gretel: Generalized Path Analysis for Social Networks

#' gretel: Generalized Path Analysis for Social Networks
#' 
#' This package contains two categories of functions. The first category is concerned
#' with assigning values to user specified paths, while the second identifies
#' paths of optimal value.
#' 
#' Key functions in the path value calculation category are
#' - \code{gpv}, which calculates Generalized Path Value
#' - \code{ppv}, which calculates Probabilistic Path Value
#' - \code{binary_distance}, \code{peay_path_value}, \code{flament_path_length},
#'   \code{peay_average_path_value}, and \code{flament_average_path_length}, which
#'   calculate path value measures described in \emph{Yang, Knoke} (2001).
#' - \code{generate_proximities}, which generates a matrix of values representing the
#'   measures of optimal paths from each source node (row index) to each target node 
#'   (column index).
#'   
#' Key functions in the optimal path identification category are
#' - \code{opt_gpv}, which identifies the path of optimal Generalized Path Value from 
#'   a particular source node to a particular target node
#' - \code{opt_ppv}, which identifies the path of optimal Probabilistic Path Value from
#'   a particular source node to a particular target node
#' - \code{all_opt_gpv}, which identifies the 'gpv'-optimal paths from every source node 
#'   to every target node
#' - \code{all_opt_ppv}, which identifies the 'ppv'-optimal paths from every source node
#'   to every target node
#' - \code{unpack}, which unpacks the Dijkstra-format encoded shortest paths returned by
#'   \code{all_opt_gpv} and \code{all_opt_ppv}. See their help pages for details.
#'
#' @docType package
#' @name gretel
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#' @useDynLib gretel, .registration = TRUE
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#' @importFrom Rcpp sourceCpp
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