dynamicmultiplex

• R fit functions now default to seed = 123, as in the Python package (previously NULL, the caller's random number stream). Pass seed = NULL for unseeded detection. Because bootstrap refits are now seeded, R values of s differ slightly from 1.3.x (for example 0.916 to 0.921 on a test network, within Monte Carlo error).
• allow_unequal_nodes = TRUE now requires vertex names on every layer even when layers have the same size, since nodes can enter and leave without the size changing. Previously same-size unnamed layers were matched by position. (Python: adjacency matrices are rejected with allow_unequal_nodes=True.)

New features

• repair_chains and repair_min_similarity (two-stage fit functions, default off). Stage-2 modularity tends to cut a long-lived community into time segments (a resolution-limit effect on chains of communities). With repair_chains = TRUE a group that ends at layer s and one that starts at a linked layer t are joined when their communities are mutual best matches (Kawadia and Sreenivasan 2012) with similarity at least repair_min_similarity (default 0.2; Greene et al. 2010 use a matching threshold). Groups that coexist are never merged. Joins are logged in chain_repairs.
• tracking = "filtered" (two-stage fit functions; default "smoothed", the previous behaviour). Stage 2 runs forward in time: the meta ids of layers 1..t depend only on layers 1..t, so fitted labels carry no information from later layers. Works with Leiden and Louvain and with repair_chains. Needs a seed (seed = NULL is an error), because unseeded stage-1 fits of later layers would shift the random stream that stage 2 uses.
• layers_from_membership() builds one weighted layer per time slice from group memberships (wide 0/1 matrices or long node-group tables), with weighting = "count", "size" or "kumar" (Kumar et al. hypergraph reweighting, as in hypernetx 2.4.3). There is no default weighting, each slice is built on its own, and rescale = "mean" puts slices on a common scale for the multislice coupling.
• bootstrap_multilayer() gains allow_unequal_nodes (nodes entering and leaving) and passes tracking, repair_chains and repair_min_similarity to the refits. Layers with different named node sets now stop with an error unless allow_unequal_nodes = TRUE.

Python only (by design)

• The two packages deliberately diverge here, because the libraries these rely on exist in Python and not in R. partition_membership_bipartite() partitions membership slices by Barber's bipartite modularity (leidenalg's bipartite construction, Leiden only); partition_membership_strict() by strict (all-or-nothing) hypergraph modularity (hypernetx reference pipeline, optional extra [hypergraph], which needs pandas < 3); fit_multilayer_from_partitions() tracks supplied per-slice partitions with the DynMux second stage or Hungarian matching. They are comparison specifications; in pilot simulations both did worse than the projections in layers_from_membership().

Bug fixes

• fit_multilayer_identity_ties() (undirected): the multislice local mover scored staying in a community the vertex has no tie into as 0, which could refuse an improving move (the same flaw was fixed for directed slices in 1.3.2). The stay score is now exact. Results on the package's regression networks are unchanged.
• Python: bootstrap_multilayer() silently paired nodes by position when networkx layers had the same size but different nodes; it now stops unless allow_unequal_nodes=True, which matches nodes by label.