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-4.7-any)kpcaIG_1.0.1.tar.gz(r-4.6-any)
kpcaIG_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
kpcaIG/json (API)

# Install 'kpcaIG' in R:
install.packages('kpcaIG', repos = c('https://cran.r-universe.dev', '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 2 stars 2 scripts 112 downloads 4 exports 50 dependencies

Last updated from:51237f39f4. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK132
source / vignettesOK199
linux-release-x86_64OK137
wasm-releaseOK163

Exports:kernelpcakpca_igradplot_kpca2Dplot_kpca3D

Dependencies:base64encbslibcachemclicpp11crayondigestevaluatefarverfastmapfontawesomefsggplot2gluegridExtragtablehighrhmshtmltoolshtmlwidgetsisobandjquerylibjsonlitekernlabknitrlabelinglifecyclemagrittrmemoisemimepkgconfigprettyunitsprogressR6rappdirsRColorBrewerrglrlangrmarkdownS7sassscalestinytexvctrsviridisviridisLiteWallomicsDatawithrxfunyaml