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) . Many applications have been successfully realized. See < https://hadrienlorenzo.netlify.com/> for more information, documentation and examples.


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

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install.packages("ddsPLS")

1.0.91 by Hadrien Lorenzo, 21 days ago


https://hadrienlorenzo.netlify.com/


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


Authors: Hadrien Lorenzo [aut, cre] , Misbah Razzaq [ctb] , 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, corrplot, Rcpp

Suggests knitr, rmarkdown, htmltools

Linking to Rcpp


See at CRAN