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: 2.1.9
Depends: R (≥ 3.1.0)
Imports: SuperLearner, stats, dplyr, magrittr, ROCR, tibble, rlang, MASS
Suggests: knitr, rmarkdown, gam, xgboost, glmnet, ranger, polspline, quadprog, covr, testthat, ggplot2, cowplot, RCurl, cvAUC
Published: 2021-03-01
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 fredhutch.org>
BugReports: https://github.com/bdwilliamson/vimp/issues
License: MIT + file LICENSE
URL: https://bdwilliamson.github.io/vimp/, https://github.com/bdwilliamson/vimp
NeedsCompilation: no
Materials: NEWS
CRAN checks: vimp results

Downloads:

Reference manual: vimp.pdf
Vignettes: Introduction to 'vimp'
Using precomputed regression function estimates in 'vimp'
Types of VIMs
Package source: vimp_2.1.9.tar.gz
Windows binaries: r-devel: vimp_2.1.6.zip, r-release: vimp_2.1.9.zip, r-oldrel: vimp_2.1.6.zip
macOS binaries: r-release: vimp_2.1.6.tgz, r-oldrel: vimp_2.1.6.tgz
Old sources: vimp archive

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