rmweather: Tools to Conduct Meteorological Normalisation and Counterfactual
Modelling for Air Quality Data
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: |
0.2.63 |
| 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: |
2025-08-22 |
| 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:
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