NEWS.md
add_info()
add_info(.data) (so no further arguments) now checks to see whether metadata may be added and prompts the user to add it if soadd_info(.data, optional = TRUE) extends this to further, optional metadataexpect_nodes()/expect_ties() and introduces internal active-context helpersas_diffusion.igraph() to derive diffusion events from changelistsas_globallist() for extracting global variables from ‘igraph’ and ‘stocnet’ objectsas_stocnet.data.frame() for coercing edgelists to stocnet objects (closes #138)make_stocnet() construction, indexing, and validation of nodes, ties, changes, info, and globalsas_stocnet() coercion for data.frame and tbl_graph inputsmake_stocnet() aborts early and informatively when node labels do not match (thanks @auzaheta)add_tie_attribute()
bind_nodes() for adding nodes and ties from another networkarrange_changes() and arrange_nodes() as generics for reordering nodes and ties in a networkgather_changes() as a generic for gathering changes up to a time pointrename_changes() for renaming change variables and rename_globals() for renaming global variables
rename_*() methods to rename variables when no arguments givenselect_ties(), select_changes(), and select_globals() for selecting variables
select_*() methods to reorder variables when no arguments givenfilter_nodes(), filter_ties(), and filter_changes() as generics for filtering nodes, ties, and changes in a networksummarise_ties() for summarising tie variablesjoin_nodes.stocnet() for joining a nodelist to a stocnet objectarrange_ties(), bind_ties(), mutate_nodes(), mutate_ties(), and rename_nodes()
add_changes() to bind_changes.tbl_graph()
bind_changes.stocnet() for binding on groups of changes to a stocnet objectfrom_ties() to accept named arguments instead of a named list, improving consistency with other modifying functions
from_ties.stocnet() for creating a multiplex stocnet object from more than one network, including handling of tie types as layers and carrying forward nodal attributesstocnet objects so ties and changes remain alignedjoin_nodes() for joining two nodelistsmutate_globals() for mutating global variables in a networkto_*() default methods to coerce through supported graph classes and then restore original classesto_subgraph()
is_cognitive() for identifying cognitive social structure networksis_egonet() for identifying egocentric networksis_*() handling across stocnet, igraph, and tbl_graph inputsis_*() methods to return logical values reliably after coercionis_dynamic.stocnet() to look for more time-type variablesnet_nodes(), net_node_attributes(), and net_tie_attributes() coverage for table and network-like inputsnet_nodes.stocnet() and net_dims.stocnet() to work with unlabelled networks|>), the dplyr R dependence anywayrun_tute(), extract_tute(), and tutorial 0 to migraph
create_ego() to collect_ego() to better signal the different input structure expectedread_cran() and read_pkg() to collect_cran() and collect_pkg() for consistency with other collecting functionscreate_cycle() and create_wheel() (undirected by default)generate_permutation() as no longer necessaryplay_segregation() to be less verbosestocnet class for representing multilevel, multiplex, multimodal, signed, dynamic, or longitudinal networks as a structured list of tibbles and metadata
make_stocnet() for straightforward construction of stocnet objectsvalidate_stocnet() for checking and suggesting improvements to stocnet structureas_stocnet.igraph() for converting igraph objects to stocnet, including converting tie types to layersvalue class to hold and print values of different classes held in a list vectoras_matrix.igraph() for multiplex networksas_matrix.network() and as_igraph.network() bugs related to inheriting integer namesdiffnet coercion so that netdiffuseR is no longer requiredcollect_changes() to gather_changes()
add_info() so that if no further arguments are given it checks to see whether metadata may be added
add_info.stocnet() for adding metadata to stocnet objectsmutate_net() to mutate_info() for consistencynet_info() to as_infolist() for claritynetwork method for add_node_attribute()
join_nodes() to work with unlabelled networksto_ties.igraph() for unlabelled networksto_mode1() and to_mode2() to work with unlabelled networksto_no_missing() rlang data pronoun issueto_tree() to use updated igraph function namefrom_ties() to be quieter when joiningseq_nodes() and seq_ties() for indexing network elements, exported for use in other packagesnet_modes() and net_layers() for describing network structurenet_nodes.matrix() for extracting node count from matrix objectsnet_node_attributes() and net_tie_attributes() into generics with stocnet methodsnet_node_names() and net_tie_names() to mode_names() and layer_names() respectively for consistency with net_modes() and net_layers()
