Function reference • textmineR

All functions
`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 a topic model
`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 sampling
`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
`CalcLikelihoodC` `CalcSumSquares` `dtm_to_lexicon_c` `Dtm2DocsC` `fit_lda_c` `predict_lda_c` `Hellinger_cpp` `HellingerMat` `JSD_cpp` `JSDmat`	Internal helper functions for `textmineR`
`LabelTopics()`	Get some topic labels using a "more probable" method of terms
`SummarizeTopics()`	Summarize topics in a topic model
`TermDocFreq()`	Get term frequencies and document frequencies from a document term matrix.
`TmParallelApply()`	An OS-independent parallel version of `lapply`
`nih_sample` `nih_sample_dtm` `nih_sample_topic_model`	Abstracts and metadata from NIH research grants awarded in 2014
`posterior()`	Posterior methods for topic models
`posterior(<lda_topic_model>)`	Draw from the posterior of an LDA topic model
`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
`textmineR`	textmineR
`update()`	Update methods for topic models
`update(<lda_topic_model>)`	Update a Latent Dirichlet Allocation topic model with new data

Reference

All functions