• using R version 4.1.2 (2021-11-01)
• using platform: x86_64-apple-darwin17.0 (64-bit)
• using session charset: UTF-8
• checking for file ‘PRSim/DESCRIPTION’ ... OK
• checking extension type ... Package
• this is package ‘PRSim’ version ‘1.4-1’
• package encoding: UTF-8
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• checking whether package ‘PRSim’ can be installed ... [8s/8s] OK
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• checking DESCRIPTION meta-information ... OK
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• checking examples ... [3s/3s] OK
• checking differences from ‘PRSim-Ex.Rout’ to ‘PRSim-Ex.Rout.save’ ... OK
  
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  < > ##D demo("PRSim_weather")
  < > ##D demo("PRSim_weather-validate")
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  < This is mgcv 1.8-39. For overview type 'help("mgcv-package")'.
  ---
  > This is mgcv 1.8-31. For overview type 'help("mgcv-package")'.
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  < This is mgcv 1.8-39. For overview type 'help("mgcv-package")'.
  ---
  > This is mgcv 1.8-31. For overview type 'help("mgcv-package")'.
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  < > nameEx("fun_stoch_sim_weather")
  < > ### * fun_stoch_sim_weather
  < >
  < > flush(stderr()); flush(stdout())
  < >
  < > ### Name: pRsim.weather
  < > ### Title: Weather simulation (temperature and precipitation) for multiple
  < > ### stations
  < > ### Aliases: PRsim.weather prsim.weather prsim_weather
  < > ### Keywords: ts
  < >
  < > ### ** Examples
  < >
  < > data(weather_multi_sites)
  < > ## Not run:
  < > ##D # The following call requires half minute or so to execute.
  < > ##D prsim.weather(data_p=data_p, data_t=data_t, number_sim=1, p_margin='egpd',t_margin='sep')
  < > ## End(Not run)
  < >
  < >
  < > ## Not run:
  < > ##D # The following call requires 5 seconds to execute
  < > ##D ### define normal distribution
  < > ##D library(fitdistrplus)
  < > ##D rNORM <- function(n, theta) rnorm(n, theta[1], theta[2])
  < > ##D pNORM <- function(x, theta) pnorm(x, theta[1], theta[2])
  < > ##D NORM_fit <- function( xdat, ...) fitdistr( xdat, 'normal', show=FALSE, ...)$estimate
  < > ##D ### define GEV distribution
  < > ##D require("evd")
  < > ##D require("ismev")
  < > ##D rGEV <- function(n, theta) rgev(n, theta[1], theta[2], theta[3])
  < > ##D pGEV <- function(x, theta) pgev(x, theta[1], theta[2], theta[3])
  < > ##D GEV_fit <- function( xdat, ...) gev.fit(xdat, show=FALSE, ...)$mle
  < > ##D
  < > ##D ### apply function using alternative distributions
  < > ##D out <- prsim.weather(data_p=data_p, data_t=data_t, number_sim=1,p_margin='GEV',t_margin='NORM')
  < > ## End(Not run)
  < >
  < >
  < >
  < >
  < > cleanEx()
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  < > nameEx("runoff_multi_site_T")
  < > ### * runoff_multi_site_T
  < >
  < > flush(stderr()); flush(stdout())
  < >
  < > ### Name: runoff_multi_site_T
  < > ### Title: Sample runoff and temperature data of two catchments with a
  < > ### similar discharge regime
  < > ### Aliases: runoff_multi_site_T 'runoff multi site T'
  < > ### Keywords: datasets
  < >
  < > ### ** Examples
  < >
  < > data(runoff_multi_site_T)
  < > str(runoff_multi_site_T)
  < List of 2
  < $ :'data.frame': 11323 obs. of 5 variables:
  < ..$ YYYY: chr [1:11323] "1980" "1980" "1980" "1980" ...
  < ..$ MM : chr [1:11323] "01" "01" "01" "01" ...
