stm: 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) <doi:10.1111/ajps.12103> and Roberts et al (2016) <doi:10.1080/01621459.2016.1141684>. Vignette is Roberts et al (2019) <doi:10.18637/jss.v091.i02>.

Version: 1.3.4
Depends: R (≥ 3.2.2), methods
Imports: Rcpp (≥ 0.11.3), data.table, glmnet, grDevices, graphics, lda, Matrix, matrixStats, parallel, quadprog, quanteda, slam, splines, stats, stringr, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: clue, geometry, huge, igraph, LDAvis, KernSmooth, NLP, rsvd, Rtsne, SnowballC, spelling, testthat, tm (≥ 0.6), wordcloud
Published: 2019-10-30
Author: Margaret Roberts [aut], Brandon Stewart [aut, cre], Dustin Tingley [aut], Kenneth Benoit [ctb]
Maintainer: Brandon Stewart <bms4 at>
License: MIT + file LICENSE
NeedsCompilation: yes
Language: en-US
Citation: stm citation info
Materials: NEWS
In views: NaturalLanguageProcessing
CRAN checks: stm results


Reference manual: stm.pdf
Vignettes: Using stm
Package source: stm_1.3.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: stm_1.3.3.tgz, r-oldrel: stm_1.3.3.tgz
Old sources: stm archive

Reverse dependencies:

Reverse depends: stmgui
Reverse imports: stmCorrViz, stminsights, themetagenomics
Reverse suggests: quanteda, tidytext


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