  Penalized regression models such as the lasso have been extensively applied
to analyzing high-dimensional data sets. However, due to memory limitations,
existing R packages like glmnet and ncvreg are not capable of fitting
lasso-type models for ultrahigh-dimensional, multi-gigabyte data sets that are
increasingly seen in many areas such as genetics, genomics, biomedical imaging,
and high-frequency finance. In this research, we implement an R package called
biglasso that tackles this challenge. biglasso utilizes memory-mapped files to
store the massive data on the disk, only reading data into memory when
necessary during model fitting, and is thus able to handle out-of-core
computation seamlessly. Moreover, it's equipped with newly proposed, more
efficient feature screening rules, which substantially accelerate the
computation. Benchmarking experiments show that our biglasso package, as
compared to existing popular ones like glmnet, is much more memory- and
computation-efficient. We further analyze a 31 GB real data set on a laptop
with only 16 GB RAM to demonstrate the out-of-core computation capability of
biglasso in analyzing massive data sets that cannot be accommodated by existing
R packages.
