The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

cooccure 0.1.1

• 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).

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.
  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().

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.