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The R package wdnet provides functions to conduct network analysis:

  1. Assortativity, centrality, clustering coefficient for weighted and directed networks;
  2. Rewire an unweighted network with given assortativity coefficient(s);
  3. Preferential attachment (PA) network generation with user-defined preference functions.

Installation

You may install the released version from CRAN.

install.packages("wdnet")

Development

The development version of this package is available on Gitlab.

License

GNU General Public License (≥ 3)

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Version

Install

install.packages('wdnet')

Monthly Downloads

125

Version

1.2.3

License

GPL (>= 3.0)

Maintainer

Yelie Yuan

Last Published

March 3rd, 2024

Functions in wdnet (1.2.3)

dprewire_directed

Degree preserving rewiring for directed networks
dprewire_directed_cpp

Degree preserving rewiring process for directed networks.
get_dist

Get the node-level joint distributions and some empirical distributions with given edgelist.
dw_assort

Compute the assortativity coefficient of a weighted and directed network.
find_node_cpp

Fill missing nodes in the node sequence. Defined for wdnet::rpanet.
fill_weight_cpp

Fill edgeweight into the adjacency matrix. Defined for function edgelist_to_adj.
dw_feature_assort

Feature based assortativity coefficient
get_values

Get the value of an object from the optimization problem. This function is defined for get_eta_directed().
get_constr

Get the constraints for the optimization problem. This function is defined for get_eta_directed().
find_node_undirected_cpp

Fill missing values in node sequence. Defined for wdnet::rpanet.
dprewire_undirected

Degree preserving rewiring for undirected networks
dprewire_undirected_cpp

Degree preserving rewiring process for undirected networks.
get_eta_undirected

Compute edge-level distribution for undirected networks with respect to desired assortativity level.
print_control_details

Prints rpacontrol in terminal
print_control_edgeweight

Prints rpa_control_edgeweight() in terminal
print_control_preference

Prints rpa_control_preference() in terminal
print_control_newedge

Prints rpa_control_newedge() in terminal
print.wdnet

Prints the input network
print.rpacontrol

Prints rpacontrol objects
get_eta_directed

Compute edge-level distributions for directed networks with respect to desired assortativity level(s).
is_wdnet

Checks if the input is a wdnet object
dprewire.range

Range of assortativity coefficients.
dprewire

Degree preserving rewiring.
print_control_reciprocal

Prints rpa_control_reciprocal() in terminal
print_control_scenario

Prints rpa_control_scenario() in terminal
rpa_control_edgeweight

Control weight of new edges. Defined for rpanet.
rpacontrol

rpacontrol: Controls the Preferential Attachment (PA) Network Generation Process
rpa_control_default

Default controls for rpanet
node_strength_cpp

Aggregate edgeweight into nodes' strength.
plot.wdnet

Plots the input network
rpa_control_newedge

Control new edges in each step. Defined for rpanet.
igraph_to_wdnet

Converts an igraph object to a wdnet object
is_rpacontrol

Checks whether the input is a rpacontrol object
edgelist_to_wdnet

Creates a wdnet object using edgelist.
edgelist_to_adj

Convert edgelist and edgeweight to adjacency matrix.
rpa_control_reciprocal

Control reciprocal edges. Defined for rpanet.
wpr

Weighted PageRank centrality
wdnet_to_igraph

Converts a wdnet object to an igraph object
+.rpacontrol

Add components to the control list
rpa_control_preference

Set preference function(s). Defined for rpanet.
rpanet_linear_directed_cpp

Preferential attachment network generation.
rpanet

Generate PA networks.
rpanet_linear_undirected_cpp

Preferential attachment network generation.
rpa_control_scenario

Control edge scenarios. Defined for rpanet.
rpanet_binary_directed

Preferential attachment network generation.
rpanet_binary_undirected_cpp

Preferential attachment network generation.
wdnet-package

wdnet: Weighted and Directed Networks
sample_node_cpp

Uniformly draw a node from existing nodes for each time step. Defined for wdnet::rpanet().
rpanet_general

Internal functions for generating PA networks
rpanet_bag_cpp

Preferential attachment algorithm for simple situations, i.e., edge weight equals 1, each step adds one new edge.
assortcoef

Compute the assortativity coefficient(s) for a network.
closeness_c

Closeness centrality
adj_to_wdnet

Creates a wdnet object using an adjacency matrix
centrality

Centrality measures
clustcoef

Directed clustering coefficient
cvxr_control

Parameters passed to CVXR::solve().
create_wdnet

Creates a wdnet object from input data.
adj_to_edgelist

Converts an adjacency matrix to edgelist and edgeweight using the igraph package.
compile_pref_func

Compile preference functions via RcppXPtrUtils.
degree_c

Degree-based centrality