to_*() functions into reformatting (changing
properties) and transforming (changing dimensions) documentationedge_*() to tie_*() to offer more
(SNA) consistent vocabularynetwork.goldfish objects
(and linked events and nodelists) to migraph-compatible objects (closed
#96)add_node_attributes() to
add_node_attribute() and add_edge_attributes()
to add_tie_attribute()is_*() are now considered graph-level ‘marks’node_is_isolate() for marking isolatesnode_is_max(), node_is_min(),
tie_is_max(), tie_is_min() for converting
‘measures’ into ‘marks’graph_motif (fixed #234)\dontruncluster_concor() and
cluster_hierarchical() are now exportedautographr() no longer requires “highlight_measure” and
“identify_function” arguments as users can now convert ‘measures’ to
‘marks’ and use these for “node_color” or “edge_color”@format for more
consistent documentationison_brandes2 dataset, a two-mode version of the
original one-mode datasetmpn_cow_trade and mpn_cow_igo
datasets (thanks @JaelTan)mpn_elite_mexnode_reach() for calculating reach centrality
(closed #196){concaveman}, {ggdendro},
{oaqc} are now Suggests; the user is prompted to install
them when required in autographr(),
plot.member(), and node_quad_census()
respectively{stringr} and {tibble} replaced with base
or other code and therefore no longer necessary@family tags for improved cross-referencingprint(), plot(),
summary(), have been added for them (see appropriate
sections below)create_ and generate_ functions now:
n passed an existing networkcreate_tree(),
generate_smallworld(),
generate_scalefree()create_ functions can now take a membership vector
or split into equal partitions by default
create_components() no longer accepts a number of
components, instead relying on the membership vectorcreate_core() for creating core-periphery
graphsgenerate_random() now inherits attributes from any
networkto_ functions useful for working with
networks of different types
to_redirected() for adding or swapping direction
to networks (closed #219)to_blocks() for reducing a network down by a
membership vector; blockmodel() and
reduce_graph() are now deprecatedto_multilevel.igraph() now only works on two-mode
networks; returns the original network if passed a one-mode networkis_ functions
is_signed.data.frame() and
is_signed.matrix() now rely on new helper
is.wholenumber() rather than misleading
is.integer()is_directed.igraph() and
is_directed.matrix() now return FALSE for two-mode
networksis_connected() now returns result for strong components
if directed and weak components if undirectedas_igraph.data.frame() now infers third column as
weightedge_multiple(), edge_loop(),
edge_reciprocal() moved from measuresedge_bridges()"edge_measure" S3 class has been added, along
with print() and plot() methodssummary.node_measure() method for printing a
summary by a membership vector; summarise_statistics() is
now deprecated"graph_measure" class results
graph_components() now calculates strong components for
directed networks else weak componentsprint.graph_measure() now correctly labels two-mode
results where a vector is givengraph_smallworld()
graph_core() for calculating correlation of an
observed network to a core-periphery network of the same dimensions
(closed #39)graph_factions() for calculating correlation of
an observed network to a component network of the same dimensions
(closed #40)graph_modularity() for calculating modularity of
an observed network, including modularity for two-mode networks (closed
#144)node_constraint()
node_bridges(), node_redundancy(),
node_effsize(), node_efficiency(),
node_hierarchy()node_betweenness() no longer needs
nobigint argument; just uses default from
{igraph}"node_motif" S3 class for the output of
node_*_census() functions
print.node_motif() for tibble-printing of census
resultssummary.node_motif() to summarise censuses by a
membership vector, replacing group_tie_census() and
group_triad_census(), which are now deprecated"graph_motif" S3 class for the output of
graph_*_census() functionsnode_path_census() for returning the shortest
distances from each node to every other node (closed #222)node_tie_census() now creates unique column names"node_member" S3 class for vectors of nodes’
cluster memberships
plot.node_member() replaces ggtree()node_equivalence() for identifying nodes’
membership in classes equivalent with respect to some arbitrary motif
census
"hierarchical" and "concor" now options
for cluster within node_equivalence();
blockmodel_concor() is now deprecated (closed #123)"elbow" now an option for k selection
within node_equivalence();
ggidentify_clusters() is now deprecated"silhouette" and "strict" options
for k selection (closed #197)k to be defineddistance passed to
