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.
- Version1.0.1
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release02/01/2022
Documentation
Team
Xiang Lyu
Lexin Li
Show author detailsRolesAuthorXiaowu Dai
Show author detailsRolesAuthor
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Last 30 days
This package has been downloaded 146 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 8 times.
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Last 365 days
This package has been downloaded 2,195 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Jul 21, 2024 with 67 downloads.
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Dependencies
- Imports5 packages
- Suggests3 packages