PRSim: Stochastic Simulation of Streamflow Time Series using Phase
Randomization
Provides a simulation framework to simulate streamflow time series with similar
  main characteristics as observed data. These characteristics include the distribution of daily 
  streamflow values and their temporal correlation as expressed by short- and long-range 
  dependence. The approach is based on the randomization of the phases of the Fourier 
  transform or the phases of the wavelet transform. The function prsim() is applicable 
  to single site simulation and uses the Fourier transform. 
  The function prsim.wave() extends the approach to multiple sites 
  and is based on the complex wavelet transform. The function prsim.weather()
  extends the approach to multiple variables for weather generation.
  We further use the flexible four-parameter Kappa distribution, 
  which allows for the extrapolation to yet unobserved low and high flows. 
  Alternatively, the empirical or any other distribution can be used. 
  A detailed description of the simulation approach for single sites 
  and an application example can be found in <doi:10.5194/hess-23-3175-2019>.
  A detailed description and evaluation of the wavelet-based multi-site approach
  can be found in <doi:10.5194/hess-24-3967-2020>.
  A detailed description and evaluation of the multi-variable and multi-site
  weather generator can be found in <doi:10.5194/esd-12-621-2021>.
| Version: | 
1.4-2 | 
| Depends: | 
R (≥ 3.5.0) | 
| Imports: | 
stats, methods, lmomco, mev, goftest, wavScalogram, splus2R | 
| Suggests: | 
lattice, ismev, evd, GB2, boot, MASS | 
| Published: | 
2022-09-20 | 
| Author: | 
Manuela Brunner  
    [aut, cre],
  Reinhard Furrer  
    [aut] | 
| Maintainer: | 
Manuela Brunner  <manuela.i.brunner at gmail.com> | 
| BugReports: | 
https://git.math.uzh.ch/reinhard.furrer/PRSim-devel/-/issues | 
| License: | 
GPL-3 | 
| URL: | 
https://git.math.uzh.ch/reinhard.furrer/PRSim-devel | 
| NeedsCompilation: | 
yes | 
| CRAN checks: | 
PRSim results | 
Documentation:
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