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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.
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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("nanxstats/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.

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