tim edits

paper/paper.md

@@ -10,36 +10,43 @@ tags:
 authors:
   - name: Andrew R. McCluskey
     orcid: 0000-0003-3381-5911
-    affiliation: "1, 2" 
+    affiliation: "1, 2"
+  - name: Tim Snow
+    orcid: 0000-0001-7146-6885
+    affiliation: "1, 3"
 affiliations:
  - name: Diamond Light Source, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
    index: 1
  - name: Department of Chemistry, University of Bath, Claverton Down, Bath, BA2 7AY, UK
    index: 2
+ - name: School of Chemistry, University of Bristol, Bristol, BS8 1TS, UK
+   index: 3
 date: 04 May 2020
 bibliography: paper.bib
 ---
 
 # Summary
 
-`uravu` offers an easy to use interface to Bayesian modelling for scientific analysis in the Python programming language, making Bayesian modelling as easy to use as the `scipy.optimize.curve_fit()` class.  
+`uravu` offers an easy to use interface to Bayesian modelling for scientific analysis in the Python programming language, making Bayesian modelling as easy to use as the `scipy.optimize.curve_fit()` method.  
 This software acts to lower the barrier of entry to the use of packages such as:
-- scipy: for maximum likelihood estimation [@virtanen_scipy_2020]
-- emcee: for Markov chain Monte Carlo investigation of posterior probabilities [@foremanmackey_emcee_2019]
-- dynesty: for nested sampling of the Bayesian evidence [@speagle_dynesty_2020].
-While, adding additional utility, such as the inclusion of measurement units (important for scientific analysis) with the pint package, and publication-quality plots of relationships, data, and distributions with matplotlib [@hunter_matplotlib_2007] and corner [@foremanmackey_corner_2019]. 
+
+- `scipy`: for maximum likelihood estimation [@virtanen_scipy_2020]
+- `emcee`: for Markov chain Monte Carlo investigation of posterior probabilities [@foremanmackey_emcee_2019]
+- `dynesty`: for nested sampling of the Bayesian evidence [@speagle_dynesty_2020].
+
+In addition to lowering the entry barrier uravu also adds additional utility, such as the inclusion of measurement units (important for scientific analysis) with the `pint` package, and publication-quality plots of relationships, data, and distributions with `matplotlib` [@hunter_matplotlib_2007] and `corner` [@foremanmackey_corner_2019]. 
 
 In addition to the straightforward interface, the `uravu` documentation offers brief tutorials (uravu.rtfd.io/en/latest/tutorials.html) in all aspects of the package.
 This enables those unfamiliar with Bayesian modelling to get to grips with these important tools for data analysis.
 `uravu` is being actively applied to scientific problems, such as data reduction at large scale scientific facilities and the modelling of diffusion in battery materials.
 
 # Statement of Need
 
-The Python language as a large number of powerful packages related to the application of Bayesian modelling. 
+The Python language has a large number of powerful packages related to the application of Bayesian modelling. 
 However, to apply these methods to their problems, scientific users need a straightforward environment. 
 For maximum-likelihood modelling, this is achieved using the `scipy.optimize.curve_fit()` method for many users but, to the best of the author's knowledge, there is no equivalent method for Bayesian modelling.
 `uravu` fills this gap by offering easy access to powerful Python packages to perform Markov chain Monte Carlo and nested sampling. 
-Furthermore, the tutorials available as documentation online allow users to become more comfortable with the use of Bayesian methods for data modelling. 
+Furthermore, the tutorials, available as documentation, online allow users to become more comfortable with the use of Bayesian methods for data modelling.
 
 # Acknowledgements