Bug-fix release. 0.8.4 was published on r-universe but never reached CRAN.
plot() on net_entropy_bayes and
net_sequence_comparison results no longer fails where the
graphics device cannot represent the arrow glyph. Edge labels and the
comparison subtitle previously used
U+2192/U+2190, which a non-UTF-8 locale cannot
convert; the graphics engine raised an error
(conversion failure ... in 'mbcsToSbcs') rather than
substituting a character, so the plot could not be drawn at all. Both
now use the ASCII arrow notation (A -> B) already used
by the higher-order network verbs. Console output is
unaffected.
plot() on net_entropy_bayes results no
longer emits a deprecation warning under ggplot2 4.0, which removed
geom_errorbarh().
entropy_network() now renders with its intended
transition-network styling on cograph 2.4.4, the current CRAN version.
The object states its full style through cograph’s
meta$splot producer contract instead of relying on cograph
recognising the "entropy" method name, so no cograph update
is required. Output is identical under cograph 2.4.4 and 2.4.5.print() and plot() examples for the
MMM clustering attribute now reach it through
build_network(), matching the 0.8.4 change that made
cluster_mmm() return the fitted net_mmm
object.transition_entropy() — entropy rate H (stationary
distribution from the eigendecomposition at lambda = 1), stationary
entropy, redundancy, per-state branching entropies, all with the
normalized (scale-free) variants; print/summary/plot and
netobject_group dispatch.entropy_network() — the exact edge-level decomposition
of H: each edge carries its term pi_i P_ij log(1/P_ij); weights sum to
H. Displays the summands of the entropy-rate equation — no new quantity
is estimated. scaling = "share" relabels edges as
percentages of H (sum = 100). Additional weights:
"surprisal" (optionally scaling = "chance")
and "production" (irreversibility). The result is a regular
netobject that inherits its source network’s styling and
ships an entropy house style via cograph’s meta$splot
producer contract, which states the styling outright rather than relying
on cograph recognising the method name (cograph >= 2.4.4).entropy_trajectory() — sliding-window entropy over the
transition stream (the windowed design of Krejtz et al. 2025): tidy
per-window table, per-group trajectories, loess-trend plot; the
per-window estimator weights rows by observed occupancy (robust to
non-ergodic window fragments).entropy_bayes() — Dirichlet-posterior estimation:
credible intervals for H, per-state entropies, and per-edge
contributions; edges flagged credible when their share of H credibly
exceeds min_share; $model holds the pruned
stable entropy network.cluster_mmm() now returns the fitted
net_mmm clustering object, retaining assignments, posterior
probabilities, mixing proportions, fit criteria, and fitted component
models. Network materialization remains the responsibility of
build_network(fit) or the one-step
cluster_network(..., cluster_by = "mmm") workflow.as_htna() gains a net_mmm method. An MMM
fit created from an HTNA model is materialized into an
htna_group without rerunning the MMM fit, while the
original actor partition and clustering diagnostics are preserved.as_htna() still rebuilds one full node-level network
from the original source, preserving every between-cluster transition,
and now completes the canonical HTNA contract. Its result inherits from
htna, netobject, and
cograph_network; stores character actor labels in
$node_groups$group, a factor in $nodes$groups,
and actor order in $actor_levels; and retains actor-order
metadata on $node_groups for lossless partition round
trips, plus the legacy $nodes$cluster and
"cluster_members" metadata.prepare() now identifies sessions from the observed
combinations of the actor and session columns
instead of base::interaction(). Three defects are
fixed:
interaction() codes
a combination over the marginal level space, which exceeds
.Machine$integer.max once both columns pass 46,341 distinct
values. The resulting NAs were pasted into the literal
string "NA", merging unrelated events into one
pseudo-session. On 50,000 actor-session pairs this silently discarded
14% of sessions and manufactured transitions that no input sequence
contained, including self-loops on terminal states." | " before being used as a grouping and metadata merge
key, so ("a | b", "c") and ("a", "b | c")
collapsed into a single session. Grouping now keys on the original
columns; the readable label is display-only and is exposed as
.session_label in meta_data.Group numbering reproduces interaction()’s ordering, so
prepared row order and finite same-seed bootstrap results are
unchanged.
time_threshold = FALSE switches session-interval
splitting off, so each actor (or actor-session) forms a single sequence
regardless of gap length. Accepted by prepare(),
build_network() and build_mcml().
interaction() for row-order
compatibility.Distance clustering and mixed-Markov clustering now preserve HTNA
inputs. build_clusters() and cluster_mmm()
carry the node-to-actor partition into network materialization, while
cluster_network() returns an htna_group
directly. Every child remains an htna object with
$node_groups, $nodes$groups, and
$actor_levels; clustering assignments, posterior
probabilities, fit diagnostics, and other outer attributes remain
attached.
