Recent research in nuclear structure physics is constantly trending towards more exotic nuclei than previously studied. This trend is also pushing the boundary towards lower and lower yields, resulting in recorded spectra with low count rates. For the analysis of such low statistics spectra, traditional nonlinear least squares does not provide the correct answer due to the assumptions inherent in the algorithms. In response to this need, a Python package was written to provide an easy interface between the data and the chosen algorithm, be it nonlinear least squares or immediate likelihood optimisation. Uncertainty estimates can be made by both the proces outlined in the MINUIT guide and by probing the Bayesian posterior likelihood through a random walk in parameter space.