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flevr

Flexible, Ensemble-Based Variable Selection with Potentially Missing Data

v0.0.5 · Dec 6, 2025 · MIT + file LICENSE

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>.

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r-devel-windows-x86_64 OK
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Check History

OK 13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Jun 9, 2026
ERROR 12 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE Jun 8, 2026
ERROR r-devel-linux-x86_64-debian-gcc

package dependencies

Package required but not available: ‘caret’

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See section ‘The DESCRIPTION file’ in the ‘Writing R Extensions’
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OK 13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE May 2, 2026
ERROR 11 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE Apr 25, 2026
ERROR r-release-macos-x86_64

package dependencies

Package required but not available: ‘caret’

Package suggested but not available for checking: ‘mice’

See section ‘The DESCRIPTION file’ in the ‘Writing R Extensions’
manual.
OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026

Dependency Network

Dependencies Reverse dependencies SuperLearner dplyr magrittr tibble caret mvtnorm kernlab rlang ranger flevr

Version History

3 tracked
new 0.0.5 Mar 10, 2026
updated 0.0.5 ← 0.0.4 diff Dec 5, 2025
new 0.0.4 Nov 29, 2023