Added extensive randomized equivalence coverage across distance clustering, mixed-Markov clustering, all transition-network estimators, actor-absent clusters, and HTNA-versus-plain input paths.
The Bayesian verbs get their own reference section, placed
directly after Network Estimation: certainty(),
bayes_compare(), subtract_networks() and
as_netdifference(). They were previously buried in a
fourteen-entry “Bootstrap & Inference” list.
bootstrap_network() now points at certainty()
as its closed-form counterpart, and permutation() points at
bayes_compare() as its Bayesian complement, so each pair is
reachable from either side.
frequencies() is no longer marked
\keyword{internal}. The topic page and the exported
function share a roxygen topic name, so the keyword from the topic block
leaked onto the function’s own help page even though the function is
exported (and called by the package). cluster_data() keeps
its internal keyword: it is a deprecated alias for
build_clusters() and is meant to stay out of the
index.
Dropped the utils help page, which documented no
exported object. The @importFrom directives it carried are
retained.
audit_codex/ is no longer tracked; it holds
generated audit artifacts.
Suggests: cograph (>= 2.4.4). The netdifference
verbs added in 0.7.8 need cograph 2.4.x: CRAN’s cograph 2.3.6 contains
no netdifference support, so
cograph::plot_difference() does not exist there and
cograph::splot() on a netdifference falls
through to the plain netobject renderer and silently draws
an unsigned network. Nestimate must not be submitted to CRAN before
cograph 2.4.4 is available there.subtract_networks() /
as_netdifference() — verbs for the difference between two
networks. subtract_networks(x, y) returns the edge-wise
difference as a netdifference object;
as_netdifference() promotes an existing comparison result
to the same class — a bayes_compare() result, or a
netdifference, which passes through; anything else errors —
so a difference computed by any route prints the same way. Adds
print.netdifference.
bayes_compare() accepts two
net_edge_betweenness() objects (source method
"relative" only). Edge betweenness is recomputed on every
posterior draw, giving the Bayesian analogue of
permutation()’s edge-betweenness dispatch, with posterior
mean betweenness matrices and the plug-in
observed_diff.
permutation() gains a measures argument
for centrality permutation tests, matching the tna
package’s dispatch.
bayes_compare()’s probability-of-direction column is
renamed pd -> p_difference in the
summary() frame, and the result now carries class
c("net_bayes", "netdifference", "net_permutation") so it
dispatches to the difference verbs as well as the permutation
ones.
Non-ASCII characters normalized across R sources and man pages.
centrality_stability() no longer errors with
“missing value where TRUE/FALSE needed” when a requested measure is
undefined on the network (e.g. Diffusion is
NaN on a small cyclic net): sd() returned
NA, which poisoned if (!any(keep)). Such
measures now drop like zero-variance ones.
centrality_stability()’s default
measures is restored to
c("InStrength", "OutStrength", "Betweenness"). 0.7.7 had
swapped OutStrength for Diffusion, which broke
the package: it calls centrality_stability() with no
measures and compares the result against its own explicit
trio. centrality() / net_centrality() keep the
Diffusion default; only centrality_stability()
reverts.
Suggests: cograph relaxed from
(>= 2.4.4) to (>= 2.3.6), the version
available on CRAN. Additional_repositories removed — every
declared dependency now resolves from CRAN.
plot() on a net_centrality_group gains
type = "delta", showing the between-group difference per
measure, and now supports three or more groups. Zero-valued edges can be
blanked with drop_zero = TRUE.plot() on a net_edge_betweenness() result
(plot.net_edge_betweenness).plot() on centrality results gains alternative views:
type = c("bar", "line", "heatmap") for a single
net_centrality, and
type = c("bar", "line", "delta") for a
net_centrality_group. Count-like measures get integer axis
labels.Version bump only; no user-visible changes.
