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 145 articles )

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  1. Calafiore, Giuseppe C.; Novara, Carlo; Taragna, Michele: Leading impulse response identification via the elastic net criterion (2017)
  2. Chow, Yat Tin; Wu, Tianyu; Yin, Wotao: Cyclic coordinate-update algorithms for fixed-point problems: analysis and applications (2017)
  3. Fu, Zhixuan; Parikh, Chirag R.; Zhou, Bingqing: Penalized variable selection in competing risks regression (2017)
  4. Johnstone, Patrick R.; Moulin, Pierre: Local and global convergence of a general inertial proximal splitting scheme for minimizing composite functions (2017)
  5. Maciak, Matúš: Testing shape constraints in Lasso regularized joinpoint regression (2017)
  6. Moradi Rekabdarkolaee, Hossein; Wang, Qin: Variable selection through adaptive MAVE (2017)
  7. Simon Mak, C. F. Jeff Wu: cmenet: a new method for bi-level variable selection of conditional main effects (2017) arXiv
  8. Ternès, Nils; Rotolo, Federico; Heinze, Georg; Michiels, Stefan: Identification of biomarker-by-treatment interactions in randomized clinical trials with survival outcomes and high-dimensional spaces (2017)
  9. Yamamoto, Michio; Hwang, Heungsun: Dimension-reduced clustering of functional data via subspace separation (2017)
  10. Zhang, Haixiang; Zheng, Yinan; Yoon, Grace; Zhang, Zhou; Gao, Tao; Joyce, Brian; Zhang, Wei; Schwartz, Joel; Vokonas, Pantel; Colicino, Elena; Baccarelli, Andrea; Hou, Lifang; Liu, Lei: Regularized estimation in sparse high-dimensional multivariate regression, with application to a DNA methylation study (2017)
  11. Angelopoulos, Nicos; Abdallah, Samer; Giamas, Georgios: Advances in integrative statistics for logic programming (2016)
  12. Beinrucker, Andre; Dogan, Ürün; Blanchard, Gilles: Extensions of stability selection using subsamples of observations and covariates (2016)
  13. Blum, Yuna; Houée-Bigot, Magalie; Causeur, David: Sparse factor model for co-expression networks with an application using prior biological knowledge (2016)
  14. Fitzpatrick, Trevor; Mues, Christophe: An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market (2016)
  15. Fountoulakis, Kimon; Gondzio, Jacek: Performance of first- and second-order methods for $\ell_1$-regularized least squares problems (2016)
  16. Frandi, Emanuele; Ñanculef, Ricardo; Lodi, Stefano; Sartori, Claudio; Suykens, Johan A.K.: Fast and scalable Lasso via stochastic Frank-Wolfe methods with a convergence guarantee (2016)
  17. Furmańczyk, Konrad: Variable selection using stepdown procedures in high-dimensional linear models (2016)
  18. Gillberg, Jussi; Marttinen, Pekka; Pirinen, Matti; Kangas, Antti J.; Soininen, Pasi; Ali, Mehreen; Havulinna, Aki S.; Järvelin, Marjo-Riitta; Ala-Korpela, Mika; Kaski, Samuel: Multiple output regression with latent noise (2016)
  19. Guhaniyogi, Rajarshi; Dunson, David B.: Compressed Gaussian process for manifold regression (2016)
  20. Kwemou, Marius: Non-asymptotic oracle inequalities for the Lasso and group Lasso in high dimensional logistic model (2016)

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