Skip to contents

Changelog

Source: NEWS.md

Nestimate 0.9.24

Documentation

  • Fixes to the 0.9.23 help-page merge, found in review:
    • Pages are named after their function again: build_mcml and plot_state_frequencies had been named after a print() method, and their usage now lists the function before its methods.
    • The descriptions of 51 methods (what a plot draws, what a print shows) are restored, as a “Methods” section on each function’s page.
    • Each page’s ... entry states what each method does with extra arguments: methods that reject them say so, plot.net_bayes() names cograph::splot(), and the rest say they are ignored.
    • compare_networks: the ... and labels entries no longer repeat; net_edge_betweenness and net_pruning_details no longer list a non-existent class.
    • Every S3 method has a documented return value.

Behaviour notes

  • The mgm estimator rejects scale = FALSE with a classed error (nestimate_mgm_unscaled); this started in 0.9.23 and was not listed there. Missing values are handled pairwise, now passed explicitly.

Tests

  • Two more reference-implementation tests (a hand-written linear solve for passage_time(), a manual multinom re-computation) and unused reference helpers moved to the local suite; section headers that claimed equivalence tests are renamed. New tests: mgm on data with missing values, and the scale = FALSE error.

Nestimate 0.9.23

Internal changes

  • The mgm and ising estimators delegate to psychnets::mgm_fit() and psychnets::ising_fit() (glmnet engine), joining cor, pcor and glasso. Results are unchanged: identical to the former in-package code (max absolute difference 0 for Ising over 180 configurations, 5.6e-17 for MGM over 48). Nestimate keeps type detection, input validation and the result fields. The Ising result no longer carries the unused asymm_weights and lambda_selected fields. The unused internal moderated-MGM code is removed.

Documentation

Bug fixes

  • build_network(method = "mgm") no longer fails on data with missing values (“number of observations in y not equal to the number of rows of x”).

Tests

  • Equivalence tests against other packages (tna, igraph, mgm, stringdist, glm()) and naive-loop reference implementations are no longer shipped.
  • CRAN runs a core of 15 test files (data preparation, estimation registry, extraction, Markov, link prediction, Bayesian verbs, pruning, and the htna contract); every other file is skipped on CRAN and runs locally and in CI. The CRAN test run drops from about 300 s to 22 s.
  • Slow examples run with fewer iterations (boot_glasso(), nct(), permutation_diagnostics(), build_gimme()) and ?sequence_plot draws fewer figures.

Nestimate 0.9.21

Bug fixes

  • summary() of a predict_links() result reports NA scores for a method with no predictions (every possible link already exists). It returned NaN / -Inf / Inf with one warning per column.

Documentation

  • Every help page now has example code that runs under R CMD check. 31 pages had all of it inside \donttest{} or \dontrun{}; fast examples are unwrapped, and slow ones (nct(), permutation_diagnostics(), entropy_bayes()) run live with fewer iterations.
  • The predict_links() family of examples uses fixed data that leaves links to predict, and the loading_stability() / as_networks() examples use items with real two-factor structure, so neither emits warnings.
  • pkgdown: the cograph-tutorial-nestimate article was a 47.8 MB page (300 dpi figures inlined as base64); it is now about 70 KB with figures as separate files. The README no longer lists outdated imports.

Nestimate 0.9.20

Documentation

  • The documentation site is now https://pak.dynasite.org/Nestimate/ (DESCRIPTION URL, _pkgdown.yml, README links). saqr.me/Nestimate was a stale copy. The README lists the permutation-nested-data and compare-networks articles.

Nestimate 0.9.18

Documentation

Nestimate 0.9.17

New features

  • compare_networks() gains actor =, passed to the permutation backend: whole actors are reassigned between the networks, the printed header names the actor column, and global_differences() gains the rows ICC, Design effect (edges) and Design effect (M) (category "Nesting"). Requires test = "permutation" (error class nestimate_compare_actor_needs_permutation).

