From ebd2b83eb203f124eef382ec40dbd19c551e8795 Mon Sep 17 00:00:00 2001
From: "George G. Vega Yon" <g.vegayon@gmail.com>
Date: Mon, 25 Nov 2024 11:45:56 -0700
Subject: [PATCH 1/3] Adding disadopt

---
 R/RcppExports.R     | 349 ----------------------------
 R/rdiffnet.r        |  22 +-
 src/RcppExports.cpp | 552 --------------------------------------------
 3 files changed, 20 insertions(+), 903 deletions(-)
 delete mode 100644 R/RcppExports.R
 delete mode 100644 src/RcppExports.cpp

diff --git a/R/RcppExports.R b/R/RcppExports.R
deleted file mode 100644
index b756fc7..0000000
--- a/R/RcppExports.R
+++ /dev/null
@@ -1,349 +0,0 @@
-# Generated by using Rcpp::compileAttributes() -> do not edit by hand
-# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
-
-edgelist_to_adjmat_cpp <- function(edgelist, weights = as.numeric( c()), n = 0L, undirected = FALSE, self = FALSE, multiple = FALSE) {
-    .Call(`_netdiffuseR_edgelist_to_adjmat_cpp`, edgelist, weights, n, undirected, self, multiple)
-}
-
-adjmat_to_edgelist_cpp <- function(adjmat, undirected = TRUE) {
-    .Call(`_netdiffuseR_adjmat_to_edgelist_cpp`, adjmat, undirected)
-}
-
-toa_diff_cpp <- function(year) {
-    .Call(`_netdiffuseR_toa_diff_cpp`, year)
-}
-
-egonet_attrs_cpp <- function(graph, V, outer = TRUE, self = TRUE, valued = TRUE) {
-    .Call(`_netdiffuseR_egonet_attrs_cpp`, graph, V, outer, self, valued)
-}
-
-approx_geodesicCpp <- function(G, n = 6L, warn = FALSE) {
-    .Call(`_netdiffuseR_approx_geodesicCpp`, G, n, warn)
-}
-
-infection_cpp <- function(graph, times, normalize = TRUE, K = 1L, r = 0.5, expdiscount = FALSE, n = 0L, valued = FALSE, outgoing = TRUE) {
-    .Call(`_netdiffuseR_infection_cpp`, graph, times, normalize, K, r, expdiscount, n, valued, outgoing)
-}
-
-susceptibility_cpp <- function(graph, times, normalize = TRUE, K = 1L, r = 0.5, expdiscount = FALSE, n = 0L, valued = FALSE, outgoing = TRUE) {
-    .Call(`_netdiffuseR_susceptibility_cpp`, graph, times, normalize, K, r, expdiscount, n, valued, outgoing)
-}
-
-select_egoalter_cpp <- function(adjmat_t0, adjmat_t1, adopt_t0, adopt_t1) {
-    .Call(`_netdiffuseR_select_egoalter_cpp`, adjmat_t0, adjmat_t1, adopt_t0, adopt_t1)
-}
-
-sp_trimatl <- function(x) {
-    .Call(`_netdiffuseR_sp_trimatl`, x)
-}
-
-sp_diag <- function(x, v) {
-    .Call(`_netdiffuseR_sp_diag`, x, v)
-}
-
-unif_rand_w_exclusion <- function(n, e) {
-    .Call(`_netdiffuseR_unif_rand_w_exclusion`, n, e)
-}
-
-sp_as_undirected <- function(x) {
-    .Call(`_netdiffuseR_sp_as_undirected`, x)
-}
-
-bootnet_fillself <- function(graph, index, E) {
-    .Call(`_netdiffuseR_bootnet_fillself`, graph, index, E)
-}
-
-#' Distribution over a grid
-#'
-#' Distribution of pairs over a grid of fix size.
-#'
-#' @param x Numeric vector of size \eqn{n}
-#' @param y Numeric vector of size \eqn{n}
-#' @param nlevels Integer scalar. Number of bins to return
-#' @details
-#'
-#' This function ment for internal use only.
-#'
-#' @export
-#' @keywords misc dplot
-#' @family visualizations
-#' @seealso Used by \code{\link{plot_infectsuscep}}
-#' @return Returns a list with three elements
-#' \item{x}{Numeric vector of size \code{nlevels} with the class marks for x}
-#' \item{y}{Numeric vector of size \code{nlevels} with the class marks for y}
-#' \item{z}{Numeric matrix of size \code{nlevels} by \code{nlevels} with the distribution %
-#' of the elements in terms of frequency}
-#' @section Examples:
-#' \preformatted{
-#' # Generating random vectors of size 100
-#' x <- rnorm(100)
-#' y <- rnorm(100)
-#'
-#' # Calculating distribution
-#' grid_distribution(x,y,20)
-#' }
-grid_distribution <- function(x, y, nlevels = 100L) {
-    .Call(`_netdiffuseR_grid_distribution`, x, y, nlevels)
-}
-
-#' Compute ego/alter edge coordinates considering alter's size and aspect ratio
-#'
-#' Given a graph, vertices' positions and sizes, calculates the absolute positions
-#' of the endpoints of the edges considering the plot's aspect ratio.
-#'
-#' @param graph A square matrix of size \eqn{n}. Adjacency matrix.
-#' @param toa Integer vector of size \eqn{n}. Times of adoption.
-#' @param x Numeric vector of size \eqn{n}. x-coordinta of vertices.
-#' @param y Numeric vector of size \eqn{n}. y-coordinta of vertices.
-#' @param vertex_cex Numeric vector of size \eqn{n}. Vertices' sizes in terms
-#' of the x-axis (see \code{\link{symbols}}).
-#' @param undirected Logical scalar. Whether the graph is undirected or not.
-#' @param no_contemporary Logical scalar. Whether to return (compute) edges'
-#' coordiantes for vertices with the same time of adoption (see details).
-#' @param dev Numeric vector of size 2. Height and width of the device (see details).
-#' @param ran Numeric vector of size 2. Range of the x and y axis (see details).
-#' @param curved Logical vector.
-#' @return A numeric matrix of size \eqn{m\times 5}{m * 5} with the following
-#' columns:
-#' \item{x0, y0}{Edge origin}
-#' \item{x1, y1}{Edge target}
-#' \item{alpha}{Relative angle between \code{(x0,y0)} and \code{(x1,y1)} in terms
-#' of radians}
-#' With \eqn{m} as the number of resulting edges.
-#' @details
-#'
-#' In order to make the plot's visualization more appealing, this function provides
-#' a straight forward way of computing the tips of the edges considering the
-#' aspect ratio of the axes range. In particular, the following corrections are
-#' made at the moment of calculating the egdes coords:
-#'
-#' \itemize{
-#' \item{Instead of using the actual distance between ego and alter, a relative
-#' one is calculated as follows
-#' \deqn{d'=\left[(x_0-x_1)^2 + (y_0' - y_1')^2\right]^\frac{1}{2}}{d'=sqrt[(x0-x1)^2 + (y0'-y1')^2]}
-#' where \eqn{%
-#' y_i'=y_i\times\frac{\max x - \min x}{\max y - \min y} }{%
-#' yi' = yi * [max(x) - min(x)]/[max(y) - min(y)]}
-#' }
-#' \item{Then, for the relative elevation angle, \code{alpha}, the relative distance \eqn{d'}
-#' is used, \eqn{\alpha'=\arccos\left( (x_0 - x_1)/d' \right)}{\alpha' = acos[ (x0 - x1)/d' ]}}
-#' \item{Finally, the edge's endpoint's (alter) coordinates are computed as follows: %
-#' \deqn{%
-#'   x_1' = x_1 + \cos(\alpha')\times v_1}{%
-#'   x1' = x1 + cos(\alpha') * v1
-#' }
-#' \deqn{%
-#'   y_1' = y_1 -+ \sin(\alpha')\times v_1 \times\frac{\max y - \min y}{\max x - \min x} }{%
-#'   y1' = y1 -+ sin(\alpha')*[max(y) - min(y)]/[max(x) - min(x)]
-#' }
-#' Where \eqn{v_1}{v1} is alter's size in terms of the x-axis, and the sign of
-#' the second term in \eqn{y_1'}{y1'} is negative iff \eqn{y_0 < y_1}{y0<y1}.
-#' }
-#' }
-#'
-#' The same process (with sign inverted) is applied to the edge starting piont.
-#' The resulting values, \eqn{x_1',y_1'}{x1',y1'} can be used with the function
-#' \code{\link{arrows}}. This is the workhorse function used in \code{\link{plot_threshold}}.
-#'
-#' The \code{dev} argument provides a reference to rescale the plot accordingly
-#' to the device, and former, considering the size of the margins as well (this
-#' can be easily fetched via \code{par("pin")}, plot area in inches).
-#'
-#' On the other hand, \code{ran} provides a reference for the adjustment
-#' according to the range of the data, this is \code{range(x)[2] - range(x)[1]}
-#' and \code{range(y)[2] - range(y)[1]} respectively.
-#'
-#' @keywords misc dplot
-#' @examples
-#' # --------------------------------------------------------------------------
-#' data(medInnovationsDiffNet)
-#' library(sna)
-#'
-#' # Computing coordinates
-#' set.seed(79)
-#' coords <- sna::gplot(as.matrix(medInnovationsDiffNet$graph[[1]]))
-#'
-#' # Getting edge coordinates
-#' vcex <- rep(1.5, nnodes(medInnovationsDiffNet))
-#' ecoords <- edges_coords(
-#'   medInnovationsDiffNet$graph[[1]],
-#'   diffnet.toa(medInnovationsDiffNet),
-#'   x = coords[,1], y = coords[,2],
-#'   vertex_cex = vcex,
-#'   dev = par("pin")
-#'   )
-#'
-#' ecoords <- as.data.frame(ecoords)
-#'
-#' # Plotting
-#' symbols(coords[,1], coords[,2], circles=vcex,
-#'   inches=FALSE, xaxs="i", yaxs="i")
-#'
-#' with(ecoords, arrows(x0,y0,x1,y1, length=.1))
-#' @export
-edges_coords <- function(graph, toa, x, y, vertex_cex, undirected = TRUE, no_contemporary = TRUE, dev = as.numeric( c()), ran = as.numeric( c()), curved = as.logical( c())) {
-    .Call(`_netdiffuseR_edges_coords`, graph, toa, x, y, vertex_cex, undirected, no_contemporary, dev, ran, curved)
-}
-
-edges_arrow <- function(x0, y0, x1, y1, height, width, beta = 1.5707963267949, dev = as.numeric( c()), ran = as.numeric( c()), curved = FALSE) {
-    .Call(`_netdiffuseR_edges_arrow`, x0, y0, x1, y1, height, width, beta, dev, ran, curved)
-}
-
-vertices_coords <- function(x, y, size, nsides, rot, dev = as.numeric( c()), ran = as.numeric( c())) {
-    .Call(`_netdiffuseR_vertices_coords`, x, y, size, nsides, rot, dev, ran)
-}
-
-rgraph_er_cpp <- function(n = 10L, p = 0.3, undirected = TRUE, weighted = FALSE, self = FALSE) {
-    .Call(`_netdiffuseR_rgraph_er_cpp`, n, p, undirected, weighted, self)
-}
-
-#' Ring lattice graph
-#'
-#' Creates a ring lattice with \eqn{n} vertices, each one of degree (at most) \eqn{k}
-#' as an undirected graph. This is the basis of \code{\link{rgraph_ws}}.
-#' @param n Integer scalar. Size of the graph.
-#' @param k Integer scalar. Out-degree of each vertex.
-#' @param undirected Logical scalar. Whether the graph is undirected or not.
-#' @details when \code{undirected=TRUE}, the degree of each node always
-#' even. So if \code{k=3}, then the degree will be \code{2}.
-#' @return A sparse matrix of class \code{\link[Matrix:dgCMatrix-class]{dgCMatrix}} of size
-#' \eqn{n\times n}{n * n}.
-#' @references Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of
-#' “small-world” networks. Nature, 393(6684), 440–2. \doi{10.1038/30918}
-#' @export
-#' @family simulation functions
-ring_lattice <- function(n, k, undirected = FALSE) {
-    .Call(`_netdiffuseR_ring_lattice`, n, k, undirected)
-}
-
-rewire_endpoints <- function(graph, p, both_ends = FALSE, self = FALSE, multiple = FALSE, undirected = FALSE) {
-    .Call(`_netdiffuseR_rewire_endpoints`, graph, p, both_ends, self, multiple, undirected)
-}
-
-rewire_swap <- function(graph, nsteps = 100L, self = FALSE, multiple = FALSE, undirected = FALSE, pr_rewire = 0.5) {
-    .Call(`_netdiffuseR_rewire_swap`, graph, nsteps, self, multiple, undirected, pr_rewire)
-}
-
-rewire_ws <- function(G, K, p = 0.0, self = FALSE, multiple = FALSE) {
-    .Call(`_netdiffuseR_rewire_ws`, G, K, p, self, multiple)
-}
-
-permute_graph_cpp <- function(x, self = FALSE, multiple = FALSE) {
-    .Call(`_netdiffuseR_permute_graph_cpp`, x, self, multiple)
-}
-
-rgraph_ba_cpp <- function(graph, dgr, m = 1L, t = 10L, self = TRUE) {
-    .Call(`_netdiffuseR_rgraph_ba_cpp`, graph, dgr, m, t, self)
-}
-
-rgraph_ba_new_cpp <- function(m0 = 1L, m = 1L, t = 10L, self = TRUE) {
-    .Call(`_netdiffuseR_rgraph_ba_new_cpp`, m0, m, t, self)
-}
-
-rgraph_sf_homo <- function(eta, graph, dgr, m = 1L, t = 10L, self = TRUE) {
-    .Call(`_netdiffuseR_rgraph_sf_homo`, eta, graph, dgr, m, t, self)
-}
-
-rgraph_sf_homo_new <- function(eta, m0 = 1L, m = 1L, t = 10L, self = TRUE) {
-    .Call(`_netdiffuseR_rgraph_sf_homo_new`, eta, m0, m, t, self)
-}
-
-#' @export
-#' @rdname vertex_covariate_dist
-vertex_covariate_dist <- function(graph, X, p = 2.0) {
-    .Call(`_netdiffuseR_vertex_covariate_dist`, graph, X, p)
-}
-
-vertex_mahalanobis_dist_cpp <- function(graph, X, S) {
-    .Call(`_netdiffuseR_vertex_mahalanobis_dist_cpp`, graph, X, S)
-}
-
-#' Comparisons at dyadic level
-#'
-#' @param graph A matrix of size \eqn{n\times n}{n*n} of class \code{dgCMatrix}.
-#' @param X A numeric vector of length \eqn{n}.
-#' @param funname Character scalar. Comparison to make (see details).
-#' @details
-#'
-#' This auxiliary function takes advantage of the sparseness of \code{graph} and
-#' applies a function in the form of \eqn{funname(x_i,x_j)}{funname(X[i],X[j])}
-#' only to \eqn{(i,j)} that have no empty entry. In other words, applies a compares
-#' elements of \code{X} only between vertices that have a link; making
-#' \code{nlinks(graph)} comparisons instead of looping through \eqn{n\times n}{n*n},
-#' which is much faster.
-#'
-#' \code{funname} can take any of the following values:
-#' \code{"distance"}, \code{"^2"} or \code{"quaddistance"}, \code{">"} or \code{"greater"},
-#' \code{"<"} or \code{"smaller"}, \code{">="} or \code{"greaterequal"},
-#' \code{"<="} or \code{"smallerequal"}, \code{"=="} or \code{"equal"}.
-#'
-#'
-#' @return A matrix \code{dgCMatrix} of size \eqn{n\times n}{n*n} with values in
-#' the form of \eqn{funname(x_i,x_j)}{funname(X[i],X[j])}.
-#' @family dyadic-level comparison functions
-#' @examples
-#'
-#' # Basic example ------------------------------------------------------------
-#' set.seed(1313)
-#' G <- rgraph_ws(10, 4, .2)
-#' x <- rnorm(10)
-#'
-#' vertex_covariate_compare(G, x, "distance")
-#' vertex_covariate_compare(G, x, "^2")
-#' vertex_covariate_compare(G, x, ">=")
-#' vertex_covariate_compare(G, x, "<=")
-#' @export
-vertex_covariate_compare <- function(graph, X, funname) {
-    .Call(`_netdiffuseR_vertex_covariate_compare`, graph, X, funname)
-}
-
-moran_cpp <- function(x, w) {
-    .Call(`_netdiffuseR_moran_cpp`, x, w)
-}
-
-matrix_compareCpp <- function(A, B, fun) {
-    .Call(`_netdiffuseR_matrix_compareCpp`, A, B, fun)
-}
-
-struct_test_mean <- function(y, funname, self = FALSE) {
-    .Call(`_netdiffuseR_struct_test_mean`, y, funname, self)
-}
-
-struct_test_var <- function(y, funname, self = FALSE) {
-    .Call(`_netdiffuseR_struct_test_var`, y, funname, self)
-}
-
-#' Computes variance of \eqn{Y} at ego level
-#' @param graph A matrix of size \eqn{n\times n}{n*n} of class \code{dgCMatrix}.
-#' @param Y A numeric vector of length \eqn{n}.
-#' @param funname Character scalar. Comparison to make (see \code{\link{vertex_covariate_compare}}).
-#' @param all Logical scalar. When \code{FALSE} (default) \eqn{f_i} is mean at
-#' ego level. Otherwise is fix for all i (see details).
-#' @details
-#'
-#' For each vertex \eqn{i} the variance is computed as follows
-#'
-#' \deqn{%
-#' (\sum_j a_{ij})^{-1}\sum_j a_{ij} \left[f(y_i,y_j) - f_i\right]^2
-#' }{%
-#' (sum_j a(ij))^(-1) * \sum_j a(ij) * [f(y(i),y(j)) - f(i)]^2
-#' }
-#'
-#' Where \eqn{a_{ij}}{a(ij)} is the ij-th element of \code{graph}, \eqn{f} is
-#' the function specified in \code{funname}, and, if \code{all=FALSE}
-#' \eqn{f_i = \sum_j a_{ij}f(y_i,y_j)^2/\sum_ja_{ij}}{f(i)=\sum_j a(ij)f(y(i), y(j))^2/\sum_j a(ij)},
-#' otherwise \eqn{f_i = f_j = \frac{1}{n^2}\sum_{i,j}f(y_i,y_j)}{f(i)=f(j)=(1/n^2)\sum_(i,j) f(y_i,y_j)}
-#'
-#'
-#' This is an auxiliary function for \code{\link{struct_test}}. The idea is
-#' to compute an adjusted measure of disimilarity between vertices, so the
-#' closest in terms of \eqn{f} is \eqn{i} to its neighbors, the smaller the
-#' relative variance.
-#' @return A numeric vector of length \eqn{n}.
-#' @export
-#' @seealso \code{\link{struct_test}}
-#' @family statistics
-ego_variance <- function(graph, Y, funname, all = FALSE) {
-    .Call(`_netdiffuseR_ego_variance`, graph, Y, funname, all)
-}
-
diff --git a/R/rdiffnet.r b/R/rdiffnet.r
index 7abcd17..cd000a0 100644
--- a/R/rdiffnet.r
+++ b/R/rdiffnet.r
@@ -320,7 +320,8 @@ rdiffnet <- function(
     exposure.args  = list(),
     name           = "A diffusion network",
     behavior       = "Random contagion",
-    stop.no.diff   = TRUE
+    stop.no.diff   = TRUE,
+    disadopt       = NULL
   ) {
 
