ddsPLS: Data-Driven Sparse PLS Robust to Missing Samples for Mono and Multi-Block Data Sets

Allows to build Multi-Data-Driven Sparse PLS models. Multi-blocks with high-dimensional settings are particularly sensible to this.

Version: 1.0.61
Depends: R (≥ 2.10)
Imports: RColorBrewer, MASS, graphics, stats, Rdpack, doParallel, foreach, parallel
Suggests: knitr, rmarkdown
Published: 2019-01-21
Author: Hadrien Lorenzo [aut, cre], Jerome Saracco [aut], Rodolphe Thiebaut [aut]
Maintainer: Hadrien Lorenzo <hadrien.lorenzo.2015 at>
License: MIT + file LICENSE
NeedsCompilation: no
Citation: ddsPLS citation info
Materials: README
In views: MissingData
CRAN checks: ddsPLS results


Reference manual: ddsPLS.pdf
Vignettes: Mono and Multi-block Data-Driven sparse PLS (mdd-sPLS)
Package source: ddsPLS_1.0.61.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: ddsPLS_1.0.61.tgz, r-oldrel: ddsPLS_1.0.61.tgz
Old sources: ddsPLS archive


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