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

Allows to build Multi-Data-Driven Sparse Partial Least Squares models. Multi-blocks with high-dimensional settings are particularly sensible to this. It comes with visualization functions and uses 'Rcpp' functions for fast computations and 'doParallel' to parallelize cross-validation. This is based on H Lorenzo, J Saracco, R Thiebaut (2019) <arXiv:1901.04380>. Many applications have been successfully realized. See <https://hadrienlorenzo.netlify.com/> for more information, documentation and examples.

Version: 1.0.91
Depends: R (≥ 3.5.0)
Imports: RColorBrewer, MASS, graphics, stats, Rdpack, doParallel, foreach, parallel, corrplot, Rcpp (≥ 0.12.18)
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, htmltools
Published: 2019-08-28
Author: Hadrien Lorenzo [aut, cre], Misbah Razzaq [ctb], Jerome Saracco [aut], Rodolphe Thiebaut [aut]
Maintainer: Hadrien Lorenzo <hadrien.lorenzo.2015 at gmail.com>
License: MIT + file LICENSE
URL: https://hadrienlorenzo.netlify.com/
NeedsCompilation: yes
Citation: ddsPLS citation info
Materials: README
In views: MissingData
CRAN checks: ddsPLS results

Downloads:

Reference manual: ddsPLS.pdf
Vignettes: Mono and Multi-block Data-Driven sparse PLS (mdd-sPLS)
Package source: ddsPLS_1.0.91.tar.gz
Windows binaries: r-devel: ddsPLS_1.0.91.zip, r-release: ddsPLS_1.0.91.zip, r-oldrel: ddsPLS_1.0.91.zip
OS X binaries: r-release: ddsPLS_1.0.91.tgz, r-oldrel: ddsPLS_1.0.91.tgz
Old sources: ddsPLS archive

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