Man pages for textmineR
Functions for Text Mining and Topic Modeling

CalcGammaCalculate a matrix whose rows represent P(topic_i|tokens)
CalcHellingerDistCalculate Hellinger Distance
CalcJSDivergenceCalculate Jensen-Shannon Divergence
CalcLikelihoodCalculate the log likelihood of a document term matrix given...
CalcProbCoherenceProbabilistic coherence of topics
CalcTopicModelR2Calculate the R-squared of a topic model.
Cluster2TopicModelRepresent a document clustering as a topic model
CreateDtmConvert a character vector to a document term matrix.
CreateTcmConvert a character vector to a term co-occurrence matrix.
Dtm2DocsConvert a DTM to a Character Vector of documents
Dtm2LexiconTurn a document term matrix into a list for LDA Gibbs...
Dtm2TcmTurn a document term matrix into a term co-occurrence matrix
FitCtmModelFit a Correlated Topic Model
FitLdaModelFit a Latent Dirichlet Allocation topic model
FitLsaModelFit a topic model using Latent Semantic Analysis
GetProbableTermsGet cluster labels using a "more probable" method of terms
GetTopTermsGet Top Terms for each topic from a topic model
InternalFunctionsInternal helper functions for 'textmineR'
LabelTopicsGet some topic labels using a "more probable" method of terms
nihAbstracts and metadata from NIH research grants awarded in...
predict.ctm_topic_modelPredict method for Correlated topic models (CTM)
predict.lda_topic_modelGet predictions from a Latent Dirichlet Allocation model
predict.lsa_topic_modelPredict method for LSA topic models
SummarizeTopicsSummarize topics in a topic model
TermDocFreqGet term frequencies and document frequencies from a document...
textmineR-deprecatedDeprecated functions in package 'textmineR'.
TmParallelApplyAn OS-independent parallel version of 'lapply'
textmineR documentation built on Oct. 31, 2018, 5:05 p.m.