An algorithmic framework for measuring feature importance, outlier detection, model applicability domain evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions.
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.
enpls from CRAN:
Or try the development version on GitHub:
See the vignette (or open with
vignette("enpls") in R) for a quick-start guide.
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.
cvfoldsnow available in all applicable functions for finer control of cross-validation folds in automatic parameter selection of each PLS/SPLS model.
maxcomp = NULL(maximum number of components not specified explicitly) in
enpls.functions. Now it should correctly determine the maximum number of components to use, considering both cross-validation and special cases such as n < p. Thanks to Dr. You-Wu Lin for the feedback.
enspls.ad()for PLS and sparse PLS model applicability domain evaluation.
plot.enpls.ad()for exploring model applicability domain evaluation results with traditional static plot support and interactive plot support.
alphaavailable for setting transparency level (to reduce overplotting) in
enpls.rmsle()for computing RMSE, MAE, and RMSLE.
cv.enplsand plotting functions.