From 136fec0771149a16b57781a8aacc64a8bbebd976 Mon Sep 17 00:00:00 2001
From: GitHub Actions Site built with pkgdown 2.1.0. Site built with pkgdown 2.1.1. 2024-11-06 2024-09-03Page not found (404)
diff --git a/CODE_OF_CONDUCT.html b/CODE_OF_CONDUCT.html
index 0d7708a5..325b4fc2 100644
--- a/CODE_OF_CONDUCT.html
+++ b/CODE_OF_CONDUCT.html
@@ -17,7 +17,7 @@
@@ -82,7 +82,7 @@ Contributor Code of Conduct
diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html
index e44ac9cf..d046c4c2 100644
--- a/CONTRIBUTING.html
+++ b/CONTRIBUTING.html
@@ -17,7 +17,7 @@
@@ -117,7 +117,7 @@ Documentation
-
License
diff --git a/LICENSE.html b/LICENSE.html
index 58e8007e..c5bd4577 100644
--- a/LICENSE.html
+++ b/LICENSE.html
@@ -17,7 +17,7 @@
@@ -80,7 +80,7 @@ MIT License
diff --git a/authors.html b/authors.html
index 92b0c1db..0b0686ce 100644
--- a/authors.html
+++ b/authors.html
@@ -17,7 +17,7 @@
@@ -107,7 +107,7 @@ Citation
diff --git a/index.html b/index.html
index 604b4107..12f40883 100644
--- a/index.html
+++ b/index.html
@@ -40,7 +40,7 @@
@@ -268,7 +268,7 @@ Developers
diff --git a/news/index.html b/news/index.html
index ed092cc5..2c154c8b 100644
--- a/news/index.html
+++ b/news/index.html
@@ -17,7 +17,7 @@
@@ -60,15 +60,69 @@ Changelog
+
+ migraph 1.4.32024-11-06
+Package
+Models
+test_configuration()
+Data
+mpn_bristol
with infompn_ryanair
with infompn_elite_mex
with infompn_elite_usa
+mpn_senate
+mpn_cow
+Tutorial
+alter(Discipline)
from net_regression()
callmigraph 1.4.22024-09-04
+Models
test_permutation()
now uses manynet::to_permuted()
instead of the older manynet::generate_permutation()
syntaxtest_permutation()
now uses manynet::to_permuted()
instead of the older manynet::generate_permutation()
syntaxmigraph 1.4.12024-07-19
@@ -112,7 +166,7 @@ Modelstest_fit() no longer measures fit against steps where there is no covariance
-
test_random()
where parameters were passed to manynet::generate_random()
instead of the original object, which is processed more intuitively within manynet::generate_random()
(thanks @RWKrause)test_random()
where parameters were passed to manynet::generate_random()
instead of the original object, which is processed more intuitively within manynet::generate_random()
(thanks @RWKrause)test_random()
returns results on edge-conditioned uniform graphs, not sizeggplot2::scale_y_discrete()
migraph 1.3.3Measures
node_brokering_activity()
and node_brokering_exclusivity()
from Hamilton et al (2020)node_degree()
now returns strength centrality (alpha = 1) for weighted networks by defaultnode_degree()
now returns strength centrality (alpha = 1) for weighted networks by default
node_redundancy()
now works for weighted onemode and twomode networks (closed #292)Measuresnetwork_change() to measure the Hamming distance between two or more networks
network_stability()
to measure the Jaccard index between two or more networksnetwork_spatial()
to measure spatial association (Moran’s I, closes #209)node_deg()
for a non-normalised version of node_degree()
+node_deg()
for a non-normalised version of node_degree()
node_degree()
now normalises strength centralitynode_induced()
to measure nodes’ induced betweenness centralitiesnode_flow()
to measure nodes’ flow betweenness centralities (closes #195)node_information()
to measure nodes’ information or current-flow closeness centralities (closes #194)node_multidegree()
to measure the ratio of one type of tie in a multiplex network to anothernode_posneg()
measures the PN centrality of signed networksnode_degree()
now normalises strength centrality
+node_induced()
to measure nodes’ induced betweenness centralitiesnode_flow()
to measure nodes’ flow betweenness centralities (closes #195)node_information()
to measure nodes’ information or current-flow closeness centralities (closes #194)node_multidegree()
to measure the ratio of one type of tie in a multiplex network to anothernode_posneg()
measures the PN centrality of signed networksMembers
@@ -229,7 +283,7 @@ Members
Models
-play_*()
functions to manynet, including as_diffusion()
and the diffusion tutorialplay_*()
functions to manynet, including as_diffusion()
and the diffusion tutorialnetwork_reg()
so that specifications that include a ‘sim’ or ‘same’ effect for a variable are encouraged to also include more elementary ‘ego’ and ‘alter’ effectsnetwork_reg()
now ignores the LHS of the formula for uniplex networksmigraph 1.2.1
Measures
-node_adoption_time()
+node_adoption_time()
MarksMeasures
network_hazard()
for calculating the hazard rate for each time point of a diff_model objectnode_adoption_time()
