LassoBacktracking: Modelling Interactions in High-Dimensional Data with Backtracking

Implementation of the algorithm introduced in "Shah, R. D. (2016) Modelling interactions in high-dimensional data with Backtracking, JMLR, to appear". Data with thousands of predictors can be handled. The algorithm performs sequential Lasso (Tibshirani, 1996) 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: 0.1.1
Imports: Matrix, parallel, Rcpp
LinkingTo: Rcpp
Published: 2016-04-14
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_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Mavericks binaries: r-release: LassoBacktracking_0.1.1.tgz, r-oldrel: LassoBacktracking_0.1.1.tgz


Please use the canonical form to link to this page.