diff --git a/DESCRIPTION b/DESCRIPTION
index ba4d31b..2ca9eb5 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: netrankr
 Type: Package
 Title: Analyzing Partial Rankings in Networks
-Version: 1.2.3.9000
+Version: 1.2.4
 Authors@R: c(
     person("David", "Schoch", email = "david@schochastics.net", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-2952-4812")),
@@ -25,7 +25,7 @@ Imports: igraph (>= 1.0.1),
     Rcpp (>= 0.12.8),
     Matrix
 LinkingTo: Rcpp,RcppArmadillo
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2
 Roxygen: list(markdown = TRUE)
 Suggests: 
     knitr,
diff --git a/NAMESPACE b/NAMESPACE
index 88990c0..e729788 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -31,6 +31,10 @@ export(neighborhood_inclusion)
 export(positional_dominance)
 export(rank_intervals)
 export(spectral_gap)
+export(swan_closeness)
+export(swan_combinatory)
+export(swan_connectivity)
+export(swan_efficiency)
 export(threshold_graph)
 export(transitive_reduction)
 export(walks_attenuated)
diff --git a/NEWS.md b/NEWS.md
index 5bdac16..1c54124 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,7 @@
+# netrankr 1.2.4
+
+* added functions from archived NetSwan package
+
 # netrankr 1.2.3
 
 * removed test causing issues on some platforms
diff --git a/R/netswan.R b/R/netswan.R
new file mode 100644
index 0000000..8b397dd
--- /dev/null
+++ b/R/netswan.R
@@ -0,0 +1,293 @@
+#' @name swan_closeness
+#' @title Impact on closeness when a node is removed
+#'
+#' @description
+#' `swan_closeness` measures the change in the sum of the inverse of distances between all node pairs
+#' when excluding that node.#'
+#' @param g An `igraph` object representing the graph to analyze.
+#'
+#' @details
+#' `swan_closeness` measures the impact of a node's removal by computing the change in
+#' the sum of inverse distances between all node pairs.
+#'
+#' The code is an adaptation from the NetSwan package that was archived on CRAN.
+#' @return
+#' A numeric vector containing the `swan_closeness` values for all vertices.
+#'
+#' @references
+#' Lhomme S. (2015). *Analyse spatiale de la structure des réseaux techniques dans un contexte de risques*.
+#' Cybergeo: European Journal of Geography.
+#' @examples
+#' library(igraph)
+#' # Example graph (electrical network structure)
+#' elec <- matrix(ncol = 2, byrow = TRUE, c(
+#'   11,1, 11,10, 1,2, 2,3, 2,9,
+#'   3,4, 3,8, 4,5, 5,6, 5,7,
+#'   6,7, 7,8, 8,9, 9,10
+#' ))
+#' gra <- graph_from_edgelist(elec, directed = FALSE)
+#'
+#' # Compute swan_closeness
+#' f2 <- swan_closeness(gra)
+#'
+#' # Compare with betweenness centrality
+#' bet <- betweenness(gra)
+#' reg <- lm(bet ~ f2)
+#' summary(reg)
+#' @export
+swan_closeness <- function(g) {
+  n <- igraph::vcount(g)
+  swancc <- rep(0, n)
+  cc <- igraph::distances(g)
+  ccb <- 1 / cc
+  ccb[is.infinite(ccb)] <- 0
+  tot <- sum(ccb)
+  for (i in seq_len(n)) {
+    g2 <- g
+    g2 <- igraph::delete_vertices(g2, i)
+    cc_no_i <- igraph::distances(g2)
+    cc_no_ib <- 1 / cc_no_i
+    cc_no_ib[is.infinite(cc_no_ib)] <- 0
+    tot2 <- sum(cc_no_ib)
+    swancc[i] <- tot2 - (tot - sum(ccb[i, ]) - sum(ccb[, i]))
+  }
+  return(swancc)
+}
+
+#' @name swan_combinatory
+#' @title Error and attack tolerance of complex networks
+#'
+#' @description
+#' `swan_combinatory` assesses network vulnerability and the resistance of networks
+#' to node removals, whether due to random failures or intentional attacks.
+#'
+#' @param g An `igraph` object representing the graph to analyze.
+#' @param k The number of iterations for assessing the impact of random failures.
+#'
+#' @details
+#' Many complex systems display a surprising degree of tolerance against random failures.
+#' However, this resilience often comes at the cost of extreme vulnerability to targeted attacks,
+#' where removing key nodes (high-degree or high-betweenness nodes) can severely impact network connectivity.
+#'
+#' `swan_combinatory` simulates different attack strategies:
+#' - **Random failure:** Nodes are removed randomly over multiple iterations.