stats::dist that defines the distance metric (closed
#36)range constrains the number of
k evaluated by "elbow" and
"silhouette" to improve parsimony and avoid long elapsed
timesnode_automorphic_equivalence() for identifying
nodes’ membership in automorphically-equivalent classes (closed
#187)node_structural_equivalence() replaces
cluster_structural_equivalence()node_regular_equivalence() replaces
cluster_regular_equivalence()node_kernaghinlin() for identifying nodes’
membership in communities based on the Kernaghin-Lin algorithm (thank
you, @jaeltan, closed
#198)node_coreness() for nodes’ k-core score
(closed #200)node_strong_components() and
node_weak_components() for more direct calls;
node_components() now calculates strong components for
directed networks else weak components"graph_test" S3 class replaces
"cug_test" and "qap_test"
plot.graph_test() replaces plot.cug_test()
and plot.qap_test()print.graph_test() methodplot.matrix() now plots adjacency/incidence matrices
with sorting and horizontal/vertical lines if a membership vector is
provided, effectively replacing plot.block_model()autographr() can highlight nodes that max (by default)
some measure (thank you, @BBieri, closed #224)layout_tbl_graph_stressgrid() as an extra
optionggatyear() is deprecatedison_algebra’s edge attributes now named “friends”,
“social”, and “tasks”{roxytest}m argument into p for
generate_random(), p can now be passed an
integer to indicate the number of ties the network should haveto_edges() to be ~26 times faster on
averageto_edges()to_subgraph() now instead of
dplyr::filter() or strain()times argument instead of
maxiter"measure" class "node_measure" and
added "graph_measure" class with print methodnormalized is now the second argumentdirected and weights arguments have been
removed and are now imputed, if this is undesired please use
to_*() firstnode_degree() now calculates strength centrality if
network is weightednode_eigenvector() and graph_eigenvector()
both work with two-mode networksedge_degree() and
edge_eigenvector(), which both just apply the corresponding
nodal measure to the edge graphedge_mutual() renamed to
edge_reciprocal()graph_assortativity()mpn_elite_mexison_brandesis_multiplex.igraph() and
is_multiplex.tbl_graph() now checks for multiple edge
attributesstrain() as wrapper for {dplyr}’s
filter(), renamed to avoid conflicts with
{stats}ison_algebra now unlabelled{readxl} is now suggested, but required if
importing from an Excel sheet{patchwork} replaces {gridExtra} to make
for more concise multiplot visualisations{dplyr} also serves to export {magrittr}’s
pipe{RColorBrewer} has been dropped and the
Dark2 discrete set of colors is now internaldirected and direction
arguments in some functions; whereas directed is always
logical (TRUE/FALSE), direction expects a character string,
e.g. “in”, “out”, or “undirected”generate_permutation() now has an additional logical
argument, with_attr, that indicates whether any attributes
from the original data should be passed to the permuted objectcreate_*() functions now accept existing objects as
their first argument and will create networks with the same
dimensionsread_pajek() now imports nodal attributes alongside the
main edgesread_ucinet() now enjoys clearer documentationas_*() functions now retain weights where present;
if you want an unweighted result, use is_unweighted()
afterwards
as_edgelist.network() now better handles edge
weightsas_matrix.igraph() now better handles edge signsis_twomode(), is_directed(),
is_weighted(), is_labelled(),
is_signed(), is_multiplex(),
is_complex(), and is_graph()as_edgelist(), and
to_unweighted(), and improved the data.frame method for
as_matrix()to_named() and
to_unsigned()to_edges() for creating adjacency matrices using
a network’s edges as nodesproject_rows() and project_cols()
functions to to_mode1() and to_mode2(), which
is both more consistent with other functions naming conventions and more
generic by avoiding the matrix-based row/column distinctionnode_mode(), which returns a vector of the mode
assignments of the nodes in a networkedge_signs(), which returns a vector of the sign
assignments of the edges in a networkautographr() works and how {ggraph} extends
thisautographr() now incorporates ggidentify()
functionality (closed #150){patchwork} is now used to assemble multiple plots
togetherggatyear()measure class and directed most
node_*() functions to create objects of this class
ggdistrib() and offers “hist”
and “dens” methods for histograms and density plots respectivelyedge_betweenness() wraps {igraph}’s
function of the same nameedge_closeness() measures the closeness centrality of
nodes in an edge adjacencynode_cuts() identifies articulation points (nodes) in a
