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.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_0.0.4.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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