https://docs.ropensci.org/hunspell/ (docs),
https://github.com/ropensci/hunspell (devel)
https://hunspell.github.io (upstream)
spelling, testthat, pdftools, janeaustenr, wordcloud2, knitr,
stopwords, rmarkdown

Zero-Inflated and Hurdle Modelling Using Bayesian Inference
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.
c(person("Earvin", "Balderama", email = "ebalderama@luc.edu", role = c("aut", "cre")),person("Taylor", "Trippe", email = "taylortbertrand@gmail.com", role = "aut"))
Earvin Balderama [aut, cre],
Taylor Trippe [aut]

Explore and Map County-Level Hurricane Exposure in the United
States
c(person("Brooke", "Anderson",
email = "brooke.anderson@colostate.edu", role = c("aut", "cre")),
person("Meilin", "Yan",
email = "meilin.yan@colostate.edu", role = "aut"),
person("Joshua", "Ferreri",
email = "joshua.m.ferreri@gmail.com", role = "aut"),
person("William", "Crosson",
email = "bill.crosson@nasa.gov", role = "ctb"),
person("Mohammad", "Al-Hamdan",
email = "mohammad.alhamdan@nasa.gov", role = "ctb"),
person("Andrea", "Schumacher",
email = "andrea.schumacher@colostate.edu", role = "ctb"),
person("Dirk", "Eddelbuettel",
email = "edd@debian.org", role = "ctb")
)
Allows users to create time series of tropical storm
exposure histories for chosen counties for a number of hazard metrics (wind,
rain, distance from the storm, etc.). This package interacts with data available
through the 'hurricaneexposuredata' package, which is available in a 'drat'
repository. To access this data package, run 'install.packages("hurricaneexposuredata",
repos = "https://geanders.github.io/drat/", type = "source")'. The size of the
'hurricaneexposuredata' package is approximately 25 MB. This work was supported in
part by grants from the National Institute of Environmental Health Sciences
(R00ES022631), the National Science Foundation (1331399), and a NASA Applied
Sciences Program/Public Health Program Grant (NNX09AV81G).
hurricaneexposuredata (>= 0.0.1), knitr, pander, rmarkdown,
weathermetrics
Brooke Anderson [aut, cre],
Meilin Yan [aut],
Joshua Ferreri [aut],
William Crosson [ctb],
Mohammad Al-Hamdan [ctb],
Andrea Schumacher [ctb],
Dirk Eddelbuettel [ctb]