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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.