rmcmc - Robust Markov Chain Monte Carlo Methods

Functions for simulating Markov chains to compute Markov chain Monte Carlo (MCMC) estimates of expectations with respect to a target distribution on a real-valued vector space. The package has built-in support for using Metropolis-Hastings transitions with random-walk, Langevin, Hamiltonian or Barker proposals, and is designed to be easy to extend with additional algorithms. Proposal shape and scale parameters can be automatically adapted in an initial warm-up stage. The package has a modular design allowing different algorithmic components such as proposals and adapters to be mixed and matched. If 'BridgeStan's R interface <https://roualdes.us/bridgestan/latest/languages/r.html>, available on GitHub <https://github.com/roualdes/bridgestan>, is installed, then it can be used to specify the target distribution to sample from.