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' 4.1.2 (2021-07-26), 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-272
The log-likelihood improved by 2.118.
Iteration 2 of at most 60:
Convergence test P-value:2.9e-184
The log-likelihood improved by 1.46.
Iteration 3 of at most 60:
Convergence test P-value:4.8e-42
The log-likelihood improved by 0.1913.
Iteration 4 of at most 60:
Convergence test P-value:2.9e-10
The log-likelihood improved by 0.04196.
Iteration 5 of at most 60:
Convergence test P-value:1.1e-02
The log-likelihood improved by 0.01193.
Iteration 6 of at most 60:
Convergence test P-value:2.6e-03
The log-likelihood improved by 0.01626.
Iteration 7 of at most 60:
Convergence test P-value:1.2e-01
The log-likelihood improved by 0.006844.
Iteration 8 of at most 60:
Convergence test P-value:7.2e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.002242.
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 0.1442.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0501.
Convergence test p-value: 0.3374. 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 0.0321.
Convergence test p-value: 0.1786. 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 0.0163.
Convergence test p-value: 0.0035. 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:2e-107
The log-likelihood improved by 1.212.
Iteration 2 of at most 60:
Convergence test P-value:3.8e-52
The log-likelihood improved by 0.6472.
Iteration 3 of at most 60:
Convergence test P-value:2.7e-19
The log-likelihood improved by 0.172.
Iteration 4 of at most 60:
Convergence test P-value:9.2e-14
The log-likelihood improved by 0.1183.
Iteration 5 of at most 60:
Convergence test P-value:2.7e-13
The log-likelihood improved by 0.1202.
Iteration 6 of at most 60:
Convergence test P-value:1.3e-05
The log-likelihood improved by 0.04359.
Iteration 7 of at most 60:
Convergence test P-value:3.4e-02
The log-likelihood improved by 0.01289.
Iteration 8 of at most 60:
Convergence test P-value:6.2e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.001857.
Finished CD.
Starting Monte Carlo maximum likelihood estimation (MCMLE):
Iteration 1 of at most 60:
Optimizing with step length 0.7979.
The log-likelihood improved by 5.6658.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1012.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1301.
Estimating equations are not within tolerance region.
Iteration 4 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0206.
Convergence test p-value: 0.0616. 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 0.0248.
Convergence test p-value: 0.0431. 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 0.0144.
Convergence test p-value: 0.0073. 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 1.765.
Iteration 2 of at most 60:
Convergence test P-value:5.3e-205
The log-likelihood improved by 1.34.
Iteration 3 of at most 60:
Convergence test P-value:9.4e-39
The log-likelihood improved by 0.1473.
Iteration 4 of at most 60:
Convergence test P-value:3.4e-04
The log-likelihood improved by 0.01684.
Iteration 5 of at most 60:
Convergence test P-value:4.1e-01
The log-likelihood improved by 0.00403.
Iteration 6 of at most 60:
Convergence test P-value:1.6e-01
The log-likelihood improved by 0.00645.
Iteration 7 of at most 60:
Convergence test P-value:1.3e-02
The log-likelihood improved by 0.01075.
Iteration 8 of at most 60:
Convergence test P-value:8.3e-02
The log-likelihood improved by 0.007791.
Iteration 9 of at most 60:
Convergence test P-value:3.2e-01
The log-likelihood improved by 0.004719.
Iteration 10 of at most 60:
Convergence test P-value:4.1e-01
The log-likelihood improved by 0.004027.
Iteration 11 of at most 60:
Convergence test P-value:2.5e-01
The log-likelihood improved by 0.005235.
Iteration 12 of at most 60:
Convergence test P-value:2.8e-01
The log-likelihood improved by 0.004918.
Iteration 13 of at most 60:
Convergence test P-value:3.2e-01
The log-likelihood improved by 0.004551.
Iteration 14 of at most 60:
Convergence test P-value:8.2e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.001916.
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 1.8677.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.5968.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0840.
Convergence test p-value: 0.7966. 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 0.0800.
Convergence test p-value: 0.1561. 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 0.0748.
Convergence test p-value: 0.0573. 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 0.0207.
Convergence test p-value: 0.0004. 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 1.336.
Iteration 2 of at most 60:
Convergence test P-value:6.3e-171
The log-likelihood improved by 1.284.
Iteration 3 of at most 60:
Convergence test P-value:1.1e-32
The log-likelihood improved by 0.15.
Iteration 4 of at most 60:
Convergence test P-value:5.2e-11
The log-likelihood improved by 0.0479.
Iteration 5 of at most 60:
Convergence test P-value:5e-02
The log-likelihood improved by 0.008795.
Iteration 6 of at most 60:
Convergence test P-value:6.7e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.002512.
Finished CD.
Starting Monte Carlo maximum likelihood estimation (MCMLE):
Iteration 1 of at most 60:
Optimizing with step length 0.9045.
The log-likelihood improved by 3.2879.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.3968.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.7150.
Estimating equations are not within tolerance region.
Iteration 4 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.5815.
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 0.1563.
Estimating equations are not within tolerance region.
Iteration 6 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1130.
Estimating equations are not within tolerance region.
Iteration 7 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0373.
Convergence test p-value: 0.5791. 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 0.0647.
Convergence test p-value: 0.1102. 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 0.0924.
Convergence test p-value: 0.0078. 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 1.292.
Iteration 2 of at most 60:
Convergence test P-value:0e+00
The log-likelihood improved by 1.31.
Iteration 3 of at most 60:
Convergence test P-value:1.1e-202
The log-likelihood improved by 0.9084.
Iteration 4 of at most 60:
Convergence test P-value:3.3e-28
The log-likelihood improved by 0.08123.
Iteration 5 of at most 60:
Convergence test P-value:8.5e-04
The log-likelihood improved by 0.01358.
Iteration 6 of at most 60:
Convergence test P-value:3.6e-03
The log-likelihood improved by 0.01135.
Iteration 7 of at most 60:
Convergence test P-value:4.2e-01
The log-likelihood improved by 0.003989.
Iteration 8 of at most 60:
Convergence test P-value:7.4e-02
The log-likelihood improved by 0.007016.
Iteration 9 of at most 60:
Convergence test P-value:4.8e-01
The log-likelihood improved by 0.003739.
Iteration 10 of at most 60:
Convergence test P-value:5.6e-02
The log-likelihood improved by 0.007608.
Iteration 11 of at most 60:
Convergence test P-value:8.7e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.001878.
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 
                1.18759518                -4.57397915 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.10944653                 0.04905715 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.31547961                -1.06372406 
  absdiff.sum.faction.id.4                  nodecovar 
               -1.17753728                 3.18115510 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                 469.12115                   26.84394 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 -51.07598                  139.59138 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  63.45585                   90.03491 
  absdiff.sum.faction.id.4                  nodecovar 
                  82.26078                  225.56703 
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 2.1950.
Estimating equations are not within tolerance region.

