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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.

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

Documentation:

Reference manual: LassoBacktracking.pdf

Downloads:

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

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.