Flexible, Ensemble-Based Variable Selection with Potentially Missing Data R package flevr version 0.0.5 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>. Software 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 Brian D. Williamson <brian.d.williamson@kp.org> Comprehensive R Archive Network (CRAN) Brian D. Williamson [aut, cre] (ORCID: <https://orcid.org/0000-0002-7024-548X>) MIT + file LICENSE (https://CRAN.R-project.org/package=flevr/LICENSE) 2025-12-06 application/tgz https://CRAN.R-project.org/package=flevr doi:10.32614/CRAN.package.flevr