Package: pcaPP 2.0-5
pcaPP: Robust PCA by Projection Pursuit
Provides functions for robust PCA by projection pursuit. The methods are described in Croux et al. (2006) <doi:10.2139/ssrn.968376>, Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>.
Authors:
pcaPP_2.0-5.tar.gz
pcaPP_2.0-5.zip(r-4.7)pcaPP_2.0-5.zip(r-4.6)pcaPP_2.0-5.zip(r-4.5)
pcaPP_2.0-5.tgz(r-4.6-x86_64)pcaPP_2.0-5.tgz(r-4.6-arm64)pcaPP_2.0-5.tgz(r-4.5-x86_64)pcaPP_2.0-5.tgz(r-4.5-arm64)
pcaPP_2.0-5.tar.gz(r-4.7-arm64)pcaPP_2.0-5.tar.gz(r-4.7-x86_64)pcaPP_2.0-5.tar.gz(r-4.6-arm64)pcaPP_2.0-5.tar.gz(r-4.6-x86_64)
pcaPP_2.0-5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
pcaPP/json (API)
| # Install 'pcaPP' in R: |
| install.packages('pcaPP', repos = c('https://valentint.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/valentint/pcapp/issues
Last updated from:f2d90c38b3. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 128 | ||
| linux-devel-x86_64 | OK | 106 | ||
| source / vignettes | OK | 212 | ||
| linux-release-arm64 | OK | 123 | ||
| linux-release-x86_64 | OK | 103 | ||
| macos-release-arm64 | OK | 81 | ||
| macos-release-x86_64 | OK | 207 | ||
| macos-oldrel-arm64 | OK | 127 | ||
| macos-oldrel-x86_64 | OK | 149 | ||
| windows-devel | OK | 105 | ||
| windows-release | OK | 88 | ||
| windows-oldrel | OK | 114 | ||
| wasm-release | OK | 108 |
Exports:cor.fkcovPCcovPCAgridcovPCAprojdata.Zoul1medianl1median_BFGSl1median_CGl1median_HoCrl1median_NLMl1median_NMl1median_VaZhobjplotopt.BICopt.TPOPCAgridPCAprojPCdiagplotplotcovqnScaleAdvsPCAgrid
Dependencies:mvtnorm
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Fast estimation of Kendall's tau rank correlation coefficient | cor.fk |
| Covariance Matrix Estimation from princomp Object | covPC |
| Robust Covariance Matrix Estimation | covPCAgrid covPCAproj |
| Test Data Generation for Sparse PCA examples | data.Zou |
| Multivariate L1 Median | l1median |
| Multivariate L1 Median | l1median_BFGS l1median_CG l1median_HoCr l1median_NLM l1median_NM l1median_VaZh |
| Objective Function Plot for Sparse PCs | objplot |
| Model Selection for Sparse (Robust) Principal Components | opt.BIC opt.TPO |
| (Sparse) Robust Principal Components using the Grid search algorithm | PCAgrid sPCAgrid |
| Robust Principal Components using the algorithm of Croux and Ruiz-Gazen (2005) | PCAproj |
| Diagnostic plot for principal components | PCdiagplot |
| Tradeoff Curves for Sparse PCs | plot.opt.BIC plot.opt.TPO |
| Compare two Covariance Matrices in Plots | plotcov |
| scale estimation using the robust Qn estimator | qn |
| centers and rescales data | ScaleAdv |
