Package: plsdof 0.3-2
plsdof: Degrees of Freedom and Statistical Inference for Partial Least Squares Regression
The plsdof package provides Degrees of Freedom estimates for Partial Least Squares (PLS) Regression. Model selection for PLS is based on various information criteria (aic, bic, gmdl) or on cross-validation. Estimates for the mean and covariance of the PLS regression coefficients are available. They allow the construction of approximate confidence intervals and the application of test procedures (Kramer and Sugiyama 2012 <doi:10.1198/jasa.2011.tm10107>). Further, cross-validation procedures for Ridge Regression and Principal Components Regression are available.
Authors:
plsdof_0.3-2.tar.gz
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plsdof_0.3-2.tgz(r-4.4-any)plsdof_0.3-2.tgz(r-4.3-any)
plsdof_0.3-2.tar.gz(r-4.5-noble)plsdof_0.3-2.tar.gz(r-4.4-noble)
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plsdof.pdf |plsdof.html✨
plsdof/json (API)
NEWS
| # Install 'plsdof' in R: |
| install.packages('plsdof', repos = c('https://fbertran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/fbertran/plsdof/issues
Last updated 2 years agofrom:c46661633e. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 18 2024 |
| R-4.5-win | OK | Nov 18 2024 |
| R-4.5-linux | OK | Nov 18 2024 |
| R-4.4-win | OK | Nov 18 2024 |
| R-4.4-mac | OK | Nov 18 2024 |
| R-4.3-win | OK | Nov 18 2024 |
| R-4.3-mac | OK | Nov 18 2024 |
Exports:benchmark.plsbenchmark.regressioncompute.lower.bounddAdnormalizedvvtzfirst.local.minimuminformation.criteriakernel.pls.fitkrylovlinear.pls.fitnormalizepcrpcr.cvpls.cvpls.dofpls.icpls.modelridge.cvtrvvtz
Dependencies:MASS
