Kernel Knockoffs Selection for Nonparametric Additive Models
Overview
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.
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Health
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Xiang Lyu <xianglyu@berkeley.edu>’ The Description field contains Nonparametric Additive Models”. arXiv preprint <arXiv:2105.11659>. Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
- NOTE11 OK, 2 NOTENow
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 6
- Examples that run
- 100%6 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
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Code & Tests
People & History
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- 1.0.1Latest2022-02-01 · current release
- RR 4.1.0 released · 2021-05-18
Package metadata
- First published
- 2022-02-01
- Total releases
- 1 / 4 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.6.3
- Download size
- 38 KB
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