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.


A sparse PLS formulation for mono and multi-block data sets with missing samples

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Reference manual

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install.packages("ddsPLS")

1.0.61 by Hadrien Lorenzo, 4 months ago


Browse source code at https://github.com/cran/ddsPLS


Authors: Hadrien Lorenzo [aut, cre] , Jerome Saracco [aut] , Rodolphe Thiebaut [aut]


Documentation:   PDF Manual  


Task views: Missing Data


MIT + file LICENSE license


Imports RColorBrewer, MASS, graphics, stats, Rdpack, doParallel, foreach, parallel

Suggests knitr, rmarkdown


See at CRAN