The flare package for high dimensional linear regression and precision matrix estimation in R

Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu

Research output: Contribution to journalArticle

30 Scopus citations

Abstract

This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, ℓq 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.

Original languageEnglish (US)
Pages (from-to)553-557
Number of pages5
JournalJournal of Machine Learning Research
Volume16
StatePublished - Mar 1 2015

Keywords

  • Alternating direction method of multipliers
  • Robustness
  • Sparse linear regression
  • Sparse precision matrix estimation
  • Tuning insensitiveness

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Statistics and Probability
  • Artificial Intelligence

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