enpls
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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.
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 this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.