Error(s) in re-building vignettes:
  ...
--- re-building ‘valued.Rmd’ using rmarkdown
Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-1'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Loading required package: ergm
Loading required package: network

'network' 1.17.1 (2021-06-12), part of the Statnet Project
* 'news(package="network")' for changes since last version
* 'citation("network")' for citation information
* 'https://statnet.org' for help, support, and other information


'ergm' 4.0.1 (2021-06-20), part of the Statnet Project
* 'news(package="ergm")' for changes since last version
* 'citation("ergm")' for citation information
* 'https://statnet.org' for help, support, and other information

'ergm' 4 is a major update that introduces some backwards-incompatible
changes. Please type 'news(package="ergm")' for a list of major
changes.


'ergm.count' 4.0.2 (2021-06-18), part of the Statnet Project
* 'news(package="ergm.count")' for changes since last version
* 'citation("ergm.count")' for citation information
* 'https://statnet.org' for help, support, and other information

Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-2'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-3'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-4'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-5'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-6'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-7'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-8'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-9'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse'.
Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse'.
Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse'.
Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-10'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-11'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Best valid proposal 'Geometric' cannot take into account hint(s) 'sparse'.
Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-12'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-13'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Best valid proposal 'Geometric' cannot take into account hint(s) 'sparse'.
Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-14'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-15'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Starting contrastive divergence estimation via CD-MCMLE:
Iteration 1 of at most 60:
Convergence test P-value:1.1e-293
The log-likelihood improved by 1.543.
Iteration 2 of at most 60:
Convergence test P-value:7.6e-151
The log-likelihood improved by 0.9824.
Iteration 3 of at most 60:
Convergence test P-value:1.8e-54
The log-likelihood improved by 0.2493.
Iteration 4 of at most 60:
Convergence test P-value:5.1e-11
The log-likelihood improved by 0.04637.
Iteration 5 of at most 60:
Convergence test P-value:1.1e-06
The log-likelihood improved by 0.02911.
Iteration 6 of at most 60:
Convergence test P-value:2.5e-01
The log-likelihood improved by 0.005257.
Iteration 7 of at most 60:
Convergence test P-value:9.9e-02
The log-likelihood improved by 0.007549.
Iteration 8 of at most 60:
Convergence test P-value:2.3e-01
The log-likelihood improved by 0.005541.
Iteration 9 of at most 60:
Convergence test P-value:3.1e-01
The log-likelihood improved by 0.004684.
Iteration 10 of at most 60:
Convergence test P-value:7.2e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.002306.
Finished CD.
Starting Monte Carlo maximum likelihood estimation (MCMLE):
Iteration 1 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.6246.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0713.
Convergence test p-value: 0.5110. Not converged with 99% confidence; increasing sample size.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0897.
Convergence test p-value: 0.1206. Not converged with 99% confidence; increasing sample size.
Iteration 4 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0096.
Convergence test p-value: 0.0076. Converged with 99% confidence.
Finished MCMLE.
Evaluating log-likelihood at the estimate. Setting up bridge sampling...
Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Using 16 bridges: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 .
Note: Null model likelihood calculation is not implemented for valued
ERGMs at this time.  This means that all likelihood-based inference
(LRT, Analysis of Deviance, AIC, BIC, etc.) is only valid between
models with the same reference distribution and constraints.
This model was fit using MCMC.  To examine model diagnostics and check
for degeneracy, use the mcmc.diagnostics() function.
Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-16'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-17'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-18'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Starting contrastive divergence estimation via CD-MCMLE:
Iteration 1 of at most 60:
Convergence test P-value:8.8e-110
The log-likelihood improved by 1.864.
Iteration 2 of at most 60:
Convergence test P-value:7.8e-47
The log-likelihood improved by 0.5369.
Iteration 3 of at most 60:
Convergence test P-value:6.9e-28
The log-likelihood improved by 0.2731.
Iteration 4 of at most 60:
Convergence test P-value:2.7e-12
The log-likelihood improved by 0.1087.
Iteration 5 of at most 60:
Convergence test P-value:3.6e-08
The log-likelihood improved by 0.06742.
Iteration 6 of at most 60:
Convergence test P-value:1.4e-05
The log-likelihood improved by 0.04414.
Iteration 7 of at most 60:
Convergence test P-value:5.5e-03
The log-likelihood improved by 0.01994.
Iteration 8 of at most 60:
Convergence test P-value:3.5e-01
The log-likelihood improved by 0.004009.
Iteration 9 of at most 60:
Convergence test P-value:5.2e-02
The log-likelihood improved by 0.01205.
Iteration 10 of at most 60:
Convergence test P-value:1.9e-04
The log-likelihood improved by 0.03332.
Iteration 11 of at most 60:
Convergence test P-value:2.8e-02
The log-likelihood improved by 0.01394.
Iteration 12 of at most 60:
Convergence test P-value:1.6e-01
The log-likelihood improved by 0.006985.
Iteration 13 of at most 60:
Convergence test P-value:9.2e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.0003196.
Finished CD.
Starting Monte Carlo maximum likelihood estimation (MCMLE):
Iteration 1 of at most 60:
Optimizing with step length 0.5407.
The log-likelihood improved by 3.2169.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 1.2898.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0975.
