{ "_id": "6a5144ba9b92047c28d50487", "Package": "climasus4r", "Title": "Integrated Analysis Toolkit for Health, Climate, and\nEnvironmental Data in Brazil: Reproducible Workflows for\nClimate-Health Research", "Version": "1.0.0", "Authors@R": "c(person(given = \"Max\",\nfamily = \"Anjos\",\nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"max.anjos@ufjf.br\",\ncomment = c(ORCID = \"0000-0001-6394-1324\")),\nperson(given = \"Thaua\",\nfamily = \"Menezes\",\nrole = c(\"ctb\"),\nemail = \"thaua.menezes@unesp.br\",\ncomment = c(ORCID = \"0000-0002-3015-2270\")),\nperson(given = \"Marlon\",\nfamily = \"Faria\",\nrole = c(\"ctb\"),\nemail = \"marlon.faria@usp.br\",\ncomment = c(ORCID = \"0000-0002-9485-4079\"))\n)", "Description": "climasus4r is a comprehensive R toolkit for integrated\nanalysis of health, climate, and environmental data in Brazil.\nThe package automates the entire data pipeline: (1) importing\nand cleaning health data from the Brazilian Unified Health\nSystem (SUS) across six systems (SIM, SIH, SINAN, SIA, CNES,\nSINASC); (2) integrating climate data (INMET, ERA5), air\nquality (AQI), environmental data (MapBiomas), and\nsocioeconomic indicators (IBGE); (3) performing flexible\nspatiotemporal aggregation at multiple scales; (4) constructing\nreproducible analytical pipelines (RAPs) that ensure\ntransparency and replicability. Designed for researchers\nstudying climate-health interactions, environmental\nvulnerability, and adaptation strategies in the Brazilian\ncontext. Supports multilingual output (Portuguese, Spanish,\nEnglish) and parallel processing for large datasets.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "Remotes": [ "rfsaldanha/microdatasus" ], "Additional_repositories": "https://inla.r-inla-download.org/R/stable", "URL": "https://bymaxanjos.r-universe.dev/climasus4r,\nhttps://github.com/ByMaxAnjos/climasus4r", "BugReports": "https://github.com/ByMaxAnjos/climasus4r/issues", "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev libssl-dev\nxz-utils", "Repository": "https://bymaxanjos.r-universe.dev", "Date/Publication": "2026-07-10 16:26:21 UTC", "RemoteUrl": "https://github.com/ByMaxAnjos/climasus4r", "RemoteRef": "HEAD", "RemoteSha": "6115248fc85ed4a8d5c5e873af7657fa1da1c261", "NeedsCompilation": "no", "Packaged": { "Date": "2026-07-10 18:21:26 UTC", "User": "root" }, "Author": "Max Anjos [aut, cre, cph] (ORCID:\n),\nThaua Menezes [ctb] (ORCID: ),\nMarlon Faria 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"sus_climate_anomaly", "title": "Climate Anomaly Computation vs. INMET Climatological Normals", "topics": [ "sus_climate_anomaly" ] }, { "page": "sus_climate_compute_coldwaves", "title": "Detect coldwaves using multiple standard methodologies", "topics": [ "sus_climate_compute_coldwaves" ] }, { "page": "sus_climate_compute_heatwaves", "title": "Detect heatwaves using multiple standard methodologies", "topics": [ "sus_climate_compute_heatwaves" ] }, { "page": "sus_climate_compute_indicators", "title": "Compute Bioclimatic and Thermal Stress Indicators", "topics": [ "sus_climate_compute_indicators" ] }, { "page": "sus_climate_compute_spei", "title": "Compute Standardized Precipitation-Evapotranspiration Index (SPEI)", "topics": [ "sus_climate_compute_spei" ] }, { "page": "sus_climate_compute_spi", "title": "Compute Standardized Precipitation Index (SPI)", "topics": [ "sus_climate_compute_spi" ] }, { "page": "sus_climate_fill_inmet", "title": "Fill gaps in INMET climate time series