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) and Williamson et al. (arXiv, 2020+) <arXiv:2004.03683>.

Version: 2.0.1
Depends: R (≥ 3.1.0)
Imports: SuperLearner, stats, dplyr, magrittr, ROCR, tibble, rlang
Suggests: knitr, rmarkdown, MASS, gam, xgboost, glmnet, ranger, quadprog, covr, testthat, ggplot2, cowplot, RCurl, forcats
Published: 2020-04-11
Author: Brian D. Williamson ORCID iD [aut, cre], Noah Simon ORCID iD [aut], Marco Carone ORCID iD [aut]
Maintainer: Brian D. Williamson <brianw26 at uw.edu>
BugReports: https://github.com/bdwilliamson/vimp/issues
License: MIT + file LICENSE
URL: https://github.com/bdwilliamson/vimp
NeedsCompilation: no
CRAN checks: vimp results

Downloads:

Reference manual: vimp.pdf
Vignettes: Introduction to vimp
Package source: vimp_2.0.1.tar.gz
Windows binaries: r-prerelease: vimp_2.0.1.zip, r-release: vimp_2.0.1.zip, r-oldrel: vimp_2.0.1.zip
macOS binaries: r-prerelease: vimp_2.0.1.tgz, r-release: vimp_2.0.1.tgz, r-oldrel: vimp_1.1.6.tgz
Old sources: vimp archive

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