doc2concrete: Measuring Concreteness in Natural Language

Models for detecting concreteness in natural language. This package is built in support of Yeomans (2020) <doi:10.17605/OSF.IO/DYZN6>, which reviews linguistic models of concreteness in several domains. Here, we provide an implementation of the best-performing domain-general model (from Brysbaert et al., (2014) <doi:10.3758/s13428-013-0403-5>) as well as two pre-trained models for the feedback and plan-making domains.

Version: 0.5.0
Depends: R (≥ 3.5.0)
Imports: tm, quanteda, ggplot2, parallel, glmnet, stringr, dplyr, english, textstem, SnowballC, textclean
Suggests: knitr, rmarkdown, testthat
Published: 2020-11-16
Author: Mike Yeomans
Maintainer: Mike Yeomans <mk.yeomans at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: doc2concrete results


Reference manual: doc2concrete.pdf
Vignettes: doc2concrete
Package source: doc2concrete_0.5.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: doc2concrete_0.5.0.tgz, r-oldrel: doc2concrete_0.5.0.tgz
Old sources: doc2concrete archive


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