Likelihood

This release introduced some great new features. Most important among those are the return of the scipy.optimize.minimize wrappers Minimize and Maximize.
Furthermore, the Likelihood object allows fitting using the principle of maximum likelihood.
Lastly, submodels can now be evaluated more easely than ever before, allowing the following:

pop_1 = A_1 * exp(-(x - x0_1)**2/(2 * sig_1**2))
pop_2 = A_2 * exp(-(x - x0_2)**2/(2 * sig_2**2))
model = pop_1 + pop_2

fit = Fit(model, xdata, ydata)
fit_result = fit.execute()

y = model(x=xdata, **fit_result.params)
y_1 = pop_1(x=xdata, **fit_result.params)