Metadata-Version: 2.1
Name: pylogit
Version: 1.0.1
Summary: Created on Mon Mar 14 15:33:07 2016
Home-page: https://github.com/timothyb0912/pylogit
License: BSD-3-Clause
Keywords: conditional logit,discrete choice,econometrics,choice models
Author: Timothy Brathwaite
Author-email: timothyb0912@gmail.com
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Environment :: Console
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: License :: OSI Approved :: BSD License
Requires-Dist: pandas >= 0.16.2
Requires-Dist: numpy >= 1.10.2
Requires-Dist: scipy >= 0.16.1
Requires-Dist: future >= 0.16
Requires-Dist: statsmodels >= 0.6.1
Requires-Dist: tqdm >= 4.15.0

![PyLogit Logo](./images/PyLogit_Final-small-04.png)

![Tests](https://github.com/timothyb0912/pylogit/workflows/Testing/badge.svg)

# 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](https://pypi.python.org/pypi/pylogit):
```
pip install pylogit
```

Available through [Anaconda](https://anaconda.org/conda-forge/pylogit):
```
conda install -c conda-forge pylogit
```

or

```
conda install -c timothyb0912 pylogit
```

## Usage
For Jupyter notebooks filled with examples, see [examples](./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](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). See [here](./LICENSE.txt).

## Changelog
See [here](./CHANGELOG.rst).

