Metadata-Version: 1.1
Name: pylogit
Version: 0.2.2
Summary: Maximum likelihood estimation of conditional logit models
Home-page: http://github.com/timothyb0912/pylogit
Author: Timothy Brathwaite
Author-email: timothyb0912@berkeley.edu
License: BSD License
Description-Content-Type: UNKNOWN
Description: .. image:: https://travis-ci.org/timothyb0912/pylogit.svg?branch=master
            :target: https://travis-ci.org/timothyb0912/pylogit
        .. image:: https://coveralls.io/repos/github/timothyb0912/pylogit/badge.svg?branch=master
        
        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
        
            https://pypi.python.org/pypi/pylogit/0.1.2
        
        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)
        
        Changelog
        =========
        
        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.
        
Keywords: conditional logit discrete choice econometrics
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
