The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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  [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 |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=enpls
to link to this page.
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.
Health stats visible at Monitor.