An implementation by Chen, Li, and Zhang (2022) <doi:10.1093/bioadv/vbac041> of the Depth Importance in Precision Medicine (DIPM) method in Chen and Zhang (2022) <doi:10.1093/biostatistics/kxaa021> and Chen and Zhang (2020) <doi:10.1007/978-3-030-46161-4_16>. The DIPM method is a classification tree that searches for subgroups with especially poor or strong performance in a given treatment group.
| Version: | 1.12 |
| Depends: | R (≥ 3.0.0) |
| Imports: | stats, utils,survival,partykit (≥ 1.2-6),ggplot2,rlang, grid |
| Published: | 2025-11-10 |
| DOI: | 10.32614/CRAN.package.dipm |
| Author: | Cai Li [aut, cre], Victoria Chen [aut], Heping Zhang [aut] |
| Maintainer: | Cai Li <cai.li.stats at gmail.com> |
| License: | GPL-2 |GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| In views: | MachineLearning |
| CRAN checks: | dipm results |
| Reference manual: | dipm.html ,dipm.pdf |
| Package source: | dipm_1.12.tar.gz |
| Windows binaries: | r-devel:dipm_1.12.zip, r-release:dipm_1.12.zip, r-oldrel:dipm_1.12.zip |
| macOS binaries: | r-release (arm64):dipm_1.12.tgz, r-oldrel (arm64):dipm_1.12.tgz, r-release (x86_64):dipm_1.12.tgz, r-oldrel (x86_64):dipm_1.12.tgz |
| Old sources: | dipm archive |
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