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ddsPLS  

Data-Driven Sparse Partial Least Squares
View on CRAN: Click here


Download and install ddsPLS package within the R console
Install from CRAN:
install.packages("ddsPLS")

Install from Github:
library("remotes")
install_github("cran/ddsPLS")

Install by package version:
library("remotes")
install_version("ddsPLS", "1.2.1")



Attach the package and use:
library("ddsPLS")
Maintained by
Hadrien Lorenzo
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-09-19
Latest Update: 2024-01-30
Description:
A sparse Partial Least Squares implementation which uses soft-threshold estimation of the covariance matrices and therein introduces sparsity. Number of components and regularization coefficients are automatically set.
How to cite:
Hadrien Lorenzo (2018). ddsPLS: Data-Driven Sparse Partial Least Squares. R package version 1.2.1, https://cran.r-project.org/web/packages/ddsPLS. Accessed 29 Jun. 2026.
Previous versions and publish date:
1.0.0 (2018-09-19 17:10), 1.0.1 (2018-10-12 09:40), 1.0.2 (2018-11-06 15:40), 1.0.3 (2018-11-09 15:20), 1.0.4 (2018-11-29 14:00), 1.0.5 (2018-12-05 10:40), 1.0.51 (2019-01-03 11:00), 1.0.52 (2019-01-03 17:10), 1.0.53 (2019-01-08 10:50), 1.0.61 (2019-01-21 11:20), 1.0.91 (2019-08-28 16:00), 1.1.1 (2019-09-26 17:30), 1.1.4 (2020-03-02 12:20), 1.2.0 (2023-05-15 10:20)
Other packages that cited ddsPLS R package
View ddsPLS citation profile
Other R packages that ddsPLS depends, imports, suggests or enhances
Complete documentation for ddsPLS
Functions, R codes and Examples using the ddsPLS R package
Some associated functions: bootstrapWrap . bootstrap_Rcpp . ddsPLS . ddsPLS_App . modelddsPLSCpp_Rcpp . plot.ddsPLS . predict.ddsPLS . print.ddsPLS . summary.ddsPLS . 
Some associated R codes: RcppExports.R . app.R . ddspls.R . plot.ddsPLS.R . predict.ddsPLS.R . summary.ddsPLS.R .  Full ddsPLS package functions and examples
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