@@ -19543,3 +19543,40 @@ and co-authors and the C++ code for fitting LDA models using Gibbs sampling by
Xuan-Hieu Phan and co-authors.")
(license license:gpl2)))
+(define-public r-stm
+ (package
+ (name "r-stm")
+ (version "1.3.5")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (cran-uri "stm" version))
+ (sha256
+ (base32
+ "1yyfxaxqc6yq0yq68zhdnhpwpvsyp71dlmivn7zxixfmp932s6cn"))))
+ (properties `((upstream-name . "stm")))
+ (build-system r-build-system)
+ (propagated-inputs
+ `(("r-data-table" ,r-data-table)
+ ("r-glmnet" ,r-glmnet)
+ ("r-lda" ,r-lda)
+ ("r-matrix" ,r-matrix)
+ ("r-matrixstats" ,r-matrixstats)
+ ("r-quadprog" ,r-quadprog)
+ ("r-quanteda" ,r-quanteda)
+ ("r-rcpp" ,r-rcpp)
+ ("r-rcpparmadillo" ,r-rcpparmadillo)
+ ("r-slam" ,r-slam)
+ ("r-stringr" ,r-stringr)))
+ (home-page "http://www.structuraltopicmodel.com/")
+ (synopsis
+ "Estimation of the Structural Topic Model")
+ (description
+ "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>.")
+ (license license:expat)))
+