Maximum likelihood estimation of conditional logit models
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PyLogit is a Python package for performing maximum likelihood estimation of conditional logit models and similar discrete choice models.
Conditional Logit (Type) Models
Multinomial Asymmetric Models
Supports datasets where the choice set differs across observations
Supports model specifications where the coefficient for a given variable may be
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 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)
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:
Changed sparse matrix creation to enable estimation of larger datasets.
Refactored internal code organization and classes for estimation.
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.
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.
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.