LassoBacktracking: Modelling Interactions in High-Dimensional Data with Backtracking

Implementation of the algorithm introduced in Shah, R. D. (2016) <http://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: 0.1.2
Imports: Matrix, parallel, Rcpp
LinkingTo: Rcpp
Published: 2017-04-04
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: www.jmlr.org/papers/volume17/13-515/13-515.pdf
NeedsCompilation: yes
CRAN checks: LassoBacktracking results

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Reference manual: LassoBacktracking.pdf
Package source: LassoBacktracking_0.1.2.tar.gz
Windows binaries: r-devel: LassoBacktracking_0.1.2.zip, r-release: LassoBacktracking_0.1.2.zip, r-oldrel: LassoBacktracking_0.1.2.zip
OS X El Capitan binaries: r-release: LassoBacktracking_0.1.2.tgz
OS X Mavericks binaries: r-oldrel: LassoBacktracking_0.1.2.tgz
Old sources: LassoBacktracking archive

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