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:
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')) |
- AssociatedPress - Associated Press data
- JSS_papers - JSS Papers Dublin Core Metadata
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 months agofrom:9f331d1955. Checks:OK: 1 NOTE: 1. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Aug 20 2024 |
| R-4.5-linux-x86_64 | NOTE | Aug 20 2024 |
Exports:CTMdistHellingerdtm2ldaformatget_termsget_topicsLDAldaformat2dtmlogLikperplexityposteriortermstopics
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Associated Press data | AssociatedPress |
| Correlated Topic Model | CTM |
| Compute Hellinger distance | distHellinger distHellinger.default distHellinger.simple_triplet_matrix |
| JSS Papers Dublin Core Metadata | JSS_papers |
| Latent Dirichlet Allocation | LDA |
| Transform data from and for use with the 'lda' package | dtm2ldaformat ldaformat2dtm |
| Methods for Function logLik | logLik,Gibbs_list-method logLik,TopicModel-method |
| Methods for Function perplexity | perplexity 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 probabilities | posterior,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 models | coerce,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 |