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Linear and nonlinear regression methods based on Partial Least Squares and Penalization Techniques. Model parameters are selected via cross-validation, and confidence intervals ans tests for the regression coefficients can be conducted via jackknifing. The method is described and applied to simulated and experimental data in Kraemer et al. (2008) <doi:10.1016/j.chemolab.2008.06.009>.
| Version: | 2.0.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | splines, MASS |
| Published: | 2025-07-22 |
| DOI: | 10.32614/CRAN.package.ppls |
| Author: | Nicole Kraemer [aut], Anne-Laure Boulesteix [aut], Vincent Guillemot [cre, aut] |
| Maintainer: | Vincent Guillemot <vincent.guillemot at pasteur.fr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Citation: | ppls citation info |
| Materials: | README, NEWS |
| CRAN checks: | ppls results |
| Reference manual: | ppls.html , ppls.pdf |
| Package source: | ppls_2.0.0.tar.gz |
| Windows binaries: | r-devel: ppls_2.0.0.zip, r-release: ppls_2.0.0.zip, r-oldrel: ppls_2.0.0.zip |
| macOS binaries: | r-release (arm64): ppls_2.0.0.tgz, r-oldrel (arm64): ppls_2.0.0.tgz, r-release (x86_64): ppls_2.0.0.tgz, r-oldrel (x86_64): ppls_2.0.0.tgz |
| Old sources: | ppls archive |
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