Multilevel Supervised Topic Models with Multiple Outcomes R package mlstm version 0.1.6 Fits latent Dirichlet allocation (LDA), supervised topic models, and multilevel supervised topic models for text data with multiple outcome variables. Core estimation routines are implemented in C++ using the 'Rcpp' ecosystem. For topic models, see Blei et al. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>. For supervised topic models, see Blei and McAuliffe (2007) <https://papers.nips.cc/paper_files/paper/2007/hash/d56b9fc4b0f1be8871f5e1c40c0067e7-Abstract.html>. Software Depends: R (>= 4.0.0) Imports: Rcpp, Matrix, data.table, RcppParallel, stats LinkingTo: Rcpp, RcppArmadillo, RcppParallel, BH Suggests: knitr, rmarkdown, testthat (>= 3.0.0) Tomoya Himeno <bd24f002@g.hit-u.ac.jp> Comprehensive R Archive Network (CRAN) Tomoya Himeno [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=mlstm/LICENSE) 2026-04-03 application/tgz https://CRAN.R-project.org/package=mlstm doi:10.32614/CRAN.package.mlstm