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gp3bayes: Contract-First Bayesian Workflows for Hierarchical Behavioural Data

Source: R/gp3bayes-package.R
gp3bayes-package.Rd

gp3bayes provides package-neutral infrastructure for transparent, contract-first Bayesian workflows for repeated-measures and hierarchical behavioural data. It implements approved Bernoulli-logit and positive lognormal duration workflows with deterministic simulation, recorded preparation, inspectable priors, restricted optional full-MCMC fitting, sampling diagnostics, posterior predictive checks, prior sensitivity, simulation-based recovery, 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.

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() and fit_duration_model() interfaces retain the fixed rstan route, while the backend-portable fit_binary_model_backend() and fit_duration_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.

See also

Useful links:

Author

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

Authors: