CausalMixGPD
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Track: Customization

When to use this track

Choose this path if you want to go beyond defaults: custom priors, covariate links, alternative threshold behavior, or manual NIMBLE pipelines for inspection/debugging.

Path (recommended)

  1. Model umbrella
  2. Advanced: customization and tuning
  3. Theory: customization maps + extension points
  4. Developers: registry
ImportantDeveloper notes

If you are adding new kernels/tails, treat the registry as the source of truth. Update support declarations, defaults, and compatibility before touching bundle/runner/consumer code.

Prereqs

  • Required packages and data for this page are listed in the setup chunks above.

Outputs

  • This page renders model fits, diagnostics, and summary artifacts generated by package APIs.

Interpretation

  • Canonical concept page: Customization And Tuning
  • Treat this page as an application/example view and use the canonical page for core definitions.

Next

  • Continue to the linked canonical concept page, then return for implementation-specific details.
(c) CausalMixGPD - Bayesian semiparametric modeling for heavy-tailed data
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