CRAN/E | vimp

vimp

Perform Inference on Algorithm-Agnostic Variable Importance

Installation

About

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).

bdwilliamson.github.io/vimp/
github.com/bdwilliamson/vimp
bdwilliamson.github.io/vimp/
Bug report File report

Key Metrics

Version 2.3.3
R ≥ 3.1.0
Published 2023-08-28 236 days ago
Needs compilation? no
License MIT
License File
CRAN checks vimp results

Downloads

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Maintainer

Maintainer

Brian D. Williamson

brian.d.williamson@kp.org

Authors

Brian D. Williamson

aut / cre

Jean Feng

ctb

Charlie Wolock

ctb

Noah Simon

ths

Marco Carone

ths

Material

NEWS
Reference manual
Package source

Vignettes

Introduction to 'vimp'
Variable importance with coarsened data
Using precomputed regression function estimates in 'vimp'
Types of VIMs

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

vimp archive

Depends

R ≥ 3.1.0

Imports

SuperLearner
stats
dplyr
magrittr
ROCR
tibble
rlang
MASS
boot
data.table

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