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]

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mdw.pdf |mdw.html
mdw/json (API)

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

Peer review:

On CRAN:

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

2.34 score 22 scripts 207 downloads 7 exports 4 dependencies

Last updated 3 months agofrom:7369a927ca. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-winOKOct 30 2024
R-4.5-linuxOKOct 30 2024
R-4.4-winOKOct 30 2024
R-4.4-macOKOct 30 2024
R-4.3-winOKOct 30 2024
R-4.3-macOKOct 30 2024

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

Dependencies:kyotillatticeMASSMatrix

Maximum Diversity Weighting

Rendered frommdw-vignette.pdf.asisusingR.rsp::asison Oct 30 2024.

Last update: 2020-06-18
Started: 2020-06-18