kko
Kernel Knockoffs Selection for Nonparametric Additive Models
A variable selection procedure, dubbed KKO, for nonparametric additive model with finite-sample false discovery rate control guarantee. The method integrates three key components: knockoffs, subsampling for stability, and random feature mapping for nonparametric function approximation. For more information, see the accompanying paper: Dai, X., Lyu, X., & Li, L. (2021). “Kernel Knockoffs Selection for Nonparametric Additive Models”. arXiv preprint <arXiv:2105.11659>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.1 |
rolling linux/jammy R-4.5 | kko_1.0.1.tar.gz |
71.8 KiB |
1.0.1 |
rolling linux/noble R-4.5 | kko_1.0.1.tar.gz |
71.7 KiB |
1.0.1 |
rolling source/ R- | kko_1.0.1.tar.gz |
38.1 KiB |
1.0.1 |
latest linux/jammy R-4.5 | kko_1.0.1.tar.gz |
71.8 KiB |
1.0.1 |
latest linux/noble R-4.5 | kko_1.0.1.tar.gz |
71.7 KiB |
1.0.1 |
latest source/ R- | kko_1.0.1.tar.gz |
38.1 KiB |
1.0.1 |
2026-04-26 source/ R- | kko_1.0.1.tar.gz |
38.1 KiB |
1.0.1 |
2026-04-23 source/ R- | kko_1.0.1.tar.gz |
38.1 KiB |
1.0.1 |
2026-04-09 windows/windows R-4.5 | kko_1.0.1.zip |
72.2 KiB |
1.0.1 |
2025-04-20 source/ R- | kko_1.0.1.tar.gz |
38.1 KiB |