# Nestimate 0.8.0

## Documentation

* 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 \pkg{htna} 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.

## Dependencies

* `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.

# Nestimate 0.7.8

## New features

* `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.

## Enhancements

* `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.

## Bug fixes

* `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 \pkg{htna} 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.

# Nestimate 0.7.7

## Enhancements

* `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`.

# Nestimate 0.7.6

## New features

* `plot()` on a `net_edge_betweenness()` result (`plot.net_edge_betweenness`).

## Enhancements

* `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.

# Nestimate 0.7.5

Version bump only; no user-visible changes.

# Nestimate 0.7.4

## New features

* `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()`.

## Enhancements

* 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.

# Nestimate 0.7.3

## New features

* `as_netobject()` / `validate_netobject()` — the boundary layer between
  \pkg{psychnet} (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`; `netobject`s 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`).

## Enhancements

* `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.

# Nestimate 0.7.2

## New features

* `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.

# Nestimate 0.7.1

## New features

* `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.

## Documentation

* Vignettes and articles now call package verbs directly instead of
  hand-assembled base-R subsetting rituals: `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()`.

# Nestimate 0.7.0

## New features (experimental)

* `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.

# Nestimate 0.6.5

## New features

* `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"`.

# Nestimate 0.6.4

## Bug fixes

* `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.

# Nestimate 0.6.3

## New features

* `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.

## Enhancements

* `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.

# Nestimate 0.6.2

## Bug fixes

* Corrected stale rank-scaling assertions in the test suite. No user-visible
  change.

# Nestimate 0.6.1

## New features

* `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.

# Nestimate 0.6.0

## New features

* `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).
* Full persistent homology with a Vietoris-Rips filtration
  (`persistent_homology()`, `build_simplicial(type = "vr")`) plus diagram
  tools `bottleneck_distance()` and `persistence_landscape()`.
* Network comparison: `compare_model()` (with `netobject_group` dispatch),
  `summary.netobject()`, `plot.net_comparison()`, and `rename_models()` for
  relabelling grouped network objects.

## Documentation

* The pkgdown reference index now lists every exported function;
  `magnitude_difference()`, `casedrop_reliability()`, `build_hypergraph()`,
  `hypergraph_measures()`, and `cluster_data()` were previously absent.

## Packaging

* Removed the `Remotes:` field; `cograph` and `tna` are available from CRAN,
  so no non-CRAN source pin is needed.

# Nestimate 0.5.1

## Audit follow-up (clustering + MCML)
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).

### Bug fixes
* `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)

### New parameters
* `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)

### Improvements
* `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)

### Documentation
* `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)

### Tests
* +12 new tests pinning the corrected contracts and the documented
  edge cases (misordered `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.

# Nestimate 0.5.0

## Bug fixes
* `.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.

## New features
* `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.

# Nestimate 0.4.4

## Bug fixes
* `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.

## New parameters
* `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.

## Removed
* `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.

## Documentation
* `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.

## Testing infrastructure
* New numerical-equivalence tests (gated by `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.
* New HON-family equivalence tests under `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`.
* Branch-matrix coverage added for the four many-mode APIs (`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.

# Nestimate 0.4.3

## CRAN resubmission (addresses incoming-check NOTEs on 0.4.2)
* Tarball now ships `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.

# Nestimate 0.4.2

## CRAN resubmission
* Full `--as-cran --run-donttest` audit pass.
* Purged stale `.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.

# Nestimate 0.4.1

## CRAN resubmission
* Pre-built vignettes included in `inst/doc/` as required by CRAN.
* Fixed 301-redirect URLs in README.
* Added `skip_on_cran()` to slow test block to keep check time under 10 minutes.

# Nestimate 0.4.0

## New functions
* `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.

## New datasets
* `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).

## API
* `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.

## Bug fixes
* `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 p-values corrected; `hypa_score` column renamed to `p_value`.

## CRAN compliance
* `.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.
* Equivalence test files excluded from the built tarball.

## Performance
* `do.call(rbind, ...)` replaced with `data.table::rbindlist()` in `mcml.R` and `sequence_compare.R`.

# Nestimate 0.3.4

* HYPA: Renamed `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.
* HYPA: `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.

# Nestimate 0.2.15

* Preparing for publication

# Nestimate 0.2.0

* Reduced hard dependencies from 6 to 4 Imports (ggplot2, glasso, data.table, cluster).
* Removed igraph from Imports — `centrality_stability()` and `boot_glasso()` now accept a `centrality_fn` parameter for external centrality computation.
* Removed tna from Imports — moved to Suggests (only used for input class detection).
* Implemented `graphical_var()` from scratch using coordinate descent lasso + graphical lasso with EBIC model selection, eliminating the graphicalVAR dependency.
* Dropped `ml_graphical_var()` — users should use `mlvar()` for multilevel VAR.
* Removed cograph plot wrappers — `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.
* Added `attention` estimator for decay-weighted transition networks.
* Increased test coverage from 84.5% to 96.1% (2780 tests).
* Passes R CMD check with 0 errors, 0 warnings, 0 notes.

# Nestimate 0.1.0

* Initial release. Split from Saqrlab v0.3.0.
* Core estimation via `build_network()` with 8 built-in estimators.
* Bootstrap inference (`bootstrap_network()`), permutation testing (`permutation()`), EBICglasso bootstrap (`boot_glasso()`).
* Higher-order networks: HON, HONEM, HYPA, MOGen.
* GIMME, MCML, multilevel VAR, graphical VAR.
* Temporal network analysis and velocity TNA.
* Dual-class `c("netobject", "cograph_network")` output for cograph compatibility.
