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: 2026-09-24 08:13:38 UTC; root
Author: Mitja Briscik [aut, cre], Mohamed Heimida [aut], Sébastien
        Déjean [aut]
Repository: https://cran.r-universe.dev
Date/Publication: 2025-03-28 14:30:07 UTC
RemoteUrl: https://github.com/cran/kpcaIG
RemoteRef: HEAD
RemoteSha: 51237f39f41257be201a32290802b7d5cce4850b