Running examples in 'clue-Ex.R' failed
The error most likely occurred in:

> ### Name: cl_consensus
> ### Title: Consensus Partitions and Hierarchies
> ### Aliases: cl_consensus
> ### Keywords: cluster
> 
> ### ** Examples
> 
> ## Consensus partition for the Rosenberg-Kim kinship terms partition
> ## data based on co-membership dissimilarities.
> data("Kinship82")
> m1 <- cl_consensus(Kinship82, method = "GV3",
+                    control = list(k = 3, verbose = TRUE))
SUMT run: 1
Iteration: 0 Rho: 0.0978703 P: 51.1484
Iteration: 1 Rho: 0.0978703 P: 51.1484
Iteration: 2 Rho: 0.978703 P: 0.269306
Iteration: 3 Rho: 9.78703 P: 0.183769
Iteration: 4 Rho: 97.8703 P: 0.0327726
Iteration: 5 Rho: 978.703 P: 0.000817076
Iteration: 6 Rho: 9787.03 P: 9.28508e-06
Iteration: 7 Rho: 97870.3 P: 9.40848e-08
Iteration: 8 Rho: 978703 P: 9.4229e-10
Iteration: 9 Rho: 9.78703e+06 P: 9.42419e-12
Iteration: 10 Rho: 9.78703e+07 P: 9.42432e-14
Iteration: 11 Rho: 9.78703e+08 P: 9.42477e-16
Minimum: 6.43155
> ## (Note that one should really use several replicates of this.)
> ## Value for criterion function to be minimized:
> sum(cl_dissimilarity(Kinship82, m1, "comem") ^ 2)
[1] 2411.388
> ## Compare to the consensus solution given in Gordon & Vichi (2001).
> data("Kinship82_Consensus")
> m2 <- Kinship82_Consensus[["JMF"]]
> sum(cl_dissimilarity(Kinship82, m2, "comem") ^ 2)
[1] 2422.397
> ## Seems we get a better solution ...
> ## How dissimilar are these solutions?
> cl_dissimilarity(m1, m2, "comem")
Cross-dissimilarities using Euclidean comembership distance:
          [,1]
[1,] 0.3708906
> ## How "fuzzy" are they?
> cl_fuzziness(cl_ensemble(m1, m2))
Fuzziness using normalized partition coefficient:
[1] 0.4360393 0.3894000
> ## Do the "nearest" hard partitions fully agree?
> cl_dissimilarity(as.cl_hard_partition(m1),
+                  as.cl_hard_partition(m2))
Cross-dissimilarities using minimal Euclidean membership distance:
     [,1]
[1,]    0
> 
> ## Consensus partition for the Gordon and Vichi (2001) macroeconomic
> ## partition data based on Euclidean dissimilarities.
> data("GVME")
> set.seed(1)
> ## First, using k = 2 classes.
> m1 <- cl_consensus(GVME, method = "GV1",
+                    control = list(k = 2, verbose = TRUE))
Iteration: 0 *** value: 6.96343
Iteration: 1 *** value: 2.3701
Iteration: 2 *** value: 1.55361
Iteration: 3 *** value: 1.55361
Minimum: 1.55361
> ## (Note that one should really use several replicates of this.)
> ## Value of criterion function to be minimized:
> sum(cl_dissimilarity(GVME, m1, "GV1") ^ 2)
[1] 7.76807
> ## Compare to the consensus solution given in Gordon & Vichi (2001).
> data("GVME_Consensus")
> m2 <- GVME_Consensus[["MF1/2"]]
> sum(cl_dissimilarity(GVME, m2, "GV1") ^ 2)
[1] 7.76815
> ## Seems we get a slightly  better solution ...
> ## But note that
> cl_dissimilarity(m1, m2, "GV1")
Cross-dissimilarities using Gordon-Vichi Delta_1 dissimilarity:
      [,1]
[1,] 0.004
> ## and that the maximal deviation of the memberships is
> max(abs(cl_membership(m1) - cl_membership(m2)))
[1] 0.001
> ## so the differences seem to be due to rounding.
> ## Do the "nearest" hard partitions fully agree?
> table(cl_class_ids(m1), cl_class_ids(m2))
   
     1  2
  1 14  0
  2  0  7
> 
> ## And now for k = 3 classes.
> m1 <- cl_consensus(GVME, method = "GV1",
+                    control = list(k = 3, verbose = TRUE))
Iteration: 0 *** value: 7.09768
Error in loadNamespace(x) : there is no package called 'quadprog'
Calls: cl_consensus ... loadNamespace -> withRestarts -> withOneRestart -> doWithOneRestart
Execution halted