Convergence test p-value: 0.2745. Not converged with 99% confidence; increasing sample size.
Iteration 4 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0426.
Convergence test p-value: 0.0070. Converged with 99% confidence.
Finished MCMLE.
Evaluating log-likelihood at the estimate. Setting up bridge sampling...
Using 16 bridges: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 .
Note: Null model likelihood calculation is not implemented for valued
ERGMs at this time.  This means that all likelihood-based inference
(LRT, Analysis of Deviance, AIC, BIC, etc.) is only valid between
models with the same reference distribution and constraints.
This model was fit using MCMC.  To examine model diagnostics and check
for degeneracy, use the mcmc.diagnostics() function.
Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-19'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-20'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Starting contrastive divergence estimation via CD-MCMLE:
Iteration 1 of at most 60:
Convergence test P-value:0e+00
The log-likelihood improved by 1.652.
Iteration 2 of at most 60:
Convergence test P-value:6.9e-226
The log-likelihood improved by 1.577.
Iteration 3 of at most 60:
Convergence test P-value:2.7e-29
The log-likelihood improved by 0.1075.
Iteration 4 of at most 60:
Convergence test P-value:1.1e-08
The log-likelihood improved by 0.03325.
Iteration 5 of at most 60:
Convergence test P-value:9e-03
The log-likelihood improved by 0.01136.
Iteration 6 of at most 60:
Convergence test P-value:5.8e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.003058.
Finished CD.
Starting Monte Carlo maximum likelihood estimation (MCMLE):
Iteration 1 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 1.8627.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1287.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1796.
Estimating equations are not within tolerance region.
Iteration 4 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.7186.
Estimating equations are not within tolerance region.
Estimating equations did not move closer to tolerance region more than 1 time(s) in 4 steps; increasing sample size.
Iteration 5 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.2229.
Estimating equations are not within tolerance region.
Iteration 6 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1020.
Estimating equations are not within tolerance region.
Iteration 7 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0550.
Convergence test p-value: 0.2757. Not converged with 99% confidence; increasing sample size.
Iteration 8 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0326.
Convergence test p-value: 0.0110. Not converged with 99% confidence; increasing sample size.
Iteration 9 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0282.
Convergence test p-value: 0.0055. Converged with 99% confidence.
Finished MCMLE.
Evaluating log-likelihood at the estimate. Setting up bridge sampling...
Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Using 16 bridges: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 .
Note: Null model likelihood calculation is not implemented for valued
ERGMs at this time.  This means that all likelihood-based inference
(LRT, Analysis of Deviance, AIC, BIC, etc.) is only valid between
models with the same reference distribution and constraints.
This model was fit using MCMC.  To examine model diagnostics and check
for degeneracy, use the mcmc.diagnostics() function.
Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-21'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-22'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Starting contrastive divergence estimation via CD-MCMLE:
Iteration 1 of at most 60:
Convergence test P-value:0e+00
The log-likelihood improved by 1.092.
Iteration 2 of at most 60:
Convergence test P-value:4.5e-220
The log-likelihood improved by 1.469.
Iteration 3 of at most 60:
Convergence test P-value:2.6e-49
The log-likelihood improved by 0.2289.
Iteration 4 of at most 60:
Convergence test P-value:3.4e-10
The log-likelihood improved by 0.04452.
Iteration 5 of at most 60:
Convergence test P-value:6.7e-03
The log-likelihood improved by 0.01274.
Iteration 6 of at most 60:
Convergence test P-value:4.7e-02
The log-likelihood improved by 0.008946.
Iteration 7 of at most 60:
Convergence test P-value:2e-01
The log-likelihood improved by 0.005729.
Iteration 8 of at most 60:
Convergence test P-value:5.1e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.003365.
Finished CD.
Starting Monte Carlo maximum likelihood estimation (MCMLE):
Iteration 1 of at most 60:
Optimizing with step length 0.6554.
The log-likelihood improved by 1.7963.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.2007.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0635.
Convergence test p-value: 0.8605. Not converged with 99% confidence; increasing sample size.
Iteration 4 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0747.
Convergence test p-value: 0.5228. Not converged with 99% confidence; increasing sample size.
Iteration 5 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0290.
Convergence test p-value: 0.0950. Not converged with 99% confidence; increasing sample size.
Iteration 6 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0066.
Convergence test p-value: < 0.0001. Converged with 99% confidence.
Finished MCMLE.
Evaluating log-likelihood at the estimate. Setting up bridge sampling...
Best valid proposal 'Binomial' cannot take into account hint(s) 'sparse'.
Using 16 bridges: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 .
Note: Null model likelihood calculation is not implemented for valued
ERGMs at this time.  This means that all likelihood-based inference
(LRT, Analysis of Deviance, AIC, BIC, etc.) is only valid between
models with the same reference distribution and constraints.
This model was fit using MCMC.  To examine model diagnostics and check
for degeneracy, use the mcmc.diagnostics() function.
Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-23'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-24'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Warning in eng_r(options) :
  Failed to tidy R code in chunk 'unnamed-chunk-25'. Reason:
Error : The formatR package is required by the chunk option tidy = TRUE but not installed; tidy = TRUE will be ignored.

