CausalMixGPD

CausalMixGPD provides Bayesian Dirichlet process mixture modeling for heavy-tailed outcomes, with optional generalized Pareto tails and extensions for causal inference and predictor-dependent clustering.

Start Here

The package documentation is organized around three main workflows:

• One-arm modeling
• Causal inference
• Predictor-dependent clustering

Reference

• Function reference

Installation

# install.packages("remotes")
remotes::install_github(
  "arnabaich96/CausalMixGPD",
  build_vignettes = TRUE,
  INSTALL_opts = c("--html")
)

Notes

• The article pages above are the best entry points for model setup, interpretation, and worked examples.
• The reference section is useful once you know which workflow and function family you need.