chainladder (python)
chainladder - Property and Casualty Loss Reserving in Python
This package gets inspiration from the popular R ChainLadder package.
This package strives to be minimalistic in needing its own API. Think in pandas for data manipulation and scikit-learn for model construction. An actuary already versed in these tools will pick up this package with ease. Save your mental energy for actuarial work.
Documentation
Please visit the Documentation page for examples, how-tos, and source code documentation.
Available Estimators
chainladder
has an ever growing list of estimators that work seemlessly together:
Loss Development | Tails Factors | IBNR Models | Adjustments | Workflow |
---|---|---|---|---|
Development | TailCurve | Chainladder | BootstrapODPSample | VotingChainladder |
DevelopmentConstant | TailConstant | MackChainladder | BerquistSherman | Pipeline |
MunichAdjustment | TailBondy | BornhuettterFerguson | ParallelogramOLF | GridSearch |
ClarkLDF | TailClark | Benktander | Trend | |
IncrementalAdditive | CapeCod | |||
CaseOutstanding | ||||
TweedieGLM | ||||
DevelopmentML | ||||
BarnettZehnwirth |
Getting Started Tutorials
Tutorial notebooks are available for download here.
- Working with Triangles
- Selecting Development Patterns
- Extending Development Patterns with Tails
- Applying Deterministic Methods
- Applying Stochastic Methods
- Large Datasets
Installation
To install using pip: pip install chainladder
To instal using conda: conda install -c conda-forge chainladder
Alternatively for pre-release functionality, install directly from github: pip install git+https://github.com/casact/chainladder-python/
Note: This package requires Python>=3.5 pandas 0.23.0 and later, sparse 0.9 and later, scikit-learn 0.23.0 and later.
Questions or Ideas?
Join in on the github discussions. Your question is more likely to get answered here than on Stack Overflow. We're always happy to answer any usage questions or hear ideas on how to make chainladder
better.
Want to contribute?
Check out our contributing guidelines.