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WLogit  

Variable Selection in High-Dimensional Logistic Regression Models using a Whitening Approach
View on CRAN: Click here


Download and install WLogit package within the R console
Install from CRAN:
install.packages("WLogit")

Install from Github:
library("remotes")
install_github("cran/WLogit")

Install by package version:
library("remotes")
install_version("WLogit", "2.1")



Attach the package and use:
library("WLogit")
Maintained by
Wencan Zhu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-07-02
Latest Update: 2023-07-17
Description:
It proposes a novel variable selection approach in classification problem that takes into account the correlations that may exist between the predictors of the design matrix in a high-dimensional logistic model. Our approach consists in rewriting the initial high-dimensional logistic model to remove the correlation between the predictors and in applying the generalized Lasso criterion.
How to cite:
Wencan Zhu (2022). WLogit: Variable Selection in High-Dimensional Logistic Regression Models using a Whitening Approach. R package version 2.1, https://cran.r-project.org/web/packages/WLogit. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0 (2022-07-02 14:20), 2.0 (2023-06-08 02:02)
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Complete documentation for WLogit
Functions, R codes and Examples using the WLogit R package
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