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Unified Sensitivity Suites and Evidence Inventories

Python-facing port of sensitivity-evidence-workflow.Rmd from the frozen R gp3bayes 0.5.0 reference. The statistical and governance framing below follows the canonical vignette; executable Python workflows use the mapped APIs listed later.

Orchestration without automatic robustness claims

gp3bayespy 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

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

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

Evidence is an inventory

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

Reports require an explicit file path:

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.

Python API mapping

  • gp3bayespy.collect_model_evidence
  • gp3bayespy.create_group_deletion_sensitivity_plan
  • gp3bayespy.create_model_evidence_report
  • gp3bayespy.create_random_slope_sensitivity_plan
  • gp3bayespy.create_sensitivity_suite_plan
  • gp3bayespy.run_sensitivity_suite
  • gp3bayespy.summarise_sensitivity_suite

Python usage

import gp3bayespy as gp

# All functions listed above are available from the package root.
# Use help(gp.<function>) or the API reference for the exact Python signature.

An executable workflow for this family is included in ../../examples/sensitivity_workflow.py.