ddsPLS (1.1.1)

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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 for more information, documentation and examples.

Maintainer: Hadrien Lorenzo
Author(s): Hadrien Lorenzo [aut, cre], Misbah Razzaq [ctb], Jerome Saracco [aut], Rodolphe Thiebaut [aut]

License: MIT + file LICENSE

Uses: corrplot, doParallel, foreach, MASS, RColorBrewer, Rcpp, Rdpack, knitr, htmltools, rmarkdown

Released 4 months ago.

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