pylogit 0.1.0

Maximum likelihood estimation of conditional logit models

What PyLogit is

PyLogit is a Python package for performing maximum likelihood estimation of conditional logit models and similar logit-like models.

Main Features

• Conditional Logit (Type) Models

  • Multinomial Logit Models

  • Multinomial Asymmetric Models

    • Multinomial Clog-log Model

    • Multinomial Scobit Model

    • Multinomial Uneven Logit Model

    • Multinomial Asymmetric Logit Model

  • Nested Logit Models

  • Mixed Logit Models (with Normal mixing distributions)

• Supports datasets where the choice set differs across observations

• Supports model specifications where the coefficient for a given variable may be

  • completely alternative-specific (i.e. one coefficient per alternative, subject to identification of the coefficients),

  • subset-specific (i.e. one coefficient per subset of alternatives, where each alternative belongs to only one subset, and there are more than 1 but less than J subsets, where J is the maximum number of available alternatives in the dataset),

  • completely generic (i.e. one coefficient across all alternatives).

Where to get it

Available from PyPi

https://pypi.python.org/pypi/pylogit/0.1.0

Available through Anaconda::

conda install -c timothyb0912 pylogit

For More Information

For more information about the asymmetric models that can be estimated with PyLogit, see the following paper

Brathwaite, Timothy, and Joan Walker. “Asymmetric, Closed-Form, Finite-Parameter Models of Multinomial Choice.” arXiv preprint arXiv:1606.05900 (2016). http://arxiv.org/abs/1606.05900.

Attribution

If PyLogit (or its constituent models) is useful in your research or work, please cite this package by citing the paper above.

License

Modified BSD (3-clause)