Extreme Value Theory Python Packages

powerlawrs

A fast Python library for analyzing Power-Law distributions in empirical data built on Rust.

unibm

Dependence-aware block-maxima inference for severity, persistence, and design-life levels.

nsevt

Calibrated GPD tail inference in Python: continuous and grouped fits, profile intervals, permutation trends, Monte Carlo calibration, and multi-source robustness.

quantlite

A fat-tail-native quantitative finance toolkit for Python.

extremeloss

Extreme value analysis for insurance losses: POT/GPD, GEV with uncertainty, threshold diagnostics, return levels, body-tail splicing.

heavytails

heavytails is a Python library implementing heavy-tailed probability distributions, built from first principles with NumPy-backed vectorized evaluation. The library provides comprehensive support for continuous and discrete heavy-tailed distributions, tail index estimation methods, and diagnostic utilities

tailestim

Estimate tail parameters of heavy-tailed distributions (including power law exponent gamma) in Python

insurance-severity

Severity modelling for insurance pricing - spliced distributions, DRN, composite regression, EQRN extreme quantiles

fitdistcp

Python package for fitting statistical models using calibrating priors.

tsrbench

EVT-based noise injection toolkit for evaluating time series forecasting robustness

pioneer-detection

Official Python implementation of the Pioneer Detection Method (PDM) — convergence-based expert aggregation and opinion pooling under structural change. Code for Vansteenberghe (2026), The Geneva Papers 51(1).

scikit-extremes

A specialized Python library for sparse multivariate extreme value analysis, structure learning, and robust spectral measure estimation using extremal graphical models.