Provides R routine for the so called two-sample
Cramer-Test.  This nonparametric two-sample-test on equality
of the underlying distributions can be applied to
multivariate data as well as univariate data. It offers two
possibilities to approximate the critical value both of which
are included in this package.

Multivariate Nonparametric Cramer-Test for the
Two-Sample-Problem

c(person("Thomas J.", "Leeper", email = "thosjleeper@gmail.com", role = c("aut", "cre")))
Provides a data.frame listing of known data-only and data-heavy
packages available on CRAN.

Provides a data frame listing of known data-only and data-heavy
packages available on CRAN.

Network Analysis of Dependencies of CRAN Packages
person("Clement", "Lee", email = "clement.lee.tm@outlook.com", role = c("aut", "cre"))
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.

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.
xml2, rvest, stringr, dplyr, igraph, Rcpp

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.
ggplot2, tibble, visNetwork, knitr, rmarkdown