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 (2023+) <doi:10.48550/arXiv.2202.12989>.
| Version: | 0.0.4 |
| 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: | 2023-11-30 |
| 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 + file LICENSE |
| URL: | https://github.com/bdwilliamson/flevr |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | flevr results |
Documentation:
| Reference manual: | flevr.pdf |
| Vignettes: |
Extrinsic variable selection Intrinsic variable selection Introduction to 'flevr' |
Downloads:
| Package source: | flevr_0.0.4.tar.gz |
| Windows binaries: | r-devel: flevr_0.0.4.zip, r-release: flevr_0.0.4.zip, r-oldrel: flevr_0.0.4.zip |
| macOS binaries: | r-release (arm64): flevr_0.0.4.tgz, r-oldrel (arm64): flevr_0.0.4.tgz, r-release (x86_64): flevr_0.0.4.tgz, r-oldrel (x86_64): flevr_0.0.4.tgz |
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