Package: guidedPLS 1.2.1

Koki Tsuyuzaki

guidedPLS: Supervised Dimensional Reduction by Guided Partial Least Squares

Guided partial least squares (guided-PLS) is the combination of partial least squares by singular value decomposition (PLS-SVD) and guided principal component analysis (guided-PCA). This package provides implementations of PLS-SVD, guided-PLS, and guided-PCA for supervised dimensionality reduction. The guided-PCA function (new in v1.1.0) automatically handles mixed data types (continuous and categorical) in the supervision matrix and provides detailed contribution analysis for interpretability. For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/guidedPLS>.

Authors:Koki Tsuyuzaki [aut, cre]

guidedPLS_1.2.1.tar.gz
guidedPLS_1.2.1.zip(r-4.7)guidedPLS_1.2.1.zip(r-4.6)guidedPLS_1.2.1.zip(r-4.5)
guidedPLS_1.2.1.tgz(r-4.6-any)guidedPLS_1.2.1.tgz(r-4.5-any)
guidedPLS_1.2.1.tar.gz(r-4.7-any)guidedPLS_1.2.1.tar.gz(r-4.6-any)
guidedPLS_1.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
guidedPLS/json (API)

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

Bug tracker:https://github.com/rikenbit/guidedpls/issues

On CRAN:

Conda:

4.48 score 1 stars 2 scripts 515 downloads 7 exports 3 dependencies

Last updated from:ab5a14ebba. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK169
source / vignettesOK337
linux-release-x86_64OK214
macos-release-arm64OK91
macos-oldrel-arm64OK88
windows-develOK110
windows-releaseOK122
windows-oldrelOK125
wasm-releaseOK160

Exports:dummyMatrixguidedPCAguidedPLSPLSSVDsoftThrsPLSDAtoyModel

Dependencies:irlbalatticeMatrix

Guided Partial Least Squares (guided-PLS)
Introduction | Guided Partial Least Squares (guided-PLS) | Basic Usage | Session Information | References
Rendered fromguidedPLS-2.Rmdusingknitr::rmarkdown

Last update: 2023-03-22
Started: 2023-03-22

Partial Least Squares (PLS) Models
Introduction | Partial Least Squares by Singular Value Decomposition (PLS-SVD) | Basic Usage | Partial Least Squares Discriminant Analysis (PLS-DA) | Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) | Comparison with unsupervised learning | Session Information | References
Rendered fromguidedPLS-1.Rmdusingknitr::rmarkdown

Last update: 2023-03-22
Started: 2023-03-22