docs styling

docs/source/index.rst

@@ -6,31 +6,31 @@
 making Bayesian modelling easy(er)
 ==================================
 
-``uravu`` is about the relationship between some data and a function that may be used to describe the data. 
+:py:mod:`uravu` is about the relationship between some data and a function that may be used to describe the data. 
 
-The aim of ``uravu`` is to make using the **amazing** Bayesian inference libraries that are available in Python as easy as `scipy.optimize.curve_fit`_.
+The aim of :py:mod:`uravu` is to make using the **amazing** Bayesian inference libraries that are available in Python as easy as :py:function:`scipy.optimize.curve_fit`.
 Therefore enabling many more to make use of these exciting tools and powerful libraries.
 Plus, we have some nice plotting functionalities available in the :mod:`plotting` module, capable of generating publication quality figures.
 
 .. image:: sample_fig.png
   :alt: An example of the type of figures that uravu can produce. Showing straight line distribution with increasing uncertainty. 
 
-In an effort to make the ``uravu`` API friendly to those new to Bayesian inference, ``uravu`` is *opinionated*, making assumptions about priors amoung other things. 
+In an effort to make the :py:mod:`uravu` API friendly to those new to Bayesian inference, :py:mod:`uravu` is *opinionated*, making assumptions about priors amoung other things. 
 However, we have endevoured to make it straightforward to ignore these opinions.
 
 In addition to the library and API, we also have some `basic tutorials`_ discussing how Bayesian inference methods can be used in the analysis of data. 
 
-``uravu`` is under active development, more details of which can be found on `Github`_.
+:py:mod:`uravu` is under active development, more details of which can be found on `Github`_.
 
 Bayesian inference in Python
 ----------------------------
 
-There are a couple of fantastic Bayesian inference libraries available in Python that `uravu` makes use of:
+There are a couple of fantastic Bayesian inference libraries available in Python that :py:mod:`uravu` makes use of:
 
-- `emcee`_: enables the use of the `Goodman & Weare’s Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler`_ to evaluate the structure of the model parameter posterior distributions,
-- `dynesty`_: implements the `nested sampling`_ algorithm for evidence estimation.
+- :py:mod:`emcee`: enables the use of the `Goodman & Weare’s Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler`_ to evaluate the structure of the model parameter posterior distributions,
+- :py:mod:`dynesty`: implements the `nested sampling`_ algorithm for evidence estimation.
 
-To see which publications you should reference in your work, the :class:`Relationship` has a property that will return the relevant citations based on what analyses have been performed.
+To see which publications you should reference in your work, the :py:class:`~uravu.relationship.Relationship` has a property that will return the relevant citations based on what analyses have been performed.
 
 .. code-block:: python 
 
@@ -43,11 +43,8 @@ To see which publications you should reference in your work, the :class:`Relatio
    
    print(modeller.citations)
 
-.. _scipy.optimize.curve_fit: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html
 .. _basic tutorials: ./tutorials.html
 .. _Github: https://github.com/arm61/uravu
-.. _emcee: https://emcee.readthedocs.io/
-.. _dynesty: https://dynesty.readthedocs.io/
 .. _Goodman & Weare’s Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler: https://doi.org/10.2140/camcos.2010.5.65
 .. _nested sampling: https://doi.org/10.1063/1.1835238
 

docs/source/installation.rst

@@ -1,7 +1,7 @@
 Installation
 ============
 
-``uravu`` can be installed from the PyPI package manager with ``pip``:
+:py:mod:`uravu` can be installed from the PyPI package manager with py:mod:`pip`:
 
 .. code-block:: bash