Statistical Tools for Modelling Climate-Health Impacts // 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.