Title: Network Analysis of Dependencies of CRAN Packages
Authors@R: person("Clement", "Lee", email = "clement.lee.tm@outlook.com", role = c("aut", "cre"))
Description: The dependencies of CRAN packages can be analysed in a network fashion. Through scrapping the page of a package on CRAN, we can obtain the packages that it depends, imports, suggests, etc. By iterating this procedure over a number of packages, we can build the dependency network and represent it by a graph object, most easily through the 'igraph' package. Subsequently, the dependency network can be visualised and analysed using social network analysis methods, enabling us to have a bird's-eye view of the CRAN ecosystem.
Imports: xml2, rvest, stringr, dplyr, igraph
Suggests: ggplot2, tibble, knitr, rmarkdown
Packaged: 2020-05-12 14:02:31 UTC; clement_lee

Description: The dependencies of CRAN packages can be analysed in a network fashion. For each package we can obtain the packages that it depends, imports, suggests, etc. By iterating this procedure over a number of packages, we can build, visualise, and analyse the dependency network, enabling us to have a bird's-eye view of the CRAN ecosystem. One aspect of interest is the number of reverse dependencies of the packages, or equivalently the in-degree distribution of the dependency network. This can be fitted by the power law and/or an extreme value mixture distribution, of which functions are provided.
Imports: xml2, rvest, stringr, dplyr, igraph, Rcpp

Description: The dependencies of CRAN packages can be analysed in a network fashion. For each package we can obtain the packages that it depends, imports, suggests, etc. By iterating this procedure over a number of packages, we can build, visualise, and analyse the dependency network, enabling us to have a bird's-eye view of the CRAN ecosystem. One aspect of interest is the number of reverse dependencies of the packages, or equivalently the in-degree distribution of the dependency network. This can be fitted by the power law and/or an extreme value mixture distribution <arXiv:2008.03073>, of which functions are provided.
Suggests: ggplot2, tibble, visNetwork, knitr, rmarkdown