CausalMixGPD
0.8.0Bayesian Nonparametric Conditional Density Modeling in Causal Inference and Clustering with a Heavy-Tail Extension
Overview
The presence of a heavy tail is a feature of many scenarios when risk management involves extremely rare events. While parametric distributions may give adequate representation of the mode of data, they are likely to misrepresent heavy tails, and completely nonparametric approaches lack a rigorous mechanism for tail extrapolation; see Pickands (1975) doi:10.1214/aos/1176343003. The package 'CausalMixGPD' implements tools for Bayesian analysis of heavy-tailed outcomes by combining Dirichlet process mixture models for the body of the distribution with optional generalized Pareto tails. The method allows for unconditional and covariate-modulated mixtures, implements MCMC estimation using 'nimble', and extends to mixtures of different arms' outcomes with application to causal inference in the Rubin (1974) doi:10.1037/h0037350 framework. Posterior summaries include density functions, quantiles, expected values, survival functions, and causal effects, with an emphasis on tail quantiles and functional measures sensitive to the tail.
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- OK2026-04-228 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 59%
- Documented parameters
- 90%
- Return-value docs
- 100%
- References docs
- 1%
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Code & Tests
- Cyclomatic complexity
- 4.0 median / 481 max
- Test cases
- 444 / 0.50 per code line
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Functions
450 145 exported
Complexity
9.9 avg / 481 max
Call network
450 nodes / 648 edges
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2 releases. Pick two to compare their code metrics. R releases are shown for context.
- 0.8.0Latest
- RR 4.6.0 released · 2026-04-24
- 0.7.02026-04-22
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-04-22
- Total releases
- 2 / 1 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 4.0.0
- Bundled data
- 98 KB / 12 files
- Download size
- not tracked yet
- Installed size
- not tracked yet
- With dependencies
- not tracked yet