pyl

pylogit

A python package for estimating conditional logit models.

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PyLogit

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

Main Features

  • It supports
    • 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)
  • It supports datasets where the choice set differs across observations
  • It 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).

Installation

Available from PyPi:

pip install pylogit

Available through Anaconda:

conda install -c conda-forge pylogit

or

conda install -c timothyb0912 pylogit

Usage

For Jupyter notebooks filled with examples, see examples.

For More Information

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

Brathwaite, T., & Walker, J. L. (2018). Asymmetric, closed-form, finite-parameter models of multinomial choice. Journal of Choice Modelling, 29, 78–112. https://doi.org/10.1016/j.jocm.2018.01.002

A free and better formatted version is available at ArXiv.

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). See here.

Changelog

See here.

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