The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R.
This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, ℓqLasso, and Dantzig selector) and their extensions to sparse precision matrix estimation (TIGER and CLIME). These methods exploit different nonsmooth loss functions to gain modeling exibility, estimation robustness, and tuning insensitiveness. The developed solver is based on the alternating direction method of multipliers (ADMM), which is further accelerated by the multistage screening approach. The package flare is coded in double precision C, and called from R by a user-friendly interface. The memory usage is optimized by using the sparse matrix output. The experiments show that flare is efficient and can scale up to large problems.
Department of Electrical and Computer Engineering, University of Minnesota Twin Cities.
Xingguo
X
Li
Department of Computer Science, Johns Hopkins University.
Tuo
T
Zhao
Department of Mathematics, Hong Kong Baptist University.
Xiaoming
X
Yuan
Department of Operations Research and Financial Engineering, Princeton University.
Han
H
Liu