rmweather: Tools to Conduct Meteorological Normalisation on Air Quality Data

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>.

Version: 0.2.4
Depends: R (≥ 3.2.0)
Imports: dplyr (≥ 1.0.1), ggplot2, lubridate, magrittr, pdp, purrr, ranger, stringr, strucchange, tibble, viridis, tidyr
Suggests: testthat, openair
Published: 2022-11-08
Author: Stuart K. Grange ORCID iD [cre, aut]
Maintainer: Stuart K. Grange <stuart.grange at york.ac.uk>
BugReports: https://github.com/skgrange/rmweather/issues
License: GPL-3 | file LICENSE
URL: https://github.com/skgrange/rmweather
NeedsCompilation: no
Citation: rmweather citation info
CRAN checks: rmweather results


Reference manual: rmweather.pdf


Package source: rmweather_0.2.4.tar.gz
Windows binaries: r-devel: rmweather_0.2.4.zip, r-release: rmweather_0.2.4.zip, r-oldrel: rmweather_0.2.4.zip
macOS binaries: r-release (arm64): rmweather_0.2.4.tgz, r-oldrel (arm64): rmweather_0.2.4.tgz, r-release (x86_64): rmweather_0.2.4.tgz, r-oldrel (x86_64): rmweather_0.2.4.tgz
Old sources: rmweather archive


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