Functions for Text Mining and Topic Modeling

CalcGamma | Calculate a matrix whose rows represent P(topic_i|tokens) |

CalcHellingerDist | Calculate Hellinger Distance |

CalcJSDivergence | Calculate Jensen-Shannon Divergence |

CalcLikelihood | Calculate the log likelihood of a document term matrix given... |

CalcProbCoherence | Probabilistic coherence of topics |

CalcTopicModelR2 | Calculate the R-squared of a topic model. |

Cluster2TopicModel | Represent a document clustering as a topic model |

CreateDtm | Convert a character vector to a document term matrix. |

CreateTcm | Convert a character vector to a term co-occurrence matrix. |

Dtm2Docs | Convert a DTM to a Character Vector of documents |

Dtm2Lexicon | Turn a document term matrix into a list for LDA Gibbs... |

Dtm2Tcm | Turn a document term matrix into a term co-occurrence matrix |

FitCtmModel | Fit a Correlated Topic Model |

FitLdaModel | Fit a Latent Dirichlet Allocation topic model |

FitLsaModel | Fit a topic model using Latent Semantic Analysis |

GetProbableTerms | Get cluster labels using a "more probable" method of terms |

GetTopTerms | Get Top Terms for each topic from a topic model |

InternalFunctions | Internal helper functions for 'textmineR' |

LabelTopics | Get some topic labels using a "more probable" method of terms |

nih | Abstracts and metadata from NIH research grants awarded in... |

predict.ctm_topic_model | Predict method for Correlated topic models (CTM) |

predict.lda_topic_model | Get predictions from a Latent Dirichlet Allocation model |

predict.lsa_topic_model | Predict method for LSA topic models |

SummarizeTopics | Summarize topics in a topic model |

TermDocFreq | Get term frequencies and document frequencies from a document... |

textmineR | textmineR |

textmineR-deprecated | Deprecated functions in package 'textmineR'. |

TmParallelApply | An OS-independent parallel version of 'lapply' |

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