Algorithmic framework for measuring feature importance, outlier detection, model applicability evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions.
R
Switch branches/tags
Clone or download
Latest commit 0782cd4 Jun 26, 2018
Permalink
Failed to load latest commit information.
R fix table code Jun 26, 2018
data v4.5 Sep 15, 2016
docs update docs site Jun 26, 2018
man
vignettes move from enpls.org to nanx.me/enpls/ May 8, 2018
.Rbuildignore move docs site from gh-pages to docs/ Apr 19, 2018
.gitattributes add appveyor for Windows CI Dec 21, 2016
.gitignore add travis Jun 19, 2016
.travis.yml https for enpls Jan 18, 2017
CONDUCT.md
CONTRIBUTING.md
DESCRIPTION v6.0 staging May 13, 2018
LICENSE add license; add logo Dec 23, 2016
NAMESPACE
NEWS.md v6.0 staging May 13, 2018
README.md move from enpls.org to nanx.me/enpls/ May 8, 2018
TODO v5.9 staging Sep 27, 2017
_pkgdown.yml update theme May 14, 2018
appveyor.yml add appveyor for Windows CI Dec 21, 2016
enpls.Rproj bump to v3.0 Jun 23, 2016

README.md

enpls logo

Build Status AppVeyor Build Status CRAN Version Downloads from the RStudio CRAN mirror

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

Install enpls from CRAN:

install.packages("enpls")

Or try the development version on GitHub:

# install.packages("devtools")
devtools::install_github("road2stat/enpls")

See the vignette (or open with vignette("enpls") in R) for a quick-start guide.

Gallery

Measuring Feature Importance

enpls-fs

Outlier Detection

enpls-od

Model Applicability Domain Evaluation / Ensemble Predictive Modeling

enpls-fit

Links

Contribute

To contribute to this project, please take a look at the Contributing Guidelines first. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.