R package grpreg. Efficient algorithms for fitting the regularization path of linear or logistic 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.
Keywords for this software
References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Li, Xinyi; Wang, Li; Nettleton, Dan: Sparse model identification and learning for ultra-high-dimensional additive partially linear models (2019)
- Zhao, Weihua; Zhang, Fode; Wang, Xuejun; Li, Rui; Lian, Heng: Principal varying coefficient estimator for high-dimensional models (2019)
- Liu, Ruiping; Wang, Huiwen; Wang, Shanshan: Functional variable selection via Gram-Schmidt orthogonalization for multiple functional linear regression (2018)
- Honda, Toshio; Yabe, Ryota: Variable selection and structure identification for varying coefficient Cox models (2017)
- Tutz, Gerhard; Schmid, Matthias: Modeling discrete time-to-event data (2016)
- Hirose, Kei; Yamamoto, Michio: Estimation of an oblique structure via penalized likelihood factor analysis (2014)