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 387 articles , 1 standard article )

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  1. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  2. Cao, Xuan; Khare, Kshitij; Ghosh, Malay: High-dimensional posterior consistency for hierarchical non-local priors in regression (2020)
  3. Choiruddin, Achmad; Cuevas-Pacheco, Francisco; Coeurjolly, Jean-François; Waagepetersen, Rasmus: Regularized estimation for highly multivariate log Gaussian Cox processes (2020)
  4. Feng, Yang; Liu, Qingfeng; Okui, Ryo: On the sparsity of Mallows model averaging estimator (2020)
  5. Furmańczyk, Konrad; Rejchel, Wojciech: High-dimensional linear model selection motivated by multiple testing (2020)
  6. Huang, Yimin; Kong, Xiangshun; Ai, Mingyao: Optimal designs in sparse linear models (2020)
  7. Lai, Yuanhao; McLeod, Ian: Ensemble quantile classifier (2020)
  8. Pan, Yuqing; Mai, Qing: Efficient computation for differential network analysis with applications to quadratic discriminant analysis (2020)
  9. Posch, Konstantin; Arbeiter, Maximilian; Pilz, Juergen: A novel Bayesian approach for variable selection in linear regression models (2020)
  10. Rachael C. Aikens, Joseph Rigdon, Justin Lee, Michael Baiocchi, Jonathan Chen: Stratified Pilot Matching in R: The stratamatch Package (2020) arXiv
  11. Ren, Sheng; Kang, Emily L.; Lu, Jason L.: MCEN: a method of simultaneous variable selection and clustering for high-dimensional multinomial regression (2020)
  12. Tang, Lu; Zhou, Ling; Song, Peter X.-K.: Distributed simultaneous inference in generalized linear models via confidence distribution (2020)
  13. Wang, Fan; Mukherjee, Sach; Richardson, Sylvia; Hill, Steven M.: High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking (2020)
  14. Xu, Xinyi; Li, Xiangjie; Zhang, Jingxiao: Regularization methods for high-dimensional sparse control function models (2020)
  15. Agor, Joseph; Özaltın, Osman Y.: Feature selection for classification models via bilevel optimization (2019)
  16. Ahonen, Ilmari; Nevalainen, Jaakko; Larocque, Denis: Prediction with a flexible finite mixture-of-regressions (2019)
  17. Algamal, Zakariya Yahya; Lee, Muhammad Hisyam: A two-stage sparse logistic regression for optimal gene selection in high-dimensional microarray data classification (2019)
  18. Angela Bitto-Nemling, Annalisa Cadonna, Sylvia Frühwirth-Schnatter, Peter Knaus: Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP (2019) arXiv
  19. Audouze, Christophe; Nair, Prasanth B.: Sparse low-rank separated representation models for learning from data (2019)
  20. Bhadra, Anindya; Datta, Jyotishka; Polson, Nicholas G.; Willard, Brandon: Lasso meets horseshoe: a survey (2019)

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