Package: flevr
Title: Flexible, Ensemble-Based Variable Selection with Potentially
        Missing Data
Version: 0.0.5
Authors@R: 
    person(given = "Brian D.",
           family = "Williamson",
           role = c("aut", "cre"),
           email = "brian.d.williamson@kp.org",
           comment = c(ORCID = "0000-0002-7024-548X"))
Description: 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>.
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
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
URL: https://github.com/bdwilliamson/flevr
BugReports: https://github.com/bdwilliamson/flevr/issues
VignetteBuilder: knitr
License: MIT + file LICENSE
NeedsCompilation: no
Packaged: 2025-12-05 17:43:10 UTC; L107067
Author: Brian D. Williamson [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-7024-548X>)
Maintainer: Brian D. Williamson <brian.d.williamson@kp.org>
Repository: CRAN
Date/Publication: 2025-12-06 14:30:02 UTC
Built: R 4.6.0; ; 2026-04-30 11:21:15 UTC; unix
