Welcome to ChoiceDesign’s documentation!

ChoiceDesign is a Python package for generating D-efficient designs for Discrete Choice Experiments (DCE). It provides a clean API for defining utility functions symbolically, optimising designs under constraints, and evaluating design quality with multiple optimality criteria.

User guide

• Getting Started
  • Installation
  • Basic workflow
  • Evaluating an existing design
• Concepts
  • Discrete choice experiments
  • D-error and optimality criteria
  • The expression system
  • Condition syntax
  • Stopping criteria
  • Optimisation algorithms
  • Utility balance
• Examples
  • Example of a D-efficient RUM design with ChoiceDesign
  • Example of a D-efficient RUM design with dummies in ChoiceDesign
  • Example of a D-efficient RUM design with alternative-specific constants (ASC) in ChoiceDesign
  • Example of a D-efficient RUM design with ChoiceDesign considering an opt-out alternative
  • Example of a D-efficient RUM design with availability conditions per alternative in ChoiceDesign
  • Example of a D-efficient RUM design with conditions in ChoiceDesign
  • Example of a D-efficient RUM design using the Modified Federov algorithm
  • Example of a D-efficient RUM design using the RSC algorithm
  • Example of an A-efficient RUM design with ChoiceDesign
  • Example of a C-efficient RUM design with ChoiceDesign
  • Example of a Db-efficient RUM design with ChoiceDesign
  • Example of a full-factorial design with ChoiceDesign

choicedesign.design	Classes for constructing efficient experimental designs.
choicedesign.expressions	Symbolic expression system for ChoiceDesign utility functions.
choicedesign.criteria	Optimality criteria for discrete choice experimental designs.
choicedesign.algorithms	Optimisation algorithms for efficient experimental designs.
choicedesign.utils	Utility functions for design generation and condition handling.