Package: topicmodels 0.2-17

Bettina Grün

topicmodels: Topic Models

Provides an interface to the C code for Latent Dirichlet Allocation (LDA) models and Correlated Topics Models (CTM) by David M. Blei and co-authors and the C++ code for fitting LDA models using Gibbs sampling by Xuan-Hieu Phan and co-authors.

Authors:Bettina Grün [aut, cre], Kurt Hornik [aut], David M Blei [ctb, cph], John D Lafferty [ctb, cph], Xuan-Hieu Phan [ctb, cph], Makoto Matsumoto [ctb, cph], Takuji Nishimura [ctb, cph], Shawn Cokus [ctb]

topicmodels_0.2-17.tar.gz
topicmodels_0.2-17.tar.gz(r-4.5-noble)topicmodels_0.2-17.tar.gz(r-4.4-noble)
topicmodels_0.2-17.tgz(r-4.4-emscripten)topicmodels_0.2-17.tgz(r-4.3-emscripten)
topicmodels.pdf |topicmodels.html✨
topicmodels/json (API)
NEWS

# Install 'topicmodels' in R:
install.packages('topicmodels', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • gsl– GNU Scientific Library (GSL)
  • c++– GNU Standard C++ Library v3
Datasets:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

9.57 score 8 stars 15 packages 4.9k scripts 14k downloads 5 mentions 12 exports 9 dependencies

Last updated 3 months agofrom:9f331d1955. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 19 2024
R-4.5-linux-x86_64OKNov 19 2024

Exports:CTMdistHellingerdtm2ldaformatget_termsget_topicsLDAldaformat2dtmlogLikperplexityposteriortermstopics

Dependencies:BHclimodeltoolsNLPRcpprlangslamtmxml2

topicmodels: An R Package for Fitting Topic Models

Rendered fromtopicmodels.Rnwusingutils::Sweaveon Nov 19 2024.

Last update: 2024-01-10
Started: 2012-04-17

Readme and manuals

Help Manual

Help pageTopics
Associated Press dataAssociatedPress
Correlated Topic ModelCTM
Compute Hellinger distancedistHellinger distHellinger.default distHellinger.simple_triplet_matrix
JSS Papers Dublin Core MetadataJSS_papers
Latent Dirichlet AllocationLDA
Transform data from and for use with the 'lda' packagedtm2ldaformat ldaformat2dtm
Methods for Function logLiklogLik,Gibbs_list-method logLik,TopicModel-method
Methods for Function perplexityperplexity perplexity,ANY,DocumentTermMatrix-method perplexity,ANY,matrix-method perplexity,Gibbs,simple_triplet_matrix-method perplexity,Gibbs_list,simple_triplet_matrix-method perplexity,list,missing-method perplexity,list,simple_triplet_matrix-method perplexity,VEM,missing-method perplexity,VEM,simple_triplet_matrix-method
Determine posterior probabilitiesposterior,TopicModel,ANY-method posterior,TopicModel,missing-method
Extract most likely terms or topics.get_terms get_topics terms,TopicModel-method topics topics,TopicModel-method
Virtual class "TopicModel"CTM-class LDA-class show,TopicModel-method TopicModel-class
Different classes for controlling the estimation of topic modelscoerce,list,CTM_VEMcontrol-method coerce,list,LDA_VEMcontrol-method coerce,list,OPTcontrol-method coerce,NULL,CTM_VEMcontrol-method coerce,NULL,LDA_VEMcontrol-method coerce,NULL,LDcontrol-method coerce,NULL,OPTcontrol-method CTM_VEMcontrol-class LDAcontrol-class LDA_Gibbscontrol-class LDA_VEMcontrol-class OPTcontrol-class TopicModelcontrol-class