dipm: Depth Importance in Precision Medicine (DIPM) Method

An implementation 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: 1.7
Depends: R (≥ 3.0.0)
Imports: stats, utils, survival, partykit (≥ 1.2-6), ggplot2, grid
Published: 2022-05-18
Author: Cai Li [aut, cre], Victoria Chen [aut], Heping Zhang [aut]
Maintainer: Cai Li <cli9 at ncsu.edu>
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_1.7.tar.gz
Windows binaries: r-devel: dipm_1.7.zip, r-release: dipm_1.7.zip, r-oldrel: dipm_1.7.zip
macOS binaries: r-release (arm64): dipm_1.7.tgz, r-oldrel (arm64): dipm_1.7.tgz, r-release (x86_64): dipm_1.7.tgz, r-oldrel (x86_64): dipm_1.7.tgz
Old sources: dipm archive


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