mode_names() and layer_names() to look for older grand-nested variable names where necessarynet_nodes(), net_dims(), net_ties(), net_name(), mode_names(), and layer_names()
network method for net_name()
is_egonet() to return correct value (FALSE) for non-netlistsis_signed() to no longer expect integersdiff_model method for is_changing()
is_manynet() now recognises the stocnet classstocnet methods for is_labelled(), is_signed(), is_complex(), is_multiplex(), is_changing(), is_longitudinal(), is_dynamic(), is_twomode(), and is_directed()
stopifnot(is.scalar(ties)) in generate_citations()
generate_citations() to be clear that it doesn’t yet work on two-mode networksdescribe_network() to include “network” as a suffixnet_attributes() for returning the names of graph attributesnet_info() for returning the list of graph attributesnet_waves() to now return waves = 1 for cross-sectional networksnode_is_isolate() to work with signed networksnode_is_pendant() to work with signed networksexpect_nodes() and expect_ties() for use in other packagesnet_name() for obtaining network name if availablenet_node_names() and net_tie_names() for network element informationwrite_graphml() to coerce to igraph firstto_mode1() and to_mode2() to carry forward nodal and tie attributes during projectionto_mode1() and to_mode2() to drop unnecessary nodal informationto_uniplex() to handle multimodal multiplex filtering correctly and retain tie type informationadd_changes() to work with tbl_graphfilter_nodes() to use dplyr::all_of()
interpolate() helper for injecting missing datanet_by_mixed() to work with multiplex networksprint_all()
delete_changes() for deleting all changes from a changing networkto_waves() to work with networks that are changing, longitudinal, or bothas_diffusion() to not trim off final wave in the reportas_diffusion() to return correct E and R compartments for waning diffusion modelsis_longitudinal() to only check for tie waves and not nodal changesnet_diversity() and node_diversity()
over_membership()
net_diversity() and node_diversity() to use and declare methods appropriate for the vector of attributesnet_homophily() and node_homophily() for measuring homophily according to different methods including
node_is_latent(), node_is_infected(), node_is_recoverd() to work with changing networksnode_is_exposed() to work with changing networks and now accepts a time argumentto_directed() and to_redirected() in the data tutorialgenerate_configuration() methods since igraph >= v2.1.0 requiredgenerate_configuration() to use recent versions of igraph::sample_degseq() correctlyread_gdf() for importing GDF filesjoin_nodes() not recognising nodelists correctlynode_randomwalk() closeness centralitynode_subgraph() centralityfict_actually to be a multiplex network (thanks @korakotbua)read_pkg() for creating a network of the function interdependencies within a packagenode_coreness() where data wasn’t recognised within pipesnet_by_hierarchy() for characterising networks by their graph theoretic dimensions of hierarchyison_emotions
irps_nuclear
describe_changes() to work with time as well as begin/end tie attributescreate_windmill()
create_wheel()
create_cycle()
to_uniplex() test so that it doesn’t rely on random samplingis_multiplex() to recognise more tie attributesto_no_missing() to record missing removalto_no_isolates() to record isolate removalto_giant() to record giant component scopingto_matching() to record matchingto_matching() triggering warnings from handling NAs (thanks @schochastics for fixing #109)to_mentoring() to record mentoringto_eulerian() to record Eulerian pathingnet_core() with method options for calculating correlation, distance, ndiff, and diffnode_coreness() to implement Borgatti and Everett’s continuous coreness algorithmnode_coreness() to node_kcoreness()
graphr(), graphs(), and grapht() to autograph
to_mode1() and to_mode2() to record projection changeto_no_missing() for removing nodes with missing dataas_diffusion.mnet() so that it includes “diff_model” classnode_richness() documentationtable_data() so that it lists components, longitudinal, dynamic, and changing datafict_lotr
irps_revere dataplay_learning()
play_diffusion() for e.g. SID modelscreate_ego()
