doi:10.1007/s10994-021-06030-6>. Implements a conditional variable importance measure which can be applied to any supervised learning algorithm and loss function. Provides statistical inference procedures without parametric assumptions and applies equally well to continuous and categorical predictors and outcomes.">

cpi: Conditional Predictive Impact (original) (raw)

A general test for conditional independence in supervised learning algorithms as proposed by Watson & Wright (2021) <doi:10.1007/s10994-021-06030-6>. Implements a conditional variable importance measure which can be applied to any supervised learning algorithm and loss function. Provides statistical inference procedures without parametric assumptions and applies equally well to continuous and categorical predictors and outcomes.

Version: 0.1.5
Imports: foreach, mlr3, lgr, knockoff
Suggests: mlr3learners, ranger, glmnet, testthat (≥ 3.0.0), knitr, rmarkdown, doParallel
Published: 2024-11-25
DOI: 10.32614/CRAN.package.cpi
Author: Marvin N. Wright ORCID iD [aut, cre], David S. Watson [aut]
Maintainer: Marvin N. Wright
BugReports: https://github.com/bips-hb/cpi/issues
License: GPL (≥ 3)
URL: https://github.com/bips-hb/cpi, https://bips-hb.github.io/cpi/
NeedsCompilation: no
Citation: cpi citation info
Materials: README, NEWS
CRAN checks: cpi results

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