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Implementation of the algorithm introduced in Shah, R. D. (2016) <https://www.jmlr.org/papers/volume17/13-515/13-515.pdf>. Data with thousands of predictors can be handled. The algorithm performs sequential Lasso fits on design matrices containing increasing sets of candidate interactions. Previous fits are used to greatly speed up subsequent fits, so the algorithm is very efficient.
| Version: | 1.1 |
| Imports: | Matrix, parallel, Rcpp |
| LinkingTo: | Rcpp |
| Published: | 2022-12-08 |
| DOI: | 10.32614/CRAN.package.LassoBacktracking |
| Author: | Rajen Shah [aut, cre] |
| Maintainer: | Rajen Shah <r.shah at statslab.cam.ac.uk> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://www.jmlr.org/papers/volume17/13-515/13-515.pdf |
| NeedsCompilation: | yes |
| CRAN checks: | LassoBacktracking results |
| Reference manual: | LassoBacktracking.pdf |
| Package source: | LassoBacktracking_1.1.tar.gz |
| Windows binaries: | r-devel: LassoBacktracking_1.1.zip, r-release: LassoBacktracking_1.1.zip, r-oldrel: LassoBacktracking_1.1.zip |
| macOS binaries: | r-release (arm64): LassoBacktracking_1.1.tgz, r-oldrel (arm64): LassoBacktracking_1.1.tgz, r-release (x86_64): LassoBacktracking_1.1.tgz, r-oldrel (x86_64): LassoBacktracking_1.1.tgz |
| Old sources: | LassoBacktracking archive |
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These binaries (installable software) and packages are in development.
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