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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=flevr to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4