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 482 articles , 1 standard article )

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  1. Genç, Murat; Özkale, M. Revan: Usage of the GO estimator in high dimensional linear models (2021)
  2. Guastavino, S.; Benvenuto, F.: A mathematical model for image saturation with an application to the restoration of solar images via adaptive sparse deconvolution (2021)
  3. Pietrosanu, Matthew; Gao, Jueyu; Kong, Linglong; Jiang, Bei; Niu, Di: Advanced algorithms for penalized quantile and composite quantile regression (2021)
  4. Suchit Mehrotra, Arnab Maity: Variational Inference for Shrinkage Priors: The R package vir (2021) arXiv
  5. Sun, Ruoyu; Ye, Yinyu: Worst-case complexity of cyclic coordinate descent: (O(n^2)) gap with randomized version (2021)
  6. Vishwakarma, Gajendra K.; Thomas, Abin; Bhattacharjee, Atanu: A weight function method for selection of proteins to predict an outcome using protein expression data (2021)
  7. Augugliaro, Luigi; Sottile, Gianluca; Vinciotti, Veronica: The conditional censored graphical Lasso estimator (2020)
  8. Bertsimas, Dimitris; Pauphilet, Jean; van Parys, Bart: Sparse regression: scalable algorithms and empirical performance (2020)
  9. Bertsimas, Dimitris; van Parys, Bart: Sparse high-dimensional regression: exact scalable algorithms and phase transitions (2020)
  10. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  11. Canhong Wen, Aijun Zhang, Shijie Quan, Xueqin Wang: BeSS: An R Package for Best Subset Selection in Linear, Logistic and Cox Proportional Hazards Models (2020) not zbMATH
  12. Cao, Xuan; Khare, Kshitij; Ghosh, Malay: High-dimensional posterior consistency for hierarchical non-local priors in regression (2020)
  13. Cerqueira, Vitor; Torgo, Luis; Mozetič, Igor: Evaluating time series forecasting models: an empirical study on performance estimation methods (2020)
  14. Chavez, Gordon V.: Dynamic tail inference with log-Laplace volatility (2020)
  15. Chen, Kedong; Li, William; Wang, Sijian: An easy-to-implement hierarchical standardization for variable selection under strong heredity constraint (2020)
  16. Chen, Yuansi; Taeb, Armeen; Bühlmann, Peter: A look at robustness and stability of (\ell_1)-versus (\ell_0)-regularization: discussion of papers by Bertsimas et al. and Hastie et al. (2020)
  17. Choiruddin, Achmad; Cuevas-Pacheco, Francisco; Coeurjolly, Jean-François; Waagepetersen, Rasmus: Regularized estimation for highly multivariate log Gaussian Cox processes (2020)
  18. Dai, Yutong; Weng, Yang: Synchronous parallel block coordinate descent method for nonsmooth convex function minimization (2020)
  19. Fan, Jianqing; Ke, Yuan; Wang, Kaizheng: Factor-adjusted regularized model selection (2020)
  20. Feng, Yang; Liu, Qingfeng; Okui, Ryo: On the sparsity of Mallows model averaging estimator (2020)

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