   # Checking options
@@ -451,6 +452,8 @@ rdiffnet <- function(
   # Step 3.0: Running the simulation -------------------------------------------
 
   for (i in 2:t) {
+
+    # 3.1 Computing exposure
     if (exists("attrs_arr")){
       exposure.args[c("attrs")] <- list(attrs_arr[,i, ,drop=FALSE])
     }
@@ -460,16 +463,31 @@ rdiffnet <- function(
 
     for (q in 1:num_of_behaviors) {
 
+      # 3.2 Identifying who adopts based on the threshold
       whoadopts <- which( (expo[,,q] >= thr[,q]) )
+
+      # 3.3 Updating the cumadopt
       cumadopt[whoadopts, i:t, q] <- 1L
-                                                        # ADD SOMETHING TO DISADOPT
 
+      # 3.4` Updating the toa
+      # toa[cbind(whoadopts, q)] <- t
       toa[, q] <- apply(cumadopt[,, q], 1, function(x) {
         first_adopt <- which(x == 1)
         if (length(first_adopt) > 0) first_adopt[1] else NA
       })
 
     }
+
+    if (length(disadopt)) {
+
+      # Run the disadoption algorithm. This will return the following:
+      # - The updated cupadopt
+      # - A vector of who disadopted
+      # - A list of the same length of what was disadopted.
+      disadopt_res <- disadopt(expo, cupadopt, t)
+
+
+    }
   }
 
