# Functions for Text Mining and Topic Modeling

## Documentation for package ‘textmineR’ version 3.0.4

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

textmineR-package | textmineR
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