chainladder 0.8.6

Chainladder Package - P&C Loss Reserving package

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.