- Update to 6.17
  * Too many changes to list, for details see
    https://dakota.sandia.gov/content/dakota-617
- Move (unversioned) libraries from devel subpackage to main
  package.
- Clean up spec file, remove some packaging issues

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- Update to 6.12
* The efficient_global method for optimization and least squares now
supports concurrent refinement (adding multiple points).
* (Experimental) The MIT Uncertainty Quantification (MUQ) MUQ2 library
(Parno, Davis, Marzouk, et al.) enhances Dakota's Bayesian inference
capability with new Markov Chain Monte Carlo (MCMC) sampling methods.
MCMC samplers available in Dakota (under method > bayes_calibration >
muq) include Metropolis-Hastings and Adaptive Metropolis. Future work
will activate MUQ's more advanced samplers, including surrogate-based
and derivative-enhanced sampling, as well as delayed rejection schemes.
* (Experimental) Dakota 6.12 extends functional tensor train (FTT)
surrogate models from the C3 library (Gorodetsky, University of
Michigan) to support building FTT approximations across a sequence of
model fidelities (multifidelity FTT) or model resolutions (multilevel
FTT).

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Update version to 6.11, comment out patch that has been fixed upstream

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- Switch from python2 to python3 interface
- Add memory-constraints build requirement to handle memory
  requirements

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- Update to 6.10
 * Evaluation data (variables and responses) may now be output to disk
 in HDF5 format. HDF5 support has been added to all of our downloads.
 See the Dakota HDF5 Output section of the Reference Manual for full
 details.
 * Capabilities for multilevel polynomial chaos expansion (ML PCE) and
 stochastic collocation (MC SC) have been expanded and hardened to
 improve their efficiency, completeness, and accuracy.

- Add a dakota.pth file for python package imports
- Remove patches, just use a few one-liners in spec file

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Made requested changes to devel packages and exclude files

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