Package: kpcaIG 1.0.1

Mitja Briscik

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.

Authors:Mitja Briscik [aut, cre], Mohamed Heimida [aut], Sébastien Déjean [aut]

kpcaIG_1.0.1.tar.gz
kpcaIG_1.0.1.tar.gz(r-4.5-noble)kpcaIG_1.0.1.tar.gz(r-4.4-noble)
kpcaIG_1.0.1.tgz(r-4.4-emscripten)kpcaIG_1.0.1.tgz(r-4.3-emscripten)
kpcaIG.pdf |kpcaIG.html✨
kpcaIG/json (API)

# Install 'kpcaIG' in R:
install.packages('kpcaIG', repos = 'https://cloud.r-project.org')

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.30 score 1 stars 141 downloads 4 exports 59 dependencies

Last updated 5 days agofrom:51237f39f4. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 28 2025
R-4.5-linuxOKMar 28 2025
R-4.4-linuxOKMar 28 2025

Exports:kernelpcakpca_igradplot_kpca2Dplot_kpca3D

Dependencies:base64encbslibcachemclicolorspacecrayondigestevaluatefansifarverfastmapfontawesomefsggplot2gluegridExtragtablehighrhmshtmltoolshtmlwidgetsisobandjquerylibjsonlitekernlabknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmepillarpkgconfigprettyunitsprogressR6rappdirsRColorBrewerrglrlangrmarkdownsassscalestibbletinytexutf8vctrsviridisviridisLiteWallomicsDatawithrxfunyaml

Citation

To cite package ‘kpcaIG’ in publications use:

Briscik M, Heimida M, Déjean S (2025). kpcaIG: Variables Interpretability with Kernel PCA. R package version 1.0.1, https://CRAN.R-project.org/package=kpcaIG.

Corresponding BibTeX entry: 

  @Manual{,
    title = {kpcaIG: Variables Interpretability with Kernel PCA},
    author = {Mitja Briscik and Mohamed Heimida and Sébastien Déjean},
    year = {2025},
    note = {R package version 1.0.1},
    url = {https://CRAN.R-project.org/package=kpcaIG},
  }