snet_prompt() for easier to read consolessnet_minor_info() for reminding users to assign the functionto_matching() to work on the more general class of stable matching problems, including where nodes have different capacitiesprint.mnet() to print sections more prettily and accommodate changes when presentprint_all() for printing infinite rowsplay_diffusions() to migraph (but not play_diffusion())play_diffusion() to return an ‘mnet’ class object with changes instead of a ‘diff_model’ object with hidden network
old_version = TRUE
write_*() functions by printing picked pathnames to the consoleas_changelist() for extracting changelists from ‘mnet’ objectsas_diffusion.mnet() for maintaining backward compatibility for diffusion measuresas_tidygraph.networkDynamic() and as_igraph.networkDynamic() for coercing ‘networkDynamic’ objects into ‘mnet’, ‘tidygraph’, and ‘igraph’ objectsadd_changes() for adding changes to a network, including checks and imputing activity and susceptibilitymutate_changes() to mutate changesselect_changes() to select change variablesfilter_changes() to filter changescollect_changes() to collect changes up to a time pointapply_changes() to apply collected changes to a time pointto_time() for scoping a longitudinal network to a time point, including nodal and/or tie changesselect_nodes() for selecting only some nodal variablesmutate_net() for working with network informationis_changing() for identifying networks with a change componentnode_is_mean() for identifying typical nodesnode_is_*() diffusion marks to work with new .data outputnet_strength() for measuring the number of ties that would need to be removed from a network to increase its number of componentsnet_toughness() for measuring the number of nodes that would need to be removed from a network to increase its number of componentsover_*() to migraph
node_*() diffusion measures to work with new .data outputnet_modularity() by informing users when a bipartition is usednet_waves() for measuring the number of waves in a networknet_hazard() to net_by_hazard()
node_by_*() diffusion motifs to work with new .data outputfict_potter with composition changesison_starwars to fict_starwars, updating it with composition changes and additional codingirps_911 network data on 9/11 hijackers and associatesnode_in_community()
tie_is_bridge() and node_bridges()
to_waves() where result was unorderedgraphr() now uses categorical palettes (effectively closing #60)net_correlation() for calculating the product-moment correlation between networksis_aperiodic() where it would not work in tutorial chunkscreate_ego(), create_empty(), create_filled(), create_ring(), create_star(), create_lattice())as_matrix() handles signed networksas_nodelist() for extracting nodelists from networks into tibblesto_cosine()
to_galois() until it can be refactoredto_signed() for adding signs to networksto_weighted() for adding weights to networksgraphr() where line types were inferred incorrectlygraphr() so that layouts can now be snapped to a grid, mileage may varynode_is_pendant() for identifying pendant nodesnode_is_neighbor() for identifying adjacent nodestie_is_imbalanced() for identifying ties in imbalanced configurationssummary.network_measure() to return z-scores and p-values for measuresnode_vitality() for measuring closeness vitality centralitynode_eigenvector()
node_in_community() which runs through most salient community detection algorithms to find and return the one with the highest modularitynode_in_regular() to inform user which census is being usednode_by_quad() to node_by_tetrad() to be more consistent with Greek origins
summary.network_motif() which returns the z-scores for the motif scores based on random or configurational networks, traces progressplot.network_motif() where motif names were not identified correctly, internal make_network_motif now inherits call informationcluster_cosine() for another equivalence optionrun_tute() fuzzy matched so that insertions are not as costlygloss(), clear_glossary(), and print_glossary() for adding glossaries to tutorialsirps_wwi, a dynamic, signed networkison_blogs to irps_blogs, added infoison_books to irps_books, added infoison_usstates to irps_usgeo, added infoison_friends to fict_friends, added info and fixed directed issueison_greys to fict_greys, added infoison_lotr to fict_lotr, added infoison_thrones to fict_thrones, added info and some additional nodal attributesison_potter to fict_potter, added info and combined waves into single objectgraphr() where user not informed about concaveman dependencygraphr() examplesto_dominating() for extracting the dominating tree of a given networkgraphr() to make function more concise and consistent (thanks @henriquesposito)