networkedge_bridges() identifies edges that serve as bridges
in a networkgraph_cohesion() measures how many nodes would need to
be removed to increase the number of components (closed #192)graph_adhesion() measures how many edges would need to
be removed to increase the number of componentsgraph_length() measures the average path lengthgraph_diameter() measures the longest path lengthnode_smallworld() and added
graph_smallworld(), which works with both one- and two-mode
networks (fixed #214)node_quad_census()network_reg()’s formula-based system
network_reg() can now handle binary and multiple
categorical variables (factors and characters, closed #211);network_reg() can now manage interactions specified in
the common syntax; var1 * var2 expands to
var1 + var2 + var1:var2 (closed #163)dist() and sim() effects have been added
(closed #207)network_reg() now employs logistic regression to
estimate a binary outcome and linear regression to estimate a continuous
outcome (closed #184)network_reg() now uses Dekker et al’s semi-partialling
procedure by default for multivariate specifications (closed #206),
defaulting to y-permutations in the case of a single predictor
(closed #208)network_reg(), relying on
{furrr} for potential parallelisation and
{progressr} for progress reports (closed #185, #186)test_random() and
test_permutation(), relying on {furrr} for
potential parallelisation and {progressr} for progress
reports; note that nSim argument now times
(closed #199){broom} S3 methods for netlm and
netlogit class objects (closed #183)
tidy() extracts coefficients and related valuesglance()extracts model-level values such as
R^2netlm and netlogit
class objects (closed #216), which plots the empirical distribution for
each test statistic, indicates percentiles relating to common critical
values, and superimposes the observed coefficientscug_test and
qap_test class objects, which plots the empirical
distribution, highlighting tails beyond some critical value (closed
#213), and superimposing the observed coefficient and, possibly, 0{sna}
print.block_model() replaces
print.blockmodel()plot.block_model() replaces
plot.blockmodel()ison_southern_women instead of
southern_womenison_brandes instead of brandesison_networkers instead of ison_eiesison_algebra instead of ison_m182ison_adolescents instead of
ison_colemanmpn_elite_mex is extended with data from Pajek and with
help from Frank Heberison_networkers becomes named with information from
{tnet}mpn_* and
ison_* datasets, including references/sourcescluster_structural_equivalence() for isolates
{patchwork} to suggested packages
in DESCRIPTIONadd_ functions
mutate_edges()add_node_attributes()is_ methods: is_multiplex(),
is_uniplex(), is_acyclic()edge_ functions to identify edges by properties:
edge_mutual(), edge_multiple(),
edge_loop()read_nodelist() and read_edgelist()as_network() method to convert
correctly form an {igraph} to a {network}
object.ggraphgrid() documentationread_edgelist() and
read_nodelist() to readxl to avoid Java dependencywrite_edgelist() and
write_nodelist() to avoid Java dependency
as_network(),
as_igraph(), and is_directed()read_ and write_ functions and
updated documentation
read_edgelist() for importing
edgelists from Excel and csv filesread_pajek() for importing .net
and .paj fileswrite_edgelist(), write_nodelist,
write_pajek(), and write_ucinet() for
exporting into various file formats (Excel, csv, Pajek, and UCINET)is_graph() to check if an object is a graph or
notas_network() to retain attributesas_ and to_ functions
as_ functions to convert from dataframes
instead of tibblesas_igraph() functionto_undirected() function to work with
network objectsto_main_component() function so that it
retains vertex attributes in network objectsedge_attribute() to grab a named edge attribute
from a graph/networkto_unweighted() to prevent conversion of
network object into igraph object when deleting weightssummarise_statistics()network_reg() exampleautographr()ison_eies dataset for use in practical 7
vignetteas_matrix() method for networks now works with
two-mode and weighted networksas_igraph() method for matrices now checks for
weights independently of coercionas_igraph() method for networks now works with
two-mode and weighted networksas_network() method for matrices now works with
two-mode and weighted networksas_network() method for edgelists, igraph, and
tidygraphs now works with weighted networksto_unnamed() method for edge liststo_simplex() method for matricesto_main_component() method for networksto_multilevel() method for matricesmutate_edges() now coalesces rows of edgesgraph_blau_index()generate_permutation() and thus
