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stm (version 1.3.6)

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

2,700

Version

1.3.6

License

MIT + file LICENSE

Maintainer

Brandon Stewart

Last Published

September 18th, 2020

Functions in stm (1.3.6)

alignCorpus

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

STM Corpus Coercion
convertCorpus

Convert stm formatted documents to another format
checkBeta

Looks for words that load exclusively onto a topic
estimateEffect

Estimates regressions using an STM object
calcscore

Calculate Score Words
cloud

Plot a wordcloud
checkResiduals

Residual dispersion test for topic number
calclift

Calculate Lift Words
make.heldout

Heldout Likelihood by Document Completion
findThoughts

Find Thoughts
calcfrex

Calculate FREX (FRequency and EXclusivity) Words
findTopic

Find topics that contain user specified words.
fitNewDocuments

Fit New Documents
gadarian

Gadarian and Albertson data
labelTopics

Label topics
make.dt

Make a data.table of topic proportions.
exclusivity

Exclusivity
makeDesignMatrix

Make a Design Matrix
js.estimate

A James-Stein Estimator Shrinking to a Uniform Distribution
optimizeDocument

Optimize Document
plot.searchK

Plots diagnostic values resulting from searchK
plot.topicCorr

Plot a topic correlation graph
permutationTest

Permutation test of a binary covariate.
plot.STMpermute

Plot an STM permutation test.
plot.estimateEffect

Plot effect of covariates on topics
plot.MultimodDiagnostic

Plotting Method for Multimodality Diagnostic Objects
plot.STM

Functions for plotting STM objects
manyTopics

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

Prepare documents for analysis with stm
rmvnorm

Draw from a Multivariate Normal
readCorpus

Read in a corpus file.
readLdac

Read in a .ldac Formatted File
multiSTM

Analyze Stability of Local STM Mode
plotRemoved

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

Make a B-spline Basis Function
plotQuote

Plots strings
plotModels

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

Plot some effects with loess
poliblog5k

CMU 2008 Political Blog Corpus
selectModel

Assists the user in selecting the best STM model.
summary.STM

Summary Function for the STM objects
sageLabels

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

Semantic Coherence
stm-package

Structural Topic Model
stm

Variational EM for the Structural Topic Model
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.
topicQuality

Plots semantic coherence and exclusivity for each topic.
topicCorr

Estimate topic correlation
unpack.glmnet

Unpack a glmnet object
searchK

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

Plot predictions using topics
summary.estimateEffect

Summary for estimateEffect
writeLdac

Write a .ldac file
textProcessor

Process a vector of raw texts
thetaPosterior

Draw from Theta Posterior