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 <> for more information, documentation and examples.

Version: 1.1.4
Depends: R (≥ 2.10)
Imports: RColorBrewer, MASS, graphics, stats, Rdpack, doParallel, foreach, parallel, corrplot, Rcpp (≥ 0.12.18)
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, htmltools
Published: 2020-03-02
Author: Hadrien Lorenzo [aut, cre], Misbah Razzaq [ctb], Jerome Saracco [aut], Rodolphe Thiebaut [aut]
Maintainer: Hadrien Lorenzo <hadrien.lorenzo.2015 at>
License: MIT + file LICENSE
NeedsCompilation: yes
Citation: ddsPLS citation info
Materials: README
In views: MissingData
CRAN checks: ddsPLS results


Reference manual: ddsPLS.pdf
Vignettes: ddsPLS : A Package to Deal with Multi-Block Supervised Problems with Missing Samples in High Dimension
Package source: ddsPLS_1.1.4.tar.gz
Windows binaries: r-prerelease:, r-release:, r-oldrel:
macOS binaries: r-prerelease: ddsPLS_1.1.4.tgz, r-release: ddsPLS_1.1.4.tgz, r-oldrel: ddsPLS_1.1.4.tgz
Old sources: ddsPLS archive


Please use the canonical form to link to this page.