kpcaIG: Variables Interpretability with Kernel PCA

The kernelized version of principal component analysis (KPCA) has proven to be a valid nonlinear alternative for tackling the nonlinearity of biological sample spaces. However, it poses new challenges in terms of the interpretability of the original variables. 'kpcaIG' aims to provide a tool to select the most relevant variables based on the kernel PCA representation of the data as in Briscik et al. (2023) <doi:10.1186/s12859-023-05404-y>. It also includes functions for 2D and 3D visualization of the original variables (as arrows) into the kernel principal components axes, highlighting the contribution of the most important ones.

Version: 1.0
Imports: grDevices, rgl, kernlab, ggplot2, stats, progress, viridis, WallomicsData
Published: 2024-07-21
DOI: 10.32614/CRAN.package.kpcaIG
Author: Mitja Briscik [aut, cre], Mohamed Heimida [aut], Sébastien Déjean [aut]
Maintainer: Mitja Briscik <mitja.briscik at math.univ-toulouse.fr>
License: GPL-3
NeedsCompilation: no
CRAN checks: kpcaIG results

Documentation:

Reference manual: kpcaIG.pdf

Downloads:

Package source: kpcaIG_1.0.tar.gz
Windows binaries: r-devel: kpcaIG_1.0.zip, r-release: kpcaIG_1.0.zip, r-oldrel: kpcaIG_1.0.zip
macOS binaries: r-release (arm64): kpcaIG_1.0.tgz, r-oldrel (arm64): kpcaIG_1.0.tgz, r-release (x86_64): kpcaIG_1.0.tgz, r-oldrel (x86_64): kpcaIG_1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=kpcaIG to link to this page.