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)
Published: 2023年11月30日
Author: Brian D. Williamson ORCID iD [aut, cre]
Maintainer: Brian D. Williamson <brian.d.williamson at kp.org>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: flevr results [issues need fixing before 2025年12月17日]

Documentation:

Reference manual: flevr.html , flevr.pdf

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

Linking:

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