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

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: s_generate_data_mars
> ### Title: Generate non linear response and test and training sets for
> ###   non-linear simulation study
> ### Aliases: s_generate_data_mars
> 
> ### ** Examples
> 
> library(magrittr)
> 
> # simulation parameters
> rho = 0.90; p = 500 ;SNR = 1 ; n = 200; n0 = n1 = 100 ; nActive = p*0.10 ; cluster_distance = "tom";
> Ecluster_distance = "difftom"; rhoOther = 0.6; betaMean = 2;
> alphaMean = 1; betaE = 3; distanceMethod = "euclidean"; clustMethod = "hclust";
> cutMethod = "dynamic"; agglomerationMethod = "average"
> 
> #in this simulation its blocks 3 and 4 that are important
> #leaveOut:  optional specification of modules that should be left out
> #of the simulation, that is their genes will be simulated as unrelated
> #("grey"). This can be useful when simulating several sets, in some which a module
> #is present while in others it is absent.
> d0 <- s_modules(n = n0, p = p, rho = 0, exposed = FALSE,
+                 modProportions = c(0.15,0.15,0.15,0.15,0.15,0.25),
+                 minCor = 0.01,
+                 maxCor = 1,
+                 corPower = 1,
+                 propNegativeCor = 0.3,
+                 backgroundNoise = 0.5,
+                 signed = FALSE,
+                 leaveOut = 1:4)
 simulateDatExpr: simulating 500 genes in 5 modules.
> 
> d1 <- s_modules(n = n1, p = p, rho = rho, exposed = TRUE,
+                 modProportions = c(0.15,0.15,0.15,0.15,0.15,0.25),
+                 minCor = 0.4,
+                 maxCor = 1,
+                 corPower = 0.3,
+                 propNegativeCor = 0.3,
+                 backgroundNoise = 0.5,
+                 signed = FALSE)
 simulateDatExpr: simulating 500 genes in 5 modules.
> 
> truemodule1 <- d1$setLabels
> 
> X <- rbind(d0$datExpr, d1$datExpr) %>%
+   magrittr::set_colnames(paste0("Gene", 1:p)) %>%
+   magrittr::set_rownames(paste0("Subject",1:n))
> 
> betaMainEffect <- vector("double", length = p)
> 
> # the first nActive/2 in the 3rd block are active
> betaMainEffect[which(truemodule1 %in% 3)[1:(nActive/2)]] <- runif(
+   nActive/2, betaMean - 0.1, betaMean + 0.1)
> 
> # the first nActive/2 in the 4th block are active
> betaMainEffect[which(truemodule1 %in% 4)[1:(nActive/2)]] <- runif(
+   nActive/2, betaMean+2 - 0.1, betaMean+2 + 0.1)
> beta <- c(betaMainEffect, betaE)
> 
> result <- s_generate_data_mars(p = p, X = X,
+                                beta = beta,
+                                binary_outcome = FALSE,
+                                truemodule = truemodule1,
+                                nActive = nActive,
+                                include_interaction = FALSE,
+                                cluster_distance = cluster_distance,
+                                n = n, n0 = n0,
+                                eclust_distance = Ecluster_distance,
+                                signal_to_noise_ratio = SNR,
+                                distance_method = distanceMethod,
+                                cluster_method = clustMethod,
+                                cut_method = cutMethod,
+                                agglomeration_method = agglomerationMethod,
+                                nPC = 1)
Creating data and simulating response for MARS model
Warning in k * error :
  Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.
Calculating similarity matrices
TOM calculation: adjacency..
..will not use multithreading.
 Fraction of slow calculations: 0.000000
..connectivity..
..matrix multiplication (system BLAS)..
..normalization..
..done.
TOM calculation: adjacency..
..will not use multithreading.
 Fraction of slow calculations: 0.000000
..connectivity..
..matrix multiplication (system BLAS)..
..normalization..
..done.
TOM calculation: adjacency..
..will not use multithreading.
 Fraction of slow calculations: 0.000000
..connectivity..
..matrix multiplication (system BLAS)..
..normalization..
..done.
Creating CV folds from training data
Calculating number of clusters based on tom using hclust with average
                  linkage and the dynamic to determine the number of clusters
 ..cutHeight not given, setting it to 0.999  ===>  99% of the (truncated) height range in dendro.
 ..done.
Error in loadNamespace(x) : there is no package called ‘factoextra’
Calls: s_generate_data_mars ... loadNamespace -> withRestarts -> withOneRestart -> doWithOneRestart
Execution halted