  Quasi-Monte Carlo (QMC) methods have developed over several decades. With the
explosion in computational science, there is a need for great software that
implements QMC algorithms. We summarize the QMC software that has been
developed to date, propose some criteria for developing great QMC software, and
suggest some steps toward achieving great software. We illustrate these
criteria and steps with the Quasi-Monte Carlo Python library (QMCPy), an
open-source community software framework, extensible by design with common
programming interfaces to an increasing number of existing or emerging QMC
libraries developed by the greater community of QMC researchers.