+#' - **Degree-based attack:** Nodes are removed in decreasing order of their degree.
+#' - **Betweenness-based attack:** Nodes are removed in decreasing order of their betweenness centrality.
+#' - **Cascading failure:** Nodes are removed based on recalculated betweenness after each removal.
+#'
+#' The function returns a matrix showing the connectivity loss for each attack scenario.
+#'
+#' The code is an adaptation from the NetSwan package that was archived on CRAN.
+#' @return
+#' A matrix with five columns:
+#' \itemize{
+#'   \item Column 1: Fraction of nodes removed.
+#'   \item Column 2: Connectivity loss from betweenness-based attack.
+#'   \item Column 3: Connectivity loss from degree-based attack.
+#'   \item Column 4: Connectivity loss from cascading failure.
+#'   \item Column 5: Connectivity loss from random failures (averaged over `k` iterations).
+#' }
+#'
+#' @references
+#' Albert R., Jeong H., Barabási A. (2000). *Error and attack tolerance of complex networks*.
+#' Nature, 406(6794), 378-382.
+#'
+#' @examples
+#' library(igraph)
+#' # Example electrical network graph
+#' elec <- matrix(ncol = 2, byrow = TRUE, c(
+#'   11,1, 11,10, 1,2, 2,3, 2,9,
+#'   3,4, 3,8, 4,5, 5,6, 5,7,
+#'   6,7, 7,8, 8,9, 9,10
+#' ))
+#' gra <- graph_from_edgelist(elec, directed = FALSE)
+#'
+#' # Compute vulnerability measures
+#' f4 <- swan_combinatory(gra, 10)
+#' @export
+swan_combinatory <- function(g, k) {
+  n <- igraph::vcount(g)
+  dist <- igraph::distances(g)
+  dist[is.infinite(dist)] <- 0
+  dist[dist > 0] <- 1
+  tot <- sum(dist)
+  fin <- matrix(ncol = 5, nrow = n, 0)
+  mat <- matrix(ncol = 2, nrow = n, 0)
+  mat[, 1] <- 1:n
+  bet <- igraph::betweenness(g)
+  mat[, 2] <- bet
+  matri <- mat[order(mat[, 2]), ]
+  g2 <- g
+  for (i in seq_len(n)) {
+    v = n + 1 - i
+    g2 <- igraph::delete_vertices(g2, matri[v, 1])
+    dist2 <- igraph::distances(g2)
+    dist2[is.infinite(dist2)] <- 0
+    dist2[dist2 > 0] <- 1
+    tot2 <- sum(dist2)
+    fin[i, 1] <- i / n
+    fin[i, 2] <- tot - tot2
+    matri[matri[, 1] > matri[v, 1], 1] <- matri[matri[, 1] > matri[v, 1], 1] - 1
+  }
+  mat <- matrix(ncol = 2, nrow = n, 0) #degree attack
+  mat[, 1] <- 1:n
+  deg <- igraph::degree(g)
+  mat[, 2] <- deg
+  matri <- mat[order(mat[, 2]), ]
+  g2 <- g
+  for (i in seq_len(n)) {
+    v = n + 1 - i
+    g2 <- igraph::delete_vertices(g2, matri[v, 1])
+    dist2 <- igraph::distances(g2)
+    dist2[is.infinite(dist2)] <- 0
+    dist2[dist2 > 0] <- 1
+    tot2 <- sum(dist2)
+    fin[i, 3] <- tot - tot2
+    matri[matri[, 1] > matri[v, 1], 1] <- matri[matri[, 1] > matri[v, 1], 1] -
+      1 #bluff
+  }
+  g2 <- g #cascading
+  npro <- n
+  lim <- n - 1
+  for (i in 1:lim) {
+    mat <- matrix(ncol = 2, nrow = npro, 0)
+    mat[, 1] <- 1:npro
+    bet <- igraph::betweenness(g2)
+    mat[, 2] <- bet
+    matri <- mat[order(mat[, 2]), ]
+    g2 <- igraph::delete_vertices(g2, matri[npro, 1])
+    dist2 <- igraph::distances(g2)
+    dist2[is.infinite(dist2)] <- 0
+    dist2[dist2 > 0] <- 1
+    tot2 <- sum(dist2)
+    fin[i, 4] <- tot - tot2
+    npro <- npro - 1
+  }
+  fin[n, 4] <- tot
+  #random
+  for (l in seq_len(k)) {
+    al <- sample(1:n, n)
+    g2 <- g
+    for (i in seq_len(k)) {
+      g2 <- igraph::delete_vertices(g2, al[i])
+      dist2 <- igraph::distances(g2)
+      dist2[is.infinite(dist2)] <- 0
+      dist2[dist2 > 0] <- 1
+      tot2 <- sum(dist2)
+      fin[i, 5] <- fin[i, 5] + (tot - tot2)
+      al[al > al[i]] <- al[al > al[i]] - 1 #bluff
+    }
+  }
+  fin[, 2:4] <- fin[, 2:4] / tot
+  fin[, 5] <- fin[, 5] / tot / k
+  return(fin)
+}
+
+#' @name swan_connectivity
+#' @title Impact on connectivity when a node is removed
+#'
+#' @description
+#' `swan_connectivity` measures the loss of connectivity when a node is removed from the network.
