c(
person("Marta", "Karas", email = "marta.karass@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-5889-3970")),
person("Jacek", "Urbanek", role = c("aut"),
comment = c(ORCID = "0000-0002-1890-8899")),
person("John", "Muschelli", role = c("ctb"),
comment = c(ORCID = "0000-0001-6469-1750")),
person("Lacey", "Etzkorn", role = c("ctb"))
)
Marta Karas [aut, cre] (<https://orcid.org/0000-0001-5889-3970>),
Jacek Urbanek [aut] (<https://orcid.org/0000-0002-1890-8899>),
John Muschelli [ctb] (<https://orcid.org/0000-0001-6469-1750>),
Lacey Etzkorn [ctb]

XML, httr (>= 0.6.1), plyr, foreach, stringr, testthat
An interface to the web service methods provided by the United States Department of Agriculture (USDA). The Agricultural Research Service (ARS) provides a large set of databases. The current version of the package holds interfaces to the Systematic Mycology and Microbiology Laboratory (SMML), which consists of four databases: Fungus-Host Distributions, Specimens, Literature and the Nomenclature database. It provides functions for querying these databases. The main function is \code{associations}, which allows searching for fungus-host combinations.

XML, httr (>= 0.6.1), plyr, foreach, stringr, testthat,
taxize, RCurl

Beautiful Graphical Representation of Multiplication Tables on a
Modular Circle
person("Vincent", "Guyader", email = "vincent@thinkr.fr", role = c("aut", "cre"))
By placing on a circle 10 points numbered from 1 to 10, and connecting them by a straight line to the point corresponding to its multiplication by 2. (1 must be connected to 1 * 2 = 2, point 2 must be set to 2 * 2 = 4, point 3 to 3 * 2 = 6 and so on). You will obtain an amazing geometric figure that complicates and beautifies itself by varying the number of points and the multiplication table you use.
dplyr, ggforce, ggplot2, reshape2, shiny, tidyr

person("Paul", "Northrop", email = "p.northrop@ucl.ac.uk",
role = c("aut", "cre", "cph"))
Uses the generalised ratio-of-uniforms (RU) method to simulate
from univariate and (low-dimensional) multivariate continuous distributions.
The user specifies the log-density, up to an additive constant. The RU
algorithm is applied after relocation of mode of the density to zero, and
the user can choose a tuning parameter r. For details see Wakefield, Gelfand
and Smith (1991) <DOI:10.1007/BF01889987>, Efficient generation of random variates via the
ratio-of-uniforms method, Statistics and Computing (1991) 1, 129-133.
A Box-Cox variable transformation can be used to make the input density
suitable for the RU method and to improve efficiency.  In the multivariate
case rotation of axes can also be used to improve efficiency.
See the rust website for more information, documentation and examples.