Metadata-Version: 2.1
Name: glimix-core
Version: 1.4.0
Summary: Fast inference over mean and covariance parameters for Generalised Linear Mixed Models
Home-page: https://github.com/limix/glimix-core
Author: Danilo Horta
Author-email: horta@ebi.ac.uk
Maintainer: Danilo Horta
Maintainer-email: horta@ebi.ac.uk
License: MIT
Download-URL: https://github.com/limix/glimix-core
Description: glimix-core
        ===========
        
        |Travis| |AppVeyor| |Documentation|
        
        Fast inference over mean and covariance parameters for Generalised
        Linear Mixed Models.
        
        It implements the mathematical tricks of
        `FaST-LMM <https://github.com/MicrosoftGenomics/FaST-LMM>`__ for the
        special case of Linear Mixed Models with a linear covariance matrix and
        provides an interface to perform inference over millions of covariates
        in seconds. The Generalised Linear Mixed Model inference is implemented
        via Expectation Propagation and also makes use of several mathematical
        tricks to handle large data sets with thousands of samples and millions
        of covariates.
        
        Install
        -------
        
        We recommend installing it via
        `conda <http://conda.pydata.org/docs/index.html>`__:
        
        .. code:: bash
        
            conda install -c conda-forge glimix-core
        
        Alternatively, glimix-core can also be installed using
        `pip <https://pypi.python.org/pypi/pip>`__:
        
        .. code:: bash
        
            pip install glimix-core
        
        The second installation option requires from the user to install
        `liknorm <https://github.com/limix/liknorm>`__ beforehand.
        
        Running the tests
        -----------------
        
        After installation, you can test it
        
        .. code:: bash
        
            python -c "import glimix_core; glimix_core.test()"
        
        as long as you have `pytest <https://docs.pytest.org/en/latest/>`__.
        
        Usage
        -----
        
        Here it is a very simple example to get you started:
        
        .. code:: python
        
            >>> from numpy import array, ones
            >>> from numpy_sugar.linalg import economic_qs_linear
            >>> from glimix_core.lmm import LMM
            >>>
            >>> X = array([[1, 2], [3, -1], [1.1, 0.5], [0.5, -0.4]], float)
            >>> QS = economic_qs_linear(X)
            >>> X = ones((4, 1))
            >>> y = array([-1, 2, 0.3, 0.5])
            >>> lmm = LMM(y, X, QS)
            >>> lmm.fit(verbose=False)
            >>> lmm.lml()
            -2.2726234086180557
        
        An extensive documentation of the library can be found at
        http://glimix-core.readthedocs.org/.
        
        Authors
        -------
        
        -  `Danilo Horta <https://github.com/horta>`__
        
        License
        -------
        
        This project is licensed under the `MIT
        License <https://raw.githubusercontent.com/limix/glimix-core/master/LICENSE.md>`__.
        
        .. |Travis| image:: https://img.shields.io/travis/limix/glimix-core.svg?style=flat-square&label=linux%20%2F%20macos%20build
           :target: https://travis-ci.org/limix/glimix-core
        .. |AppVeyor| image:: https://img.shields.io/appveyor/ci/Horta/glimix-core.svg?style=flat-square&label=windows%20build
           :target: https://ci.appveyor.com/project/Horta/glimix-core
        .. |Documentation| image:: https://img.shields.io/readthedocs/glimix-core.svg?style=flat-square&version=stable
           :target: https://glimix-core.readthedocs.io/
        
Keywords: function,optimisation
Platform: Windows
Platform: MacOS
Platform: Linux
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Description-Content-Type: text/markdown
