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 228 articles )

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  1. Tsao, Min: Estimable group effects for strongly correlated variables in linear models (2019)
  2. Bogaert, Matthias; Ballings, Michel; Van den Poel, Dirk: Evaluating the importance of different communication types in romantic tie prediction on social media (2018)
  3. Choiruddin, Achmad; Coeurjolly, Jean-François; Letué, Frédérique: Convex and non-convex regularization methods for spatial point processes intensity estimation (2018)
  4. Heinze, Georg; Wallisch, Christine; Dunkler, Daniela: Variable selection - A review and recommendations for the practicing Statistician (2018)
  5. Jung, Yoonsuh: Multiple predicting $K$-fold cross-validation for model selection (2018)
  6. Lin, Bingqing; Pang, Zhen; Wang, Qihua: Cluster feature selection in high-dimensional linear models (2018)
  7. Pazira, Hassan; Augugliaro, Luigi; Wit, Ernst: Extended differential geometric LARS for high-dimensional GLMs with general dispersion parameter (2018)
  8. Sagaert, Yves R.; Aghezzaf, El-Houssaine; Kourentzes, Nikolaos; Desmet, Bram: Tactical sales forecasting using a very large set of macroeconomic indicators (2018)
  9. Shi, Yue-Yong; Cao, Yong-Xiu; Yu, Ji-Chang; Jiao, Yu-Ling: Variable selection via generalized SELO-penalized linear regression models (2018)
  10. Topuz, Kazim; Uner, Hasmet; Oztekin, Asil; Yildirim, Mehmet Bayram: Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network (2018)
  11. Wang, Yanxin; Fan, Qibin; Zhu, Li: Variable selection and estimation using a continuous approximation to the $L_0$ penalty (2018)
  12. Aravkin, Aleksandr; Burke, James V.; Ljung, Lennart; Lozano, Aurelie; Pillonetto, Gianluigi: Generalized Kalman smoothing: modeling and algorithms (2017)
  13. Bertsimas, Dimitris; King, Angela: Logistic regression: from art to science (2017)
  14. Calafiore, Giuseppe C.; Novara, Carlo; Taragna, Michele: Leading impulse response identification via the elastic net criterion (2017)
  15. Chiquet, Julien; Mary-Huard, Tristan; Robin, Stéphane: Structured regularization for conditional Gaussian graphical models (2017)
  16. Chow, Yat Tin; Wu, Tianyu; Yin, Wotao: Cyclic coordinate-update algorithms for fixed-point problems: analysis and applications (2017)
  17. Fu, Zhixuan; Parikh, Chirag R.; Zhou, Bingqing: Penalized variable selection in competing risks regression (2017)
  18. Giurcanu, Mihai C.: Oracle M-estimation for time series models (2017)
  19. Groll, Andreas; Tutz, Gerhard: Variable selection in discrete survival models including heterogeneity (2017)
  20. Jhong, Jae-Hwan; Koo, Ja-Yong; Lee, Seong-Whan: Penalized B-spline estimator for regression functions using total variation penalty (2017)

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