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A method for fitting the entire regularization path of the principal components lasso for linear and logistic regression models. The algorithm uses cyclic coordinate descent in a path-wise fashion. See URL below for more information on the algorithm. See Tay, K., Friedman, J. ,Tibshirani, R., (2014) 'Principal component-guided sparse regression' <doi:10.48550/arXiv.1810.04651>.
| Version: | 1.2 |
| Imports: | svd |
| Suggests: | knitr, rmarkdown |
| Published: | 2020-09-03 |
| Author: | Jerome Friedman, Kenneth Tay, Robert Tibshirani |
| Maintainer: | Rob Tibshirani <tibs at stanford.edu> |
| License: | GPL-3 |
| URL: | https://arxiv.org/abs/1810.04651 |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | pcLasso results |
| Reference manual: | pcLasso.pdf |
| Vignettes: |
Introduction to pcLasso |
| Package source: | pcLasso_1.2.tar.gz |
| Windows binaries: | r-devel: pcLasso_1.2.zip, r-release: pcLasso_1.2.zip, r-oldrel: pcLasso_1.2.zip |
| macOS binaries: | r-release (arm64): pcLasso_1.2.tgz, r-oldrel (arm64): pcLasso_1.2.tgz, r-release (x86_64): pcLasso_1.2.tgz, r-oldrel (x86_64): pcLasso_1.2.tgz |
| Old sources: | pcLasso archive |
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