now works with incomplete diffusionsnode_adoption_time()
now works with incomplete diffusionsnode_exposure()
now uses node_is_infected()
for more flexibilitynode_exposure()
now uses node_is_infected()
for more flexibility
node_thresholds()
now works with incomplete diffusionsnode_thresholds()
now works with incomplete diffusionsMembers
@@ -272,10 +326,10 @@ Members
Models
play_diffusion()
node_is_exposed()
and node_exposure()
internallyplay_diffusion()
node_is_exposed()
and node_exposure()
internallyplay_diffusion()
+play_diffusion()
MarksMeasures
network_reproduction()
function that calculates the R-nought value.network_immunity()
function to calculate the Herd Immunity Threshold for the network.node_exposure()
function to calculate the number of infected/adopting nodes to which each susceptible node is exposed.node_exposure()
function to calculate the number of infected/adopting nodes to which each susceptible node is exposed.node_*()
and network_*()
measures.Models
-as_diffusion()
function to convert a diffusion event table into a diff_model
object.as_diffusion()
function to convert a diffusion event table into a diff_model
object.test_gof()
function for testing goodness-of-fit in diffusion models.migraph 1.1.82023-12-06
Measures
-node_thresholds()
so that it infers nodes’ thresholds from the amount of exposure they had when they became infected or exposed.node_adoption_time()
and node_adopter()
to return node_member
and node_measure
objects, which makes printing and summarising better.node_thresholds()
so that it infers nodes’ thresholds from the amount of exposure they had when they became infected or exposed.node_adoption_time()
and node_adopter()
to return node_member
and node_measure
objects, which makes printing and summarising better.Members
Models
diff_model
object to carry original network data for plotting.play_diffusion()
to forward exposure/contact information to the events table.play_diffusion()
to forward exposure/contact information to the events table.Tutorials
@@ -341,7 +395,7 @@ Marks
Models
play_segregation()
to sample randomly from those unoccupied options less than the desired threshold.play_segregation()
to sample randomly from those unoccupied options less than the desired threshold.Tutorials
@@ -352,10 +406,10 @@ migraph 1.1.62023-11-08
Measures
-network_transmissability()
, node_infection_length()
, network_infection_length()
, network_reproduction()
, node_adoption_time()
, node_adopter()
, node_thresholds()
.network_transmissability()
, node_infection_length()
, network_infection_length()
, network_reproduction()
, node_adoption_time()
, node_adopter()
, node_thresholds()
.Models
-play_diffusion()
.play_diffusion()
.Measuresnetwork_efficiency()
network_upperbound()
node_eccentricity()
, which wraps igraph::eccentricity()
+node_eccentricity()
, which wraps igraph::eccentricity()
node_neighbours_degree()
, which wraps igraph::knn()
Package
Measures
-node_outdegree()
, node_indegree()
, network_outdegee()
, and network_indegree()
wrappersnode_outdegree()
, node_indegree()
, network_outdegee()
, and network_indegree()
wrappersnetwork_reach()
node_harmonic()
and network_harmonic()
+node_harmonic()
and network_harmonic()
node_pagerank()
+node_pagerank()
plot.node_measure()
now returns a single plot for one-mode networks with a frequency histogram and a density overlayDatamigraph 1.0.12023-07-20
Measures
-node_closeness()
and node_betweenness()
in preparation for an igraph deprecation (merci @maelle)node_closeness()
and node_betweenness()
in preparation for an igraph deprecation (merci @maelle)Models
test_random()
and test_permutation()
rely on graph dimensions rather than graph order (thank you finding this @rabenton)Package
Measures
-over_time()
and over_waves()
to measure (potentially parallelised) over split graphsnetwork_measures
objectover_time()
and over_waves()
to measure (potentially parallelised) over split graphsnetwork_measures
objectnode_alpha()
for calculating alpha centrality, mostly just a wrapper for igraph::alpha_centrality()
+node_alpha()
for calculating alpha centrality, mostly just a wrapper for igraph::alpha_centrality()
node_power()
for calculating beta or Bonacich centrality, mostly just a wrapper for igraph::power_centrality()
, though also correctly accounts for two-mode networksnode_power()
for calculating beta or Bonacich centrality, mostly just a wrapper for igraph::power_centrality()
, though also correctly accounts for two-mode networksnode_homophily()
to node_heterophily()
