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### Running command:
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###   /Library/Frameworks/R.framework/Resources/bin/R CMD build --keep-empty-dirs --no-resave-data supraHex
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* checking for file ‘supraHex/DESCRIPTION’ ... OK
* preparing ‘supraHex’:
* checking DESCRIPTION meta-information ... OK
* installing the package to build vignettes
* creating vignettes ... ERROR
--- re-building ‘supraHex_vignettes.Rnw’ using Sweave
Loading required package: hexbin
Start at 2024-11-08 19:21:48.527866

First, define topology of a map grid (2024-11-08 19:21:48.528805)...
Second, initialise the codebook matrix (169 X 6) using 'linear' initialisation, given a topology and input data (2024-11-08 19:21:48.528805)...
Third, get training at the rough stage (2024-11-08 19:21:48.534208)...
	1 out of 2 (2024-11-08 19:21:48.535066)
	updated (2024-11-08 19:21:48.542896)
	2 out of 2 (2024-11-08 19:21:48.54307)
	updated (2024-11-08 19:21:48.550917)
Fourth, get training at the finetune stage (2024-11-08 19:21:48.551073)...
	1 out of 7 (2024-11-08 19:21:48.551531)
	updated (2024-11-08 19:21:48.564682)
	2 out of 7 (2024-11-08 19:21:48.564819)
	updated (2024-11-08 19:21:48.57241)
	3 out of 7 (2024-11-08 19:21:48.572529)
	updated (2024-11-08 19:21:48.5801)
	4 out of 7 (2024-11-08 19:21:48.58022)
	updated (2024-11-08 19:21:48.587678)
	5 out of 7 (2024-11-08 19:21:48.587801)
	updated (2024-11-08 19:21:48.599858)
	6 out of 7 (2024-11-08 19:21:48.599991)
	updated (2024-11-08 19:21:48.60747)
	7 out of 7 (2024-11-08 19:21:48.607594)
	updated (2024-11-08 19:21:48.615089)
Next, identify the best-matching hexagon/rectangle for the input data (2024-11-08 19:21:48.615243)...
Finally, append the response data (hits and mqe) into the sMap object (2024-11-08 19:21:48.627076)...

Below are the summaries of the training results:
   dimension of input data: 1000x6
   xy-dimension of map grid: xdim=15, ydim=15, r=8
   grid lattice: hexa
   grid shape: suprahex
   dimension of grid coord: 169x2
   initialisation method: linear
   dimension of codebook matrix: 169x6
   mean quantization error: 2.07057047899621

Below are the details of trainology:
   training algorithm: batch
   alpha type: invert
   training neighborhood kernel: gaussian
   trainlength (x input data length): 2 at rough stage; 7 at finetune stage
   radius (at rough stage): from 4 to 1
   radius (at finetune stage): from 1 to 1

End at 2024-11-08 19:21:48.7464
Runtime in total is: 0 secs

Warning: The `path` argument of `write_delim()` is deprecated as of readr 1.4.0.
ℹ Please use the `file` argument instead.
ℹ The deprecated feature was likely used in the supraHex package.
  Please report the issue to the authors.

Error: processing vignette 'supraHex_vignettes.Rnw' failed with diagnostics:
 chunk 18 
Error in igraph::as_data_frame(., what = "edge") : 
  `arg` must be one of "edges", "vertices", or "both", not "edge".
ℹ Did you mean "edges"?

--- failed re-building ‘supraHex_vignettes.Rnw’

SUMMARY: processing the following file failed:
  ‘supraHex_vignettes.Rnw’

Error: Vignette re-building failed.
Execution halted