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powerlaw
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Power-Law Distribution Analysis
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Download README
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powerlaw
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# powerlaw [](http://powerlaw.readthedocs.org/en/latest/?badge=latest) [](https://zenodo.org/badge/latestdoi/48375467) Power-Law Distribution Analysis based on [Power-law distributions in Empirical data](http://arxiv.org/pdf/0706.1062.pdf) paper ([Summary](/paper/README.md)). ## Basic use ``` from powerlaw.regression import estimate_parameters, goodness_of_fit data = [1.1, 2.2, 3.3, 4.4] (xmin, alpha, ks_statistics) = estimate_parameters(data) p_value = goodness_of_fit(data, xmin, alpha, ks_statistics) ``` ## Install ``` sudo pip install git+https://github.com/shagunsodhani/powerlaw.git ``` #### Alternatively ``` git clone https://github.com/shagunsodhani/powerlaw.git cd powerlaw sudo python setup.py install ``` ## Features The current implementation supports fitting both continuous and discrete data to a power-law (using both Linear Regression and Maximum Likelihood Estimator method) and calculating the goodness of fit for the fitted power-law. Additionally, there are methods to generate random numbers for power-law, exponential and stretched exponential series. The complete documentation can be found [here](https://powerlaw.readthedocs.org). A short summary of the paper can be found [here](/paper/README.md). ## References [Clauset, Aaron, Cosma Rohilla Shalizi, and Mark EJ Newman. "Power-law distributions in empirical data." SIAM review 51.4 (2009): 661-703.](http://arxiv.org/pdf/0706.1062.pdf) ## License [MIT](http://shagun.mit-license.org/)