, which is more accurate and in line with the scale’s directionnode_heterophily()
now calculates EI indices in a faster, vectorised form, instead of the older, slower solution that calculated network_homophily()
over all ego networksnetwork_homophily()
to network_heterophily()
Measures
Models
k_strict()
, k_elbow()
and k_silhouette()
are now exported and documentedplay_segregation()
for playing Schelling segregation models with various parametersk_strict()
, k_elbow()
and k_silhouette()
are now exported and documentedplay_segregation()
for playing Schelling segregation models with various parametersdiff_model
and diffs_model
plotting irregularitiescluster_concor()
+cluster_concor()
Package
Makes
-read_matrix()
for importing CSVs of adjacency/incidence matricesread_dynetml()
for importing DynetML .xml files (closed #261)read_matrix()
for importing CSVs of adjacency/incidence matricesread_dynetml()
for importing DynetML .xml files (closed #261)Manipulations
-as_tidygraph()
for merging nodelists and edgelistsas_siena()
as_tidygraph()
for merging nodelists and edgelistsas_siena()
Package
Makes
-read_pajek()
where multiple networks/ties were causing an issue for partition assignmentread_pajek()
where multiple networks/ties were causing an issue for partition assignmentModels
-play_diffusion()
relating to latency inversionplay_diffusion()
relating to latency inversionmigraph 0.13.02022-11-30
@@ -582,7 +636,7 @@ Package
Marks
-is_aperiodic()
for testing whether a network is aperiodic (the greatest common divisor for all cycles in the network is 1)is_aperiodic()
for testing whether a network is aperiodic (the greatest common divisor for all cycles in the network is 1)node_is_max()
and node_is_min()
now take a “rank” argument for selecting more than the first ranked maxima or minimaMeasuresnetwork_richness() and
node_richness()
for calculating the richness (a common diversity measure) of an attribute in a network
Models
-play_diffusion()
to include more compartment and transition optionsplay_diffusion()
to include more compartment and transition optionsModelsplay_diffusions() for running a diffusion model multiple times
play_diffusions()
for running a diffusion model multiple timesplay_learning()
for running a DeGroot learning modelplay_learning()
for running a DeGroot learning modelMarksnode_is_random() for selecting n nodes at random
Models
-play_diffusion()
modelplay_diffusion()
modelmigraph 0.12.5
@@ -658,11 +712,11 @@ Package
Makes
-generate_random()
+generate_random()
Manipulating
-to_mode1()
and to_mode2()
)to_mode1()
and to_mode2()
)to_redirected.igraph()
where routing through an edgelist caused problemsMake
Marks
-is_eulerian()
for a logical expression of whether the network has an Eulerian pathis_eulerian()
for a logical expression of whether the network has an Eulerian pathMeasures
migraph 0.12.1<
Manipulations
to_twomode()
now returns an undirected networkto_anti()
for obtaining the complement of the given networkto_twomode()
now returns an undirected network
+to_anti()
for obtaining the complement of the given networkMarks
-is_perfect_matching()
for a logical expression of whether the maximum matching of a network is also perfectis_perfect_matching()
for a logical expression of whether the maximum matching of a network is also perfectnode_is_core()
for a logical vector of which nodes are members of the coreMeasures
node_degree()
now has an additional parameter for trading off between degree and strength in the case of weighted networksnode_power()
for Bonacich power centrality for both one- and two-mode networks (closed #193)node_degree()
now has an additional parameter for trading off between degree and strength in the case of weighted networks
+node_power()
for Bonacich power centrality for both one- and two-mode networks (closed #193)Package
Manipulation
-as_graphAM()
methods for all migraph-consistent object classes so {Rgraphviz}
can be used effectivelyas_igraph()
, as_tidygraph()
, and as_network()
methods for RSiena sienaData objects (thanks @JaelTan, closed #94)as_edgelist()
and as_matrix()
methods for network.goldfish
class objects"twomode"
argument in as_matrix()
is now NULL
by default, allowing both one-mode and two-mode coercionas_graphAM()
methods for all migraph-consistent object classes so {Rgraphviz}
can be used effectivelyas_igraph()
, as_tidygraph()
, and as_network()
methods for RSiena sienaData objects (thanks @JaelTan, closed #94)as_edgelist()
and as_matrix()
methods for network.goldfish
class objects"twomode"
argument in as_matrix()
is now NULL
