Package: ddsPLS 1.2.1
ddsPLS: Data-Driven Sparse Partial Least Squares
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.
Authors:
ddsPLS_1.2.1.tar.gz
ddsPLS_1.2.1.zip(r-4.5)ddsPLS_1.2.1.zip(r-4.4)ddsPLS_1.2.1.zip(r-4.3)
ddsPLS_1.2.1.tgz(r-4.4-x86_64)ddsPLS_1.2.1.tgz(r-4.4-arm64)ddsPLS_1.2.1.tgz(r-4.3-x86_64)ddsPLS_1.2.1.tgz(r-4.3-arm64)
ddsPLS_1.2.1.tar.gz(r-4.5-noble)ddsPLS_1.2.1.tar.gz(r-4.4-noble)
ddsPLS_1.2.1.tgz(r-4.4-emscripten)ddsPLS_1.2.1.tgz(r-4.3-emscripten)
ddsPLS.pdf |ddsPLS.html✨
ddsPLS/json (API)
# Install 'ddsPLS' in R: |
install.packages('ddsPLS', repos = c('https://hlorenzo.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/hlorenzo/ddspls/issues
missing-datamulti-blockplssupervised-learningsvdvariable-selection
Last updated 10 months agofrom:3a57108fa0. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 27 2024 |
R-4.5-win-x86_64 | OK | Oct 27 2024 |
R-4.5-linux-x86_64 | OK | Oct 27 2024 |
R-4.4-win-x86_64 | OK | Oct 27 2024 |
R-4.4-mac-x86_64 | OK | Oct 27 2024 |
R-4.4-mac-aarch64 | OK | Oct 27 2024 |
R-4.3-win-x86_64 | OK | Oct 27 2024 |
R-4.3-mac-x86_64 | OK | Oct 27 2024 |
R-4.3-mac-aarch64 | OK | Oct 27 2024 |
Exports:ddsPLSddsPLS_App
Dependencies:base64encbslibcachemclicodetoolscommonmarkcrayondigestdoParallelfastmapfontawesomeforeachfsgluehtmltoolshttpuviteratorsjquerylibjsonlitelaterlifecyclemagrittrmemoisemimepromisesR6rappdirsRColorBrewerRcppRcppEigenrlangsassshinysourcetoolswithrxtable
Readme and manuals
Help Manual
Help page | Topics |
---|---|
C++ implementation of the bootstrap operations | bootstrap_Rcpp |
C++ wrapper for bootstrap function | bootstrapWrap |
Data-Driven Sparse Partial Least Squares | ddsPLS |
Applet to start ddsPLS | ddsPLS_App |
C++ code to build models, internal function | modelddsPLSCpp_Rcpp |
Function to plot bootstrap performance results of the ddsPLS algorithm | plot.ddsPLS |
Function to predict from ddsPLS objects | predict.ddsPLS |
Function to sum up bootstrap performance results of the ddsPLS algorithm | print.ddsPLS |
Function to sum up bootstrap performance results of the ddsPLS algorithm | summary.ddsPLS |