Symfit

Bugfix release. Most important fixes include
- Arguments no longer use inspect to find their name, it is recommended to provide names explicitly. The old syntax is still supported, although it will raise a deprication warning.
- `numpy >= 1.12` is demanded
- `symfit` `Argument`-objects are full pickelable
- `ODEModel`s can now be integrated back in time too.
- `ODEModel`s now have a `__str__`, and to declare a `__str__` has been made mandatory by adding it as an `abstractmethod` on `BaseModel`.
- More minor bug fixes

Major overhaul of the internal API, making future development of `symfit` easier.

Additionally covariance matrices are now calculated for all current fitting types, meaning all parameters uncertainties are now provided. Additionally, gradients can now also be calculated for `ODEModels` due to the addition of finite differences as a default means of calculating gradients.

Bugfix release.

Apart from bug-fixes, the most important change in this version is the addition of the contrib module. This will hold useful side-project which depend on the ``symfit`` core but are not a part of it.

Currently, this contains a visual guess tool, which aims to make providing good guesses for your model a lot easier. Give it a shot!

This version of symfit introduces a lot of improvements to the Fit object. Global fitting now works better, and the Fit object takes constraints.

Apart from this, it features many minor improvements and bug fixes.

What better way to start the new year than with a version of `symfit`?

This version introduces two great new fitting types: `LinearLeastSquares` and `NonLinearLeastSquares`.

Up until now all fitting in `symfit` was done numerically and iteratively. However, `LinearLeastSquares` is an implementation of the analytical solution to the least squares problem. Therefore, no more iterations. It's one step and you'll have your answer!

However, this only works with models linear in the parameters. For nonlinear models there is `NonLinearLeastSquares`. `NonLinearLeastSquares` works by approximating your model by it's first-order Taylor expansion and then iteratively improving the fit using `LinearLeastSquares`.

These objects are an exciting step towards my goal of implementing constrained fitting in a sexy and generic manner throughout `symfit`.

p.s. It also features some minor bug fixes.