Package: topicmodels.etm
Type: Package
Title: Topic Modelling in Embedding Spaces
Version: 0.1.0
Maintainer: Jan Wijffels <jwijffels@bnosac.be>
Authors@R: c(
    person('Jan', 'Wijffels', role = c('aut', 'cre', 'cph'), email = 'jwijffels@bnosac.be', comment = "R implementation"), 
    person('BNOSAC', role = 'cph', comment = "R implementation"), 
    person('Adji B. Dieng', role = c('ctb', 'cph'), comment = "original Python implementation in inst/orig"),
    person('Francisco J. R. Ruiz', role = c('ctb', 'cph'), comment = "original Python implementation in inst/orig"),
    person('David M. Blei', role = c('ctb', 'cph'), comment = "original Python implementation in inst/orig"))
Description: Find topics in texts which are semantically embedded using techniques like word2vec or Glove. 
    This topic modelling technique models each word with a categorical distribution whose natural parameter is the inner product between a word embedding and an embedding of its assigned topic.
    The techniques are explained in detail in the paper 'Topic Modeling in Embedding Spaces' by Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei (2019), available at <arXiv:1907.04907>.
License: MIT + file LICENSE
Encoding: UTF-8
SystemRequirements: LibTorch (https://pytorch.org/)
Depends: R (>= 2.10)
Imports: graphics, stats, Matrix, torch (>= 0.5.0)
Suggests: udpipe (>= 0.8.4), word2vec, uwot, tinytest, textplot (>=
        0.2.0), ggrepel, ggalt
RoxygenNote: 7.1.2
NeedsCompilation: no
Packaged: 2021-11-05 11:01:22 UTC; Jan
Author: Jan Wijffels [aut, cre, cph] (R implementation),
  BNOSAC [cph] (R implementation),
  Adji B. Dieng [ctb, cph] (original Python implementation in inst/orig),
  Francisco J. R. Ruiz [ctb, cph] (original Python implementation in
    inst/orig),
  David M. Blei [ctb, cph] (original Python implementation in inst/orig)
Repository: CRAN
Date/Publication: 2021-11-08 08:40:02 UTC
Built: R 4.2.0; ; 2023-07-10 23:55:32 UTC; unix