as_htna() — builds a grouped node-level network from
data and a clustering, keeping every node (unlike
cluster_summary(), which collapses to a cluster-level macro
summary). Intended for cograph::plot_htna().Centrality gains the tna-parity measures.
net_centrality(x, measures = "all") now returns
OutStrength, InStrength,
ClosenessIn, ClosenessOut,
Closeness, Betweenness,
BetweennessRSP, Diffusion and
Clustering — previously only the strengths,
Closeness and Betweenness. Adds
plot.net_centrality and
plot.net_centrality_group.
sequence_plot() and the MCML plots gain layout
refinements.
as_netobject() / validate_netobject() —
the boundary layer between (which owns the psychometric-network math and
emits a lean cograph_network) and Nestimate (which owns the
canonical netobject schema). as_netobject()
promotes a psychnet result or a bare
cograph_network to the dual-class
c("netobject", "cograph_network") so it dispatches to every
Nestimate verb, parking psychnet-specific fields (including the GLASSO
KKT certificate) under $meta$psychnet;
netobjects pass through unchanged.
validate_netobject() enforces the shared structural
contract so schema drift on either side fails loudly.
psychnet is not a declared dependency — Nestimate never
calls it; the converter works by S3 dispatch on whatever
psychnet object the caller supplies.
certainty() — analytic Bayesian counterpart of
bootstrap_network() for transition networks. Models each
state’s outgoing transitions as a Dirichlet-Multinomial process
(Jeffreys prior) and returns posterior mean, sd, credible interval and a
stability decision per edge in closed form (no resampling). Returns the
exact net_bootstrap object layout and carries class
c("net_certainty", "net_bootstrap"), so it is a drop-in:
every net_bootstrap method works on it. Completes the
assessment trio certainty / stability (bootstrap_network) /
reliability (reliability).
sequence_plot() gains a multichannel view for
mcml objects built from sequences.
sequence_plot(fit) draws one carpet panel per cluster
channel plus a macro Summary panel — each channel’s own
states solid, the other clusters a faded wash, finished cells white,
rows aligned by the macro sequence.
sequence_plot(fit, type = "distribution") stacks the
prevalence (own states + faded other clusters + an explicit
NA band, to 100%), and normalize = TRUE gives
a TraMineR-style seqdplot where each time point sums to 1.
ggplot-based and dependency-free; returns a ggplot
object.
bayes_compare() results are now 100% compatible with
the permutation() format: the object carries class
c("net_bayes", "net_permutation") with all
net_permutation slots (diff_sig,
p_values, effect_size, iter,
alpha, paired, adjust), and its
summary is a superset of
summary.net_permutation
(from, to, weight_x, weight_y, diff, effect_size, p_value, sig
plus the Bayesian extras
count_x, count_y, ci_lower, ci_upper, ci_width, pd). A
bayes_compare() result is now a drop-in wherever a
net_permutation is consumed.
bayes_compare() — Bayesian Dirichlet-Multinomial
comparison of two transition networks, a complement to
permutation(). Models each source state’s outgoing
transitions as a Dirichlet-Multinomial process (Jeffreys prior) and
returns, per edge, a posterior mean difference, a credible interval, the
probability of direction (pd) and its two-sided
p-equivalent. Adds
print/summary/plot methods and
netobject_group dispatch (all-pairwise or matched). Method
source: Johnston & Jendoubi (2026), How Delivery Mode Reshapes
Resource Engagement: A Bayesian Differential Network Analysis, TNA
Workshop 2026.as_networks() — promote a build_mcml_pc()
result into a netobject_group (the psychometric-network
counterpart of as_tna()). Singleton clusters with no
within-network are dropped with a warning; an existing
netobject_group passes through unchanged.markov_order_test() reads sequences straight from a fitted
network (markov_order_test(net)); HYPA anomaly tables use
summary(hypa, order_by = "ratio"); higher-order pathways
use pathways(hon, top = ); grouped-clustering inspection
uses cluster_diagnostics().build_mcml_pc() — MCML aggregation for psychometric
networks (cor / pcor / EBICglasso). Five aggregation methods with
explicitly different statuses: "average" (descriptive
block-mean; works without raw data), "composite" (cluster
scores re-estimated with the chosen estimator — a genuine cluster-level
network), "loadings" (composites weighted by mean
within-cluster connection strength — Nestimate’s own weighting, not an
EGA reimplementation), "rv" (Escoufier’s RV matrix
correlation between blocks), and "canonical" (first
canonical correlation — the upper bound for composite methods).
