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| lda-package | Collapsed Gibbs Samplers and Related Utility Functions for LDA-type Models |
| cora | A subset of the Cora dataset of scientific documents. |
| cora.cites | A subset of the Cora dataset of scientific documents. |
| cora.documents | A subset of the Cora dataset of scientific documents. |
| cora.titles | A subset of the Cora dataset of scientific documents. |
| cora.vocab | A subset of the Cora dataset of scientific documents. |
| document.lengths | Compute Summary Statistics of a Corpus |
| filter.words | Functions to manipulate text corpora in LDA format. |
| lda | Collapsed Gibbs Samplers and Related Utility Functions for LDA-type Models |
| lda.collapsed.gibbs.sampler | Functions to Fit LDA-type models |
| lexicalize | Generate LDA Documents from Raw Text |
| links.as.edgelist | Convert a set of links keyed on source to a single list of edges. |
| merge.documents | Functions to manipulate text corpora in LDA format. |
| mmsb.collapsed.gibbs.sampler | Functions to Fit LDA-type models |
| nubbi.collapsed.gibbs.sampler | Collapsed Gibbs Sampling for the Networks Uncovered By Bayesian Inference (NUBBI) Model. |
| poliblog | A collection of political blogs with ratings. |
| poliblog.documents | A collection of political blogs with ratings. |
| poliblog.ratings | A collection of political blogs with ratings. |
| poliblog.vocab | A collection of political blogs with ratings. |
| predictive.distribution | Compute predictive distributions for fitted LDA-type models. |
| predictive.link.probability | Use the RTM to predict whether a link exists between two documents. |
| read.documents | Read LDA-formatted Document and Vocabulary Files |
| read.vocab | Read LDA-formatted Document and Vocabulary Files |
| rtm.collapsed.gibbs.sampler | Collapsed Gibbs Sampling for the Relational Topic Model (RTM). |
| sampson | Sampson monk data |
| shift.word.indices | Functions to manipulate text corpora in LDA format. |
| slda.em | Functions to Fit LDA-type models |
| top.topic.documents | Get the Top Words and Documents in Each Topic |
| top.topic.words | Get the Top Words and Documents in Each Topic |
| word.counts | Compute Summary Statistics of a Corpus |