+#'
+#' @usage
+#' swan_connectivity(g)
+#'
+#' @param g An `igraph` object representing the graph to analyze.
+#'
+#' @details
+#' Connectivity loss indices quantify the decrease in the number of relationships between nodes
+#' when one or more components are removed. `swan_connectivity` computes the connectivity loss
+#' by systematically excluding each node and evaluating the resulting changes in the network structure.
+#'
+#' The code is an adaptation from the NetSwan package that was archived on CRAN.
+#' @return
+#' A numeric vector where each entry represents the connectivity loss when the corresponding node is removed.
+#'
+#' @references
+#' Lhomme S. (2015). *Analyse spatiale de la structure des réseaux techniques dans un contexte de risques*.
+#' Cybergeo: European Journal of Geography.
+#' @examples
+#' library(igraph)
+#' # Example graph (electrical network structure)
+#' elec <- matrix(ncol = 2, byrow = TRUE, c(
+#'   11,1, 11,10, 1,2, 2,3, 2,9,
+#'   3,4, 3,8, 4,5, 5,6, 5,7,
+#'   6,7, 7,8, 8,9, 9,10
+#' ))
+#' gra <- graph_from_edgelist(elec, directed = FALSE)
+#'
+#' # Compute connectivity loss
+#' f3 <- swan_connectivity(gra)
+#' @export
+swan_connectivity <- function(g) {
+  n <- igraph::vcount(g)
+  fin <- rep(0, n)
+  dist <- igraph::distances(g)
+  dist[!is.infinite(dist)] <- 0
+  dist[is.infinite(dist)] <- 1
+  con <- sum(dist)
+  for (i in seq_len(n)) {
+    g2 <- g
+    g2 <- igraph::delete_vertices(g2, i)
+    dist2 <- igraph::distances(g2)
+    dist2[!is.infinite(dist2)] <- 0
+    dist2[is.infinite(dist2)] <- 1
+    con2 <- sum(dist2)
+    fin[i] <- con2 - con
+  }
+  return(fin)
+}
+
+#' @name swan_efficiency
+#' @title Impact on farness when a node is removed
+#'
+#' @description
+#' `swan_efficiency` measures the change in the sum of distances between all node pairs
+#' when excluding a node from the network.
+#'
+#' @param g An `igraph` object representing the graph to analyze.
+#' `swan_efficiency` is based on geographic accessibility, similar to indices used for
+#' assessing transportation network performance, such as closeness accessibility.
+#' It quantifies the impact of node removal by calculating the change in the sum of
+#' distances between all node pairs.
+#'
+#' The code is an adaptation from the NetSwan package that was archived on CRAN.
+#'
+#' @return
+#' A numeric vector where each entry represents the `swan_efficiency` value for the
+#' corresponding node.
+#'
+#' @references
+#' Lhomme S. (2015). *Analyse spatiale de la structure des réseaux techniques dans un
+#' contexte de risques*. Cybergeo: European Journal of Geography.
