Package: LassoBacktracking 1.1

LassoBacktracking: Modelling Interactions in High-Dimensional Data with Backtracking

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.

Authors:Rajen Shah [aut, cre]

LassoBacktracking_1.1.tar.gz
LassoBacktracking_1.1.zip(r-4.7)LassoBacktracking_1.1.zip(r-4.6)LassoBacktracking_1.1.zip(r-4.5)
LassoBacktracking_1.1.tgz(r-4.6-x86_64)LassoBacktracking_1.1.tgz(r-4.6-arm64)LassoBacktracking_1.1.tgz(r-4.5-x86_64)LassoBacktracking_1.1.tgz(r-4.5-arm64)
LassoBacktracking_1.1.tar.gz(r-4.7-arm64)LassoBacktracking_1.1.tar.gz(r-4.7-x86_64)LassoBacktracking_1.1.tar.gz(r-4.6-arm64)LassoBacktracking_1.1.tar.gz(r-4.6-x86_64)
LassoBacktracking_1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
LassoBacktracking/json (API)

# Install 'LassoBacktracking' in R:
install.packages('LassoBacktracking', repos = c('https://rajenshah.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

1.00 score 1 stars 3 scripts 222 downloads 2 exports 3 dependencies

Last updated from:698fffffe4. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK163
linux-devel-x86_64OK142
source / vignettesOK166
linux-release-arm64OK160
linux-release-x86_64OK138
macos-release-arm64OK153
macos-release-x86_64OK218
macos-oldrel-arm64OK169
macos-oldrel-x86_64OK288
windows-develOK130
windows-releaseOK94
windows-oldrelOK135
wasm-releaseOK114

Exports:cvLassoBTLassoBT

Dependencies:latticeMatrixRcpp