Hierarchical Conformal Prediction for Clustered Data with Missing Responses R package HCPclust version 0.1.1 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. Software Imports: stats, grf, quantreg, xgboost, quantregForest Suggests: foreach, doParallel, doRNG, parallel, testthat (>= 3.0.0), knitr, rmarkdown, FNN, rstudioapi Menghan Yi <menghany@umich.edu> Comprehensive R Archive Network (CRAN) Menghan Yi [aut, cre], Judy Wang [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=HCPclust/LICENSE) 2026-01-30 application/tgz https://CRAN.R-project.org/package=HCPclust doi:10.32614/CRAN.package.HCPclust