CausalMixGPD
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Track: Kernels & tails

When to use this track

Choose this path if your main question is “what distribution family should I use for the bulk, and when do I need a GPD tail?”.

Path (recommended)

  1. Kernels hub
  2. Kernel catalog
  3. Summary: distribution/backend reference
  4. Intro: GPD kernel and conventions ## Quick decision aids
  • Use kernel_support_table() to confirm what each kernel supports.
  • Use examples as templates to compare kernels on the same data.

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: Introduction With Gpd Kernel
  • 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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