An integrated set of tools to allow data users to conduct meteorological normalisation and counterfactual modelling for air quality data. The 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>. The random forest models can also be used for counterfactual or business as usual (BAU) modelling by using the models to predict, from the model's perspective, the future. For an example, see Grange et al. (2021) <doi:10.5194/acp-2020-1171>.
Version: 0.2.62 Depends: R (≥ 3.2.0) Imports: dplyr (≥ 1.0.1), ggplot2, lubridate, magrittr, pdp, purrr (≥ 1.0.0), ranger, stringr, strucchange, tibble, viridis, tidyr, cli Suggests: testthat, openair Published: 2025-02-21 DOI: 10.32614/CRAN.package.rmweather Author: Stuart K. Grange [cre, aut] Maintainer: Stuart K. Grange <s.k.grange at gmail.com> 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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=rmweather to link to this page.
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