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.
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