Minor revisions for JOSS

README.md

@@ -1,8 +1,10 @@
-## Geometrically Weighted Degree
+# Geometrically Weighted Degree
 
 ![travis badge](https://travis-ci.org/michaellevy/gwdegree.svg?branch=master)
 [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/gwdegree)](https://cran.r-project.org/package=gwdegree)
 
+### Overview
+
 There is ambiguity and confusion in the research community about how to interpret GWDegree estimates in exponential random graph models (ERGMs). This app aims to help by providing an interactive platform that demonstrates:
 
 1. how the GWD statistic responds to adding edges to nodes of various degrees, contingent on the value of the shape parameter, $\theta_S$;
@@ -11,10 +13,21 @@ There is ambiguity and confusion in the research community about how to interpre
 
 1. how GWD and GWESP -- an ERGM term used to model triadic closure -- interact to affect network centralization and clustering.
 
-All three tabs aim to provide intuition on how GWD parameter values and shape parameter values relate to network structures. For the applied researcher trying to decide whether to estimate or fix the shape parameter value, how to choose or interpret the shape parameter value, and how to interpret the GWD parameter value, the second tab, "Parameter & Degree Distribution" may be particularly useful. Adjust the sliders to match the observed network's size and density. To choose a fixed decay parameter value, examine the possible degree distribution shapes for a given decay parameter value. Once an estimate of the GWD parameter is obtained, examine the implication of the parameter estimate on the degree distribution, *ceteris parabus*.
+### Installation
+
+The application is available as a web-app [online](https://michaellevy.shinyapps.io/gwdegree/), but with conservative simulation limits and limited bandwidth. If you want to do anything more than play with it, install the `gwdegree` package for R from CRAN via `install.packages("gwdegree")`.
+
+### Use
+
+To run the application from R, first attach the package with `library(gwdegree)`, then launch it with `gwdegree()`.
+
+The three functionalities described above are split into separate tabs in the application. Researchers trying to decide whether to estimate or fix the shape parameter value, how to choose or interpret the shape parameter value, or how to interpret the GWD parameter value may find the second tab, "Parameter & Degree Distribution" especially useful by setting the network size and density to match the observed network and exploring the influence of the two parameters on the shape of the degree distribution.
+
+### Feedback, questions, and contributions
+
+If you would like to contribute to this app, please submit a pull request to the [GitHub repository](https://github.com/michaellevy/gwdegree). Feel free to report issues, provide feedback, suggest improvements, and ask questions by opening a [new issue](https://github.com/michaellevy/gwdegree/issues).
 
-There is a working version [online](michaellevy.shinyapps.io/gwdegree), but it has pretty conservative limits. If you want to do anything more than play with the app, install it with `install.packages("gwdegree")` and launch the app locally with `library(gwdegree); gwdegree()`.
 
-### Related Conference Poster
+## Related Conference Poster
 
-I presented a poster on this at Political Networks 2016. You can view that [here](https://figshare.com/articles/Interpretation_of_GW-Degree_Estimates_in_ERGMs/3465020). It got an honorable mention for methodological contribution at the conference.
+I presented a poster on this at the 2016 Political Networks conference. You can view that [here](https://figshare.com/articles/Interpretation_of_GW-Degree_Estimates_in_ERGMs/3465020). It won an honorable mention for methodological contribution at the conference.

paper/paper.md

@@ -25,10 +25,5 @@ Exponential random graph models (ERGMs) are maximum entropy statistical models t
 
 1. how GWD and GWESP -- an ERGM term used to model triadic closure -- interact to affect network centralization and clustering.
 
-### Use
-
-The application is [available online](https://michaellevy.shinyapps.io/gwdegree/), but with conservative simulation limits and limited bandwidth. It is also bundled as an R package that can be launched by installing the `gwdegree` package from CRAN (`install.packages("gwdegree")`), attaching the package (`library(gwdegree)`), and running the app (`gwdegree()`). Pull requests, feature suggestions, and other feedback is welcome at [https://github.com/michaellevy/gwdegree](https://github.com/michaellevy/gwdegree). 
-
-The app has three tabs, which correspond to the three functionalities described above. Researchers trying to choose a $\theta_S$ value or interpret a $\theta_{GWD}$ estimate may find the second tab especially useful by setting the network size and density to match the observed network and exploring the influence of the two parameters on the shape of the degree distribution.
 
 # References