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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.
Getting started
Browse package contents
Package details |
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| Author | Anthony Christidis [aut, cre], Gabriela Cohen-Freue [aut] |
| Maintainer | Anthony Christidis <anthony.christidis@stat.ubc.ca> |
| License | GPL (>= 2) |
| Version | 3.1.0 |
| Package repository | View on CRAN |
| Installation |
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