Variable Selection for Highly Correlated Predictors

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 (Zhu et al., 2020).


Reference manual

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1.0 by Wencan Zhu, a year ago

Browse source code at

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

Documentation:   PDF Manual  

GPL-2 license

Imports Matrix, genlasso, tibble, MASS, ggplot2

Suggests knitr, markdown

See at CRAN