Package: vimpclust 0.1.0
Madalina Olteanu
vimpclust: Variable Importance in Clustering
An implementation of methods related to sparse clustering and variable importance in clustering. The package currently allows to perform sparse k-means clustering with a group penalty, so that it automatically selects groups of numerical features. It also allows to perform sparse clustering and variable selection on mixed data (categorical and numerical features), by preprocessing each categorical feature as a group of numerical features. Several methods for visualizing and exploring the results are also provided. M. Chavent, J. Lacaille, A. Mourer and M. Olteanu (2020)<https://www.esann.org/sites/default/files/proceedings/2020/ES2020-103.pdf>.
Authors:
vimpclust_0.1.0.tar.gz
vimpclust_0.1.0.tar.gz(r-4.5-noble)vimpclust_0.1.0.tar.gz(r-4.4-noble)
vimpclust_0.1.0.tgz(r-4.4-emscripten)vimpclust_0.1.0.tgz(r-4.3-emscripten)
vimpclust.pdf |vimpclust.html✨
vimpclust/json (API)
| # Install 'vimpclust' in R: |
| install.packages('vimpclust', repos = c('https://cran.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 4 years agofrom:dae854c9a2. Checks:OK: 1 NOTE: 1. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 27 2024 |
| R-4.5-linux | NOTE | Oct 27 2024 |
Exports:groupsoftgroupsparsewkminfo_clustrecodmixsparsewkmweightedss
Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmclustmgcvmunsellnlmePCAmixdatapillarpkgconfigPolychromeR6RColorBrewerrlangscalesscatterplot3dtibbleutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Mice Protein Expression Data Set | DataMice |
| Group soft-thresholding operator | groupsoft |
| Group-sparse weighted k-means | groupsparsewkm |
| Statlog (Heart) Data Set | HDdata |
| Description of a set of partitions | info_clust |
| Plots from a "spwkm" object | plot.spwkm |
| Recoding mixed data | recodmix |
| Sparse weighted k-means | sparsewkm |
| Weighted sum-of-squares criteria | weightedss |