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 426 articles , 1 standard article )

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  1. Bradic, Jelena; Fan, Jianqing; Jiang, Jiancheng: Regularization for Cox’s proportional hazards model with NP-dimensionality (2011)
  2. Breheny, Patrick; Huang, Jian: Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection (2011)
  3. Chen, Jun; Xie, Jichun; Li, Hongzhe: A penalized likelihood approach for bivariate conditional normal models for dynamic co-expression analysis (2011)
  4. McShane, Blakeley B.; Wyner, Abraham J.: Rejoinder (2011)
  5. Noah Simon; Jerome Friedman; Trevor Hastie; Rob Tibshirani: Regularization Paths for Cox’s Proportional Hazards Model via Coordinate Descent (2011) not zbMATH
  6. Percival, Daniel; Roeder, Kathryn; Rosenfeld, Roni; Wasserman, Larry: Structured, sparse regression with application to HIV drug resistance (2011)
  7. Radchenko, Peter; James, Gareth M.: Improved variable selection with forward-lasso adaptive shrinkage (2011)
  8. Schelldorfer, Jürg; Bühlmann, Peter; van de Geer, Sara: Estimation for high-dimensional linear mixed-effects models using (\ell_1)-penalization (2011)
  9. Tingley, Martin P.: Spurious predictions with random time series: the lasso in the context of paleoclimatic reconstructions. Discussion of: A statistical analysis of multiple temperature proxies: are reconstructions of surface temperatures over the last 1000 years reliable? (2011)
  10. van de Geer, Sara; Bühlmann, Peter; Zhou, Shuheng: The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso) (2011)
  11. Wand, M. P.; Ormerod, J. T.: Penalized wavelets: embedding wavelets into semiparametric regression (2011)
  12. Wang, Pei; Chao, Dennis L.; Hsu, Li: Learning oncogenic pathways from binary genomic instability data (2011)
  13. Yen, Tso-Jung: A majorization-minimization approach to variable selection using spike and slab priors (2011)
  14. Zhou, Shuheng; Rütimann, Philipp; Xu, Min; Bühlmann, Peter: High-dimensional covariance estimation based on Gaussian graphical models (2011)
  15. Bunea, Florentina; Tsybakov, Alexandre B.; Wegkamp, Marten H.; Barbu, Adrian: SPADES and mixture models (2010)
  16. Jerome Friedman; Trevor Hastie; Rob Tibshirani: Regularization Paths for Generalized Linear Models via Coordinate Descent (2010) not zbMATH
  17. Kolar, Mladen; Song, Le; Ahmed, Amr; Xing, Eric P.: Estimating time-varying networks (2010)
  18. Peng, Jie; Zhu, Ji; Bergamaschi, Anna; Han, Wonshik; Noh, Dong-Young; Pollack, Jonathan R.; Wang, Pei: Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer (2010)
  19. Schifano, Elizabeth D.; Strawderman, Robert L.; Wells, Martin T.: Majorization-minimization algorithms for nonsmoothly penalized objective functions (2010)
  20. Städler, Nicolas; Bühlmann, Peter; Geer, Sara Van de: (\ell_1)-penalization for mixture regression models (2010)

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