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 422 articles , 1 standard article )

Showing results 1 to 20 of 422.
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  1. Bertsimas, Dimitris; van Parys, Bart: Sparse high-dimensional regression: exact scalable algorithms and phase transitions (2020)
  2. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  3. Cao, Xuan; Khare, Kshitij; Ghosh, Malay: High-dimensional posterior consistency for hierarchical non-local priors in regression (2020)
  4. Chavez, Gordon V.: Dynamic tail inference with log-Laplace volatility (2020)
  5. Choiruddin, Achmad; Cuevas-Pacheco, Francisco; Coeurjolly, Jean-François; Waagepetersen, Rasmus: Regularized estimation for highly multivariate log Gaussian Cox processes (2020)
  6. Fan, Jianqing; Ke, Yuan; Wang, Kaizheng: Factor-adjusted regularized model selection (2020)
  7. Feng, Yang; Liu, Qingfeng; Okui, Ryo: On the sparsity of Mallows model averaging estimator (2020)
  8. Furmańczyk, Konrad; Rejchel, Wojciech: High-dimensional linear model selection motivated by multiple testing (2020)
  9. Gold, David; Lederer, Johannes; Tao, Jing: Inference for high-dimensional instrumental variables regression (2020)
  10. Huang, Yimin; Kong, Xiangshun; Ai, Mingyao: Optimal designs in sparse linear models (2020)
  11. Jeon, Jong-June; Kim, Yongdai; Won, Sungho; Choi, Hosik: Primal path algorithm for compositional data analysis (2020)
  12. Lai, Yuanhao; McLeod, Ian: Ensemble quantile classifier (2020)
  13. Liu, Wenchen; Tang, Yincai; Wu, Xianyi: Separating variables to accelerate non-convex regularized optimization (2020)
  14. Oda, Ryoya; Yanagihara, Hirokazu: A fast and consistent variable selection method for high-dimensional multivariate linear regression with a large number of explanatory variables (2020)
  15. Pang, Tongyao; Wu, Chunlin; Liu, Zhifang: A cubic spline penalty for sparse approximation under tight frame balanced model (2020)
  16. Pan, Yuqing; Mai, Qing: Efficient computation for differential network analysis with applications to quadratic discriminant analysis (2020)
  17. Piironen, Juho; Paasiniemi, Markus; Vehtari, Aki: Projective inference in high-dimensional problems: prediction and feature selection (2020)
  18. Posch, Konstantin; Arbeiter, Maximilian; Pilz, Juergen: A novel Bayesian approach for variable selection in linear regression models (2020)
  19. Rachael C. Aikens, Joseph Rigdon, Justin Lee, Michael Baiocchi, Jonathan Chen: Stratified Pilot Matching in R: The stratamatch Package (2020) arXiv
  20. Renaux, Claude; Buzdugan, Laura; Kalisch, Markus; Bühlmann, Peter: Hierarchical inference for genome-wide association studies: a view on methodology with software (2020)

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