API client for 'ClimMob', an open source software for decentralized 
    large-N trials with the 'tricot' approach <https://climmob.net/>. 
    Developed by van Etten et al. (2019) <doi:10.1017/S0014479716000739>, it turns the 
    research paradigm on its head; instead of a few researchers designing complicated 
    trials to compare several technologies in search of the best solutions for the 
    target environment, it enables many participants to carry out reasonably simple 
    experiments that taken together can offer even more information. 'ClimMobTools' 
    enables project managers to deep explore and analyse their 'ClimMob' data in R.

An automated and streamlined workflow for predictive climate 
             mapping using climate station data. Works within an environment 
             the user provides a destined path to - otherwise it's tempdir().
             Quick and relatively easy creation of resilient and reproducible 
             climate models, predictions and climate maps, shortening the
             usually long and complicated work of predictive modelling. 
             For more information, please find the provided URL.
             Many methods in this package are new, but the main method is based
             on a workflow from 
             Meyer (2019) <doi:10.1016/j.ecolmodel.2019.108815> 
             and 
             Meyer (2022) <doi:10.1038/s41467-022-29838-9> , however, it was generalized and adjusted in the context of this package.

Set of tools to compute metrics and indices for climate analysis.
    The package provides functions to compute extreme indices, evaluate the
    agreement between models and combine theses models into an ensemble. Multi-model
    time series of climate indices can be computed either after averaging the 2-D
    fields from different models provided they share a common grid or by combining
    time series computed on the model native grid. Indices can be assigned weights
    and/or combined to construct new indices. The package makes use of some of the
    methods described in:
    N. Manubens et al. (2018) <doi:10.1016/j.envsoft.2018.01.018>.