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: 2.3.0
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
Published: 2022-11-14
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 <brian.d.williamson at kp.org>
BugReports: https://github.com/bdwilliamson/vimp/issues
License: MIT + file LICENSE
URL: https://bdwilliamson.github.io/vimp/, https://github.com/bdwilliamson/vimp, http://bdwilliamson.github.io/vimp/
NeedsCompilation: no
Citation: vimp citation info
Materials: NEWS
CRAN checks: vimp results

Documentation:

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

Downloads:

Package source: vimp_2.3.0.tar.gz
Windows binaries: r-devel: vimp_2.3.0.zip, r-release: vimp_2.3.0.zip, r-oldrel: vimp_2.3.0.zip
macOS binaries: r-release (arm64): vimp_2.3.0.tgz, r-oldrel (arm64): vimp_2.3.0.tgz, r-release (x86_64): vimp_2.3.0.tgz, r-oldrel (x86_64): vimp_2.3.0.tgz
Old sources: vimp archive

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