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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>.
Getting started
Browse package contents
Package details |
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| Author | Xiaowu Dai [aut], Xiang Lyu [aut, cre], Lexin Li [aut] |
| Maintainer | Xiang Lyu <xianglyu@berkeley.edu> |
| License | GPL (>= 2) |
| Version | 1.0.1 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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