Markov Chain Monte Carlo algorithms are a general technique for learning probability distributions. However, they tend to mix slowly in complex, high-dimensional models, and scale poorly to large datasets. This package arose from the need for conducting high dimensional inference in large models using R. It provides a distributed version of stochastic based gradient variations of common continuous-based Metropolis algorithms, and utilizes the theory of optimal acceptance rates of Metropolis algorithms to automatically tune the proposal distribution to its optimal value. We describe how to use the package to learn complex distributions, and compare to other packages such as RStan.
