netrankr: Analyzing Partial Rankings in Networks

#' netrankr: An R package for centrality and partial rankings in networks
#'
#' @description netrankr provides several functions to analyze partial rankings for network
#' centrality. The main focus lies on methods that do not necessarily rely on indices like degree,
#' betweenness or closeness. However, the package also provides more than 20 indices,
#' which can be constructed via a Rstudio addin.
#'
#' The package follows the philosophy, that centrality
#' can be decomposed in a series of micro steps. Starting from a network,
#' [indirect_relations] can be derived which can either be aggregated into an index with
#' [aggregate_positions], or alternatively turned into a partial ranking with [positional_dominance].
#' The partial ranking can then be further analyzed with [exact_rank_prob], to obtain
#' probabilistic centrality rankings.
#'
#' @details Some features of the package are:
#'
#' \itemize{
#' \item Working with the neighborhood inclusion preorder. This forms the bases
#' for any centrality analysis on undirected and unweighted graphs.
#' More details can be found in the dedicated vignette:
#' `vignette("neighborhood_inclusion",package = "netrankr")`
#' \item Constructing graphs with a unique centrality ranking.
#' This class of graphs, known as threshold graphs, can be used to benchmark
#' centrality indices, since they only allow for one ranking of the nodes.
#' For more details consult the vignette: `vignette("threshold_graph",package = "netrankr")`
#' \item Probabilistic centrality. Why apply a handful of indices and choosing
#' the one that fits best, when it is possible to analyze **all** centrality rankings at once?
#' The package includes several function to calculate rank probabilities of nodes
#' in a network. These include expected ranks and relative rank probabilities
#' (how likely is it that a node is more central than another?)
#' Consult `vignette("probabilistic_cent",package = "netrankr")` for more info.
#' }
#'
#' The package provides several additional vignettes that explain the functionality
#' of netrankr and its conceptual ideas. See `browseVignettes(package = 'netrankr')`
#'
#' @keywords internal
#' @aliases netrankr-package
"_PACKAGE"