[aut,
cre],
Dong-Sheng Cao [aut],
Miao-Zhu Li [aut],
Qing-Song Xu [aut]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 |
| 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: enpls_6.1.tgz, r-oldrel: enpls_6.1.tgz |
| Old sources: | enpls archive |
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