VSOLassoBag
Variable Selection Oriented LASSO Bagging Algorithm
A wrapped LASSO approach by integrating an ensemble learning strategy to help select efficient, stable, and high confidential variables from omics-based data. Using a bagging strategy in combination of a parametric method or inflection point search method for cut-off threshold determination. This package can integrate and vote variables generated from multiple LASSO models to determine the optimal candidates. Luo H, Zhao Q, et al (2020) <doi:10.1126/scitranslmed.aax7533> for more details.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0 |
rolling linux/jammy R-4.5 | VSOLassoBag_1.0.tar.gz |
1.4 MiB |
1.0 |
rolling linux/noble R-4.5 | VSOLassoBag_1.0.tar.gz |
1.4 MiB |
1.0 |
rolling source/ R- | VSOLassoBag_1.0.tar.gz |
1.4 MiB |
1.0 |
latest linux/jammy R-4.5 | VSOLassoBag_1.0.tar.gz |
1.4 MiB |
1.0 |
latest linux/noble R-4.5 | VSOLassoBag_1.0.tar.gz |
1.4 MiB |
1.0 |
latest source/ R- | VSOLassoBag_1.0.tar.gz |
1.4 MiB |
1.0 |
2026-04-26 source/ R- | VSOLassoBag_1.0.tar.gz |
1.4 MiB |
1.0 |
2026-04-23 source/ R- | VSOLassoBag_1.0.tar.gz |
1.4 MiB |
0.99.1 |
2025-04-20 source/ R- | VSOLassoBag_0.99.1.tar.gz |
1.3 MiB |