CausalMixGPD
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Track: Modeling (one-arm)

When to use this track

Choose this path if you want density estimation and prediction for a single outcome (y), optionally with covariates (X), and optionally with a spliced GPD tail.

Path (recommended)

Bulk-only (unconditional → conditional)

  1. ex01 — Unconditional DPM (CRP)

  2. ex02 — Unconditional DPM (SB)

  3. ex05 — Conditional DPM (CRP)

  4. ex06 — Conditional DPM (SB) ### Bulk + tail (GPD augmentation)

  5. ex03 — Unconditional DPM+GPD (CRP)

  6. ex04 — Unconditional DPM+GPD (SB)

  7. ex07 — Conditional DPM+GPD (CRP)

  8. ex08 — Conditional DPM+GPD (SB) ## Concepts to read alongside (non-redundant)

  • Model umbrella
  • Theory: GPD tails + DPM bulk + splicing

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: Model Umbrella
  • 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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