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-4.5)amap_0.8-20.zip(r-4.4)amap_0.8-20.zip(r-4.3)
amap_0.8-20.tgz(r-4.5-x86_64)amap_0.8-20.tgz(r-4.5-arm64)amap_0.8-20.tgz(r-4.4-x86_64)amap_0.8-20.tgz(r-4.4-arm64)amap_0.8-20.tgz(r-4.3-x86_64)amap_0.8-20.tgz(r-4.3-arm64)
amap_0.8-20.tar.gz(r-4.5-noble)amap_0.8-20.tar.gz(r-4.4-noble)
amap_0.8-20.tgz(r-4.4-emscripten)amap_0.8-20.tgz(r-4.3-emscripten)
amap.pdf |amap.html✨
amap/json (API)
| # Install 'amap' in R: |
| install.packages('amap', repos = c('https://antoinelucas64.r-universe.dev', 'https://cloud.r-project.org')) |
- 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 5 months agofrom:0a7ceeb400. Checks:12 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 21 2025 |
| R-4.5-win-x86_64 | OK | Mar 21 2025 |
| R-4.5-mac-x86_64 | OK | Mar 21 2025 |
| R-4.5-mac-aarch64 | OK | Mar 21 2025 |
| R-4.5-linux-x86_64 | OK | Mar 21 2025 |
| R-4.4-win-x86_64 | OK | Mar 21 2025 |
| R-4.4-mac-x86_64 | OK | Mar 21 2025 |
| R-4.4-mac-aarch64 | OK | Mar 21 2025 |
| R-4.4-linux-x86_64 | OK | Mar 21 2025 |
| R-4.3-win-x86_64 | OK | Mar 21 2025 |
| R-4.3-mac-x86_64 | OK | Mar 21 2025 |
| R-4.3-mac-aarch64 | OK | Mar 21 2025 |
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 |
