Package: amap 0.8-20
amap: Another Multidimensional Analysis Package
Tools for Clustering and Principal Component Analysis (With robust methods, and parallelized functions).
Authors:
amap_0.8-20.tar.gz
amap_0.8-20.zip(r-4.7)amap_0.8-20.zip(r-4.6)amap_0.8-20.zip(r-4.5)
amap_0.8-20.tgz(r-4.6-x86_64)amap_0.8-20.tgz(r-4.6-arm64)amap_0.8-20.tgz(r-4.5-x86_64)amap_0.8-20.tgz(r-4.5-arm64)
amap_0.8-20.tar.gz(r-4.7-arm64)amap_0.8-20.tar.gz(r-4.7-x86_64)amap_0.8-20.tar.gz(r-4.6-arm64)amap_0.8-20.tar.gz(r-4.6-x86_64)
amap_0.8-20.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
amap/json (API)
| # Install 'amap' in R: |
| install.packages('amap', repos = c('https://antoinelucas64.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- lubisch - Dataset Lubischew
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:0a7ceeb400. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 103 | ||
| linux-devel-x86_64 | OK | 105 | ||
| source / vignettes | OK | 183 | ||
| linux-release-arm64 | OK | 100 | ||
| linux-release-x86_64 | OK | 99 | ||
| macos-release-arm64 | OK | 68 | ||
| macos-release-x86_64 | OK | 167 | ||
| macos-oldrel-arm64 | OK | 93 | ||
| macos-oldrel-x86_64 | OK | 178 | ||
| windows-devel | OK | 83 | ||
| windows-release | OK | 83 | ||
| windows-oldrel | OK | 117 | ||
| wasm-release | OK | 84 |
Exports:acpacpgenacprobafcbiplot.acpburtdissDisthclusterhclusterparKKmeansmatlogicpcaplot.acpplot2plotAllpopprint.acpvarrobW
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Principal component analysis | acp pca print.acp |
| Generalised principal component analysis | acpgen K W |
| Robust principal component analysis | acprob |
| Correspondance factorial analysis. | afc |
| Compute burt table from a factor dataframe. | burt matlogic |
| Compute a dissimilarity matrix | diss |
| Distance Matrix Computation | Dist |
| Hierarchical Clustering | hcluster hclusterpar |
| K-Means Clustering | Kmeans |
| Dataset Lubischew | lubisch |
| Graphics for Principal component Analysis | biplot.acp plot.acp plot2 plotAll |
| Optimal Partition (classification). | pop |
| Robust variance | varrob |
