c(person(given = "Keith",
family = "McNulty",
role = c("cre", "aut", "cph"),
email = "keith.mcnulty@gmail.com",
comment = c(ORCID = "0000-0002-2332-1654")))
Keith McNulty [cre, aut, cph] (<https://orcid.org/0000-0002-2332-1654>)

c(person(given = "Keith",
family = "McNulty",
role = c("cre", "aut", "cph"),
email = "keith.mcnulty@gmail.com",
comment = c(ORCID = "0000-0002-2332-1654")),
person(given = "Liz",
family = "Romero",
role = c("ctb"),
email = "liz.romero357@gmail.com"))
Keith McNulty [cre, aut, cph] (<https://orcid.org/0000-0002-2332-1654>),
Liz Romero [ctb]

c(person(given = "Colin",
family = "Fay",
role = c("cre", "aut"),
email = "contact@colinfay.me",
comment = structure("0000-0001-7343-1846", .Names = "ORCID")),
person(given = "ThinkR",
role = "cph"),
person(given = "Neo4J",
role = "spn"))
A Modern and Flexible 'Neo4J' Driver, allowing you to query
data on a 'Neo4J' server and handle the results in R. It's modern in
the sense it provides a driver
that can be easily integrated in a data analysis workflow, especially by
providing an API working smoothly with other data analysis and graph
packages. It's flexible in the  way it returns the results, by
trying to stay as close as
possible to the way 'Neo4J' returns data. That way, you have the control
over the way you will compute the results. At the same time, the result
is not too complex, so that the "heavy lifting" of data wrangling is not
left to the user.
attempt, data.table, glue, httr, igraph, jsonlite, magrittr,
purrr, R6, rlang, rstudioapi, shiny, tibble, tidyr, tidyselect,
utils
Colin Fay [cre, aut] (<https://orcid.org/0000-0001-7343-1846>),
ThinkR [cph],
Neo4J [spn]

Basic Data Wrangling for NEON Observational Data
c(person("Claire","Lunch",email="clunch@battelleecology.org",role=c("aut","cre","ctb"),comment=c(ORCID="0000-0001-8753-6593")), person("Eric","Sokol",role=c("aut","ctb"),comment=c(ORCID="0000-0001-5923-0917")), person("Natalie","Robinson",role=c("aut","ctb"),comment=c(ORCID="0000-0002-1144-418X")), person("NEON (National Ecological Observatory Network)", role="aut"))
NEON observational data are provided via the NEON Data Portal <https://www.neonscience.org>
and NEON API, and can be downloaded and reformatted by the 'neonUtilities' package. NEON observational data
(human-observed measurements, and analyses derived from human-collected samples, such as tree diameters and
algal chemistry) are published in a format consisting of one or more tabular data files. This package provides
tools for performing common operations on NEON observational data, including checking for duplicates and
joining tables.
Claire Lunch [aut, cre, ctb] (<https://orcid.org/0000-0001-8753-6593>),
Eric Sokol [aut, ctb] (<https://orcid.org/0000-0001-5923-0917>),
Natalie Robinson [aut, ctb] (<https://orcid.org/0000-0002-1144-418X>),
NEON (National Ecological Observatory Network) [aut]