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 482 articles , 1 standard article )

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  1. Piironen, Juho; Paasiniemi, Markus; Vehtari, Aki: Projective inference in high-dimensional problems: prediction and feature selection (2020)
  2. Posch, Konstantin; Arbeiter, Maximilian; Pilz, Juergen: A novel Bayesian approach for variable selection in linear regression models (2020)
  3. Rachael C. Aikens, Joseph Rigdon, Justin Lee, Michael Baiocchi, Jonathan Chen: Stratified Pilot Matching in R: The stratamatch Package (2020) arXiv
  4. Rauschenberger, Armin; Ciocănea-Teodorescu, Iuliana; Jonker, Marianne A.; Menezes, Renée X.; van de Wiel, Mark A.: Sparse classification with paired covariates (2020)
  5. Renaux, Claude; Buzdugan, Laura; Kalisch, Markus; Bühlmann, Peter: Hierarchical inference for genome-wide association studies: a view on methodology with software (2020)
  6. Ren, Sheng; Kang, Emily L.; Lu, Jason L.: MCEN: a method of simultaneous variable selection and clustering for high-dimensional multinomial regression (2020)
  7. Robin, Geneviève; Klopp, Olga; Josse, Julie; Moulines, Éric; Tibshirani, Robert: Main effects and interactions in mixed and incomplete data frames (2020)
  8. Sauk, Benjamin; Ploskas, Nikolaos; Sahinidis, Nikolaos: GPU parameter tuning for tall and skinny dense linear least squares problems (2020)
  9. Sayan Putatunda, Dayananda Ubrangala, Kiran Rama, Ravi Kondapalli: DriveML: An R Package for Driverless Machine Learning (2020) arXiv
  10. Schmid, Matthias; Welchowski, Thomas; Wright, Marvin N.; Berger, Moritz: Discrete-time survival forests with Hellinger distance decision trees (2020)
  11. Schomaker, Michael; Heumann, Christian: When and when not to use optimal model averaging (2020)
  12. Song, Hyebin; Raskutti, Garvesh: PUlasso: high-dimensional variable selection with presence-only data (2020)
  13. Sottile, Gianluca; Frumento, Paolo; Chiodi, Marcello; Bottai, Matteo: A penalized approach to covariate selection through quantile regression coefficient models (2020)
  14. Takada, Masaaki; Suzuki, Taiji; Fujisawa, Hironori: Independently interpretable Lasso for generalized linear models (2020)
  15. Takano, Yuichi; Miyashiro, Ryuhei: Best subset selection via cross-validation criterion (2020)
  16. Tang, Lu; Zhou, Ling; Song, Peter X.-K.: Distributed simultaneous inference in generalized linear models via confidence distribution (2020)
  17. Tang, Xiwei; Bi, Xuan; Qu, Annie: Individualized multilayer tensor learning with an application in imaging analysis (2020)
  18. Tan, Zhiqiang: Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data (2020)
  19. Tardivel, Patrick J. C.; Servien, Rémi; Concordet, Didier: Simple expressions of the Lasso and SLOPE estimators in low-dimension (2020)
  20. van der Wurp, Hendrik; Groll, Andreas; Kneib, Thomas; Marra, Giampiero; Radice, Rosalba: Generalised joint regression for count data: a penalty extension for competitive settings (2020)

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