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A robust Partial Least-Squares (PLS) method is implemented that is robust to outliers in the residuals as well as to leverage points. A specific weighting scheme is applied which avoids iterations, and leads to a highly efficient robust PLS estimator.
| Version: | 0.6.0 |
| Imports: | pcaPP, robustbase |
| Published: | 2020-05-07 |
| Author: | Peter Filzmoser, Sukru Acitas, Birdal Senoglu and Maximilian Plattner |
| Maintainer: | Peter Filzmoser <peter.filzmoser at tuwien.ac.at> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| CRAN checks: | rpls results |
| Reference manual: | rpls.pdf |
| Package source: | rpls_0.6.0.tar.gz |
| Windows binaries: | r-devel: rpls_0.6.0.zip, r-release: rpls_0.6.0.zip, r-oldrel: rpls_0.6.0.zip |
| macOS binaries: | r-release (arm64): rpls_0.6.0.tgz, r-oldrel (arm64): rpls_0.6.0.tgz, r-release (x86_64): rpls_0.6.0.tgz, r-oldrel (x86_64): rpls_0.6.0.tgz |
| Old sources: | rpls 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.