Package: amap 0.8-20
Antoine Lucas
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.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://cran.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 2 days agofrom:0a7ceeb400. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 22 2024 |
| R-4.5-linux-x86_64 | OK | Oct 22 2024 |
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 |