   for (i in 1:num_of_behaviors) {
diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp
deleted file mode 100644
index f4c1945..0000000
--- a/src/RcppExports.cpp
+++ /dev/null
@@ -1,552 +0,0 @@
-// Generated by using Rcpp::compileAttributes() -> do not edit by hand
-// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
-
-#include <RcppArmadillo.h>
-#include <Rcpp.h>
-
-using namespace Rcpp;
-
-#ifdef RCPP_USE_GLOBAL_ROSTREAM
-Rcpp::Rostream<true>&  Rcpp::Rcout = Rcpp::Rcpp_cout_get();
-Rcpp::Rostream<false>& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get();
-#endif
-
-// edgelist_to_adjmat_cpp
-arma::sp_mat edgelist_to_adjmat_cpp(const arma::mat& edgelist, NumericVector weights, int n, bool undirected, bool self, bool multiple);
-RcppExport SEXP _netdiffuseR_edgelist_to_adjmat_cpp(SEXP edgelistSEXP, SEXP weightsSEXP, SEXP nSEXP, SEXP undirectedSEXP, SEXP selfSEXP, SEXP multipleSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::mat& >::type edgelist(edgelistSEXP);
-    Rcpp::traits::input_parameter< NumericVector >::type weights(weightsSEXP);
-    Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    Rcpp::traits::input_parameter< bool >::type undirected(undirectedSEXP);
-    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
-    Rcpp::traits::input_parameter< bool >::type multiple(multipleSEXP);
-    rcpp_result_gen = Rcpp::wrap(edgelist_to_adjmat_cpp(edgelist, weights, n, undirected, self, multiple));
-    return rcpp_result_gen;
-END_RCPP
-}
-// adjmat_to_edgelist_cpp
-arma::mat adjmat_to_edgelist_cpp(const arma::sp_mat& adjmat, bool undirected);
-RcppExport SEXP _netdiffuseR_adjmat_to_edgelist_cpp(SEXP adjmatSEXP, SEXP undirectedSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type adjmat(adjmatSEXP);
-    Rcpp::traits::input_parameter< bool >::type undirected(undirectedSEXP);
-    rcpp_result_gen = Rcpp::wrap(adjmat_to_edgelist_cpp(adjmat, undirected));
-    return rcpp_result_gen;
-END_RCPP
-}
-// toa_diff_cpp
-IntegerMatrix toa_diff_cpp(const IntegerVector& year);
-RcppExport SEXP _netdiffuseR_toa_diff_cpp(SEXP yearSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const IntegerVector& >::type year(yearSEXP);
-    rcpp_result_gen = Rcpp::wrap(toa_diff_cpp(year));
-    return rcpp_result_gen;
-END_RCPP
-}
-// egonet_attrs_cpp
-List egonet_attrs_cpp(const arma::sp_mat& graph, const arma::uvec V, bool outer, bool self, bool valued);
-RcppExport SEXP _netdiffuseR_egonet_attrs_cpp(SEXP graphSEXP, SEXP VSEXP, SEXP outerSEXP, SEXP selfSEXP, SEXP valuedSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
-    Rcpp::traits::input_parameter< const arma::uvec >::type V(VSEXP);
-    Rcpp::traits::input_parameter< bool >::type outer(outerSEXP);
-    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
-    Rcpp::traits::input_parameter< bool >::type valued(valuedSEXP);
-    rcpp_result_gen = Rcpp::wrap(egonet_attrs_cpp(graph, V, outer, self, valued));
-    return rcpp_result_gen;
-END_RCPP
-}
-// approx_geodesicCpp
-arma::sp_mat approx_geodesicCpp(const arma::sp_mat& G, unsigned int n, bool warn);
-RcppExport SEXP _netdiffuseR_approx_geodesicCpp(SEXP GSEXP, SEXP nSEXP, SEXP warnSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type G(GSEXP);
-    Rcpp::traits::input_parameter< unsigned int >::type n(nSEXP);
-    Rcpp::traits::input_parameter< bool >::type warn(warnSEXP);
-    rcpp_result_gen = Rcpp::wrap(approx_geodesicCpp(G, n, warn));
-    return rcpp_result_gen;
-END_RCPP
-}
-// infection_cpp
-NumericVector infection_cpp(List graph, const arma::colvec& times, bool normalize, int K, double r, bool expdiscount, int n, bool valued, bool outgoing);
-RcppExport SEXP _netdiffuseR_infection_cpp(SEXP graphSEXP, SEXP timesSEXP, SEXP normalizeSEXP, SEXP KSEXP, SEXP rSEXP, SEXP expdiscountSEXP, SEXP nSEXP, SEXP valuedSEXP, SEXP outgoingSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< List >::type graph(graphSEXP);
-    Rcpp::traits::input_parameter< const arma::colvec& >::type times(timesSEXP);
-    Rcpp::traits::input_parameter< bool >::type normalize(normalizeSEXP);
-    Rcpp::traits::input_parameter< int >::type K(KSEXP);
-    Rcpp::traits::input_parameter< double >::type r(rSEXP);
-    Rcpp::traits::input_parameter< bool >::type expdiscount(expdiscountSEXP);
-    Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    Rcpp::traits::input_parameter< bool >::type valued(valuedSEXP);
-    Rcpp::traits::input_parameter< bool >::type outgoing(outgoingSEXP);
-    rcpp_result_gen = Rcpp::wrap(infection_cpp(graph, times, normalize, K, r, expdiscount, n, valued, outgoing));
-    return rcpp_result_gen;
-END_RCPP
-}
-// susceptibility_cpp
-NumericVector susceptibility_cpp(List graph, const arma::colvec& times, bool normalize, int K, double r, bool expdiscount, int n, bool valued, bool outgoing);
-RcppExport SEXP _netdiffuseR_susceptibility_cpp(SEXP graphSEXP, SEXP timesSEXP, SEXP normalizeSEXP, SEXP KSEXP, SEXP rSEXP, SEXP expdiscountSEXP, SEXP nSEXP, SEXP valuedSEXP, SEXP outgoingSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< List >::type graph(graphSEXP);
-    Rcpp::traits::input_parameter< const arma::colvec& >::type times(timesSEXP);
-    Rcpp::traits::input_parameter< bool >::type normalize(normalizeSEXP);
-    Rcpp::traits::input_parameter< int >::type K(KSEXP);
-    Rcpp::traits::input_parameter< double >::type r(rSEXP);
-    Rcpp::traits::input_parameter< bool >::type expdiscount(expdiscountSEXP);
-    Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    Rcpp::traits::input_parameter< bool >::type valued(valuedSEXP);
-    Rcpp::traits::input_parameter< bool >::type outgoing(outgoingSEXP);
-    rcpp_result_gen = Rcpp::wrap(susceptibility_cpp(graph, times, normalize, K, r, expdiscount, n, valued, outgoing));
-    return rcpp_result_gen;
-END_RCPP
-}
-// select_egoalter_cpp
-DataFrame select_egoalter_cpp(const arma::sp_mat& adjmat_t0, const arma::sp_mat& adjmat_t1, const NumericVector& adopt_t0, const NumericVector& adopt_t1);
-RcppExport SEXP _netdiffuseR_select_egoalter_cpp(SEXP adjmat_t0SEXP, SEXP adjmat_t1SEXP, SEXP adopt_t0SEXP, SEXP adopt_t1SEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type adjmat_t0(adjmat_t0SEXP);
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type adjmat_t1(adjmat_t1SEXP);
-    Rcpp::traits::input_parameter< const NumericVector& >::type adopt_t0(adopt_t0SEXP);
-    Rcpp::traits::input_parameter< const NumericVector& >::type adopt_t1(adopt_t1SEXP);
-    rcpp_result_gen = Rcpp::wrap(select_egoalter_cpp(adjmat_t0, adjmat_t1, adopt_t0, adopt_t1));
-    return rcpp_result_gen;
-END_RCPP
-}
-// sp_trimatl
-arma::sp_mat sp_trimatl(const arma::sp_mat& x);
-RcppExport SEXP _netdiffuseR_sp_trimatl(SEXP xSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type x(xSEXP);
-    rcpp_result_gen = Rcpp::wrap(sp_trimatl(x));
-    return rcpp_result_gen;
-END_RCPP
-}
-// sp_diag
-arma::sp_mat sp_diag(const arma::sp_mat& x, const arma::vec& v);
-RcppExport SEXP _netdiffuseR_sp_diag(SEXP xSEXP, SEXP vSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type x(xSEXP);
-    Rcpp::traits::input_parameter< const arma::vec& >::type v(vSEXP);
-    rcpp_result_gen = Rcpp::wrap(sp_diag(x, v));
-    return rcpp_result_gen;
-END_RCPP
-}
-// unif_rand_w_exclusion
-int unif_rand_w_exclusion(int n, int e);
-RcppExport SEXP _netdiffuseR_unif_rand_w_exclusion(SEXP nSEXP, SEXP eSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    Rcpp::traits::input_parameter< int >::type e(eSEXP);
-    rcpp_result_gen = Rcpp::wrap(unif_rand_w_exclusion(n, e));
-    return rcpp_result_gen;
-END_RCPP
-}
-// sp_as_undirected
-arma::sp_mat sp_as_undirected(const arma::sp_mat& x);
-RcppExport SEXP _netdiffuseR_sp_as_undirected(SEXP xSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type x(xSEXP);
-    rcpp_result_gen = Rcpp::wrap(sp_as_undirected(x));
-    return rcpp_result_gen;
-END_RCPP
-}
-// bootnet_fillself
-arma::sp_mat bootnet_fillself(arma::sp_mat& graph, const IntegerVector& index, const NumericVector& E);
-RcppExport SEXP _netdiffuseR_bootnet_fillself(SEXP graphSEXP, SEXP indexSEXP, SEXP ESEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< arma::sp_mat& >::type graph(graphSEXP);
-    Rcpp::traits::input_parameter< const IntegerVector& >::type index(indexSEXP);
-    Rcpp::traits::input_parameter< const NumericVector& >::type E(ESEXP);
-    rcpp_result_gen = Rcpp::wrap(bootnet_fillself(graph, index, E));
-    return rcpp_result_gen;
-END_RCPP
-}
-// grid_distribution
-List grid_distribution(const arma::vec& x, const arma::vec& y, int nlevels);
-RcppExport SEXP _netdiffuseR_grid_distribution(SEXP xSEXP, SEXP ySEXP, SEXP nlevelsSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::vec& >::type x(xSEXP);
-    Rcpp::traits::input_parameter< const arma::vec& >::type y(ySEXP);
-    Rcpp::traits::input_parameter< int >::type nlevels(nlevelsSEXP);
-    rcpp_result_gen = Rcpp::wrap(grid_distribution(x, y, nlevels));
-    return rcpp_result_gen;
-END_RCPP
-}
-// edges_coords
-NumericMatrix edges_coords(const arma::sp_mat& graph, const arma::colvec& toa, const arma::colvec& x, const arma::colvec& y, const arma::colvec& vertex_cex, bool undirected, bool no_contemporary, NumericVector dev, NumericVector ran, LogicalVector curved);
-RcppExport SEXP _netdiffuseR_edges_coords(SEXP graphSEXP, SEXP toaSEXP, SEXP xSEXP, SEXP ySEXP, SEXP vertex_cexSEXP, SEXP undirectedSEXP, SEXP no_contemporarySEXP, SEXP devSEXP, SEXP ranSEXP, SEXP curvedSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
-    Rcpp::traits::input_parameter< const arma::colvec& >::type toa(toaSEXP);
-    Rcpp::traits::input_parameter< const arma::colvec& >::type x(xSEXP);
-    Rcpp::traits::input_parameter< const arma::colvec& >::type y(ySEXP);
-    Rcpp::traits::input_parameter< const arma::colvec& >::type vertex_cex(vertex_cexSEXP);
-    Rcpp::traits::input_parameter< bool >::type undirected(undirectedSEXP);
-    Rcpp::traits::input_parameter< bool >::type no_contemporary(no_contemporarySEXP);
-    Rcpp::traits::input_parameter< NumericVector >::type dev(devSEXP);
-    Rcpp::traits::input_parameter< NumericVector >::type ran(ranSEXP);
-    Rcpp::traits::input_parameter< LogicalVector >::type curved(curvedSEXP);
-    rcpp_result_gen = Rcpp::wrap(edges_coords(graph, toa, x, y, vertex_cex, undirected, no_contemporary, dev, ran, curved));
-    return rcpp_result_gen;
-END_RCPP
-}
-// edges_arrow
-List edges_arrow(const double& x0, const double& y0, const double& x1, const double& y1, const double& height, const double& width, const double beta, NumericVector dev, NumericVector ran, bool curved);
-RcppExport SEXP _netdiffuseR_edges_arrow(SEXP x0SEXP, SEXP y0SEXP, SEXP x1SEXP, SEXP y1SEXP, SEXP heightSEXP, SEXP widthSEXP, SEXP betaSEXP, SEXP devSEXP, SEXP ranSEXP, SEXP curvedSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const double& >::type x0(x0SEXP);
-    Rcpp::traits::input_parameter< const double& >::type y0(y0SEXP);
-    Rcpp::traits::input_parameter< const double& >::type x1(x1SEXP);
-    Rcpp::traits::input_parameter< const double& >::type y1(y1SEXP);
-    Rcpp::traits::input_parameter< const double& >::type height(heightSEXP);
-    Rcpp::traits::input_parameter< const double& >::type width(widthSEXP);
-    Rcpp::traits::input_parameter< const double >::type beta(betaSEXP);
-    Rcpp::traits::input_parameter< NumericVector >::type dev(devSEXP);
-    Rcpp::traits::input_parameter< NumericVector >::type ran(ranSEXP);
-    Rcpp::traits::input_parameter< bool >::type curved(curvedSEXP);
-    rcpp_result_gen = Rcpp::wrap(edges_arrow(x0, y0, x1, y1, height, width, beta, dev, ran, curved));
-    return rcpp_result_gen;
-END_RCPP
-}
-// vertices_coords
-List vertices_coords(const arma::colvec& x, const arma::colvec& y, const arma::colvec& size, const arma::colvec& nsides, const arma::colvec& rot, NumericVector dev, NumericVector ran);
-RcppExport SEXP _netdiffuseR_vertices_coords(SEXP xSEXP, SEXP ySEXP, SEXP sizeSEXP, SEXP nsidesSEXP, SEXP rotSEXP, SEXP devSEXP, SEXP ranSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::colvec& >::type x(xSEXP);
-    Rcpp::traits::input_parameter< const arma::colvec& >::type y(ySEXP);
-    Rcpp::traits::input_parameter< const arma::colvec& >::type size(sizeSEXP);
-    Rcpp::traits::input_parameter< const arma::colvec& >::type nsides(nsidesSEXP);
-    Rcpp::traits::input_parameter< const arma::colvec& >::type rot(rotSEXP);
-    Rcpp::traits::input_parameter< NumericVector >::type dev(devSEXP);
-    Rcpp::traits::input_parameter< NumericVector >::type ran(ranSEXP);
-    rcpp_result_gen = Rcpp::wrap(vertices_coords(x, y, size, nsides, rot, dev, ran));
-    return rcpp_result_gen;
-END_RCPP
-}
-// rgraph_er_cpp
-arma::sp_mat rgraph_er_cpp(int n, double p, bool undirected, bool weighted, bool self);
-RcppExport SEXP _netdiffuseR_rgraph_er_cpp(SEXP nSEXP, SEXP pSEXP, SEXP undirectedSEXP, SEXP weightedSEXP, SEXP selfSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    Rcpp::traits::input_parameter< double >::type p(pSEXP);
-    Rcpp::traits::input_parameter< bool >::type undirected(undirectedSEXP);
-    Rcpp::traits::input_parameter< bool >::type weighted(weightedSEXP);
-    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
-    rcpp_result_gen = Rcpp::wrap(rgraph_er_cpp(n, p, undirected, weighted, self));
-    return rcpp_result_gen;
-END_RCPP