Evaluating network in model.
Initializing unconstrained Metropolis-Hastings proposal: 'ergm.count:MH_PoissonTNT'.
Initializing model...
Model initialized.
Using initial method 'CD'.
Fitting initial model.
Starting contrastive divergence estimation via CD-MCMLE:
Iteration 1 of at most 60:
Convergence test P-value:0e+00
The log-likelihood improved by 1.451.
Iteration 2 of at most 60:
Convergence test P-value:0e+00
The log-likelihood improved by 1.293.
Iteration 3 of at most 60:
Convergence test P-value:3.4e-190
The log-likelihood improved by 0.8863.
Iteration 4 of at most 60:
Convergence test P-value:2.3e-14
The log-likelihood improved by 0.04311.
Iteration 5 of at most 60:
Convergence test P-value:2e-04
The log-likelihood improved by 0.01558.
Iteration 6 of at most 60:
Convergence test P-value:5.2e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.003524.
Finished CD.
Starting Monte Carlo maximum likelihood estimation (MCMLE):
Density guard set to 10000 from an initial count of 78 edges.

Iteration 1 of at most 50 with parameter:
                       sum                    nonzero 
                1.15328884                -4.51004945 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.14648668                 0.08708082 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.25871614                -0.97062915 
  absdiff.sum.faction.id.4                  nodecovar 
               -1.09478906                 3.30921406 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                 550.01636                   25.98569 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 132.90389                  280.58078 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  95.71575                  120.72188 
  absdiff.sum.faction.id.4                  nodecovar 
                  27.28016                  262.84950 
Starting MCMLE Optimization...
Optimizing with step length 0.0527.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.1528.
Estimating equations are not within tolerance region.

Iteration 2 of at most 50 with parameter:
                       sum                    nonzero 
                1.33204276                -5.44480177 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.14792349                 0.06972001 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.24939133                -0.90587168 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.96217916                 2.68929904 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                581.923077                  24.516484 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -49.161172                 375.805861 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                108.505495                 142.538462 
  absdiff.sum.faction.id.4                  nodecovar 
                 -9.344322                 274.193383 
Starting MCMLE Optimization...
Optimizing with step length 0.0405.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.4040.
Distance from origin on tolerance region scale: 29251.8669312122 (previously 27287.5781795425).
Estimating equations are not within tolerance region.

