heavytails

A pure-Python library of heavy-tailed probability distributions

heavytails implements continuous and discrete heavy-tailed distributions, tail index estimators, and diagnostic utilities — using only the Python standard library. Every density, quantile and sampler is derived from first principles, so the implementation can be read, checked and taught rather than taken on faith.

It targets research, teaching and simulation work in risk, finance, insurance and extreme-value analysis.

Features

• No runtime dependencies. The library imports nothing outside math, random and friends, so it installs anywhere Python does.
• Complete distribution interface. PDF/PMF, CDF, survival function, quantile function and random sampling for every family, with survival functions computed directly so they stay accurate far into the tail where 1 - cdf(x) has lost every significant digit.
• Reproducible sampling through a deterministic RNG wrapper.
• Special functions from scratch — incomplete gamma and incomplete beta — plus a safeguarded-Newton numeric PPF for families with no closed form.
• Tail index estimation with the Hill, Pickands and moment estimators.
• Parameter fitting by maximum likelihood and method of moments, with AIC/BIC model comparison.
• Diagnostics for log–log tail plots and QQ plots.
• A command-line interface for sampling, fitting, comparison and benchmarking.
• Typed throughout, with a py.typed marker so downstream type checkers see the annotations.

Installation

pip install heavytails

The command-line interface needs two extra packages; install it with the cli extra:

pip install "heavytails[cli]"

To work on the library itself:

git clone https://github.com/DiogoRibeiro7/heavytails.git
cd heavytails
poetry install --with dev,docs

Requires Python 3.10 or newer.

Quick start

from heavytails import BurrXII, Pareto, hill_estimator

pareto = Pareto(alpha=1.5, xm=1.0)

pareto.pdf(2.0)        # density
pareto.cdf(2.0)        # distribution function
pareto.sf(10.0)        # survival function: P(X > 10)
pareto.ppf(0.99)       # 99th percentile
samples = pareto.rvs(10_000, seed=42)

# Recover the tail index from the sample. The estimators return the
# extreme-value index gamma = 1 / alpha, so invert it to read alpha back.
gamma = hill_estimator(samples, k=100)   # ≈ 0.65
alpha = 1 / gamma                        # ≈ 1.53, against a true 1.5

burr = BurrXII(c=1.2, k=2.5, s=3.0)
burr.ppf(0.95)

Command line

heavytails list-distributions
heavytails sample pareto --params '{"alpha": 2.0, "xm": 1.0}' -n 1000 -o samples.txt
heavytails estimate-tail samples.txt --method hill
heavytails compare samples.txt

Run heavytails --help for the full command list.

Available distributions

Continuous

Discrete

Every continuous family provides pdf, cdf, sf, ppf and rvs; every discrete family provides pmf, cdf, ppf and rvs.

Estimation and diagnostics

Documentation

Full documentation, including the mathematical background, is at https://diogoribeiro7.github.io/heavytails.

To build it locally:

make docs-serve

Development

make install-dev   # install every dependency group
make hooks         # install the pre-commit hooks
make check         # everything CI runs: lint, format, types, tests, security

Individual targets are listed by make help. Contributions are welcome — see CONTRIBUTING.md for the branch flow, commit conventions and review process, and ROADMAP.md for what is planned next.

Notable changes are recorded in CHANGELOG.md.

License

MIT License © 2025 Diogo Ribeiro. See LICENSE.

Citation

If you use this package in research or teaching, please cite it. GitHub's "Cite this repository" button reads CITATION.cff, or use:

Ribeiro, D. (2025). heavytails: A Pure-Python Library for Heavy-Tailed Probability Distributions. https://github.com/DiogoRibeiro7/heavytails

Shared citation metadata is maintained in CITATION.cff; Zenodo-specific archive metadata is maintained in .zenodo.json. The DOI will be added after the first archived GitHub release.