vimp: Perform Inference on Algorithm-Agnostic Variable Importance

Calculate point estimates of and valid confidence intervals for nonparametric, algorithm-agnostic variable importance measures in high and low dimensions, using flexible estimators of the underlying regression functions. For more information about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (arXiv, 2020+) <arXiv:2004.03683>, and Williamson and Feng (ICML, 2020).

Version: 2.2.2
Depends: R (≥ 3.1.0)
Imports: SuperLearner, stats, dplyr, magrittr, ROCR, tibble, rlang, MASS, boot, data.table
Suggests: knitr, rmarkdown, gam, xgboost, glmnet, ranger, polspline, quadprog, covr, testthat, ggplot2, cowplot, RCurl, cvAUC
Published: 2021-06-14
Author: Brian D. Williamson ORCID iD [aut, cre], Jean Feng [ctb], Noah Simon ORCID iD [ths], Marco Carone ORCID iD [ths]
Maintainer: Brian D. Williamson <bwillia2 at>
License: MIT + file LICENSE
NeedsCompilation: no
Citation: vimp citation info
Materials: NEWS
CRAN checks: vimp results


Reference manual: vimp.pdf
Vignettes: Introduction to 'vimp'
Using precomputed regression function estimates in 'vimp'
Types of VIMs
Package source: vimp_2.2.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): vimp_2.2.2.tgz, r-release (x86_64): vimp_2.2.2.tgz, r-oldrel: vimp_2.2.2.tgz
Old sources: vimp archive


Please use the canonical form to link to this page.