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LassoBacktracking  

Modelling Interactions in High-Dimensional Data with Backtracking
View on CRAN: Click here


Download and install LassoBacktracking package within the R console
Install from CRAN:
install.packages("LassoBacktracking")

Install from Github:
library("remotes")
install_github("cran/LassoBacktracking")

Install by package version:
library("remotes")
install_version("LassoBacktracking", "1.1")



Attach the package and use:
library("LassoBacktracking")
Maintained by
Rajen Shah
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-04-14
Latest Update: 2022-12-08
Description:
Implementation of the algorithm introduced in Shah, R. D. (2016) . 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.
How to cite:
Rajen Shah (2016). LassoBacktracking: Modelling Interactions in High-Dimensional Data with Backtracking. R package version 1.1, https://cran.r-project.org/web/packages/LassoBacktracking. Accessed 10 Oct. 2026.
Previous versions and publish date:
0.1.1 (2016-04-14 14:49), 0.1.2 (2017-04-04 10:48), 1.0 (2022-10-20 16:07), (2026-07-09 08:08)
Other packages that cited LassoBacktracking R package
View LassoBacktracking citation profile
Other R packages that LassoBacktracking depends, imports, suggests or enhances
Complete documentation for LassoBacktracking
Functions, R codes and Examples using the LassoBacktracking R package
Some associated functions: LassoBT . cvLassoBT . predict.BT . 
Some associated R codes: LassoBT.R . RcppExports.R . aux_functions.R . cvBT.R . predict_BT.R .  Full LassoBacktracking package functions and examples
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