enpls

enpls-package

<img src="figures/logo.png?package=enpls&version=6.1.1" style="float: right" alt="logo" width="120">
An algorithmic framework for measuring feature importance, outlier detection, model applicability domain evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions.

internal

An algorithmic framework for measuring feature importance,
outlier detection, model applicability domain evaluation,
and ensemble predictive modeling with (sparse)
partial least squares regressions.

Nan Xiao

Ensemble Partial Least Squares Regression

Dong-Sheng Cao

Miao-Zhu Li

Qing-Song Xu

enpls-package function

Maintainer: Nan Xiao <a href="/link/me%40nanx.me?package=enpls&version=6.1.1" data-mini-rdoc="enpls::me@nanx.me">me@nanx.me</a> (<a href="https://orcid.org/0000-0002-0250-5673">ORCID</a>)
Authors:<ul>
<li>Dong-Sheng Cao</li>
<li>Miao-Zhu Li <a href="/link/miaozhu.li%40duke.edu?package=enpls&version=6.1.1" data-mini-rdoc="enpls::miaozhu.li@duke.edu">miaozhu.li@duke.edu</a></li>
<li>Qing-Song Xu <a href="/link/qsxu%40csu.edu.cn?package=enpls&version=6.1.1" data-mini-rdoc="enpls::qsxu@csu.edu.cn">qsxu@csu.edu.cn</a></li>
</ul>

Author

<img src='figures/logo.png' style='float: right' alt='logo' width='120' />
An algorithmic framework for measuring feature importance, outlier detection, model applicability domain evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions.

enpls: Ensemble Partial Least Squares Regression — enpls-package

Maintainer: Nan Xiao <a href='mailto:me@nanx.me'>me@nanx.me</a> (<a href='https://orcid.org/0000-0002-0250-5673'>ORCID</a>)
Authors:<ul>
<li>Dong-Sheng Cao</li>
<li>Miao-Zhu Li <a href='mailto:miaozhu.li@duke.edu'>miaozhu.li@duke.edu</a></li>
<li>Qing-Song Xu <a href='mailto:qsxu@csu.edu.cn'>qsxu@csu.edu.cn</a></li>
</ul>

enpls-package: enpls: Ensemble Partial Least Squares Regression

Description

Arguments

Author

See Also