Model path for tree-based lasso framework for selecting rare features
The package fits the linear model with tree-based lasso regularization proposed in Yan and Bien (2018) using alternating direction method of multipliers (ADMM). The ADMM algorithm is proposed in Algorithm 1 of the same paper. The package also provides tools for tuning regularization parameters, making predictions from the fitted model and visualizing recovered groups of the covariates in a dendrogram. package
Its main functions are rarefit, rarefit.cv, rarefit.predict, group.recover and group.plot.
Yan, X. and Bien, J. (2018) Rare Feature Selection in High Dimensions, https://arxiv.org/abs/1803.06675.
Xiaohan Yan xy257@cornell.edu , Jacob Bien