Nestimate 0.9.16

Breaking changes (development versions only)

  • The nesting argument added in 0.9.13 (permutation(), permutation_diagnostics()) and 0.9.15 (bootstrap_network()) is renamed from block to actor, the column identifying whose sequences they are: the same vocabulary as build_network(actor = ). Results carry actor and n_actors; diagnostic columns use _sequence (sequences reassigned) and _actor (actors reassigned), e.g. p_global_sequence / p_global_actor; conditions are nestimate_bad_actor, nestimate_actor_missing, nestimate_actor_misaligned, nestimate_actor_unsupported and nestimate_few_actors. The printout reads Actor: Group (200 actors) and Nesting in Group: ICC = .... Neither name was ever on CRAN.

Bug fixes

  • build_network(): columns named in metadata_cols (or left out of state_cols) are no longer read as sequence positions in wide data. They were moved to $metadata only after estimation, so their values became states and created spurious transitions, in single and grouped networks. For cor, pcor, glasso, ising and mgm, declared metadata columns are no longer entered as variables.

  • An ICC that cannot be estimated (one block, or no variation) is reported as not estimable instead of NaN [NA, NA] by permutation(), bootstrap_network() and permutation_diagnostics().

Nestimate 0.9.15

New features

  • bootstrap_network() gains block =, the column identifying the unit that sequences are nested in. Whole units are resampled with replacement, keeping their sequences together (cluster bootstrap, top level only; Davison & Hinkley, 1997; Field & Welsh, 2007). The result reports the ICC (Shrout & Fleiss, 1979) and the design effect (Kish, 1965) for the edges, printed under the bootstrap summary and returned as clustering and clustering_edges. With one sequence per unit it reproduces the ordinary bootstrap exactly. Transition networks only.

Nestimate 0.9.14

Documentation

  • New pkgdown article “Comparing two networks with compare_networks()”: high vs low achievers in group_regulation_long, every summary table and all eight plot views with a short reading of each.

Nestimate 0.9.13

New features

  • permutation() gains block =, the column identifying the unit that sequences are nested in (sessions in students, students in teams). Whole units are reassigned between the groups; units with sequences in both groups, as in repeated-measures designs, have their sequences reassigned within the unit; mixed designs combine the two (Good, 2005; Anderson & ter Braak, 2003). Supported for transition networks (relative, frequency, co_occurrence); association networks raise nestimate_block_unsupported. A warning of class nestimate_few_blocks is raised when no p-value could fall below alpha.

  • With block, permutation() reports the nesting effect: the intraclass correlation (Shrout & Fleiss, 1979) with a jackknife 95% interval, and the design effect (Kish, 1965), the blocked over the unblocked null variance, for the edges and the global statistic M.

  • permutation_diagnostics(x, block =) runs the ordinary and the blocked test on the same data and returns them side by side as a tidy data frame, one row per pair or, with level = "edges", per edge.

Improvements

  • print() of a net_permutation shows the global test (M and S, with p-values) and, with block, the ICC and design effects. A grouped result prints every pair in full.

  • The permutation() help page is reorganised into sections: what is tested, nested data and block, ICC and design effect, reading the printed output.

  • Building a transition network from a single long sequence now raises a message instead of a warning; running bootstrap_network() or permutation() on such a network still warns. Both carry the class nestimate_single_sequence.

Bug fixes

Nestimate 0.9.12

New verbs

  • set_state_colors(x, colors) attaches a palette to a netobject, netobject_group, mcml or htna, and every figure drawn from that object then uses it – sequence_plot(), distribution_plot(), plot_state_frequencies() and cograph::splot(). The cograph half goes through the documented meta$splot producer contract (defaults$node_fill, stamped in node order), so splot(net) needs no arguments and renders byte-identically to splot(net, node_fill = ...). A state_colors(x) <- replacement form is also available.