Iteration 3 of at most 50 with parameter:
                       sum                    nonzero 
                1.59813449                -6.54685824 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.03593479                -0.06485969 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.33427394                -1.02553945 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.52793683                 2.11781397 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                518.015152                  22.428030 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -44.632576                 347.325758 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                102.821970                 105.753788 
  absdiff.sum.faction.id.4                  nodecovar 
                 -8.219697                 231.096836 
Starting MCMLE Optimization...
Optimizing with step length 0.0783.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.7818.
Distance from origin on tolerance region scale: 5794.95948444032 (previously 7135.97537553121).
Estimating equations are not within tolerance region.

Iteration 4 of at most 50 with parameter:
                       sum                    nonzero 
                 1.6364476                 -6.7841325 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 0.1647196                 -0.1540591 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                -0.3980964                 -1.0349812 
  absdiff.sum.faction.id.4                  nodecovar 
                -0.3678698                  2.0552931 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                487.366812                  -3.209607 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                608.078603                  36.305677 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 47.157205                  45.113537 
  absdiff.sum.faction.id.4                  nodecovar 
                208.550218                 249.406175 
Starting MCMLE Optimization...
Optimizing with step length 0.0717.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.8890.
Distance from origin on tolerance region scale: 7321.68474410734 (previously 403334.833170297).
Estimating equations are not within tolerance region.

Iteration 5 of at most 50 with parameter:
                       sum                    nonzero 
                1.74724678                -7.33409286 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.05830928                -0.12698078 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.42920264                -0.98405415 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.36100569                 1.86679016 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                462.518135                  -1.435233 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                563.103627                  34.984456 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 41.093264                  41.067358 
  absdiff.sum.faction.id.4                  nodecovar 
                195.300518                 229.200807 
Starting MCMLE Optimization...
Optimizing with step length 0.0844.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.7704.
Distance from origin on tolerance region scale: 4941.33911236783 (previously 5793.92799627002).
Estimating equations are not within tolerance region.

Iteration 6 of at most 50 with parameter:
                       sum                    nonzero 
                1.81477401                -7.70613777 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.11881015                -0.09085791 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.45186992                -0.95722411 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.36154286                 1.97456963 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                414.982646                  -2.388286 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                513.000000                  34.555315 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 36.629067                  36.967462 
  absdiff.sum.faction.id.4                  nodecovar 
                177.555315                 209.374467 
Starting MCMLE Optimization...
Optimizing with step length 0.1015.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.1208.
Distance from origin on tolerance region scale: 4110.20376947713 (previously 4965.15185632449).
Estimating equations are not within tolerance region.

Iteration 7 of at most 50 with parameter:
                       sum                    nonzero 
                1.87571845                -8.03157241 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.30305907                -0.07553763 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.44267468                -0.91851443 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.35830626                 2.08814782 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                 378.53333                   -1.22500 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 461.51667                   31.20556 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  33.19444                   33.14444 
  absdiff.sum.faction.id.4                  nodecovar 
                 163.41389                  188.24236 
Starting MCMLE Optimization...
Optimizing with step length 0.1058.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.7647.
Distance from origin on tolerance region scale: 3145.32211191376 (previously 3838.10793372359).
Estimating equations are not within tolerance region.

Iteration 8 of at most 50 with parameter:
                       sum                    nonzero 
                1.89905780                -8.19459626 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.42894724                -0.07674839 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.45934318                -0.85935566 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.40227565                 2.20244917 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                325.767157                  -3.166667 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                408.274510                  26.193627 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 28.867647                  27.894608 
  absdiff.sum.faction.id.4                  nodecovar 
                139.536765                 165.348160 
Starting MCMLE Optimization...
Optimizing with step length 0.1219.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.0906.
Distance from origin on tolerance region scale: 2808.49222922407 (previously 3562.14499348748).
Estimating equations are not within tolerance region.

