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
Roxygen: list(markdown = 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
Config/pak/sysreqs: libicu-dev
Repository: https://bdwilliamson.r-universe.dev
Date/Publication: 2025-12-05 17:42:39 UTC
RemoteUrl: https://github.com/bdwilliamson/flevr
RemoteRef: HEAD
RemoteSha: 9c4fc952ca454d78773ec4263da990369dac82ba
NeedsCompilation: no
Packaged: 2026-07-03 06:58:23 UTC; root
Author: Brian D. Williamson [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-7024-548X>)
Maintainer: Brian D. Williamson <brian.d.williamson@kp.org>