+#'
+#' @examples
+#' library(igraph)
+#' # Example graph (electrical network structure)
+#' elec <- matrix(ncol = 2, byrow = TRUE, c(
+#'   11,1, 11,10, 1,2, 2,3, 2,9,
+#'   3,4, 3,8, 4,5, 5,6, 5,7,
+#'   6,7, 7,8, 8,9, 9,10
+#' ))
+#' gra <- graph_from_edgelist(elec, directed = FALSE)
+#'
+#' # Compute efficiency impact of node removal
+#' f2 <- swan_efficiency(gra)
+#' bet <- betweenness(gra)
+#' reg <- lm(bet ~ f2)
+#' summary(reg)
+#' @export
+swan_efficiency <- function(g) {
+  n <- igraph::vcount(g)
+  fin <- rep(0, n)
+  dt = igraph::distances(g)
+  tot = sum(dt)
+  for (i in 1:n) {
+    g2 <- g
+    g2 <- igraph::delete_vertices(g2, i)
+    dt2 <- igraph::distances(g2)
+    tot2 <- sum(dt2)
+    fin[i] <- tot2 - (tot - sum(dt[i, ]) - sum(dt[, i]))
+  }
+  return(fin)
+}
diff --git a/cran-comments.md b/cran-comments.md
index b5264b8..1f9b8d7 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -1,7 +1,6 @@
-# Update from 1.2.2 to 1.2.3
+# Update from 1.2.3 to 1.2.4
 
-quick bugfix release to prevent package from being deleted from CRAN due to
-failing test
+added functions from an archived CRAN package
 
 ## Test environments
 * ubuntu 22.04, R 4.3.2
diff --git a/man/netrankr-package.Rd b/man/netrankr-package.Rd
index daae2cf..cdf942b 100644
--- a/man/netrankr-package.Rd
+++ b/man/netrankr-package.Rd
@@ -2,8 +2,8 @@
 % Please edit documentation in R/netrankr.R
 \docType{package}
 \name{netrankr-package}
+\alias{netrankr}
 \alias{netrankr-package}
-\alias{_PACKAGE}
 \title{netrankr: An R package for centrality and partial rankings in networks}
 \description{
 netrankr provides several functions to analyze partial rankings for network
diff --git a/man/swan_closeness.Rd b/man/swan_closeness.Rd
new file mode 100644
index 0000000..45b7d81
--- /dev/null
+++ b/man/swan_closeness.Rd
@@ -0,0 +1,46 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/netswan.R
+\name{swan_closeness}
+\alias{swan_closeness}
+\title{Impact on closeness when a node is removed}
+\usage{
+swan_closeness(g)
+}
+\arguments{
+\item{g}{An \code{igraph} object representing the graph to analyze.}
+}
+\value{
+A numeric vector containing the \code{swan_closeness} values for all vertices.
+}
+\description{
+\code{swan_closeness} measures the change in the sum of the inverse of distances between all node pairs
+when excluding that node.#'
+}
+\details{
+\code{swan_closeness} measures the impact of a node's removal by computing the change in
+the sum of inverse distances between all node pairs.
+
+The code is an adaptation from the NetSwan package that was archived on CRAN.
+}
+\examples{
+library(igraph)
+# Example graph (electrical network structure)
+elec <- matrix(ncol = 2, byrow = TRUE, c(
+  11,1, 11,10, 1,2, 2,3, 2,9,
+  3,4, 3,8, 4,5, 5,6, 5,7,
+  6,7, 7,8, 8,9, 9,10
+))
+gra <- graph_from_edgelist(elec, directed = FALSE)
+
+# Compute swan_closeness
+f2 <- swan_closeness(gra)
+
+# Compare with betweenness centrality
+bet <- betweenness(gra)
+reg <- lm(bet ~ f2)
+summary(reg)
+}
+\references{
+Lhomme S. (2015). \emph{Analyse spatiale de la structure des réseaux techniques dans un contexte de risques}.
+Cybergeo: European Journal of Geography.
+}
diff --git a/man/swan_combinatory.Rd b/man/swan_combinatory.Rd
new file mode 100644
index 0000000..45b02ec
--- /dev/null
+++ b/man/swan_combinatory.Rd
@@ -0,0 +1,61 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/netswan.R
+\name{swan_combinatory}
+\alias{swan_combinatory}
+\title{Error and attack tolerance of complex networks}
+\usage{
+swan_combinatory(g, k)
+}
+\arguments{
+\item{g}{An \code{igraph} object representing the graph to analyze.}
+
+\item{k}{The number of iterations for assessing the impact of random failures.}
+}
+\value{
+A matrix with five columns:
+\itemize{
+\item Column 1: Fraction of nodes removed.
+\item Column 2: Connectivity loss from betweenness-based attack.
+\item Column 3: Connectivity loss from degree-based attack.
+\item Column 4: Connectivity loss from cascading failure.
+\item Column 5: Connectivity loss from random failures (averaged over \code{k} iterations).
+}
+}
+\description{
+\code{swan_combinatory} assesses network vulnerability and the resistance of networks
+to node removals, whether due to random failures or intentional attacks.
+}
+\details{
+Many complex systems display a surprising degree of tolerance against random failures.
+However, this resilience often comes at the cost of extreme vulnerability to targeted attacks,
+where removing key nodes (high-degree or high-betweenness nodes) can severely impact network connectivity.
+
+\code{swan_combinatory} simulates different attack strategies:
+\itemize{
+\item \strong{Random failure:} Nodes are removed randomly over multiple iterations.
+\item \strong{Degree-based attack:} Nodes are removed in decreasing order of their degree.
