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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: | 2024-04-12 |
DOI: | 10.32614/CRAN.package.gspcr |
Author: | Edoardo Costantini
|
Maintainer: | Edoardo Costantini <costantini.edoardo at yahoo.com> |
License: | MIT + file LICENSE |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | gspcr results |
Reference manual: | gspcr.pdf |
Vignettes: |
Vignette 1: Example analysis with GSPCR Vignette 2: GSPCR specification options Vignette 3: Alternatives approaches |
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 |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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