enpls: 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.

Version: 6.1
Depends: R (≥ 3.0.2)
Imports: pls, spls, foreach, doParallel, ggplot2, reshape2, plotly
Suggests: knitr, rmarkdown
Published: 2019-05-18
DOI: 10.32614/CRAN.package.enpls
Author: Nan Xiao ORCID iD [aut, cre], Dong-Sheng Cao [aut], Miao-Zhu Li [aut], Qing-Song Xu [aut]
Maintainer: Nan Xiao <me at nanx.me>
BugReports: https://github.com/nanxstats/enpls/issues
License: GPL-3 | file LICENSE
URL: https://nanx.me/enpls/, https://github.com/nanxstats/enpls
NeedsCompilation: no
Materials: README NEWS
In views: ChemPhys
CRAN checks: enpls results


Reference manual: enpls.pdf
Vignettes: A Brief Introduction to enpls


Package source: enpls_6.1.tar.gz
Windows binaries: r-devel: enpls_6.1.zip, r-release: enpls_6.1.zip, r-oldrel: enpls_6.1.zip
macOS binaries: r-release (arm64): enpls_6.1.tgz, r-oldrel (arm64): enpls_6.1.tgz, r-release (x86_64): enpls_6.1.tgz, r-oldrel (x86_64): enpls_6.1.tgz
Old sources: enpls archive


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