* using log directory 'd:/RCompile/CRANincoming/R-devel/GeoModels.Rcheck' * using R version 4.4.0 alpha (2024-04-01 r86264 ucrt) * using platform: x86_64-w64-mingw32 * R was compiled by gcc.exe (GCC) 13.2.0 GNU Fortran (GCC) 13.2.0 * running under: Windows Server 2022 x64 (build 20348) * using session charset: UTF-8 * checking for file 'GeoModels/DESCRIPTION' ... OK * checking extension type ... Package * this is package 'GeoModels' version '2.0.0' * package encoding: UTF-8 * checking CRAN incoming feasibility ... Note_to_CRAN_maintainers Maintainer: 'Moreno Bevilacqua ' * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking whether package 'GeoModels' can be installed ... 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OK * checking use of PKG_*FLAGS in Makefiles ... OK * checking use of SHLIB_OPENMP_*FLAGS in Makefiles ... OK * checking pragmas in C/C++ headers and code ... OK * checking compilation flags used ... OK * checking compiled code ... OK * checking usage of KIND in Fortran files ... OK * checking examples ... [47s] ERROR Running examples in 'GeoModels-Ex.R' failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: GeoSimapprox > ### Title: Fast simulation of Gaussian and non Gaussian Random Fields. > ### Aliases: GeoSimapprox > ### Keywords: Simulation > > ### ** Examples > > library(GeoModels) > > > ################################################################ > ### > ### Example 1. Simulation of a large spatial Gaussian RF > ### with Matern covariance model > ### using circulant embeeding method > ### It works only for regular grid > ### for any typpe of correlation model > ############################################################### > set.seed(68) > x = seq(0,1,0.005) > y = seq(0,1,0.005) > param=list(smooth=1.5,mean=0,sill=1,scale=0.2/3,nugget=0) > # Simulation of a spatial Gaussian RF with Matern correlation function > data1 <- GeoSimapprox(coordx=x,coordy=y, grid=TRUE,corrmodel="Matern", model="Gaussian", + method="CE",param=param)$data > fields::image.plot( matrix(data1, length(x), length(y), byrow = TRUE) ) > > ################################################################ > ### > ### Example 2. Simulation of a large spatial Gaussian RF > ### with Matern covariance model > ### using Turning band method > ### It works for (ir)regular grid > ### for the Matern model > ############################################################### > set.seed(68) > x = runif(10000) > y = runif(10000) > coords=cbind(x,y) > param=list(smooth=0.5,mean=0,sill=1,scale=0.1,nugget=0) > # Simulation of a spatial Gaussian RF with Matern correlation function > data1 <- GeoSimapprox(coords, corrmodel="Matern", model="Gaussian", + method="TB",param=param)$data > quilt.plot(coords,data1) > > > ################################################################ > ### > ### Example 3. Simulation of a large spatial Gaussian RF > ### with matern covariance model > ### using Vecchia's method > ### It works for (ir)regular grid > ### for the Matern model > ############################################################### > set.seed(68) > x = runif(6000) > y = runif(6000) > coords=cbind(x,y) > param=list(smooth=0.5,mean=0,sill=1,scale=0.1,nugget=0) > # Simulation of a spatial Gaussian RF with Matern correlation function > data1 <- GeoSimapprox(coords, corrmodel="Matern", model="Gaussian", + method="Vecchia",param=param)$data > quilt.plot(coords,data1) > > > > ################################################################ > ### > ### Example 4. Simulation of a large spacetime Gaussian RF > ### with separable matern covariance model > ### using Circular embeeding method > ### It works for (large) regular time grid > ### and separable spacetime models > ############################################################### > set.seed(68) > coordt <- (0:100) > coords <- cbind( runif(100, 0 ,1), runif(100, 0 ,1)) > param <- list(mean = 0, sill = 1, nugget = 0.25, + scale_s = 0.05, scale_t = 2, + smooth_s = 0.5, smooth_t = 0.5) > # Simulation of a spatial Gaussian RF with Matern correlation function > param<-list(nugget=0,mean=0,scale_s=0.2/3,scale_t=2/3,sill=1,smooth_s=0.5,smooth_t=0.5) > > data <- GeoSimapprox(coordx=coords, coordt=coordt, corrmodel="Matern_Matern", + model="Gaussian",method="CE",param=param)$data > dim(data) [1] 101 100 > > ################################################################ > ### > ### Example 5. Simulation of a large spacetime Gaussian RF > ### with separable GenWend covariance model > ### using Circular embeeding method in time > ############################################################### > set.seed(68) > # Simulation of a spatial Gaussian RF with Matern correlation function > param<-list(nugget=0,mean=0,scale_s=0.2,scale_t=3,sill=1, + smooth_s=0,smooth_t=0, power2_s=4,power2_t=4) > > data <- GeoSimapprox(coordx=coords, coordt=coordt, corrmodel="GenWend_GenWend", + model="Gaussian",method="CE",param=param)$data > dim(data) [1] 101 100 > > > > ################################################################ > ### > ### Example 6. Simulation of a large bivariate Gaussian RF > ### with bivariate Matern correlation model using TB method > ### > ############################################################### > > # Define the spatial-coordinates of the points: > x <- runif(1500, 0, 2) > y <- runif(1500, 0, 2) > coords <- cbind(x,y) > > # Simulation of a bivariate spatial Gaussian RF: > # with a separable Bivariate Matern > set.seed(12) > param=list(mean_1=4,mean_2=2,smooth_1=0.5,smooth_2=0.5,smooth_12=0.5, + scale_1=0.12,scale_2=0.1,scale_12=0.15, + sill_1=1,sill_2=1,nugget_1=0,nugget_2=0,pcol=0.5) > data <- GeoSimapprox(coordx=coords,corrmodel="Bi_matern", + param=param)$data * checking PDF version of manual ... [16s] OK * checking HTML version of manual ... [13s] OK * DONE Status: 1 ERROR