using XGBoost", "topics": [ "sus_climate_fill_inmet" ] }, { "page": "sus_climate_inmet", "title": "Import and Process INMET Meteorological Data", "topics": [ "sus_climate_inmet" ] }, { "page": "sus_climate_normals", "title": "Download and Process INMET Climate Normals", "topics": [ "sus_climate_normals" ] }, { "page": "sus_climate_normals_meta", "title": "Browse the INMET Climate Normals Catalogue", "topics": [ "sus_climate_normals_meta" ] }, { "page": "sus_climate_plot_aggregate", "title": "Visualise Climate-Health Aggregate Data", "topics": [ "sus_climate_plot_aggregate" ] }, { "page": "sus_climate_plot_coldwaves", "title": "Plot and Analyze Coldwave Events", "topics": [ "sus_climate_plot_coldwaves" ] }, { "page": "sus_climate_plot_fill", "title": "Visualise time-series gap-filling from sus_climate_fill()", "topics": [ "sus_climate_plot_fill" ] }, { "page": "sus_climate_plot_heatwaves", "title": "Plot and Analyze Heatwave Events", "topics": [ "sus_climate_plot_heatwaves" ] }, { "page": "sus_climate_uniplu", "title": "Import UNIPLU-BR: Unified Brazilian Rainfall Dataset", "topics": [ "sus_climate_uniplu" ] }, { "page": "sus_data_aggregate", "title": "Aggregate Health Data into Time Series", "topics": [ "sus_data_aggregate" ] }, { "page": "sus_data_cid_select", "title": "Interactive Disease Groups Explorer", "topics": [ "sus_data_cid_select" ] }, { "page": "sus_data_clean_encoding", "title": "Detect and correct character encoding issues", "topics": [ "sus_data_clean_encoding" ] }, { "page": "sus_data_create_variables", "title": "Create Derived Variables for Epidemiological Analysis", "topics": [ "sus_data_create_variables" ] }, { "page": "sus_data_export", "title": "Export Processed Health Data with Metadata", "topics": [ "sus_data_export" ] }, { "page": "sus_data_filter_cid", "title": "Filter SUS health data by ICD-10 codes or disease groups with multilingual support", "topics": [ "sus_data_filter_cid" ] }, { "page": "sus_data_filter_demographics", "title": "Filter Health Data by Demographic Variables", "topics": [ "sus_data_filter_demographics" ] }, { "page": "sus_data_import", "title": "Import and preprocess data from DATASUS with intelligent caching", "topics": [ "sus_data_import" ] }, { "page": "sus_data_plot_aggregate_map", "title": "Plot Municipal Map of Aggregated Health Data", "topics": [ "sus_data_plot_aggregate_map" ] }, { "page": "sus_data_plot_aggregate_ts", "title": "Time-Series Plot of Aggregated DATASUS Health Outcomes", "topics": [ "sus_data_plot_aggregate_ts" ] }, { "page": "sus_data_plot_demographics", "title": "Visualise Demographic Profiles from DATASUS Systems", "topics": [ "sus_data_plot_demographics" ] }, { "page": "sus_data_quality_report", "title": "Pipeline-aware data quality report for health data", "topics": [ "sus_data_quality_report" ] }, { "page": "sus_data_read", "title": "Read Processed Health Data with Batch and Parallel Support", "topics": [ "sus_data_read" ] }, { "page": "sus_data_standardize", "title": "Standardize SUS data column names and values", "topics": [ "sus_data_standardize" ] }, { "page": "sus_data_ts_quality", "title": "Time-Series Quality Control for Daily Municipal Health Counts", "topics": [ "sus_data_ts_quality" ] }, { "page": "sus_grid_chirps", "title": "Import CHIRPS Rainfall Data for Brazilian Municipalities", "topics": [ "sus_grid_chirps" ] }, { "page": "sus_grid_era5", "title": "Import ERA5-Land Daily Climate Data for Brazilian Municipalities", "topics": [ "sus_grid_era5" ] }, { "page": "sus_grid_fires", "title": "Import Fire Hotspot Data for Brazilian Municipalities", "topics": [ "sus_grid_fires" ] }, { "page": "sus_grid_join", "title": "Join Gridded Environmental Data to Health Data", "topics": [ "sus_grid_join" ] }, { "page": "sus_grid_koppen", "title": "Assign Kopppen-Geiger Climate Zones to Brazilian Municipalities", "topics": [ "sus_grid_koppen" ] }, { "page": "sus_grid_pdsi", "title": "Import Palmer Drought Severity Index (PDSI) for Brazilian Municipalities", "topics": [ "sus_grid_pdsi" ] }, { "page": "sus_grid_pollution_cams", "title": "Import CAMS Pollution Data for Brazilian Municipalities", "topics": [ "sus_grid_pollution_cams" ] }, { "page": "sus_grid_pollution_ghap", "title": "Import GHAP High-Resolution Pollution Data for Brazilian Municipalities", "topics": [ "sus_grid_pollution_ghap" ] }, { "page": "sus_grid_pollution_merra2", "title": "Import MERRA-2 Pollution Data for Brazilian Municipalities", "topics": [ "sus_grid_pollution_merra2" ] }, { "page": "sus_grid_prodes", "title": "Import PRODES Deforestation Data from TerraBrasilis for Brazilian Municipalities", "topics": [ "sus_grid_prodes" ] }, { "page": "sus_grid_smvi", "title": "Import Soil Moisture Volatility Index (SMVI / Flash Drought) Data", "topics": [ "sus_grid_smvi" ] }, { "page": "sus_install_deps", "title": "Install Optional Dependencies for climasus4r", "topics": [ "sus_install_deps" ] }, { "page": "sus_meta", "title": "Manage climasus_df S3 Class Metadata and Storage Backends", "topics": [ "sus_meta" ] }, { "page": "sus_mod_af", "title": "Attributable Fraction and Number from a DLNM Fit", "topics": [ "sus_mod_af" ] }, { "page": "sus_mod_burden", "title": "Ranked Disease Burden Table Across Cities or Strata", "topics": [ "sus_mod_burden" ] }, { "page": "sus_mod_casecrossover", "title": "Time-Stratified Case-Crossover Analysis for Climate-Health Data", "topics": [ "sus_mod_casecrossover" ] }, { "page": "sus_mod_dlnm", "title": "Distributed Lag Non-linear Model (DLNM) for Climate-Health Analyses", "topics": [ "sus_mod_dlnm" ] }, { "page": "sus_mod_excess", "title": "Excess Mortality and Morbidity from Climate-Health Time Series", "topics": [ "sus_mod_excess" ] }, { "page": "sus_mod_its", "title": "Interrupted Time Series Analysis for Health Outcome Counts", "topics": [ "sus_mod_its" ] }, { "page": "sus_mod_metaregression", "title": "Meta-Regression of Pooled DLNM Estimates with City-Level Covariates", "topics": [ "sus_mod_metaregression" ] }, { "page": "sus_mod_ml", "title": "XGBoost Machine Learning for Climate-Health Outcome Prediction", "topics": [ "sus_mod_ml" ] }, { "page": "sus_mod_plot_af", "title": "Plots and Tables from an Attributable Fraction Analysis", "topics": [ "sus_mod_plot_af" ] }, { "page": "sus_mod_plot_burden", "title": "Plots and Tables from a City-Level Disease Burden Analysis", "topics": [ "sus_mod_plot_burden" ] }, { "page": "sus_mod_plot_dlnm", "title": "Scientific Plots and Tables from a DLNM Fit", "topics": [ "sus_mod_plot_dlnm" ] }, { "page": "sus_mod_plot_ml", "title": "Plots and Tables from an XGBoost Machine Learning Model", "topics": [ "sus_mod_plot_ml" ] }, { "page": "sus_mod_plot_pool", "title": "Plots and Tables from a Pooled DLNM Meta-Analysis", "topics": [ "sus_mod_plot_pool" ] }, { "page": "sus_mod_plot_sensitivity", "title": "Plots and Tables from a Multi-Stratum Sensitivity Analysis", "topics": [ "sus_mod_plot_sensitivity" ] }, { "page": 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