Changes in version 0.6.0 (2026-05-31)                  

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.

                        Changes in version 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.

                        Changes in version 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.

                        Changes in version 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.

                 Changes in version 0.4.3 (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.

                        Changes in version 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.

                        Changes in version 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.

                        Changes in version 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.

                        Changes in version 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.

                       Changes in version 0.2.15                        

  - Preparing for publication

                        Changes in version 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.

                        Changes in version 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.