  • state_colors(x) reads the resolved palette back as a tidy data.frame: one row per key, with state, color and source ("set" or "default"). It is what the figures actually draw, not just what was passed in.

Improvements

  • States a named palette does not mention are now dealt the Okabe-Ito colours the palette has not used, one each, instead of taking their positional slot. Pinning plan = "#0072B2" no longer leaves another state defaulting to that same blue. Affects the named form only; an unnamed state_colors and the all-default palette are unchanged.

  • An attached palette resolves once, in the object’s own key order, so every figure drawn from it colours a state identically. Previously only the colours you set were carried and each figure dealt the remaining defaults in its own sort order – plot_state_frequencies() sorts states by frequency and sequence_plot() alphabetically, so one state could come out amber in one figure and black in the other.

  • In-tile labels in plot_state_frequencies() take whichever ink (dark or white) has the higher WCAG contrast ratio against the tile, instead of one fixed grey. A percentage on a dark tile – black, navy, dark wine – is now readable, whether the colour came from the default palette or from a palette you set.

Nestimate 0.9.11

Fixes

  • A named state_colors no longer has to match the figure exactly. Names the plot does not draw are dropped with a message() naming them, so one project-wide palette – states from a wider coding scheme, clusters a given call merged away – can be handed to every plot and each takes the keys that apply to it. 0.9.10 raised an error instead, which made a shared palette unusable.

Nestimate 0.9.10

Improvements

  • state_colors accepts a named vector everywhere sequence_plot(), distribution_plot() and plot_state_frequencies() draw, and the names are a lookup rather than a positional list: only the keys you name are overridden, every other key keeps its default, and the vector may be shorter than the number of states. An unnamed vector is still positional, unchanged.

  • For an mcml, that lookup covers the whole multichannel figure, not only the states. A cluster name colours its Summary band, its channel strip and its faded band in the other panels; a group merged by combine = is named by its label (the list name, or "A + B"); rest_label is a key too. So sequence_plot(fit, combine = list(Task = c("Cognitive", "Regulation")), state_colors = c(Task = "#0072B2")) recolours the combined cluster and leaves the rest of the palette alone. Previously the cluster and combined keys were drawn from a fixed internal palette with no way to set them.

  • A state_colors name that matches no key in the figure is now an error naming the available keys, instead of being silently ignored.

Nestimate 0.9.9

New verbs

  • build_mcml() gains combine = and expand =. On new input they change the partition before estimation, in any clusters form and for every input type: build_mcml(data, clusters = cl, combine = c("A", "B")) equals a build with A and B merged in cl (the merged cluster lists its states in cluster-name order). On an existing mcml they re-partition it: build_mcml(mc, combine =) merges clusters, build_mcml(mc, expand =) splits clusters into one cluster per state, and build_mcml(mc, clusters =) applies a new partition. The model (macro network, within-cluster networks, sequences) is re-estimated from the sequences the mcml carries, with its original type, method and directed. With the partition unchanged the result equals the input; expand = "all" reproduces the node-level transition network of the same type. Raises nestimate_mcml_no_sequences for an mcml built from a matrix or an edge list (which keeps only within-cluster edges), and errors when sequence-shaping arguments (trim, exclude, end, labels, actor, …) are passed with a re-partition, since the carried sequences already reflect them.
  • session_ids() names the session behind every sequence of a network built from long data, and of a build_mmm() or build_clusters() fit on such a network. It returns one row per sequence in model order: sequence, the actor and session columns under their own names, session_label, and, for a fit, cluster (plus posterior for a mixture). The fit’s assignments can then be joined to the input by those columns instead of parsing the "actor | session" label, which breaks when an id contains " | ". Raises nestimate_no_session_ids for wide-data input or for fits made before this version, and nestimate_session_ids_misaligned when the metadata and the sequences differ in number.
  • item_loadings() returns the tidy item-diagnostic table of a build_mcml_pc() fit (node, cluster, loading, weight, sign, max_cross, cross_cluster, misfit); misfit = TRUE/FALSE filters it.
  • composites() returns the per-respondent cluster scores of a build_mcml_pc() fit: one row per input row (input order and row names, NA where all of a cluster’s items are missing) and one column per cluster. Raises nestimate_no_composites for the descriptive aggregations ("average", "escoufier", "cancor"), which form no score.

