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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.1 |
rolling linux/jammy R-4.5 | kpcaIG_1.0.1.tar.gz |
43.4 KiB |
1.0.1 |
rolling linux/noble R-4.5 | kpcaIG_1.0.1.tar.gz |
43.4 KiB |
1.0.1 |
rolling source/ R- | kpcaIG_1.0.1.tar.gz |
8.0 KiB |
1.0.1 |
latest linux/jammy R-4.5 | kpcaIG_1.0.1.tar.gz |
43.4 KiB |
1.0.1 |
latest linux/noble R-4.5 | kpcaIG_1.0.1.tar.gz |
43.4 KiB |
1.0.1 |
latest source/ R- | kpcaIG_1.0.1.tar.gz |
8.0 KiB |
1.0.1 |
2026-04-26 source/ R- | kpcaIG_1.0.1.tar.gz |
8.0 KiB |
1.0.1 |
2026-04-23 source/ R- | kpcaIG_1.0.1.tar.gz |
8.0 KiB |
1.0.1 |
2026-04-09 windows/windows R-4.5 | kpcaIG_1.0.1.zip |
45.9 KiB |
1.0.1 |
2025-04-20 source/ R- | kpcaIG_1.0.1.tar.gz |
8.0 KiB |