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>.

Version: 1.2.0
Depends: foreach, doParallel, shiny
Imports: Rcpp (≥ 1.0.5)
LinkingTo: Rcpp, RcppEigen
Suggests: knitr, rmarkdown
Published: 2023-05-15
Author: Hadrien Lorenzo [aut, cre], Misbah Razzaq [ctb], Olivier Cloarec [aut], Jerome Saracco [aut]
Maintainer: Hadrien Lorenzo <hadrien.lorenzo.2015 at>
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: README
CRAN checks: ddsPLS results


Reference manual: ddsPLS.pdf
Vignettes: Data-Driven Sparse PLS 2 (ddsPLS)


Package source: ddsPLS_1.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): ddsPLS_1.2.0.tgz, r-oldrel (arm64): ddsPLS_1.2.0.tgz, r-release (x86_64): ddsPLS_1.2.0.tgz, r-oldrel (x86_64): not available
Old sources: ddsPLS archive


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