node_eccentricity() to allow normalisation, appear in closeness documentationnode_stress() as a new betweenness-like centrality measurenode_leverage() as a new degree-like centrality measureread_*() now print the command used to the console if the (default) file.choose() is usedread_gml()
node_power()
options(manynet_verbosity ="quiet")
create_ego() for collecting ego networks through interviews, including arguments for:
create_motifs() for creating networks that correspond to the isomorphic subgraphs of certain size and formatprint.mnet()
add_info() for adding grand info to tidygraph objects
to_unweighted() so that it passes through unweighted networks correctlyset_manynet_theme() to set theme (re #60), but not yet fully implementedis_multiplex() to ignore “name” tie attributesnode_authority() and node_hub() centrality measuresnode_equivalency() for calculating four-cycle closure by nodenet_equivalency() to one-mode networkscreate_motifs()
node_by_dyad() for node level dyad censusnet_by_quad() for network level quad censusnode_by_quad() to avoid oaqc dependency (#89), more flexible but slowerprint.node_motif() to convert to tibble and add modes and names where available only upon print
suppressPackageStartupMessages()
stop() replaced by cli::cli_abort()
{grDevices} and png dependenciesrun_tute() and extract_tute() to look for installed packages and report progressread_cran() for creating networks of package dependencies on CRAN
generate_man() for generating dyad census conditional uniform graphsgenerate_islands() only takes a single integer and not a vectoras_tidygraph() to add an additional class ‘mnet’ that is used for prettier printing
make_mnet() (internally) for future-proofingprint.tbl_graph() renamed to print.mnet()
print.mnet() uses ‘grand’ data if availablebind_ties() to be more flexible about the input it accepts, converting all input into the required edgelistto_ego() for obtaining a single neighbourhooddelete_nodes() and delete_ties()
add_ties() and delete_ties() in documentationto_unnamed.igraph() when used with already unlabelled networkstie_is_path() for tracing the ties on a particular pathtie_is_triplet() for returning all the ties that are members of transitive tripletstie_is_forbidden() for identifying ties in forbidden triadstie_is_transitive() efficiency, now only retrieves the edgelist onceis_aperiodic() to remove minMSE dependency and offer a progress bar if it takes longer than 2 secondstie_is_triangular() to do with altpath namingnode_distance() for measuring the distance from or to particular nodesnode_degree() processed isolates in calculating strength in weighted networksison_ data with new as_tidygraph()
ison_adolescents as a testison_thrones on kinship arcs between Game of Thrones characters, with ‘grand’ dataison_monastery_ data into ison_monks, a single multiplex, signed, weighted, longitudinal networkcreate_degree() for creating networks of a given degree sequence, including k-regular graphsgenerate_citations() for citation modelsgenerate_fire() for forest-fire modelsgenerate_islands() for island modelscreate_explicit() now has its own documentationtie_is_triangular() for identifying ties in trianglestie_is_cyclical() for identifying ties in cyclestie_is_transitive() for identifying ties involved in transitive closuretie_is_simmelian() for identifying Simmelian tiesgenerate_permutation() renamed to to_permuted()
graphr() plots edges in directed networkstable_data() can now report on data from multiple packages
table_data() can now filter by any reported formats, such as ‘directed’ or ‘twomode’as_matrix.igraph() now only draws from the “weight” attribute and not, e.g. “type”to_blocks() related to categorical membership variablesmutate_ties()
node_names() now returns names of the form “N01” etc for unlabelled networksplot.matrix() works for unlabelled networksgraphr()
graphr()
graphr() functiongraphs() automatically uses “star” layout to plot ego networksgraphr(), graphs(), and grapht() also accept British spellingsgraphs() and grapht()
to_subgraphs() no longer sampled{minMse} dependencynetwork_* prefix to net_* for concisenesscreate_core() where the membership inferred when passing an existing network was incorrectgenerate_configuration() for generating configuration models (including for two-mode networks)play_diffusion() now includes an explicit contact argument to control the basis of exposurenode_is_*() functions now infer network data contextnode_is_independent() for identifying nodes among largest independent setsis_multiplex() now excludes reserved tie attribute names other than type, such as “weight”, “sign”, or “wave”is_attributed() to check for non-name nodal attributesnode_is_latent(), node_is_recovered(), and node_is_infected() (closes #71)is_twomode(), is_labelled(), and is_complex()
