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rmweather
View on CRAN: Click
here
Download and install rmweather package within the R console
Install from CRAN:
install.packages("rmweather")
Install from Github:
library("remotes")
install_github("cran/rmweather")
Install by package version:
library("remotes")
install_version("rmweather", "0.2.62")
Attach the package and use:
library("rmweather")
Maintained by
Stuart K. Grange
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.rmweather . https://github.com/skgrange/rmweather/issues . https://github.com/skgrange/rmweather . rmweather citation info . rmweather results . rmweather.pdf . rmweather_0.2.62.tar.gz . rmweather_0.2.62.zip . rmweather_0.2.62.zip . rmweather_0.2.62.zip . rmweather_0.2.62.tgz . rmweather_0.2.62.tgz . rmweather_0.2.62.tgz . rmweather_0.2.62.tgz . rmweather archive . https://CRAN.R-project.org/package=rmweather .
First Published: 2018-05-08
Latest Update: 2022-11-08
Description:
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) and
Grange and Carslaw (2019) . 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) .
How to cite:
Stuart K. Grange (2018). rmweather: Tools to Conduct Meteorological Normalisation and Counterfactual Modelling for Air Quality Data. R package version 0.2.62, https://cran.r-project.org/web/packages/rmweather. Accessed 16 Apr. 2025.
Previous versions and publish date:
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Other R packages that rmweather depends,
imports, suggests or enhances
Complete documentation for rmweather
Functions, R codes and Examples using
the rmweather R package
Some associated functions: base-functions . data_london . data_london_normalised . dplyr-functions . model_london . pipe . rmw_calculate_model_errors . rmw_clip . rmw_do_all . rmw_find_breakpoints . rmw_model_nested_sets . rmw_model_statistics . rmw_nest_for_modelling . rmw_normalise . rmw_partial_dependencies . rmw_plot_importance . rmw_plot_normalised . rmw_plot_partial_dependencies . rmw_plot_test_prediction . rmw_predict . rmw_predict_nested_partial_dependencies . rmw_predict_nested_sets . rmw_predict_nested_sets_by_year . rmw_predict_the_test_set . rmw_prepare_data . rmw_train_model . system_cpu_core_count . wday_monday . zzz .
Some associated R codes: base_re_exports.R . data_london.R . data_london_normalised.R . dplyr_re_exports.R . model_london.R . rmw_calculate_model_errors.R . rmw_clip.R . rmw_do_all.R . rmw_find_breakpoints.R . rmw_model_nested_sets.R . rmw_model_statistics.R . rmw_nest_for_modelling.R . rmw_normalise.R . rmw_partial_dependencies.R . rmw_predict.R . rmw_predict_nested_partial_dependencies.R . rmw_predict_nested_sets.R . rmw_predict_nested_sets_by_year.R . rmw_predict_the_test_set.R . rmw_prepare_data.R . rmw_train_model.R . rmweather_helpers.R . rmweather_plotting_functions.R . zzz.R . Full rmweather package functions and examples
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