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Changelog

Source: NEWS.md

cooccure 0.4.0

  • cooccurrence() gains a window = parameter for sliding-window co-occurrence on ordered sequence input (lists of vectors and wide TraMineR-style data frames via field = "all"). Each window of w consecutive positions becomes one transaction; states inside the same window co-occur. window = sequence_length reduces to the existing bag-of-states behavior. TraMineR void markers (NA, "", "%", "*", "NaN") are dropped before windowing. Pure base R via embed().
  • cooccurrence() gains aggregate_by = and aggregate = parameters. aggregate_by groups the data by a column (e.g. journal id), computes the per-group co-occurrence network with whatever similarity / counting / scale / window was chosen, then combines the per-group edge weights into ONE final network. aggregate = c("sum", "mean", "min", "max") controls the combiner; count is always summed. Differs from split_by, which keeps groups separate. Cannot be combined with split_by. threshold and top_n apply after aggregation.
  • counting = "attention" adds positional gap-decay weighting: each pair within an ordered transaction contributes exp(-|pos_i - pos_j| / lambda) to the edge. Closer positions give a stronger edge, distant pairs decay. Matches the tna::build_model(type = "attention") semantics, undirected. The new lambda = parameter (default 1.0) controls the decay rate.
  • group = is now accepted as an alias for split_by =.
  • summary.cooccurrence() now returns a structured summary object with network size, density, mean degree, isolates, node-level degree and strength, and optional group-level summaries.
  • print.cooccurrence() now shows a compact diagnostic header before the edge preview, including density, mean degree, isolates, method metadata, and top nodes.
  • plot.cooccurrence(type = "degree") adds a base R degree-distribution plot.
  • cooccurrence() accepts a raw event log directly via action = (the event column), with optional actor =, time =, session =, order =, and time_threshold = (default 900 seconds). The log is sessionized into ordered sequences and each session becomes one transaction, so window = and counting = "attention" apply. Sessionization — timestamp parsing and gap splitting — is delegated to Nestimate::prepare() rather than reimplemented, so Nestimate is required for this input only.
  • cooccurrence() gains vars =, naming the indicator (one-hot) columns directly. The specification is resolved like select in base subset(), so a bare range (vars = A:D), bare names (vars = c(A, B, C)), positions (vars = 2:5), negative selection (vars = -c(id, note)), a logical mask, and a plain character vector all work. Other columns — ids, timestamps, metadata — are ignored, so a one-hot table no longer has to be stripped down to a bare matrix first. Cell values are read as presence, so count tables such as document-term matrices work alongside 0/1 and TRUE/FALSE.
  • Logical (TRUE/FALSE) indicator tables are now auto-detected; previously only numeric 0/1 was recognized.
  • NA in an indicator table is treated as absent. It previously reached Matrix::sparseMatrix() as an NA index and failed with an internal error.
  • cooccurrence() accepts a nestimate_data object from Nestimate::prepare() and uses its sequence_data, so event logs can be sessionized by prepare() (time gaps, timestamp parsing) and networked here without duplicating that logic. Passing one used to fall into the list-of-transactions branch and silently produce items built from the object’s internal components.
  • Bug fix: unknown arguments were silently swallowed by ..., so a typo such as simliarity = "jaccard" returned raw counts with no warning. Unknown or unnamed arguments now raise an error.
  • as.data.frame() on a summary.cooccurrence object returns tidy node-level metrics (what = "nodes", the default) or group-level metrics (what = "groups"), so callers no longer reach into the summary object’s internals.
  • similarity = "relative" is asymmetric, so each direction is now thresholded on its own value. Filtering by the undirected pair could retain a direction whose weight was below threshold.
  • Added a cross-implementation test suite pinning cooccurrence() to Nestimate::cooccurrence() across all eight similarity measures, all five shared input formats, min_occur, threshold, top_n, their combinations, degenerate inputs, and randomized networks. The two agree exactly once conventions are aligned: Nestimate stores item frequency on the matrix diagonal (diagonal = TRUE) where cooccure zeroes it, and Nestimate keeps every edge tied at the top_n cut where cooccure truncates strictly at N.
  • Bug fix: weight_by combined with min_occur > 1 errored with “‘dims’ must contain all (i,j) pairs”. The binary companion matrix was built from unfiltered column indices against filtered dimensions.
  • Bug fix: min_occur support for weighted input counted rows rather than distinct documents, so duplicate rows for one document could keep an item that appears in only that document.
  • Bug fix: threshold and top_n filtered the edge list but not the stored matrix, so as_matrix(), the heatmap, and the cograph and netobject converters disagreed with the printed edges (top_n = 1 returned one edge but a three-edge matrix). The matrix is now rebuilt from the surviving edges.
  • Bug fix: with counting = "attention", a window covering the whole sequence did not reproduce the unwindowed result. embed() emits each window most-recent-first, and attention reads positional gaps off the transaction, so the decay was mirrored. Windows are restored to reading order before deduplication; set-based counting is unaffected.
  • as_igraph() and as_matrix() now accept output = "gephi" objects, whose columns are named Source/Target/Weight/Count. They previously failed with “undefined columns selected”.
  • A group that fails during split_by now warns and names the group. Genuinely edgeless groups are still dropped quietly.
  • threshold = 0 is documented as meaning no filtering rather than “drop negative weights”, so centering scalings such as scale = "zscore" keep their negative half.
  • Bug fix: the argument order now reproduces the CRAN 0.1.1 signature for its first thirteen arguments, so positional calls written against 0.1.1 keep their meaning. Arguments added in 0.1.2 (vars, the event-log arguments, group, aggregate_by, aggregate, window, lambda) follow them.
  • Bug fix: a column repeated in vars was counted twice by the sparse builder, inflating weight and count for every pair involving it. Selected columns are now deduplicated.
  • Bug fix: a split_by result inherited the FIRST group’s matrix, items, frequencies, and n_transactions attributes from rbind(), so as_matrix() and the other converters silently described only that group. Those attributes are now dropped from split results, and as_matrix() rebuilds from the full edge list. Per-group support is recorded in the new group_items and group_transactions attributes.
  • Bug fix: the groups attribute of a split_by result listed every input level, including groups that produced no edges and were dropped from the data. It now lists only the groups present.
  • Bug fix: per-group density in summary() counted only nodes that appear in an edge, so a group containing an isolated node reported a density above its true value (a group with edge A-B plus isolated C reported density 1 instead of 1/3).
  • Bug fix: summary() and print() reported pooled simple-graph metrics for split_by results, where a node pair can occur once per group. Density could exceed 1 and node degree could exceed n_nodes - 1. density, mean_degree, and possible_edges are now NA for split results, and the per-group table reports the correct values.

