Package: flevr
Title: Flexible, Ensemble-Based Variable Selection with Potentially
        Missing Data
Version: 0.0.4
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 (2023+) <arXiv:2202.12989>.
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
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: 2023-11-29 19:23:19 UTC; L107067
Author: Brian D. Williamson [aut, cre]
    (<https://orcid.org/0000-0002-7024-548X>)
Maintainer: Brian D. Williamson <brian.d.williamson@kp.org>
Repository: CRAN
Date/Publication: 2023-11-30 10:20:05 UTC
Built: R 4.4.0; ; 2024-04-07 02:09:06 UTC; unix
