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

Documentation:

Reference manual:	kko.pdf
Vignettes:	Vignette of R package kko

Downloads:

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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