by default, allowing both one-mode and two-mode coercionto_mode1()
and to_mode2()
now take an extra argument to produce weighted projections by different “similarity” measuresto_mode1()
and to_mode2()
now take an extra argument to produce weighted projections by different “similarity” measuresto_matching()
methods to transform a two-mode network or network with some other (binary) “mark” attribute into a network of matching tiesto_main_component()
to to_giant()
to be more space efficientto_blocks()
(closed #242)to_blocks()
where NA blocks couldn’t be subsequently coercedto_matching()
methods to transform a two-mode network or network with some other (binary) “mark” attribute into a network of matching tiesto_main_component()
to to_giant()
to be more space efficientto_blocks()
(closed #242)to_blocks()
where NA blocks couldn’t be subsequently coercedto_blocks()
, to_subgraph()
, and to_ties()
are now S3 methods, returning objects of the same class as givento_onemode()
method for matricesto_twomode()
methods for igraph, tidygraph, and networkto_named()
, to_redirected()
, to_uniplex()
, and to_unsigned()
+to_blocks()
, to_subgraph()
, and to_ties()
are now S3 methods, returning objects of the same class as givento_onemode()
method for matricesto_twomode()
methods for igraph, tidygraph, and networkto_named()
, to_redirected()
, to_uniplex()
, and to_unsigned()
to_undirected()
and to_uniplex()
+to_undirected()
and to_uniplex()
to_uniplex()
+to_uniplex()
to_unweighted()
didn’t respect the “threshold” specifiedto_unweighted()
didn’t respect the “threshold” specifiedMarks
node_mode()
couldn’t produce a “mark” class objectis_twomode()
where labelling information was being ignoredis_twomode()
where labelling information was being ignoredMeasures
@@ -863,7 +917,7 @@ Measures
Membership
node_core()
for partitioning nodes into core and periphery membershipsk_strict()
+k_strict()
Package
Manipulation
network.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()
+add_node_attributes()
to add_node_attribute()
and add_edge_attributes()
to add_tie_attribute()
Memberships
Models
cluster_concor()
and cluster_hierarchical()
are now exportedcluster_concor()
and cluster_hierarchical()
are now exportedMapping
-Membershipsmigraph 0.10.1
Measures
-node_reach()
for calculating reach centrality (closed #196)node_reach()
for calculating reach centrality (closed #196)migraph 0.10.0<
Package
@@ -996,7 +1050,7 @@ {oaqc}
are now Suggests; the user is prompted to install them when required in autographr()
, plot.member()
, and node_quad_census()
respectivelyautographr()
, plot.member()
, and node_quad_census()
respectivelyPackage
Making
create_
and generate_
functions now:n
passed an existing networkcreate_tree()
, generate_smallworld()
, generate_scalefree()
+create_tree()
, generate_smallworld()
, generate_scalefree()
Makingcreate_core() for creating core-periphery graphs
generate_random()
now inherits attributes from any networkgenerate_random()
now inherits attributes from any network
Manipulation
-to_
functions useful for working with networks of different typesto_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_
functions useful for working with networks of different typesto_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 networkMeasuresnode_constraint()
node_bridges()
, node_redundancy()
, node_effsize()
, node_efficiency()
, node_hierarchy()
node_betweenness()
no longer needs nobigint
argument; just uses default from igraph
+node_betweenness()
no longer needs nobigint
argument; just uses default from igraph
Package
Making
-m
argument into p
for generate_random()
, p
can now be passed an integer to indicate the number of ties the network should havem
argument into p
for generate_random()
, p
can now be passed an integer to indicate the number of ties the network should haveManipulation
to_edges()
to be ~26 times faster on averageMeasuresnode_degree() now calculates strength centrality if network is weighted
+
node_degree()
now calculates strength centrality if network is weighted
node_eigenvector()
and graph_eigenvector()
both work with two-mode networksnode_eigenvector()
and graph_eigenvector()
both work with two-mode networks
edge_degree()
and edge_eigenvector()
, which both just apply the corresponding nodal measure to the edge graphMaking@describeIn documentation (closed #215)
directed
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 objectgenerate_permutation()
now has an additional logical argument, with_attr
, that indicates whether any attributes from the original data should be passed to the permuted object