-}
-// ring_lattice
-arma::sp_mat ring_lattice(int n, int k, bool undirected);
-RcppExport SEXP _netdiffuseR_ring_lattice(SEXP nSEXP, SEXP kSEXP, SEXP undirectedSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< int >::type n(nSEXP);
-    Rcpp::traits::input_parameter< int >::type k(kSEXP);
-    Rcpp::traits::input_parameter< bool >::type undirected(undirectedSEXP);
-    rcpp_result_gen = Rcpp::wrap(ring_lattice(n, k, undirected));
-    return rcpp_result_gen;
-END_RCPP
-}
-// rewire_endpoints
-arma::sp_mat rewire_endpoints(const arma::sp_mat& graph, double p, bool both_ends, bool self, bool multiple, bool undirected);
-RcppExport SEXP _netdiffuseR_rewire_endpoints(SEXP graphSEXP, SEXP pSEXP, SEXP both_endsSEXP, SEXP selfSEXP, SEXP multipleSEXP, SEXP undirectedSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
-    Rcpp::traits::input_parameter< double >::type p(pSEXP);
-    Rcpp::traits::input_parameter< bool >::type both_ends(both_endsSEXP);
-    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
-    Rcpp::traits::input_parameter< bool >::type multiple(multipleSEXP);
-    Rcpp::traits::input_parameter< bool >::type undirected(undirectedSEXP);
-    rcpp_result_gen = Rcpp::wrap(rewire_endpoints(graph, p, both_ends, self, multiple, undirected));
-    return rcpp_result_gen;
-END_RCPP
-}
-// rewire_swap
-arma::sp_mat rewire_swap(const arma::sp_mat& graph, int nsteps, bool self, bool multiple, bool undirected, double pr_rewire);
-RcppExport SEXP _netdiffuseR_rewire_swap(SEXP graphSEXP, SEXP nstepsSEXP, SEXP selfSEXP, SEXP multipleSEXP, SEXP undirectedSEXP, SEXP pr_rewireSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
-    Rcpp::traits::input_parameter< int >::type nsteps(nstepsSEXP);
-    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
-    Rcpp::traits::input_parameter< bool >::type multiple(multipleSEXP);
-    Rcpp::traits::input_parameter< bool >::type undirected(undirectedSEXP);
-    Rcpp::traits::input_parameter< double >::type pr_rewire(pr_rewireSEXP);
-    rcpp_result_gen = Rcpp::wrap(rewire_swap(graph, nsteps, self, multiple, undirected, pr_rewire));
-    return rcpp_result_gen;
-END_RCPP
-}
-// rewire_ws
-arma::sp_mat rewire_ws(arma::sp_mat G, int K, double p, bool self, bool multiple);
-RcppExport SEXP _netdiffuseR_rewire_ws(SEXP GSEXP, SEXP KSEXP, SEXP pSEXP, SEXP selfSEXP, SEXP multipleSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< arma::sp_mat >::type G(GSEXP);
-    Rcpp::traits::input_parameter< int >::type K(KSEXP);
-    Rcpp::traits::input_parameter< double >::type p(pSEXP);
-    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
-    Rcpp::traits::input_parameter< bool >::type multiple(multipleSEXP);
-    rcpp_result_gen = Rcpp::wrap(rewire_ws(G, K, p, self, multiple));
-    return rcpp_result_gen;
-END_RCPP
-}
-// permute_graph_cpp
-arma::sp_mat permute_graph_cpp(const arma::sp_mat& x, bool self, bool multiple);
-RcppExport SEXP _netdiffuseR_permute_graph_cpp(SEXP xSEXP, SEXP selfSEXP, SEXP multipleSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type x(xSEXP);
-    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
-    Rcpp::traits::input_parameter< bool >::type multiple(multipleSEXP);
-    rcpp_result_gen = Rcpp::wrap(permute_graph_cpp(x, self, multiple));
-    return rcpp_result_gen;
-END_RCPP
-}
-// rgraph_ba_cpp
-arma::sp_mat rgraph_ba_cpp(const arma::sp_mat& graph, const arma::colvec& dgr, int m, int t, bool self);
-RcppExport SEXP _netdiffuseR_rgraph_ba_cpp(SEXP graphSEXP, SEXP dgrSEXP, SEXP mSEXP, SEXP tSEXP, SEXP selfSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
-    Rcpp::traits::input_parameter< const arma::colvec& >::type dgr(dgrSEXP);
-    Rcpp::traits::input_parameter< int >::type m(mSEXP);
-    Rcpp::traits::input_parameter< int >::type t(tSEXP);
-    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
-    rcpp_result_gen = Rcpp::wrap(rgraph_ba_cpp(graph, dgr, m, t, self));
-    return rcpp_result_gen;
-END_RCPP
-}
-// rgraph_ba_new_cpp
-arma::sp_mat rgraph_ba_new_cpp(int m0, int m, int t, bool self);
-RcppExport SEXP _netdiffuseR_rgraph_ba_new_cpp(SEXP m0SEXP, SEXP mSEXP, SEXP tSEXP, SEXP selfSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< int >::type m0(m0SEXP);
-    Rcpp::traits::input_parameter< int >::type m(mSEXP);
-    Rcpp::traits::input_parameter< int >::type t(tSEXP);
-    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
-    rcpp_result_gen = Rcpp::wrap(rgraph_ba_new_cpp(m0, m, t, self));
-    return rcpp_result_gen;
-END_RCPP
-}
-// rgraph_sf_homo
-arma::sp_mat rgraph_sf_homo(const arma::colvec& eta, const arma::sp_mat& graph, const arma::colvec& dgr, int m, int t, bool self);
-RcppExport SEXP _netdiffuseR_rgraph_sf_homo(SEXP etaSEXP, SEXP graphSEXP, SEXP dgrSEXP, SEXP mSEXP, SEXP tSEXP, SEXP selfSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::colvec& >::type eta(etaSEXP);
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
-    Rcpp::traits::input_parameter< const arma::colvec& >::type dgr(dgrSEXP);
-    Rcpp::traits::input_parameter< int >::type m(mSEXP);
-    Rcpp::traits::input_parameter< int >::type t(tSEXP);
-    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
-    rcpp_result_gen = Rcpp::wrap(rgraph_sf_homo(eta, graph, dgr, m, t, self));
-    return rcpp_result_gen;
-END_RCPP
-}
-// rgraph_sf_homo_new
-arma::sp_mat rgraph_sf_homo_new(const arma::colvec& eta, int m0, int m, int t, bool self);
-RcppExport SEXP _netdiffuseR_rgraph_sf_homo_new(SEXP etaSEXP, SEXP m0SEXP, SEXP mSEXP, SEXP tSEXP, SEXP selfSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::colvec& >::type eta(etaSEXP);
-    Rcpp::traits::input_parameter< int >::type m0(m0SEXP);
-    Rcpp::traits::input_parameter< int >::type m(mSEXP);
-    Rcpp::traits::input_parameter< int >::type t(tSEXP);
-    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
-    rcpp_result_gen = Rcpp::wrap(rgraph_sf_homo_new(eta, m0, m, t, self));
-    return rcpp_result_gen;
-END_RCPP
-}
-// vertex_covariate_dist
-arma::sp_mat vertex_covariate_dist(const arma::sp_mat& graph, const arma::mat& X, double p);
-RcppExport SEXP _netdiffuseR_vertex_covariate_dist(SEXP graphSEXP, SEXP XSEXP, SEXP pSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
-    Rcpp::traits::input_parameter< const arma::mat& >::type X(XSEXP);
-    Rcpp::traits::input_parameter< double >::type p(pSEXP);
-    rcpp_result_gen = Rcpp::wrap(vertex_covariate_dist(graph, X, p));
-    return rcpp_result_gen;
-END_RCPP
-}
-// vertex_mahalanobis_dist_cpp
-arma::sp_mat vertex_mahalanobis_dist_cpp(const arma::sp_mat& graph, const arma::mat& X, const arma::mat& S);
-RcppExport SEXP _netdiffuseR_vertex_mahalanobis_dist_cpp(SEXP graphSEXP, SEXP XSEXP, SEXP SSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
-    Rcpp::traits::input_parameter< const arma::mat& >::type X(XSEXP);
-    Rcpp::traits::input_parameter< const arma::mat& >::type S(SSEXP);
-    rcpp_result_gen = Rcpp::wrap(vertex_mahalanobis_dist_cpp(graph, X, S));
-    return rcpp_result_gen;
-END_RCPP
-}
-// vertex_covariate_compare
-arma::sp_mat vertex_covariate_compare(const arma::sp_mat& graph, const NumericVector& X, std::string funname);
-RcppExport SEXP _netdiffuseR_vertex_covariate_compare(SEXP graphSEXP, SEXP XSEXP, SEXP funnameSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
-    Rcpp::traits::input_parameter< const NumericVector& >::type X(XSEXP);
-    Rcpp::traits::input_parameter< std::string >::type funname(funnameSEXP);
-    rcpp_result_gen = Rcpp::wrap(vertex_covariate_compare(graph, X, funname));
-    return rcpp_result_gen;
-END_RCPP
-}
-// moran_cpp
-List moran_cpp(const arma::colvec& x, const arma::sp_mat& w);
-RcppExport SEXP _netdiffuseR_moran_cpp(SEXP xSEXP, SEXP wSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::traits::input_parameter< const arma::colvec& >::type x(xSEXP);
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type w(wSEXP);
-    rcpp_result_gen = Rcpp::wrap(moran_cpp(x, w));
-    return rcpp_result_gen;
-END_RCPP
-}
-// matrix_compareCpp
-arma::sp_mat matrix_compareCpp(const arma::sp_mat& A, const arma::sp_mat& B, Function fun);
-RcppExport SEXP _netdiffuseR_matrix_compareCpp(SEXP ASEXP, SEXP BSEXP, SEXP funSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type A(ASEXP);
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type B(BSEXP);
-    Rcpp::traits::input_parameter< Function >::type fun(funSEXP);
-    rcpp_result_gen = Rcpp::wrap(matrix_compareCpp(A, B, fun));
-    return rcpp_result_gen;
-END_RCPP
-}
-// struct_test_mean
-double struct_test_mean(NumericVector& y, std::string funname, bool self);
-RcppExport SEXP _netdiffuseR_struct_test_mean(SEXP ySEXP, SEXP funnameSEXP, SEXP selfSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< NumericVector& >::type y(ySEXP);
-    Rcpp::traits::input_parameter< std::string >::type funname(funnameSEXP);
-    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
-    rcpp_result_gen = Rcpp::wrap(struct_test_mean(y, funname, self));
-    return rcpp_result_gen;
-END_RCPP
-}
-// struct_test_var
-double struct_test_var(NumericVector& y, std::string funname, bool self);
-RcppExport SEXP _netdiffuseR_struct_test_var(SEXP ySEXP, SEXP funnameSEXP, SEXP selfSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< NumericVector& >::type y(ySEXP);
-    Rcpp::traits::input_parameter< std::string >::type funname(funnameSEXP);
-    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
-    rcpp_result_gen = Rcpp::wrap(struct_test_var(y, funname, self));
-    return rcpp_result_gen;
-END_RCPP
-}
-// ego_variance
-NumericVector ego_variance(const arma::sp_mat& graph, const NumericVector& Y, std::string funname, bool all);
-RcppExport SEXP _netdiffuseR_ego_variance(SEXP graphSEXP, SEXP YSEXP, SEXP funnameSEXP, SEXP allSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
-    Rcpp::traits::input_parameter< const NumericVector& >::type Y(YSEXP);
-    Rcpp::traits::input_parameter< std::string >::type funname(funnameSEXP);
-    Rcpp::traits::input_parameter< bool >::type all(allSEXP);
-    rcpp_result_gen = Rcpp::wrap(ego_variance(graph, Y, funname, all));
-    return rcpp_result_gen;
-END_RCPP
-}
-
-static const R_CallMethodDef CallEntries[] = {
-    {"_netdiffuseR_edgelist_to_adjmat_cpp", (DL_FUNC) &_netdiffuseR_edgelist_to_adjmat_cpp, 6},
-    {"_netdiffuseR_adjmat_to_edgelist_cpp", (DL_FUNC) &_netdiffuseR_adjmat_to_edgelist_cpp, 2},
-    {"_netdiffuseR_toa_diff_cpp", (DL_FUNC) &_netdiffuseR_toa_diff_cpp, 1},
-    {"_netdiffuseR_egonet_attrs_cpp", (DL_FUNC) &_netdiffuseR_egonet_attrs_cpp, 5},
-    {"_netdiffuseR_approx_geodesicCpp", (DL_FUNC) &_netdiffuseR_approx_geodesicCpp, 3},
-    {"_netdiffuseR_infection_cpp", (DL_FUNC) &_netdiffuseR_infection_cpp, 9},
-    {"_netdiffuseR_susceptibility_cpp", (DL_FUNC) &_netdiffuseR_susceptibility_cpp, 9},
-    {"_netdiffuseR_select_egoalter_cpp", (DL_FUNC) &_netdiffuseR_select_egoalter_cpp, 4},
-    {"_netdiffuseR_sp_trimatl", (DL_FUNC) &_netdiffuseR_sp_trimatl, 1},
-    {"_netdiffuseR_sp_diag", (DL_FUNC) &_netdiffuseR_sp_diag, 2},
-    {"_netdiffuseR_unif_rand_w_exclusion", (DL_FUNC) &_netdiffuseR_unif_rand_w_exclusion, 2},
-    {"_netdiffuseR_sp_as_undirected", (DL_FUNC) &_netdiffuseR_sp_as_undirected, 1},
-    {"_netdiffuseR_bootnet_fillself", (DL_FUNC) &_netdiffuseR_bootnet_fillself, 3},
-    {"_netdiffuseR_grid_distribution", (DL_FUNC) &_netdiffuseR_grid_distribution, 3},
-    {"_netdiffuseR_edges_coords", (DL_FUNC) &_netdiffuseR_edges_coords, 10},
-    {"_netdiffuseR_edges_arrow", (DL_FUNC) &_netdiffuseR_edges_arrow, 10},
-    {"_netdiffuseR_vertices_coords", (DL_FUNC) &_netdiffuseR_vertices_coords, 7},
-    {"_netdiffuseR_rgraph_er_cpp", (DL_FUNC) &_netdiffuseR_rgraph_er_cpp, 5},
-    {"_netdiffuseR_ring_lattice", (DL_FUNC) &_netdiffuseR_ring_lattice, 3},
-    {"_netdiffuseR_rewire_endpoints", (DL_FUNC) &_netdiffuseR_rewire_endpoints, 6},
-    {"_netdiffuseR_rewire_swap", (DL_FUNC) &_netdiffuseR_rewire_swap, 6},
-    {"_netdiffuseR_rewire_ws", (DL_FUNC) &_netdiffuseR_rewire_ws, 5},
-    {"_netdiffuseR_permute_graph_cpp", (DL_FUNC) &_netdiffuseR_permute_graph_cpp, 3},
-    {"_netdiffuseR_rgraph_ba_cpp", (DL_FUNC) &_netdiffuseR_rgraph_ba_cpp, 5},
-    {"_netdiffuseR_rgraph_ba_new_cpp", (DL_FUNC) &_netdiffuseR_rgraph_ba_new_cpp, 4},
-    {"_netdiffuseR_rgraph_sf_homo", (DL_FUNC) &_netdiffuseR_rgraph_sf_homo, 6},
-    {"_netdiffuseR_rgraph_sf_homo_new", (DL_FUNC) &_netdiffuseR_rgraph_sf_homo_new, 5},
-    {"_netdiffuseR_vertex_covariate_dist", (DL_FUNC) &_netdiffuseR_vertex_covariate_dist, 3},
-    {"_netdiffuseR_vertex_mahalanobis_dist_cpp", (DL_FUNC) &_netdiffuseR_vertex_mahalanobis_dist_cpp, 3},
-    {"_netdiffuseR_vertex_covariate_compare", (DL_FUNC) &_netdiffuseR_vertex_covariate_compare, 3},
-    {"_netdiffuseR_moran_cpp", (DL_FUNC) &_netdiffuseR_moran_cpp, 2},
-    {"_netdiffuseR_matrix_compareCpp", (DL_FUNC) &_netdiffuseR_matrix_compareCpp, 3},
-    {"_netdiffuseR_struct_test_mean", (DL_FUNC) &_netdiffuseR_struct_test_mean, 3},
-    {"_netdiffuseR_struct_test_var", (DL_FUNC) &_netdiffuseR_struct_test_var, 3},
-    {"_netdiffuseR_ego_variance", (DL_FUNC) &_netdiffuseR_ego_variance, 4},
-    {NULL, NULL, 0}
-};
-
-RcppExport void R_init_netdiffuseR(DllInfo *dll) {
-    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
-    R_useDynamicSymbols(dll, FALSE);
-}

