Contains 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.
| Version: | 1.6-1.1 |
| Depends: | R (≥ 2.10), splines, MASS |
| Published: | 2018-07-20 |
| Author: | Nicole Kraemer Anne-Laure Boulesteix |
| Maintainer: | ORPHANED |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Citation: | ppls citation info |
| Materials: | ChangeLog |
| In views: | ChemPhys, Multivariate |
| CRAN checks: | ppls results |
| Reference manual: | ppls.pdf |
| Package source: | ppls_1.6-1.1.tar.gz |
| Windows binaries: | r-devel: ppls_1.6-1.1.zip, r-devel-gcc8: ppls_1.6-1.1.zip, r-release: ppls_1.6-1.1.zip, r-oldrel: ppls_1.6-1.1.zip |
| OS X binaries: | r-release: ppls_1.6-1.1.tgz, r-oldrel: ppls_1.6-1.1.tgz |
| Old sources: | ppls archive |
| Reverse depends: | parcor, SODC |
| Reverse imports: | clustRcompaR |
| Reverse suggests: | groc |
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