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| -rw-r--r-- | gnu/packages/cran.scm | 32 |
1 files changed, 32 insertions, 0 deletions
diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm index 33cc882030..89af513c8b 100644 --- a/gnu/packages/cran.scm +++ b/gnu/packages/cran.scm @@ -17314,6 +17314,38 @@ manipulate tree data.") R packages (on CRAN, Bioconductor or Github).") (license license:artistic2.0))) +(define-public r-doc2vec + (package + (name "r-doc2vec") + (version "0.2.0") + (source (origin + (method url-fetch) + (uri (cran-uri "doc2vec" version)) + (sha256 + (base32 + "0249hm0103kxxsi4gks4h20wf6p00gbrk9jf8c148mbja1l56f6v")))) + (properties `((upstream-name . "doc2vec"))) + (build-system r-build-system) + (propagated-inputs (list r-rcpp)) + (home-page "https://github.com/bnosac/doc2vec") + (synopsis "Distributed representations of sentences, documents and topics") + (description + "Learn vector representations of sentences, paragraphs or documents by +using the Paragraph Vector algorithms, namely the distributed bag of +words (PV-DBOW) and the distributed memory (PV-DM) model. Top2vec finds +clusters in text documents by combining techniques to embed documents and +words and density-based clustering. It does this by embedding documents in +the semantic space as defined by the doc2vec algorithm. Next it maps these +document embeddings to a lower-dimensional space using the Uniform Manifold +Approximation and Projection (UMAP) clustering algorithm and finds dense areas +in that space using a Hierarchical Density-Based Clustering +technique (HDBSCAN). These dense areas are the topic clusters which can be +represented by the corresponding topic vector which is an aggregate of the +document embeddings of the documents which are part of that topic cluster. In +the same semantic space similar words can be found which are representative of +the topic.") + (license license:expat))) + (define-public r-docopt (package (name "r-docopt") |