gnu: Add r-btm.
* gnu/packages/cran.scm (r-btm): New variable.
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@ -28045,3 +28045,34 @@ (define-public r-eyelinker
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"Imports plain-text ASC data files from EyeLink eye trackers into
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(relatively) tidy data frames for analysis and visualization.")
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(license license:gpl3)))
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(define-public r-btm
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(package
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(name "r-btm")
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(version "0.3.5")
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(source
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(origin
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(method url-fetch)
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(uri (cran-uri "BTM" version))
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(sha256
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(base32
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"1x6bncb7r97z8bdyxnn2frdi9kyawfy6c2041mv9f42zdrfzm6jb"))))
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(properties `((upstream-name . "BTM")))
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(build-system r-build-system)
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(propagated-inputs `(("r-rcpp" ,r-rcpp)))
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(home-page "https://github.com/bnosac/BTM")
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(synopsis "Biterm Topic Models for Short Text")
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(description
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"Biterm Topic Models find topics in collections of short texts. It is a
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word co-occurrence based topic model that learns topics by modeling word-word
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co-occurrences patterns which are called biterms. This in contrast to
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traditional topic models like Latent Dirichlet Allocation and Probabilistic
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Latent Semantic Analysis which are word-document co-occurrence topic models. A
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biterm consists of two words co-occurring in the same short text window. This
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context window can for example be a twitter message, a short answer on a
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survey, a sentence of a text or a document identifier. The techniques are
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explained in detail in the paper 'A Biterm Topic Model For Short Text' by
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Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng (2013)
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@url{https://github.com/xiaohuiyan/xiaohuiyan.github.io/blob/master/paper/\
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BTM-WWW13.pdf}.")
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(license license:asl2.0)))
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