test_permutation()netlm() to network_reg() to avoid
frustrating conflicts
network_reg() now accepts migraph-consistent
objectsnetwork_reg() now accepts formula terms such as
ego(), alter(), and same()generate_permutation() which takes an object and
returns an object with the edges permuted, but retaining all nodal
attributesgenerate_random() also work with an existing
object as input, in which it will return a random graph with the same
dimensions and densitymutate_edges() for adding new edges as attributes
to existing edges in an objectgraph_blau_index()graph_ei_index()test_random()
(defunct test_cug())autographr()
arguments take variable names in quotation marksnode_names() for quickly accessing node labelsnode_attribute() for quickly accessing a certain nodal
attributeedge_weights() for quickly accessing edge weightsgraph_nodes() for quickly accessing a count of nodes in
the graph, note that for two-mode networks this will be a vector of
length 2graph_edges() for quickly accessing a count of edges in
the graphgraph_dimensions() is currently a copy of
graph_nodes()add_node_attributes() for adding particular nodal
attributesadd_edge_attributes() for adding edges from another
graphcopy_edge_attributes() for copying all nodal attributes
from another graphgraph_blau_index() for summarising diversity of an
attribute in a network or groupgraph_ei_index() for summarising diversity of an
attribute over a network’s tiesnode_quad_census(), especially
useful for two-mode blockmodellinggraph_mixed_census()test_random() carries out a conditional uniform graph
(CUG) testtest_permutation() carries out a quadratic assignment
procedure (QAP) testaes() from
{ggplot2}node_shape option to
autographr()as_edgelist() methods for
converting other objects into edgelists
to_unnamed() on ‘network’ objects now operates on
them directlyto_ documentation significantlyto_onemode() that was tripping
blockmodel() on networks that are already one-modeis_connected() to test whether network is
connected, method = argument can be specified as
weak or strongcreate_tree() and create_lattice(),
and made create_star() a bit faster for one-mode
networksgenerate_smallworld() and
generate_scalefree(), though only for one-mode networks
currently=2graph_dyad_census() for more graph profile
optionsblockmodel_concor() when an object was
of class ‘igraph’ but not ‘tbl_graph’blockmodel() was treating two-mode
networks"elbow" and
"strict" methods for k-identification
ggidentify_clusters() for
speed
ggdistrib() for easy plotting of degree and other
node score distributionsggsave(), xlab() and
ylab() from {ggplot2} for easier plot
annotationas_matrix() where availablegraph_equivalency() into the same for two-mode
networks and graph_congruency()for three-mode (two
two-mode) networksgraph_reciprocity() methodgraph_components() and
node_components()node_tie_census() output so
that it’s consistent with node_triad_census() and future
node_census functionscluster_triad_census() to
group_triad_census()group_tie_census()autographr() for plotting
convex/concave hullsggraphgrid() a set of layout
functions:
layout_tbl_graph_frgrid() or
autographr(object, "frgrid") for snapping
Fruchterman-Reingold to a gridlayout_tbl_graph_kkgrid() or
autographr(object, "kkgrid") for snapping Kamada-Kawai to a
gridlayout_tbl_graph_gogrid() or
autographr(object, "gogrid") for snapping graph
optimisation to a gridggraphgrid() has been deprecatedison_m182 documentationas_igraph()to_uniplex() was not returning a
weighted graphblockmodel() was not retaining node
names in all parts of the object structureplot_releases() to another packageis_signed() to logically test whether the network
is a signed networkto_unsigned() for extracting networks of either
“positive” or “negative” ties from a signed networktbl_graph methods for all other to_
functionsactivate() from
{tidygraph}autographr()plot_releases() from this packagegraph_balance() to be much faster, following
David Schoch’s {signnet} package (see that package for
further extensions)ison_marvel_relationships to be a double
(-1/1) to be compatible with the new
graph_balance() and {signnet}{igraph} from data frames and updated
read scriptto_main_component() to extract the main component
of a networkto_onemode() for moving to multimodal igraph
objectsto_uniplex() method to delete edge types and
their edges from multiplex networksto_simplex() method to delete loops from a
networkto_named() method for randomly naming unlabeled
networksison_mm, ison_mb,
ison_bm, and ison_bb projection illustration
dataison_karateka community detection illustration
dataison_marvel_teams and
ison_marvel_relationships datasetsison_m182 dataset of friends, social and task
