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

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1.3.6 by Brandon Stewart, a year ago

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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