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>).">

gspcr: Generalized Supervised Principal Component Regression (original) (raw)

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 + file
NeedsCompilation: no
Materials: README
CRAN checks: gspcr results

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