R Under development (unstable) (2024-09-15 r87152 ucrt) -- "Unsuffered Consequences"
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> library(testthat)
>  Sys.setenv('OMP_THREAD_LIMIT'=2)
>  library(rlibkriging)

Attaching package: 'rlibkriging'

The following objects are masked from 'package:base':

    load, save

> 
> ##library(rlibkriging, lib.loc="bindings/R/Rlibs")
> ##library(testthat)
> kernel="gauss"
> 
> for (kernel in c("exp","matern3_2","matern5_2","gauss")) {
+   context(paste0("Check logMargPost for kernel ",kernel))
+   
+   f = function(x) 1-1/2*(sin(12*x)/(1+x)+2*cos(7*x)*x^5+0.7)
+   plot(f)
+   n <- 5
+   set.seed(123)
+   X <- as.matrix(runif(n))
+   y = f(X)
+   points(X,y)
+   
+   ##library(rlibkriging)
+   r <- NuggetKriging(y, X, kernel, objective="LMP", parameters=list(nugget=0,is_nugget_estim=TRUE))
+   
+ eps = 0.00001 # used for comparison bw computed grad and num grad
+ precision = 0.01
+ 
+   alpha0=r$sigma2()/(r$sigma2()+r$nugget())
+   ll2_theta = function(theta) logMargPostFun(r,c(theta,alpha0))$logMargPost
+   # second arg is alpha=1 for nugget=0
+   plot(Vectorize(ll2_theta),ylab="LMP",xlab="theta",xlim=c(0.01,1))
+   for (x in seq(0.01,1,,11)){
+     envx = new.env()
+     ll2x = logMargPostFun(r,c(x,alpha0))$logMargPost[1]
+     gll2x = logMargPostFun(r,c(x,alpha0),return_grad = T)$logMargPostGrad[,1]
+     arrows(x,ll2x,x+.1,ll2x+.1*gll2x,col='red')
+ 
+     ll2x_eps = logMargPostFun(r,c(x+eps,alpha0))$logMargPost[1]
+     test_that(desc=paste0("Numerical and analytical gradient are (almost) the same at theta=",x),
+           expect_equal((ll2x_eps-ll2x)/eps,gll2x,tol = precision))
+   }
+   
+   theta0 = r$theta()
+   ll2_alpha = function(alpha) logMargPostFun(r,c(theta0,alpha))$logMargPost
+   plot(Vectorize(ll2_alpha),ylab="LMP",xlab="alpha",xlim=c(0.01,1))
+   for (x in seq(0.01,0.99,,11)){
+     envx = new.env()
+     ll2x = logMargPostFun(r,c(theta0,x))$logMargPost[1]
+     gll2x = logMargPostFun(r,c(theta0,x),return_grad = T)$logMargPostGrad[,2]
+     arrows(x,ll2x,x+.1,ll2x+.1*gll2x,col='red')
+ 
+     ll2x_eps = logMargPostFun(r,c(theta0,x+eps))$logMargPost[1]
+     test_that(desc=paste0("Numerical and analytical gradient are (almost) the same at alpha=",x),
+           expect_equal((ll2x_eps-ll2x)/eps,gll2x,tol = precision))
+   }
+ 
+ 
+ }
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> 
> proc.time()
   user  system elapsed 
   6.98    0.57    7.54