From ae830622ad0f31a82c052c56cb4486213983d1d0 Mon Sep 17 00:00:00 2001
From: "George G. Vega Yon" <g.vegayon@gmail.com>
Date: Mon, 25 Nov 2024 12:36:58 -0700
Subject: [PATCH 2/3] Adding prototype of disadopt

---
 R/RcppExports.R                | 349 +++++++++++++++++++++++++++++++++
 R/rdiffnet.r                   |  43 ++--
 tests/testthat/test-rdiffnet.R |  27 +++
 3 files changed, 405 insertions(+), 14 deletions(-)
 create mode 100644 R/RcppExports.R

diff --git a/R/RcppExports.R b/R/RcppExports.R
new file mode 100644
index 0000000..b756fc7
--- /dev/null
+++ b/R/RcppExports.R
@@ -0,0 +1,349 @@
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+edgelist_to_adjmat_cpp <- function(edgelist, weights = as.numeric( c()), n = 0L, undirected = FALSE, self = FALSE, multiple = FALSE) {
+    .Call(`_netdiffuseR_edgelist_to_adjmat_cpp`, edgelist, weights, n, undirected, self, multiple)
+}
+
+adjmat_to_edgelist_cpp <- function(adjmat, undirected = TRUE) {
+    .Call(`_netdiffuseR_adjmat_to_edgelist_cpp`, adjmat, undirected)
+}
+
+toa_diff_cpp <- function(year) {
+    .Call(`_netdiffuseR_toa_diff_cpp`, year)
+}
+
+egonet_attrs_cpp <- function(graph, V, outer = TRUE, self = TRUE, valued = TRUE) {
+    .Call(`_netdiffuseR_egonet_attrs_cpp`, graph, V, outer, self, valued)
+}
+
+approx_geodesicCpp <- function(G, n = 6L, warn = FALSE) {
+    .Call(`_netdiffuseR_approx_geodesicCpp`, G, n, warn)
+}
+
+infection_cpp <- function(graph, times, normalize = TRUE, K = 1L, r = 0.5, expdiscount = FALSE, n = 0L, valued = FALSE, outgoing = TRUE) {
+    .Call(`_netdiffuseR_infection_cpp`, graph, times, normalize, K, r, expdiscount, n, valued, outgoing)
+}
+
+susceptibility_cpp <- function(graph, times, normalize = TRUE, K = 1L, r = 0.5, expdiscount = FALSE, n = 0L, valued = FALSE, outgoing = TRUE) {
+    .Call(`_netdiffuseR_susceptibility_cpp`, graph, times, normalize, K, r, expdiscount, n, valued, outgoing)
+}
+
+select_egoalter_cpp <- function(adjmat_t0, adjmat_t1, adopt_t0, adopt_t1) {
+    .Call(`_netdiffuseR_select_egoalter_cpp`, adjmat_t0, adjmat_t1, adopt_t0, adopt_t1)
+}
+
+sp_trimatl <- function(x) {
+    .Call(`_netdiffuseR_sp_trimatl`, x)
+}
+
+sp_diag <- function(x, v) {
+    .Call(`_netdiffuseR_sp_diag`, x, v)
+}
+
+unif_rand_w_exclusion <- function(n, e) {
+    .Call(`_netdiffuseR_unif_rand_w_exclusion`, n, e)
+}
+
+sp_as_undirected <- function(x) {
+    .Call(`_netdiffuseR_sp_as_undirected`, x)
+}
+
+bootnet_fillself <- function(graph, index, E) {
+    .Call(`_netdiffuseR_bootnet_fillself`, graph, index, E)
+}
+
+#' Distribution over a grid
+#'
+#' Distribution of pairs over a grid of fix size.
+#'
+#' @param x Numeric vector of size \eqn{n}
+#' @param y Numeric vector of size \eqn{n}
+#' @param nlevels Integer scalar. Number of bins to return
+#' @details
+#'
+#' This function ment for internal use only.
+#'
+#' @export
+#' @keywords misc dplot
+#' @family visualizations
+#' @seealso Used by \code{\link{plot_infectsuscep}}
+#' @return Returns a list with three elements
+#' \item{x}{Numeric vector of size \code{nlevels} with the class marks for x}
+#' \item{y}{Numeric vector of size \code{nlevels} with the class marks for y}
+#' \item{z}{Numeric matrix of size \code{nlevels} by \code{nlevels} with the distribution %
+#' of the elements in terms of frequency}
+#' @section Examples:
+#' \preformatted{
+#' # Generating random vectors of size 100
+#' x <- rnorm(100)
+#' y <- rnorm(100)
+#'
+#' # Calculating distribution
+#' grid_distribution(x,y,20)
+#' }
+grid_distribution <- function(x, y, nlevels = 100L) {
+    .Call(`_netdiffuseR_grid_distribution`, x, y, nlevels)
+}
+
+#' Compute ego/alter edge coordinates considering alter's size and aspect ratio
+#'
+#' Given a graph, vertices' positions and sizes, calculates the absolute positions
+#' of the endpoints of the edges considering the plot's aspect ratio.
+#'
+#' @param graph A square matrix of size \eqn{n}. Adjacency matrix.
+#' @param toa Integer vector of size \eqn{n}. Times of adoption.
+#' @param x Numeric vector of size \eqn{n}. x-coordinta of vertices.
+#' @param y Numeric vector of size \eqn{n}. y-coordinta of vertices.
+#' @param vertex_cex Numeric vector of size \eqn{n}. Vertices' sizes in terms
+#' of the x-axis (see \code{\link{symbols}}).
+#' @param undirected Logical scalar. Whether the graph is undirected or not.
+#' @param no_contemporary Logical scalar. Whether to return (compute) edges'
+#' coordiantes for vertices with the same time of adoption (see details).
+#' @param dev Numeric vector of size 2. Height and width of the device (see details).
+#' @param ran Numeric vector of size 2. Range of the x and y axis (see details).
+#' @param curved Logical vector.
+#' @return A numeric matrix of size \eqn{m\times 5}{m * 5} with the following
+#' columns:
+#' \item{x0, y0}{Edge origin}
+#' \item{x1, y1}{Edge target}
+#' \item{alpha}{Relative angle between \code{(x0,y0)} and \code{(x1,y1)} in terms
+#' of radians}
+#' With \eqn{m} as the number of resulting edges.
+#' @details
+#'
+#' In order to make the plot's visualization more appealing, this function provides
+#' a straight forward way of computing the tips of the edges considering the
+#' aspect ratio of the axes range. In particular, the following corrections are
+#' made at the moment of calculating the egdes coords:
+#'
+#' \itemize{
+#' \item{Instead of using the actual distance between ego and alter, a relative
+#' one is calculated as follows
+#' \deqn{d'=\left[(x_0-x_1)^2 + (y_0' - y_1')^2\right]^\frac{1}{2}}{d'=sqrt[(x0-x1)^2 + (y0'-y1')^2]}
+#' where \eqn{%
+#' y_i'=y_i\times\frac{\max x - \min x}{\max y - \min y} }{%
+#' yi' = yi * [max(x) - min(x)]/[max(y) - min(y)]}
+#' }
+#' \item{Then, for the relative elevation angle, \code{alpha}, the relative distance \eqn{d'}
+#' is used, \eqn{\alpha'=\arccos\left( (x_0 - x_1)/d' \right)}{\alpha' = acos[ (x0 - x1)/d' ]}}
+#' \item{Finally, the edge's endpoint's (alter) coordinates are computed as follows: %
+#' \deqn{%
+#'   x_1' = x_1 + \cos(\alpha')\times v_1}{%
+#'   x1' = x1 + cos(\alpha') * v1
+#' }
+#' \deqn{%
+#'   y_1' = y_1 -+ \sin(\alpha')\times v_1 \times\frac{\max y - \min y}{\max x - \min x} }{%
+#'   y1' = y1 -+ sin(\alpha')*[max(y) - min(y)]/[max(x) - min(x)]
+#' }
+#' Where \eqn{v_1}{v1} is alter's size in terms of the x-axis, and the sign of
+#' the second term in \eqn{y_1'}{y1'} is negative iff \eqn{y_0 < y_1}{y0<y1}.
+#' }
+#' }
+#'
+#' The same process (with sign inverted) is applied to the edge starting piont.
+#' The resulting values, \eqn{x_1',y_1'}{x1',y1'} can be used with the function
+#' \code{\link{arrows}}. This is the workhorse function used in \code{\link{plot_threshold}}.
+#'
+#' The \code{dev} argument provides a reference to rescale the plot accordingly
+#' to the device, and former, considering the size of the margins as well (this
+#' can be easily fetched via \code{par("pin")}, plot area in inches).
+#'
+#' On the other hand, \code{ran} provides a reference for the adjustment
+#' according to the range of the data, this is \code{range(x)[2] - range(x)[1]}
+#' and \code{range(y)[2] - range(y)[1]} respectively.
+#'
+#' @keywords misc dplot
+#' @examples
+#' # --------------------------------------------------------------------------
+#' data(medInnovationsDiffNet)
+#' library(sna)
+#'
+#' # Computing coordinates
+#' set.seed(79)
+#' coords <- sna::gplot(as.matrix(medInnovationsDiffNet$graph[[1]]))
+#'
+#' # Getting edge coordinates
+#' vcex <- rep(1.5, nnodes(medInnovationsDiffNet))
+#' ecoords <- edges_coords(
+#'   medInnovationsDiffNet$graph[[1]],
+#'   diffnet.toa(medInnovationsDiffNet),
+#'   x = coords[,1], y = coords[,2],
+#'   vertex_cex = vcex,
+#'   dev = par("pin")
+#'   )
+#'
+#' ecoords <- as.data.frame(ecoords)
+#'
+#' # Plotting
+#' symbols(coords[,1], coords[,2], circles=vcex,
+#'   inches=FALSE, xaxs="i", yaxs="i")
+#'
+#' with(ecoords, arrows(x0,y0,x1,y1, length=.1))
+#' @export
+edges_coords <- function(graph, toa, x, y, vertex_cex, undirected = TRUE, no_contemporary = TRUE, dev = as.numeric( c()), ran = as.numeric( c()), curved = as.logical( c())) {
+    .Call(`_netdiffuseR_edges_coords`, graph, toa, x, y, vertex_cex, undirected, no_contemporary, dev, ran, curved)
+}
+
+edges_arrow <- function(x0, y0, x1, y1, height, width, beta = 1.5707963267949, dev = as.numeric( c()), ran = as.numeric( c()), curved = FALSE) {
+    .Call(`_netdiffuseR_edges_arrow`, x0, y0, x1, y1, height, width, beta, dev, ran, curved)
+}
+
+vertices_coords <- function(x, y, size, nsides, rot, dev = as.numeric( c()), ran = as.numeric( c())) {
+    .Call(`_netdiffuseR_vertices_coords`, x, y, size, nsides, rot, dev, ran)
+}
+
+rgraph_er_cpp <- function(n = 10L, p = 0.3, undirected = TRUE, weighted = FALSE, self = FALSE) {
+    .Call(`_netdiffuseR_rgraph_er_cpp`, n, p, undirected, weighted, self)
+}
+
+#' Ring lattice graph
+#'
+#' Creates a ring lattice with \eqn{n} vertices, each one of degree (at most) \eqn{k}
+#' as an undirected graph. This is the basis of \code{\link{rgraph_ws}}.
+#' @param n Integer scalar. Size of the graph.
+#' @param k Integer scalar. Out-degree of each vertex.
+#' @param undirected Logical scalar. Whether the graph is undirected or not.
+#' @details when \code{undirected=TRUE}, the degree of each node always
+#' even. So if \code{k=3}, then the degree will be \code{2}.
+#' @return A sparse matrix of class \code{\link[Matrix:dgCMatrix-class]{dgCMatrix}} of size
+#' \eqn{n\times n}{n * n}.
+#' @references Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of
+#' “small-world” networks. Nature, 393(6684), 440–2. \doi{10.1038/30918}
+#' @export
+#' @family simulation functions
+ring_lattice <- function(n, k, undirected = FALSE) {
+    .Call(`_netdiffuseR_ring_lattice`, n, k, undirected)
+}
+
+rewire_endpoints <- function(graph, p, both_ends = FALSE, self = FALSE, multiple = FALSE, undirected = FALSE) {
+    .Call(`_netdiffuseR_rewire_endpoints`, graph, p, both_ends, self, multiple, undirected)
+}
+
+rewire_swap <- function(graph, nsteps = 100L, self = FALSE, multiple = FALSE, undirected = FALSE, pr_rewire = 0.5) {
+    .Call(`_netdiffuseR_rewire_swap`, graph, nsteps, self, multiple, undirected, pr_rewire)
+}
+
+rewire_ws <- function(G, K, p = 0.0, self = FALSE, multiple = FALSE) {
+    .Call(`_netdiffuseR_rewire_ws`, G, K, p, self, multiple)
+}
+
+permute_graph_cpp <- function(x, self = FALSE, multiple = FALSE) {
+    .Call(`_netdiffuseR_permute_graph_cpp`, x, self, multiple)
+}
+
+rgraph_ba_cpp <- function(graph, dgr, m = 1L, t = 10L, self = TRUE) {
+    .Call(`_netdiffuseR_rgraph_ba_cpp`, graph, dgr, m, t, self)
+}
+
+rgraph_ba_new_cpp <- function(m0 = 1L, m = 1L, t = 10L, self = TRUE) {
+    .Call(`_netdiffuseR_rgraph_ba_new_cpp`, m0, m, t, self)
+}
+
+rgraph_sf_homo <- function(eta, graph, dgr, m = 1L, t = 10L, self = TRUE) {
+    .Call(`_netdiffuseR_rgraph_sf_homo`, eta, graph, dgr, m, t, self)
+}
+
+rgraph_sf_homo_new <- function(eta, m0 = 1L, m = 1L, t = 10L, self = TRUE) {
+    .Call(`_netdiffuseR_rgraph_sf_homo_new`, eta, m0, m, t, self)
+}
+
+#' @export
+#' @rdname vertex_covariate_dist
+vertex_covariate_dist <- function(graph, X, p = 2.0) {
+    .Call(`_netdiffuseR_vertex_covariate_dist`, graph, X, p)
+}
+
+vertex_mahalanobis_dist_cpp <- function(graph, X, S) {
+    .Call(`_netdiffuseR_vertex_mahalanobis_dist_cpp`, graph, X, S)
+}
+
+#' Comparisons at dyadic level
+#'
+#' @param graph A matrix of size \eqn{n\times n}{n*n} of class \code{dgCMatrix}.
+#' @param X A numeric vector of length \eqn{n}.
+#' @param funname Character scalar. Comparison to make (see details).
+#' @details
+#'
+#' This auxiliary function takes advantage of the sparseness of \code{graph} and
+#' applies a function in the form of \eqn{funname(x_i,x_j)}{funname(X[i],X[j])}
+#' only to \eqn{(i,j)} that have no empty entry. In other words, applies a compares
+#' elements of \code{X} only between vertices that have a link; making
+#' \code{nlinks(graph)} comparisons instead of looping through \eqn{n\times n}{n*n},
+#' which is much faster.
+#'
+#' \code{funname} can take any of the following values:
+#' \code{"distance"}, \code{"^2"} or \code{"quaddistance"}, \code{">"} or \code{"greater"},
+#' \code{"<"} or \code{"smaller"}, \code{">="} or \code{"greaterequal"},
+#' \code{"<="} or \code{"smallerequal"}, \code{"=="} or \code{"equal"}.
+#'
+#'
+#' @return A matrix \code{dgCMatrix} of size \eqn{n\times n}{n*n} with values in
+#' the form of \eqn{funname(x_i,x_j)}{funname(X[i],X[j])}.
+#' @family dyadic-level comparison functions
+#' @examples
+#'
+#' # Basic example ------------------------------------------------------------
+#' set.seed(1313)
+#' G <- rgraph_ws(10, 4, .2)
+#' x <- rnorm(10)
+#'
+#' vertex_covariate_compare(G, x, "distance")
+#' vertex_covariate_compare(G, x, "^2")
+#' vertex_covariate_compare(G, x, ">=")
+#' vertex_covariate_compare(G, x, "<=")
+#' @export
+vertex_covariate_compare <- function(graph, X, funname) {
+    .Call(`_netdiffuseR_vertex_covariate_compare`, graph, X, funname)
+}
+
+moran_cpp <- function(x, w) {
+    .Call(`_netdiffuseR_moran_cpp`, x, w)
+}
+
+matrix_compareCpp <- function(A, B, fun) {
+    .Call(`_netdiffuseR_matrix_compareCpp`, A, B, fun)
+}
+
+struct_test_mean <- function(y, funname, self = FALSE) {
+    .Call(`_netdiffuseR_struct_test_mean`, y, funname, self)
+}
+
+struct_test_var <- function(y, funname, self = FALSE) {
+    .Call(`_netdiffuseR_struct_test_var`, y, funname, self)
+}
+
+#' Computes variance of \eqn{Y} at ego level
+#' @param graph A matrix of size \eqn{n\times n}{n*n} of class \code{dgCMatrix}.
+#' @param Y A numeric vector of length \eqn{n}.
+#' @param funname Character scalar. Comparison to make (see \code{\link{vertex_covariate_compare}}).
+#' @param all Logical scalar. When \code{FALSE} (default) \eqn{f_i} is mean at
+#' ego level. Otherwise is fix for all i (see details).
+#' @details
+#'
+#' For each vertex \eqn{i} the variance is computed as follows
+#'
+#' \deqn{%
+#' (\sum_j a_{ij})^{-1}\sum_j a_{ij} \left[f(y_i,y_j) - f_i\right]^2
+#' }{%
+#' (sum_j a(ij))^(-1) * \sum_j a(ij) * [f(y(i),y(j)) - f(i)]^2
+#' }
+#'
+#' Where \eqn{a_{ij}}{a(ij)} is the ij-th element of \code{graph}, \eqn{f} is
+#' the function specified in \code{funname}, and, if \code{all=FALSE}
+#' \eqn{f_i = \sum_j a_{ij}f(y_i,y_j)^2/\sum_ja_{ij}}{f(i)=\sum_j a(ij)f(y(i), y(j))^2/\sum_j a(ij)},
+#' otherwise \eqn{f_i = f_j = \frac{1}{n^2}\sum_{i,j}f(y_i,y_j)}{f(i)=f(j)=(1/n^2)\sum_(i,j) f(y_i,y_j)}
+#'
+#'
+#' This is an auxiliary function for \code{\link{struct_test}}. The idea is
+#' to compute an adjusted measure of disimilarity between vertices, so the
+#' closest in terms of \eqn{f} is \eqn{i} to its neighbors, the smaller the
+#' relative variance.
+#' @return A numeric vector of length \eqn{n}.
+#' @export
+#' @seealso \code{\link{struct_test}}
+#' @family statistics
+ego_variance <- function(graph, Y, funname, all = FALSE) {
+    .Call(`_netdiffuseR_ego_variance`, graph, Y, funname, all)
+}
+
diff --git a/R/rdiffnet.r b/R/rdiffnet.r
index cd000a0..b44d3b0 100644
--- a/R/rdiffnet.r
+++ b/R/rdiffnet.r
@@ -470,7 +470,7 @@ rdiffnet <- function(
       cumadopt[whoadopts, i:t, q] <- 1L
 
