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) .


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Reference manual

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install.packages("stm")

1.3.6 by Brandon Stewart, 10 months 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  


Task views: Natural Language Processing


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, igraph, LDAvis, KernSmooth, NLP, rsvd, Rtsne, SnowballC, spelling, testthat, tm, wordcloud

Linking to Rcpp, RcppArmadillo


Imported by stmCorrViz, stminsights, themetagenomics.

Depended on by stmgui.

Suggested by oolong, quanteda, tidytext.


See at CRAN