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

Documentation:

Reference manual:

HCPclust.html , HCPclust.pdf

Downloads:

Package source:	HCPclust_0.1.1.tar.gz
Windows binaries:	r-devel: HCPclust_0.1.1.zip, r-release: not available, r-oldrel: HCPclust_0.1.1.zip
macOS binaries:	r-release (arm64): HCPclust_0.1.1.tgz, r-oldrel (arm64): HCPclust_0.1.1.tgz, r-release (x86_64): HCPclust_0.1.1.tgz, r-oldrel (x86_64): HCPclust_0.1.1.tgz

Linking:

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