Iteration 2 of at most 50 with parameter:
                       sum                    nonzero 
                1.33859701                -5.51686911 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.05080691                 0.04220333 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.29340917                -0.97430308 
  absdiff.sum.faction.id.4                  nodecovar 
               -1.10885881                 2.58381775 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                301.656818                   1.440909 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -54.759091                 113.768182 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 47.079545                  75.747727 
  absdiff.sum.faction.id.4                  nodecovar 
                 34.659091                 166.501240 
Starting MCMLE Optimization...
Optimizing with step length 0.1213.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 3.5451.
Distance from origin on tolerance region scale: 4810.45123581989 (previously 10141.3824273441).
Estimating equations are not within tolerance region.

Iteration 3 of at most 50 with parameter:
                       sum                    nonzero 
                1.42217909                -6.05532554 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.23543647                 0.03327402 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.29923074                -0.90183569 
  absdiff.sum.faction.id.4                  nodecovar 
               -1.03468328                 2.10595225 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                 430.51587                   14.84127 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 422.89286                  105.55952 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  56.86111                   60.86508 
  absdiff.sum.faction.id.4                  nodecovar 
                  94.44048                  201.00452 
Starting MCMLE Optimization...
Optimizing with step length 0.1265.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.6575.
Distance from origin on tolerance region scale: 2063.94111544982 (previously 4267.74372326028).
Estimating equations are not within tolerance region.

Iteration 4 of at most 50 with parameter:
                       sum                    nonzero 
                1.43595272                -5.94125828 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.17480908                -0.02092995 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.31749721                -0.94402954 
  absdiff.sum.faction.id.4                  nodecovar 
               -1.01247407                 2.10116648 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                313.781145                   1.252525 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                411.723906                  36.360269 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 41.680135                  36.599327 
  absdiff.sum.faction.id.4                  nodecovar 
                 99.370370                 166.527734 
Starting MCMLE Optimization...
Optimizing with step length 0.0970.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.0217.
Distance from origin on tolerance region scale: 4287.09425233805 (previously 5157.47548503817).
Estimating equations are not within tolerance region.

