person("Benjamin", "Lauderdale", email = "b.e.lauderdale@lse.ac.uk", role = "aut", comment = c(ORCID = "0000-0003-3090-0969")),
Suggests: spelling, ca, dplyr, DT, e1071, ExPosition, lda, lsa, proxy,

person("Jiong Wei", "Lua", email = "J.W.Lua@lse.ac.uk", role = "aut"),
Suggests: ca, dplyr, DT, e1071, entropy, ExPosition, lda, lsa, proxy,
purrr, RColorBrewer, rmarkdown, slam, spelling, stm, svs,
testthat, text2vec, tibble, tidytext, tm (>= 0.6), topicmodels,

spacyr, stopwords, stringi, xml2, yaml, proxyC (>= 0.1.4)

'fcm-subsetting.R' 'fcm.R' 'future.R' 'kwic.R' 'nfunctions.R'

2.2.0), ggrepel, jsonlite, magrittr, Matrix (>= 1.2), network,
Rcpp (>= 0.12.12), RcppParallel, sna, SnowballC, stopwords,
Suggests: dplyr, DT, e1071, entropy, ExPosition, lda, lsa, proxy,
purrr, quanteda.textmodels, RColorBrewer, rmarkdown, slam,
spacyr, spelling, stm, svs, testthat, text2vec, tibble,
tidytext, tm (>= 0.6), tokenizers, topicmodels, xtable, knitr,
Collate: 'RcppExports.R' 'View.R' 'meta.R' 'quanteda-documentation.R'
'fcm-subsetting.R' 'fcm.R' 'fcm_select.R' 'kwic.R' 'metadoc.R'

stringi, xml2, yaml, proxyC (>= 0.1.4), digest
formatR, tidytext, tm (>= 0.6), tokenizers, topicmodels,

Imports: fastmatch, magrittr, Matrix (>= 1.2), Rcpp (>= 0.12.12),
RcppParallel, SnowballC, stopwords, stringi, xml2, yaml
Suggests: rmarkdown, spelling, testthat, formatR, tm (>= 0.6),
tokenizers, knitr, lda, lsa, dplyr, purrr, quanteda.textmodels,
spacyr, stm, text2vec, topicmodels, jsonlite, quanteda, tibble,
Collate: 'RcppExports.R' 'meta.R' 'quanteda-documentation.R' 'aaa.R'
'fcm_select.R' 'index.R' 'kwic.R' 'message.R' 'nfunctions.R'
'tokens_subset.R' 'utils.R' 'validator.R' 'wordstem.R' 'zzz.R'

creating and manipulating tokens and n-grams, exploring keywords in context,

Title: Scaling Models and Classifiers for Textual Data
Description: Scaling models and classifiers for sparse matrix objects representing
textual data in the form of a document-feature matrix.  Includes original
implementations of 'Laver', 'Benoit', and Garry's (2003) <doi:10.1017/S0003055403000698>,
'Wordscores' model, Perry and 'Benoit's' (2017) <arXiv:1710.08963> class affinity scaling model,
model, as well as methods for correspondence analysis, latent semantic analysis,
and fast Naive Bayes and linear 'SVMs' specially designed for sparse textual data.
Imports: ggplot2, LiblineaR, Matrix (>= 1.2), quanteda (>= 2.0),
RSpectra, Rcpp (>= 0.12.12), RcppParallel, RSSL, SparseM,
Suggests: ca, covr, knitr, lsa, spelling, RColorBrewer, testthat
LinkingTo: Rcpp, RcppParallel, RcppArmadillo (>= 0.7.600.1.0), quanteda