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

In this article, we describe an R package for sampling from an empirical likelihood-based posterior using a Hamiltonian Monte Carlo method. Empirical likelihood-based methodologies have been used in the Bayesian modeling of many problems of interest in recent times. This semiparametric procedure can easily combine the flexibility of a nonparametric distribution estimator together with the interpretability of a parametric model. The model is specified by estimating equation-based constraints. Drawing inference from a Bayesian empirical likelihood (BayesEL) posterior is challenging. The likelihood is computed numerically, so no closed-form expression of the posterior exists. Moreover, for any sample of finite size, the support of the likelihood is non-convex, which hinders fast mixing of many Markov Chain Monte Carlo (MCMC) procedures. It has been recently shown that using the properties of the gradient of the 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.