Package: mdw 2024.8-1

mdw: Maximum Diversity Weighting

Dimension-reduction methods aim at defining a score that maximizes signal diversity. Three approaches, tree weight, maximum entropy weights, and maximum variance weights are provided. These methods are described in He and Fong (2019) <doi:10.1002/sim.8212>.

Authors:Zonglin He [aut], Youyi Fong [cre]

mdw_2024.8-1.tar.gz
mdw_2024.8-1.zip(r-4.7)mdw_2024.8-1.zip(r-4.6)mdw_2024.8-1.zip(r-4.5)
mdw_2024.8-1.tgz(r-4.6-any)mdw_2024.8-1.tgz(r-4.5-any)
mdw_2024.8-1.tar.gz(r-4.7-any)mdw_2024.8-1.tar.gz(r-4.6-any)
mdw_2024.8-1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
mdw/json (API)

# Install 'mdw' in R:
install.packages('mdw', repos = c('https://youyifong.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/youyifong/mdw/issues

On CRAN:

Conda:

3.41 score 26 scripts 193 downloads 7 exports 4 dependencies

Last updated from:a0cd0683c5. Checks:7 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING120
source / vignettesOK165
linux-release-x86_64WARNING169
macos-release-arm64WARNING200
macos-oldrel-arm64WARNING193
windows-develWARNING90
windows-releaseWARNING85
windows-oldrelWARNING216
wasm-releaseOK99

Exports:asym.v.easym.v.ventropy.weightget.bwpca.weighttree.weightvar.weight

Dependencies:kyotillatticeMASSMatrix

Maximum Diversity Weighting

Rendered frommdw-vignette.pdf.asisusingR.rsp::asison May 11 2026.

Last update: 2025-07-27
Started: 2025-07-27