ties between 16 anonymous studentsadolescent_society dataset to
ison_colemanfor consistencygraph_eigenvector() for one mode networksgraph_balance() for measuring structural
balancenode_tie_census(),
node_triad_census(), cluster_triad_census(),
and graph_triad_census()graph_clustering() into the cohesion
measures graph_density(), graph_reciprocity(),
graph_transitivity(), and
graph_equivalence()node_smallworld() to use separated cohesion
measuresblockmodel() which masks its {sna}
namesake but has the advantages of working with two-mode networks and
retaining node names where available
cluster_structural_equivalence() and
cluster_regular_equivalence() as bases for
blockmodellingreduce_graph() for creating a network from a
blockmodelautographr() for plotting graphs with sensible
defaults
ggraphlabel() since core functionality now
provided by autographrggidentify() to identify the node
with the highest value of a specified node-level measureggatyear() for subsetting and plotting edgelists
at yeargglineage() to return a graph colored according
to lineage
ggtree() for neatly visualising hierarchical
clustersggidentify_clusters() for identifying which
number of clusters is most appropriate following the elbow methodggraph::theme_graph() present in a
few different visualisation functionsbrandes dataset for teaching centrality
measuresadolescent_society dataset for teaching
friendship paradoxread_edgelist() for importing Excel-created
edgelists directlyggraphlabel() for one-function (1F) plotting
label-based network graphsggevolution() for 1F-plotting begin/end graph
comparisonsggraphgrid() for 1F snap-to-grid graph layouts
based on Fruchterman-Reingold or Kamada-Kawaiggidentify() for 1F identifying nodes with
maximum scores based on some arbitrary functionto_undirected() for symmetrising networks of all
typesto_ functions S3 methodsClosed #100 by converting as_ coercion functions to
S3 methods
as_matrix() weightingas_tidygraph()gglineage() for graphing a
citation network through timeggevolution() for graphing two
timepoints of the same network side by sideggraphgrid() for locking a graph
to a gridplot.igraph() defaultsnode_ functionsFixed bug in as_matrix() with frame matrix by
dropping (rarely necessary) functionality
as_ functionsFixed bugs in plot_releases() with more graceful
handling of http errors
{R6},
{ggraph})netlm()
testbinarise() to to_unweighted()to_unnamed() for unlabelling networksbinarise() for unweighting networksas_tidygraph() when passed a tbl_graph
directlyplot_releases() for more general useplot.igraph() with layouts and one-mode
graphsUpdated README
Added CITATION details
Separated coercion (previously conversion) and manipulation
Added some more inter-class coercion tests
Fixed bug in how as_network() sometimes coerced
two-mode networks into much larger dimension matrices
Added more is_ tests for class-independent property
tests
is_weighted()is_directed()is_labelled()Added @csteglich ’s read_ucinet()
and write_ucinet() functions
read_ucinet() offers a file-picker when file path
unknownread_ucinet() now imports to an igraph-class object by
default, with an argument to allow other alternativeswrite_ucinet() works with all migraph-compatible
objectsUpdated mpn_bristol documentation
Added create_star() function
directed =
argumentcreate_ documentationRenamed sample_affiliation() to
generate_random()
generate_random() to be able to generate random
one- or two-mode networksprint.blockmodel()2021-04-13
usethis::use_mit_license("James Hollway"). MIT
License file now contains only the standard two lines.\dontrun from examples. netlm()
now runs in <5 seconds.2021-04-11
as_network() to coerce objects
into network class2021-03-03
jhollway/ to
snlab-ch/ organisationElaborated documentation for the remainder of the datasets
graph_degree() where data was hard-coded
inClosed #18 by adding blockmodel_concor() for
employing the CONCOR algorithm to blockmodel both one-mode and two-mode
networks
print.blockmodel() method in the {sna} package
that also prints blockmodel results for two-mode networks
consistently{ggplot2} for pretty plotting and that better
inherits names from the underlying object2021-02-06
{migraph} depend on R versions 4.0
or aboveas_matrix() function to coerce objects into an
adjacency or incidence matrix classas_igraph() function to coerce objects into an
{igraph} graph classas_tidygraph() function to coerce objects into an
{tidygraph} tbl_graph classis_twomode() function to check
whether network is two-mode on all object typesRenamed several datasets and elaborated their documentation
mpn_mexicanpower was renamed to
mpn_elite_mexmpn_powerelite was renamed to
mpn_elite_usa_advicempn_opensecrets was renamed to
