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'))

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 219 downloads 7 exports 4 dependencies

Last updated 4 months agofrom:7369a927ca. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 21 2024
R-4.5-win	OK	Nov 21 2024
R-4.5-linux	OK	Nov 21 2024
R-4.4-win	OK	Nov 21 2024
R-4.4-mac	OK	Nov 21 2024
R-4.3-win	OK	Nov 21 2024
R-4.3-mac	OK	Nov 21 2024

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::asison Nov 21 2024.

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

Citation

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