
<!-- README.md is generated from README.Rmd. Please edit that file -->

# applicable <a href="https://applicable.tidymodels.org"><img src="man/figures/logo.png" align="right" height="138" alt="applicable website" /></a>

<!-- badges: start -->

[![R-CMD-check](https://github.com/tidymodels/applicable/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/tidymodels/applicable/actions/workflows/R-CMD-check.yaml)
[![Codecov test
coverage](https://codecov.io/gh/tidymodels/applicable/graph/badge.svg)](https://app.codecov.io/gh/tidymodels/applicable)
[![Lifecycle:experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html)
[![CRAN
status](https://www.r-pkg.org/badges/version/applicable)](https://cran.r-project.org/package=applicable)
<!-- badges: end -->

## Introduction

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, its 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).

## Installation

You can install the released version of applicable from
[CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("applicable")
```

Install the development version of applicable from
[GitHub](https://github.com/) with:

``` r
# install.packages("pak")
pak::pak("tidymodels/applicable")
```

# Vignettes

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.

## Contributing

Please note that the applicable project is released with a [Contributor
Code of
Conduct](https://applicable.tidymodels.org/CODE_OF_CONDUCT.html). By
contributing to this project, you agree to abide by its terms.

- For questions and discussions about tidymodels packages, modeling, and
  machine learning, please [post on Posit
  Community](https://forum.posit.co/new-topic?category_id=15&tags=tidymodels,question).

- If you think you have encountered a bug, please [submit an
  issue](https://github.com/tidymodels/applicable/issues).

- Either way, learn how to create and share a
  [reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html)
  (a minimal, reproducible example), to clearly communicate about your
  code.

- Check out further details on [contributing guidelines for tidymodels
  packages](https://www.tidymodels.org/contribute/) and [how to get
  help](https://www.tidymodels.org/help/).
