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 ORCID iD [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


Reference manual: flevr.pdf
Vignettes: Extrinsic variable selection
Intrinsic variable selection
Introduction to 'flevr'


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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