  This paper describes an R package named flare, which implements a family of
new high dimensional regression methods (LAD Lasso, SQRT Lasso, $\ell_q$ Lasso,
and Dantzig selector) and their extensions to sparse precision matrix
estimation (TIGER and CLIME). These methods exploit different nonsmooth loss
functions to gain modeling flexibility, estimation robustness, and tuning
insensitiveness. The developed solver is based on the alternating direction
method of multipliers (ADMM). 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.
