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Posterior predictive checks compare observed features with replicated data. They are evidence objects, not automatic model-validity certificates.
ppc <- check_pupil_posterior_predictive(
pupil_fit,
ndraws = 200,
window = c(0.3, 1.0)
)
pupil_ppc_table(ppc)
plot_pupil_ppc(ppc)The implementation summarizes observed and replicated trajectories, declared window summaries, AUC, peak response and latency, residual structure, and measurement-context overlays when corresponding indicators are available.
diag <- diagnose_pupil_fit(pupil_fit)
as.data.frame(diag)
acf_table <- pupil_residual_acf(pupil_fit, max_lag = 12)
head(acf_table)
plot_pupil_residual_acf(acf_table)Sampling diagnostics reuse the package’s posterior/MCMC infrastructure and report quantities such as R-hat, effective sample size, divergences, treedepth, and available energy diagnostics. Temporal diagnostics additionally show residual autocorrelation and support over event-relative time.
No single threshold is labelled proof of model adequacy. Measurement limitations, specification uncertainty, and the prediction target remain separate questions.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.