  The discrete class algorithm presented in this paper is an efficient
simulation tool for stochastic processes governed by a reasonably small set of
transition rates. The algorithm is presented, its performance compared to
prevailing methods and applications to epitaxial growth and neuronal models are
sketched.
  Source code is available from the author's WWW-site.