  < ..$ DD : chr [1:11323] "01" "02" "03" "04" ...
  < ..$ Qobs: num [1:11323] 31.4 29.1 28.5 27.7 38.4 ...
  < ..$ T : num [1:11323] -3.372 -4.512 -4.938 -2.783 0.421 ...
  < $ :'data.frame': 11323 obs. of 5 variables:
  < ..$ YYYY: chr [1:11323] "1980" "1980" "1980" "1980" ...
  < ..$ MM : chr [1:11323] "01" "01" "01" "01" ...
  < ..$ DD : chr [1:11323] "01" "02" "03" "04" ...
  < ..$ Qobs: num [1:11323] 0.2 0.16 0.17 0.17 0.16 0.15 0.15 0.14 0.14 0.13 ...
  < ..$ T : num [1:11323] -9.89 -11.27 -11.47 -7.39 -2.75 ...
  < > runoff_multi_site_T[[1]]$timestamp <- paste(runoff_multi_site_T[[1]]$YYYY,
  < + runoff_multi_site_T[[1]]$MM, runoff_multi_site_T[[1]]$DD, sep=" ")
  < > runoff_multi_site_T[[1]]$timestamp <-
  < + as.POSIXct(strptime(runoff_multi_site_T[[1]]$timestamp,format="%Y %m %d", tz="GMT"))
  < > plot(runoff_multi_site_T[[1]]$timestamp[1:1000], runoff_multi_site_T[[1]]$Qobs[1:1000], type="l",
  < + xlab="Time [d]", ylab=expression(paste("Discharge [m"^3,"/s]")))
  < >
  < >
  < >
  < > cleanEx()
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  < [1] 6570 11
  ---
  > [1] 6570 56
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  < > cleanEx()
  < > nameEx("weather_multi_sites")
  < > ### * weather_multi_sites
  < >
  < > flush(stderr()); flush(stdout())
  < >
  < > ### Name: weather_multi_sites
  < > ### Title: Sample temperature and precipitation of four catchments derived
  < > ### from the ERA5-Land gridded dataset
  < > ### Aliases: weather_multi_sites 'weather multi sites'
  < > ### Keywords: datasets
  < >
  < > ### ** Examples
  < >
  < > data(weather_multi_sites)
  < > weather_multi_sites[[1]][[1]]$timestamp <- paste(weather_multi_sites[[1]][[1]]$YYYY,
  < + weather_multi_sites[[1]][[1]]$MM, weather_multi_sites[[1]][[1]]$DD, sep=" ")
  < > weather_multi_sites[[1]][[1]]$timestamp <-
  < + as.POSIXct(strptime(weather_multi_sites[[1]][[1]]$timestamp,
  < + format="%Y %m %d", tz="GMT"))
  < > plot(weather_multi_sites[[1]][[1]]$timestamp[1:1000],
  < + weather_multi_sites[[1]][[1]]$Qobs[1:1000], type="l",
  < + xlab="Time [d]", ylab=expression(paste("Temperature [degrees]")))
  < >
  < >
  < >
  < > cleanEx()
  < > nameEx("weather_sim_multi_sites")
  < > ### * weather_sim_multi_sites
  < >
  < > flush(stderr()); flush(stdout())
  < >
  < > ### Name: weather_sim_multi_sites
  < > ### Title: Simulated temperature and precipitation for two grid cells
  < > ### Aliases: weather.sim.multi.sites weather_sim_multi_sites
  < > ### Keywords: datasets
  < >
  < > ### ** Examples
  < >
  < > data(weather_sim_multi_sites)
  < > sim <- weather_sim_multi_sites
  < > ### define plotting colors
  < > col_sim <- adjustcolor("#fd8d3c",alpha=0.8)
  < > col_sim_tran <- adjustcolor("#fd8d3c",alpha=0.2)
  < > col_obs <- adjustcolor( "black", alpha.f = 0.2)
  < > ### greys
  < > col_vect_obs <- c('#cccccc','#969696','#636363','#252525')
