flevr: Flexible, Ensemble-Based Variable Selection with Potentially Missing Data

A framework for flexible, ensemble-based variable selection using either extrinsic or intrinsic variable importance. You provide the data and a library of candidate algorithms for estimating the conditional mean outcome given covariates; flevr handles the rest.

Author(s)

Maintainer: Brian Williamson https://bdwilliamson.github.io/

Methodology authors:

• Brian D. Williamson

• Ying Huang

See Also

Papers:

• https://arxiv.org/abs/2202.12989

Other useful links:

• https://bdwilliamson.github.io/flevr/

• https://github.com/bdwilliamson/flevr

• Report bugs at https://github.com/bdwilliamson/flevr/issues

Imports

The packages that we import either make the internal code nice (dplyr, magrittr, tibble) or are directly relevant for estimating variable importance (SuperLearner, caret).

We suggest several other packages: xgboost, ranger, glmnet, kernlab, polspline and quadprog allow a flexible library of candidate learners in the Super Learner; stabs allows importance to be embedded within stability selection; testthat and covr help with unit tests; and knitr, rmarkdown,and RCurl help with the vignettes and examples.