Variables Interpretability with Kernel PCA
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 4
- Examples that run
- 25%1 run in a plain check · 3 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 100%
Downloads
Dependencies
Code & Tests
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 1.0.1Latest
- 1.02024-07-21
- RR 4.4.0 released · 2024-04-24
Package metadata
- First published
- 2024-07-21
- Total releases
- 2 / 2 yrs
- License
- GPL-3 OSI
- Download size
- 8.1 KB
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