mpn_elite_usa_moneyReconstructed several creation functions to take universal
(one-mode/two-mode) input: specifying n = 5 creates a
one-mode network, while specifying n = c(5, 5) creates a
two-mode network
create_empty()create_complete()create_ring() to create rings
of varying breadthcreate_components() (renamed
from create_silos()) to create networks with varying
numbers of componentssample_affiliation() for random two-mode
networkscreate_match() and
create_nest()Renamed centrality_ functions with
node_ prefix and ensured they all also wrapped one-mode
measures
centrality_degree() renamed to
node_degree()centrality_closeness() renamed to
node_closeness()centrality_betweenness() renamed to
node_betweenness()node_eigenvector()Re-added node_constraint() for calculating Burt’s
constraint measure for one- and two-mode networks
Re-added node_smallworld() for calculating
Watts-Strogatz measure of small-worldness for two-mode networks
Closed #32 by re-adding centralization functions for one- and two-mode networks
graph_degree() for degree centralizationgraph_closeness() for closeness centralizationgraph_betweenness() for betweenness centralizationRe-added graph_clustering() for calculating (see
Knoke et al 2021):
Re-added netlm() for performing linear regression
for multimodal network data
netlm() to accept a
formula-based inputprint.summary.netlm() for
netlm() regressionsplot.igraph() with
sensible defaults for two-mode networks2021-01-11
{igraph} and
{tidygraph} functions for internal useconvert_ and project_
documentationAdded centrality measures that take (and if necessary return) matrix, igraph, or tidygraph objects, and offer a correct normalization for two-mode networks
centrality_degree()centrality_closeness()centrality_betweenness()2021-01-08
Package name change from roctopus to {migraph}
Now builds Linux binary too
project_rows() and project_cols() to
make it easier to project two-mode networks in different formats
(matrix, igraph, tidygraph) into projected versions in the same
formatas_incidence_matrix()2020-11-06
Data creation updated for {tidygraph} defaults
create_lattice() to create_chain()
to avoid conflicts with tidygraph::create_lattice()create_poles() to
create_silos()create_random() to play_twomode()
to avoid conflicts with tidygraph::play_bipartite()create_ and
play_ functions as tbl_graph (default),
igraph, and base matrixcreate_ and play_
function namesPackaged data updated for {tidygraph} defaults
mpn_2020-08-18
twomode_netlm() to netlm2()netlm2()netlm2()create_random()opensecrets datapowerelite data2020-07-21
create_() functionscontext()
declarationcreate_nest() now exports matrix object2020-07-20
row_project() and col_project()
functions to make it easier to remember project directions2020-07-19
mexicanpower datasetsenate112 datasetsevs datasets to matricesdf_to_mat() function for converting regular
adjacency and incidence data frames into matrices2020-07-17
ryanair dataset from Christopoulos 20062020-07-03
evs datasets from Knoke et al 20202020-06-30
bristol dataset from Knoke et al 20202018-12-20
twomode_modularity() to calculate modularity in
two-mode networksplot_multilevel() that rotates a force-directed
igraph plot in three dimensions to reveal multilevel
structureplot_globalnet() to map a multilevel network on
to a javascript, rotatable global2018-08-25
twomode_smallworld() to calculate
observed/expected clustering, observed/expected path-length, and the
observed/expected clustering ratiotwomode_2x2() to identify dominance and coherence
values for networks over timetwomode_coherence() to allow for introduction
of second-mode attributestwomode_fragmentation() to
twomode_components()plot_2x2() to plot values through a two-by-two
matrix2018-08-14
roctopustwomode_fragmentation() to calculate number of
components in two-mode networks and identify their membershiptwomode_dominance() to allow an nodal attribute
to be passed to the function to weight the centralization measuretwomode_coherence() to calculate Jaccard
similarityAdded plot_twomode(), which wraps
plot(igraph::graph_from_incidence_matrix()) with some
useful defaults
2018-07-30
README.md file with instructions on how to
install packageLICENSE file and pointed to bug/issue
trackerNEWS.md file to track changes to the
packagetwomode_clustering() to calculate percentage of
three-paths closed by four-pathstwomode_lattice() to create two-mode
latticestwomode_centralization_degree() to calculate
degree centralization in two-mode networks, for rows, columns, or
bothtwomode_centralization_between() to calculate
betweenness centralization in two-mode networkstwomode_constraint() to calculate network
constraint in two-mode networksarrange.vars() to rearrange variables by
position