create_*()
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_pajek()
now imports nodal attributes alongside the main edges
read_ucinet()
now enjoys clearer documentationread_ucinet()
now enjoys clearer documentation
Manipulation
@@ -1231,15 +1285,15 @@ Manipulation
as_matrix.igraph()
now better handles edge signs
-is_twomode()
, is_directed()
, is_weighted()
, is_labelled()
, is_signed()
, is_multiplex()
, is_complex()
, and is_graph()
+is_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()
+as_edgelist()
, and to_unweighted()
, and improved the data.frame method for as_matrix()
to_named()
and to_unsigned()
+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 distinctionproject_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 networkManipulationClosed #179 by inferring an attribute vector is for one of the two modes where possible in
add_node_attributes()
is_
methods: is_multiplex()
, is_uniplex()
, is_acyclic()
+is_
methods: is_multiplex()
, is_uniplex()
, is_acyclic()
edge_
functions to identify edges by properties: edge_mutual()
, edge_multiple()
, edge_loop()
Manipulationmigraph 0.8.11
Import and export
-read_nodelist()
and read_edgelist()
+read_nodelist()
and read_edgelist()
Package
-as_network()
method to convert correctly form an igraph to a network object.as_network()
method to convert correctly form an igraph to a network object.ggraphgrid()
documentationPackagemigraph 0.8.10
Import and export
-read_edgelist()
and read_nodelist()
to readxl to avoid Java dependencywrite_edgelist()
and write_nodelist()
to avoid Java dependencyread_edgelist()
and read_nodelist()
to readxl to avoid Java dependencywrite_edgelist()
and write_nodelist()
to avoid Java dependencyManipulation
-as_network()
, as_igraph()
, and is_directed()
+as_network()
, as_igraph()
, and is_directed()
Package#139 by adding vignette on importing and connecting data
Import and export
-read_
and write_
functions and updated documentationread_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)read_
and write_
functions and updated documentationread_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)Manipulation
-is_graph()
to check if an object is a graph or notas_network()
to retain attributesis_graph()
to check if an object is a graph or notas_network()
to retain attributesas_
and to_
functionsas_
functions to convert from dataframes instead of tibblesas_igraph()
functionto_undirected()
function to work with network objectsas_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 weightsto_unweighted()
to prevent conversion of network object into igraph object when deleting weightsMeasures
@@ -1440,15 +1494,15 @@ Data
ison_eies
dataset for use in practical 7 vignetteManipulation
-as_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 matricesas_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 matricesto_multilevel()
method for matricesmutate_edges()
now coalesces rows of edgesMeasures
Modelling
-generate_permutation()
and thus test_permutation()
+generate_permutation()
and thus test_permutation()
netlm()
to network_reg()
to avoid frustrating conflictsnetwork_reg()
now accepts migraph-consistent objectsPackage
Data
-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 densitygenerate_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 densityPackagemigraph 0.8.4
Classes
-as_edgelist()
methods for converting other objects into edgeliststo_unnamed()
on ‘network’ objects now operates on them directlyas_edgelist()
methods for converting other objects into edgeliststo_unnamed()
on ‘network’ objects now operates on them directlyto_
documentation significantlyto_onemode()
that was tripping blockmodel()
on networks that are already one-modeto_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 strong
Data
create_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 currentlygenerate_smallworld()
and generate_scalefree()
, though only for one-mode networks currentlyMeasures
@@ -1604,7 +1658,7 @@ Package#108 by adding cohesion and community vignette
Classes
-as_matrix()
where availableas_matrix()
where availableMeasures
graph_equivalency()
into the same for two-mode networks and graph_congruency()
for three-mode (two two-mode) networksPackage
Classes
-as_igraph()
+as_igraph()
to_uniplex()
was not returning a weighted graphto_uniplex()
was not returning a weighted graphModels
@@ -1670,8 +1724,8 @@ Package
Classes
-is_signed()
to logically test whether the network is a signed networkto_unsigned()
for extracting networks of either “positive” or “negative” ties from a signed networkis_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
