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>.

Version: 1.3.3
Depends: R (≥ 3.2.2)
Imports: matrixStats, splines, slam, lda, quanteda, stringr, Matrix, glmnet, Rcpp (≥ 0.11.3), grDevices, graphics, stats, utils, data.table, quadprog, parallel, methods
LinkingTo: Rcpp, RcppArmadillo
Suggests: igraph, SnowballC, tm (≥ 0.6), huge, clue, wordcloud, KernSmooth, NLP, LDAvis, geometry, Rtsne, testthat, rsvd
Published: 2018-01-28
Author: Margaret Roberts [aut, cre], Brandon Stewart [aut, cre], Dustin Tingley [aut, cre], Kenneth Benoit [ctb]
Maintainer: Brandon Stewart <bms4 at>
License: MIT + file LICENSE
NeedsCompilation: yes
Citation: stm citation info
Materials: NEWS
In views: NaturalLanguageProcessing
CRAN checks: stm results


Reference manual: stm.pdf
Vignettes: Using stm
Package source: stm_1.3.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: stm_1.3.3.tgz
OS X Mavericks binaries: r-oldrel: stm_1.3.0.tgz
Old sources: stm archive

Reverse dependencies:

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


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