Iteration 9 of at most 50 with parameter:
                       sum                    nonzero 
                1.91317945                -8.20685066 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.58897360                -0.06720392 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.49858856                -0.79776554 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.45083415                 2.39795289 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                638.352778                  -7.958333 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -52.161111                  71.083333 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 56.430556                  73.500000 
  absdiff.sum.faction.id.4                  nodecovar 
                239.219444                 318.859011 
Starting MCMLE Optimization...
Optimizing with step length 0.0647.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.3728.
Distance from origin on tolerance region scale: 11315.7886279574 (previously 79612.6937065041).
Estimating equations are not within tolerance region.

Iteration 10 of at most 50 with parameter:
                       sum                    nonzero 
                1.97700523                -8.78589405 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.29441248                -0.06929742 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.46178850                -0.74990313 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.45612385                 2.06065886 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                610.719243                  -8.078864 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -47.343849                  66.296530 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 55.113565                  69.384858 
  absdiff.sum.faction.id.4                  nodecovar 
                227.227129                 306.201470 
Starting MCMLE Optimization...
Optimizing with step length 0.0555.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.7609.
Distance from origin on tolerance region scale: 11390.4823014589 (previously 12607.3252604701).
Estimating equations are not within tolerance region.

Iteration 11 of at most 50 with parameter:
                       sum                    nonzero 
                2.05183639                -9.55468021 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.07811539                -0.03352225 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.44205885                -0.70511937 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.45298458                 1.69362169 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                589.011111                  -5.269444 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -39.286111                  75.808333 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 48.266667                  66.477778 
  absdiff.sum.faction.id.4                  nodecovar 
                210.513889                 283.855559 
Starting MCMLE Optimization...
Optimizing with step length 0.0677.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.8543.
Distance from origin on tolerance region scale: 8074.09527012239 (previously 9474.29298287077).
Estimating equations are not within tolerance region.

Iteration 12 of at most 50 with parameter:
                       sum                    nonzero 
                2.06258842                -9.81981661 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.03470193                -0.03193843 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.41352992                -0.68146129 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.48824783                 1.50429447 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                533.850688                  -5.646365 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -33.738703                  64.756385 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 46.351670                  58.929273 
  absdiff.sum.faction.id.4                  nodecovar 
                189.994106                 258.828937 
Starting MCMLE Optimization...
Optimizing with step length 0.0975.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.1737.
Distance from origin on tolerance region scale: 4567.12286806865 (previously 5513.24846108041).
Estimating equations are not within tolerance region.

Iteration 13 of at most 50 with parameter:
                       sum                    nonzero 
                2.03738275                -9.79757499 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.09128548                -0.01154882 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.40593454                -0.65726690 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.48446439                 1.37707117 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                483.915129                  -5.199262 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -20.952030                  65.391144 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 40.885609                  47.014760 
  absdiff.sum.faction.id.4                  nodecovar 
                169.420664                 223.606881 
Starting MCMLE Optimization...
Optimizing with step length 0.1047.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.1480.
Distance from origin on tolerance region scale: 3900.93430824936 (previously 4758.09576939652).
Estimating equations are not within tolerance region.

Iteration 14 of at most 50 with parameter:
                       sum                    nonzero 
               1.946273858               -9.243867463 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               0.128681282               -0.006648232 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
              -0.394667958               -0.640440557 
  absdiff.sum.faction.id.4                  nodecovar 
              -0.480900778                1.397476413 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 4096.
Average estimating function values:
                       sum                    nonzero 
                 249.54274                  -28.77778 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 311.48291                   24.39744 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  20.66239                   31.08974 
  absdiff.sum.faction.id.4                  nodecovar 
                  88.71795                  136.21368 
Starting MCMLE Optimization...
Optimizing with step length 0.0768.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.2748.
Distance from origin on tolerance region scale: 4309.64949814696 (previously 22268.8079763973).
Estimating equations are not within tolerance region.

Iteration 15 of at most 50 with parameter:
                       sum                    nonzero 
               1.852073412               -8.468531342 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               0.135708115               -0.006325243 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
              -0.372972528               -0.630351611 
  absdiff.sum.faction.id.4                  nodecovar 
              -0.498294755                1.479590797 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                487.531401                  -3.217391 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                586.695652                  61.381643 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 44.816425                  57.014493 
  absdiff.sum.faction.id.4                  nodecovar 
                172.917874                 238.465747 
Starting MCMLE Optimization...
Optimizing with step length 0.0855.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.0754.
Distance from origin on tolerance region scale: 5651.21364979195 (previously 3723.72050273462).
Estimating equations are not within tolerance region.

