https://github.com/nanxstats/enpls

Algorithmic framework for measuring feature importance, outlier detection, model applicability evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions.
https://github.com/nanxstats/enpls

chemometrics dimensionality-reduction ensemble-learning machine-learning outlier-detection partial-least-squares-regression

Last synced: 2 months ago
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Algorithmic framework for measuring feature importance, outlier detection, model applicability evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions.

• Host: GitHub
• URL: https://github.com/nanxstats/enpls
• Owner: nanxstats
• License: gpl-3.0
• Created: 2014-10-03T19:33:03.000Z (over 10 years ago)
• Default Branch: master
• Last Pushed: 2021-12-21T03:56:32.000Z (over 3 years ago)
• Last Synced: 2025-04-18T20:31:52.458Z (2 months ago)
• Topics: chemometrics, dimensionality-reduction, ensemble-learning, machine-learning, outlier-detection, partial-least-squares-regression
• Language: R
• Homepage: https://nanx.me/enpls/
• Size: 27.5 MB
• Stars: 18
• Watchers: 4
• Forks: 8
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

Awesome Lists containing this project

README

        
---

output: github_document

---

```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# enpls 

[![R-CMD-check](https://github.com/nanxstats/enpls/workflows/R-CMD-check/badge.svg)](https://github.com/nanxstats/enpls/actions)

[![CRAN Version](https://www.r-pkg.org/badges/version/enpls)](https://cran.r-project.org/package=enpls)

[![Downloads from the RStudio CRAN mirror](https://cranlogs.r-pkg.org/badges/enpls)](https://cranlogs.r-pkg.org/badges/enpls)

`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](CONTRIBUTING.md) first. Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms.