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.

Provides a principled framework for sampling Virtual Control Group (VCG) using energy distance-based covariate balancing. The package offers visualization tools to assess covariate balance and includes a permutation test to evaluate the statistical significance of observed deviations.

The purpose of this library is to compute the optimal charging cost function for a electric vehicle (EV). It is well known that the charging function of a EV is a concave function that can be approximated by a piece-wise linear function, so bigger the state of charge, slower the charging process is. Moreover, the other important function is the one that gives the electricity price. This function is usually step-wise, since depending on the time of the day, the price of the electricity is different. Then, the problem of charging an EV to a certain state of charge is not trivial. This library implements an algorithm to compute the optimal charging cost function, that is, it plots for a given state of charge r (between 0 and 1) the minimum cost we need to pay in order to charge the EV to that state of charge r. The details of the algorithm are described in González-Rodríguez et at (2023) <https://inria.hal.science/hal-04362876v1>.