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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 |
| 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 |
| Reference manual: | kko.pdf |
| Vignettes: |
Vignette of R package kko |
| Package source: | kko_1.0.1.tar.gz |
| Windows binaries: | r-devel: kko_1.0.1.zip, r-release: kko_1.0.1.zip, r-oldrel: kko_1.0.1.zip |
| macOS binaries: | r-release (arm64): kko_1.0.1.tgz, r-oldrel (arm64): kko_1.0.1.tgz, r-release (x86_64): kko_1.0.1.tgz, r-oldrel (x86_64): kko_1.0.1.tgz |
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