graphr(), graphs(), and grapht() (autographr(), autographs(), and autographd() are now deprecated)scale_size(range = c(...,...)) to be usedscale_size() from ggplot2
graphr() now rescales node size depending on network size (closes #51)to_named() now randomly generates and adds an alphabetic sequence of names, where previously this was just a random sample, which may assist pedagogical use
to_correlation() that implements pairwise correlation on networkarrange_ties() for dplyr-like reordering of ties based on some attributeto_correlation() for calculating the Pearson correlation
as_diff_model() where events were out of order and namedis_multiplex() now recognises “date”, “begin”, and “end” as reservednode_degree(), node_deg(), node_indegree(), node_outdegree(), node_multidegree(), node_posneg(), tie_degree(), net_degree(), net_indegree(), and net_outdegree()
node_betweenness(), node_induced(), node_flow(), tie_betweenness(), and net_betweenness()
node_closeness(), node_reach(), node_harmonic(), node_information(), tie_closeness(), net_closeness(), net_reach(), and net_harmonic()
node_eigenvector(), node_power(), node_alpha(), node_pagerank(), tie_eigenvector(), and net_eigenvector()
net_reciprocity(), node_reciprocity(), net_transitivity(), node_transitivity(), net_equivalency(), and net_congruency()
net_density(), net_components(), net_cohesion(),net_adhesion(), net_diameter(), net_length(), and net_independence()
net_transmissibility(), net_recovery(), net_reproduction(), net_immunity(), net_hazard(), net_infection_complete(), net_infection_total(), net_infection_peak(), node_adoption_time(), node_thresholds(), node_recovery(), and node_exposure()
net_richness(), node_richness(), net_diversity(), node_diversity(), net_heterophily(), node_heterophily(), net_assortativity(), and net_spatial()
net_reciprocity(), net_connectedness(), net_efficiency(), and net_upperbound()
node_bridges(), node_redundancy(), node_effsize(),node_efficiency(), node_constraint(), node_hierarchy(), node_eccentricity(), node_neighbours_degree(), and tie_cohesion()
net_core(), net_richclub(), net_factions(), node_partition(), net_modularity(), net_smallworld(), net_scalefree(), net_balance(), net_change(), and net_stability()
node_mode() (deprecated) to node_is_mode() since it returns a logical vectornode_attribute() and tie_attribute() to return measures when the output is numericnode_exposure() to work with two-mode and signed networksnode_constraint() to work with weighted two-mode networks, thanks to Toshitaka Izumi for spotting thismutate() without specifying .data
net_independence() for calculating the number of nodes in the largest independent setnode_coreness() now returns ‘node_measure’ outputnode_exposure() now sums tie weights where passed a weighted networknode_in_roulette() (previously node_roulette())node_in_optimal(), node_in_partition() (previously node_kernaghinlin()), node_in_infomap(), node_in_spinglass(), node_in_fluid(), node_in_louvain(), node_in_leiden(), node_in_betweenness(), node_in_greedy(), node_in_eigen(), and node_in_walktrap()
node_in_component(), node_in_weak(), and node_in_strong() (NB: node_in_component() is no longer phrased in the plural)node_is_core() and node_coreness()
node_in_adopter()
node_in_equivalence(), node_in_structural(), node_in_regular(), and node_in_automorphic()
node_*(), but including the preposition _in_ is more consistent.node_member class is now categorical
make_node_member() now converts numeric results to LETTER character resultsprint.node_member() now works with categorical membership vectorsprint.node_member() now declares how many groups before reporting the vectorsmutate() without specifying .data
node_by_tie(), node_by_triad(), node_by_quad(), node_by_path(), net_by_dyad(), net_by_triad(), net_by_mixed(), node_by_brokerage(), net_by_brokerage()
*_*_census(), but the preposition _by_ is more consistent.node_tie_census() now works on longitudinal network dataprint.node_motif() wasn’t printing the requested number of linescluster_hierarchical() and cluster_concor()
k_strict(), k_elbow(), and k_silhouette()
cluster_concor()
cluster_concor() now uses to_correlation() for initial correlationstats::cor() for subsequent iterationscluster_concor() handles unlabelled networkscluster_concor() handles two-mode networkscluster_concor() cutoff resulted in unsplit groupscluster_hierarchical() now also uses to_correlation()
ison_greys dataset, including some corrections to that published in {networkdata}
