Linear Model with Tree-Based Lasso Regularization for Rare Features
Find all descendant leaves of a node in an hclust tree
Visualize groups by coloring branches and leaves of an hclust tree
Recover aggregated groups of leaf indices
Model path for tree-based lasso framework for selecting rare features
Perform K-fold cross validation
Make predictions from a rarefit object and a rarefit.cv object
Fit the rare feature selection model
Generate matrix A encoding ancestor-descendant relationships in an hcl...
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 (2020) <doi:10.1080/01621459.2020.1796677>. 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.