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.

Package details

AuthorHadrien Lorenzo [aut, cre], Misbah Razzaq [ctb], Jerome Saracco [aut], Rodolphe Thiebaut [aut]
MaintainerHadrien Lorenzo <>
LicenseMIT + file LICENSE
Package repositoryView on CRAN
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ddsPLS documentation built on March 2, 2020, 5:08 p.m.