ison_friends dataset to be explicitly longitudinalison_usstates dataset with population data (Alaska and Hawaii missing)ison_southern_women dataset with surnames, titles, event dates, and corrected ties2024-03-13
autographr() examples that were taking too long to runautographr(), autographs(), and autographd() functionsautographr(), autographs(), and autographd() functionsnode_is_infected(), node_is_recovery(), node_is_latent() work for network lists2024-03-12
play_diffusions() to revert future plan on exitgenerate_random() works for two-mode networks with specified number of tiesautographr() more flexible and efficient in setting variables to aesthetics2023-12-24
run_tute()
2023-12-24
pkg_data() to report an overview of data contained within the package(s)play_diffusion() and play_diffusions() from migraph
play_learning() and play_segregation() from migraph
create_tree() where it was not returning a two-mode network correctlyas_diffusion() to coerce a table of diffusion events into diff_model class
as_*() functions are now considered modificationsmutate_nodes()
filter_nodes()
rename_nodes()
bind_ties()
delete_ties()
to_tree() to find one or more spanning trees amongst a network’s tiesfrom_ties() to collect multiple networks into a multiplex networkis.igraph() to is_igraph() for igraph v2.0.0is_list() for identifying a list of networksnode_is_core(), node_is_cutpoint(), node_is_fold(), node_is_isolate(), node_is_mentor() from migraph
node_is_exposed(), node_is_infected(), node_is_latent(), node_is_recovered() from migraph
node_is_max(), node_is_min(), node_is_random() from migraph
tie_is_bridge(), tie_is_loop(), tie_is_multiple(), tie_is_reciprocated() from migraph
tie_is_feedback()
tie_is_max(), tie_is_min() from migraph
tie_is_random()
scale_edge_color_centres(), scale_edge_color_ethz(), scale_edge_color_iheid(), scale_edge_color_rug(), scale_edge_color_sdgs()
autographr() now provides legends by default where multiple colours are used (closes #52)autographs() now labels legends correctly for binary variables (closes #38)autographs() now graphs just the first and last networks in a list (closes #45)autographs() now includes an option whether the layout should be based on the first, last, or both of two networks (closes #48)ison_konigsberg to ison_koenigsberg and named the bridgesison_algebra now in long multiplex formatison_karateka now weighted, anonymous members are named by number, and “obc” variable renamed “allegiance”ison_lawfirm enlarged from 36 to 71 nodes and now consists of three multiplex, directed networksison_southern_women names are now title caseison_hightech, a multiplex, directed network from Krackhardt 1987ison_monastery datasets, three of which are signed and weighted, and the other is longitudinal, from Sampson 1969 (closes #49)ison_potter datasets in a list of networks, from Bossaert and Meidert 2013 (closes #47)ison_usstates data on the contiguity of US states, from Meghanathan 20172023-12-17
to_waves.diff_model() now adds three logical vectors as variables, “Infected”, “Exposed”, and “Recovered”
node_is_latent(), node_is_infected(), and node_is_recovered()
autographr() now shapes seed, adopter, and non-adopter nodes using a parallel to migraph’s node_adoption_time() for
autographs() now colors susceptible, exposed, infected, and recovered nodes correctlyautographd() now colors susceptible, exposed, infected, and recovered nodes correctly2023-12-15
as_tidygraph() method for diff_model objectsas_siena() method for tidygraph objectsto_waves() now works on diff_model objects, add attributes and namesis_multiplex() now recognises a tie/edge ‘type’ attribute as evidence of multiplexityigraph::is_bipartite() is superseded by is_twomode()
tidygraph::activate() is superseded by mutate_ties() and similar functionsigraph::as_incidence_matrix() and igraph::graph_from_incidence_matrix() with igraph::as_biadjacency_matrix() and igraph::graph_from_biadjacency_matrix()
autographr() now plots diff_model objects, showing the diffusion as a heatmap on the verticesautographs() and autographd() now utilise network information in diff_model objects to provide better layouts (closed #17)node_size in autographd()
many_palettes replaces iheid_palette
theme_ethz(), scale_color_ethz()/scale_colour_ethz(), and scale_fill_ethz() for ETH Zürichtheme_uzh(), scale_color_uzh()/scale_colour_uzh(), and scale_fill_uzh() for Uni Zürichtheme_rug(), scale_color_rug()/scale_colour_rug(), and scale_fill_rug() for Uni Gröningen2023-12-06
read_graphml() and write_graphml() for importing and exporting graphml objects, mostly wrappers for igraph functions.autographd() and autographs() can now be used for plotting diffusion models.
