Package: kko
Title: Kernel Knockoffs Selection for Nonparametric Additive Models
Version: 1.0.1
Authors@R: 
    c(person("Xiaowu", "Dai", role = c("aut")),
    person("Xiang", "Lyu", role = c("aut","cre"), email="xianglyu@berkeley.edu"),
    person("Lexin", "Li", role = c("aut")))
Description: 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>.
License: GPL (>= 2)
Depends: R (>= 3.6.3)
Imports: grpreg, knockoff, doParallel, parallel, foreach, ExtDist
Suggests: knitr, rmarkdown, ggplot2
Encoding: UTF-8
LazyData: false
RoxygenNote: 7.1.2
VignetteBuilder: knitr
Author: Xiaowu Dai [aut],
  Xiang Lyu [aut, cre],
  Lexin Li [aut]
Maintainer: Xiang Lyu <xianglyu@berkeley.edu>
NeedsCompilation: no
Packaged: 2022-01-31 03:32:30 UTC; xianglyu
Repository: CRAN
Date/Publication: 2022-02-01 09:10:05 UTC
Built: R 4.6.0; ; 2026-04-26 08:20:34 UTC; unix
