{ "info": { "author": "UDST", "author_email": "", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Information Analysis" ], "description": "[![Build Status](https://travis-ci.org/UDST/choicemodels.svg?branch=master)](https://travis-ci.org/UDST/choicemodels)\n[![Coverage Status](https://coveralls.io/repos/github/UDST/choicemodels/badge.svg?branch=master)](https://coveralls.io/github/UDST/choicemodels?branch=master)\n[![Docs Status](https://readthedocs.org/projects/choicemodels/badge/?version=latest)](https://choicemodels.readthedocs.io)\n\n# ChoiceModels\n\nChoiceModels is a Python library for discrete choice modeling, with utilities for sampling, simulation, and other ancillary tasks. It's part of the [Urban Data Science Toolkit](https://github.com/udst>) (UDST).\n\n\n### Features\n\nThe library currently focuses on tools to help integrate discrete choice models into larger workflows, drawing on other packages such as the excellent [PyLogit](https://github.com/timothyb0912/pylogit) for most estimation of models. \n\nChoiceModels can automate the creation of choice tables for estimation or simulation, using uniform or weighted random sampling of alternatives, as well as interaction terms or cartesian merges. \n\nIt also provides general-purpose tools for Monte Carlo simulation of choices given probability distributions from fitted models, with fast algorithms for independent or capacity-constrained choices. \n\nChoiceModels includes a custom engine for Multinomial Logit estimation that's optimized for fast performance with large numbers of alternatives.\n\n\n### Installation\n\nChoiceModels can be installed using the Pip or Conda package managers:\n\n```\npip install 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