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# enpls <img src="man/figures/logo.png" align="right" width="120" />

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enpls offers an algorithmic framework for measuring feature importance,
outlier detection, model applicability domain evaluation, and ensemble
predictive modeling with (sparse) partial least squares regressions.

## Installation

You can install enpls from CRAN:

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

Or try the development version on GitHub:

``` r
remotes::install_github("nanxstats/enpls")
```

See `vignette("enpls")` for a quick-start guide.

## Gallery

### Feature importance

![](man/figures/feature-importance.png)

### Outlier detection

![](man/figures/outlier-detection.png)

### Model applicability domain evaluation and ensemble predictive modeling

![](man/figures/ensemble-modeling.png)

## Contribute

To contribute to this project, please take a look at the [Contributing
Guidelines](https://nanx.me/enpls/CONTRIBUTING.html) first. Please note
that the RECA project is released with a [Contributor Code of
Conduct](https://nanx.me/enpls/CODE_OF_CONDUCT.html). By contributing to
this project, you agree to abide by its terms.
