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

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: plot.all.impact
> ### Title: Plot "all.impact" objects
> ### Aliases: plot.all.impact
> 
> ### ** Examples
> 
> out <- impact(depression[,1:5])
Warning in EBICglassoCore(S = S, n = n, gamma = gamma, penalize.diagonal = penalize.diagonal,  :
  A dense regularized network was selected (lambda < 0.1 * lambda.max). Recent work indicates a possible drop in specificity. Interpret the presence of the smallest edges with care. Setting threshold = TRUE will enforce high specificity, at the cost of sensitivity.
Warning in EBICglassoCore(S = S, n = n, gamma = gamma, penalize.diagonal = penalize.diagonal,  :
  A dense regularized network was selected (lambda < 0.1 * lambda.max). Recent work indicates a possible drop in specificity. Interpret the presence of the smallest edges with care. Setting threshold = TRUE will enforce high specificity, at the cost of sensitivity.
Warning in EBICglassoCore(S = S, n = n, gamma = gamma, penalize.diagonal = penalize.diagonal,  :
  Network with lowest lambda selected as best network. Try setting 'lambda.min.ratio' lower.
Warning in EBICglassoCore(S = S, n = n, gamma = gamma, penalize.diagonal = penalize.diagonal,  :
  A dense regularized network was selected (lambda < 0.1 * lambda.max). Recent work indicates a possible drop in specificity. Interpret the presence of the smallest edges with care. Setting threshold = TRUE will enforce high specificity, at the cost of sensitivity.
Warning in EBICglassoCore(S = S, n = n, gamma = gamma, penalize.diagonal = penalize.diagonal,  :
  Network with lowest lambda selected as best network. Try setting 'lambda.min.ratio' lower.
Warning in EBICglassoCore(S = S, n = n, gamma = gamma, penalize.diagonal = penalize.diagonal,  :
  A dense regularized network was selected (lambda < 0.1 * lambda.max). Recent work indicates a possible drop in specificity. Interpret the presence of the smallest edges with care. Setting threshold = TRUE will enforce high specificity, at the cost of sensitivity.
Warning in EBICglassoCore(S = S, n = n, gamma = gamma, penalize.diagonal = penalize.diagonal,  :
  Network with lowest lambda selected as best network. Try setting 'lambda.min.ratio' lower.
Warning in EBICglassoCore(S = S, n = n, gamma = gamma, penalize.diagonal = penalize.diagonal,  :
  A dense regularized network was selected (lambda < 0.1 * lambda.max). Recent work indicates a possible drop in specificity. Interpret the presence of the smallest edges with care. Setting threshold = TRUE will enforce high specificity, at the cost of sensitivity.
Warning in EBICglassoCore(S = S, n = n, gamma = gamma, penalize.diagonal = penalize.diagonal,  :
  Network with lowest lambda selected as best network. Try setting 'lambda.min.ratio' lower.
Warning in EBICglassoCore(S = S, n = n, gamma = gamma, penalize.diagonal = penalize.diagonal,  :
  A dense regularized network was selected (lambda < 0.1 * lambda.max). Recent work indicates a possible drop in specificity. Interpret the presence of the smallest edges with care. Setting threshold = TRUE will enforce high specificity, at the cost of sensitivity.
Warning in EBICglassoCore(S = S, n = n, gamma = gamma, penalize.diagonal = penalize.diagonal,  :
  Network with lowest lambda selected as best network. Try setting 'lambda.min.ratio' lower.
Warning in EBICglassoCore(S = S, n = n, gamma = gamma, penalize.diagonal = penalize.diagonal,  :
  A dense regularized network was selected (lambda < 0.1 * lambda.max). Recent work indicates a possible drop in specificity. Interpret the presence of the smallest edges with care. Setting threshold = TRUE will enforce high specificity, at the cost of sensitivity.
Warning in EBICglassoCore(S = S, n = n, gamma = gamma, penalize.diagonal = penalize.diagonal,  :
  Network with lowest lambda selected as best network. Try setting 'lambda.min.ratio' lower.
Warning in EBICglassoCore(S = S, n = n, gamma = gamma, penalize.diagonal = penalize.diagonal,  :
  A dense regularized network was selected (lambda < 0.1 * lambda.max). Recent work indicates a possible drop in specificity. Interpret the presence of the smallest edges with care. Setting threshold = TRUE will enforce high specificity, at the cost of sensitivity.
> plot(out)
Error in Summary.unit(c(4.4, 4.4), na.rm = FALSE) : 
  VECTOR_ELT() can only be applied to a 'list', not a 'double'
Calls: plot ... render_strips -> build_strip -> ggstrip -> Summary.unit
Execution halted