# powerlaw

Power-Law Distribution Analysis based on Power-law distributions in Empirical data paper. (Summary)

## Basic use

```
from powerlaw.regression import estimate_parameters, goodness_of_fit
data = [1.1, 2.2, ...]
(xmin, alpha, ks_statistics) = estimate_parameters(data)
p_value = goodness_of_fit(series, 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.

A short summary of the paper can be found here.

## References

Clauset, Aaron, Cosma Rohilla Shalizi, and Mark EJ Newman. “Power-law distributions in empirical data.” SIAM review 51.4 (2009): 661-703.

## License

MIT