HCPclust: Hierarchical Conformal Prediction for Clustered Data with
Missing Responses
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.
| Version: |
0.1.1 |
| Imports: |
stats, grf, quantreg, xgboost, quantregForest |
| Suggests: |
foreach, doParallel, doRNG, parallel, testthat (≥ 3.0.0), knitr, rmarkdown, FNN, rstudioapi |
| Published: |
2026-01-30 |
| DOI: |
10.32614/CRAN.package.HCPclust (may not be active yet) |
| Author: |
Menghan Yi [aut, cre],
Judy Wang [aut] |
| Maintainer: |
Menghan Yi <menghany at umich.edu> |
| BugReports: |
https://github.com/judywangstat/HCP/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/judywangstat/HCP |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
HCPclust results |
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