This function finds the bivariate joint probability or the binary correlation from the corresponding Gaussian correlation
value of expected correlation between the corresponding Gaussian-distributed variables
probability of no precipitation occurences for the v1 and v2 time series respectively. See
logical numeric value. Default is
probability of no precipitation occurence in both v1 and v2 simultaneously. It is a matrix if
x is a matrix.
This function makes use of normal copula. A graphical introduction to this function (with its inverse) makes is present in the following URL references: http://onlinelibrary.wiley.com/doi/10.1002/joc.2305/abstract
and http://www.sciencedirect.com/science/article/pii/S0022169498001863 (See fig. 1 and par. 3.2)
If the argument
p0_v2, the two marginal probabily values must be given as a vector through the argument
x is a correlation/covariance matrix the marginal probabilities are given as a vector through the argument
D.S. Wilks (1998), Multisite Generalization of a Daily Stochastic Precipitation Generation Model, Journal of Hydrology, Volume 210, Issues 1-4, September 1998, Pages 178-191, http://www.sciencedirect.com/science/article/pii/S0022169498001863
Muamaraldin Mhanna and Willy Bauwens (2011) A Stochastic Space-Time Model for the Generation of Daily Rainfall in the Gaza Strip, International Journal of Climatology, Volume 32, Issue 7, pages 1098-1112, http://dx.doi.org/10.1002/joc.2305
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