Hierarchical Conformal Prediction for Clustered Data with Missing Responses
Overview
Implements hierarchical conformal prediction for clustered data with missing responses. The method uses repeated cluster-level splitting and within-cluster subsampling to accommodate dependence, and inverse-probability weighting to correct distribution shift induced by missingness. Conditional densities are estimated by inverting fitted conditional quantiles (linear quantile regression or quantile regression forests), and p-values are aggregated across resampling and splitting steps using the Cauchy combination test.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 6
- Examples that run
- 100%6 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 94%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Repository
- PRs merged (all time)
- 0%
- Median days to close, 50 most recently updated closed issues
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- Median days to close, 50 most recently updated closed PRs
- 0
Forks over time
1 development-tooling and community-health practice detected across 1 family in the upstream repository
Checks run against github.com/judywangstat/hcp on 2026-10-11.
Dependencies
Installing HCPclust adds 15 packages (12 with compiled code, 16 MB of source tarballs). Heaviest: data.table 5.7 MB, Rcpp 1.8 MB, RcppEigen 1.7 MB.
Code & Tests
People & History
- RR 4.6.0 released · 2026-04-24
- 0.1.1Latest2026-01-30 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-01-30
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 33 KB
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