LearnPCA: Functions, Data Sets and Vignettes to Aid in Learning Principal Components Analysis (PCA) (original) (raw)
Principal component analysis (PCA) is one of the most widely used data analysis techniques. This package provides a series of vignettes explaining PCA starting from basic concepts. The primary purpose is to serve as a self-study resource for anyone wishing to understand PCA better. A few convenience functions are provided as well.
| Version: | 0.3.4 |
|---|---|
| Depends: | rpart, class, nnet |
| Imports: | markdown, shiny, stats, graphics |
| Suggests: | ChemoSpec, chemometrics, knitr, tinytest, roxut, rmarkdown, plot3D, ade4, plotrix, latex2exp, plotly, xtable, bookdown |
| Published: | 2024-04-26 |
| DOI: | 10.32614/CRAN.package.LearnPCA |
| Author: | Bryan A. Hanson  [aut, cre], David T. Harvey [aut] |
| Maintainer: | Bryan A. Hanson |
| BugReports: | https://github.com/bryanhanson/LearnPCA/issues |
| License: | GPL-3 |
| URL: | https://bryanhanson.github.io/LearnPCA/ |
| NeedsCompilation: | no |
| Materials: | NEWS |
| In views: | ChemPhys |
| CRAN checks: | LearnPCA results |
Documentation:
Downloads:
Linking:
Please use the canonical formhttps://CRAN.R-project.org/package=LearnPCAto link to this page.