SplitKnockoff: Split Knockoffs for Structural Sparsity (original) (raw)
Split Knockoff is a data adaptive variable selection framework for controlling the (directional) false discovery rate (FDR) in structural sparsity, where variable selection on linear transformation of parameters is of concern. This proposed scheme relaxes the linear subspace constraint to its neighborhood, often known as variable splitting in optimization. Simulation experiments can be reproduced following the Vignette. 'Split Knockoffs' is first defined in Cao et al. (2021) <doi:10.48550/arXiv.2103.16159>.
| Version: | 2.1 |
|---|---|
| Depends: | R (≥ 3.5.0) |
| Imports: | glmnet, MASS, latex2exp, RSpectra, ggplot2, Matrix, stats, mvtnorm |
| Suggests: | knitr, rmarkdown |
| Published: | 2024-10-14 |
| DOI: | 10.32614/CRAN.package.SplitKnockoff |
| Author: | Yuxuan Chen [aut, cre] (Development of the latest version of the packages), Haoxue Wang [aut] (Development of the first version of the packages), Yang Cao [aut] (Revison of this package), Xinwei Sun [aut] (Original ideas about the package), Yuan Yao [aut] (Testing for the package and management of the development) |
| Maintainer: | Yuxuan Chen <yx.chen at connect.ust.hk> |
| License: | MIT + file |
| NeedsCompilation: | no |
| CRAN checks: | SplitKnockoff results |
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