What PyLogit is

PyLogit is a Python package for performing maximum likelihood estimation of conditional logit models and similar discrete choice 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::

pip install pylogit

https://pypi.python.org/pypi/pylogit/0.1.2

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)

Changelog

0.2.2 (December 11, 2017)

• Changed tqdm dependency to allow for anaconda compatibility.

0.2.1 (December 11, 2017)

• Added statsmodels and tqdm as package dependencies to fix errors with 0.2.0.

0.2.0 (December 10, 2017)

• Added support for Python 3.4 - 3.6
• Added AIC and BIC to summary tables of all models.
• Added support for bootstrapping and calculation of bootstrap confidence intervals: - percentile intervals - bias-corrected and accelerated (BCa) bootstrap confidence intervals - approximate bootstrap confidence (ABC) intervals.
• Changed sparse matrix creation to enable estimation of larger datasets.
• Refactored internal code organization and classes for estimation.

0.1.2 (December 4th, 2016)

• Added support to all logit-type models for parameter constraints during model estimation. All models now support the use of the constrained_pos keyword argument.
• Added new argument checks to provide user-friendly error messages.
• Created more than 175 tests, bringing statement coverage to 99%.
• Added new example notebooks demonstrating prediction, mixed logit, and converting long-format datasets to wide-format.
• Edited docstrings for clarity throughout the library.
• Extensively refactored codebase.
• Updated the underflow and overflow protections to make use of L’Hopital’s rule where appropriate.
• Fixed bugs with the nested logit model. In particular, the predict function, the BHHH approximation to the Fisher Information Matrix, and the ridge regression penalty in the log-likelihood, gradient, and hessian functions have been fixed.

0.1.1 (August 30th, 2016)

• Added python notebook examples demonstrating how to estimate the asymmetric choice models and the nested logit model.
• Corrected the docstrings in various places.
• Added new datasets to the github repo.

0.1.0 (August 29th, 2016)

• Added asymmetric choice models.
• Added nested logit and mixed logit models.
• Added tests for mixed logit models.
• Fixed typos in library documentation.
• Made print statements compatible with python3.
• Changed documentation to numpy doctoring standard.
• Internal refactoring.
• Added an example notebook demonstrating how to estimate the mixed logit model.

0.0.0 (March 15th, 2016)

• Initial package release with support for the conditional logit (MNL) model.