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
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mdw_2024.8-1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
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:

3.41 score 26 scripts 186 downloads 7 exports 4 dependencies

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

Target	Result	Time
linux-devel-x86_64	WARNING	123
source / vignettes	OK	143
linux-release-x86_64	WARNING	159
macos-release-arm64	WARNING	118
macos-oldrel-arm64	WARNING	114
windows-devel	WARNING	89
windows-release	WARNING	92
windows-oldrel	WARNING	74
wasm-release	OK	95

Exports:asym.v.e asym.v.v entropy.weight get.bw pca.weight tree.weight var.weight

Dependencies:kyotil lattice MASS Matrix

Maximum Diversity Weighting

Rendered frommdw-vignette.pdf.asisusingR.rsp::asis

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

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Asymptotic variance for maximum entropy weights	asym.v.e
Asymptotic variance for maximum variance weights	asym.v.v
Maximum entropy weights	entropy.weight
Bandwidth Selection	get.bw
mdw Package	mdw
Weights based on PCA	pca.weight
Weights based on GSC Tree Method	tree.weight
Maximum variance weights	var.weight