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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 [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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.