Governed Predictive Model Comparison (source, R code)
Grouped PSIS-LOO Influence (source, R code)
Hierarchical Effect and Variance Atlases (source, R code)
Hierarchical Effects and Predictive Uncertainty (source, R code)
LOO Influence and Predictive Model Comparison (source, R code)
Pointwise LOO Influence Atlases (source, R code)
Measurement Uncertainty and Missing Pupil Data (source, R code)
Mediation evidence gallery (source, R code)
Model Cards and Reporting Inventories (source, R code)
Prior sensitivity and independent information: an analytical Bayesian example (source, R code)
Optional Bayesian Backend Installation (source, R code)
Pathological Simulation Scenarios (source, R code)
Sampling Diagnostics and Conservative Decisions (source, R code)
Posterior Exploration and Publication Graphics (source, R code)
Pre-fit Design-Support Diagnostics (source, R code)
Prediction, Calibration, and Scoring (source, R code)
Prediction Contrasts, Rankings, and Groups (source, R code)
Prediction Profiles, Surfaces, and Contrast Profiles (source, R code)
Predictive Distribution and Calibration Uncertainty (source, R code)
Complete Public API Map (source, R code)
Publication-Ready Analysis Bundles (source, R code)
Publication Registries and Diagnostic Dashboards (source, R code)
Baseline, gaze/PFE, and luminance sensitivity (source, R code)
Pupil posterior predictive checks and temporal diagnostics (source, R code)
Preparing and auditing pupil time courses (source, R code)
Validation for temporally dependent pupil data (source, R code)
Posterior pupil trajectories and declared estimands (source, R code)
Quality Hardening and Failure Contracts (source, R code)
Parameter Recovery Diagnostics for Publication (source, R code)
A Reproducible 0.2.0 Release Case Study (source, R code)
Analysis Manifests and Reproducible Bayesian Workflows (source, R code)
Robust and Distributional Pupil Models (source, R code)
Simulation-Based Calibration Diagnostics (source, R code)
Retention-first Bayesian SCR responsivity (source)
Prior Sensitivity and Simulation-Based Recovery (source, R code)
Sensitivity Atlases (source, R code)
Unified Sensitivity Suites and Evidence Inventories (source, R code)
Specification Closure: Strict Readiness and Governed Validation (source, R code)
A Stable Unified Workflow API (source, R code)
Synthetic Advanced Pupillometry Gallery (source, R code)
Synthetic Gazepoint pupillometry case study (source, R code)
Transformation Replay and Detailed Posterior Predictive Checks (source, R code)
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