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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling source/ R- | HCPclust_0.1.1.tar.gz |
33.0 KiB |
0.1.1 |
rolling linux/jammy R-4.5 | HCPclust_0.1.1.tar.gz |
118.9 KiB |
0.1.1 |
rolling linux/noble R-4.5 | HCPclust_0.1.1.tar.gz |
118.8 KiB |
0.1.1 |
latest source/ R- | HCPclust_0.1.1.tar.gz |
33.0 KiB |
0.1.1 |
latest linux/jammy R-4.5 | HCPclust_0.1.1.tar.gz |
118.9 KiB |
0.1.1 |
latest linux/noble R-4.5 | HCPclust_0.1.1.tar.gz |
118.8 KiB |
0.1.1 |
2026-04-23 source/ R- | HCPclust_0.1.1.tar.gz |
33.0 KiB |
0.1.1 |
2026-04-09 windows/windows R-4.5 | HCPclust_0.1.1.zip |
121.3 KiB |