First commit of GeometricMCMC package

This package implements two strategies recently published in the MCMC literature, both exploiting information contained in the derivative of the log-target, which we assume to be available in closed form, to increased efficiency: differential geometric MCMC algorithms aimed at exploiting the fact that the derivative of the log-target captures the Hamiltonian dynamics on the Riemannian manifold of the parameter space, thus allowing automated and more efficient proposal transitions to be achieved; zero variance (ZV) techniques aimed at reducing the variance of the resulting MCMC estimators by post-processing an existing Markov path and by constructing control variates that exploit the well-known fact that the expected value of the derivative of the log-likelihood, here substituted with the log-target, is zero under mild regularity conditions related to the possibility of interchanging the order of differentiation and integration.