limpidR separates data validation, descriptive analysis,
compositional analysis, model fitting, validation and visualization.
Missing environmental context remains missing. The package does not interpret blank cells as zero and does not automatically impute climate, water-quality, land-use or population covariates.
Use lake-grouped cross-validation for cross-lake generalization questions. For temporal forecasting within a sentinel lake, use a time-blocked validation design outside the default leave-one-lake-out workflow.
classify_hotspots() and map_mp_hotspots()
provide relative point classifications. They do not claim kriging,
transport modelling or hydrodynamic interpolation.
The bundled synthetic example is event-level.
plot_depth_profile() therefore refuses a
limpid_db object and requires a genuinely depth-resolved
data frame.
Risk is assumption-sensitive. calculate_risk() exposes
contamination, polymer-hazard and fine-particle components separately. A
composite score is produced only when the user supplies both weights and
explicit component maxima.