       # 3.4` Updating the toa
-      # toa[cbind(whoadopts, q)] <- t
+      # toa[cbind(whoadopts, q)] <- i
       toa[, q] <- apply(cumadopt[,, q], 1, function(x) {
         first_adopt <- which(x == 1)
         if (length(first_adopt) > 0) first_adopt[1] else NA
@@ -481,10 +481,28 @@ rdiffnet <- function(
     if (length(disadopt)) {
 
       # Run the disadoption algorithm. This will return the following:
-      # - The updated cupadopt
-      # - A vector of who disadopted
-      # - A list of the same length of what was disadopted.
-      disadopt_res <- disadopt(expo, cupadopt, t)
+      # - A list of length q with the nodes that disadopted
+      disadopt_res <- disadopt(expo, cumadopt, i)
+
+      for (q in seq_along(disadopt_res)) {
+
+        # So only doing this is there's disadoption
+        if (length(disadopt_res[[q]]) == 0)
+          next
+
+        # Checking this makes sense (only adopters can disadopt)
+        q_adopters <- which(!is.na(toa[, q]))
+
+        if (length(setdiff(disadopt_res[[q]], q_adopters)) > 0)
+          stop("Some nodes that disadopted were not adopters.")
+
+        # Updating the cumadopt
+        cumadopt[disadopt_res[[q]], i:t, q] <- 0L
+
+        # Updating toa
+        toa[cbind(disadopt_res[[q]], q)] <- NA
+
+      }
 
 
     }
@@ -495,7 +513,12 @@ rdiffnet <- function(
 
     if (reachedt == 1) {
       if (stop.no.diff)
-        stop(paste("No diffusion in this network for behavior", i, "(Ups!) try changing the seed or the parameters."))
+        stop(
+          paste(
+            "No diffusion in this network for behavior", i,
+            "(Ups!) try changing the seed or the parameters."
+            )
+          )
       else
         warning(paste("No diffusion for behavior", i, " in this network."))
     }
@@ -657,11 +680,3 @@ split_behaviors <- function(diffnet_obj) {
   return(diffnets)
 }
 
-
-  list(
-    seed.p.adopt = seed.p.adopt,
-    seed.nodes = seed.nodes,
-    behavior = behavior,
-    num_of_behaviors = length(seed.p.adopt)
-  )
-}
diff --git a/tests/testthat/test-rdiffnet.R b/tests/testthat/test-rdiffnet.R
index 2733e03..0b5a366 100644
--- a/tests/testthat/test-rdiffnet.R
+++ b/tests/testthat/test-rdiffnet.R
@@ -180,6 +180,33 @@ test_that("toa, adopt, and cumadopt should be equal! (split_behaviors tests)", {
   expect_equal(net_single_from_multiple_1$cumadopt, net_single$cumadopt)
 })
 
+test_that("Disadoption works", {
+
+
+  set.seed(1231)
+  n <- 500
+
+  d_adopt <- function(expo, cumadopt, time) {
+
+    # Id double adopters
+    ids <- which(apply(cumadopt[, 3, , drop=FALSE], 1, sum) > 1)
+
+    if (length(ids) == 0)
+      return(list(integer(), integer()))
+
+    # Otherwise, make them pick one (literally, you can only adopt
+    # A single behavior, will drop the second)
+    return(list(ids, integer()))
+
+  }
+
+  ans <- rdiffnet(n = n, t = 10, disadopt = d_adopt, seed.p.adopt = list(0.1, 0.1))
+
+  tmat <- toa_mat(ans)
+  should_be_ones_or_zeros <- tmat[[1]]$cumadopt[, 10] + tmat[[2]]$cumadopt[, 10]
+  expect_true(all(should_be_ones_or_zeros %in% c(0,1)))
+
+})
 
 #rdiffnet(100, 5, seed.p.adopt = 0.9, threshold.dist = 2, exposure.args = list(normalized=FALSE))
 

From 746f310e46a7bffbbe63663a1842d2f08c9ac47f Mon Sep 17 00:00:00 2001
From: "George G. Vega Yon" <g.vegayon@gmail.com>
Date: Mon, 25 Nov 2024 12:37:28 -0700
Subject: [PATCH 3/3] Adding missing file

---
 src/RcppExports.cpp | 552 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 552 insertions(+)
 create mode 100644 src/RcppExports.cpp

diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp
new file mode 100644
index 0000000..f4c1945
--- /dev/null
+++ b/src/RcppExports.cpp
@@ -0,0 +1,552 @@
+// Generated by using Rcpp::compileAttributes() -> do not edit by hand
+// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+#include <RcppArmadillo.h>
+#include <Rcpp.h>
+
+using namespace Rcpp;
+
+#ifdef RCPP_USE_GLOBAL_ROSTREAM
+Rcpp::Rostream<true>&  Rcpp::Rcout = Rcpp::Rcpp_cout_get();
+Rcpp::Rostream<false>& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get();
+#endif
+
+// edgelist_to_adjmat_cpp
+arma::sp_mat edgelist_to_adjmat_cpp(const arma::mat& edgelist, NumericVector weights, int n, bool undirected, bool self, bool multiple);
+RcppExport SEXP _netdiffuseR_edgelist_to_adjmat_cpp(SEXP edgelistSEXP, SEXP weightsSEXP, SEXP nSEXP, SEXP undirectedSEXP, SEXP selfSEXP, SEXP multipleSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::mat& >::type edgelist(edgelistSEXP);
+    Rcpp::traits::input_parameter< NumericVector >::type weights(weightsSEXP);
+    Rcpp::traits::input_parameter< int >::type n(nSEXP);
+    Rcpp::traits::input_parameter< bool >::type undirected(undirectedSEXP);
+    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
+    Rcpp::traits::input_parameter< bool >::type multiple(multipleSEXP);
+    rcpp_result_gen = Rcpp::wrap(edgelist_to_adjmat_cpp(edgelist, weights, n, undirected, self, multiple));
+    return rcpp_result_gen;
+END_RCPP
+}
+// adjmat_to_edgelist_cpp
+arma::mat adjmat_to_edgelist_cpp(const arma::sp_mat& adjmat, bool undirected);
+RcppExport SEXP _netdiffuseR_adjmat_to_edgelist_cpp(SEXP adjmatSEXP, SEXP undirectedSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type adjmat(adjmatSEXP);
+    Rcpp::traits::input_parameter< bool >::type undirected(undirectedSEXP);
+    rcpp_result_gen = Rcpp::wrap(adjmat_to_edgelist_cpp(adjmat, undirected));
+    return rcpp_result_gen;
+END_RCPP
+}
+// toa_diff_cpp
+IntegerMatrix toa_diff_cpp(const IntegerVector& year);
+RcppExport SEXP _netdiffuseR_toa_diff_cpp(SEXP yearSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const IntegerVector& >::type year(yearSEXP);
+    rcpp_result_gen = Rcpp::wrap(toa_diff_cpp(year));
+    return rcpp_result_gen;
+END_RCPP
+}
+// egonet_attrs_cpp
+List egonet_attrs_cpp(const arma::sp_mat& graph, const arma::uvec V, bool outer, bool self, bool valued);
+RcppExport SEXP _netdiffuseR_egonet_attrs_cpp(SEXP graphSEXP, SEXP VSEXP, SEXP outerSEXP, SEXP selfSEXP, SEXP valuedSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
+    Rcpp::traits::input_parameter< const arma::uvec >::type V(VSEXP);
+    Rcpp::traits::input_parameter< bool >::type outer(outerSEXP);
+    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
+    Rcpp::traits::input_parameter< bool >::type valued(valuedSEXP);
+    rcpp_result_gen = Rcpp::wrap(egonet_attrs_cpp(graph, V, outer, self, valued));
+    return rcpp_result_gen;
+END_RCPP
+}
+// approx_geodesicCpp
+arma::sp_mat approx_geodesicCpp(const arma::sp_mat& G, unsigned int n, bool warn);
+RcppExport SEXP _netdiffuseR_approx_geodesicCpp(SEXP GSEXP, SEXP nSEXP, SEXP warnSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type G(GSEXP);
+    Rcpp::traits::input_parameter< unsigned int >::type n(nSEXP);
+    Rcpp::traits::input_parameter< bool >::type warn(warnSEXP);
+    rcpp_result_gen = Rcpp::wrap(approx_geodesicCpp(G, n, warn));
+    return rcpp_result_gen;
+END_RCPP
+}
+// infection_cpp
+NumericVector infection_cpp(List graph, const arma::colvec& times, bool normalize, int K, double r, bool expdiscount, int n, bool valued, bool outgoing);
+RcppExport SEXP _netdiffuseR_infection_cpp(SEXP graphSEXP, SEXP timesSEXP, SEXP normalizeSEXP, SEXP KSEXP, SEXP rSEXP, SEXP expdiscountSEXP, SEXP nSEXP, SEXP valuedSEXP, SEXP outgoingSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< List >::type graph(graphSEXP);
+    Rcpp::traits::input_parameter< const arma::colvec& >::type times(timesSEXP);
+    Rcpp::traits::input_parameter< bool >::type normalize(normalizeSEXP);
+    Rcpp::traits::input_parameter< int >::type K(KSEXP);
+    Rcpp::traits::input_parameter< double >::type r(rSEXP);
+    Rcpp::traits::input_parameter< bool >::type expdiscount(expdiscountSEXP);
+    Rcpp::traits::input_parameter< int >::type n(nSEXP);
+    Rcpp::traits::input_parameter< bool >::type valued(valuedSEXP);
+    Rcpp::traits::input_parameter< bool >::type outgoing(outgoingSEXP);
+    rcpp_result_gen = Rcpp::wrap(infection_cpp(graph, times, normalize, K, r, expdiscount, n, valued, outgoing));
+    return rcpp_result_gen;
+END_RCPP
+}
+// susceptibility_cpp
+NumericVector susceptibility_cpp(List graph, const arma::colvec& times, bool normalize, int K, double r, bool expdiscount, int n, bool valued, bool outgoing);
+RcppExport SEXP _netdiffuseR_susceptibility_cpp(SEXP graphSEXP, SEXP timesSEXP, SEXP normalizeSEXP, SEXP KSEXP, SEXP rSEXP, SEXP expdiscountSEXP, SEXP nSEXP, SEXP valuedSEXP, SEXP outgoingSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< List >::type graph(graphSEXP);
+    Rcpp::traits::input_parameter< const arma::colvec& >::type times(timesSEXP);
+    Rcpp::traits::input_parameter< bool >::type normalize(normalizeSEXP);
+    Rcpp::traits::input_parameter< int >::type K(KSEXP);
+    Rcpp::traits::input_parameter< double >::type r(rSEXP);
+    Rcpp::traits::input_parameter< bool >::type expdiscount(expdiscountSEXP);
+    Rcpp::traits::input_parameter< int >::type n(nSEXP);
+    Rcpp::traits::input_parameter< bool >::type valued(valuedSEXP);
+    Rcpp::traits::input_parameter< bool >::type outgoing(outgoingSEXP);
+    rcpp_result_gen = Rcpp::wrap(susceptibility_cpp(graph, times, normalize, K, r, expdiscount, n, valued, outgoing));
+    return rcpp_result_gen;
+END_RCPP
+}
+// select_egoalter_cpp
+DataFrame select_egoalter_cpp(const arma::sp_mat& adjmat_t0, const arma::sp_mat& adjmat_t1, const NumericVector& adopt_t0, const NumericVector& adopt_t1);
+RcppExport SEXP _netdiffuseR_select_egoalter_cpp(SEXP adjmat_t0SEXP, SEXP adjmat_t1SEXP, SEXP adopt_t0SEXP, SEXP adopt_t1SEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type adjmat_t0(adjmat_t0SEXP);
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type adjmat_t1(adjmat_t1SEXP);
+    Rcpp::traits::input_parameter< const NumericVector& >::type adopt_t0(adopt_t0SEXP);
+    Rcpp::traits::input_parameter< const NumericVector& >::type adopt_t1(adopt_t1SEXP);
+    rcpp_result_gen = Rcpp::wrap(select_egoalter_cpp(adjmat_t0, adjmat_t1, adopt_t0, adopt_t1));
+    return rcpp_result_gen;
+END_RCPP
+}
+// sp_trimatl
+arma::sp_mat sp_trimatl(const arma::sp_mat& x);
+RcppExport SEXP _netdiffuseR_sp_trimatl(SEXP xSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type x(xSEXP);
+    rcpp_result_gen = Rcpp::wrap(sp_trimatl(x));
+    return rcpp_result_gen;
+END_RCPP
+}
+// sp_diag
+arma::sp_mat sp_diag(const arma::sp_mat& x, const arma::vec& v);
+RcppExport SEXP _netdiffuseR_sp_diag(SEXP xSEXP, SEXP vSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type x(xSEXP);
+    Rcpp::traits::input_parameter< const arma::vec& >::type v(vSEXP);
+    rcpp_result_gen = Rcpp::wrap(sp_diag(x, v));
+    return rcpp_result_gen;
+END_RCPP
+}
+// unif_rand_w_exclusion
+int unif_rand_w_exclusion(int n, int e);
+RcppExport SEXP _netdiffuseR_unif_rand_w_exclusion(SEXP nSEXP, SEXP eSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< int >::type n(nSEXP);
+    Rcpp::traits::input_parameter< int >::type e(eSEXP);
+    rcpp_result_gen = Rcpp::wrap(unif_rand_w_exclusion(n, e));
+    return rcpp_result_gen;
+END_RCPP
+}
+// sp_as_undirected
+arma::sp_mat sp_as_undirected(const arma::sp_mat& x);
+RcppExport SEXP _netdiffuseR_sp_as_undirected(SEXP xSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type x(xSEXP);
+    rcpp_result_gen = Rcpp::wrap(sp_as_undirected(x));
+    return rcpp_result_gen;
+END_RCPP
+}
+// bootnet_fillself
+arma::sp_mat bootnet_fillself(arma::sp_mat& graph, const IntegerVector& index, const NumericVector& E);
+RcppExport SEXP _netdiffuseR_bootnet_fillself(SEXP graphSEXP, SEXP indexSEXP, SEXP ESEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< arma::sp_mat& >::type graph(graphSEXP);
+    Rcpp::traits::input_parameter< const IntegerVector& >::type index(indexSEXP);
+    Rcpp::traits::input_parameter< const NumericVector& >::type E(ESEXP);
+    rcpp_result_gen = Rcpp::wrap(bootnet_fillself(graph, index, E));
+    return rcpp_result_gen;
+END_RCPP
+}
+// grid_distribution
+List grid_distribution(const arma::vec& x, const arma::vec& y, int nlevels);
+RcppExport SEXP _netdiffuseR_grid_distribution(SEXP xSEXP, SEXP ySEXP, SEXP nlevelsSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::vec& >::type x(xSEXP);
+    Rcpp::traits::input_parameter< const arma::vec& >::type y(ySEXP);
+    Rcpp::traits::input_parameter< int >::type nlevels(nlevelsSEXP);
+    rcpp_result_gen = Rcpp::wrap(grid_distribution(x, y, nlevels));
+    return rcpp_result_gen;
+END_RCPP
+}
+// edges_coords
+NumericMatrix edges_coords(const arma::sp_mat& graph, const arma::colvec& toa, const arma::colvec& x, const arma::colvec& y, const arma::colvec& vertex_cex, bool undirected, bool no_contemporary, NumericVector dev, NumericVector ran, LogicalVector curved);
+RcppExport SEXP _netdiffuseR_edges_coords(SEXP graphSEXP, SEXP toaSEXP, SEXP xSEXP, SEXP ySEXP, SEXP vertex_cexSEXP, SEXP undirectedSEXP, SEXP no_contemporarySEXP, SEXP devSEXP, SEXP ranSEXP, SEXP curvedSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
+    Rcpp::traits::input_parameter< const arma::colvec& >::type toa(toaSEXP);
+    Rcpp::traits::input_parameter< const arma::colvec& >::type x(xSEXP);
+    Rcpp::traits::input_parameter< const arma::colvec& >::type y(ySEXP);
+    Rcpp::traits::input_parameter< const arma::colvec& >::type vertex_cex(vertex_cexSEXP);
+    Rcpp::traits::input_parameter< bool >::type undirected(undirectedSEXP);
+    Rcpp::traits::input_parameter< bool >::type no_contemporary(no_contemporarySEXP);
+    Rcpp::traits::input_parameter< NumericVector >::type dev(devSEXP);
+    Rcpp::traits::input_parameter< NumericVector >::type ran(ranSEXP);
+    Rcpp::traits::input_parameter< LogicalVector >::type curved(curvedSEXP);
+    rcpp_result_gen = Rcpp::wrap(edges_coords(graph, toa, x, y, vertex_cex, undirected, no_contemporary, dev, ran, curved));
+    return rcpp_result_gen;
+END_RCPP
+}
+// edges_arrow
+List edges_arrow(const double& x0, const double& y0, const double& x1, const double& y1, const double& height, const double& width, const double beta, NumericVector dev, NumericVector ran, bool curved);
+RcppExport SEXP _netdiffuseR_edges_arrow(SEXP x0SEXP, SEXP y0SEXP, SEXP x1SEXP, SEXP y1SEXP, SEXP heightSEXP, SEXP widthSEXP, SEXP betaSEXP, SEXP devSEXP, SEXP ranSEXP, SEXP curvedSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const double& >::type x0(x0SEXP);
+    Rcpp::traits::input_parameter< const double& >::type y0(y0SEXP);
+    Rcpp::traits::input_parameter< const double& >::type x1(x1SEXP);
+    Rcpp::traits::input_parameter< const double& >::type y1(y1SEXP);
+    Rcpp::traits::input_parameter< const double& >::type height(heightSEXP);
+    Rcpp::traits::input_parameter< const double& >::type width(widthSEXP);
+    Rcpp::traits::input_parameter< const double >::type beta(betaSEXP);
+    Rcpp::traits::input_parameter< NumericVector >::type dev(devSEXP);
+    Rcpp::traits::input_parameter< NumericVector >::type ran(ranSEXP);
+    Rcpp::traits::input_parameter< bool >::type curved(curvedSEXP);
+    rcpp_result_gen = Rcpp::wrap(edges_arrow(x0, y0, x1, y1, height, width, beta, dev, ran, curved));
+    return rcpp_result_gen;
+END_RCPP
+}
+// vertices_coords
+List vertices_coords(const arma::colvec& x, const arma::colvec& y, const arma::colvec& size, const arma::colvec& nsides, const arma::colvec& rot, NumericVector dev, NumericVector ran);
+RcppExport SEXP _netdiffuseR_vertices_coords(SEXP xSEXP, SEXP ySEXP, SEXP sizeSEXP, SEXP nsidesSEXP, SEXP rotSEXP, SEXP devSEXP, SEXP ranSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::colvec& >::type x(xSEXP);
+    Rcpp::traits::input_parameter< const arma::colvec& >::type y(ySEXP);
+    Rcpp::traits::input_parameter< const arma::colvec& >::type size(sizeSEXP);
+    Rcpp::traits::input_parameter< const arma::colvec& >::type nsides(nsidesSEXP);
+    Rcpp::traits::input_parameter< const arma::colvec& >::type rot(rotSEXP);
+    Rcpp::traits::input_parameter< NumericVector >::type dev(devSEXP);
+    Rcpp::traits::input_parameter< NumericVector >::type ran(ranSEXP);
+    rcpp_result_gen = Rcpp::wrap(vertices_coords(x, y, size, nsides, rot, dev, ran));
+    return rcpp_result_gen;
+END_RCPP
+}
+// rgraph_er_cpp
+arma::sp_mat rgraph_er_cpp(int n, double p, bool undirected, bool weighted, bool self);
+RcppExport SEXP _netdiffuseR_rgraph_er_cpp(SEXP nSEXP, SEXP pSEXP, SEXP undirectedSEXP, SEXP weightedSEXP, SEXP selfSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< int >::type n(nSEXP);
+    Rcpp::traits::input_parameter< double >::type p(pSEXP);
+    Rcpp::traits::input_parameter< bool >::type undirected(undirectedSEXP);
+    Rcpp::traits::input_parameter< bool >::type weighted(weightedSEXP);
+    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
+    rcpp_result_gen = Rcpp::wrap(rgraph_er_cpp(n, p, undirected, weighted, self));
+    return rcpp_result_gen;
+END_RCPP
+}
+// ring_lattice
+arma::sp_mat ring_lattice(int n, int k, bool undirected);
