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 495 articles , 1 standard article )

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  1. Lockhart, Richard; Taylor, Jonathan; Tibshirani, Ryan J.; Tibshirani, Robert: Rejoinder: “A significance test for the lasso” (2014)
  2. Lockhart, Richard; Taylor, Jonathan; Tibshirani, Ryan J.; Tibshirani, Robert: A significance test for the lasso (2014)
  3. Luigi Augugliaro; Angelo Mineo; Ernst Wit: dglars: An R Package to Estimate Sparse Generalized Linear Models (2014) not zbMATH
  4. Lv, Jinchi; Zheng, Zemin: Discussion: “A significance test for the lasso” (2014)
  5. Marchetti, Yuliya; Zhou, Qing: Solution path clustering with adaptive concave penalty (2014)
  6. Martin Sill; Thomas Hielscher; Natalia Becker; Manuela Zucknick: c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models (2014) not zbMATH
  7. Matsui, Hidetoshi: Variable and boundary selection for functional data via multiclass logistic regression modeling (2014)
  8. Monni, Stefano: Bayesian variable selection for correlated covariates via colored cliques (2014)
  9. Neykov, N. M.; Filzmoser, P.; Neytchev, P. N.: Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator (2014)
  10. Pillonetto, Gianluigi; Dinuzzo, Francesco; Chen, Tianshi; De Nicolao, Giuseppe; Ljung, Lennart: Kernel methods in system identification, machine learning and function estimation: a survey (2014)
  11. Prangle, Dennis; Fearnhead, Paul; Cox, Murray P.; Biggs, Patrick J.; French, Nigel P.: Semi-automatic selection of summary statistics for ABC model choice (2014)
  12. Ramirez, Alexandro D.; Paninski, Liam: Fast inference in generalized linear models via expected log-likelihoods (2014)
  13. Stephen Reid; Rob Tibshirani: Regularization Paths for Conditional Logistic Regression: The clogitL1 Package (2014) not zbMATH
  14. Tseng, Paul; Yun, Sangwoon: Incrementally updated gradient methods for constrained and regularized optimization (2014)
  15. van de Geer, Sara; Bühlmann, Peter; Ritov, Ya’acov; Dezeure, Ruben: On asymptotically optimal confidence regions and tests for high-dimensional models (2014)
  16. Viallon, Vivian; Banerjee, Onureena; Jougla, Eric; Rey, Grégoire; Coste, Joel: Empirical comparison study of approximate methods for structure selection in binary graphical models (2014)
  17. Vincent, Martin; Hansen, Niels Richard: Sparse group lasso and high dimensional multinomial classification (2014)
  18. Wang, Jin-Jia; Lu, Yang: Coordinate descent based hierarchical interactive Lasso penalized logistic regression and its application to classification problems (2014)
  19. Wang, Mingqiu; Wang, Xiuli: Adaptive Lasso estimators for ultrahigh dimensional generalized linear models (2014)
  20. Wang, Mingqiu; Wang, Xiuli; Wang, Xiaoguang: A note on the one-step estimator for ultrahigh dimensionality (2014)

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