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 <doi:10.48550/arXiv.2105.11659>.
| Version: | 1.0.1 |
| Depends: | R (≥ 3.6.3) |
| Imports: | grpreg,knockoff,doParallel, parallel,foreach,ExtDist |
| Suggests: | knitr,rmarkdown,ggplot2 |
| Published: | 2022-02-01 |
| DOI: | 10.32614/CRAN.package.kko |
| Author: | Xiaowu Dai [aut], Xiang Lyu [aut, cre], Lexin Li [aut] |
| Maintainer: | Xiang Lyu <xianglyu at berkeley.edu> |
| License: | GPL-2 |GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | kko results |
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