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gspcr: Generalized Supervised Principal Component Regression

Generalization of supervised principal component regression (SPCR; Bair et al., 2006, <doi:10.1198/016214505000000628>) to support continuous, binary, and discrete variables as outcomes and predictors (inspired by the 'superpc' R package <https://cran.r-project.org/package=superpc>).

Version:0.9.5
Depends:R (≥ 2.10)
Imports:dplyr,FactoMineR,ggplot2,MASS,MLmetrics,nnet,PCAmixdata,reshape2,rlang
Suggests:knitr,lmtest,patchwork,rmarkdown,superpc,testthat (≥3.0.0)
Published:2025-11-08
DOI:10.32614/CRAN.package.gspcr
Author:Edoardo CostantiniORCID iD [aut, cre]
Maintainer:Edoardo Costantini <costantini.edoardo at yahoo.com>
License:MIT + fileLICENSE
NeedsCompilation:no
Materials:README
CRAN checks:gspcr results

Documentation:

Reference manual:gspcr.html ,gspcr.pdf
Vignettes:Vignette 1: Example analysis with GSPCR (source)
Vignette 2: GSPCR specification options (source)
Vignette 3: Alternatives approaches (source)

Downloads:

Package source: gspcr_0.9.5.tar.gz
Windows binaries: r-devel:gspcr_0.9.5.zip, r-release:gspcr_0.9.5.zip, r-oldrel:gspcr_0.9.5.zip
macOS binaries: r-release (arm64):gspcr_0.9.5.tgz, r-oldrel (arm64):gspcr_0.9.5.tgz, r-release (x86_64):gspcr_0.9.5.tgz, r-oldrel (x86_64):gspcr_0.9.5.tgz
Old sources: gspcr archive

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=gspcrto link to this page.


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