R package biglasso. Extending Lasso Model Fitting to Big Data. 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.
Keywords for this software
References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Zeng, Yaohui; Yang, Tianbao; Breheny, Patrick: Hybrid safe-strong rules for efficient optimization in Lasso-type problems (2021)
- Angela Bitto-Nemling, Annalisa Cadonna, Sylvia Frühwirth-Schnatter, Peter Knaus: Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP (2019) arXiv
- De Micheaux, Pierre Lafaye; Liquet, Benoît; Sutton, Matthew: PLS for Big Data: a unified parallel algorithm for regularised group PLS (2019)
- Ndiaye, Eugene; Fercoq, Olivier; Gramfort, Alexandre; Salmon, Joseph: Gap safe screening rules for sparsity enforcing penalties (2017)
- Yaohui Zeng, Patrick Breheny: The biglasso Package: A Memory- and Computation-Efficient Solver for Lasso Model Fitting with Big Data in R (2017) arXiv