Features

• Parameter Estimation: Estimates the parameters (x_min, alpha) of a power-law distribution from data.

• Goodness-of-Fit: Uses the Kolmogorov-Smirnov (KS) statistic to find the best-fitting parameters.

• Data Visualization: Includes a plot() method to visually inspect the data and the fitted model on a log-log scale.

• Vuongs Closeness Test: Model selection by comparing vectors of Log-Likelihoods from two distributions.

• Additional Distributions: Provides functionality for other distributions, such as the exponential distribution.

• High Performance: Computationally intensive tasks are parallelized in the Rust core for significant speedups.

• Flexible API: Offers both a simple functional API for quick analyses and a class-based API for more detailed work.