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.

AuthorRajen Shah [aut, cre]
Date of publication2016-04-14 14:49:49
MaintainerRajen Shah <r.shah@statslab.cam.ac.uk>
LicenseGPL (>= 2)
Version0.1.1

View on CRAN

Files

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

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