Changes

  • sequence_plot() on an mcml gains combine =: named clusters are merged into one channel (a character vector for one group, a list for several; list names label the merged channels, default "A + B"). The merged group acts as one cluster across the figure (one panel, one Summary key, one faded band) and can itself be opened with expand =.

  • sequence_plot() on an mcml gains rest = c("clusters", "pooled", "none"): how a cluster’s panel shows the time spent in other clusters (one faded band per cluster, one pooled grey band, or blank, leaving only the panel’s own states). Applies to the carpet and distribution views. rest_label = (default "Other states") sets the legend text: the pooled band takes it as is, per-cluster bands read "Social (Other states)". This replaces the former "(elsewhere)" wording.

  • sequence_plot() on an mcml now honours na = in the distribution view: na = FALSE drops the NA (ended) band and shows each time point as shares of the sequences still running, as distribution_plot() already did.

  • macro_network() accepts an mcml_pc fit and returns its cluster-level network; expand = on an mcml_pc raises nestimate_no_expand, and method = or ... error (the estimator is set in build_mcml_pc()).

  • sequence_plot() errors when combine, expand, rest or rest_label is passed for input that is not an mcml, instead of ignoring them.

  • print.mcml_pc() and its build-time warnings name item_loadings() instead of pointing at $loadings.

  • prepare() (and so build_network() on long data) keeps the session column(s) in the per-sequence metadata under their own names, and returns the metadata explicitly in sequence row order (it was assembled with merge(sort = FALSE), whose order is unspecified).

  • build_mmm() and build_clusters() keep the input network’s $metadata (restricted to the fitted rows when build_mmm() drops sequences with missing covariates).

Fixes

  • sequence_plot() on an mcml with type = "heatmap"/"index" and expand = drew the other-cluster wash as blank cells: the wash was keyed by the Summary keys, which are states once a cluster is expanded. It is now keyed by cluster.
  • sequence_plot() on an mcml with type = "distribution" and expand = no longer fails with “subscript out of bounds” in the default (normalize = FALSE) view. The Summary band now opens the expanded cluster into its states, and the other panels draw it as one faded "<cluster> (<rest_label>)" band.
  • sequence_plot() on an mcml with a single channel (one cluster, or every cluster merged by combine) no longer fails in the carpet view with “replacement has 1 row, data has 0”.
  • sequence_plot() on an mcml: with expand =, each cluster’s faded band in the other panels now has its own colour (expanded clusters all shared one).

Nestimate 0.9.5

New verbs

  • macro_network() returns the macro (cluster-level) network of an mcml with one or more named clusters expanded back into their member states and every other cluster left collapsed — a network at mixed resolution. It re-counts from the recoded sequence data rather than splitting the k x k aggregate, which cannot be disaggregated; a matrix-derived mcml raises nestimate_no_expand_source. $node_groups maps each expanded state back to its parent cluster, so the result plots grouped.
  • extract_pathways() cuts a long event log into pathways and returns one row per pathway. Three cuts via type =: "unit" (one pathway per group), "segments" (one per contiguous run), "anchored" (spans around an anchor event). resolve = appends a resolution label as the closing state.
  • outcome_model() regresses a unit-level outcome on sequence or network predictors — pattern indicators, simplicial_features() output, or any numeric covariate — with family auto-detection, an optional lme4 random intercept, select = "split" hold-out selection, BH-corrected p-values, confidence intervals and odds ratios. effects_table() is the tidy accessor; summary() returns the same table.
  • simplicial_features() returns topological summaries of one or many networks as a tidy data.frame, one row per network per threshold, ready to use as regression predictors. Accepts a netobject, netobject_group, mcml, a square weight matrix, or a named list of any of these.