Within-cluster networks re-estimated by default
(within = "reestimate") since a pcor submatrix is not the
subsystem’s pcor network. Item diagnostics in $loadings:
signed loadings (reverse-keyed items detected via the leading
eigenvector of the within-block matrix and flipped in composites),
cross-cluster strengths, and a misfit flag when an item is
more connected to another cluster than its own (warned). Composites
tolerate missing data (row-wise renormalized weighted means); Composite
item weights are selectable via weighting — ten built-in
schemes spanning three views of the cluster: the scale as scored
("equal", "item_total"), the network’s view
("strength", "eigen",
"closeness", "betweenness",
"expected_influence", "specificity" — the
misfit margin as a weighting, zeroing items that belong as much to
another cluster), and the latent-variable view ("pca",
"factor"); plus fully custom weighting via a named numeric
vector or a function(W_block, data_block, nodes).
aggregation = "loadings" is the alias for composite +
strength. The "factor" weighting exposes its extraction
method via fa_method: "ml" (factanal),
"paf" (iterated principal axis), "minres"
(ULS), or "cfa" (one-factor lavaan model; with
cor_method = "polychoric" the categorical DWLS factor
model) — all operating on the cor_method-consistent
correlation structure. Reverse-keyed handling works under every
sign-carrying scheme (item-total correlations are computed on
eigen-sign-pre-oriented columns so a reversed member cannot contaminate
small clusters). cor_method = "polychoric" (via lavaan)
supports ordinal items; id_col drops identifier columns so
convert_sequence_format(format = "frequency")
actor-profiles feed the function directly (the within-person
co-occurrence view of event data). Returns class mcml_pc
(macro + within netobjects, all undirected) with print/summary/plot; the
composite/loadings macro is a full netobject, so
bootstrap_network(), vertex_bootstrap(), and
vertex_compare() apply to it directly.
cograph::plot_mcml() (>= 2.3.8) renders the two-layer
undirected MCML view. Experimental: API and formulas may change.loading_stability() — case-bootstrap stability of the
build_mcml_pc() composite weights (percentile CIs,
sign-flip rates), with print and forest-style plot.vertex_bootstrap() — Snijders & Borgatti (1999)
vertex bootstrap for network-level statistics (density, mean weight,
strength centralization, weighted reciprocity, plus custom
statistic_fn). Needs only the weight matrix, so it works on
data-less netobjects (build_mlvar() constituents,
as_tna(mcml) elements, plain matrices) where
bootstrap_network() cannot run. Returns a tidy
one-row-per-statistic net_vertex_bootstrap with
print/summary/plot. Self-loops are preserved (diagonal carries the
resampled vertex’s own self-weight); undirected replicates stay
symmetric.vertex_compare() — the Snijders & Borgatti
two-network test the vertex bootstrap was originally proposed for:
z-tests and normal-approximation CIs for differences in network-level
statistics between two networks (netobjects, matrices, or precomputed
net_vertex_bootstrap objects). Tidy
net_vertex_comparison result with print/summary/plot
(forest plot of differences).bootstrap_network() and vertex_bootstrap()
gain ci_method = c("percentile", "basic"): basic intervals
(Davison & Hinkley 1997, eq. 5.6) reflect the percentile bounds
around the observed estimate, correcting first-order bootstrap bias.
Default remains "percentile".build_mcml() (sequence and edge-list paths) now records
the effective directedness in $meta$directed:
FALSE when type = "cooccurrence", whose
weights are symmetrized, instead of echoing the directed
argument unchanged. Renderers that auto-detect directedness (e.g.,
cograph::plot_mcml() with directed = NULL) now
draw co-occurrence MCML objects as undirected networks
automatically.mosaic_analysis(data, var1, var2) — two-variable mosaic
analysis on a data.frame: chi-square or Fisher test,
Cramer’s V (df-adjusted effect size) and a flat mosaic plot. Returns
class mosaic_analysis with a tidy one-row-per-cell
$counts, a one-row $stats, and
print/summary/plot. Distinct from mosaic_plot(), which
draws from a fitted network object.mosaic_plot() gains
style = c("classic", "flat"). The flat style uses
variable-width columns, white gutters and in-tile or side labels,
sharing the classic style’s geometry and diverging palette;
values = TRUE prints residuals inside the tiles.build_network() and the transition wrappers
(build_tna(), build_ftna(),
build_atna(), build_cna()) gain
start and end boundary markers:
FALSE (default), TRUE (labels
"Start" / "End") or a custom string.
start prepends a source state to every sequence;
end places a sink in the single cell after each sequence’s
last non-NA state (not absorbing — see
mark_terminal_state() for that). Honoured by the
relative, frequency,
co_occurrence and attention estimators; other
methods error.
build_mmm() gains covariate_effect.
"em" (default) folds covariates into the EM as
covariate-dependent mixing, changing the fit; "posthoc"
fits a plain mixture and uses covariates only for the after-fit
multinomial logit, leaving the clustering bit-identical to a
no-covariate fit.
magnitude_difference() compares the frequency (FTNA)
and probability (TNA) views of a transition network and quantifies the
per-edge discrepancy on a common scale, with five metrics, four
scalings, and two polar plot() portraits (stacked and
circular).persistent_homology(),
build_simplicial(type = "vr")) plus diagram tools
bottleneck_distance() and
persistence_landscape().compare_model() (with
netobject_group dispatch),
summary.netobject(), plot.net_comparison(),
and rename_models() for relabelling grouped network
objects.magnitude_difference(),
casedrop_reliability(), build_hypergraph(),
hypergraph_measures(), and cluster_data() were
previously absent.Remotes: field; cograph and
tna are available from CRAN, so no non-CRAN source pin is
needed.Followed codex_docs/audit_clustering and
codex_docs/audit_mcml recommendations across two modules.
Eleven of thirteen findings addressed; two deferred pending design
decisions on numeric semantics (directed = FALSE raw-data
MCML, MMM first-non-NA initial state).
cluster_network() now forwards distance-clustering
arguments (na_syms, weighted,
lambda, seed, q, p,
covariates) to build_clusters() instead of
silently passing them to build_network(). The split runs on
caller ... only — netobject build_args
continue to flow only to the build_network() step,
protecting attention-method (atna) network history from
being re-routed to weighted Hamming. (audit_clustering #1).auto_detect_clusters() (used by
build_mcml() and cluster_summary()) now
requires node_groups to carry a node identifier column when
shaped as a data.frame, or be a named atomic vector keyed by node label.
Previously, a bare cluster-only data.frame was read
positionally — silently mis-assigning nodes whenever
node_groups rows were in a different order than
x$nodes. (audit_mcml #1)build_clusters() now rejects all-missing input early
with a clear message instead of failing indirectly downstream in
pam/hclust. (audit_clustering #4)compare_mmm(return_fits = FALSE) — when
TRUE, the fitted net_mmm models are attached
as attr(result, "fits") keyed by k, so users
can pick the chosen model without re-running EM. Default behaviour
unchanged. (audit_clustering #6)build_clusters() validation messages now name the
offending argument ("'k' must be at least 2 (got k = 1)")
rather than dumping the failing predicate. Top-level type checks
switched to named-condition stopifnot() for the same
reason. (audit_clustering #2)summary.mcml() roxygen corrected — was claiming a
printing side effect that doesn’t exist. (audit_mcml #5)build_mcml() clusters = "<col>" mode
now documents its narrow contract: assigns each row’s group label to
both endpoints, so it only makes sense for within-group edge lists.
(audit_mcml #2)build_mcml() method parameter doc now
steers raw sequence / event-log inputs to "sum", since the
function counts observed transitions. Other methods are for weighted
edge lists or pre-existing matrices. (audit_mcml #4)as_tna.mcml() “Excluded Clusters” section corrected —
drop emits a warning() (was claimed silent) and only fires
for relative method (was claimed unconditional).
(audit_mcml #6)build_clusters() na_syms doc adds an
explicit “Missing-value distance rule” subsection: NA becomes a
comparable sentinel state, not pairwise deletion. (audit_clustering
#3)build_mmm() adds an “Initial states” section explaining
first-column-verbatim init and that build_mmm does NOT honor
build_clusters-style na_syms — only actual NA
cells become NA inits. (audit_clustering #5, doc-only path)node_groups alignment, label
propagation through state_distribution(),
as_tna.mcml() drop-warning fixture, MMM first-column NA
behaviour, and the four-way cluster_network() arg-routing
contract). Full sweep: 1628 / 1628 pass, 0 fail..extract_edges_from_matrix() no longer drops the
diagonal. Netobjects built via .wrap_netobject() (and
therefore everything from build_network(),
build_mcml(), bootstrap_network(),
build_mmm(), wtna(), as_tna())
now have $edges containing every non-zero matrix entry,
including self-loops. Previously $weights and
$edges were silently inconsistent on any matrix with a
non-zero diagonal, causing downstream consumers
(e.g. cograph::centrality() on an MCML macro) to
under-count node degree by 2.plot_state_frequencies() — native S3 generic for
state-frequency plots across netobject,
netobject_group, mcml, and htna.
Defaults to a marimekko (mosaic) layout where column widths reflect
per-group totals and segment heights reflect within-group state
proportions; also supports a colored-bars style and a per-group faceted
marimekko. Uses the package Okabe-Ito palette throughout.plot_mosaic() — exported low-level marimekko primitive
built on geom_rect() with cumulative-width /
cumulative-height geometry. Reusable for any tidy
data.frame(group, state, weight) input.passage_time() and markov_stability() now
raise an explicit error naming the dead state when a transition-matrix
row sums to zero, instead of silently propagating NaN
through eigen/solve. Zero rows mean the chain
is not ergodic; mean first passage times are undefined. Shared helper
.mpt_normalize_rows() factored out of both entry
points..prepare_association_input() no longer hard-rejects
non-square numeric matrices. For association methods (glasso, pcor, cor)
the netobject’s $data slot is a numeric matrix (not a
data.frame). Any downstream caller that row-subsetted $data
and re-invoked the estimator (centrality_stability(),
bootstrap_network(), reliability()) was
silently producing NULL centralities caught by tryCatch,
which surfaced as an “all centrality measures have zero variance”
warning or all-NaN correlations. The matrix branch now
recognises non-square input as raw observation data and recursively
re-enters through the data-frame branch. Square symmetric matrices
(pre-computed correlation / covariance) still go through the
symmetric-matrix path with the symmetry check intact.build_network() gains state_cols and
metadata_cols parameters (both default NULL).
Explicit overrides for the state-vs-metadata column classifier, which
previously used a “values-in-nodes” heuristic that silently
misclassifies metadata columns whose values coincide with node labels
(e.g. a condition column with levels
"A","B","C" when nodes are "A","B","C").
Validation: error on overlap between the two vectors, error on column
names not present in the input data. Forwarded through the
group = ... recursive dispatch so per-group calls honour
the override.plot.net_link_prediction() and plot.mcml()
removed. Nestimate is a computation engine — visualization is the user’s
concern. Previously both methods called cograph:: directly,
violating the stated dependency invariant (Nestimate -> cograph
direction forbidden). Users call cograph::splot(net) or
cograph::plot_mcml(fit) directly.wtna() @param type now flags that
type = "relative" combined with
method = "cooccurrence" produces an asymmetric matrix
(conditional co-occurrence given row state), not a symmetric undirected
weight matrix. Use type = "frequency" if symmetric counts
are required.NESTIMATE_EQUIV_TESTS=true):
test-equiv-permutation.R (vs. stats::p.adjust
+ hand-coded base-R permutation loop), test-equiv-mlvar.R
(vs. mlVAR::mlVAR at machine precision),
test-equiv-association-rules.R (vs.
arules::apriori), test-equiv-link-prediction.R
(vs. clean-room matrix algebra + igraph::similarity),
test-equiv-centrality-stability.R
(vs. bootnet::corStability). Total ~162k per-value
comparisons; all within machine precision except centrality-stability
which uses a documented drop-grid tolerance because bootnet uses
igraph path-based centrality and Nestimate uses
Floyd-Warshall.local_testing_and_equivalence/ validating HON, HONEM, HYPA,
MOGen, and hypergraph against pathpy 2.2.0 (via reticulate),
BiasedUrn, RSpectra, and HyperG.
