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Quantify extrapolation of new samples given a training set
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Lifecycle: experimental CRAN status Travis build status


There are times when a model’s prediction should be taken with some skepticism. For example, if a new data point is substantially different from the training set, it’s predicted value may be suspect. In chemistry, it is not uncommon to create an “applicability domain” model that measures the amount of potential extrapolation new samples have from the training set. applicable contains different methods to measure how much a new data point is an extrapolation from the original data (if at all).


You can install the development version of applicable from GitHub with:

# install.packages("devtools")


To learn about how to use applicable, check out the vignettes:

  • vignette("binary-data", "applicable"): Learn different methods to analyze binary data.

  • vignette("continuous-data", "applicable"): Learn different methods to analyze continuous data.

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