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:3.3e-279
The log-likelihood improved by 1.297.
Iteration 2 of at most 60:
Convergence test P-value:4e-185
The log-likelihood improved by 1.458.
Iteration 3 of at most 60:
Convergence test P-value:6.4e-55
The log-likelihood improved by 0.2596.
Iteration 4 of at most 60:
Convergence test P-value:4.2e-14
The log-likelihood improved by 0.06163.
Iteration 5 of at most 60:
Convergence test P-value:2.7e-01
The log-likelihood improved by 0.005006.
Iteration 6 of at most 60:
Convergence test P-value:8.1e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.00178.
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 0.7053.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0232.
Convergence test p-value: 0.4658. 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 0.0295.
Convergence test p-value: 0.0932. 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 0.0093.
Convergence test p-value: 0.0151. 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 0.0318.
Convergence test p-value: 0.0025. 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-108
The log-likelihood improved by 1.315.
Iteration 2 of at most 60:
Convergence test P-value:5e-33
The log-likelihood improved by 0.349.
Iteration 3 of at most 60:
Convergence test P-value:1.7e-16
The log-likelihood improved by 0.146.
Iteration 4 of at most 60:
Convergence test P-value:2.5e-16
The log-likelihood improved by 0.1516.
Iteration 5 of at most 60:
Convergence test P-value:2.1e-08
The log-likelihood improved by 0.07242.
Iteration 6 of at most 60:
Convergence test P-value:2.6e-03
The log-likelihood improved by 0.02264.
Iteration 7 of at most 60:
Convergence test P-value:5.5e-07
The log-likelihood improved by 0.05663.
Iteration 8 of at most 60:
Convergence test P-value:2e-01
The log-likelihood improved by 0.006218.
Iteration 9 of at most 60:
Convergence test P-value:1.7e-01
The log-likelihood improved by 0.006939.
Iteration 10 of at most 60:
Convergence test P-value:1.9e-01
The log-likelihood improved by 0.006409.
Iteration 11 of at most 60:
Convergence test P-value:2.9e-01
The log-likelihood improved by 0.004791.
Iteration 12 of at most 60:
Convergence test P-value:3.5e-03
The log-likelihood improved by 0.02227.
Iteration 13 of at most 60:
Convergence test P-value:6.4e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.001779.
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 2.4791.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 2.7327.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0901.
Convergence test p-value: 0.2350. 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 0.0294.
Convergence test p-value: 0.0318. 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 0.0033.
Convergence test p-value: 0.0008. 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 1.218.
Iteration 2 of at most 60:
Convergence test P-value:9.8e-244
The log-likelihood improved by 1.802.
Iteration 3 of at most 60:
Convergence test P-value:3.3e-36
The log-likelihood improved by 0.1305.
Iteration 4 of at most 60:
Convergence test P-value:5.6e-06
The log-likelihood improved by 0.02307.
Iteration 5 of at most 60:
Convergence test P-value:1.7e-01
The log-likelihood improved by 0.005973.
Iteration 6 of at most 60:
Convergence test P-value:4e-02
The log-likelihood improved by 0.008666.
Iteration 7 of at most 60:
Convergence test P-value:6.1e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.003014.
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 2.5986.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.5853.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0777.
Convergence test p-value: 0.5390. 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 0.0548.
Convergence test p-value: 0.2829. 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 0.0328.
Convergence test p-value: 0.0071. 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 1.663.
Iteration 2 of at most 60:
Convergence test P-value:1.4e-175
The log-likelihood improved by 0.997.
Iteration 3 of at most 60:
Convergence test P-value:3.6e-29
The log-likelihood improved by 0.1263.
Iteration 4 of at most 60:
Convergence test P-value:2.5e-07
The log-likelihood improved by 0.0308.
Iteration 5 of at most 60:
Convergence test P-value:6.2e-05
The log-likelihood improved by 0.02166.
Iteration 6 of at most 60:
Convergence test P-value:6.4e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.002668.
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 2.2468.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1946.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0091.
Convergence test p-value: 0.6206. 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 0.0509.
Convergence test p-value: 0.6156. 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 0.0202.
Convergence test p-value: 0.1427. 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 0.0104.
Convergence test p-value: 0.0109. Not converged with 99% confidence; increasing sample size.
Iteration 7 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0129.
Convergence test p-value: 0.0041. 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 1.323.
Iteration 2 of at most 60:
Convergence test P-value:0e+00
The log-likelihood improved by 1.196.
Iteration 3 of at most 60:
Convergence test P-value:4.4e-200
The log-likelihood improved by 0.8732.
Iteration 4 of at most 60:
Convergence test P-value:1.8e-17
The log-likelihood improved by 0.05095.
Iteration 5 of at most 60:
Convergence test P-value:1.2e-04
The log-likelihood improved by 0.01576.
Iteration 6 of at most 60:
Convergence test P-value:3.4e-01
The log-likelihood improved by 0.004483.
Iteration 7 of at most 60:
Convergence test P-value:4.5e-01
The log-likelihood improved by 0.003863.
Iteration 8 of at most 60:
Convergence test P-value:1.1e-01
The log-likelihood improved by 0.006412.
Iteration 9 of at most 60:
Convergence test P-value:1.1e-01
The log-likelihood improved by 0.006426.
Iteration 10 of at most 60:
Convergence test P-value:4.5e-01
The log-likelihood improved by 0.003903.
Iteration 11 of at most 60:
Convergence test P-value:3e-02
The log-likelihood improved by 0.008486.
Iteration 12 of at most 60:
Convergence test P-value:2.6e-01
The log-likelihood improved by 0.004944.
Iteration 13 of at most 60:
Convergence test P-value:5.4e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.003409.
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 
                1.17554694                -4.60114396 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.13083776                 0.06408765 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.25516179                -0.98993072 
  absdiff.sum.faction.id.4                  nodecovar 
               -1.09102554                 3.21083028 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                645.609700                  24.575058 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -43.404157                 247.378753 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                398.050808                  49.819861 
  absdiff.sum.faction.id.4                  nodecovar 
                 -9.605081                 304.040917 
Starting MCMLE Optimization...
Optimizing with step length 0.0383.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.1308.
Estimating equations are not within tolerance region.

