doc2concrete: Measuring Concreteness in Natural Language

Models for detecting concreteness in natural language. This package is built in support of Yeomans (2021) <doi:10.1016/j.obhdp.2020.10.008>, 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:	0.6.0
Depends:	R (≥ 3.5.0)
Imports:	tm, quanteda, parallel, glmnet, stringr, english, textstem, SnowballC, stringi
Suggests:	knitr, rmarkdown, testthat
Published:	2024-01-23
DOI:	10.32614/CRAN.package.doc2concrete
Author:	Mike Yeomans
Maintainer:	Mike Yeomans <mk.yeomans at gmail.com>
License:	MIT + file LICENSE
NeedsCompilation:	no
Materials:	README
CRAN checks:	doc2concrete results

Documentation:

Reference manual:	doc2concrete.pdf
Vignettes:	doc2concrete

Downloads:

Package source:	doc2concrete_0.6.0.tar.gz
Windows binaries:	r-devel: doc2concrete_0.6.0.zip, r-release: doc2concrete_0.6.0.zip, r-oldrel: doc2concrete_0.6.0.zip
macOS binaries:	r-release (arm64): doc2concrete_0.6.0.tgz, r-oldrel (arm64): doc2concrete_0.6.0.tgz, r-release (x86_64): doc2concrete_0.6.0.tgz, r-oldrel (x86_64): doc2concrete_0.6.0.tgz
Old sources:	doc2concrete archive

Reverse dependencies:

Reverse imports:

DICEM

Linking:

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