Package: clim4health 0.1.0

Carles Milà

clim4health: Post-Processing of Climate Data for Health Applications

Obtain, transform and export climate data including reanalyses, (seasonal) forecasts and hindcasts, and weather stations for their use in epidemiological analyses. It is organised in three sequential blocks, input (download and load data), transform (downscaling, verification, spatiotemporal aggregation and threshold-based indicators) and output (visualising and exporting data). Downscaling methods include those described in Duzenli et al. (2026) <doi:10.1038/s41598-026-45067-2> and verification methods are based on those in Manubens et al. (2018) <doi:10.1016/j.envsoft.2018.01.018>.

Authors:Emily Ball [aut], Carles Milà [aut, cre], Alba Llabrés [aut], Raul Capellan [aut], Rebeca Nunes [aut], Daniela Lührsen [aut], Anna B. Kawiecki [aut], Rachel Lowe [aut]

clim4health_0.1.0.tar.gz
clim4health_0.1.0.tar.gz(r-4.7-any)clim4health_0.1.0.tar.gz(r-4.6-any)
clim4health_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
clim4health/json (API)

# Install 'clim4health' in R:
install.packages('clim4health', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/bsc-es/ghrtools/issues

On CRAN:

Conda:

3.48 score 14 exports 115 dependencies

Last updated from:7a6ae26904. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK218
source / vignettesOK268
linux-release-x86_64OK227
wasm-releaseOK222

Exports:c4h_collapsec4h_convertc4h_convert_unitsc4h_downscalec4h_getc4h_get_helpc4h_indexc4h_loadc4h_plotc4h_plotskillc4h_savec4h_spacec4h_timec4h_verify

Dependencies:abindaskpassBHbigmemorybigmemory.sribootcachemCircStatsclassclassIntcliClimProjDiagscodetoolscolorspacecowplotcpp11CSDownscaleCSToolscurldata.tableDBIdigestdoParalleldotCall64dplyrdtwe1071easyNCDFeasyVerificationecmwfrfarverfastmapfieldsfilelockfitdistrplusforeachfuturegenericsgetPassggpatternggplot2GHRexploreglobalsgluegridpatterngtablehttrisobanditeratorsjsonliteKernSmoothkeyringlabelinglatticelifecyclelistenvlubridatemagrittrmapprojmapsMASSMatrixmemoisemimemultiApplyNbClustncdf4nnetopensslparallellypbapplypillarpkgconfigplyrpngproxypurrrqmapR6rainfarmrRColorBrewerRcppRcppArmadilloreshape2rlangrstudioapis2s2dvS7scalessfsignalspamSpecsVerificationstarsstartRstringistringrsurvivalsysterratibbletidyrtidyselecttimechangeunitsutf8uuidvctrsverificationviridisLitewithrwkyamlzoo

clim4health downscale
Overview | 0. Load the package and sample data | 1. Key c4h_downscale arguments | 2. Downscaling methods (gridded data) | Method 1: Interpolation | Method 2: Interpolation plus bias correction | Method 3: Interpolation plus linear regression | Method 4: Analogs | 3. Downscaling to point locations | 4. Calibration | 5. Considerations for downscaling common variables | Interpolation-based methods | Specific recommendations for precipitation downscaling | General recommendations | Parameter selection
Rendered fromclim4health_downscaling.Rmdusingknitr::rmarkdown

Last update: 2026-06-30
Started: 2026-06-30

clim4health get
Overview | 0. Load the package | 1. c4h_get arguments | 2. c4h_get_help | 3. Specifying arguments in c4h_get() | Specifying temporal range | Specifying spatial extent | Specifying variables | Specifying the file path and name | 4. Obtain your Personal Access Token (PAT) | 5. Example downloads | 5.1. ERA5-Land data | 5.2. Hindcast data | 5.3. Forecast data
Rendered fromclim4health_get.Rmdusingknitr::rmarkdown

Last update: 2026-06-30
Started: 2026-06-30

clim4health glossary
Overview | Glossary
Rendered fromclim4health_glossary.Rmdusingknitr::rmarkdown

Last update: 2026-06-30
Started: 2026-06-30

clim4health overview
Overview | Installation | Data requirements | clim4health structure | 1. Obtain input data | 2. Transform and process the data | 3. Prepare outputs | clim4health workflow | 1. Loading, inspecting, and preprocessing data | Dataset description | Data preprocessing | Plot the raw data | 2. Processing the data | Spatial aggregation | Downscaling | Verification and postprocessing | Masking data | Aggregating data temporally | Collapsing data across a dimension | 3. Outputting the data | Convert data from an s2dv_cube to a different data type | Save the data
Rendered fromclim4health_overview.Rmdusingknitr::rmarkdown

Last update: 2026-06-30
Started: 2026-06-30

clim4health verification
Overview | 0. Load the package and sample data | 1. Key c4h_verify arguments | 2. Verification metrics | "BSS" - Brier Skill Score | "RPSS" - Ranked Probability Skill Score | "CRPSS" - Continuous Ranked Probability Skill Score | "AbsBiasSS" - Absolute Bias Skill Score | "MSE" - Mean Squared Error | "MSSS" - Mean Squared Error Skill Score | "RMSE" - Root Mean Squared Error | "RMSSS" - Root Mean Square Skill Score | "ROCSS" - Relative Operating Characteristic Skill Score | Statistical Significance | 3. Example skill assessment | Choosing arguments in c4h_verify | ref | brier_thresholds | prob_thresholds | sig_method and sig_method.type | alpha | ncores | N.eff | indices_for_clim | cross.val and clim.cross.val | weights_exp and weights_ref | comp_dim and limits | conf | na.rm | sign and pval | rocss_cat
Rendered fromclim4health_verification.Rmdusingknitr::rmarkdown

Last update: 2026-06-30
Started: 2026-06-30

Introduction to s2dv_cube objects
Overview | 0. Load the package | 1. Forecasts, hindcasts, and reanalyses | 2. Climate data in clim4health | Loading multi-dimensional data with c4h_load | Loading example data | The forecast data is stored with dimensions | The hindcast data is stored with dimensions | The reanalysis data is stored with dimensions | Exploring the loaded data | Data dimensions in clim4health | Exploring the s2dv_cube structure | 3. Constructing an s2dv_cube from scratch | 1. Load the data | 2. Reshape the data | 3. Add dimensions, coordinates, and attributes | 4. Create the s2dv_cube object
Rendered fromclim4health_s2dv_cubes.Rmdusingknitr::rmarkdown

Last update: 2026-06-30
Started: 2026-06-30