Extended

  • build_mcml() gains the exclude, trim, end and end_by sequence arguments, applied in that fixed order.
  • as_tna() is now a generic. as_tna.mcml(expand =) delegates to macro_network() for the macro layer only; per-cluster layers are untouched. An as_tna.default() covers everything else.
  • sequence_plot() and its mcml method gain panel = c("both", "summary", "channels").

Fixes

  • frequencies(format = "frequency") returned zero rows for any long-format data whose id column was not integer-valued. The sequence key was coerced with as.integer() before the merge, turning every character id into NA; the merge then matched nothing and failed silently. Sequence keys are now aligned in their character form.
  • sequence_plot() on an mcml errored whenever a singleton cluster was named after its own state: the shared fill scale built its levels with factor(levels = c(states, clusters)), and duplicate levels are an error. Levels and values are now de-duplicated.
  • as.data.frame() methods for net_hypergraph_transduction and net_hypergraph_cluster placed what in the generic’s row.names slot, so as.data.frame(x, "scores") bound "scores" to row.names. The generic’s arguments now come first and row.names is honoured (R CMD check “S3 generic/method consistency”).

Nestimate 0.9.4

  • pkgdown reference index repaired: the five topics added in 0.9.3 were unindexed and build_reference_index() failed. The S3 Methods section now wildcards as.data.frame. alongside print., summary. and plot., so a future result class with the house-standard accessor cannot repeat it.

Nestimate 0.9.3

New verb: compare_networks()

  • Compares two or more networks (netobject, netobject_group, tna, group_tna, mcml, cograph_network, matrices, or a list of them) in one object. summary() returns the tidy one-row-per-pair overview, as every other Nestimate summary() does; the full tables are their own verbs: edge_differences(), node_differences(), global_differences(), network_metrics(), each with a pair column. All pairs by default, or every network against one reference =.
  • Ratios are guarded (NA, never Inf/NaN); every cell is kept, absent edges appear at weight 0; higher (colour) and status (shape) columns drive the plots.
  • plot(x) draws one view per call, selected with type = (the package-wide argument name; what = is accepted as an alias): "networks" (default; each network drawn once via cograph::splot()), "difference" (the signed difference network of each pair), "edges" (ranked dumbbell), "nodes", "global", "heatmap", "scatter", and "inference" (a forest of the edge differences on the difference scale, with credible intervals when the Bayesian backend ran, a filled marker for a significant edge and the p-value in a right-hand column; it raises nestimate_compare_no_test when test = "none"). One sign-to-colour contract (#4A6FE3 = first/reference higher, #D33F6A = second higher) backed by line type and marker shape; the heatmap scale follows the data.
  • plot(x, combined = FALSE) splits a multi-pair view into a named list of single-pair plots (one per pair) instead of facetting them into one figure, matching the combined argument of plot.net_reliability(), plot.simplicial_complex() and friends. The base-graphics views draw one panel per page.
  • Optional inference on the same tables: test = "permutation", "bayes" (with an optional rope), "bootstrap", combinable. Non-significant results are faded, never deleted.
  • permutation() gains a $global data frame with NCT-style M (sum of absolute differences) and S (largest absolute difference) statistics and their permutation p-values, computed from the same null.
  • compare_model() is unchanged and will be soft-deprecated once compare_networks() has been through one release.

Hypergraph suite

Nestimate 0.9.1

tna-parity release: sequence-side gaps against tna::build_model() and tna::prepare_data() closed.

New estimators

  • build_network(method = "ngram") (params n_gram, default 2), "gap" (params max_gap, default 1) and "reverse" (params weighted) mirror the tna types "n-gram", "gap" and "reverse". Weights are numerically identical to tna on shared inputs (tested). All three accept wide or long input, start/end boundary markers, group = dispatch and scaling = "normalize" for row probabilities.
  • New aliases: "n-gram" / "n_gram" -> "ngram", "co-occurrence" -> "co_occurrence".

