flevr
Flexible, Ensemble-Based Variable Selection with Potentially Missing Data
Perform variable selection in settings with possibly missing data based on extrinsic (algorithm-specific) and intrinsic (population-level) variable importance. Uses a Super Learner ensemble to estimate the underlying prediction functions that give rise to estimates of variable importance. For more information about the methods, please see Williamson and Huang (2024) <doi:10.1515/ijb-2023-0059>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.0.5 |
rolling source/ R- | flevr_0.0.5.tar.gz |
172.3 KiB |
0.0.5 |
latest source/ R- | flevr_0.0.5.tar.gz |
172.3 KiB |
0.0.5 |
2026-04-23 source/ R- | flevr_0.0.5.tar.gz |
172.3 KiB |
0.0.5 |
2026-04-09 windows/windows R-4.5 | flevr_0.0.5.zip |
244.8 KiB |