This package implements both the discrete and continuous maximum likelihood estimators for fitting the power-law distribution to data. Additionally, a goodness-of-fit based approach is used to estimate the lower cutoff for the scaling region.

analyzing power-laws fits of financial time series data

This repository contains FDP'18 presentations and R scripts.

Prediction with Pretrained autoencoders

Curve fitting routines for astrophysical (SDSS) spectrographs

R package for convolution of Pareto distributions

Star Data Base

Python wrapper for the Powerlaw Collision Avoidance model

Code behind the FlightRank website and the data mining that went into it

This is a sample implementation of "Power-law Distribution Aware Trust Prediction", IJCAI 2018.

This project evolves around the concept of networks, centralities, and community detection, and infection simulation.

Shows the Bitcoin & Kaspa power law price channels

Simulation of Rayleigh Benard Convection of a powerlaw fluid in rectangular and cylindrical cavities using rheoTool

The project for the course "Web Information Retrieval"

CS Master’s thesis from SFU, Fall 2018

The entropy module can be found here: https://github.com/nikdon/pyEntropy/blob/master/pyentrp/entropy.py

Fit powerlaw distributions with a maximum-likelihood fitting procedure

DsFeatFreqDist – Dataset Feature-Frequency Distributions Fitting

A Bayesian take on fitting power law (and alternatives) to data