migraph 0.8.0Classes
to_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 networksto_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 networksData
@@ -1751,7 +1805,7 @@ migraph 0.7.2Data
brandes
dataset for teaching centrality measuresadolescent_society
dataset for teaching friendship paradoxread_edgelist()
for importing Excel-created edgelists directlyread_edgelist()
for importing Excel-created edgelists directlyManipulation
-to_undirected()
for symmetrising networks of all typesto_undirected()
for symmetrising networks of all typesto_
functions S3 methodsClasses#100 by converting
as_
coercion functions to S3 methods
as_matrix()
weightingas_tidygraph()
+as_matrix()
weightingas_tidygraph()
Fixed bug in as_matrix()
with frame matrix by dropping (rarely necessary) functionality
Fixed bug in as_matrix()
with frame matrix by dropping (rarely necessary) functionality
as_
functionsbinarise()
to to_unweighted()
+binarise()
to to_unweighted()
to_unnamed()
for unlabelling networksto_unnamed()
for unlabelling networksbinarise()
for unweighting networksas_tidygraph()
when passed a tbl_graph directlyas_tidygraph()
when passed a tbl_graph directlySeparated 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
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()
+is_labelled()
Added @csteglich ’s read_ucinet()
and write_ucinet()
functions
Added @csteglich ’s read_ucinet()
and write_ucinet()
functions
read_ucinet()
offers a file-picker when file path unknownread_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 alternativesread_ucinet()
now imports to an igraph-class object by default, with an argument to allow other alternatives
write_ucinet()
works with all migraph-compatible objectswrite_ucinet()
works with all migraph-compatible objects
Updated mpn_bristol
documentation
create_
documentationRenamed sample_affiliation()
to generate_random()
generate_random()
to be able to generate random one- or two-mode networksRenamed sample_affiliation()
to generate_random()
generate_random()
to be able to generate random one- or two-mode networksas_network()
to coerce objects into network classas_network()
to coerce objects into network classas_matrix()
function to coerce objects into an adjacency or incidence matrix classas_matrix()
function to coerce objects into an adjacency or incidence matrix classas_igraph()
function to coerce objects into an igraph graph classas_igraph()
function to coerce objects into an igraph graph class
as_tidygraph()
function to coerce objects into an tidygraph tbl_graph classis_twomode()
function to check whether network is two-mode on all object typesas_tidygraph()
function to coerce objects into an tidygraph tbl_graph class
+is_twomode()
function to check whether network is two-mode on all object typescentrality_degree()
renamed to node_degree()
centrality_closeness()
renamed to node_closeness()
+centrality_closeness()
renamed to node_closeness()
centrality_betweenness()
renamed to node_betweenness()
+centrality_betweenness()
renamed to node_betweenness()
node_eigenvector()
+node_eigenvector()
Re-added node_constraint()
for calculating Burt’s constraint measure for one- and two-mode networks
Tests of network distributions
Tests of network measures
One-mode EU policy influence network, June 2004 (Christopoulos 2006)
Two-mode 112th Congress Senate Voting (Knoke et al. 2021)
#> # A labelled, two-mode network with 264 nodes and 1496 ties
-#> # A tibble: 264 x 3
-#> name type lvl
-#> <chr> <lgl> <dbl>
-#> 1 101 FALSE 1
-#> 2 102 FALSE 1
-#> 3 103 FALSE 1
-#> 4 104 FALSE 1
-#> 5 105 FALSE 1
-#> 6 106 FALSE 1
-#> # i 258 more rows
-#> # A tibble: 1,496 x 2
-#> from to
-#> <int> <int>
-#> 1 36 151
-#> 2 40 151
-#> 3 73 151
-#> 4 94 151
-#> 5 138 151
-#> 6 145 151
-#> # i 1,490 more rows
#> # Bristol protest event network
+#> # A labelled, two-mode network of 150 individuals and 1496 affiliation and
+#> participation ties
+#> # A tibble: 264 x 3
+#> name type lvl
+#> <chr> <lgl> <dbl>
+#> 1 101 FALSE 1
+#> 2 102 FALSE 1
+#> 3 103 FALSE 1
+#> 4 104 FALSE 1
+#> 5 105 FALSE 1
+#> 6 106 FALSE 1
+#> # i 258 more rows
+#> # A tibble: 1,496 x 2
+#> from to
+#> <int> <int>
+#> 1 36 151
+#> 2 40 151
+#> 3 73 151
+#> 4 94 151
+#> 5 138 151
+#> 6 145 151
+#> # i 1,490 more rows
#> # A labelled, weighted, directed network with 116 nodes and 11489 arcs
+ #> # A labelled, weighted, directed network of 116 nodes and 11489 arcs
#> # A tibble: 116 x 1
#> name
#> <chr>
@@ -93,7 +93,7 @@ Format
#> 5 1 6 2188.
