Package: dipm 1.12

dipm: Depth Importance in Precision Medicine (DIPM) Method

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.

Authors:Cai Li [aut, cre], Victoria Chen [aut], Heping Zhang [aut]

dipm_1.12.tar.gz
dipm_1.12.zip(r-4.7)dipm_1.12.zip(r-4.6)dipm_1.12.zip(r-4.5)
dipm_1.12.tgz(r-4.6-x86_64)dipm_1.12.tgz(r-4.6-arm64)dipm_1.12.tgz(r-4.5-x86_64)dipm_1.12.tgz(r-4.5-arm64)
dipm_1.12.tar.gz(r-4.7-arm64)dipm_1.12.tar.gz(r-4.7-x86_64)dipm_1.12.tar.gz(r-4.6-arm64)dipm_1.12.tar.gz(r-4.6-x86_64)
dipm_1.12.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
dipm/json (API)

# Install 'dipm' in R:
install.packages('dipm', repos = c('https://cli9.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openmp

1.30 score 354 downloads 4 exports 26 dependencies

Last updated from:515ed5d6bf. Checks:12 OK, 1 NOTE. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK153
linux-devel-x86_64NOTE144
source / vignettesOK161
linux-release-arm64OK130
linux-release-x86_64OK149
macos-release-arm64OK124
macos-release-x86_64OK240
macos-oldrel-arm64OK93
macos-oldrel-x86_64OK343
windows-develOK106
windows-releaseOK100
windows-oldrelOK110
wasm-releaseOK98

Exports:dipmnode_dipmpmprunespmtree

Dependencies:clicpp11farverFormulaggplot2gluegtableinumisobandlabelinglatticelibcoinlifecycleMatrixmvtnormpartykitR6RColorBrewerrlangrpartS7scalessurvivalvctrsviridisLitewithr