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