complexity-science

You can test if your data follows a power-law distribution by estimating scaling exponents and statistically comparing the power-law fit against alternative distributions like lognormal or exponential.

You estimate the Hurst exponent and sample entropy for a time series by applying R/S-based long-range dependence estimation and information-theoretic calculations to quantify temporal persistence and complexity in your data.

Yes, you can run agent-based modeling simulations using Mesa to simulate emergent dynamics, such as executing Schelling segregation models to quantify emergent ordering within complex systems.

Yes, this approach supports estimating bond percolation thresholds using Monte Carlo methods and measuring fractal structure by computing fractal dimensions via box-counting estimation for spatial complexity.

You compute Shannon entropy and mutual information to compare signals by applying information-theoretic analysis, which calculates joint entropy and normalized mutual information to relate dependencies between processes.

The best way to analyze a citation network for scale-free connectivity is to test the degree sequence for power-law behavior to determine if the network connectivity exhibits heavy-tailed distribution scaling.