#> 6 1 7 123677
#> # i 11,483 more rows
-
diff --git a/reference/mpn_elite_mex.html b/reference/mpn_elite_mex.html
index 0c964152..7cd75155 100644
--- a/reference/mpn_elite_mex.html
+++ b/reference/mpn_elite_mex.html
@@ -31,7 +31,7 @@
#> # A labelled, undirected network with 35 nodes and 117 ties
-#> # A tibble: 35 x 8
-#> name full_name entry_year military in_mpn PlaceOfBirth state region
-#> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <dbl>
-#> 1 Trevino Trevino, Jacint~ 1910 1 0 Guerrero Coah~ 1
-#> 2 Madero Madero, Francis~ 1911 0 0 Parras de l~ Coah~ 1
-#> 3 Carranza Carranza, Venus~ 1913 1 0 Cuatro Cien~ Coah~ 1
-#> 4 Aguilar Aguilar, Candido 1918 1 0 Cordoba Vera~ 3
-#> 5 Obregon Obregon, Alvaro 1920 1 0 Siquisiva, ~ Sono~ 1
-#> 6 Calles Calles, Plutarc~ 1924 1 0 Guaymas Sono~ 1
-#> # i 29 more rows
-#> # A tibble: 117 x 2
-#> from to
-#> <int> <int>
-#> 1 2 3
-#> 2 2 5
-#> 3 2 6
-#> 4 2 4
-#> 5 1 2
-#> 6 2 8
-#> # i 111 more rows
#> # Mexican power elite network
+#> # A labelled, undirected network of 35 elites and 117 common belonging or
+#> interest ties
+#> # A tibble: 35 x 8
+#> name full_name entry_year military in_mpn PlaceOfBirth state region
+#> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <dbl>
+#> 1 Trevino Trevino, Jacint~ 1910 1 0 Guerrero Coah~ 1
+#> 2 Madero Madero, Francis~ 1911 0 0 Parras de l~ Coah~ 1
+#> 3 Carranza Carranza, Venus~ 1913 1 0 Cuatro Cien~ Coah~ 1
+#> 4 Aguilar Aguilar, Candido 1918 1 0 Cordoba Vera~ 3
+#> 5 Obregon Obregon, Alvaro 1920 1 0 Siquisiva, ~ Sono~ 1
+#> 6 Calles Calles, Plutarc~ 1924 1 0 Guaymas Sono~ 1
+#> # i 29 more rows
+#> # A tibble: 117 x 2
+#> from to
+#> <int> <int>
+#> 1 2 3
+#> 2 2 5
+#> 3 2 6
+#> 4 2 4
+#> 5 1 2
+#> 6 2 8
+#> # i 111 more rows
#> # A labelled, two-mode network with 34 nodes and 46 ties
+ #> # A labelled, two-mode network of 34 nodes and 46 ties
#> # A tibble: 34 x 2
#> type name
#> <lgl> <chr>
@@ -115,7 +115,7 @@ Format
#> 5 2 23
#> 6 2 27
#> # i 40 more rows
-#> # A labelled, two-mode network with 38 nodes and 103 ties
+#> # A labelled, two-mode network of 38 nodes and 103 ties
#> # A tibble: 38 x 2
#> type name
#> <lgl> <chr>
@@ -144,7 +144,7 @@ Details
References
Domhoff, G William. 2016. “Who Rules America? Power Elite Database.”
-The Center for Responsive Politics. 2019. “OpenSecrets.”
+The Center for Responsive Politics. 2019. “OpenSecrets.” https://www.opensecrets.org.
