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These fixes change numbers that earlier versions reported.
summary() density excludes dyads with missing values
from the denominator, matching sna::gden(). Networks
without missing dyads are unaffected.diag_to_NA = TRUE),
matching the igraph import path. Pass diag_to_NA = FALSE to
keep self-loop cells in density and summary_actor()
prop_ties.to_netify() sets the diagonal of imported statnet
network objects that do not allow loops to NA,
matching the igraph import. Density for these networks now agrees with
sna::gden(), and netify to statnet to netify round trips
keep their density.netify(..., output_format = "longit_array") respects
actor_pds and actor_time_uniform = FALSE.
Actors outside their active periods are NA in those
periods.actor_pds with gapped numeric time no longer
creates empty periods for every integer in the range.actor_pds (and
get_actor_time_info(net)) uses time labels in the class of
the time variable rather than integer positions.mixing_matrix() counts each undirected tie once, so
total_ties is halved for symmetric networks and raw
between-group cells hold half a tie in each mirrored cell. Proportions,
assortativity, and modularity are unchanged.compare_networks() counts each undirected edge once in
edge_changes. For networks with different actors,
correlation, Jaccard, Hamming, QAP, and spectral distance are computed
on the shared actors, with a message saying how many actors were left
out.bootstrap_netlet() resamples longitudinal netlets by
actor name, so the same actors are drawn in every period. Pairs formed
by two copies of the same actor are NA rather than 0,
removing a downward bias in density.subset(from =, to =) on unipartite networks returns a
square matrix over the union of the selected actors, with ties outside
the from x to block set to
NA.netify() warns when symmetric = TRUE and a
dyad appears in both directions within a period with different weights.
The value from the last row is still used; use
sum_dyads = TRUE,
aggregate_dyad(..., symmetric = TRUE), or
symmetric = FALSE to resolve it.nodal_vars is documented as describing
actor1, and netify() reports actors that only
appear as actor2, since they receive NA.subset() / subset_netify() on longitudinal
networks clips actor period bounds to the retained periods. Subsetting
to several periods without both endpoints previously made
plot(x, time_filter = ...),
decompose_netify(), and add_node_vars() fail
with “Actor period bounds are not in the netlet time labels.”subset() returns requested periods in netlet order
without duplicates.subset() down to a single period keys dyadic data as
cross-sectional, so dyad_correlation(),
attribute_report(), add_dyad_vars(),
mutate_weights(), and pivot_dyad_to_network()
find existing dyadic variables.subset() of a multilayer longitudinal array to one
period returns a cross-sectional [actor, actor, layer]
array.subset() on a bipartite network gives a clear error
when the selected actors come from only one mode.ego_netify() subsets actor_pds, nodal, and
dyadic attributes to the ego network’s actors and periods, and keeps the
period name when only one period remains.mutate_weights() with only add_constant
changes longitudinal arrays, updates weight attributes on every period
of a longitudinal list, renames the layer of single-layer networks with
new_name, and works on multilayer networks.
binarize() also works on multilayer networks.nodal_vars, dyad_vars,
add_node_vars(), and add_dyad_vars() work with
POSIXct time.plot() draws each tie once in undirected networks.plot() works for bipartite networks where an actor
appears in both modes (for example mexico).
summary_actor() returns plain actor names for these
networks with an actor_mode column.plot(..., static_actor_positions = TRUE) works for
longitudinal bipartite networks.layout = "sugiyama" works in plot() and
get_node_layout().point_layout accepts a single data.frame reused across
periods.plot_homophily(type = "distribution") works for
longitudinal networks.label_size_by, label_color_by,
label_fill_by, and label_alpha_by no longer
trigger an “Unknown parameter” warning.node_filter in longitudinal plots removes edges only in
the periods where an endpoint is filtered out.time_filter accepts numeric time labels such as
time_filter = 2012 when they are not valid indices, and
lists the available periods when nothing matches.homophily(), mixing_matrix(),
attribute_report(), and dyad_correlation()
work on multilayer longitudinal netlets stored as lists.homophily(method = "hamming") and
method = "jaccard" accept character, factor, and logical
attributes.dyad_correlation() aligns dyadic covariates to network
actors by name.compare_networks(by = ...) works for
what = "structure", "nodes", and
"attributes", and gives a clear error for edge
comparisons.netify_to_statnet() accepts factor and Date nodal
attributes.netify_to_amen(lame = TRUE) and
to_lame(lame = TRUE) order Xrow and
Xcol rows to match each period’s actors.to_lame()$ame_call snippet runs with current lame,
and from_lame_fit() returns a longitudinal netify for
longitudinal lame::lame() fits.animate_netify() example runs only when gganimate
is installed.peek() example uses actor names that exist in
icews.plot.netify(),
compare_networks(), and netify_to_statnet()
examples so they run quickly on CRAN Windows checks.unnetify() and plot() for bipartite
networks so ties in same-position row and column entries are kept. These
entries are valid cross-mode ties, not diagonal self-ties.classroom_edges,
classroom_nodes) and a matching short-form vignette
(quickstart_inference) for quick onboarding.?netify_workflows, covering unipartite, bipartite,
longitudinal, and multilayer setups, plus handoff to
statnet, amen, lame,
dbn, and igraph.to_lame() now bundles posterior round-tripping via
from_lame_fit(), including bootstrap and per-cell
credible-interval bounds for binary bipartite fits.bind_netifies() gains
align_actors = c("none", "union", "intersection") for
combining cross-sections with mismatched actor sets.drop_na_actors(), binarize(),
validate_netify(), bootstrap_netlet(),
compare_to_null(), simulate.netify(), plus
type predicates (is_binary(), is_bipartite(),
is_longitudinal(), is_multilayer(),
n_actors(), n_periods(),
n_layers()) and accessors (nodal_data()).as_tibble.netify(),
tidy.netify(), glance.netify(), plus
as.igraph.netify() and as.network.netify() S3
methods registered automatically when the relevant package is
attached.style = "heatmap" for
adjacency-matrix views, auto-diverging palettes for signed weights,
smarter label-repel for small networks, and two extra themes
(theme_publication_netify(),
theme_publication_netify_ts()).summary_actor() gains a stats = "fast"
mode (skips the expensive centralities) and auto-promotes for networks
above getOption("netify.fast_threshold", 1500).pipeline_netify_ergm, pipeline_lame_dbn,
multilayer_networks, tracking_changes,
attribute_analysis, manual_plotting, and
internals are all new or substantially rewritten.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.