Family of Lasso Regression
Extract Model Coefficients for an object with S3 class "slim"
Internal flare functions
flare: a new Family of Lasso Regression
Plot Function for "roc"
Plot Function for "select"
Plot Function for "sim"
Plot Function for "slim"
Plot Function for "sugm"
Prediction for an object with S3 class "slim"
Print Function for for an object with S3 class "roc"
Print Function for for an object with S3 class "select"
Print Function for for an object with S3 class "sim"
Print Function for an object with S3 class "slim"
Print Function for an object with S3 class "sugm"
Sparse Linear Regression using Nonsmooth Loss Functions and L1 Regular...
Data generator for sparse undirected graph estimation.
Graph visualization for an object with S3 class "sugm"
High-deimensional Sparse Undirected Graphical Models.
Draw ROC Curve for an object with S3 class "sugm"
Model selection for high-dimensional undirected graphical models
Provide the implementation of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequential L1 penalized least square minimization problem, which can be efficiently solved by linearization algorithm. A multi-stage screening approach is adopted for further acceleration. Besides the sparse linear model estimation, we also provide the extension of these Lasso variants to sparse Gaussian graphical model estimation including TIGER and CLIME using either L1 or adaptive penalty. Missing values can be tolerated for Dantzig selector and CLIME. The computation is memory-optimized using the sparse matrix output. For more information, please refer to <https://www.jmlr.org/papers/volume16/li15a/li15a.pdf>.