glmnet

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 559 articles , 1 standard article )

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  1. Bonaccolto, Giovanni; Caporin, Massimiliano; Maillet, Bertrand B.: Dynamic large financial networks \textitviaconditional expected shortfalls (2022)
  2. Chen, Baojiang; Qin, Jing; Yuan, Ao: Variable selection in the Box-Cox power transformation model (2022)
  3. Jun Woo, Jinhua Wang: bbl: Boltzmann Bayes Learner for High-Dimensional Inference with Discrete Predictors in R (2022) not zbMATH
  4. Li, Yehua; Qiu, Yumou; Xu, Yuhang: From multivariate to functional data analysis: fundamentals, recent developments, and emerging areas (2022)
  5. Nibbering, Didier; Hastie, Trevor J.: Multiclass-penalized logistic regression (2022)
  6. Qianxiang Zang, Chen Xu, Kelly Burkett: SMLE: An R Package for Joint Feature Screening in Ultrahigh-dimensional GLMs (2022) arXiv
  7. Sun, Zhihua; Liu, Yi; Chen, Kani; Li, Gang: Broken adaptive ridge regression for right-censored survival data (2022)
  8. Amir, Tal; Basri, Ronen; Nadler, Boaz: The trimmed Lasso: sparse recovery guarantees and practical optimization by the generalized soft-min penalty (2021)
  9. Bai, Ray; Ghosh, Malay: On the beta prime prior for scale parameters in high-dimensional Bayesian regression models (2021)
  10. Bak, Kwan-Young; Jhong, Jae-Hwan; Lee, JungJun; Shin, Jae-Kyung; Koo, Ja-Yong: Penalized logspline density estimation using total variation penalty (2021)
  11. Belbahri, Mouloud; Murua, Alejandro; Gandouet, Olivier; Nia, Vahid Partovi: Qini-based uplift regression (2021)
  12. Benjamin Christoffersen: dynamichazard: Dynamic Hazard Models Using State Space Models (2021) not zbMATH
  13. Bertsimas, Dimitris; Orfanoudaki, Agni; Pawlowski, Colin: Imputation of clinical covariates in time series (2021)
  14. Bertsimas, Dimitris; Pauphilet, Jean; Van Parys, Bart: Sparse classification: a scalable discrete optimization perspective (2021)
  15. Chen, Ting-Huei; Chatterjee, Nilanjan; Landi, Maria Teresa; Shi, Jianxin: A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information (2021)
  16. David Ardia, Keven Bluteau, Samuel Borms, Kris Boudt: The R package sentometrics to compute, aggregate and predict with textual sentiment (2021) arXiv
  17. Ellenbach, Nicole; Boulesteix, Anne-Laure; Bischl, Bernd; Unger, Kristian; Hornung, Roman: Improved outcome prediction across data sources through robust parameter tuning (2021)
  18. Fitzpatrick, Trevor; Mues, Christophe: How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments (2021)
  19. Genç, Murat; Özkale, M. Revan: Usage of the GO estimator in high dimensional linear models (2021)
  20. Guastavino, S.; Benvenuto, F.: A mathematical model for image saturation with an application to the restoration of solar images via adaptive sparse deconvolution (2021)

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