The Dakota toolkit provides a flexible, extensible interface between
analysis codes and iteration methods. Dakota contains algorithms for
optimization with gradient and nongradient-based methods; uncertainty
quantification with sampling, reliability, stochastic expansion,
and epistemic methods; parameter estimation with nonlinear least
squares methods; and sensitivity/variance analysis with design of
experiments and parameter study capabilities.  These capabilities may
be used on their own or as components within advanced strategies such
as surrogate-based optimization, mixed integer nonlinear programming,
or optimization under uncertainty.

Optional dependency: openmpi (for distributed memory parallel
capabilities)

You can build with multiple jobs by setting the MAKEFLAGS environment
variable.