Packaged: 2014-11-22 12:28:55 UTC; elenachatzimichali

Packaged: 2015-01-11 16:16:53 UTC; elenachatzimichali

Title: R Interface to the ClassyFire RESTful API
person("Tom", "Wilson", , "tpw2@aber.ac.uk", c("aut", "cre")))
Description: Access to the ClassyFire RESTful API <http://classyfire.wishartlab.com>. Retrieve existing entity classifications and submit new entities for classification.
Imports: httr, jsonlite, tibble, crayon, clisymbols
Suggests: testthat, covr, dplyr, purrr, tidyr

Imports: httr, jsonlite, tibble, crayon, clisymbols, purrr, dplyr,

person("Tom", "Wilson", , "tpw2@aber.ac.uk", c("aut", "cre")),
person("Jasen", "Finch", , "jsf9@aber.ac.uk", "aut"))

Author: Tom Wilson [aut, cre] (<https://orcid.org/0000-0003-3112-4682>),

Imports: httr, jsonlite, tibble, crayon, clisymbols, cli, purrr, dplyr,

person("Jasen", "Finch", , "jsf9@aber.ac.uk", "aut"),
stringr, methods, rjson, tidyjson, tidyr

Title: R Interface to the 'ClassyFire' REST API
Description: Access to the 'ClassyFire' REST API <http://classyfire.wishartlab.com>. Retrieve existing entity classifications and submit new entities for classification.
stringr, methods, rjson, RSQLite, tidyjson, tidyr
Suggests: testthat, covr, knitr, rmarkdown, stringi

Title: Google's Compact Language Detector 2
person("Dirk Sites", role = "cph", comment = "Author of CLD2 C++ library")
Description: Bindings to Google's C++ library Compact Language Detector 2
(see <https://github.com/CLD2Owners/cld2#readme> for more information). Probabilistically
detects over 80 languages in UTF-8 text (plain text or HTML). For mixed-language input it
returns the top three languages and their approximate proportion of the total classified
text bytes (e.g. 80% English and 20% French out of 1000 bytes).
Dirk Sites [cph] (Author of CLD2 C++ library)

(see <https://github.com/cld2owners/cld2#readme> for more information). Probabilistically
detects over 80 languages in plain text or HTML. For mixed-language input it returns the
top three detected languages and their approximate proportion of the total classified
text bytes (e.g. 80% English and 20% French out of 1000 bytes). There is also a 'cld3'
package on CRAN which uses a neural network model instead.