Guide
************

**Introduction**

sGWAS is a python library for performing regression with correlated observations within-class.


In the sGWAS/bin there is a script: 'sGWAS.py' (:doc:`sGWAS`). This script performs GWAS that fits both within and between family effects.

The core model is the sibreg model (:class:`sibreg.model`), which consists of a linear model for the mean along
with a vector of class labels that allows for correlations within-class. (The correlations within-class result
from modelling mean differences between classes as independent, normally distributed random effects.) For
the application in the script 'sGWAS.py', the classes are the families, so correlations are modelled
between all siblings in a family.

The documentation for the sibreg module (:doc:`sibreg`) contains information on how to define a :class:`sibreg.model`,
how to optimise a :class:`sibreg.model`, how to predict from
a :class:`sibreg.model`, and how to simulate a :class:`sibreg.model`.

**Running tests**

To check that the code is working properly and computing likelihoods and gradients accurately, you can
run tests. In the sGWAS/tests subdirectory, type

    ``python tests.py``

The output should say

    ``Ran 4 tests in...``

    ``OK``