Package: kpcaIG
Title: Variables Interpretability with Kernel PCA
Version: 1.0.1
Authors@R: c(person("Mitja", "Briscik", role = c("aut", "cre"), email="mitja.briscik@math.univ-toulouse.fr"), person("Mohamed", "Heimida", role = c("aut"), email="21067@esp.mr"),person("Sébastien", "Déjean", role = c("aut"), email="sebastien.dejean@math.univ-toulouse.f"))
Maintainer: Mitja Briscik <mitja.briscik@math.univ-toulouse.fr>
Description: 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.
License: GPL-3
Encoding: UTF-8
Imports: grDevices, rgl, kernlab, ggplot2, stats, progress, viridis,
        WallomicsData, utils
NeedsCompilation: no
Packaged: 2025-03-28 12:01:50 UTC; mbriscik
Author: Mitja Briscik [aut, cre],
  Mohamed Heimida [aut],
  Sébastien Déjean [aut]
Repository: CRAN
Date/Publication: 2025-03-28 14:30:07 UTC
Built: R 4.6.0; ; 2026-04-26 19:19:50 UTC; unix
