sparsegl: An R Package for Estimating Sparse Group Lasso
Dublin Core
Title
sparsegl: An R Package for Estimating Sparse Group Lasso
Subject
: generalized linear model, regularization, sequential strong rule, sparse matrix
Description
The sparse group lasso is a high-dimensional regression technique that is useful for
problems whose predictors have a naturally grouped structure and where sparsity is encouraged at both the group and individual predictor level. In this paper we discuss a new
R package for computing such regularized models. The intention is to provide highly optimized solution routines enabling analysis of very large datasets, especially in the context
of sparse design matrices.
problems whose predictors have a naturally grouped structure and where sparsity is encouraged at both the group and individual predictor level. In this paper we discuss a new
R package for computing such regularized models. The intention is to provide highly optimized solution routines enabling analysis of very large datasets, especially in the context
of sparse design matrices.
Creator
Xiaoxuan Liang
Source
https://www.jstatsoft.org/article/view/v110i06
Publisher
University of British Columbia
Date
August 2024
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Xiaoxuan Liang, “sparsegl: An R Package for Estimating Sparse Group Lasso,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8342.