Maximum likelihood estimation of conditional logit models
Project description
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
- 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)
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