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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>.
Getting started
Browse package contents
Package details |
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| Author | Brian D. Williamson [aut, cre] (ORCID: <https://orcid.org/0000-0002-7024-548X>) |
| Maintainer | Brian D. Williamson <brian.d.williamson@kp.org> |
| License | MIT + file LICENSE |
| Version | 0.0.5 |
| URL | https://github.com/bdwilliamson/flevr |
| Package repository | View on CRAN |
| Installation |
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