Skip to main content

Tools for discrete choice estimation

Project description

[![Build Status](]( [![Coverage Status](](

# ChoiceModels

This is a package for discrete choice model estimation and simulation, with an emphasis on large choice sets and behavioral refinements to multinomial models. Most of these models are not available in Statsmodels or Scikit-learn.

The underlying estimation routines come from two main places: (1) the urbanchoice codebase, which has been moved into ChoiceModels, and (2) Timothy Brathwaite’s PyLogit package, which handles more flexible model specifications.

## Documentation

Package documentation is available on [readthedocs](

## Installation

Install with pip:

pip install choicemodels

## Current functionality

  • Generates a merged long-format table of choosers and alternatives.


  • Fits MNL models, using either the ChoiceModels or PyLogit estimation engines.


  • Stores and reports fitted MNL models.

There’s documentation in these classes’ docstrings, and a usage demo in a Jupyter notebook.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
choicemodels-0.1.1-py2.py3-none-any.whl (20.1 kB) Copy SHA256 hash SHA256 Wheel py2.py3
choicemodels-0.1.1.tar.gz (17.1 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page