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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: | 1.9 |
| Depends: | R (≥ 3.0.0) |
| Imports: | stats, utils, survival, partykit (≥ 1.2-6), ggplot2, grid |
| Published: | 2022-10-27 |
| 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.pdf |
| Package source: | dipm_1.9.tar.gz |
| Windows binaries: | r-devel: dipm_1.9.zip, r-release: dipm_1.9.zip, r-oldrel: dipm_1.9.zip |
| macOS binaries: | r-release (arm64): dipm_1.9.tgz, r-oldrel (arm64): dipm_1.9.tgz, r-release (x86_64): dipm_1.9.tgz, r-oldrel (x86_64): dipm_1.9.tgz |
| Old sources: | dipm archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.