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stm (version 1.3.5)

Estimation of the Structural Topic Model

Description

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. Methods developed in Roberts et al (2014) and Roberts et al (2016) . Vignette is Roberts et al (2019) .

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Version

Install

install.packages('stm')

Monthly Downloads

3,318

Version

1.3.5

License

MIT + file LICENSE

Maintainer

Brandon Stewart

Last Published

December 17th, 2019

Functions in stm (1.3.5)

asSTMCorpus

STM Corpus Coercion
manyTopics

Performs model selection across separate STM's that each assume different numbers of topics.
gadarian

Gadarian and Albertson data
textProcessor

Process a vector of raw texts
readCorpus

Read in a corpus file.
js.estimate

A James-Stein Estimator Shrinking to a Uniform Distribution
readLdac

Read in a .ldac Formatted File
multiSTM

Analyze Stability of Local STM Mode
exclusivity

Exclusivity
make.heldout

Heldout Likelihood by Document Completion
poliblog5k

CMU 2008 Political Blog Corpus
findThoughts

Find Thoughts
plot.searchK

Plots diagnostic values resulting from searchK
plot.topicCorr

Plot a topic correlation graph
plot.STMpermute

Plot an STM permutation test.
fitNewDocuments

Fit New Documents
plot.estimateEffect

Plot effect of covariates on topics
findTopic

Find topics that contain user specified words.
labelTopics

Label topics
make.dt

Make a data.table of topic proportions.
plotTopicLoess

Plot some effects with loess
plotRemoved

Plot documents, words and tokens removed at various word thresholds
prepDocuments

Prepare documents for analysis with stm
stm-package

Structural Topic Model
selectModel

Assists the user in selecting the best STM model.
plotQuote

Plots strings
plot.STM

Functions for plotting STM objects
plot.MultimodDiagnostic

Plotting Method for Multimodality Diagnostic Objects
plotModels

Plots semantic coherence and exclusivity for high likelihood models outputted from selectModel.
semanticCoherence

Semantic Coherence
sageLabels

Displays verbose labels that describe topics and topic-covariate groups in depth.
stm

Variational EM for the Structural Topic Model
topicLasso

Plot predictions using topics
topicCorr

Estimate topic correlation
makeDesignMatrix

Make a Design Matrix
rmvnorm

Draw from a Multivariate Normal
summary.STM

Summary Function for the STM objects
thetaPosterior

Draw from Theta Posterior
s

Make a B-spline Basis Function
permutationTest

Permutation test of a binary covariate.
writeLdac

Write a .ldac file
optimizeDocument

Optimize Document
toLDAvisJson

Wrapper to create Json mapping for LDAvis. This can be useful in indirect render e.g. Shiny Dashboards
toLDAvis

Wrapper to launch LDAvis topic browser.
summary.estimateEffect

Summary for estimateEffect
topicQuality

Plots semantic coherence and exclusivity for each topic.
searchK

Computes diagnostic values for models with different values of K (number of topics).
unpack.glmnet

Unpack a glmnet object
estimateEffect

Estimates regressions using an STM object
checkResiduals

Residual dispersion test for topic number
cloud

Plot a wordcloud
calcscore

Calculate Score Words
checkBeta

Looks for words that load exclusively onto a topic
calcfrex

Calculate FREX (FRequency and EXclusivity) Words
convertCorpus

Convert stm formatted documents to another format
alignCorpus

Align the vocabulary of a new corpus to an old corpus
calclift

Calculate Lift Words