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.

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The stable release of the package is hosted on [CRAN]( and can be installed as usual:


The latest development version can be installed using the devtools package.


Version updates to CRAN will be published frequently after new features are implemented, so the development version is not recommended unless you plan to modify the code.


The supervisePRIM() function can be used to train a model on a dataset of all numeric columns with a binary 0/1 response. For example, using the famous iris dataset

yData <- ifelse(iris$Species == "setosa", 1L, 0L)
xData <- iris
xData$Species <- NULL
primModel <- supervisedPRIM(x = xData, y = yData)

This returns a S3 class supervisedPRIM object, and the regular S3 predict() generic can be used to apply the model to new data:

predictions <- predict(primModel, newdata = xData)

Furthermore, this supervisedPRIM objects also inherits from the "prim" package, so all the regular method there (e.g. plot()) can be used on the supervisedPRIM objects. Consult the documention of the "prim" package for more comprehensive details of the available functions and the arguments accepted for training.


This package is free software released under the GPL-3 license.


Version 2.0.0 [2016-09-30]

  • Changed API in supervisedPRIM() to accept factors for y instead of c(0, 1)

Version 1.0.1 [2016-08-22]

  • First CRAN release
  • Added usage examples
  • Added NEWS

Version 1.0.0 [2016-08-17]

  • First Github release

Reference manual

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2.0.0 by David Shaub, 5 years ago

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Browse source code at

Authors: David Shaub [aut, cre]

Documentation:   PDF Manual  

GPL-3 license

Depends on stats, prim

Suggests kernlab, testthat

See at CRAN