SplitKnockoff: Split Knockoffs for Structural Sparsity
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. We include data
(both .mat and .csv format) and application with our method of Alzheimer's Disease
study in this package.
'Split Knockoffs' is first defined in Cao et al. (2021) <arXiv:2103.16159>.
||R (≥ 3.5.0)
||glmnet, MASS, latex2exp, RSpectra, ggplot2, Matrix, stats, mvtnorm
||Haoxue Wang [aut, cre] (Development of the whole 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
||Haoxue Wang <haoxwang at student.ethz.ch>
||MIT + file LICENSE
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