psychtm: Text Mining Methods for Psychological Research

Provides text mining methods for social science research. The package implements estimation, inference, summarization, and goodness-of-fit methods for topic models including Latent Dirichlet Allocation (LDA), supervised LDA, and supervised LDA with covariates using Bayesian Markov Chain Monte Carlo. A description of the key models and estimation methods is available in Wilcox, Jacobucci, Zhang, & Ammerman (2021). <doi:10.31234/>.

Version: 2021.1.0
Depends: R (≥ 3.3.0)
Imports: coda (≥ 0.4), label.switching, methods, Rcpp (≥ 0.11.0), rlang (≥ 0.4.10), tibble (≥ 2.1.3)
LinkingTo: Rcpp (≥ 0.11.0), RcppArmadillo, RcppProgress (≥ 0.4.2)
Suggests: spelling, knitr (≥ 1.22), covr, dplyr, ggplot2, lda, testthat (≥ 3.0.2), rmarkdown
Published: 2021-11-02
Author: Kenneth Wilcox [aut, cre, cph]
Maintainer: Kenneth Wilcox <kwilcox3 at>
License: LGPL (≥ 3)
NeedsCompilation: yes
SystemRequirements: C++11
Language: en-US
Citation: psychtm citation info
Materials: README NEWS
CRAN checks: psychtm results


Reference manual: psychtm.pdf
Vignettes: Estimating SLDAX Models


Package source: psychtm_2021.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): psychtm_2021.1.0.tgz, r-oldrel (arm64): psychtm_2021.1.0.tgz, r-release (x86_64): psychtm_2021.1.0.tgz, r-oldrel (x86_64): psychtm_2021.1.0.tgz


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