text: Analyses of Text using Natural Language Processing and Machine Learning

Transforms text variables to word embeddings; where the word embeddings are used to statistically test the mean difference between set of texts, compute semantic similarity scores between texts, predict numerical variables, and visual statistically significant words according to various dimensions etc. For more information see <https://www.r-text.org>.

Version: 0.9.10
Depends: R (≥ 4.00)
Imports: dplyr, tokenizers, tibble, stringr, tidyr, ggplot2, ggrepel, cowplot, rlang, purrr, magrittr, parsnip, recipes, rsample, reticulate, tune, workflows, yardstick, future, furrr
Suggests: knitr, rmarkdown, testthat, rio, glmnet, randomForest, covr, xml2, ranger
Published: 2020-12-14
Author: Oscar Kjell ORCID iD [aut, cre], Salvatore Giorgi ORCID iD [aut], Andrew Schwartz ORCID iD [aut]
Maintainer: Oscar Kjell <oscar.kjell at psy.lu.se>
BugReports: https://github.com/OscarKjell/text/issues/
License: GPL-3
URL: https://r-text.org/, https://github.com/OscarKjell/text/
NeedsCompilation: no
SystemRequirements: Python (>= 3.6.0)
Citation: text citation info
Materials: README NEWS
CRAN checks: text results


Reference manual: text.pdf
Vignettes: Computer_Capacity
Package source: text_0.9.10.tar.gz
Windows binaries: r-devel: text_0.9.10.zip, r-release: text_0.9.10.zip, r-oldrel: not available
macOS binaries: r-release: text_0.9.10.tgz, r-oldrel: not available
Old sources: text archive


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