Running examples in ‘CompRandFld-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: FitComposite
> ### Title: Max-Likelihood-Based Fitting of Gaussian, Binary and Max-Stable
> ###   Fields
> ### Aliases: FitComposite print.FitComposite
> ### Keywords: Composite
> 
> ### ** Examples
> 
> library(CompRandFld)
> library(RandomFields)
Loading required package: sp
Loading required package: RandomFieldsUtils

Attaching package: ‘RandomFields’

The following object is masked from ‘package:RandomFieldsUtils’:

    RFoptions

The following objects are masked from ‘package:base’:

    abs, acosh, asin, asinh, atan, atan2, atanh, cos, cosh, exp, expm1,
    floor, gamma, lgamma, log, log1p, log2, logb, max, min, round, sin,
    sinh, sqrt, tan, tanh, trunc

> library(spam)
Loading required package: dotCall64
Loading required package: grid
Spam version 2.1-4 (2018-04-12) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction 
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.

Attaching package: ‘spam’

The following objects are masked from ‘package:base’:

    backsolve, forwardsolve

> set.seed(3132)
> 
> ###############################################################
> ############ Examples of spatial random fields ################
> ###############################################################
> 
> # Define the spatial-coordinates of the points:
> x <- runif(100, 0, 10)
> y <- runif(100, 0, 10)
> 
> # Set the covariance model's parameters:
> corrmodel <- "exponential"
> mean <- 0
> sill <- 1
> nugget <- 0
> scale <- 1.5
> param<-list(mean=mean,sill=sill,nugget=nugget,scale=scale)
> coords<-cbind(x,y)
> # Simulation of the spatial Gaussian random field:
> data <- RFsim(coordx=coords, corrmodel=corrmodel, param=param)$data
> 
> # Fixed parameters
> fixed<-list(mean=mean,nugget=nugget)
> 
> # Starting value for the estimated parameters
> start<-list(scale=scale,sill=sill)
> 
> 
> ################################################################
> ###
> ### Example 1. Maximum likelihood fitting of
> ### Gaussian random fields with exponential correlation.
> ### One spatial replication.
> ### Likelihood setting: composite with
> ### marginal pairwise likelihood objects.
> ###
> ###############################################################
> 
> 
> # Maximum composite-likelihood fitting of the random field:
> fit <- FitComposite(data, coordx=coords, corrmodel=corrmodel, maxdist=2,
+                     likelihood="Marginal",type="Pairwise",varest=TRUE,
+                     start=start,fixed=fixed)
> 
> # Results:
> print(fit)

##################################################################
Maximum Composite-Likelihood Fitting of Gaussian Random Fields

Setting: Marginal Composite-Likelihood 

Model associated to the likelihood objects: Gaussian 

Type of the likelihood objects: Pairwise 

Covariance model: exponential 
Number of spatial coordinates: 100 
Number of dependent temporal realisations: 1 
Number of replicates of the random field: 1 
Number of estimated parameters: 2 

Maximum log-Composite-Likelihood value: -1677.49
CLIC : 3385 

Estimated parameters:
scale   sill  
1.761  1.366  

Standard errors:
 scale    sill  
0.4213  0.2273  

Variance-covariance matrix of the estimates:
         scale      sill
scale   0.1775   0.08610
sill    0.0861   0.05165

##################################################################
> 
> ################################################################
> ###
> ### Example 2. Maximum likelihood fitting of
> ### Gaussian random fields with exponential correlation.
> ### One spatial replication.
> ### Likelihood setting: standard full likelihood.
> ###
> ###############################################################
> 
> # Maximum composite-likelihood fitting of the random field:
> fit <- FitComposite(data, coordx=coords, corrmodel=corrmodel,likelihood="Full",
+                     type="Standard",varest=TRUE,start=start,fixed=fixed)
> # Results:
> print(fit)

##################################################################
Maximum Likelihood Fitting of Gaussian Random Fields

Setting: Full Likelihood 

Model associated to the likelihood objects: Gaussian 

Type of the likelihood objects: Standard 

Covariance model: exponential 
Number of spatial coordinates: 100 
Number of dependent temporal realisations: 1 
Number of replicates of the random field: 1 
Number of estimated parameters: 2 

Maximum log-Likelihood value: -119.50
AIC : 243 

Estimated parameters:
scale   sill  
1.161  1.112  

Standard errors:
 scale    sill  
0.3679  0.2511  

Variance-covariance matrix of the estimates:
          scale      sill
scale   0.13536   0.07201
sill    0.07201   0.06305

##################################################################
> 
> ################################################################
> ###
> ### Example 3. Maximum likelihood fitting of
> ### Gaussian random fields with exponetial correlation.
> ### One spatial replication.
> ### Likelihood setting: tapered full likelihood.
> ###
> ###############################################################
> 
> # Maximum tapered likelihood fitting of the random field:
> fit <- FitComposite(data, coordx=coords, corrmodel=corrmodel,likelihood="Full",
+                     type="Tapering",taper="Wendland1",maxdist=1.5,
+                     varest=TRUE,start=start,fixed=fixed)
Error in UseMethod("determinant") : 
  no applicable method for 'determinant' applied to an object of class "spam.chol.NgPeyton"
Calls: FitComposite ... optim -> <Anonymous> -> fn -> apply -> FUN -> determinant
Execution halted