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Over the last few decades power law distributions have been suggested asforming generative mechanisms in a variety of disparate fields, such as,astrophysics, criminology and database curation. However, fitting these heavytailed distributions requires care, especially since the power law behaviourmay only be present in the distributional tail. Current state of the artmethods for fitting these models rely on estimating the cut-off parameter$x_{\min}$. This results in the majority of collected data being discarded.This paper provides an alternative, principled approached for fitting heavytailed distributions. By directly modelling the deviation from the power lawdistribution, we can fit and compare a variety of competing models in a singleunified framework.

A complete data frame work for fitting power law distributions