  In this article, we describe a {\tt R} package for sampling from an empirical
likelihood-based posterior using a Hamiltonian Monte Carlo method. Empirical
likelihood-based methodologies have been used in Bayesian modeling of many
problems of interest in recent times. This semiparametric procedure can easily
combine the flexibility of a non-parametric distribution estimator together
with the interpretability of a parametric model. The model is specified by
estimating equations-based constraints. Drawing an inference from a Bayesian
empirical likelihood (BayesEL) posterior is challenging. The likelihood is
computed numerically, so no closed expression of the posterior exists.
Moreover, for any sample of finite size, the support of the likelihood is
non-convex, which hinders the fast mixing of many Markov Chain Monte Carlo
(MCMC) procedures. It has been recently shown that using the properties of the
gradient of log empirical likelihood, one can devise an efficient Hamiltonian
Monte Carlo (HMC) algorithm to sample from a BayesEL posterior.
  The package requires the user to specify only the estimating equations, the
prior, and their respective gradients. An MCMC sample drawn from the BayesEL
posterior of the parameters, with various details required by the user is
obtained.
