Package: SCGLR 3.0.9000

Guillaume Cornu

SCGLR: Supervised Component Generalized Linear Regression

An extension of the Fisher Scoring Algorithm to combine PLS regression with GLM estimation in the multivariate context. Covariates can also be grouped in themes.

Authors:Guillaume Cornu [aut, cre], Frederic Mortier [aut], Catherine Trottier [aut], Xavier Bry [aut], Jocelyn Chauvet [aut], Sylvie Gourlet-Fleury [dtc], Claude Garcia [dtc]

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SCGLR.pdf |SCGLR.html✨
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NEWS

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

Peer review:

Bug tracker:https://github.com/scnext/scglr/issues

Datasets:
  • dataGen - Sample dataset of abundance of genera in tropical moist forest
  • genus - Sample dataset of abundance of genera in tropical moist forest
  • genus2 - Sample dataset of abundance of genera in tropical moist forest

On CRAN:

partial-least-squares-regression

4.00 score 1 stars 67 scripts 184 downloads 11 exports 37 dependencies

Last updated 16 days agofrom:76ee5c04bf. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 07 2024
R-4.5-winOKNov 07 2024
R-4.5-linuxOKNov 07 2024
R-4.4-winOKNov 07 2024
R-4.4-macOKNov 07 2024
R-4.3-winOKNov 07 2024
R-4.3-macOKNov 07 2024

Exports:critConvergenceinfoCriterionkCompRandmethodSRmultivariateFormulamultivariateGlm.fitmultivariatePredictGlmscglrscglrCrossValscglrThemescglrThemeBackward

Dependencies:ade4clicolorspacefansifarverFormulaggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpixmappkgconfigplsplyrpROCR6RColorBrewerRcppRcppArmadillorlangscalessptibbleutf8vctrsviridisLitewithr