Interface for `ParameterEstimation.jl` and `Optim.jl` or `BlackBoxOptim.jl` derivative-free optimization

A priority for future development should be to make this package compatible with arbitrary derivative-free `Optim.jl` or `BlackBoxOptim.jl` optimization algorithms. These problems are formulated not as Bayesian inverse problems but purely in terms of the objective function that needs to be minimized. However, I think we should still use the `InverseProblem`, `OutputMap` and `Tranformation` constructs to set up the problem and compute the objective; there will just be the open question of what kinds of options to provide for what form the objective function should take (e.g. regularized or unregularized).

Before I implement an API, does anyone have strong opinions about what kind of flexibility should be available? For now, I think a keyword argument `regularized` indicating which version of the EKI objective is desired might be all we need.

Start by reviewing the InverseProblem, OutputMap, and Tranformation constructs and the proposed interfaces with Optim.jl and BlackBoxOptim.jl. Resolve the desired objective-function forms, including regularized and unregularized variants; done means an agreed API supports arbitrary derivative-free optimization algorithms.

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