c(
person("Bruce", "Swihart", email="bruce.swihart@gmail.com", role=c("cre", "aut")),
person("Jim"  , "Lindsey", email="jlindsey@gen.unimaas.nl", role="aut", comment="Jim created this package, Bruce is maintaining the CRAN version"),
person("K.", "Sikorski", role=c("ctb", "cph"), comment="Wrote TOMS614/INTHP, https://calgo.acm.org/"),
person("F.", "Stenger", role=c("ctb", "cph"), comment="Wrote TOMS614/INTHP, https://calgo.acm.org/"),
person("J.", "Schwing", role=c("ctb", "cph"), comment="Wrote TOMS614/INTHP, https://calgo.acm.org/")
)
A toolkit of functions for nonlinear regression and repeated
measurements not to be used by itself but called by other Lindsey packages such
as 'gnlm', 'stable', 'growth', 'repeated', and 'event'
(available at <https://www.commanster.eu/rcode.html>).
Bruce Swihart [cre, aut],
Jim Lindsey [aut] (Jim created this package, Bruce is maintaining the
CRAN version),
K. Sikorski [ctb, cph] (Wrote TOMS614/INTHP, https://calgo.acm.org/),
F. Stenger [ctb, cph] (Wrote TOMS614/INTHP, https://calgo.acm.org/),
J. Schwing [ctb, cph] (Wrote TOMS614/INTHP, https://calgo.acm.org/)

Tools to Conduct Meteorological Normalisation on Air Quality
Data
person("Stuart K.", "Grange", email = "stuart.grange@york.ac.uk",
role = c("cre", "aut"))
An integrated set of tools to allow data users to conduct
meteorological normalisation on air quality data. This meteorological
normalisation technique uses predictive random forest models to remove
variation of pollutant concentrations so trends and interventions can be
explored in a robust way. For an example, see Grange et al. (2018)
<doi:10.5194/acp-18-6223-2018>.
dplyr, ggplot2, lubridate, magrittr, pdp, purrr, ranger,
stringr, strucchange, testthat, tibble, viridis

dplyr, ggplot2, lubridate, magrittr, pdp, purrr, ranger,
stringr, strucchange, tibble, viridis

person("Stuart K.", "Grange", email = "stuart.grange@york.ac.uk",
role = c("cre", "aut"), comment = c(ORCID = "0000-0003-4093-3596"))
An integrated set of tools to allow data users to conduct
meteorological normalisation on air quality data. This meteorological
normalisation technique uses predictive random forest models to remove
variation of pollutant concentrations so trends and interventions can be
explored in a robust way. For examples, see Grange et al. (2018)
<doi:10.5194/acp-18-6223-2018> and Grange and Carslaw (2019)
<doi:10.1016/j.scitotenv.2018.10.344>.
Stuart K. Grange [cre, aut] (<https://orcid.org/0000-0003-4093-3596>)