lossmodels

Severity and frequency fitting and aggregate loss distributions.

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Overview

lossmodels covers the loss-distribution workflow from data to aggregate: a severity catalog spanning the classic distributions and the transformed beta and transformed gamma families, frequency models, maximum likelihood that is truncation- and censoring-aware, fit diagnostics and model selection, and aggregate distributions via simulation, FFT, and Panjer recursion.

Every severity model shares one interface — moments, quantiles, limited expected values, increased-limits tables — so fitted models drop directly into coverage modifications, aggregates, and the wider ecosystem.

Installation

pip install lossmodels

Requires Python 3.10 or newer.

Quick start

from lossmodels import Poisson, Lognormal, CollectiveRiskModel

freq = Poisson(lam=2.0)
sev = Lognormal(mu=10.0, sigma=0.8)
model = CollectiveRiskModel(freq, sev)

print("Mean     :", model.mean())
print("Variance :", model.variance())
print("VaR 95%  :", model.var(0.95))
print("TVaR 95% :", model.tvar(0.95))

samples = model.sample(50_000, rng=0)
print("Simulated mean:", samples.mean())

What's inside

  • Severity — classic and extended catalogs (including the transformed beta and transformed gamma families) behind one shared interface, plus increased-limits and loss-elimination tables.
  • Frequency — Poisson, negative binomial, and related counting models.
  • Estimation — MLE with truncation and censoring support, parameter uncertainty, goodness-of-fit diagnostics, and model comparison.
  • Aggregate — CollectiveRiskModel simulation, FFT and Panjer recursion, stop-loss and risk measures.
  • Coverage — deductibles, limits, and coinsurance applied analytically.
  • Empirical — nonparametric counterparts for validation.

The full API reference and end-to-end worked examples live at openactuarial.org/lossmodels.html.

The OpenActuarial ecosystem

lossmodels is one of seven packages that share conventions — tidy tables, explicit distribution parameterizations, reproducible random-number handling — and compose across package seams:

Package Role
actuarialpy Calculation primitives the workflow packages build on
experiencestudies Experience reporting, actual-vs-expected, claimant and concentration analysis
projectionmodels Claim, premium, and expense projection over a renewal horizon
ratingmodels Manual and experience rating, credibility, indication, GLM relativities
lossmodels Severity and frequency fitting, aggregate loss distributions
extremeloss Extreme-value tails: POT/GPD, GEV, return levels, splicing
risksim Portfolio Monte Carlo, dependence, reinsurance contracts, risk measures

Install everything at once with pip install openactuarial.

Development

git clone https://github.com/OpenActuarial/lossmodels
cd lossmodels
python -m pip install -e ".[dev]"
pytest
ruff check src tests

CI runs the same gate on Python 3.10–3.14 across Linux and Windows.

Versioning and stability

All ecosystem packages are pre-1.0: minor releases may change APIs, and every release is documented in CHANGELOG.md. Current per-package API stability is tracked at openactuarial.org/stability.html.

License

MIT — see LICENSE.

Project links

Data verified by PyPI on Jul 12, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.8.2)

1 maintainer

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Avatar for michaelabryant from gravatar.com michaelabryant

Credits

Author: Michael Bryant

GitHub Statistics

Data verified by PyPI on Jul 12, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.10

Provides Extra

actuarialpy dev

Tags

actuarial frequency severity insurance loss modeling risk simulation statistics

Classifiers

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