Estimation of the Structural Topic Model

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) .

Reference manual

install.packages("stm")

1.3.8 by Brandon Stewart, a year ago

http://www.structuraltopicmodel.com/

Report a bug at https://github.com/bstewart/stm/issues

Browse source code at https://github.com/cran/stm

Authors: Margaret Roberts [aut] , Brandon Stewart [aut, cre] , Dustin Tingley [aut] , Kenneth Benoit [ctb]

Documentation: PDF Manual

MIT + file LICENSE license

Imports Rcpp, data.table, glmnet, grDevices, graphics, lda, Matrix, matrixStats, parallel, quadprog, quanteda, slam, splines, stats, stringr, utils

Depends on methods

Suggests clue, geometry, huge, hunspell, igraph, LDAvis, KernSmooth, NLP, rsvd, Rtsne, SnowballC, spelling, testthat, tm, wordcloud

Linking to Rcpp, RcppArmadillo

Imported by SportMiner, discursive, stminsights, sts, tall.

Depended on by stmgui.

Suggested by ELAplus, TextAnalysisR, birddog, oolong, sentopics, surveyframe, tidytext, tmfast, topicdoc.

Enhanced by quanteda.

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