Package: supervisedPRIM 2.0.0

supervisedPRIM: Supervised Classification Learning and Prediction using Patient Rule Induction Method (PRIM)

The Patient Rule Induction Method (PRIM) is typically used for "bump hunting" data mining to identify regions with abnormally high concentrations of data with large or small values. This package extends this methodology so that it can be applied to binary classification problems and used for prediction.

Authors:David Shaub [aut, cre]

supervisedPRIM_2.0.0.tar.gz
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supervisedPRIM_2.0.0.tgz(r-4.6-any)supervisedPRIM_2.0.0.tgz(r-4.5-any)
supervisedPRIM_2.0.0.tar.gz(r-4.7-any)supervisedPRIM_2.0.0.tar.gz(r-4.6-any)
supervisedPRIM_2.0.0.tgz(r-4.6-emscripten)
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card.svg |card.png
supervisedPRIM/json (API)
NEWS

# Install 'supervisedPRIM' in R:
install.packages('supervisedPRIM', repos = c('https://dashaub.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/dashaub/supervisedprim/issues

On CRAN:

Conda:

patient-rules-inductionsupervised-learning

2.70 score 1 stars 5 scripts 208 downloads 2 exports 13 dependencies

Last updated from:e143da991c. Checks:7 WARNING, 1 ERROR, 1 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING219
source / vignettesERROR145
linux-release-x86_64WARNING182
macos-release-arm64WARNING151
macos-oldrel-arm64WARNING107
windows-develWARNING135
windows-releaseWARNING113
windows-oldrelWARNING127
wasm-releaseOK90

Exports:predict.supervisedPRIMsupervisedPRIM

Dependencies:clifarvergluelabelinglifecyclemisc3dplot3DprimR6RColorBrewerrlangscalesviridisLite