Changes in version 0.1.2                        

  - 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 behaviour. 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.

                 Changes in version 0.1.1 (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)
    vectorised: 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).

                        Changes in version 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.
    (2016) 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().