fix

.Rbuildignore

@@ -4,3 +4,5 @@
 ^\.github$
 ^doc$
 ^Meta$
+^CRAN-SUBMISSION$
+^cran-comments\.md$

CRAN-SUBMISSION

@@ -0,0 +1,3 @@
+Version: 0.1.0
+Date: 2024-11-05 04:35:53 UTC
+SHA: c860cd2e3b56e6dcfd48f91c2afd6cf9a95787fa

DESCRIPTION

@@ -22,7 +22,7 @@ Authors@R: c(
      "Xu",
      email = "[email protected]",
      role = c("aut"),
-     comment = c(ORCHID = "0000-0002-0257-8856")
+     comment = c(ORCID = "0000-0002-0257-8856")
      )
   )
 Description: Implements the "Smith-Pittman" community detection algorithm 
@@ -31,7 +31,7 @@ Description: Implements the "Smith-Pittman" community detection algorithm
     networks, with a gradient evident in social partitioning. The package 
     provides functions for community detection, visualization, and analysis of 
     the resulting community structure. Methods are based on results from Smith, 
-    Pittman and Xu (2024) <doi>.
+    Pittman and Xu (2024) <https://doi.org/10.48550/arXiv.2411.01394>.
 License: MIT + file LICENSE
 References: 
   Reference 1
@@ -46,7 +46,7 @@ Imports:
   rlist,
   BBmisc,
   qgraph
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2
 Suggests: 
     knitr,
     rmarkdown

cran-comments.md

@@ -0,0 +1,5 @@
+## R CMD check results
+
+0 errors | 0 warnings | 1 note
+
+* Fix in Description with ORCID ID.

utils/joss_paper/paper.Rmd

@@ -24,20 +24,25 @@ affiliations:
 date: "`r Sys.Date()`"
 bibliography: paper.bib
 output:
-  # rticles::joss_article
+  #rticles::joss_article
   md_document:
     preserve_yaml: TRUE
     variant: "markdown_strict"
 journal: JOSS
+
 ---
 
+```{r, include=FALSE}
+options(tinytex.verbose = TRUE)
+```
+
 # Summary
 
-{ig.degree.betweenness} is an R [@base2022] package which implements the "Smith-Pittman" community detection algorithm [@sp_abstract] and is directly compatible with networks and sociograms constructed and loaded with `igraph` package [@igraph_software] by Csardi and Nepusz [@igraph_article]. {ig.degree.betweenness} also offers utility functions to which enable neater plotting of densely connected networks with high number of edges and a low number of nodes and preparation of unlabeled graphs for algorithm implementation.
+{ig.degree.betweenness} is an R [@base2022] package which implements the "Smith-Pittman" community detection algorithm [@sp_abstract] on networks and sociograms constructed and/or loaded with {igraph} package [@igraph_software] by Csardi and Nepusz [@igraph_article]. Additionally, {ig.degree.betweenness} offers some utility functions to which enable neater plotting of densely connected networks with high number of edges and a low number of nodes and preparation of unlabeled graphs for the Smith-Pittman algorithm's implementation.
 
 # Statement of Need
 
-The `igraph` package offers a suite of community detection algorithms, including Girvan-Newman [@Girvan_Newman_2002] and Louvain [@louvain_paper]. In densely connected complex networks it has been noted by [@sp_abstract] that 
+The {igraph} package offers a suite of community detection algorithms, including Girvan-Newman [@Girvan_Newman_2002], Louvain [@louvain_paper] and others^[For the full list of available community detection algorithms in the {igraph} package, see the {igraph} reference manual: https://igraph.org/c/html/latest/igraph-Community.html]. In densely connected complex networks it has been noted by [@sp_abstract] that considering the number of connections possessed by each individual node in a given network (degree centrality) along with edge-betweeness (as done by [@Girvan_Newman_2002]) offers an approach for identifying clusters which are more descriptive. {ig.degree.betweenness} offers a 
 
 # Minimal Example