sparsegl: An R Package for Estimating Sparse Group Lasso
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 regularize...
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| Format: | Article |
| Language: | English |
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Foundation for Open Access Statistics
2024-08-01
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| Series: | Journal of Statistical Software |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/4897 |
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| _version_ | 1846101676997476352 |
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| author | Xiaoxuan Liang Aaron Cohen Anibal Sólon Heinsfeld Franco Pestilli Daniel J. McDonald |
| author_facet | Xiaoxuan Liang Aaron Cohen Anibal Sólon Heinsfeld Franco Pestilli Daniel J. McDonald |
| author_sort | Xiaoxuan Liang |
| collection | DOAJ |
| 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.
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| format | Article |
| id | doaj-art-182f3acde8874439aaf7bec3184701e7 |
| institution | Kabale University |
| issn | 1548-7660 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj-art-182f3acde8874439aaf7bec3184701e72024-12-29T00:12:42ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602024-08-01110110.18637/jss.v110.i06sparsegl: An R Package for Estimating Sparse Group LassoXiaoxuan Liang0Aaron Cohen1Anibal Sólon Heinsfeld2Franco Pestilli3Daniel J. McDonald4University of British ColumbiaIndiana UniversityThe University of Texas at AustinThe University of Texas at AustinUniversity of British Columbia 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. https://www.jstatsoft.org/index.php/jss/article/view/4897 |
| spellingShingle | Xiaoxuan Liang Aaron Cohen Anibal Sólon Heinsfeld Franco Pestilli Daniel J. McDonald sparsegl: An R Package for Estimating Sparse Group Lasso Journal of Statistical Software |
| title | sparsegl: An R Package for Estimating Sparse Group Lasso |
| title_full | sparsegl: An R Package for Estimating Sparse Group Lasso |
| title_fullStr | sparsegl: An R Package for Estimating Sparse Group Lasso |
| title_full_unstemmed | sparsegl: An R Package for Estimating Sparse Group Lasso |
| title_short | sparsegl: An R Package for Estimating Sparse Group Lasso |
| title_sort | sparsegl an r package for estimating sparse group lasso |
| url | https://www.jstatsoft.org/index.php/jss/article/view/4897 |
| work_keys_str_mv | AT xiaoxuanliang sparseglanrpackageforestimatingsparsegrouplasso AT aaroncohen sparseglanrpackageforestimatingsparsegrouplasso AT anibalsolonheinsfeld sparseglanrpackageforestimatingsparsegrouplasso AT francopestilli sparseglanrpackageforestimatingsparsegrouplasso AT danieljmcdonald sparseglanrpackageforestimatingsparsegrouplasso |