A python package for estimating conditional logit models.
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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


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.


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


Modified BSD (3-clause)


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.