quanteda: Quantitative Analysis of Textual Data

A fast, flexible, and comprehensive framework for quantitative text analysis in R. Provides functionality for corpus management, creating and manipulating tokens and ngrams, exploring keywords in context, forming and manipulating sparse matrices of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and distances, applying content dictionaries, applying supervised and unsupervised machine learning, visually representing text and text analyses, and more.

Version: 0.99.22
Depends: R (≥ 3.4.0), methods
Imports: utils, stats, Matrix (≥ 1.2), data.table (≥ 1.9.6), SnowballC, wordcloud, Rcpp (≥ 0.12.12), RcppParallel, RSpectra, stringi, fastmatch, ggplot2 (≥ 2.2.0), XML, yaml, lubridate, magrittr, spacyr
LinkingTo: Rcpp, RcppParallel, RcppArmadillo (≥ 0.7.600.1.0)
Suggests: knitr, rmarkdown, lda, proxy, topicmodels, tm (≥ 0.6), slam, testthat, RColorBrewer, xtable, DT, ca, purrr
Published: 2017-11-13
Author: Kenneth Benoit [aut, cre, cph], Kohei Watanabe [ctb], Paul Nulty [ctb], Adam Obeng [ctb], Haiyan Wang [ctb], Benjamin Lauderdale [ctb], Will Lowe [ctb]
Maintainer: Kenneth Benoit <kbenoit at lse.ac.uk>
BugReports: https://github.com/kbenoit/quanteda/issues
License: GPL-3
URL: http://quanteda.io
NeedsCompilation: yes
SystemRequirements: C++11
Citation: quanteda citation info
Materials: README NEWS
In views: NaturalLanguageProcessing
CRAN checks: quanteda results

Downloads:

Reference manual: quanteda.pdf
Vignettes: Getting Started Guide
Package source: quanteda_0.99.22.tar.gz
Windows binaries: r-devel: quanteda_0.99.22.zip, r-release: quanteda_0.99.22.zip, r-oldrel: quanteda_0.9.9-65.zip
OS X El Capitan binaries: r-release: quanteda_0.99.22.tgz
OS X Mavericks binaries: r-oldrel: quanteda_0.9.9-65.tgz
Old sources: quanteda archive

Reverse dependencies:

Reverse depends: word.alignment
Reverse imports: clustRcompaR, gofastr, politeness, preText, sentometrics, stm, textstem
Reverse suggests: corpustools, phrasemachine, readtext, stopwords, tidytext
Reverse enhances: corpus

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