+As PyMC continues to mature and expand its functionality to accomodate more domains of application, we increasingly see cutting-edge methodologies, highly specialized statistical distributions, and complex models appear. While this adds to the functinoality and relevance of the project, it can also introduce instability and impose a burden on testing and quality control. To help address this, a `pymc-experimental` respository could act as a home for new additions to PyMC, which may include unusual probability distribitions, advanced model fitting algorithms, or any code that may be inappropriate to include in the `pymc` repository, but may want to be made available to users.
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