Extend lasso and elastic-net model fitting for ultrahigh-dimensional, multi-gigabyte data sets that cannot be loaded into memory. It's much more memory- and computation-efficient as compared to existing lasso-fitting packages like 'glmnet' and 'ncvreg', thus allowing for very powerful big data analysis even with an ordinary laptop.

Documentation

Manual: biglasso.pdf
Vignette: Tutorial

Maintainer: Yaohui Zeng <yaohui-zeng at uiowa.edu>

Author(s): Yaohui Zeng*, Patrick Breheny*

Install package and any missing dependencies by running this line in your R console:

install.packages("biglasso")

Depends R (>= 3.2.0), bigmemory(>=4.5.0), Matrix, ncvreg
Imports Rcpp(>=0.12.1), methods
Suggests parallel, testthat, R.rsp
Enhances
Linking to Rcpp, RcppArmadillo, bigmemory, BH
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Package biglasso
Materials
URL https://github.com/YaohuiZeng/biglasso https://arxiv.org/abs/1701.05936
Task Views MachineLearning
Version 1.3-3
Published 2017-01-26
License GPL-3
BugReports https://github.com/YaohuiZeng/biglasso/issues
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks biglasso check results
Package source biglasso_1.3-3.tar.gz