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xmax to x_max for consistency.invts in mcmc_*_wrapper() is set to 1.0 for no Metropolis coupling or power posterior.u_max (and v_max if applicable) with defaults in mcmc_mix*_wrapper().mcmc_pol() (for the polylog distribution) and mcmc_mix2() (for the 2-component mixture distribution). The combination of the existing argument invt for inverse temperatures and the new boolean argument mc3_or_marg determines either parallel tempering or power posterior or neither will be performed.marg_pow() for calculating the posterior density and marginal (log-)likelihood of the discrete power law i.e. the Zipf distribution.update() in C++ in preparation for marginal log-likelihood calculations for other models (Zipf-polylog, mixtures, etc.)marg_pow() added to align with mcmc_pol() (when theta = 1.0) in terms of posterior density.name has been removed in the wrapper functions as it required data frames with a specific column name.obtain_u_set_mix2() or obtain_u_set_mix3().s_alpha in mcmc_mix3_wrapper() is changed from 0.0 to 10.0.obtain_u_set_mix3(), whether there is data between v & u is checked before optimisation. This prevents a warning when the “Brent” method is used (when only alpha is optimised as theta is fixed to 1.0), when there’s no data points between v & u.dpol() & Spol(), the check for alpha <= 1.0 is made only when theta == 1.0 i.e. the power law applies.obtain_u_set_mix2() and obtain_u_set_mix3(), which are functions for obtaining the profile posterior density for the 2-component and 3-component mixture models, respectively.mcmc_pol(), mcmc_mix2() and mcmc_mix3(), all with the suffix _wrapper. Specifically, mcmc_mix2_wrapper() calls obtain_u_set_mix2() and mcmc_mix2(), and mcmc_mix3_wrapper() calls obtain_u_set_mix3() and mcmc_mix3().dupp() and mcmc_upp() are replaced by dpol() and mcmc_pol(), respectively, to facilitate a generalisation of the discrete power law, namely the Zipf-polylog distribution.dmix() and mcmc_mix() are replaced by dmix2() and mcmc_mix2(), respectively, with a new parametrisation, for the 2-component mixture distribution.dmix3() and mcmc_mix3() are added for the 3-component mixture distribution.get_dep(), html_text_vec() and get_dep_all_packages() now return an error message if Internet resources are not available.The previous functionality of get_dep() is replaced by that of get_dep_df(), while get_dep_df() is soft deprecated. This means the former is the single function for obtaining dependencies in a non-igraph object.
The argument type in get_dep() and get_graph_all_packages() now allows Enhances and Reverse enhances as the value. These two kind of dependencies are also included in the data frame obtained using get_dep_all_packages().
Examples in internal functions html_text_vec(), get_dep_str() and get_dep_vec() removed to minimise the errors due to no internet connection and/or timeout.
Multiple dependencies are now allowed in the type argument in get_graph_all_packages().
Arguments give_log in dupp() & dmix() changed to log without changing the functionality. For uniformity, Supp() & Smix() are also given the additional argument log.
The ordering of arguments in the funcions *mix() is made consistent.
For the vignette on dependencies of all CRAN packages, community detection is added.
Replace https://cran.r-project.org/web/packages/available_packages_by_name.html by https://cran.r-project.org, in the dependency network vignette, to prevent NOTE on possibly invalid URL.
In the vignette on modelling the number of reverse dependencies, a section on fitting extreme value mixture distribution is added.
Added is a citation network of the CHI conference papers, that can serve as a comparison to the CRAN dependency network, in terms of network summaries and characteristics, such as degree distribution.
Replace https://cran.r-project.org/web/packages/available_packages_by_name.html by https://cran.r-project.org, in the manual of cran_dependencies, to prevent NOTE on possibly invalid URL.
get_dep_all_packages() and get_graph_all_packages(): The former is for a data frame of all dependencies of all CRAN packages, while the latter is for the graph of one type of depenedencies of all CRAN packages.
get_dep() replaces get_dep_all(), with the same functionality. get_dep() gets soft deprecated.
The argument type in get_dep() and types in get_dep_df() allows input more flexibility. For reverse dependencies, either space or underscore is accepted for separating the words e.g. type = "reverse suggests" or type = "reverse_suggests".
The argument types in get_dep_df() allows (as before) a character vector of dependency words. Also allowed now is types = "all" which means all of the four dependencies (depends, suggests, imports, linking to) and their reverse counterparts.
Previously, there were issues with string manipulation for some packages without scraping. This is because, when using tools::CRAN_package_db(), there might be no space between the package name and the left parenthesis for the version. This is not an issue if scrape = TRUE as there is always a space on the CRAN page.
In the output of get_dep_df() and get_dep_all_packages(), any LinkingTo and Reverse linking to dependencies will become “linking to” (“linking_to” previously) in the variable type, with “FALSE” and “TRUE” in the variable reverse, respectively. This is also updated in the data cran_dependencies.
The sections on obtaining dependencies of all CRAN packages is now moved to a new vignette. In this vignette, we also provide interactive visualisation of the network of Depends of all packages.
The degree modelling vignette is now for Imports network, not Depends network. In addition to discrete power law, a discrete extreme value mixture distribution is also used to model the same data set.
dupp() and Supp(): density and survival functions, respectively, of the discrete power law (above a threshold).mcmc_upp(): fitting the discrete power law (above a threshold) to data using Markov chain Monte Carlo (MCMC).dmix(), Smix(): density and survival functions, respectively, of a discrete extreme value mixture distribution.mcmc_mix(): fitting the discrete extreme value mixture distribution to data using MCMC.get_dep_all() and get_dep_df(): scrape = TRUE is the same as previous version, while scrape = FALSE means tools::CRAN_package_db() (thanks to Dirk Eddelbuettel (#1)) will be used instead. Note that changing this argument should still give the same result; the main difference is the time taken.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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