Offers tools to identify the climate analogues of reference polygons and quantifies their transformation under future climate change scenarios. Approaches described in Mingarro and Lobo (2018) <doi:10.32800/abc.2018.41.0333> and Mingarro and Lobo (2022) <doi:10.1017/S037689292100014X>.

Automatize downloading of meteorological and hydrological data from publicly available repositories:
    OGIMET (<http://ogimet.com/index.phtml.en>), 
    University of Wyoming - atmospheric vertical profiling data (<http://weather.uwyo.edu/upperair/>),
    Polish Institute of Meteorology and Water Management - National Research Institute (<https://danepubliczne.imgw.pl>),
    and National Oceanic & Atmospheric Administration (NOAA).
    This package also allows for searching geographical coordinates for each observation and calculate distances to the nearest stations.

Tools for producing climate-health indicators and supporting
              official statistics from health and climate data. Implements analytical
              workflows for temperature-related mortality, wildfire smoke exposure,
              air pollution, suicides related to extreme heat, malaria, and
              diarrhoeal disease outcomes, with utilities for descriptive statistics, model
              validation, attributable fraction and attributable number estimation,
              relative risk estimation, minimum mortality temperature estimation,
              and plotting for reporting. These six indicators are endorsed by
              the United Nations Statistical Commission for inclusion in the
              Global Set of Environment and Climate Change Statistics.
              Implemented methods include distributed lag non-linear models (DLNM),
              quasi-Poisson time-series regression, case-crossover analysis,
              Bayesian spatio-temporal models using the Integrated Nested Laplace
              Approximation ('INLA'), and multivariate meta-analysis for
              sub-national estimates. The package is based on methods developed
              in the Standards for Official Statistics on Climate-Health
              Interactions (SOSCHI) project
              <https://climate-health.officialstatistics.org>. For methodologies,
              see Watkins et al. (2026) <doi:10.5281/zenodo.14865904>,
              Jose et al. (2026) <doi:10.5281/zenodo.14052183>,
              Pearce et al. (2026) <doi:10.5281/zenodo.14050224>,
              Byukusenge et al. (2026) <doi:10.5281/zenodo.15585042>,
              Dzakpa et al. (2026) <doi:10.5281/zenodo.14881886>, and
              Dzakpa et al. (2026) <doi:10.5281/zenodo.14871506>.