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v1.0.0 #69
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… an existing network was incorrect
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new logo looks good to me, only the grey colour for the M makes the logo look a bit faded.
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The idea here was for the logo to mimic the grey used in default graphr()
calls. But perhaps we should also reconsider that default choice?
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Thank you for all the work @jhollway !
…ptible again after recovery
…ie dates, and fixed bug with four extra ties
…_southern_women updated accordingly
Description
Package
{manynet}
logo with stocnet GitHub address and color blind safe colorway{minMse}
dependencynetwork_*
prefix tonet_*
for conciseness{migraph}
{migraph}
Making
create_core()
where the membership inferred when passing anexisting network was incorrect
generate_configuration()
for generating configuration models(including for two-mode networks)
play_diffuson()
now includes an explicit contact argument to control thebasis of exposure
Marking
node_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 Fix inconsistencies with how diffusion models are plotted withgraphs()
andgrapht()
#71)is_twomode()
,is_labelled()
, andis_complex()
Mapping
graphr()
,graphs()
, andgrapht()
(
autographr()
,autographs()
, andautographd()
are now deprecated)scale_size(range = c(...,...))
to be usedscale_size()
from{ggplot2}
graphr()
now rescales node size depending on network size (closes Improve node sizing algorithm ingraphr()
#51)Modifying
as_diff_model()
where events were out of order and namedto_correlation()
that implements pairwise correlation on networkarrange_ties()
for{dplyr}
-like reordering of ties based on some attributeto_named()
now randomly generates and adds an alphabetic sequence of names,where previously this was just a random sample,
which may assist pedagogical use
Marking
is_multiplex()
now recognises "date", "begin", and "end" as reservedMeasuring
{migraph}
node_degree()
,node_deg()
,node_indegree()
,node_outdegree()
,node_multidegree()
,node_posneg()
,tie_degree()
,net_degree()
,net_indegree()
, andnet_outdegree()
node_betweenness()
,node_induced()
,node_flow()
,tie_betweenness()
, andnet_betweenness()
node_closeness()
,node_reach()
,node_harmonic()
,node_information()
,tie_closeness()
,net_closeness()
,net_reach()
, andnet_harmonic()
node_eigenvector()
,node_power()
,node_alpha()
,node_pagerank()
,tie_eigenvector()
, andnet_eigenvector()
net_reciprocity()
,node_reciprocity()
,net_transitivity()
,node_transitivity()
,net_equivalency()
, andnet_congruency()
net_density()
,net_components()
,net_cohesion()
,net_adhesion()
,net_diameter()
,net_length()
, andnet_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()
, andnode_exposure()
net_richness()
,node_richness()
,net_diversity()
,node_diversity()
,net_heterophily()
,node_heterophily()
,net_assortativity()
, andnet_spatial()
net_reciprocity()
,net_connectedness()
,net_efficiency()
, andnet_upperbound()
node_bridges()
,node_redundancy()
,node_effsize()
,node_efficiency()
,node_constraint()
,node_hierarchy()
,node_eccentricity()
,node_neighbours_degree()
, andtie_cohesion()
net_core()
,net_richclub()
,net_factions()
,node_partition()
,net_modularity()
,net_smallworld()
,net_scalefree()
,net_balance()
,net_change()
, andnet_stability()
node_mode()
(deprecated) to node_is_mode() since it returns alogical vector
node_attribute()
andtie_attribute()
to return measureswhen the output is numeric
node_exposure()
to work with two-mode and signed networksMembers
{migraph}
node_in_roulette()
node_in_optimal()
,node_in_partition()
,node_in_infomap()
,node_in_spinglass()
,node_in_fluid()
,node_in_louvain()
,node_in_leiden()
,node_in_betweenness()
,node_in_greedy()
,node_in_eigen()
, andnode_in_walktrap()
node_in_component()
,node_in_weak()
, andnode_in_strong()
node_is_core()
andnode_coreness()
node_in_adopter()
node_in_equivalence()
,node_in_structural()
,node_in_regular()
, andnode_in_automorphic()
node_*()
, but including the preposition_in_
is more consistent.Motifs
{migraph}
,these include
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.Methods
{migraph}
cluster_hierarchical()
andcluster_concor()
k_strict()
,k_elbow()
, andk_silhouette()
Data
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 tiesChecklist: