guidedPLS-package {guidedPLS}

Supervised Dimensional Reduction by Guided Partial Least Squares

Description

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). For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/guidedPLS>.

Details

The DESCRIPTION file:

Package:	guidedPLS
Type:	Package
Title:	Supervised Dimensional Reduction by Guided Partial Least Squares
Version:	1.0.0
Authors@R:	c(person("Koki", "Tsuyuzaki", role = c("aut", "cre"), email = "[e-mail]"))
Depends:	R (>= 3.4.0)
Imports:	irlba
Suggests:	fields, knitr, rmarkdown, testthat
Description:	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). For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/guidedPLS>.
License:	MIT + file LICENSE
URL:	https://github.com/rikenbit/guidedPLS
VignetteBuilder:	knitr
Author:	Koki Tsuyuzaki [aut, cre]
Maintainer:	Koki Tsuyuzaki <[e-mail]>

Index of help topics:

dummyMatrix             Toy model data for using dNMF, dSVD, dsiNMF,
                        djNMF, dPLS, dNTF, and dNTD
guidedPLS               Guided Partial Least Squares (guied-PLS)
guidedPLS-package       Supervised Dimensional Reduction by Guided
                        Partial Least Squares
PLSSVD                  Partial Least Squares by Singular Value
                        Decomposition (PLS-SVD)
softThr                 Soft-thresholding to make a sparse vector
                        sparse
sPLSDA                  Sparse Partial Least Squares Discriminant
                        Analysis (sPLS-DA)
toyModel                Toy model data for using PLSSVD, sPLSDA, and
                        guidedPLS

Author(s)

NA

Maintainer: NA

References

Le Cao, et al. (2008). A Sparse PLS for Variable Selection when Integrating Omics Data. Statistical Applications in Genetics and Molecular Biology, 7(1)

Reese S E, et al. (2013). A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis. Bioinformatics, 29(22), 2877-2883

See Also

toyModel,PLSSVD,sPLSDA,guidedPLS

Examples

ls("package:guidedPLS")