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. (JASA, 2021), and Williamson and Feng (ICML, 2020).

Version: 2.3.3
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, cvAUC, tidyselect, WeightedROC, purrr
Published: 2023-08-28
DOI: 10.32614/CRAN.package.vimp
Author: Brian D. Williamson ORCID iD [aut, cre], Jean Feng [ctb], Charlie Wolock [ctb], Noah Simon ORCID iD [ths], Marco Carone ORCID iD [ths]
Maintainer: Brian D. Williamson <brian.d.williamson at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: NEWS
CRAN checks: vimp results


Reference manual: vimp.pdf
Vignettes: Introduction to 'vimp'
Variable importance with coarsened data
Using precomputed regression function estimates in 'vimp'
Types of VIMs


Package source: vimp_2.3.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): vimp_2.3.3.tgz, r-oldrel (arm64): vimp_2.3.3.tgz, r-release (x86_64): vimp_2.3.3.tgz, r-oldrel (x86_64): vimp_2.3.3.tgz
Old sources: vimp archive

Reverse dependencies:

Reverse suggests: flevr, tidyhte


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