Site built with pkgdown 2.1.0.
+Site built with pkgdown 2.1.1.
diff --git a/LICENSE-text.html b/LICENSE-text.html index 85e4aba4..8fd189ba 100644 --- a/LICENSE-text.html +++ b/LICENSE-text.html @@ -17,7 +17,7 @@ @@ -77,7 +77,7 @@2024-11-06
+test_configuration()
+mpn_bristol with infompn_ryanair with infompn_elite_mex with infompn_elite_usa
+mpn_senate
+mpn_cow
+alter(Discipline) from net_regression() call2024-09-03
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() syntaxtest_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()
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)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 networksplay_*() 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 networksnode_adoption_time()
+node_adoption_time()
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 diffusionsplay_diffusion()node_is_exposed() and node_exposure() internallyplay_diffusion()node_is_exposed() and node_exposure() internallyplay_diffusion()
+play_diffusion()
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.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.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.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.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.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().play_diffusion().play_diffusion().network_upperbound()node_eccentricity(), which wraps igraph::eccentricity()
+node_eccentricity(), which wraps igraph::eccentricity()
node_neighbours_degree(), which wraps igraph::knn()
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 overlaynode_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)test_random() and test_permutation() rely on graph dimensions rather than graph order (thank you finding this @rabenton)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()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()
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)as_tidygraph() for merging nodelists and edgelistsas_siena()as_tidygraph() for merging nodelists and edgelistsas_siena()read_pajek() where multiple networks/ties were causing an issue for partition assignmentread_pajek() where multiple networks/ties were causing an issue for partition assignmentplay_diffusion() relating to latency inversionplay_diffusion() relating to latency inversionis_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 minimanode_richness() for calculating the richness (a common diversity measure) of an attribute in a network
play_diffusion() to include more compartment and transition optionsplay_diffusion() to include more compartment and transition optionsplay_diffusions() for running a diffusion model multiple timesplay_learning() for running a DeGroot learning modelplay_learning() for running a DeGroot learning modelplay_diffusion() modelplay_diffusion() modelgenerate_random()
+generate_random()
to_mode1() and to_mode2())to_mode1() and to_mode2())to_redirected.igraph() where routing through an edgelist caused problemsis_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 pathto_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 networkis_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 corenode_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)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” specifiednode_mode() couldn’t produce a “mark” class objectis_twomode() where labelling information was being ignoredis_twomode() where labelling information was being ignorednode_core() for partitioning nodes into core and periphery membershipsk_strict()
+k_strict()
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()
cluster_concor() and cluster_hierarchical() are now exportedcluster_concor() and cluster_hierarchical() are now exportednode_reach() for calculating reach centrality (closed #196)node_reach() for calculating reach centrality (closed #196){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() respectivelycreate_ and generate_ functions now:n passed an existing networkcreate_tree(), generate_smallworld(), generate_scalefree()
+create_tree(), generate_smallworld(), generate_scalefree()
generate_random() now inherits attributes from any networkgenerate_random() now inherits attributes from any network
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 networknode_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
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 haveto_edges() to be ~26 times faster on averagenode_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 graphdirected 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
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 networkadd_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()
read_nodelist() and read_edgelist()
+read_nodelist() and read_edgelist()
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() documentationread_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 dependencyas_network(), as_igraph(), and is_directed()
+as_network(), as_igraph(), and is_directed()
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)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 weightsison_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 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 edgesgenerate_permutation() and thus test_permutation()
+generate_permutation() and thus test_permutation()
netlm() to network_reg() to avoid frustrating conflictsnetwork_reg() now accepts migraph-consistent objectsgenerate_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 densityas_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
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 currentlyas_matrix() where availableas_matrix() where availablegraph_equivalency() into the same for two-mode networks and graph_congruency()for three-mode (two two-mode) networksas_igraph()
+as_igraph()
to_uniplex() was not returning a weighted graphto_uniplex() was not returning a weighted graphis_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
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 networksbrandes dataset for teaching centrality measuresadolescent_society dataset for teaching friendship paradoxread_edgelist() for importing Excel-created edgelists directlyread_edgelist() for importing Excel-created edgelists directlyto_undirected() for symmetrising networks of all typesto_undirected() for symmetrising networks of all typesto_ functions S3 methodsas_ 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 classas_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
A manynet-consistent network.
-See e.g. manynet::as_tidygraph() for more details.
manynet::as_tidygraph() for more details.
A diff_model object is returned by
-play_diffusion() or as_diffusion() and contains
+play_diffusion() or as_diffusion() and contains
a single empirical or simulated diffusion.
A diff_models object is returned by
-play_diffusions() and contains a series of diffusion simulations.
play_diffusions() and contains a series of diffusion simulations.
A manynet-consistent network.
-See e.g. manynet::as_tidygraph() for more details.
manynet::as_tidygraph() for more details.
marvel_friends <- to_unsigned(ison_marvel_relationships)
-marvel_friends <- to_giant(marvel_friends) %>%
+ marvel_friends <- to_unsigned(ison_marvel_relationships)
+marvel_friends <- to_giant(marvel_friends) %>%
to_subgraph(PowerOrigin == "Human")
# (cugtest <- test_random(marvel_friends, manynet::net_heterophily, attribute = "Attractive",
# times = 200))
@@ -174,7 +183,7 @@ Examples