Package: LassoBacktracking
Type: Package
Title: Modelling Interactions in High-Dimensional Data with
        Backtracking
Version: 1.1
Date: 2022-12-08
Authors@R: person("Rajen", "Shah", email = "r.shah@statslab.cam.ac.uk",
                  role = c("aut", "cre"))
Description: 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.
License: GPL (>= 2)
Imports: Matrix, parallel, Rcpp
LinkingTo: Rcpp
URL: https://www.jmlr.org/papers/volume17/13-515/13-515.pdf
Encoding: UTF-8
RoxygenNote: 7.2.1
NeedsCompilation: yes
Author: Rajen Shah [aut, cre]
Maintainer: Rajen Shah <r.shah@statslab.cam.ac.uk>
Packaged: 2022-12-08 15:15:07 UTC; thera
Repository: CRAN
Date/Publication: 2022-12-08 15:52:30 UTC
Built: R 4.6.0; aarch64-apple-darwin23; 2026-04-26 01:02:51 UTC; unix
Archs: LassoBacktracking.so.dSYM
