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 gmail.com> |
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: ddsPLS_1.2.0.zip, r-release: ddsPLS_1.2.0.zip, r-oldrel: ddsPLS_1.2.0.zip |
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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