Update README.md

README.md

@@ -25,7 +25,7 @@ Or to access the app through a browser, visit [paulgovan.shinyapps.io/RiskNetwor
 
 # Example
 ## Home
-Launching the app brings up the Home. The Home is basically a landing page that gives a brief introduction to the app and includes two value boxes, one each for the number of *nodes* and *arcs* in the network. 
+Launching the app brings up the Home tab. The Home tab is basically a landing page that gives a brief introduction to the app and includes two value boxes, one each for the number of *nodes* and *arcs* in the network. 
 
 RiskNetwork comes with a number of simulated and "real world" data sets. This example will use the "Sample Discrete Network", which is the selected network by default.
 
@@ -53,22 +53,14 @@ To view the network score, select a score function from the The Network Score bo
 Try different combinations of structural learning algorithms and score functions in order to see the effect (if any) on the resulting network.
 
 ## Parameters
-Select the grow-shrink algorithm once again and then click Parameters in the sidepanel in order to learn the parameters of the network.
+Select the grow-shrink algorithm once again and then click Parameters in the sidepanel in order to learn the parameters of the network. The selected paramaters are automically displayed in the Network Paramaters box.
 
-The selected paramaters are automically displayed in the Network Paramaters box.
-
-Select a learning algorithm from the Parameter Learning box. This app supports both maximum-likelihood and Bayesian estimation of the parameters. Note that Bayesian parameter learning is currently only implemented for discrete data sets. 
-
-Then select the type of chart to display in the Paramter Infographic box and, for the discrete case, choose your preferred node. 
+Select a learning algorithm from the Parameter Learning box. This app supports both maximum-likelihood and Bayesian estimation of the parameters. Note that Bayesian parameter learning is currently only implemented for discrete data sets. Then select the type of chart to display in the Paramter Infographic box and, for the discrete case, choose your preferred node. For example, the selected node *A* is a discrete node with three levels: *a*, *b*, and *c*.
 
 ![Parameters](https://github.com/paulgovan/RiskNetwork/blob/master/inst/images/Parameters.PNG?raw=true)
 
-For example, the selected node *A* is a discrete node with three levels: *a*, *b*, and *c*.
-
 ## Measures
-Click Measures in the sidepanel to bring up a number of tools for classical network analysis. 
-
-The Measures tab has a number of node and network measures. The node measures include:
+Click Measures in the sidepanel to bring up a number of tools for classical network analysis. The Measures tab has a number of node and network measures. The node measures include:
 * Markov blanket
 * Neighborhood
 * Parents
@@ -87,9 +79,7 @@ The Network Control box contains different options for hierarchical clustering o
 
 ## Simulation
 
-Finally, click Simulation in the sidepanel in order to simulate data from the network for future use.
-
-Simply enter the sample size *N* in the Network Simulation box and click *Download* in order to download a random sample of data from the Bayesian network. 
+Finally, click Simulation in the sidepanel in order to simulate data from the network for future use. Simply enter the sample size *N* in the Network Simulation box and click *Download* in order to download a random sample of data from the Bayesian network. 
 
 ![Simulation](https://github.com/paulgovan/RiskNetwork/blob/master/inst/images/Simulation.PNG?raw=true)