Iteration 16 of at most 50 with parameter:
                       sum                    nonzero 
                1.92349429                -8.70768313 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.13380693                 0.01342541 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.37542062                -0.63458907 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.53119754                 1.81008769 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                441.750000                  -3.585821 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                536.339552                  61.164179 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 41.623134                  53.600746 
  absdiff.sum.faction.id.4                  nodecovar 
                156.981343                 219.471419 
Starting MCMLE Optimization...
Optimizing with step length 0.1017.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.1949.
Distance from origin on tolerance region scale: 4225.21321370571 (previously 5158.96475477039).
Estimating equations are not within tolerance region.

Iteration 17 of at most 50 with parameter:
                       sum                    nonzero 
                1.99915764                -9.04852570 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.37866366                 0.03087133 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.38828253                -0.65828185 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.56061845                 2.07841050 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                993.744548                  20.246106 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -35.404984                 566.137072 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                138.087227                 276.168224 
  absdiff.sum.faction.id.4                  nodecovar 
                 -9.850467                 431.701780 
Starting MCMLE Optimization...
Optimizing with step length 0.0310.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.9682.
Distance from origin on tolerance region scale: 40755.4176824367 (previously 541859.195804336).
Estimating equations are not within tolerance region.

Iteration 18 of at most 50 with parameter:
                       sum                    nonzero 
                2.22079315               -11.25168462 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.27007757                -0.05212153 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.39215409                -0.68472701 
  absdiff.sum.faction.id.4                  nodecovar 
                0.33227554                 1.53396126 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                2763.45347                  115.51683 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 -75.42178                  -58.52871 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 -10.97822                  -10.80594 
  absdiff.sum.faction.id.4                  nodecovar 
                1753.42970                  630.88479 
Starting MCMLE Optimization...
Optimizing with step length 0.0128.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.4722.
Distance from origin on tolerance region scale: 299141.996480161 (previously 2395021.27933505).
Estimating equations are not within tolerance region.

Iteration 19 of at most 50 with parameter:
                       sum                    nonzero 
                 2.0331352                -13.7673212 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 0.1956195                  0.4867153 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 0.1953065                  0.1792958 
  absdiff.sum.faction.id.4                  nodecovar 
                 0.3508105                  2.2855382 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 16384.
Average estimating function values:
                       sum                    nonzero 
                3152.73897                   77.00735 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 920.89338                  540.78676 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 281.14338                  375.29779 
  absdiff.sum.faction.id.4                  nodecovar 
                1333.39338                 1075.35795 
Starting MCMLE Optimization...
Optimizing with step length < 0.0001.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by < 0.0001.
Distance from origin on tolerance region scale: 8323411.87333392 (previously 18673005.9975832).
Estimating equations are not within tolerance region.

Iteration 20 of at most 50 with parameter:
                       sum                    nonzero 
                 2.0331352                -13.7673212 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 0.1956195                  0.4867153 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 0.1953065                  0.1792958 
  absdiff.sum.faction.id.4                  nodecovar 
                 0.3508105                  2.2855382 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 8192.
Average estimating function values:
                       sum                    nonzero 
                3147.62720                   77.01008 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 919.12343                  537.60705 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 280.75063                  376.33249 
  absdiff.sum.faction.id.4                  nodecovar 
                1333.03275                 1073.94047 
Starting MCMLE Optimization...
Optimizing with step length 0.0001.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 0.0049.
Distance from origin on tolerance region scale: 6453526.49080391 (previously 6452537.32470309).
Estimating equations are not within tolerance region.
Quitting from lines 765-771 (valued.Rmd) 
Error: processing vignette 'valued.Rmd' failed with diagnostics:
MCMLE estimation stuck. There may be excessive correlation between model terms, suggesting a poor model for the observed data. If target.stats are specified, try increasing SAN parameters.
--- failed re-building ‘valued.Rmd’

SUMMARY: processing the following file failed:
  ‘valued.Rmd’

Error: Vignette re-building failed.
Execution halted