Knoke, David, Mario Diani, James Hollway, and Dimitris C Christopoulos. 2021.
Multimodal Political Networks.
Cambridge University Press. Cambridge University Press.
@@ -162,7 +162,7 @@ References
diff --git a/reference/mpn_evs.html b/reference/mpn_evs.html
index aa8b6fc9..99c5c8b6 100644
--- a/reference/mpn_evs.html
+++ b/reference/mpn_evs.html
@@ -21,7 +21,7 @@
@@ -166,7 +166,7 @@ References
#> # A labelled, weighted, directed network with 20 nodes and 177 arcs
-#> # A tibble: 20 x 1
-#> name
-#> <chr>
-#> 1 1 AER
-#> 2 2 AER
-#> 3 5 AER/COR
-#> 4 7 RYANAIR
-#> 5 8 DG TRANSPORT
-#> 6 9 COR
-#> # i 14 more rows
-#> # A tibble: 177 x 3
-#> from to weight
-#> <int> <int> <dbl>
-#> 1 1 2 1
-#> 2 1 3 1
-#> 3 1 4 1
-#> 4 1 5 1
-#> 5 1 6 1
-#> 6 1 7 1
-#> # i 171 more rows
#> # EU policy influence network
+#> # A labelled, weighted, directed network of 20 policy actors and 177
+#> interaction arcs
+#> # A tibble: 20 x 1
+#> name
+#> <chr>
+#> 1 1 AER
+#> 2 2 AER
+#> 3 5 AER/COR
+#> 4 7 RYANAIR
+#> 5 8 DG TRANSPORT
+#> 6 9 COR
+#> # i 14 more rows
+#> # A tibble: 177 x 3
+#> from to weight
+#> <int> <int> <dbl>
+#> 1 1 2 1
+#> 2 1 3 1
+#> 3 1 4 1
+#> 4 1 5 1
+#> 5 1 6 1
+#> 6 1 7 1
+#> # i 171 more rows
#> # A labelled, weighted, two-mode network with 114 nodes and 2791 ties
+ #> # A labelled, weighted, two-mode network of 114 nodes and 2791 ties
#> # A tibble: 114 x 2
#> type name
#> <lgl> <chr>
@@ -127,7 +127,7 @@ Format
#> 5 1 56 1
#> 6 1 57 1
#> # i 2,785 more rows
-#> # A labelled, weighted, two-mode network with 134 nodes and 3675 ties
+
-
diff --git a/reference/mpn_DemSxP.html b/reference/mpn_senate_dem.html
similarity index 100%
rename from reference/mpn_DemSxP.html
rename to reference/mpn_senate_dem.html
diff --git a/reference/mpn_OverSxP.html b/reference/mpn_senate_over.html
similarity index 100%
rename from reference/mpn_OverSxP.html
rename to reference/mpn_senate_over.html
diff --git a/reference/mpn_RepSxP.html b/reference/mpn_senate_rep.html
similarity index 100%
rename from reference/mpn_RepSxP.html
rename to reference/mpn_senate_rep.html
diff --git a/reference/reexports.html b/reference/reexports.html
index 80285406..67a695cd 100644
--- a/reference/reexports.html
+++ b/reference/reexports.html
@@ -32,7 +32,7 @@
@@ -106,7 +106,7 @@ Objects exported from other packages
{j?7x4Q0s^sez9L$>A4=Gxx?L<48QBPQ)`
zQr?kP+8!q75XrZ0z$d`U%E}m~mnan{1*)fbVN_CB&IGU&U$jH7VgCN8OdSbwjK#?{
zs>C^CY)A)5MM=2UXPj;5)@PkOyHl?>r5~;1#lCpPajf-^Ejqgv^bXovHw|Vrl?Zu0
z3*xu_$%;?)V0T&Qx8mrfVJ2OT&w)$W3&zv5J#p!%1>?N@c57GfACr0OBJF(*tvZBNSiZE
z+X3b)tO0EmCpe}2)cWuZ3Tin-sETq53OcHTW}c;{YJD
Hfr(E)D?y(K)k6}%w#K&9h1BwxnHmF4JYc0+N
z)zl5I3n>+MJDiYHmBwnz&jKcr;7}86jz70EA+_5fAUvk-T|4JodfBu@eqCIhUDQ+q
zc<+%NhEnh?qg>*eewQz({q6`WZfZ{kM