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        "author": "Joshua D. Loyal",
        "author_email": "jloyal25@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
        ],
        "description": ".. -*- mode: rst -*-\n\n|Travis|_ |AppVeyor|_ |Coveralls|_ |CircleCI|_ |License|_\n\n.. |Travis| image:: https://travis-ci.org/joshloyal/sliced.svg?branch=master\n.. _Travis: https://travis-ci.org/joshloyal/sliced\n\n.. |AppVeyor| image:: https://ci.appveyor.com/api/projects/status/54j060q1ukol1wnu/branch/master?svg=true\n.. _AppVeyor: https://ci.appveyor.com/project/joshloyal/sliced/history\n\n.. |Coveralls| image:: https://coveralls.io/repos/github/joshloyal/sliced/badge.svg?branch=master\n.. _Coveralls: https://coveralls.io/github/joshloyal/sliced?branch=master\n\n.. |CircleCI| image:: https://circleci.com/gh/joshloyal/sliced/tree/master.svg?style=svg\n.. _CircleCI: https://circleci.com/gh/joshloyal/sliced/tree/master\n\n.. |License| image:: https://img.shields.io/badge/License-MIT-blue.svg\n.. _License: https://opensource.org/licenses/MIT\n\n.. _scikit-learn: https://github.com/scikit-learn/scikit-learn\n\nsliced\n======\nsliced is a python package offering a number of sufficient dimension reduction (SDR) techniques commonly used in high-dimensional datasets with a supervised target. It is compatible with scikit-learn_.\n\n\nAlgorithms supported:\n\n- Sliced Inverse Regression (SIR) [1]_\n- Sliced Average Variance Estimation (SAVE) [2]_\n\nDocumentation / Website: https://joshloyal.github.io/sliced/\n\n\nExample\n-------\nExample that shows how to learn a one dimensional subspace from a dataset with ten features:\n\n.. code-block:: python\n\n   from sliced.datasets import make_cubic\n   from sliced import SlicedInverseRegression\n\n   # load the 10-dimensional dataset\n   X, y = make_cubic(random_state=123)\n\n   # Set the options for SIR\n   sir = SlicedInverseRegression(n_directions=1)\n\n   # fit the model\n   sir.fit(X, y)\n\n   # transform into the new subspace\n   X_sir = sir.transform(X)\n\n\nInstallation\n------------\n\nDependencies\n------------\nsliced requires:\n\n- Python (>= 2.7 or >= 3.4)\n- NumPy (>= 1.8.2)\n- SciPy (>= 0.13.3)\n- Scikit-learn (>=0.17)\n\nAdditionally, to run examples, you need matplotlib(>=2.0.0).\n\nInstallation\n------------\nYou need a working installation of numpy and scipy to install sliced. If you have a working installation of numpy and scipy, the easiest way to install sliced is using ``pip``::\n\n    pip install -U sliced\n\nIf you prefer, you can clone the repository and run the setup.py file. Use the following commands to get the copy from GitHub and install all the dependencies::\n\n    git clone https://github.com/joshloyal/sliced.git\n    cd sliced\n    pip install .\n\nOr install using pip and GitHub::\n\n    pip install -U git+https://github.com/joshloyal/sliced.git\n\nTesting\n-------\nAfter installation, you can use pytest to run the test suite via setup.py::\n\n    python setup.py test\n\nReferences:\n-----------\n.. [1] : Li, K C. (1991)\n        \"Sliced Inverse Regression for Dimension Reduction (with discussion)\",\n        Journal of the American Statistical Association, 86, 316-342.\n.. [2] : Shao, Y, Cook, RD and Weisberg, S (2007).\n         \"Marginal Tests with Sliced Average Variance Estimation\",\n         Biometrika, 94, 285-296.\n\n\n",
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