estimate parameters which control the dependence between intensities with a MAR(1) process
fit.MAR1.amount(P.mat, isPeriod, th, copulaInt, M0, A)list with the following items
M0 covariance matrix of gaussianized prec. amounts for all pairs of stations
A omega correlations for all pairs of stations
covZ covariance matrix of the MAR(1) process
sdZ standard deviation of the diagonal elements
corZ correlation matrix of the MAR(1) process
dfStudent degrees of freedom for the Student copula if CopulaInt is equal to "Student"
precipitation matrix
vector of logical n x 1 indicating the days concerned by a 3-month period
threshold above which we consider that a day is wet (e.g. 0.2 mm)
type of dependance between inter-site amounts: 'Gaussian' or 'Student'
covariance matrix of gaussianized prec. amounts for all pairs of stations
Matrix containing the autocorrelation (temporal) correlations
Guillaume Evin
Matalas, N. C. 1967. “Mathematical Assessment of Synthetic Hydrology.” Water Resources Research 3 (4): 937–45. https://doi.org/10.1029/WR003i004p00937.
Bárdossy, A., and G. G. S. Pegram. 2009. “Copula Based Multisite Model for Daily Precipitation Simulation.” Hydrology and Earth System Sciences 13 (12): 2299–2314. https://doi.org/10.5194/hess-13-2299-2009.