  < > ### oranges
  < > col_vect_sim <- c('#fdbe85','#fd8d3c','#e6550d','#a63603')
  < >
  < > ### plot time series for multiple sites
  < >
  < > ### Temperature (first list entry)
  < > par(mfrow=c(2,1),mar=c(3,3,2,1))
  < > ### determine ylim
  < > ylim_max <- max(sim[[1]][[1]]$Temp)*1.5
  < > ### observed
  < > plot(sim[[1]][[1]]$Temp[1:1000],
  < + ylab=expression(bold(paste("Temperature [degrees]"))),
  < + xlab="Time [d]",type="l",col=col_vect_obs[1],
  < + ylim=c(0,ylim_max),main='Observations')
  < > for(l in 2){
  < + lines(sim[[l]][[1]]$Temp[1:1000],col=col_vect_obs[l])
  < + }
  < > # legend('topleft',legend=c('Station 1','Station 2'
  < > # ),lty=1,col=col_vect_obs[1:2])
  < > ### simulated (one run)
  < > plot(sim[[1]][[1]]$r1[1:1000],
  < + ylab=expression(bold(paste("Temperature [degrees]"))),
  < + xlab="Time [d]",type="l",col=col_vect_sim[1],
  < + ylim=c(0,ylim_max),main='Stochastic simulations')
  < > for(l in 2){
  < + lines(sim[[l]][[1]]$r1[1:1000],col=col_vect_sim[l])
  < + }
  < >
  < >
  < > ### precipitation (second list entry)
  < > ylim_max <- max(sim[[1]][[2]]$Prec)*1
  < > ### observed
  < > plot(sim[[1]][[2]]$Prec[1:1000],
  < + ylab=expression(bold(paste("Precipitation [mm/d]"))),
  < + xlab="Time [d]",type="l",col=col_vect_obs[1],
  < + ylim=c(0,ylim_max),main='Observations')
  < > for(l in 2){
  < + lines(sim[[l]][[2]]$Prec[1:1000],col=col_vect_obs[l])
  < + }
  < > # legend('topleft',legend=c('Station 1','Station 2'
  < > # ),lty=1,col=col_vect_obs[1:2])
  < > ### simulated (one run)
  < > plot(sim[[1]][[2]]$r1[1:1000],
  < + ylab=expression(bold(paste("Precipitation [mm/d]"))),
  < + xlab="Time [d]",type="l",col=col_vect_sim[1],
  < + ylim=c(0,ylim_max),main='Stochastic simulations')
  < > for(l in 2){
  < + lines(sim[[l]][[2]]$r1[1:1000],col=col_vect_sim[l])
  < + }
  < >
  < >
  < >
  < > graphics::par(get("par.postscript", pos = 'CheckExEnv'))
• checking for unstated dependencies in ‘tests’ ... OK
• checking tests ... [20s/20s] OK
    Running ‘basic.R’ [20s/20s]
    Comparing ‘basic.Rout’ to ‘basic.Rout.save’ ...34c34
  < This is mgcv 1.8-39. For overview type 'help("mgcv-package")'.
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  > This is mgcv 1.8-31. For overview type 'help("mgcv-package")'.
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  < > rKappa <- function(n, theta) homtest::rand.kappa(n, theta[1], theta[2], theta[3], theta[4])
  ---
  > > rKappa <- function(n, theta) rand.kappa(n, theta[1], theta[2], theta[3], theta[4])
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  < + ll <- homtest::Lmoments(xdat)
  < + unlist(homtest::par.kappa(ll[1],ll[2],ll[4],ll[5]))
  ---
  > + ll <- Lmoments(xdat)
  > + unlist(par.kappa(ll[1],ll[2],ll[4],ll[5]))
• checking PDF version of manual ... OK
• DONE
  Status: OK
• using check arguments '--no-clean-on-error '