Check the approved pupil priors predictively — check_pupil_prior

Creates a governed prior-predictive plan by default. With execute = TRUE, draws from the prior-only approved Gaussian brms model and compares replicated pupil values with the observed model-scale range. The check reports evidence only and never changes priors automatically.

Usage

check_pupil_prior_predictive(
  specification,
  execute = FALSE,
  backend = c("rstan", "cmdstanr"),
  draws = 200L,
  chains = 2L,
  iter = 1000L,
  warmup = 500L,
  cores = min(2L, chains),
  seed = 2026,
  probability = 0.95,
  max_cells = 3000000L
)

Arguments

specification: Approved pupil model specification.
execute: Whether to run prior-only MCMC. Defaults to FALSE.
backend: Approved backend, "rstan" or "cmdstanr".
draws: Number of prior predictive replicated draws to retain.
chains, iter, warmup, cores, seed: Sampling controls. Package-controlled cores are capped at two.
probability: Central predictive interval probability.
max_cells: Maximum retained draw-by-observation cells.

Value

A gp3bayes_pupil_prior_predictive evidence object.

Governance boundary

This operation does not tune priors, select a favourable prior scale, or certify a model as scientifically adequate. execute = FALSE performs no compilation or fitting.

Examples

# See vignette("bayesian-dynamic-pupillometry", package = "gp3bayes") for a complete workflow.