cooccure 0.1.1

CRAN release: 2026-04-24

  • First CRAN release. Package renamed from cooccur (GitHub-only development name) to cooccure to avoid collision with an unrelated archived CRAN package of the same name.
  • Internal engine rewritten on top of Matrix::sparseMatrix / Matrix::crossprod. The previous implementation allocated dense n x k and k x k matrices, which hit R’s vector memory limit on corpora with many documents and items (e.g. citation networks > ~100k unique references). The sparse rewrite stays in triplet form end-to-end and scales linearly with the number of non-zero co-occurrences.
  • attr(x, "matrix") and attr(x, "raw_matrix") are now sparse Matrix objects. as_matrix() densifies them on demand so existing downstream code keeps working.
  • Added Matrix to Imports.
  • Delimited parsers (.co_parse_delimited, .co_parse_multi_delimited) vectorized: trimws(), NA/empty filtering, and per-row deduplication now run as single C-level calls over the flattened token vector rather than as per-row R calls. Cuts overall runtime on a 166k-row x 20-items-per-row citation corpus from ~6.2 s to ~3.4 s (~1.8x faster).

cooccure 0.1.0

  • Initial development release (distributed as cooccur on GitHub).
  • cooccurrence() (alias co()) builds co-occurrence networks from six input formats: delimited fields, multi-column delimited, long/bipartite, binary matrices, wide sequences (field = "all"), and lists of character vectors.
  • Eight similarity measures: none, jaccard, cosine, inclusion, association, dice, equivalence, relative.
  • Eight scaling methods: minmax, log, log10, binary, zscore, sqrt, proportion, none.
  • counting = "fractional" implements the Perianes-Rodriguez et al.
    1. weighted pair count.
  • weight_by parameter supports weighted long-format input (e.g. LDA topic-document probabilities).
  • split_by computes a separate network per group and returns a combined edge data frame.
  • output argument returns edges in default, Gephi, igraph, cograph, or matrix form.
  • S3 converters: as_matrix(), as_igraph(), as_tidygraph(), as_cograph(), as_netobject().
  • Shiny app accessible via launch_app().