R/influential-package.R

In influential: Identification and Classification of the Most Influential Nodes

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#
#    Influential package description
#
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#' @keywords internal
#' @title Influential package
#' @description
#' The goal of \emph{\strong{\code{influential}}} is to help identification of the most influential nodes in a network
#' as well as the classification and ranking of top candidate features.
#' This package contains functions for the classification and ranking of features,
#' reconstruction of networks from adjacency matrices and
#' data frames, analysis of the topology of the network and calculation of centrality measures
#' as well as a novel and powerful influential node ranking.
#' The \strong{Experimental data-based Integrative Ranking (ExIR)} is a sophisticated model
#' for classification and ranking of the top candidate features based on only the experimental data.
#' The first integrative method, namely the \strong{Integrated Value of Influence (IVI)},
#' that captures all topological dimensions of the network for
#' the identification of network most influential nodes is also provided as
#' a function. Also, neighborhood connectivity, H-index, local H-index, and collective
#' influence (CI), all of which required centrality measures for the calculation of IVI,
#' are for the first time provided in an R package. Additionally, a function is provided
#' for running \strong{SIRIR} model, which is the combination of leave-one-out cross validation
#' technique and the conventional SIR model, on a network to unsupervisedly rank the true
#' influence of vertices.Furthermore, some functions have been provided for the
#' assessment of dependence and correlation of two network centrality measures as well
#' as the conditional probability of deviation from their corresponding
#' means in opposite directions.
#'
#' You may check the latest developmental version of the \emph{influential} package on its
#' \href{https://github.com/asalavaty/influential}{GitHub repository}
#'
#' Also, a web-based \href{https://influential.erc.monash.edu/}{Influential Software Package} with a convenient
#' user-interface (UI) has been developed for the comfort of all users including those without a coding background.
#'
#' @details
#' \itemize{
#'   \item Package: influential
#'   \item Type: Package
#'   \item License: GPL-3
#' }
#'
#' @author
#' Author: Adrian (Abbas) Salavaty
#'
#' Advisors: Mirana Ramialison and Peter D. Currie
#'
#'
#' Maintainer: Adrian (Abbas) Salavaty \email{abbas.salavaty@@gmail.com}
#'
#'
#' You may find more information on my personal website at \href{https://asalavaty.com/}{www.ASalavaty.com}
#'
#' @references
#' \itemize{
#'   \item Fred Viole and David Nawrocki (2013, ISBN:1490523995).
#'   \item Csardi G, Nepusz T (2006). “The igraph software package for complex network research.”
#' InterJournal, Complex Systems, 1695. \url{https://igraph.org/}.
#' }
#'
#' \strong{Note:} Adopted algorithms and sources are referenced in function document.
#'
#' @useDynLib influential, .registration = TRUE
#' @importFrom Rcpp sourceCpp
"_PACKAGE"

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