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]

SCGLR_3.0.9000.tar.gz
SCGLR_3.0.9000.zip(r-4.5)SCGLR_3.0.9000.zip(r-4.4)SCGLR_3.0.9000.zip(r-4.3)
SCGLR_3.0.9000.tgz(r-4.4-any)SCGLR_3.0.9000.tgz(r-4.3-any)
SCGLR_3.0.9000.tar.gz(r-4.5-noble)SCGLR_3.0.9000.tar.gz(r-4.4-noble)
SCGLR_3.0.9000.tgz(r-4.4-emscripten)SCGLR_3.0.9000.tgz(r-4.3-emscripten)
SCGLR.pdf |SCGLR.html✨
SCGLR/json (API)
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

11 exports 1 stars 1.02 score 37 dependencies 67 scripts 214 downloads

Last updated 5 months agofrom:5d8883ba80. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 13 2024
R-4.5-winOKSep 13 2024
R-4.5-linuxOKSep 13 2024
R-4.4-winOKSep 13 2024
R-4.4-macOKSep 13 2024
R-4.3-winOKSep 13 2024
R-4.3-macOKSep 13 2024

Exports:critConvergenceinfoCriterionkCompRandmethodSRmultivariateFormulamultivariateGlm.fitmultivariatePredictGlmscglrscglrCrossValscglrThemescglrThemeBackward

Dependencies:ade4clicolorspacefansifarverFormulaggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpixmappkgconfigplsplyrpROCR6RColorBrewerRcppRcppArmadillorlangscalessptibbleutf8vctrsviridisLitewithr