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Changelog

Source: NEWS.md

Nestimate 0.8.4

Mixed Markov clustering contract

  • 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.

Nestimate 0.8.3

HTNA expansion

  • 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.

Nestimate 0.8.2

Session grouping

  • prepare() now identifies sessions from the observed combinations of the actor and session columns instead of base::interaction(). Three defects are fixed:

    • Integer overflow. 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.
    • Separator collisions. Identifiers were pasted with " | " 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.
    • Missing identifiers. Missing values in a grouping column silently changed the session count. They now raise an error naming the columns.

    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().

Testing

  • Added a randomized grouping sweep over 1,000 seeded datasets covering actor/session column counts, identifier vocabularies including separator and UTF-8 cases, time on and off, tied timestamps, explicit order columns, shuffled input, and time-gap structures that split, do not split, or sit exactly on the threshold. Verified against ground truth and against interaction() for row-order compatibility.

Nestimate 0.8.1

HTNA interoperability

  • 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.

Nestimate 0.8.0

CRAN release: 2026-07-10

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 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 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

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

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 (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).

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

CRAN release: 2026-05-31

New features

Documentation

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 release: 2026-04-20

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

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

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

Performance

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.