LassoBacktracking: Modelling Interactions in High-Dimensional Data with Backtracking

Implementation of the algorithm introduced in Shah, R. D. (2016) <>. 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>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: LassoBacktracking results


Reference manual: LassoBacktracking.pdf


Package source: LassoBacktracking_1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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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