Ensemble Partial Least Squares Regression

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

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:

install.packages("enpls")

Or try the development version on GitHub:

remotes::install_github("nanxstats/enpls")

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

Gallery

Feature importance

Outlier detection

Model applicability domain evaluation and ensemble predictive modeling

Contribute

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