person("Jun", "Cai", role = c("aut", "cre"),
email = "cai-j12@mails.tsinghua.edu.cn",  comment = c(ORCID = "0000-0001-9495-1226"))

Description: A spell checker and morphological analyzer library
designed for languages with rich morphology and complex word
SystemRequirements: Hunspell library and headers

Title: Spell Checker and Text Parser for R
Description: A spell checker and morphological analyzer library designed for
languages with rich morphology and complex word compounding or character
encoding. The package can check and analyze individual words as well as
search for incorrect words within a text, latex or manual document.

Title: Morphological Analysis and Spell Checker for R

search for incorrect words within a text, latex or manual document. Use
the 'devtools' package to spell R documentation with 'hunspell'.
License: GPL-2 | LGPL-2.1 | Mozilla Public License

the 'devtools' package to spell check R documentation with 'hunspell'.

Title: High-Performance Stemmer, Tokenizer, and Spell Checker for R
search for incorrect words within a text, latex, html or xml document. Use
Suggests: testthat, devtools, pdftools, janeaustenr, wordcloud2, knitr,

Title: High-Performance Stemmer, Tokenizer, and Spell Checker
person("Authors of libhunspell", role = "cph", comment = "see AUTHORS file"))
Description: Low level spell checker and morphological analyzer based on the
famous 'hunspell' library <https://hunspell.github.io>. The package can analyze
or check individual words as well as parse text, latex, html or xml documents.
For a more user-friendly interface use the 'spelling' package which builds on
this package to automate checking of files, documentation and vignettes in all
Suggests: spelling, testthat, pdftools, janeaustenr, wordcloud2, knitr,
Authors of libhunspell [cph] (see AUTHORS file)

Title: Zero-Inflated and Hurdle Modelling Using Bayesian Inference
Description: When considering count data, it is often the case that many more zero counts than would be expected of some given distribution are observed. It is well established that data such as this can be reliably modelled using zero-inflated or hurdle distributions, both of which may be applied using the functions in this package. Bayesian analysis methods are used to best model problematic count data that cannot be fit to any typical distribution. The package functions are flexible and versatile, and can be applied to varying count distributions, parameter estimation with or without explanatory variable information, and are able to allow for multiple hurdles as it is also not uncommon that count data have an abundance of large-number observations which would be considered outliers of the typical distribution. In lieu of throwing out data or misspecifying the typical distribution, these extreme observations can be applied to a second, extreme distribution. With the given functions of this package, such a two-hurdle model may be easily specified in order to best manage data that is both zero-inflated and over-dispersed.
Authors@R: c(person("Earvin", "Balderama", email = "ebalderama@luc.edu", role = c("aut", "cre")),person("Taylor", "Trippe", email = "taylortbertrand@gmail.com", role = "aut"))