+RcppExport SEXP _netdiffuseR_ring_lattice(SEXP nSEXP, SEXP kSEXP, SEXP undirectedSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< int >::type n(nSEXP);
+    Rcpp::traits::input_parameter< int >::type k(kSEXP);
+    Rcpp::traits::input_parameter< bool >::type undirected(undirectedSEXP);
+    rcpp_result_gen = Rcpp::wrap(ring_lattice(n, k, undirected));
+    return rcpp_result_gen;
+END_RCPP
+}
+// rewire_endpoints
+arma::sp_mat rewire_endpoints(const arma::sp_mat& graph, double p, bool both_ends, bool self, bool multiple, bool undirected);
+RcppExport SEXP _netdiffuseR_rewire_endpoints(SEXP graphSEXP, SEXP pSEXP, SEXP both_endsSEXP, SEXP selfSEXP, SEXP multipleSEXP, SEXP undirectedSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
+    Rcpp::traits::input_parameter< double >::type p(pSEXP);
+    Rcpp::traits::input_parameter< bool >::type both_ends(both_endsSEXP);
+    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
+    Rcpp::traits::input_parameter< bool >::type multiple(multipleSEXP);
+    Rcpp::traits::input_parameter< bool >::type undirected(undirectedSEXP);
+    rcpp_result_gen = Rcpp::wrap(rewire_endpoints(graph, p, both_ends, self, multiple, undirected));
+    return rcpp_result_gen;
+END_RCPP
+}
+// rewire_swap
+arma::sp_mat rewire_swap(const arma::sp_mat& graph, int nsteps, bool self, bool multiple, bool undirected, double pr_rewire);
+RcppExport SEXP _netdiffuseR_rewire_swap(SEXP graphSEXP, SEXP nstepsSEXP, SEXP selfSEXP, SEXP multipleSEXP, SEXP undirectedSEXP, SEXP pr_rewireSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
+    Rcpp::traits::input_parameter< int >::type nsteps(nstepsSEXP);
+    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
+    Rcpp::traits::input_parameter< bool >::type multiple(multipleSEXP);
+    Rcpp::traits::input_parameter< bool >::type undirected(undirectedSEXP);
+    Rcpp::traits::input_parameter< double >::type pr_rewire(pr_rewireSEXP);
+    rcpp_result_gen = Rcpp::wrap(rewire_swap(graph, nsteps, self, multiple, undirected, pr_rewire));
+    return rcpp_result_gen;
+END_RCPP
+}
+// rewire_ws
+arma::sp_mat rewire_ws(arma::sp_mat G, int K, double p, bool self, bool multiple);
+RcppExport SEXP _netdiffuseR_rewire_ws(SEXP GSEXP, SEXP KSEXP, SEXP pSEXP, SEXP selfSEXP, SEXP multipleSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< arma::sp_mat >::type G(GSEXP);
+    Rcpp::traits::input_parameter< int >::type K(KSEXP);
+    Rcpp::traits::input_parameter< double >::type p(pSEXP);
+    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
+    Rcpp::traits::input_parameter< bool >::type multiple(multipleSEXP);
+    rcpp_result_gen = Rcpp::wrap(rewire_ws(G, K, p, self, multiple));
+    return rcpp_result_gen;
+END_RCPP
+}
+// permute_graph_cpp
+arma::sp_mat permute_graph_cpp(const arma::sp_mat& x, bool self, bool multiple);
+RcppExport SEXP _netdiffuseR_permute_graph_cpp(SEXP xSEXP, SEXP selfSEXP, SEXP multipleSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type x(xSEXP);
+    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
+    Rcpp::traits::input_parameter< bool >::type multiple(multipleSEXP);
+    rcpp_result_gen = Rcpp::wrap(permute_graph_cpp(x, self, multiple));
+    return rcpp_result_gen;
+END_RCPP
+}
+// rgraph_ba_cpp
+arma::sp_mat rgraph_ba_cpp(const arma::sp_mat& graph, const arma::colvec& dgr, int m, int t, bool self);
+RcppExport SEXP _netdiffuseR_rgraph_ba_cpp(SEXP graphSEXP, SEXP dgrSEXP, SEXP mSEXP, SEXP tSEXP, SEXP selfSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
+    Rcpp::traits::input_parameter< const arma::colvec& >::type dgr(dgrSEXP);
+    Rcpp::traits::input_parameter< int >::type m(mSEXP);
+    Rcpp::traits::input_parameter< int >::type t(tSEXP);
+    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
+    rcpp_result_gen = Rcpp::wrap(rgraph_ba_cpp(graph, dgr, m, t, self));
+    return rcpp_result_gen;
+END_RCPP
+}
+// rgraph_ba_new_cpp
+arma::sp_mat rgraph_ba_new_cpp(int m0, int m, int t, bool self);
+RcppExport SEXP _netdiffuseR_rgraph_ba_new_cpp(SEXP m0SEXP, SEXP mSEXP, SEXP tSEXP, SEXP selfSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< int >::type m0(m0SEXP);
+    Rcpp::traits::input_parameter< int >::type m(mSEXP);
+    Rcpp::traits::input_parameter< int >::type t(tSEXP);
+    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
+    rcpp_result_gen = Rcpp::wrap(rgraph_ba_new_cpp(m0, m, t, self));
+    return rcpp_result_gen;
+END_RCPP
+}
+// rgraph_sf_homo
+arma::sp_mat rgraph_sf_homo(const arma::colvec& eta, const arma::sp_mat& graph, const arma::colvec& dgr, int m, int t, bool self);
+RcppExport SEXP _netdiffuseR_rgraph_sf_homo(SEXP etaSEXP, SEXP graphSEXP, SEXP dgrSEXP, SEXP mSEXP, SEXP tSEXP, SEXP selfSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::colvec& >::type eta(etaSEXP);
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
+    Rcpp::traits::input_parameter< const arma::colvec& >::type dgr(dgrSEXP);
+    Rcpp::traits::input_parameter< int >::type m(mSEXP);
+    Rcpp::traits::input_parameter< int >::type t(tSEXP);
+    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
+    rcpp_result_gen = Rcpp::wrap(rgraph_sf_homo(eta, graph, dgr, m, t, self));
+    return rcpp_result_gen;
+END_RCPP
+}
+// rgraph_sf_homo_new
+arma::sp_mat rgraph_sf_homo_new(const arma::colvec& eta, int m0, int m, int t, bool self);
+RcppExport SEXP _netdiffuseR_rgraph_sf_homo_new(SEXP etaSEXP, SEXP m0SEXP, SEXP mSEXP, SEXP tSEXP, SEXP selfSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::colvec& >::type eta(etaSEXP);
+    Rcpp::traits::input_parameter< int >::type m0(m0SEXP);
+    Rcpp::traits::input_parameter< int >::type m(mSEXP);
+    Rcpp::traits::input_parameter< int >::type t(tSEXP);
+    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
+    rcpp_result_gen = Rcpp::wrap(rgraph_sf_homo_new(eta, m0, m, t, self));
+    return rcpp_result_gen;
+END_RCPP
+}
+// vertex_covariate_dist
+arma::sp_mat vertex_covariate_dist(const arma::sp_mat& graph, const arma::mat& X, double p);
+RcppExport SEXP _netdiffuseR_vertex_covariate_dist(SEXP graphSEXP, SEXP XSEXP, SEXP pSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
+    Rcpp::traits::input_parameter< const arma::mat& >::type X(XSEXP);
+    Rcpp::traits::input_parameter< double >::type p(pSEXP);
+    rcpp_result_gen = Rcpp::wrap(vertex_covariate_dist(graph, X, p));
+    return rcpp_result_gen;
+END_RCPP
+}
+// vertex_mahalanobis_dist_cpp
+arma::sp_mat vertex_mahalanobis_dist_cpp(const arma::sp_mat& graph, const arma::mat& X, const arma::mat& S);
+RcppExport SEXP _netdiffuseR_vertex_mahalanobis_dist_cpp(SEXP graphSEXP, SEXP XSEXP, SEXP SSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
+    Rcpp::traits::input_parameter< const arma::mat& >::type X(XSEXP);
+    Rcpp::traits::input_parameter< const arma::mat& >::type S(SSEXP);
+    rcpp_result_gen = Rcpp::wrap(vertex_mahalanobis_dist_cpp(graph, X, S));
+    return rcpp_result_gen;
+END_RCPP
+}
+// vertex_covariate_compare
+arma::sp_mat vertex_covariate_compare(const arma::sp_mat& graph, const NumericVector& X, std::string funname);
+RcppExport SEXP _netdiffuseR_vertex_covariate_compare(SEXP graphSEXP, SEXP XSEXP, SEXP funnameSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
+    Rcpp::traits::input_parameter< const NumericVector& >::type X(XSEXP);
+    Rcpp::traits::input_parameter< std::string >::type funname(funnameSEXP);
+    rcpp_result_gen = Rcpp::wrap(vertex_covariate_compare(graph, X, funname));
+    return rcpp_result_gen;
+END_RCPP
+}
+// moran_cpp
+List moran_cpp(const arma::colvec& x, const arma::sp_mat& w);
+RcppExport SEXP _netdiffuseR_moran_cpp(SEXP xSEXP, SEXP wSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::traits::input_parameter< const arma::colvec& >::type x(xSEXP);
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type w(wSEXP);
+    rcpp_result_gen = Rcpp::wrap(moran_cpp(x, w));
+    return rcpp_result_gen;
+END_RCPP
+}
+// matrix_compareCpp
+arma::sp_mat matrix_compareCpp(const arma::sp_mat& A, const arma::sp_mat& B, Function fun);
+RcppExport SEXP _netdiffuseR_matrix_compareCpp(SEXP ASEXP, SEXP BSEXP, SEXP funSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type A(ASEXP);
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type B(BSEXP);
+    Rcpp::traits::input_parameter< Function >::type fun(funSEXP);
+    rcpp_result_gen = Rcpp::wrap(matrix_compareCpp(A, B, fun));
+    return rcpp_result_gen;
+END_RCPP
+}
+// struct_test_mean
+double struct_test_mean(NumericVector& y, std::string funname, bool self);
+RcppExport SEXP _netdiffuseR_struct_test_mean(SEXP ySEXP, SEXP funnameSEXP, SEXP selfSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< NumericVector& >::type y(ySEXP);
+    Rcpp::traits::input_parameter< std::string >::type funname(funnameSEXP);
+    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
+    rcpp_result_gen = Rcpp::wrap(struct_test_mean(y, funname, self));
+    return rcpp_result_gen;
+END_RCPP
+}
+// struct_test_var
+double struct_test_var(NumericVector& y, std::string funname, bool self);
+RcppExport SEXP _netdiffuseR_struct_test_var(SEXP ySEXP, SEXP funnameSEXP, SEXP selfSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< NumericVector& >::type y(ySEXP);
+    Rcpp::traits::input_parameter< std::string >::type funname(funnameSEXP);
+    Rcpp::traits::input_parameter< bool >::type self(selfSEXP);
+    rcpp_result_gen = Rcpp::wrap(struct_test_var(y, funname, self));
+    return rcpp_result_gen;
+END_RCPP
+}
+// ego_variance
+NumericVector ego_variance(const arma::sp_mat& graph, const NumericVector& Y, std::string funname, bool all);
+RcppExport SEXP _netdiffuseR_ego_variance(SEXP graphSEXP, SEXP YSEXP, SEXP funnameSEXP, SEXP allSEXP) {
+BEGIN_RCPP
+    Rcpp::RObject rcpp_result_gen;
+    Rcpp::RNGScope rcpp_rngScope_gen;
+    Rcpp::traits::input_parameter< const arma::sp_mat& >::type graph(graphSEXP);
+    Rcpp::traits::input_parameter< const NumericVector& >::type Y(YSEXP);
+    Rcpp::traits::input_parameter< std::string >::type funname(funnameSEXP);
+    Rcpp::traits::input_parameter< bool >::type all(allSEXP);
+    rcpp_result_gen = Rcpp::wrap(ego_variance(graph, Y, funname, all));
+    return rcpp_result_gen;
+END_RCPP
+}
+
+static const R_CallMethodDef CallEntries[] = {
+    {"_netdiffuseR_edgelist_to_adjmat_cpp", (DL_FUNC) &_netdiffuseR_edgelist_to_adjmat_cpp, 6},
+    {"_netdiffuseR_adjmat_to_edgelist_cpp", (DL_FUNC) &_netdiffuseR_adjmat_to_edgelist_cpp, 2},
+    {"_netdiffuseR_toa_diff_cpp", (DL_FUNC) &_netdiffuseR_toa_diff_cpp, 1},
+    {"_netdiffuseR_egonet_attrs_cpp", (DL_FUNC) &_netdiffuseR_egonet_attrs_cpp, 5},
+    {"_netdiffuseR_approx_geodesicCpp", (DL_FUNC) &_netdiffuseR_approx_geodesicCpp, 3},
+    {"_netdiffuseR_infection_cpp", (DL_FUNC) &_netdiffuseR_infection_cpp, 9},
+    {"_netdiffuseR_susceptibility_cpp", (DL_FUNC) &_netdiffuseR_susceptibility_cpp, 9},
+    {"_netdiffuseR_select_egoalter_cpp", (DL_FUNC) &_netdiffuseR_select_egoalter_cpp, 4},
+    {"_netdiffuseR_sp_trimatl", (DL_FUNC) &_netdiffuseR_sp_trimatl, 1},
+    {"_netdiffuseR_sp_diag", (DL_FUNC) &_netdiffuseR_sp_diag, 2},
+    {"_netdiffuseR_unif_rand_w_exclusion", (DL_FUNC) &_netdiffuseR_unif_rand_w_exclusion, 2},
+    {"_netdiffuseR_sp_as_undirected", (DL_FUNC) &_netdiffuseR_sp_as_undirected, 1},
+    {"_netdiffuseR_bootnet_fillself", (DL_FUNC) &_netdiffuseR_bootnet_fillself, 3},
+    {"_netdiffuseR_grid_distribution", (DL_FUNC) &_netdiffuseR_grid_distribution, 3},
+    {"_netdiffuseR_edges_coords", (DL_FUNC) &_netdiffuseR_edges_coords, 10},
+    {"_netdiffuseR_edges_arrow", (DL_FUNC) &_netdiffuseR_edges_arrow, 10},
+    {"_netdiffuseR_vertices_coords", (DL_FUNC) &_netdiffuseR_vertices_coords, 7},
+    {"_netdiffuseR_rgraph_er_cpp", (DL_FUNC) &_netdiffuseR_rgraph_er_cpp, 5},
+    {"_netdiffuseR_ring_lattice", (DL_FUNC) &_netdiffuseR_ring_lattice, 3},
+    {"_netdiffuseR_rewire_endpoints", (DL_FUNC) &_netdiffuseR_rewire_endpoints, 6},
+    {"_netdiffuseR_rewire_swap", (DL_FUNC) &_netdiffuseR_rewire_swap, 6},
+    {"_netdiffuseR_rewire_ws", (DL_FUNC) &_netdiffuseR_rewire_ws, 5},
+    {"_netdiffuseR_permute_graph_cpp", (DL_FUNC) &_netdiffuseR_permute_graph_cpp, 3},
+    {"_netdiffuseR_rgraph_ba_cpp", (DL_FUNC) &_netdiffuseR_rgraph_ba_cpp, 5},
+    {"_netdiffuseR_rgraph_ba_new_cpp", (DL_FUNC) &_netdiffuseR_rgraph_ba_new_cpp, 4},
+    {"_netdiffuseR_rgraph_sf_homo", (DL_FUNC) &_netdiffuseR_rgraph_sf_homo, 6},
+    {"_netdiffuseR_rgraph_sf_homo_new", (DL_FUNC) &_netdiffuseR_rgraph_sf_homo_new, 5},
+    {"_netdiffuseR_vertex_covariate_dist", (DL_FUNC) &_netdiffuseR_vertex_covariate_dist, 3},
+    {"_netdiffuseR_vertex_mahalanobis_dist_cpp", (DL_FUNC) &_netdiffuseR_vertex_mahalanobis_dist_cpp, 3},
+    {"_netdiffuseR_vertex_covariate_compare", (DL_FUNC) &_netdiffuseR_vertex_covariate_compare, 3},
+    {"_netdiffuseR_moran_cpp", (DL_FUNC) &_netdiffuseR_moran_cpp, 2},
+    {"_netdiffuseR_matrix_compareCpp", (DL_FUNC) &_netdiffuseR_matrix_compareCpp, 3},
+    {"_netdiffuseR_struct_test_mean", (DL_FUNC) &_netdiffuseR_struct_test_mean, 3},
+    {"_netdiffuseR_struct_test_var", (DL_FUNC) &_netdiffuseR_struct_test_var, 3},
+    {"_netdiffuseR_ego_variance", (DL_FUNC) &_netdiffuseR_ego_variance, 4},
+    {NULL, NULL, 0}
+};
+
+RcppExport void R_init_netdiffuseR(DllInfo *dll) {
+    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
+    R_useDynamicSymbols(dll, FALSE);
+}