Network Analysis of Dependencies of CRAN Packages
Check and convert dependency word(s)
Conditionally change a string
Construct the giant component of the network from two data frames
Probability mass function (PMF) of 2-component discrete extreme value ...
Probability mass function (PMF) of 3-component discrete extreme value ...
Probability mass function (PMF) of Zipf-polylog distribution
Dependencies of all CRAN packages
Split a string to a list of dependencies
Multiple types of dependencies
Graph of dependencies of all CRAN packages
Wrapper of lpost_bulk, assuming power law (theta = 1.0)
Wrapper of lpost_mix2, assuming power law (theta = 1.0) & contrained (...
Wrapper of lpost_pol, assuming power law (theta = 1.0)
Unnormalised log-posterior density of discrete power law
Marginal log-likelihood and posterior density of discrete power law vi...
Wrapper of mcmc_mix1
Markov chain Monte Carlo for TZP-power-law mixture
Wrapper of mcmc_mix2
Markov chain Monte Carlo for 2-component discrete extreme value mixtur...
Wrapper of mcmc_mix3
Markov chain Monte Carlo for 3-component discrete extreme value mixtur...
Wrapper of mcmc_pol
Markov chain Monte Carlo for Zipf-polylog distribution
Obtain set of thresholds with high posterior density for the TZP-power...
Obtain set of thresholds with high posterior density for the constrain...
Obtain set of thresholds with high posterior density for the 2-compone...
Obtain set of thresholds with high posterior density for the 3-compone...
Reshape the data frame of dependencies
Survival function of 2-component discrete extreme value mixture distri...
Survival function of 3-component discrete extreme value mixture distri...
Survival function of Zipf-polylog distribution
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 <doi:10.1111/stan.12355>, of which functions are provided.