netUtils is a collection of tools for network analysis that may not deserve a package on their own and/or are missing from other network packages.

You can install the development version of netUtils with:

```
# install.packages("remotes")
::install_github("schochastics/netUtils") remotes
```

most functions only support igraph objects

**helper/convenience functions**

`biggest_component()`

extracts the biggest connected
component of a network.

`delete_isolates()`

deletes vertices with degree zero.

`bipartite_from_data_frame()`

creates a two mode network from
a data frame.

`graph_from_multi_edgelist()`

creates multiple graphs from a
typed edgelist.

`clique_vertex_mat()`

computes the clique vertex
matrix.

`graph_cartesian()`

computes the Cartesian product of two
graphs.

`graph_direct()`

computes the direct (or tensor) product of
graphs.

`str()`

extends str to work with igraph objects.

**methods**

`dyad_census_attr()`

calculates dyad census with node
attributes.

`triad_census_attr()`

calculates triad census with node
attributes.

`core_periphery()`

fits a discrete core periphery
model.

`graph_kpartite()`

creates a random k-partite network.

`split_graph()`

sample graph with perfect core periphery
structure.

`sample_coreseq()`

creates a random graph with given coreness
sequence.

`sample_pa_homophilic()`

creates a preferential attachment
graph with two groups of nodes.

`sample_lfr()`

create LFR benchmark graph for community
detection.

`structural_equivalence()`

finds structurally equivalent
vertices.

`fast_clique()`

computes cliques with MACE (faster than
igraph for dense graphs).

`reciprocity_cor()`

reciprocity as a correlation
coefficient.

**methods to use with caution**

*(this functions should only be used if you know what you are
doing)*

`as_adj_list1()`

extracts the adjacency list faster, but less
stable, from igraph objects.

`as_adj_weighted()`

extracts the dense weighted adjacency
matrix fast.