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). âKernel Knockoffs Selection for Nonparametric Additive Modelsâ. 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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=kko to link to this page.
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