A framework that facilitates spatio-temporal analysis of climate dynamics through exploring and measuring different dimensions of climate change in space and time.

Functions for fitting GEV and POT (via point process fitting)
    models for extremes in climate data, providing return values, return
    probabilities, and return periods for stationary and nonstationary models.
    Also provides differences in return values and differences in log return
    probabilities for contrasts of covariate values. Functions for estimating risk
    ratios for event attribution analyses, including uncertainty. Under the hood,
    many of the functions use functions from 'extRemes', including for fitting the
    statistical models. Details are given in Paciorek, Stone, and Wehner (2018)
    <doi:10.1016/j.wace.2018.01.002>.

Computes 138 standard climate indices at monthly, seasonal and annual resolution. These indices were selected, based on their direct and significant impacts on target sectors, after a thorough review of the literature in the field of extreme weather events and natural hazards. Overall, the selected indices characterize different aspects of the frequency, intensity and duration of extreme events, and are derived from a broad set of climatic variables, including surface air temperature, precipitation, relative humidity, wind speed, cloudiness, solar radiation, and snow cover. The 138 indices have been classified as follow: Temperature based indices (42), Precipitation based indices (22), Bioclimatic indices (21), Wind-based indices (5), Aridity/ continentality indices (10), Snow-based indices (13), Cloud/radiation based indices (6), Drought indices (8), Fire indices (5), Tourism indices (5).

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.