glmnet

R package glmnet: Lasso and elastic-net regularized generalized linear models. Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, poisson regression and the Cox model. Two recent additions are the multiresponse gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a pathwise fashion, as described in the paper listed below.


References in zbMATH (referenced in 505 articles , 1 standard article )

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  1. Bak, Kwan-Young; Jhong, Jae-Hwan; Lee, JungJun; Shin, Jae-Kyung; Koo, Ja-Yong: Penalized logspline density estimation using total variation penalty (2021)
  2. Chen, Ting-Huei; Chatterjee, Nilanjan; Landi, Maria Teresa; Shi, Jianxin: A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information (2021)
  3. Genç, Murat; Özkale, M. Revan: Usage of the GO estimator in high dimensional linear models (2021)
  4. Guastavino, S.; Benvenuto, F.: A mathematical model for image saturation with an application to the restoration of solar images via adaptive sparse deconvolution (2021)
  5. Guha, Sharmistha; Rodriguez, Abel: Bayesian regression with undirected network predictors with an application to brain connectome data (2021)
  6. Huang, Shih-Ting; Xie, Fang; Lederer, Johannes: Tuning-free ridge estimators for high-dimensional generalized linear models (2021)
  7. Jakob A. Dambon, Fabio Sigrist, Reinhard Furrer: varycoef: An R Package for Gaussian Process-based Spatially Varying Coefficient Models (2021) arXiv
  8. Lahiri, Soumendra N.: Necessary and sufficient conditions for variable selection consistency of the LASSO in high dimensions (2021)
  9. Ma, Shaohui; Fildes, Robert: Retail sales forecasting with meta-learning (2021)
  10. Mishra, Aditya; Dey, Dipak K.; Chen, Yong; Chen, Kun: Generalized co-sparse factor regression (2021)
  11. Park, Gunwoong; Moon, Sang Jun; Park, Sion; Jeon, Jong-June: Learning a high-dimensional linear structural equation model via (\ell_1)-regularized regression (2021)
  12. Pietrosanu, Matthew; Gao, Jueyu; Kong, Linglong; Jiang, Bei; Niu, Di: Advanced algorithms for penalized quantile and composite quantile regression (2021)
  13. Suchit Mehrotra, Arnab Maity: Variational Inference for Shrinkage Priors: The R package vir (2021) arXiv
  14. Sun, Ruoyu; Ye, Yinyu: Worst-case complexity of cyclic coordinate descent: (O(n^2)) gap with randomized version (2021)
  15. Van Belle, Jente; Guns, Tias; Verbeke, Wouter: Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains (2021)
  16. Vishwakarma, Gajendra K.; Thomas, Abin; Bhattacharjee, Atanu: A weight function method for selection of proteins to predict an outcome using protein expression data (2021)
  17. Wang, Rui; Xiu, Naihua; Zhou, Shenglong: An extended Newton-type algorithm for (\ell_2)-regularized sparse logistic regression and its efficiency for classifying large-scale datasets (2021)
  18. Wang, Yihe; Zhao, Sihai Dave: A nonparametric empirical Bayes approach to large-scale multivariate regression (2021)
  19. Yan, Xiaohan; Bien, Jacob: Rare feature selection in high dimensions (2021)
  20. Zeng, Yaohui; Yang, Tianbao; Breheny, Patrick: Hybrid safe-strong rules for efficient optimization in Lasso-type problems (2021)

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