R package elasticnet: Elastic-Net for Sparse Estimation and Sparse PCA. This package provides functions for fitting the entire solution path of the Elastic-Net and also provides functions for estimating sparse Principal Components. The Lasso solution paths can be computed by the same function. First version: 2005-10.
Keywords for this software
References in zbMATH (referenced in 12 articles )
Showing results 1 to 12 of 12.
- Kim, Sun Hye; Boukouvala, Fani: Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques (2020)
- Epifanio, Irene; Ibáñez, María Victoria; Simó, Amelia: Archetypal shapes based on landmarks and extension to handle missing data (2018)
- Mair, Patrick: Modern psychometrics with R (2018)
- Toh, Kar-Ann; Lin, Zhiping; Sun, Lei; Li, Zhengguo: Stretchy binary classification (2018)
- Ma, Shaohui; Fildes, Robert; Huang, Tao: Demand forecasting with high dimensional data: the case of SKU retail sales forecasting with intra- and inter-category promotional information (2016)
- Shi, Jianhong; Song, Weixing: Sparse principal component analysis with measurement errors (2016)
- Shah, Jasmit; Datta, Somnath; Datta, Susmita: A multi-loss super regression learner (MSRL) with application to survival prediction using proteomics (2014)
- Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
- Jerome Friedman; Trevor Hastie; Rob Tibshirani: Regularization Paths for Generalized Linear Models via Coordinate Descent (2010) not zbMATH
- Schifano, Elizabeth D.; Strawderman, Robert L.; Wells, Martin T.: Majorization-minimization algorithms for nonsmoothly penalized objective functions (2010)
- Hesterberg, Tim; Choi, Nam Hee; Meier, Lukas; Fraley, Chris: Least angle and (\ell_1) penalized regression: a review (2008)
- Max Kuhn: Building Predictive Models in R Using the caret Package (2008) not zbMATH