to_waves() and autographd() to account for ‘exposed’ nodes in diffusion models.hierarchical layout so that node name can be specified for centering the layouttheme_heid() layout2023-11-15
+.ggplot() method for visualising multiple plots in the same panetheme_iheid for plotsscale_ family of functions for changing colour scales in plotsautographr():
autographd() function2023-11-01
as_igraph() in accordance with upcoming updates to igraph package (closing #27)2023-10-25
to_redirected.tbl_graph()
print.tbl_graph() no longer mentions the object class2023-10-19
run_tute() helper for quicker access to manynet and migraph tutorialsextract_tute() for extracting the main code examples from manynet and migraph tutorialspurl = FALSE to tutorial chunks that are not needed for extraction (thanks @JaelTan)run_tute() and extract_tute()
2023-10-11
autographr()
autographr(), closes #11autographr()
autographr() now automatically bends arcs for reciprocated ties when directed network is not too large/denseautographr() now accepts unquoted variables as argumentsautographr() now uses graphlayouts::layout_igraph_multilevel where appropriate2023-08-11
thisRequiresBio() helper function to download Bioconductor packagesison_konigsberg for illustrating Seven Bridges of Konigsbergison_brandes2 and added potential modal type as extra variable to ison_brandes
ison_bb, ison_bm, ison_mb, and ison_mm into a list of networks called ison_laterals
create_explicit() for creating networks based on explicit nodes and tiesdelete_nodes() for deleting specific nodesto_eulerian() function that returns a Eulerian path network, if available, from a given networkis_ functions from migraph
is_connected() to test if network is strongly connectedis_perfect_matching() to test if there is a matching for every node in the networkis_eulerian() to test whether there is a Eulerian path for a networkis_acyclic to test whether network is a directed acyclic graphis_aperiodic to test whether network is aperiodiclayout_tbl_graph_alluvial() that places successive layers horizontallylayout_tbl_graph_concentric()that places a “hierarchy” layout around a circlelayout_tbl_graph_hierarchy() that layers the nodes along the top and bottom sequenced to minimise overlaplayout_tbl_graph_ladder()that aligns nodes across successive layers horizontallylayout_tbl_graph_railway that aligns nodes across successive layers verticallytheme_iheid() function that themes graphs with colors based on the Geneva Graduate Institute2023-06-07
.data
print.tbl_graph method that offers easy to interpret informationread_*() functions, e.g. read_edgelist()
write_*() functions, e.g. write_edgelist()
write_matrix() for exporting to matricescreate_*() functions, e.g. create_lattice()
create_*() functions return tbl_graph class objectsgenerate_*() functions, e.g. generate_smallworld()
ison_* network data, e.g. ison_southern_women
as_*() functions, e.g. as_igraph()
as_edgelist.network()
as_network.data.frame()
as_network.tbl_graph()
join_*() functions, e.g. join_ties()
add_*() functions, e.g. add_node_attribute()
create_*() functions return tbl_graph class objectsmutate_*() functions, e.g. mutate_ties()
mutate_ties(), it is no longer necessary to activate(edges)
rename_*() functions, e.g. rename_ties()
is_*() functions, e.g. is_dynamic()
is_labelled() to work correctly with multiple network formatsto_*() functions, e.g. to_mode1()
to_giant.network()
to_directed() now a methodto_subgraphs() now returns a list of tbl_graphsto_reciprocated() now works on edgelists, matrices, tbl_graphs, and networksto_acylic() now works on matrices, tbl_graphs, and networksfrom_*() functions, e.g. from_egos()
from_subgraphs()
network_nodes()
network_dims() is now a method