grpreg
Regularization Paths for Regression Models with Grouped Covariates
Efficient algorithms for fitting the regularization path of linear regression, GLM, and Cox regression models with grouped penalties. This includes group selection methods such as group lasso, group MCP, and group SCAD as well as bi-level selection methods such as the group exponential lasso, the composite MCP, and the group bridge. For more information, see Breheny and Huang (2009) doi:10.4310/sii.2009.v2.n3.a10, Huang, Breheny, and Ma (2012) doi:10.1214/12-sts392, Breheny and Huang (2015) doi:10.1007/s11222-013-9424-2, and Breheny (2015) doi:10.1111/biom.12300, or visit the package homepage https://pbreheny.github.io/grpreg/.
- Version3.5.0
- R versionunknown
- LicenseGPL-3
- Needs compilation?Yes
- grpreg citation info
- Last release09/03/2024
Documentation
Team
Patrick Breheny
Yaohui Zeng
Show author detailsRolesContributorRyan Kurth
Show author detailsRolesContributor
Insights
Last 30 days
This package has been downloaded 5,906 times in the last 30 days. Impressive! The kind of number that makes colleagues ask, 'How did you do it?' The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 301 times.
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Last 365 days
This package has been downloaded 67,181 times in the last 365 days. An impressive feat! Enough downloads to make even seasoned academics take note. The day with the most downloads was Sep 04, 2024 with 466 downloads.
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Dependencies
- Imports1 package
- Suggests4 packages
- Reverse Depends1 package
- Reverse Imports11 packages
- Reverse Suggests2 packages