WLogit
Variable Selection in High-Dimensional Logistic Regression Models using a Whitening Approach
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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.1 |
rolling linux/jammy R-4.5 | WLogit_2.1.tar.gz |
731.3 KiB |
2.1 |
rolling linux/noble R-4.5 | WLogit_2.1.tar.gz |
731.3 KiB |
2.1 |
rolling source/ R- | WLogit_2.1.tar.gz |
692.2 KiB |
2.1 |
latest linux/jammy R-4.5 | WLogit_2.1.tar.gz |
731.3 KiB |
2.1 |
latest linux/noble R-4.5 | WLogit_2.1.tar.gz |
731.3 KiB |
2.1 |
latest source/ R- | WLogit_2.1.tar.gz |
692.2 KiB |
2.1 |
2026-04-26 source/ R- | WLogit_2.1.tar.gz |
692.2 KiB |
2.1 |
2026-04-23 source/ R- | WLogit_2.1.tar.gz |
692.2 KiB |
2.1 |
2026-04-09 windows/windows R-4.5 | WLogit_2.1.zip |
734.9 KiB |
2.1 |
2025-04-20 source/ R- | WLogit_2.1.tar.gz |
692.2 KiB |