rare

R package rare: Linear Model with Tree-Based Lasso Regularization for Rare Features. Implementation of an alternating direction method of multipliers algorithm for fitting a linear model with tree-based lasso regularization, which is proposed in Algorithm 1 of Yan and Bien (2018) <arXiv:1803.06675>. The package allows efficient model fitting on the entire 2-dimensional regularization path for large datasets. The complete set of functions also makes the entire process of tuning regularization parameters and visualizing results hassle-free.

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References in zbMATH (referenced in 6 articles , 1 standard article )

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  1. Bùi, Minh N.; Combettes, Patrick L.: Multivariate monotone inclusions in saddle form (2022)
  2. Johnstone, Patrick R.; Eckstein, Jonathan: Projective splitting with forward steps (2022)
  3. Johnstone, Patrick R.; Eckstein, Jonathan: Single-forward-step projective splitting: exploiting cocoercivity (2021)
  4. Kawano, Shuichi: Sparse principal component regression via singular value decomposition approach (2021)
  5. Xu, Shirong; Dai, Ben; Wang, Junhui: Sentiment analysis with covariate-assisted word embeddings (2021)
  6. Yan, Xiaohan; Bien, Jacob: Rare feature selection in high dimensions (2021)