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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: 1.3.7
Depends: R (≥ 3.5.0), 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: 2023-12-01
DOI: 10.32614/CRAN.package.stm
Author: Margaret Roberts [aut], Brandon Stewart [aut, cre], Dustin Tingley [aut], Kenneth Benoit [ctb]
Maintainer: Brandon Stewart <bms4 at princeton.edu>
BugReports: https://github.com/bstewart/stm/issues
License: MIT + file LICENSE
URL: http://www.structuraltopicmodel.com/
NeedsCompilation: yes
Language: en-US
Citation: stm citation info
Materials: NEWS
In views: NaturalLanguageProcessing
CRAN checks: stm results

Documentation:

Reference manual: stm.pdf
Vignettes: Using stm

Downloads:

Package source: stm_1.3.7.tar.gz
Windows binaries: r-devel: stm_1.3.7.zip, r-release: stm_1.3.7.zip, r-oldrel: stm_1.3.7.zip
macOS binaries: r-release (arm64): stm_1.3.7.tgz, r-oldrel (arm64): stm_1.3.7.tgz, r-release (x86_64): stm_1.3.7.tgz, r-oldrel (x86_64): stm_1.3.7.tgz
Old sources: stm archive

Reverse dependencies:

Reverse depends: stmgui
Reverse imports: discursive, stmCorrViz, stminsights, sts, Twitmo
Reverse suggests: oolong, sentopics, tidytext, topicdoc
Reverse enhances: quanteda

Linking:

Please use the canonical form https://CRAN.R-project.org/package=stm to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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