Enpls
Algorithmic framework for measuring feature importance, outlier detection, model applicability evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions.
Install / Use
/learn @nanxstats/EnplsREADME
enpls <img src="man/figures/logo.png" align="right" width="120" />
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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.
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