Title: Scalable Least-Angle Regression and Lasso
Author: Mark Seligman, Chris Fraley, Tim Hesterberg
Description: Least-angle regression, lasso and stepwise regression for
numeric datasets in which the number of observations is greater
than the number of predictors.  The functions can be used with
the ff library to accomodate datasets that are too large to be

Title: Big Lasso: Extending Lasso Model Fitting to Big Data in R
Author: Yaohui Zeng [aut,cre], Patrick Breheny [ctb]
Description: Extend lasso and elastic-net model fitting for ultrahigh-dimensional, multi-gigabyte data sets that cannot be loaded into memory. Compared to existing lasso-fitting packages, it preserves equivalently fast computation speed but is much more memory-efficient, thus allowing for very powerful big data analysis even with only a single laptop.
Depends: bigmemory, Matrix, parallel, ncvreg

Description: 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
Depends: bigmemory (>= 4.0.0), Matrix, ncvreg

Title: Extending Lasso Model Fitting to Big Data in R
Suggests: parallel, knitr, testthat, R.rsp

Title: Extending Lasso Model Fitting to Big Data

Depends: R (>= 3.2.0), bigmemory (>= 4.5.0), Matrix, ncvreg

Author: Yaohui Zeng [aut,cre], Chuyi Wang [aut,cre], Patrick Breheny [ctb]
LinkingTo: Rcpp, RcppArmadillo (>= 0.8.600), bigmemory, BH
Suggests: parallel, testthat, R.rsp, glmnet, survival, knitr, rmarkdown

Author: Yaohui Zeng [aut], Chuyi Wang [aut], Patrick Breheny [aut]
Suggests: parallel, testthat, glmnet, survival, knitr, rmarkdown

person("Yaohui", "Zeng", role = c("aut")),
person("Chuyi", "Wang", role = c("aut")),
Description: Extend lasso and elastic-net model fitting for ultra
high-dimensional, multi-gigabyte data sets that cannot be loaded into
memory. Designed to be more memory- and computation-efficient than existing
lasso-fitting packages like 'glmnet' and 'ncvreg', thus allowing
the user to analyze big data analysis even on an ordinary laptop.
Patrick Breheny [aut, cre] (<https://orcid.org/0000-0002-0650-1119>)