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gp3bayes provides package-neutral infrastructure for transparent, contract-first Bayesian workflows for repeated-measures and hierarchical behavioural data. It implements approved Bernoulli-logit, positive lognormal duration, and governed Gaussian dynamic-pupillometry workflows with deterministic simulation, recorded preparation, scale-aware priors, restricted optional full-MCMC fitting, sampling and temporal diagnostics, posterior predictive checks, sensitivity analysis, target-specific validation, and conservative structured reporting. Fitting or passing a numerical threshold does not by itself establish convergence, posterior adequacy, causal identification, or validity.

Approved model families

The approved model-family scope is restricted to:

  • hierarchical Bernoulli-logit models for binary trial-level outcomes;

  • hierarchical lognormal models for strictly positive uncensored durations;

  • governed Gaussian hierarchical dynamic pupil time-course models with scale-aware priors and declared temporal dependence.

Additional outcome families require separate methodological approval.

Backend policy

Core validation, contract, simulation, preparation, transformation, specification, and prior-predictive functionality remains usable without a Bayesian backend. Restricted full-MCMC fitting uses the brms interface. The original fit_binary_model(), fit_duration_model(), and fit_pupil_model() interfaces retain a fixed rstan route, while the backend-portable fit_binary_model_backend(), fit_duration_model_backend(), and fit_pupil_model_backend() interfaces support either rstan or cmdstanr. Model families, formulas, priors, and algorithms remain contract-restricted.

Interpretation boundaries

Behavioural measurements do not directly reveal emotion, stress, cognition, comprehension, personality, diagnosis, deception, intention, or other latent psychological states. Associations must not be described as causal effects unless the design and estimand justify that language.

Author

Maintainer: Stefanos Balaskas s.balaskas@ac.upatras.gr (ORCID) [copyright holder]

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