Iteration 5 of at most 50 with parameter:
                       sum                    nonzero 
                1.60476056                -6.48332721 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.11724254                -0.02168769 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.37160437                -0.94140251 
  absdiff.sum.faction.id.4                  nodecovar 
               -1.03244326                 2.28384374 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                281.473430                   1.492754 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                361.014493                  38.560386 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 36.690821                  33.246377 
  absdiff.sum.faction.id.4                  nodecovar 
                 80.913043                 142.788126 
Starting MCMLE Optimization...
Optimizing with step length 0.1193.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.7701.
Distance from origin on tolerance region scale: 2474.13188126871 (previously 3088.62139268553).
Estimating equations are not within tolerance region.

Iteration 6 of at most 50 with parameter:
                       sum                    nonzero 
                1.65279532                -6.67864536 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.35583744                -0.01122095 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.42617943                -0.95110719 
  absdiff.sum.faction.id.4                  nodecovar 
               -1.05385210                 2.61226029 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                741.726190                  21.690476 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -45.952381                 344.952381 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                262.142857                 141.567460 
  absdiff.sum.faction.id.4                  nodecovar 
                 -9.777778                 339.417483 
Starting MCMLE Optimization...
Optimizing with step length 0.0406.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.2771.
Distance from origin on tolerance region scale: 27486.8090493595 (previously 211532.508984688).
Estimating equations are not within tolerance region.

Iteration 7 of at most 50 with parameter:
                       sum                    nonzero 
                1.86886323                -8.25285674 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.22360552                -0.04604143 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.44217889                -0.94637662 
  absdiff.sum.faction.id.4                  nodecovar 
                0.40006450                 2.01595238 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 4096.
Average estimating function values:
                       sum                    nonzero 
                2381.80784                  134.64706 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 112.23529                  -64.59216 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 -11.26275                  -10.78431 
  absdiff.sum.faction.id.4                  nodecovar 
                1561.95294                  537.04018 
Starting MCMLE Optimization...
Optimizing with step length 0.0132.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.1291.
Distance from origin on tolerance region scale: 244136.023001801 (previously 775392.18321506).
Estimating equations are not within tolerance region.

Iteration 8 of at most 50 with parameter:
                       sum                    nonzero 
                1.95298590               -10.18623559 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.17099332                 0.09349477 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.03214831                -0.40335589 
  absdiff.sum.faction.id.4                  nodecovar 
                0.37164593                 1.63399474 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 8192.
Average estimating function values:
                       sum                    nonzero 
               2156.035714                 115.242063 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -75.753968                 -58.301587 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 -7.694444                 -10.361111 
  absdiff.sum.faction.id.4                  nodecovar 
               1415.912698                 490.973335 
Starting MCMLE Optimization...
Optimizing with step length 0.0123.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.5816.
Distance from origin on tolerance region scale: 209934.127681517 (previously 177511.677074346).
Estimating equations are not within tolerance region.

Iteration 9 of at most 50 with parameter:
                       sum                    nonzero 
                 1.8142443                -11.8658849 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 0.1679832                  0.4757532 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 0.3654081                  0.1779486 
  absdiff.sum.faction.id.4                  nodecovar 
                 0.3881057                  2.1803149 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 4096.
Average estimating function values:
                       sum                    nonzero 
                2645.19807                  105.00000 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  97.80193                  437.08213 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 268.88406                  310.64251 
  absdiff.sum.faction.id.4                  nodecovar 
                1141.16425                  828.46581 
Starting MCMLE Optimization...
Optimizing with step length 0.0062.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 5.8111.
Distance from origin on tolerance region scale: 3046286.55162823 (previously 3528332.45984286).
Estimating equations are not within tolerance region.

Iteration 10 of at most 50 with parameter:
                       sum                    nonzero 
                 1.9119510                -29.5128246 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 0.1686606                  0.4210217 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 0.3258433                  0.1508466 
  absdiff.sum.faction.id.4                  nodecovar 
                 0.3413675                  1.9444048 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
Quitting from lines 765-771 (valued.Rmd) 
Error: processing vignette 'valued.Rmd' failed with diagnostics:
Unconstrained MCMC sampling did not mix at all. Optimization cannot continue.
--- failed re-building 'valued.Rmd'

SUMMARY: processing the following file failed:
  'valued.Rmd'

Error: Vignette re-building failed.
Execution halted