Functions for Text Mining and Topic Modeling

---

Documentation for package ‘textmineR’ version 3.0.5

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

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
CalcLikelihoodC	Internal helper functions for 'textmineR'
CalcProbCoherence	Probabilistic coherence of topics
CalcSumSquares	Internal helper functions for 'textmineR'
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
Dtm2DocsC	Internal helper functions for 'textmineR'
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
dtm_to_lexicon_c	Internal helper functions for 'textmineR'
FitCtmModel	Fit a Correlated Topic Model
FitLdaModel	Fit a Latent Dirichlet Allocation topic model
FitLsaModel	Fit a topic model using Latent Semantic Analysis
fit_lda_c	Internal helper functions for 'textmineR'
GetProbableTerms	Get cluster labels using a "more probable" method of terms
GetTopTerms	Get Top Terms for each topic from a topic model
HellingerMat	Internal helper functions for 'textmineR'
Hellinger_cpp	Internal helper functions for 'textmineR'
JSDmat	Internal helper functions for 'textmineR'
JSD_cpp	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 2014
nih_sample	Abstracts and metadata from NIH research grants awarded in 2014
nih_sample_dtm	Abstracts and metadata from NIH research grants awarded in 2014
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
predict_lda_c	Internal helper functions for 'textmineR'
SummarizeTopics	Summarize topics in a topic model
TermDocFreq	Get term frequencies and document frequencies from a document term matrix.
textmineR	textmineR
TmParallelApply	An OS-independent parallel version of 'lapply'
update	Update methods for topic models
update.lda_topic_model	Update a Latent Dirichlet Allocation topic model with new data