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 239 articles , 1 standard article )

Showing results 1 to 20 of 239.
Sorted by year (citations)

1 2 3 ... 10 11 12 next

  1. 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
  2. Ge, Jason; Li, Xingguo; Jiang, Haoming; Liu, Han; Zhang, Tong; Wang, Mengdi; Zhao, Tuo: \textttpicasso: a sparse learning library for high dimensional data analysis in \textttRand \textttPython (2019)
  3. Henrique Helfer Hoeltgebaum, Heather Battey: HCmodelSets: An R package for specifying sets of well-fitting models in regression with a large number of potential explanatory variables (2019) arXiv
  4. Piotr Pokarowski, Wojciech Rejchel, Agnieszka Soltys, Michal Frej, Jan Mielniczuk: Improving Lasso for model selection and prediction (2019) arXiv
  5. Tsao, Min: Estimable group effects for strongly correlated variables in linear models (2019)
  6. Bogaert, Matthias; Ballings, Michel; Van den Poel, Dirk: Evaluating the importance of different communication types in romantic tie prediction on social media (2018)
  7. Choiruddin, Achmad; Coeurjolly, Jean-François; Letué, Frédérique: Convex and non-convex regularization methods for spatial point processes intensity estimation (2018)
  8. Diego Saldana; Yang Feng: SIS: An R Package for Sure Independence Screening in Ultrahigh-Dimensional Statistical Models (2018) not zbMATH
  9. Heinze, Georg; Wallisch, Christine; Dunkler, Daniela: Variable selection -- a review and recommendations for the practicing statistician (2018)
  10. Jung, Yoonsuh: Multiple predicting (K)-fold cross-validation for model selection (2018)
  11. Lin, Bingqing; Pang, Zhen; Wang, Qihua: Cluster feature selection in high-dimensional linear models (2018)
  12. Park, Gunwoong; Raskutti, Garvesh: Learning quadratic variance function (QVF) DAG models via overdispersion scoring (ODS) (2018)
  13. Pazira, Hassan; Augugliaro, Luigi; Wit, Ernst: Extended differential geometric LARS for high-dimensional GLMs with general dispersion parameter (2018)
  14. Sagaert, Yves R.; Aghezzaf, El-Houssaine; Kourentzes, Nikolaos; Desmet, Bram: Tactical sales forecasting using a very large set of macroeconomic indicators (2018)
  15. Shi, Yue-Yong; Cao, Yong-Xiu; Yu, Ji-Chang; Jiao, Yu-Ling: Variable selection via generalized SELO-penalized linear regression models (2018)
  16. 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)
  17. Van der Laan, Mark J.; Rose, Sherri: Targeted learning in data science. Causal inference for complex longitudinal studies (2018)
  18. Wang, Yanxin; Fan, Qibin; Zhu, Li: Variable selection and estimation using a continuous approximation to the (L_0) penalty (2018)
  19. Aravkin, Aleksandr; Burke, James V.; Ljung, Lennart; Lozano, Aurelie; Pillonetto, Gianluigi: Generalized Kalman smoothing: modeling and algorithms (2017)
  20. Bertsimas, Dimitris; King, Angela: Logistic regression: from art to science (2017)

1 2 3 ... 10 11 12 next