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-any)guidedPLS_1.2.1.zip(r-4.6-any)guidedPLS_1.2.1.zip(r-4.5-any)
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:

4.60 score 2 stars 2 scripts 357 downloads 7 exports 3 dependencies

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

TargetResultTimeFilesSyslog
linux-develOK185
source / vignettesOK351
linux-releaseOK234
macos-releaseOK177
macos-oldrelOK226
windows-develOK116
windows-releaseOK299
windows-oldrelOK94
wasm-releaseOK304

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