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 resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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