Skip to content
View OpenActuarial's full-sized avatar

Block or report OpenActuarial

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
openactuarial/README.md

openactuarial

One install for the full OpenActuarial ecosystem.

PyPI Python

Overview

openactuarial is a meta-package: it contains no code of its own and exists to install the seven OpenActuarial packages in one step. The ecosystem covers general actuarial workflows — experience analysis, projection, rating and pricing, loss modeling, tail estimation, and portfolio simulation — as small packages that share conventions and compose across seams.

Installation

pip install openactuarial

Requires Python 3.10 or newer.

openactuarial pins exact versions of every package: each release installs the one combination that was tested together as a release train. If you want looser version ranges, install the individual packages instead — they declare compatible ranges (>=X.Y,<X.Y+1) rather than exact pins. Each package can also be installed individually (pip install actuarialpy, and so on).

What gets installed

Package Role
actuarialpy Calculation primitives, the canonical Experience data contract, and the ExperienceSet workbook layer (one construction call over source tables)
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; fits directly from a claims-listing Experience
extremeloss Extreme-value tails: POT/GPD, GEV, return levels, splicing; fits directly from a claims-listing Experience
risksim Portfolio Monte Carlo, dependence, reinsurance contracts, risk measures

Version policy

Dependencies are declared as open floors, so pip install openactuarial resolves to the latest release of every package. Cross-package compatibility is exercised nightly by the ecosystem smoke workflow, which reruns every package's test suite against current PyPI releases. For reproducible environments, pin the individual packages in your own requirements or constraints file.

Documentation

Full API references and nine end-to-end worked examples: openactuarial.org.

License

MIT — see LICENSE.

Popular repositories Loading

  1. lossmodels lossmodels Public

    Severity and frequency fitting and aggregate loss distributions: transformed beta and gamma catalogs, truncation- and censoring-aware MLE, FFT and Panjer.

    Python 1

  2. risksim risksim Public

    Portfolio Monte Carlo: aggregate simulation, Iman-Conover dependence, reinsurance layers, VaR/TVaR with bootstrap error.

    Python

  3. extremeloss extremeloss Public

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

    Python

  4. actuarialpy actuarialpy Public

    Purpose-neutral actuarial calculation primitives: metrics, reserving, trend, credibility, pooling, financial math. Foundation of the OpenActuarial ecosystem.

    Python

  5. docs docs Public

    Source for openactuarial.org: one Sphinx site covering all seven ecosystem packages, rebuilt on push, with nightly cross-package smoke tests.

    Makefile

  6. ratingmodels ratingmodels Public

    Manual and experience rating, credibility blending, and rate indication with an audit trail; GLM relativities and frequency-severity modeling.

    Python