The Structural Topic Model (STM) allows researchers to estimate topic models with document-level covariates. The package also includes tools for model selection, visualization, and estimation of topic-covariate regressions.
|Author||Margaret E. Roberts, Brandon M. Stewart and Dustin Tingley|
|Date of publication||2016-01-15 00:09:18|
|Maintainer||Brandon Stewart <email@example.com>|
|License||MIT + file LICENSE|
checkResiduals: Residual dispersion test for topic number
cloud: Plot a wordcloud
convertCorpus: Convert 'stm' formatted documents to another format
estimateEffect: Estimates regressions using an STM object
findThoughts: Find Thoughts
findTopic: Find topics that contain user specified words.
gadarian: Gadarian and Albertson data
heldout: Heldout Likelihood by Document Completion
labelTopics: Label topics
manyTopics: Performs model selection across separate STM's that each...
multiSTM: Analyze Stability of Local STM Mode
permutationTest: Permutation test of a binary covariate.
plot.estimateEffect: Plot effect of covariates on topics
plotModels: Plots semantic coherence and exclusivity for high likelihood...
plot.MultimodDiagnostic: Plotting Method for Multimodality Diagnostic Objects
plotQuote: Plots strings
plotRemoved: Plot documents, words and tokens removed at various word...
plot.searchK: Plots diagnostic values resulting from searchK
plot.STM: Plot summary of an STM object
plot.STMpermute: Plot an STM permutation test.
plot.topicCorr: Plot a topic correlation graph
plotTopicLoess: Plot some effects with loess
poliblog5k: CMU 2008 Political Blog Corpus
prepDocuments: Prepare documents for analysis with 'stm'
readCorpus: Read in a corpus file.
s: Make a B-spline Basis Function
sageLabels: Displays verbose labels that describe topics and...
searchK: Computes diagnostic values for models with different values...
selectModel: Assists the user in selecting the best STM model.
stm: Variational EM for the Structural Topic Model
stm-package: Structural Topic Model
summary.STM: Summary Function for the STM objects
textProcessor: Process a vector of raw texts
thetaPosterior: Draw from Theta Posterior
toLDAvis: Wrapper to launch LDAvis topic browser.
topicCorr: Estimate topic correlation
topicLasso: Plot predictions using topics
topicQuality: Plots semantic coherence and exclusivity for each topic.
writeLdac: Write a .ldac file