Metadata-Version: 1.1
Name: choicemodels
Version: 0.1
Summary: Tools for discrete choice estimation
Home-page: https://github.com/udst/choicemodels
Author: UDST
Author-email: UNKNOWN
License: UNKNOWN
Description-Content-Type: UNKNOWN
Description: [![Build Status](https://travis-ci.org/UDST/choicemodels.svg?branch=master)](https://travis-ci.org/UDST/choicemodels)
        [![Coverage Status](https://coveralls.io/repos/github/UDST/choicemodels/badge.svg?branch=master)](https://coveralls.io/github/UDST/choicemodels?branch=master)
        
        # 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](https://choicemodels.readthedocs.io/).
        
        
        
        ## Installation
        
        Install with pip:
        
        `pip install choicemodels`
        
        
        
        ## Current functionality
        
        `choicemodels.tools.MergedChoiceTable()`
        
        - Generates a merged long-format table of choosers and alternatives.
        
        `choicemodels.MultinomialLogit()`
        
        - Fits MNL models, using either the ChoiceModels or PyLogit estimation engines.
        
        `chociemodels.MultinomialLogitResults()`
        
        - Stores and reports fitted MNL models.
        
        There's documentation in these classes' docstrings, and a usage demo in a Jupyter notebook.
        
        https://github.com/udst/choicemodels/blob/master/notebooks/Destination-choice-models-02.ipynb
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: BSD License
