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:
mdw_2024.8-1.tar.gz
mdw_2024.8-1.zip(r-4.5)mdw_2024.8-1.zip(r-4.4)mdw_2024.8-1.zip(r-4.3)
mdw_2024.8-1.tgz(r-4.4-any)mdw_2024.8-1.tgz(r-4.3-any)
mdw_2024.8-1.tar.gz(r-4.5-noble)mdw_2024.8-1.tar.gz(r-4.4-noble)
mdw_2024.8-1.tgz(r-4.4-emscripten)mdw_2024.8-1.tgz(r-4.3-emscripten)
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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 months agofrom:7369a927ca. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 30 2024 |
R-4.5-win | OK | Oct 30 2024 |
R-4.5-linux | OK | Oct 30 2024 |
R-4.4-win | OK | Oct 30 2024 |
R-4.4-mac | OK | Oct 30 2024 |
R-4.3-win | OK | Oct 30 2024 |
R-4.3-mac | OK | Oct 30 2024 |
Exports:asym.v.easym.v.ventropy.weightget.bwpca.weighttree.weightvar.weight
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 |