Not shipped in the R-package tests/ directory; added to
.Rbuildignore.wtna, bootstrap_network,
build_clusters, sequence_plot) — systematic
cross-product tests over all combinations of mode parameters to catch
regressions where one branch silently diverges.build/vignette.rds (the vignette
index). Previous 0.4.2 build used
R CMD build --no-build-vignettes, which preserved pre-built
inst/doc/*.html but stripped the index — CRAN flagged
“VignetteBuilder field but no prebuilt vignette index.”test-gimme.R now skip_on_cran(). GIMME
tests fit a lavaan SEM per subject and took ~50s locally (2-3× on
Windows), pushing total check time to 11 min on win-devel. Full test
suite still runs in CI and local dev.--as-cran --run-donttest audit pass..Rcheck/ and Meta/ build
artifacts from working tree; added explicit
^Nestimate\.Rcheck$ and ^\.\.Rcheck$ entries
to .Rbuildignore as belt-and-suspenders against
repeat-submission contamination.inst/doc/ as required
by CRAN.skip_on_cran() to slow test block to keep check
time under 10 minutes.build_mlvar() — multilevel VAR networks from ESM/EMA
panel data. Estimates temporal (directed), contemporaneous (undirected),
and between-subjects (undirected) networks matching
mlVAR::mlVAR() at machine precision.build_mmm() / compare_mmm() — mixture of
Markov models via EM, with BIC/AIC/ICL model selection and optional
covariate regression.cooccurrence() — standalone co-occurrence network
builder supporting 6 input formats and 8 similarity methods.sequence_compare() — k-gram pattern comparison across
groups with optional permutation testing.sequence_plot() / distribution_plot() —
base-R sequence index and state distribution plots with clustering
integration.build_simplicial(), persistent_homology(),
q_analysis() — topological analysis of networks via
simplicial complexes.nct() — Network Comparison Test matching
NetworkComparisonTest::NCT() at machine precision.build_gimme() — group iterative mean estimation for
idiographic networks via lavaan.passage_time(), markov_stability() —
Markov chain passage times and stability analysis.predict_links() / evaluate_links() — link
prediction with 6 structural similarity methods.association_rules() — Apriori association rule mining
from sequences or binary matrices.predictability() — node predictability for
glasso/pcor/cor networks.build_hon(), build_honem(),
build_hypa(), build_mogen() — higher-order
network methods (HON, HONEM, HYPA, MOGen) now
cograph_network-compatible.human_long, ai_long — canonical
long-format human–AI pair programming interaction sequences (10,796
turns, 429 sessions).chatgpt_srl — ChatGPT-generated SRL scale scores for
psychological network analysis.trajectories — 138-student engagement trajectory matrix
(15 timepoints, 3 states).build_clusters(), network_reliability(),
permutation(), and prepare() replace earlier
internal names for consistency with the build_* naming
convention.mgm estimator added (method = "mgm") for
mixed continuous + categorical data via nodewise lasso, matching
mgm::mgm() at machine precision.build_mmm() no longer crashes on platforms where
parallel::detectCores() returns NA (macOS
ARM64 CRAN check failure).gimme convergence filter now correctly handles all
typed NA variants (NA_character_,
NA_real_, etc.).NaN values in numeric metadata aggregation
(all-NA sessions) normalized to NA_real_.hypa_score column renamed to
p_value..data pronoun added to
globalVariables().base::.rowSums() / base::.colSums()
replaced with rowSums() / colSums().dev.new() guarded by interactive() — no
side effects under knitr or CI.do.call(rbind, ...) replaced with
data.table::rbindlist() in mcml.R and
sequence_compare.R.hypa_score column to p_value
for clarity. Added $over, $under,
$n_over, $n_under fields to
net_hypa objects. Scores are now pre-sorted with anomalous
paths first.summary.net_hypa() now shows
over/under-represented paths separately with a configurable
n parameter.pathways.netobject(): New S3 method to extract
higher-order pathways directly from a netobject (builds HON or HYPA
internally).path_counts(): Now handles NAs in trajectories by
stripping them before k-gram counting.centrality_stability()
and boot_glasso() now accept a centrality_fn
parameter for external centrality computation.graphical_var() from scratch using
coordinate descent lasso + graphical lasso with EBIC model selection,
eliminating the graphicalVAR dependency.ml_graphical_var() — users should use
mlvar() for multilevel VAR.plot.netobject(),
plot.net_bootstrap(), plot.net_permutation(),
plot.net_hon(), plot.net_hypa() and
as_cograph() removed. Users call cograph plotting functions
directly on netobjects.attention estimator for decay-weighted transition
networks.build_network() with 8 built-in
estimators.bootstrap_network()), permutation
testing (permutation()), EBICglasso bootstrap
(boot_glasso()).c("netobject", "cograph_network") output for
cograph compatibility.