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.

Install the latest version of this package by entering the following in R:
install.packages("LassoBacktracking")
AuthorRajen Shah [aut, cre]
Date of publication2017-04-04 08:48:43 UTC
MaintainerRajen Shah <r.shah@statslab.cam.ac.uk>
LicenseGPL (>= 2)
Version0.1.2
www.jmlr.org/papers/volume17/13-515/13-515.pdf

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Files

src
src/ExportedFunctions.cpp
src/LassoBacktracking_init.c
src/RcppExports.cpp
NAMESPACE
R
R/cvBT.R R/LassoBT.R R/RcppExports.R R/predict_BT.R R/aux_functions.R
MD5
DESCRIPTION
man
man/LassoBT.Rd man/predict.BT.Rd man/cvLassoBT.Rd

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