flevr: Flexible, Ensemble-Based Variable Selection with PotentiallyMissing 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>.
| Version: | 0.0.5 |
| Depends: | R (≥ 3.1.0) |
| Imports: | SuperLearner,dplyr,magrittr,tibble,caret,mvtnorm,kernlab,rlang,ranger |
| Suggests: | vimp,stabs,testthat,knitr,rmarkdown,mice,xgboost,glmnet,polspline |
| Published: | 2025-12-06 |
| DOI: | 10.32614/CRAN.package.flevr |
| Author: | Brian D. Williamson [aut, cre] |
| Maintainer: | Brian D. Williamson <brian.d.williamson at kp.org> |
| BugReports: | https://github.com/bdwilliamson/flevr/issues |
| License: | MIT + fileLICENSE |
| URL: | https://github.com/bdwilliamson/flevr |
| NeedsCompilation: | no |
| Materials: | README,NEWS |
| CRAN checks: | flevr results |
Documentation:
| Reference manual: | flevr.html ,flevr.pdf |
| Vignettes: | Extrinsic variable selection (source,R code) Intrinsic variable selection (source,R code) Introduction to 'flevr' (source,R code) |
Downloads:
| Package source: | flevr_0.0.5.tar.gz |
| Windows binaries: | r-devel:flevr_0.0.5.zip, r-release:flevr_0.0.5.zip, r-oldrel:flevr_0.0.5.zip |
| macOS binaries: | r-release (arm64):flevr_0.0.5.tgz, r-oldrel (arm64):flevr_0.0.5.tgz, r-release (x86_64):flevr_0.0.5.tgz, r-oldrel (x86_64):flevr_0.0.5.tgz |
| Old sources: | flevr archive |
Linking:
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