  Recently developed adaptive Markov chain Monte Carlo (MCMC) methods have been
applied successfully to many problems in Bayesian statistics. Grapham is a new
open source implementation covering several such methods, with emphasis on
graphical models for directed acyclic graphs. The implemented algorithms
include the seminal Adaptive Metropolis algorithm adjusting the proposal
covariance according to the history of the chain and a Metropolis algorithm
adjusting the proposal scale based on the observed acceptance probability.
Different variants of the algorithms allow one, for example, to use these two
algorithms together, employ delayed rejection and adjust several parameters of
the algorithms. The implemented Metropolis-within-Gibbs update allows arbitrary
sampling blocks. The software is written in C and uses a simple extension
language Lua in configuration.