+\item \strong{Betweenness-based attack:} Nodes are removed in decreasing order of their betweenness centrality.
+\item \strong{Cascading failure:} Nodes are removed based on recalculated betweenness after each removal.
+}
+
+The function returns a matrix showing the connectivity loss for each attack scenario.
+
+The code is an adaptation from the NetSwan package that was archived on CRAN.
+}
+\examples{
+library(igraph)
+# Example electrical network graph
+elec <- matrix(ncol = 2, byrow = TRUE, c(
+  11,1, 11,10, 1,2, 2,3, 2,9,
+  3,4, 3,8, 4,5, 5,6, 5,7,
+  6,7, 7,8, 8,9, 9,10
+))
+gra <- graph_from_edgelist(elec, directed = FALSE)
+
+# Compute vulnerability measures
+f4 <- swan_combinatory(gra, 10)
+}
+\references{
+Albert R., Jeong H., Barabási A. (2000). \emph{Error and attack tolerance of complex networks}.
+Nature, 406(6794), 378-382.
+}
diff --git a/man/swan_connectivity.Rd b/man/swan_connectivity.Rd
new file mode 100644
index 0000000..78e44a9
--- /dev/null
+++ b/man/swan_connectivity.Rd
@@ -0,0 +1,41 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/netswan.R
+\name{swan_connectivity}
+\alias{swan_connectivity}
+\title{Impact on connectivity when a node is removed}
+\usage{
+swan_connectivity(g)
+}
+\arguments{
+\item{g}{An \code{igraph} object representing the graph to analyze.}
+}
+\value{
+A numeric vector where each entry represents the connectivity loss when the corresponding node is removed.
+}
+\description{
+\code{swan_connectivity} measures the loss of connectivity when a node is removed from the network.
+}
+\details{
+Connectivity loss indices quantify the decrease in the number of relationships between nodes
+when one or more components are removed. \code{swan_connectivity} computes the connectivity loss
+by systematically excluding each node and evaluating the resulting changes in the network structure.
+
+The code is an adaptation from the NetSwan package that was archived on CRAN.
+}
+\examples{
+library(igraph)
+# Example graph (electrical network structure)
+elec <- matrix(ncol = 2, byrow = TRUE, c(
+  11,1, 11,10, 1,2, 2,3, 2,9,
+  3,4, 3,8, 4,5, 5,6, 5,7,
+  6,7, 7,8, 8,9, 9,10
+))
+gra <- graph_from_edgelist(elec, directed = FALSE)
+
+# Compute connectivity loss
+f3 <- swan_connectivity(gra)
+}
+\references{
+Lhomme S. (2015). \emph{Analyse spatiale de la structure des réseaux techniques dans un contexte de risques}.
+Cybergeo: European Journal of Geography.
+}
diff --git a/man/swan_efficiency.Rd b/man/swan_efficiency.Rd
new file mode 100644
index 0000000..46576fb
--- /dev/null
+++ b/man/swan_efficiency.Rd
@@ -0,0 +1,45 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/netswan.R
+\name{swan_efficiency}
+\alias{swan_efficiency}
+\title{Impact on farness when a node is removed}
+\usage{
+swan_efficiency(g)
+}
+\arguments{
+\item{g}{An \code{igraph} object representing the graph to analyze.
+\code{swan_efficiency} is based on geographic accessibility, similar to indices used for
+assessing transportation network performance, such as closeness accessibility.
+It quantifies the impact of node removal by calculating the change in the sum of
+distances between all node pairs.
+
+The code is an adaptation from the NetSwan package that was archived on CRAN.}
+}
+\value{
+A numeric vector where each entry represents the \code{swan_efficiency} value for the
+corresponding node.
+}
+\description{
+\code{swan_efficiency} measures the change in the sum of distances between all node pairs
+when excluding a node from the network.
+}
+\examples{
+library(igraph)
+# Example graph (electrical network structure)
+elec <- matrix(ncol = 2, byrow = TRUE, c(
+  11,1, 11,10, 1,2, 2,3, 2,9,
+  3,4, 3,8, 4,5, 5,6, 5,7,
+  6,7, 7,8, 8,9, 9,10
+))
+gra <- graph_from_edgelist(elec, directed = FALSE)
+
+# Compute efficiency impact of node removal
+f2 <- swan_efficiency(gra)
+bet <- betweenness(gra)
+reg <- lm(bet ~ f2)
+summary(reg)
+}
+\references{
+Lhomme S. (2015). \emph{Analyse spatiale de la structure des réseaux techniques dans un
+contexte de risques}. Cybergeo: European Journal of Geography.
+}