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        "author": "Timothy Brathwaite",
        "author_email": "timothyb0912@berkeley.edu",
        "bugtrack_url": null,
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            "Development Status :: 2 - Pre-Alpha",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: End Users/Desktop",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering"
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        "description": ".. image:: https://travis-ci.org/timothyb0912/pylogit.svg?branch=master\n    :target: https://travis-ci.org/timothyb0912/pylogit\n.. image:: https://coveralls.io/repos/github/timothyb0912/pylogit/badge.svg?branch=master\n\nWhat PyLogit is\n===============\nPyLogit is a Python package for performing maximum likelihood estimation of conditional logit models and similar discrete choice models.\n\nMain Features\n=============\n\n* Conditional Logit (Type) Models\n\n   - Multinomial Logit Models\n   - Multinomial Asymmetric Models\n\n      + Multinomial Clog-log Model\n      + Multinomial Scobit Model\n      + Multinomial Uneven Logit Model\n      + Multinomial Asymmetric Logit Model\n   - Nested Logit Models\n   - Mixed Logit Models (with Normal mixing distributions)\n\n* Supports datasets where the choice set differs across observations\n* Supports model specifications where the coefficient for a given variable may be\n\n   - completely alternative-specific (i.e. one coefficient per alternative, subject to identification of the coefficients),\n   - 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),\n   - completely generic (i.e. one coefficient across all alternatives).\n\nWhere to get it\n===============\nAvailable from PyPi::\n    pip install pylogit\n\n    https://pypi.python.org/pypi/pylogit/0.1.2\n\nAvailable through Anaconda::\n    conda install -c timothyb0912 pylogit\n\nFor More Information\n====================\nFor more information about the asymmetric models that can be estimated with PyLogit, see the following paper\n    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.\n\nAttribution\n===========\nIf PyLogit (or its constituent models) is useful in your research or work, please cite this package by citing the paper above.\n\nLicense\n=======\nModified BSD (3-clause)\n\nChangelog\n=========\n\n0.2.2 (December 11, 2017)\n-------------------------\n- Changed tqdm dependency to allow for anaconda compatibility.\n\n0.2.1 (December 11, 2017)\n-------------------------\n- Added statsmodels and tqdm as package dependencies to fix errors with 0.2.0.\n\n0.2.0 (December 10, 2017)\n-------------------------\n- Added support for Python 3.4 - 3.6\n\n- Added AIC and BIC to summary tables of all models.\n\n- Added support for bootstrapping and calculation of bootstrap confidence intervals:\n  - percentile intervals\n  - bias-corrected and accelerated (BCa) bootstrap confidence intervals\n  - approximate bootstrap confidence (ABC) intervals.\n\n- Changed sparse matrix creation to enable estimation of larger datasets.\n\n- Refactored internal code organization and classes for estimation.\n\n0.1.2 (December 4th, 2016)\n--------------------------\n- 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.\n\n- Added new argument checks to provide user-friendly error messages.\n\n- Created more than 175 tests, bringing statement coverage to 99%.\n\n- Added new example notebooks demonstrating prediction, mixed logit, and converting long-format datasets to wide-format.\n\n- Edited docstrings for clarity throughout the library.\n\n- Extensively refactored codebase.\n\n- Updated the underflow and overflow protections to make use of L\u2019Hopital\u2019s rule where appropriate.\n\n- 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.\n\n0.1.1 (August 30th, 2016)\n-------------------------\n- Added python notebook examples demonstrating how to estimate the asymmetric choice models and the nested logit model.\n\n- Corrected the docstrings in various places.\n\n- Added new datasets to the github repo.\n\n0.1.0 (August 29th, 2016)\n-------------------------\n- Added asymmetric choice models.\n\n- Added nested logit and mixed logit models.\n\n- Added tests for mixed logit models.\n\n- Fixed typos in library documentation.\n\n- Made print statements compatible with python3.\n\n- Changed documentation to numpy doctoring standard.\n\n- Internal refactoring.\n\n- Added an example notebook demonstrating how to estimate the mixed logit model.\n\n0.0.0 (March 15th, 2016)\n-------------------------\n- Initial package release with support for the conditional logit (MNL) model.\n\n\n",
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