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        "author": "Jeff Gortmaker",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
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        "description": "An overview of the model, examples, references, and other documentation can be found on `Read the Docs <https://pyblp.readthedocs.io/en/stable/>`_.\n\n.. docs-start\n\nThe pyblp package is a Python 3 implementation of routines for estimating the demand for differentiated products with BLP-type random coefficients logit models. This package was created by `Jeff Gortmaker <https://jeffgortmaker.com/>`_ in collaboration with `Chris Conlon <https://chrisconlon.github.io/>`_.\n\nDevelopment of the package has been guided by the work of many researchers and practitioners. For a full list of references, including the original work of `Berry, Levinsohn, and Pakes (1995) <https://ideas.repec.org/a/ecm/emetrp/v63y1995i4p841-90.html>`_, refer to the `references <https://pyblp.readthedocs.io/en/stable/references.html>`_ section of the documentation.\n\n\nCitation\n--------\n\nIf you use pyblp in your research, we ask that you also cite `Conlon and Gortmaker (2019) <https://jeffgortmaker.com/files/pyblp.pdf>`_, which describes the advances implemented in the package.\n\n\nInstallation\n------------\n\nThe pyblp package has been tested on `Python <https://www.python.org/downloads/>`_ versions 3.6 and 3.7. The `SciPy instructions <https://scipy.org/install.html>`_ for installing related packages is a good guide for how to install a scientific Python environment. A good choice is the `Anaconda Distribution <https://www.anaconda.com/distribution/>`_, since, along with many other packages that are useful for scientific computing, it comes packaged with pyblp's only required dependencies: `NumPy <https://numpy.org/>`_, `SciPy <https://www.scipy.org/>`_, `SymPy <https://www.sympy.org/en/index.html>`_, and `Patsy <https://patsy.readthedocs.io/en/latest/>`_.\n\nHowever, pyblp may not work with old versions of its dependencies. You can update pyblp's dependencies in Anaconda with::\n\n    conda update numpy scipy sympy patsy\n\nYou can install the current release of pyblp with `pip <https://pip.pypa.io/en/latest/>`_::\n\n    pip install pyblp\n\nYou can upgrade to a newer release with the ``--upgrade`` flag::\n\n    pip install --upgrade pyblp\n\nIf you lack permissions, you can install pyblp in your user directory with the ``--user`` flag::\n\n    pip install --user pyblp\n\nAlternatively, you can download a wheel or source archive from `PyPI <https://pypi.org/project/pyblp/>`_. You can find the latest development code on `GitHub <https://github.com/jeffgortmaker/pyblp/>`_ and the latest development documentation `here <https://pyblp.readthedocs.io/en/latest/>`_.\n\n\nOther Languages\n---------------\n\nOnce installed, pyblp can be incorporated into projects written in many other languages with the help of various tools that enable interoperability with Python.\n\nFor example, the `reticulate <https://github.com/rstudio/reticulate>`_ package makes interacting with pyblp in R straightforward::\n\n    library(reticulate)\n    pyblp <- import(\"pyblp\")\n\nSimilarly, `PyCall <https://github.com/JuliaPy/PyCall.jl>`_ can be used to incorporate pyblp into a Julia workflow::\n\n    using PyCall\n    pyblp = pyimport(\"pyblp\")\n\nThe `py command <https://www.mathworks.com/help/matlab/call-python-libraries.html>`_ serves a similar purpose in MATLAB::\n\n   py.pyblp\n\n\nFeatures\n--------\n\n- R-style formula interface\n- Bertrand-Nash supply-side moments\n- Multiple equation GMM\n- Demographic interactions\n- Micro moments that match product and agent characteristic covariances\n- Fixed effect absorption\n- Nonlinear functions of product characteristics\n- Concentrating out linear parameters\n- Parameter bounds and constraints\n- Random coefficients nested logit (RCNL)\n- Varying nesting parameters across groups\n- Logit and nested logit benchmarks\n- Classic BLP instruments\n- Differentiation instruments\n- Optimal instruments\n- Tests of overidentifying and model restrictions\n- Parametric boostrapping post-estimation outputs\n- Elasticities and diversion ratios\n- Marginal costs and markups\n- Profits and consumer surplus\n- Merger simulation\n- Custom counterfactual simulation\n- Synthetic data construction\n- SciPy or Artleys Knitro optimization\n- Fixed point acceleration\n- Monte Carlo, product rule, or sparse grid integration\n- Custom optimization and iteration routines\n- Robust and clustered errors\n- Linear or log-linear marginal costs\n- Partial ownership matrices\n- Analytic gradients\n- Finite difference Hessians\n- Market-by-market parallelization\n- Extended floating point precision\n- Robust error handling\n\n\nFeatures Slated for Future Versions\n-----------------------------------\n\n- Other micro moments\n- Fast, \"Robust,\" and Approximately Correct (FRAC) estimation\n- Analytic Hessians\n- Mathematical Program with Equilibrium Constraints (MPEC)\n- Generalized Empirical Likelihood (GEL)\n- Discrete types\n- Pure characteristics model\n- Newton methods for computing equilibrium prices\n\n\nBugs and Requests\n-----------------\n\nPlease use the `GitHub issue tracker <https://github.com/jeffgortmaker/pyblp/issues>`_ to submit bugs or to request features.\n\n",
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