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ddsPLS: Data-Driven Sparse Partial Least Squares
Allows to build Data-Driven Sparse Partial Least Squares models with high-dimensional settings. Number of components and regularization coefficients are automatically set. It comes with visualization functions and uses 'Rcpp' functions for fast computations and 'doParallel' to parallelize bootstrap operations. An applet has been developed to apply this procedure. This is based on H Lorenzo, O Cloarec, R Thiebaut, J Saracco (2021) <doi:10.1002/sam.11558>.
Getting started
Browse package contents
Package details |
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| Author | Hadrien Lorenzo [aut, cre], Misbah Razzaq [ctb], Olivier Cloarec [aut], Jerome Saracco [aut] |
| Maintainer | Hadrien Lorenzo <hadrien.lorenzo.2015@gmail.com> |
| License | MIT + file LICENSE |
| Version | 1.2.0 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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