Running 'anisotropyTest.R' [13s]
  Running 'block.R' [5s]
  Running 'idw.R' [4s]
  Running 'interpolate.R' [6s]
  Running 'interpolateBlock.R' [5s]
  Running 'javaR.R' [3s]
  Running 'linearVariogram.R' [3s]
  Running 'minimal.R' [3s]
  Running 'transGaussian.R' [4s]
  Running 'unbiased.R' [5s]
Running the tests in 'tests/interpolate.R' failed.
Complete output:
  > options(error = recover)
  > set.seed(15331)
  > library(intamap)
  Loading required package: sp
  > library(automap)
  > library(gstat)
  > library(psgp)
  Error in library(psgp) : there is no package called 'psgp'
  recover called non-interactively; frames dumped, use debugger() to view
  > #loadMeuse()
  > 
  > sessionInfo()
  R Under development (unstable) (2022-03-16 r81916 ucrt)
  Platform: x86_64-w64-mingw32/x64 (64-bit)
  Running under: Windows Server x64 (build 20348)
  
  Matrix products: default
  
  locale:
  [1] LC_COLLATE=C                 LC_CTYPE=German_Germany.utf8
  [3] LC_MONETARY=C                LC_NUMERIC=C                
  [5] LC_TIME=C                   
  
  attached base packages:
  [1] stats     graphics  grDevices utils     datasets  methods   base     
  
  other attached packages:
  [1] gstat_2.0-9    automap_1.0-14 intamap_1.4-9  sp_1.4-6      
  
  loaded via a namespace (and not attached):
   [1] Rcpp_1.0.8.2      codetools_0.2-18  lattice_0.20-45   intervals_0.15.2 
   [5] mvtnorm_1.1-3     FNN_1.1.3         reshape_0.8.8     spacetime_1.2-6  
   [9] zoo_1.8-9         foreach_1.5.2     MASS_7.3-55       grid_4.2.0       
  [13] plyr_1.8.6        doParallel_1.0.17 xts_0.12.1        rgdal_1.5-29     
  [17] iterators_1.0.14  parallel_4.2.0    compiler_4.2.0    evd_2.3-4        
  > 
  > crs = CRS("+init=epsg:28992 +proj=sterea +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +towgs84=565.417,50.3319,465.552,-0.398957,0.343988,-1.8774,4.0725 +units=m +no_defs")
  Warning message:
  In showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj = prefer_proj) :
    Discarded datum Unknown based on Bessel 1841 ellipsoid in Proj4 definition,
   but +towgs84= values preserved
  > data("meuse")
  > coordinates(meuse) <- ~x+y
  > proj4string(meuse) <- crs
  > data("meuse.grid")
  > coordinates(meuse.grid) <- ~x+y
  > gridded(meuse.grid) <- TRUE
  > proj4string(meuse.grid) <- crs
  > 
  > meuse$value = log(meuse$zinc)
  > meuse.grid = meuse.grid[sample(1:dim(meuse.grid)[1], 100),]
  > output = interpolate(meuse, meuse.grid, list(mean=T, variance=T, nsim = 5), methodName = "automap")
  R 2022-03-17 11:22:34 interpolating 155 observations, 100 prediction locations
  Checking object ... OK
  There were 16 warnings (use warnings() to see them)
  > summary(t(output$outputTable), digits = 4)
         x                y               mean          variance      
   Min.   :178500   Min.   :329700   Min.   :4.812   Min.   :0.08335  
   1st Qu.:179330   1st Qu.:330540   1st Qu.:5.239   1st Qu.:0.12361  
   Median :180060   Median :331140   Median :5.523   Median :0.14205  
   Mean   :179994   Mean   :331338   Mean   :5.663   Mean   :0.16902  
   3rd Qu.:180670   3rd Qu.:332150   3rd Qu.:6.203   3rd Qu.:0.19770  
   Max.   :181300   Max.   :333380   Max.   :6.770   Max.   :0.36419  
        sim1            sim2            sim3            sim4      
   Min.   :4.297   Min.   :4.344   Min.   :4.348   Min.   :4.163  
   1st Qu.:5.212   1st Qu.:5.223   1st Qu.:5.293   1st Qu.:5.154  
   Median :5.658   Median :5.683   Median :5.709   Median :5.655  
   Mean   :5.731   Mean   :5.653   Mean   :5.685   Mean   :5.711  
   3rd Qu.:6.221   3rd Qu.:6.086   3rd Qu.:5.994   3rd Qu.:6.304  
   Max.   :7.204   Max.   :6.876   Max.   :7.475   Max.   :7.109  
        sim5      
   Min.   :3.541  
   1st Qu.:5.129  
   Median :5.602  
   Mean   :5.625  
   3rd Qu.:6.199  
   Max.   :7.125  
  > 
  > output = interpolate(meuse, meuse.grid,
  +     optList = list(idpRange = seq(0.1, 2.9, 0.5), nfold = 3), 
  +     outputWhat = list(mean=TRUE), methodName = "idw")
  R 2022-03-17 11:22:35 interpolating 155 observations, 100 prediction locations
  Checking object ... OK
  [1] "best idp value found is 2.6 rmse 0.466537817338921"
  There were 17 warnings (use warnings() to see them)
  > summary(t(output$outputTable), digits = 4)
         x                y               mean      
   Min.   :178500   Min.   :329700   Min.   :4.786  
   1st Qu.:179330   1st Qu.:330540   1st Qu.:5.348  
   Median :180060   Median :331140   Median :5.601  
   Mean   :179994   Mean   :331338   Mean   :5.691  
   3rd Qu.:180670   3rd Qu.:332150   3rd Qu.:6.100  
   Max.   :181300   Max.   :333380   Max.   :6.746  
  > 
  > 
  > output = interpolate(meuse, meuse.grid, list(mean=T, variance=T),methodName = "transGaussian")
  R 2022-03-17 11:22:35 interpolating 155 observations, 100 prediction locations
  Checking object ... OK
  There were 14 warnings (use warnings() to see them)
  > summary(t(output$outputTable), digits = 4)
         x                y               mean          variance     
   Min.   :178500   Min.   :329700   Min.   :4.811   Min.   :0.0611  
   1st Qu.:179330   1st Qu.:330540   1st Qu.:5.262   1st Qu.:0.1076  
   Median :180060   Median :331140   Median :5.521   Median :0.1297  
   Mean   :179994   Mean   :331338   Mean   :5.653   Mean   :0.1471  
   3rd Qu.:180670   3rd Qu.:332150   3rd Qu.:6.192   3rd Qu.:0.1763  
   Max.   :181300   Max.   :333380   Max.   :6.764   Max.   :0.3693  
  > 
  > 
  > set.seed(15331)
  > data(meuse)
  > meuse = meuse[sample(dim(meuse)[1],30),]
  > meuse$value=meuse$zinc
  > coordinates(meuse) = ~x+y
  > mgrid = spsample(meuse,10,"regular")
  > gridded(mgrid) = TRUE
  > output1 = interpolate(meuse, mgrid, list(mean=T, variance=T, excprob = 1000,quantile = 0.5),
  +                      methodName = "copula")
  R 2022-03-17 11:22:36 interpolating 30 observations, 12 prediction locations
  Checking object ... OK
  There were 31 warnings (use warnings() to see them)
  > 
  > output2 = interpolate(meuse, mgrid, list(mean=T, variance=T, excprob = 1000,quantile = 0.5),
  +                      methodName = "copula",optList = list(methodParameters = output1$methodParameters))
  R 2022-03-17 11:22:37 interpolating 30 observations, 12 prediction locations