glmnet

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 120 articles )

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  1. Blum, Yuna; Houée-Bigot, Magalie; Causeur, David: Sparse factor model for co-expression networks with an application using prior biological knowledge (2016)
  2. Furmańczyk, Konrad: Variable selection using stepdown procedures in high-dimensional linear models (2016)
  3. Guhaniyogi, Rajarshi; Dunson, David B.: Compressed Gaussian process for manifold regression (2016)
  4. Laurin, Charles; Boomsma, Dorret; Lubke, Gitta: The use of vector bootstrapping to improve variable selection precision in Lasso models (2016)
  5. Neykov, Matey; Liu, Jun S.; Cai, Tianxi: On the characterization of a class of Fisher-consistent loss functions and its application to boosting (2016)
  6. Oneto, Luca; Ridella, Sandro; Anguita, Davide: Tikhonov, Ivanov and Morozov regularization for support vector machine learning (2016)
  7. Perthame, Émeline; Friguet, Chloé; Causeur, David: Stability of feature selection in classification issues for high-dimensional correlated data (2016)
  8. Pillonetto, Gianluigi; Chen, Tianshi; Chiuso, Alessandro; De Nicolao, Giuseppe; Ljung, Lennart: Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint (2016)
  9. Teisseyre, Paweł; Kłopotek, Robert A.; Mielniczuk, Jan: Random subspace method for high-dimensional regression with the R package regRSM (2016)
  10. Treister, Eran; Turek, Javier S.; Yavneh, Irad: A multilevel framework for sparse optimization with application to inverse covariance estimation and logistic regression (2016)
  11. Tutz, Gerhard; Schmid, Matthias: Modeling discrete time-to-event data (2016)
  12. Vinciotti, Veronica; Augugliaro, Luigi; Abbruzzo, Antonino; Wit, Ernst C.: Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks (2016)
  13. Aragam, Bryon; Zhou, Qing: Concave penalized estimation of sparse Gaussian Bayesian networks (2015)
  14. Boche, Holger; Calderbank, Robert; Kutyniok, Gitta; Vybíral, Jan: A survey of compressed sensing (2015)
  15. Breheny, Patrick; Huang, Jian: Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors (2015)
  16. Calafiore, Giuseppe C.; El Ghaoui, Laurent M.; Novara, Carlo: Sparse identification of posynomial models (2015)
  17. Carrico, Caroline; Gennings, Chris; Wheeler, David C.; Factor-Litvak, Pam: Characterization of weighted quantile sum regression for highly correlated data in a risk analysis setting (2015)
  18. Dokukin, A.A.; Senko, O.V.: Regression model based on convex combinations best correlated with response (2015)
  19. Dong, Qian; Liu, Xin; Wen, Zai-Wen; Yuan, Ya-Xiang: A parallel line search subspace correction method for composite convex optimization (2015)
  20. Han, Fang; Lu, Huanran; Liu, Han: A direct estimation of high dimensional stationary vector autoregressions (2015)

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