# Predictive Distribution and Calibration Uncertainty

Posterior predictive diagnostics can retain uncertainty in entire outcome distributions as well as in predictive scores.

    atlas <- create_predictive_distribution_atlas(
      fit,
      ndraws = 500,
      include_group_effects = TRUE,
      seed = 2026
    )

    plot_predictive_atlas_statistics(atlas)

    quantiles <- predictive_quantile_envelope(atlas)
    plot_predictive_quantile_envelope(quantiles)

    scores <- prediction_score_uncertainty(
      fit,
      ndraws = 1000
    )

    prediction_score_uncertainty_table(scores)
    plot_prediction_score_uncertainty(scores)

For binary models:

    calibration <- binary_calibration_uncertainty(
      fit,
      bins = 10,
      ndraws = 1000
    )

    binary_calibration_uncertainty_table(calibration)
    plot_binary_calibration_uncertainty(calibration)

These summaries do not automatically establish calibration, predictive adequacy, or model superiority.