An R package and a study of methods for computing empirical likelihood

Dan Yang, Dylan S. Small

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Empirical likelihood (EL) is an important nonparametric statistical methodology. We develop a package in R called el.convex to implement EL for inference about a multivariate mean. This package contains five functions which use different optimization algorithms but meanwhile seek the same goal. These functions are based on the theory of convex optimization; they are Newton, Davidon-Fletcher-Powell, Broyden-Fletcher-Goldfarb-Shanno, conjugate gradient method, and damped Newton, respectively. We also compare them with the function el.test in the existing R package emplik, and discuss their relative advantages and disadvantages.

Original languageEnglish (US)
Pages (from-to)1363-1372
Number of pages10
JournalJournal of Statistical Computation and Simulation
Issue number7
StatePublished - Jul 2013

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


  • conjugate gradient method
  • convex optimization
  • instrumental variables
  • quasi-Newton method


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