Family of Lasso Regression

Provides implementations of a family of Lasso variants, including Dantzig Selector, LAD Lasso, SQRT Lasso, and Lq Lasso, for estimating high-dimensional sparse linear models. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequence of L1-penalized least-squares minimization problems that are efficiently solved by linearization and multi-stage screening. In addition to sparse linear model estimation, we provide extensions of these methods to sparse Gaussian graphical model estimation, including TIGER and CLIME, using either L1 or adaptive penalties. Missing values can be tolerated for Dantzig selector and CLIME. Computation is memory-optimized using sparse matrix output. For more information, see < https://www.jmlr.org/papers/volume16/li15a/li15a.pdf>.

flare

flare provides a family of sparse regression and sparse graphical model estimators, including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso, TIGER, and CLIME.

Development Notes

• The CI workflow runs:
  • R CMD check on Linux/macOS/Windows (R release)
  • R CMD check on Linux (R devel)
  • Test coverage generation and optional Codecov upload