Package: ppls 2.1.0

Vincent Guillemot

ppls: Penalized Partial Least Squares

Linear and nonlinear regression methods based on Partial Least Squares and Penalization Techniques. Model parameters are selected via cross-validation, and confidence intervals and 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>.

Authors:Nicole Kraemer [aut], Anne-Laure Boulesteix [aut], Vincent Guillemot [cre, aut]

ppls_2.1.0.tar.gz
ppls_2.1.0.zip(r-4.7-any)ppls_2.1.0.zip(r-4.6-any)ppls_2.1.0.zip(r-4.5-any)
ppls_2.1.0.tgz(r-4.6-any)ppls_2.1.0.tgz(r-4.5-any)
ppls_2.1.0.tar.gz(r-4.7-any)ppls_2.1.0.tar.gz(r-4.6-any)
ppls_2.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ppls/json (API)

# Install 'ppls' in R:
install.packages('ppls', repos = c('https://vguillemot.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/vguillemot/ppls/issues

Datasets:
  • cookie - Near-Infrared (NIR) Spectroscopy of Biscuit Doughs

On CRAN:

Conda:

3.33 score 43 scripts 253 downloads 14 exports 1 dependencies

Last updated from:f84d232c26. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK109
source / vignettesOK141
linux-releaseOK129
macos-releaseOK147
macos-oldrelOK337
windows-develOK85
windows-releaseOK61
windows-oldrelOK78
wasm-releaseOK78

Exports:graphic.ppls.splinesjack.pplsnew.penalized.plsnormalize.vectorpenalized.plspenalized.pls.cvpenalized.pls.defaultpenalized.pls.kernelpenalized.pls.selectPenalty.matrixppls.splines.cvsim.data.pplsttest.pplsX2s

Dependencies:MASS