Update README.md

README.md

@@ -11,12 +11,17 @@ Depends: R (>= 3.0.0), igraph
 
 Imports: parallel
 
-<b>Description</b>: The NetVA assists in network analysis with two approaches: (1) Performs network vulnerability analysis and helps to identify vulnerable nodes (VNs) or proteins (VPs) for a given protein-protein interaction (PPI) network. Hence, network vulnerability analysis is an approach for identifying VPs by assessing networks based on their topological properties. The VPs are identified based on fourteen different topological properties calculated for networks as constructed by deleting all nodes/proteins, one by one, from the original network under investigation. This package provides the capability to use multiple cores on Linux/macOS to parallelize the process using the parallel package. (2) Performs network influence analysis to identify influential nodes (INs) i.e. key nodes with ranking by calculating escape velocity centrality (EVC) and extended escape velocity centrality (EVC+). It also helps to identify hubs and bottlenecks present in the given network based on the well-known Pareto principle of the 80:20 rule. An illustrative tutorial on how to use various functions of this package for network vulnerability and influence analysis has been provided in <b>Tutorial_Rscript</b>.
+Vignette: Available
 
-<b>Installation</b>: The NetVA package can be installed on the R console using:
+<b>Description</b>: The NetVA assists in network analysis with two approaches: (1) Performs network vulnerability analysis and helps to identify vulnerable nodes (VNs) or proteins (VPs) for a given protein-protein interaction (PPI) network. Hence, network vulnerability analysis is an approach for identifying VPs by assessing networks based on their topological properties. The VPs are identified based on fourteen different topological properties calculated for networks as constructed by deleting all nodes/proteins, one by one, from the original network under investigation. This package provides the capability to use multiple cores on Linux/macOS to parallelize the process using the parallel package. (2) Performs network influence analysis to identify influential nodes (INs) i.e. key nodes with ranking by calculating escape velocity centrality (EVC) and extended escape velocity centrality (EVC+). It also helps to identify hubs and bottlenecks present in the given network based on the well-known Pareto principle of the 80:20 rule. An illustrative tutorial on how to use various functions of this package for network vulnerability and influence analysis has been provided in <b>Tutorial_Rscript</b>.
 
+<b>Installation</b>: The NetVA package can be installed in two ways on the R console using:
+<b>1</b> Installation without the vignette
 > devtools::install_github("kr-swapnil/NetVA")
 
+<b>2</b> Installation with the vignette
+> devtools::install_github("kr-swapnil/NetVA", build_vignettes = TRUE)
+
 <b>Available functions</b>:
 
   netva(): Performs vulnerability analysis with or without parallel processing. Parallel processing works only on Linux/macOS machines but not on Windows-based machines.