CausalMixGPD
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On this page

  • Checklist
    • Step 0 – Decide scope
    • Step 1 – Implement kernel functions
    • Step 2 – Implement Mix / MixGPD wrappers
    • Step 3 – Register it
    • Step 4 – Tests
    • Step 5 – Docs
  • Templates
  • Prereqs
  • Outputs
  • Interpretation
  • Next

How to add a kernel

This is the extension recipe contributors should follow.

Checklist

Step 0 – Decide scope

  • Support: positive-only vs real line
  • Tail option: bulk-only vs bulk+GPD
  • Backend compatibility: CRP, SB, or both

Step 1 – Implement kernel functions

Implement the kernel’s d/p/q/r surface (and any internal helpers). Keep vectorization and support checks consistent.

Step 2 – Implement Mix / MixGPD wrappers

If the kernel is used inside mixture components or spliced tails, implement the corresponding MixGPD functions.

Step 3 – Register it

Add an entry to the kernel registry so compile_model_spec() can discover it.

Step 4 – Tests

Add at least: - support checks (values outside support return 0 density, etc.) - monotonic CDF / quantile inversion checks - a tiny end-to-end bundle build test

Step 5 – Docs

  • Add website/kernels/kernel-<name>.qmd
  • Add link from website/kernels/kernels-index.qmd
  • Mention the kernel in kernel_support_table() documentation

Templates

  • R code skeleton: developers/templates/kernel-skeleton.R
  • Registry entry skeleton: developers/templates/registry-entry-skeleton.R
  • Test skeleton: developers/templates/test-kernel-skeleton.R

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: Spec And Contracts
  • 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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