Package: LassoBacktracking 1.1
LassoBacktracking: Modelling Interactions in High-Dimensional Data with Backtracking
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.
Authors:
LassoBacktracking_1.1.tar.gz
LassoBacktracking_1.1.zip(r-4.5)LassoBacktracking_1.1.zip(r-4.4)LassoBacktracking_1.1.zip(r-4.3)
LassoBacktracking_1.1.tgz(r-4.4-x86_64)LassoBacktracking_1.1.tgz(r-4.4-arm64)LassoBacktracking_1.1.tgz(r-4.3-x86_64)LassoBacktracking_1.1.tgz(r-4.3-arm64)
LassoBacktracking_1.1.tar.gz(r-4.5-noble)LassoBacktracking_1.1.tar.gz(r-4.4-noble)
LassoBacktracking_1.1.tgz(r-4.4-emscripten)LassoBacktracking_1.1.tgz(r-4.3-emscripten)
LassoBacktracking.pdf |LassoBacktracking.html✨
LassoBacktracking/json (API)
| # Install 'LassoBacktracking' in R: |
| install.packages('LassoBacktracking', repos = c('https://rajenshah.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:698fffffe4. 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 |
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Cross-validation for 'LassoBT' | cvLassoBT |
| Fit linear models with interactions using the Lasso. | LassoBT |
| Make predictions from a "'BT'" object. | coef.BT predict.BT |
