WLasso-package: Variable Selection for Highly Correlated Predictors

In WLasso: Variable Selection for Highly Correlated Predictors

Description

It proposes a novel variable selection approach taking into account the correlations that may exist between the predictors of the design matrix in a high-dimensional linear model. Our approach consists in rewriting the initial high-dimensional linear model to remove the correlation between the predictors and in applying the generalized Lasso criterion. For further details we refer the reader to the paper <arXiv:2007.10768> (Zhu et al., 2020).

Details

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This package consists of four functions: "Sigma_Estimation.R", "top.R", "top_thresh.R" and "Whitening_Lasso.R". For further information on how to use these functions, we refer the reader to the vignette of the package.

Author(s)

Wencan Zhu [aut, cre], Celine Levy-Leduc [ctb], Nils Ternes [ctb]

Maintainer: Wencan Zhu <[e-mail]>

References

W. Zhu, C. Levy-Leduc, N. Ternes. "A variable selection approach for highly correlated predictors in high-dimensional genomic data". arXiv:2007.10768.

WLasso documentation built on Aug. 13, 2020, 5:06 p.m.