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 511 articles , 1 standard article )

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  1. Bai, Ray; Ghosh, Malay: On the beta prime prior for scale parameters in high-dimensional Bayesian regression models (2021)
  2. Bak, Kwan-Young; Jhong, Jae-Hwan; Lee, JungJun; Shin, Jae-Kyung; Koo, Ja-Yong: Penalized logspline density estimation using total variation penalty (2021)
  3. 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)
  4. Genç, Murat; Özkale, M. Revan: Usage of the GO estimator in high dimensional linear models (2021)
  5. Guastavino, S.; Benvenuto, F.: A mathematical model for image saturation with an application to the restoration of solar images via adaptive sparse deconvolution (2021)
  6. Guha, Sharmistha; Rodriguez, Abel: Bayesian regression with undirected network predictors with an application to brain connectome data (2021)
  7. Huang, Shih-Ting; Xie, Fang; Lederer, Johannes: Tuning-free ridge estimators for high-dimensional generalized linear models (2021)
  8. Jakob A. Dambon, Fabio Sigrist, Reinhard Furrer: varycoef: An R Package for Gaussian Process-based Spatially Varying Coefficient Models (2021) arXiv
  9. Lahiri, Soumendra N.: Necessary and sufficient conditions for variable selection consistency of the Lasso in high dimensions (2021)
  10. Ma, Shaohui; Fildes, Robert: Retail sales forecasting with meta-learning (2021)
  11. Mishra, Aditya; Dey, Dipak K.; Chen, Yong; Chen, Kun: Generalized co-sparse factor regression (2021)
  12. Pan, Xiaoou; Sun, Qiang; Zhou, Wen-Xin: Iteratively reweighted (\ell_1)-penalized robust regression (2021)
  13. Park, Gunwoong; Moon, Sang Jun; Park, Sion; Jeon, Jong-June: Learning a high-dimensional linear structural equation model via (\ell_1)-regularized regression (2021)
  14. Pietrosanu, Matthew; Gao, Jueyu; Kong, Linglong; Jiang, Bei; Niu, Di: Advanced algorithms for penalized quantile and composite quantile regression (2021)
  15. Schultheiss, Christoph; Renaux, Claude; Bühlmann, Peter: Multicarving for high-dimensional post-selection inference (2021)
  16. Staerk, Christian; Kateri, Maria; Ntzoufras, Ioannis: High-dimensional variable selection via low-dimensional adaptive learning (2021)
  17. Suchit Mehrotra, Arnab Maity: Variational Inference for Shrinkage Priors: The R package vir (2021) arXiv
  18. Sun, Ruoyu; Ye, Yinyu: Worst-case complexity of cyclic coordinate descent: (O(n^2)) gap with randomized version (2021)
  19. Van Belle, Jente; Guns, Tias; Verbeke, Wouter: Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains (2021)
  20. Vishwakarma, Gajendra K.; Thomas, Abin; Bhattacharjee, Atanu: A weight function method for selection of proteins to predict an outcome using protein expression data (2021)

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