# Unified Sensitivity Suites and Evidence Inventories

## Orchestration without automatic robustness claims

`gp3bayes` already provides prior sensitivity, power scaling, PSIS-LOO, structural sensitivity, group-deletion sensitivity, coding/scaling variants, duration-unit invariance and exact K-fold validation. Version 0.2.0 adds a thin orchestration layer so these results can be planned and collected without turning them into an automatic “robust/not robust” verdict.

## Declare a suite before running it

    plan <- create_sensitivity_suite_plan(
      prior_scale = TRUE,
      powerscale = TRUE,
      psis_loo = TRUE
    )
    plan
    #> <gp3bayes_sensitivity_plan>
    #>   Prior-scale refit: TRUE
    #>   Power-scaling: TRUE
    #>   PSIS-LOO: TRUE
    #>   Random-slope plan: FALSE
    #>   Group-deletion plan: FALSE

Creating the plan runs nothing. Expensive components only run when `run_sensitivity_suite()` receives both a fitted model and an explicit plan.

    suite <- run_sensitivity_suite(
      fit,
      plan,
      stop_on_error = FALSE
    )

    summarise_sensitivity_suite(suite)
    plot(suite)

Structural sensitivity can be declared using the package’s existing governed plans:

    random_slope_plan <- create_random_slope_sensitivity_plan(specification)
    group_plan <- create_group_deletion_sensitivity_plan(
      specification,
      group = "participant",
      units = c("p001", "p002")
    )

    plan <- create_sensitivity_suite_plan(
      prior_scale = TRUE,
      psis_loo = TRUE,
      random_slope_plan = random_slope_plan,
      group_deletion_plan = group_plan
    )

## Evidence is an inventory

Already-computed results can be collected into one review object.

    evidence <- collect_model_evidence(
      fit = fit,
      design = design,
      diagnostics = diagnostics,
      posterior = posterior,
      ppc = ppc,
      estimands = estimands,
      loo = loo_result,
      sensitivity = suite,
      manifest = frozen_manifest
    )

    evidence
    plot(evidence)

Reports require an explicit file path:

    report <- tempfile(fileext = ".md")
    create_model_evidence_report(evidence, report)
    unlink(report)

The inventory deliberately withholds aggregate adequacy, robustness, causal, and model-selection claims. Different evidence components answer different questions and can disagree without being collapsed into a single score.