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

elhmc: An R Package for Hamiltonian Monte Carlo Sampling in Bayesian Empirical Likelihood