Timezone-safe timestamp parsing

  • prepare() and build_network() gain timezone = "UTC" (Olson name). Naive timestamps are interpreted in that zone; ISO-8601 Z/UTC/GMT markers and numeric offsets (+0200, +02:00) are converted from their offset. Parsing no longer depends on the machine’s local time zone.
  • Fixes: ...Z timestamps parsed to NA (the marker was stripped before matching), offsets were silently ignored, and wall-clock readings inside a DST gap could turn into spurious session boundaries. Unparsable values now raise an error instead of becoming session breaks.

Nestimate 0.9.0

Delegation release: Nestimate stops owning psychometric-network math and delegates it to its two clean-room home packages, psychnets (cross-sectional) and idiographic (temporal). No public API changes: every signature, default, return shape, and — verified against frozen pre-delegation baselines — every number is preserved.

Delegated to psychnets (new hard dependency, >= 0.5.2)

  • The cor, pcor, and glasso estimators keep Nestimate’s input layer, validation, and return contract; the math now runs in psychnets::cor_network() / pcor_network() / ebic_glasso(). Verified field-complete against the frozen baseline: cor exact, pcor within 5.6e-17, glasso exact on weights, precision, selected lambda, and the EBIC path (including penalize.diagonal and refit branches).
  • nct() delegates its inner EBIC-glasso solve; the NCT-specific nearPD symmetrization stays local. Seeded runs: networks and p-values exact.
  • boot_glasso() and permutation() (glasso branch) delegate the per-resample solve via psychnets::ebic_glasso(lambda_path = ), keeping the fixed-path-across-resamples semantics. Seeded permutation() is byte-identical; boot_glasso() is identical except wall-clock timing.
  • The internal pure-R glasso kernel (glasso_pure.R) and its EBIC helpers are deleted; psychnets owns that math now.
  • The ising and mgm estimators remain local for now (their delegation needs psychnets to expose per-node lambda selection and a scale passthrough) and are unchanged.

Delegated to idiographic (new hard dependency, >= 0.3.4)

  • build_mlvar() keeps its signature, S3 methods, and return object; the lmer estimation pipeline now runs in idiographic::fit_mlvar(). Verified identical at tolerance 0 (object, class, print, summary) across lag/standardize/day/beep configurations, and against mlVAR::mlVAR() at 8.8e-16 over 954 checks.
  • When the between-subjects network is not estimable (a random-intercept SD of zero), build_mlvar() now warns before returning the zero matrix; it previously returned it silently. Numbers are unchanged.
  • build_gimme() delegates its whole search to idiographic::fit_gimme(). This is the one delegation that changes results: idiographic’s search reproduces the upstream gimme package (>= 10.0) exactly, which Nestimate’s own search did not — the individual-level path search in particular under-detected person-specific paths relative to upstream. Group-level results are typically unchanged; individual-level path sets can grow. The signature and the net_gimme field contract are unchanged (the object gains idiographic’s netobject fields and now renders directly with cograph); print/summary/plot dispatch to idiographic’s methods. Treat pre-0.9.0 build_gimme() individual-level results as superseded.

Nestimate 0.8.5

CRAN release: 2026-08-21

Bug-fix release. 0.8.4 was published on r-universe but never reached CRAN.

Plotting in non-UTF-8 locales

  • 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().

Rendering entropy networks with cograph

  • 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.

Documentation

Nestimate 0.8.4

Transition matrix entropy

  • New entropy suite around the entropy rate of a transition matrix (Shannon 1948; Cover & Thomas 2006, ch. 4; the transition entropy of Krejtz et al. 2015 and the real-time mobile transition matrix entropy of Krejtz et al. 2025):
    • 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.
  • New vignette: Transition Matrix Entropy.

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.