CausalMixGPD
  • Home
  • Roadmaps
    • Website roadmap
    • Package roadmap
  • Start
    • Start Hub
    • Roadmap
    • Usage Diagrams
    • Start Here
    • Basic Compile and Run
    • Backends and Workflow
    • Troubleshooting
  • Tracks
    • Quickstart
    • Modeling (1-arm)
    • Causal
    • Clustering
    • Kernels & tails
    • Customization
  • Examples
  • Kernels
  • Advanced
  • Developers
  • Reference
    • Reference hub
    • Function reference by job
  • News
  • Cite
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  • API Reference

Roadmap

Analysis Roadmap

Map from setup to model specification, execution, diagnostics, and extraction/prediction.

Start here Basic compile and run Reference hub

Levels

  1. Usage diagrams (wrapper/parameter/predict choices)
  • Full package usage diagrams
  1. Theory background (GPD tails + DPM bulk + causal estimands)
  • Advanced theory background
  1. Setup and first run
  • Start here
  1. Model assembly and execution
  • Basic model compile and run
  1. Backend choice and workflow ordering
  • Backends and workflow
  1. Diagnose and stabilize
  • Troubleshooting
  1. Extract, predict, and compare
  • Examples hub

Quarto vs pkgdown split

  • Quarto: curated routes and workflow context.

  • pkgdown: exact function interfaces and package reference.

  • pkgdown reference

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: Start Here
  • 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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