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Fast inference over mean and covariance parameters for Generalised Linear Mixed Models

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Travis AppVeyor Documentation

Fast inference over mean and covariance parameters for Generalised Linear Mixed Models.

It implements the mathematical tricks of 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.


There are two main ways of installing it. Via pip:

pip install glimix-core

Or via conda:

conda install -c conda-forge glimix-core

Running the tests

After installation, you can test it

python -c "import glimix_core; glimix_core.test()"

as long as you have pytest.


Here it is a very simple example to get you started:

>>> 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.lml()

We also provide an extensive documentation about the library.



This project is licensed under the MIT License.

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