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A sparse Partial Least Squares implementation which uses soft-threshold estimation of the covariance matrices and therein introduces sparsity. Number of components and regularization coefficients are automatically set.
| Version: | 1.2.1 |
| Depends: | foreach, R (≥ 2.10) |
| Imports: | Rcpp (≥ 1.0.5), doParallel, shiny, RColorBrewer |
| LinkingTo: | Rcpp, RcppEigen |
| Suggests: | knitr, rmarkdown, MASS |
| Published: | 2024-01-30 |
| Author: | Hadrien Lorenzo |
| Maintainer: | Hadrien Lorenzo <hadrien.lorenzo.2015 at gmail.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | yes |
| Citation: | ddsPLS citation info |
| Materials: | README |
| CRAN checks: | ddsPLS results |
| Reference manual: | ddsPLS.pdf |
| Vignettes: |
Data-Driven Sparse PLS (ddsPLS) |
| Package source: | ddsPLS_1.2.1.tar.gz |
| Windows binaries: | r-devel: ddsPLS_1.2.1.zip, r-release: ddsPLS_1.2.1.zip, r-oldrel: ddsPLS_1.2.1.zip |
| macOS binaries: | r-release (arm64): ddsPLS_1.2.1.tgz, r-oldrel (arm64): ddsPLS_1.2.1.tgz, r-release (x86_64): ddsPLS_1.2.1.tgz, r-oldrel (x86_64): ddsPLS_1.2.1.tgz |
| Old sources: | ddsPLS archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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