Conducts sensitivity analyses for unmeasured confounding, selection bias, and measurement error (individually or in combination; VanderWeele & Ding (2017) <doi:10.7326/M16-2607>; Smith & VanderWeele (2019) <doi:10.1097/EDE.0000000000001032>; VanderWeele & Li (2019) <doi:10.1093/aje/kwz133>; Smith, Mathur, & VanderWeele (2021) <doi:10.1097/EDE.0000000000001380>). Also conducts sensitivity analyses for unmeasured confounding in meta-analyses (Mathur & VanderWeele (2020a) <doi:10.1080/01621459.2018.1529598>; Mathur & VanderWeele (2020b) <doi:10.1097/EDE.0000000000001180>) and for additive measures of effect modification (Mathur et al., <doi:10.1093/ije/dyac073>).

An R-based application for exploratory data analysis of global EvapoTranspiration (ET) datasets. 
  'evapoRe' enables users to download, validate, visualize, and analyze multi-source ET data across various spatio-temporal scales.
  Also, the package offers calculation methods for estimating potential ET (PET), including temperature-based, combined type, and radiation-based approaches described in : Oudin et al., (2005) <doi:10.1016/j.jhydrol.2004.08.026>.
  'evapoRe' supports hydrological modeling, climate studies, agricultural research, and other data-driven fields by facilitating access to ET data and offering powerful analysis capabilities.
  Users can seamlessly integrate the package into their research applications and explore diverse ET data at different resolutions.

Uses data and constants to calculate potential evapotranspiration (PET) and actual evapotranspiration (AET) from 21 different formulations including Penman, Penman-Monteith FAO 56, Priestley-Taylor and Morton formulations.

Implements local influence diagnostics for the Extreme-Value
    Birnbaum-Saunders (EVBS) regression model: joint maximum likelihood
    estimation, conformal normal curvature diagnostics under three
    perturbation schemes (case-weight, response variable, and explanatory
    variable), randomized quantile residuals with simulation envelope,
    Monte Carlo simulation utilities, and publication-quality density and
    diagnostic plots. The methods are described in Ospina, Lima, Barros,
    and Macedo (2026, submitted) and are applied to monthly maximum wind
    gust data from Itajai, Brazil.