srlars
Fast and Scalable Cellwise-Robust Ensemble
Functions to perform robust variable selection and regression using the Fast and Scalable Cellwise-Robust Ensemble (FSCRE) algorithm. The approach establishes a robust foundation using the Detect Deviating Cells (DDC) algorithm and robust correlation estimates. It then employs a competitive ensemble architecture where a robust Least Angle Regression (LARS) engine proposes candidate variables and cross-validation arbitrates their assignment. A final robust MM-estimator is applied to the selected predictors.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.0.1 |
rolling linux/jammy R-4.5 | srlars_2.0.1.tar.gz |
48.9 KiB |
2.0.1 |
rolling linux/noble R-4.5 | srlars_2.0.1.tar.gz |
48.9 KiB |
2.0.1 |
rolling source/ R- | srlars_2.0.1.tar.gz |
13.8 KiB |
2.0.1 |
latest linux/jammy R-4.5 | srlars_2.0.1.tar.gz |
48.9 KiB |
2.0.1 |
latest linux/noble R-4.5 | srlars_2.0.1.tar.gz |
48.9 KiB |
2.0.1 |
latest source/ R- | srlars_2.0.1.tar.gz |
13.8 KiB |
2.0.1 |
2026-04-26 source/ R- | srlars_2.0.1.tar.gz |
13.8 KiB |
2.0.1 |
2026-04-23 source/ R- | srlars_2.0.1.tar.gz |
13.8 KiB |
2.0.1 |
2026-04-09 windows/windows R-4.5 | srlars_2.0.1.zip |
51.6 KiB |
1.0.1 |
2025-04-20 source/ R- | srlars_1.0.1.tar.gz |
9.2 KiB |