Three predictors are used to predict an unknown variable: conditional expectation, conditional median, and conditional probability. The conditional distribution is determined by copulas. The functions support both bivariate and multivariate distributions.
The ConditionalPrediction_script.R in the scripts folder shows how to use the functions and implement conditional prediction using the example data.
The example_data.RData contains mean air temperature at one day obtained from weather stations and ERA-Interim data.
The packages sp, gstat, VineCopula, and copula are available on CRAN whereas the package spcopula on R-Forge.
Please take a look at the post "Environmental processes are linked, but how?" An introduction to copulas.
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Alidoost F., Su Z, Stein A. 2019. Evaluating the effects of climate extremes on crop yield, production and price using multivariate distributions: A new copula application. Weather and climate extremes.
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Alidoost F., Stein A., Su Z. 2019. The use of bivariate copulas for bias correction of reanalysis air temperature data. PLOS ONE.
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Alidoost, F., (2019), Copulas for integrating weather and land information in space and time (Doctoral), University of Twente.
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