Iteration 2 of at most 50 with parameter:
                       sum                    nonzero 
                1.48016453                -5.86238357 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.02024800                -0.03863576 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.33813896                -1.06995194 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.32399248                 2.54501366 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 256.
Average estimating function values:
                       sum                    nonzero 
                629.337408                  24.635697 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -40.146699                 237.273839 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                388.332518                  45.872861 
  absdiff.sum.faction.id.4                  nodecovar 
                 -7.261614                 292.973893 
Starting MCMLE Optimization...
Optimizing with step length 0.0454.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.2554.
Distance from origin on tolerance region scale: 21796.9913089443 (previously 23578.8114252397).
Estimating equations are not within tolerance region.

Iteration 3 of at most 50 with parameter:
                       sum                    nonzero 
                 1.6698741                 -6.9542892 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 0.1061192                 -0.1007924 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                -0.3955151                 -1.0727065 
  absdiff.sum.faction.id.4                  nodecovar 
                -0.3941317                  2.0328476 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
               417.2022727                 -0.6840909 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -40.1727273                124.7227273 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               359.3772727                -10.4681818 
  absdiff.sum.faction.id.4                  nodecovar 
                -5.9659091                216.1154588 
Starting MCMLE Optimization...
Optimizing with step length 0.0792.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.5487.
Distance from origin on tolerance region scale: 8106.31861653782 (previously 32405.9311365787).
Estimating equations are not within tolerance region.

Iteration 4 of at most 50 with parameter:
                       sum                    nonzero 
                 1.7781834                 -7.6052169 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 0.1023876                 -0.1526511 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                -0.4660588                 -0.5086577 
  absdiff.sum.faction.id.4                  nodecovar 
                -0.4088214                  1.6973812 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                395.668790                   3.162420 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 -4.477707                 110.047771 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                316.780255                  -5.302548 
  absdiff.sum.faction.id.4                  nodecovar 
                  2.468153                 195.741928 
Starting MCMLE Optimization...
Optimizing with step length 0.1002.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.0363.
Distance from origin on tolerance region scale: 4043.18630583389 (previously 5224.13206293731).
Estimating equations are not within tolerance region.

Iteration 5 of at most 50 with parameter:
                       sum                    nonzero 
                 1.8065756                 -7.8405002 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 0.1226489                 -0.1998495 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                -0.5380168                 -0.4740044 
  absdiff.sum.faction.id.4                  nodecovar 
                -0.4074654                  1.5805279 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                 470.19841                   -3.59127 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 580.40476                   29.24206 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  36.86508                   68.54762 
  absdiff.sum.faction.id.4                  nodecovar 
                 186.49206                  239.04328 
Starting MCMLE Optimization...
Optimizing with step length 0.0758.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.5338.
Distance from origin on tolerance region scale: 5311.22916893025 (previously 188990.702237886).
Estimating equations are not within tolerance region.

Iteration 6 of at most 50 with parameter:
                       sum                    nonzero 
                1.89101237                -8.20765891 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.03243288                -0.17542023 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.53908168                -0.49045290 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.42625750                 1.63283035 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                437.816425                  -2.874396 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                536.951691                  27.019324 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 33.893720                  59.714976 
  absdiff.sum.faction.id.4                  nodecovar 
                173.458937                 220.503275 
Starting MCMLE Optimization...
Optimizing with step length 0.0712.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.3271.
Distance from origin on tolerance region scale: 5205.10621817829 (previously 6076.29831476888).
Estimating equations are not within tolerance region.

Iteration 7 of at most 50 with parameter:
                       sum                    nonzero 
                 1.9792199                 -8.3842220 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -0.2985554                 -0.1633247 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                -0.5306195                 -0.4921858 
  absdiff.sum.faction.id.4                  nodecovar 
                -0.4324941                  1.9370505 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                 692.77459                   22.38525 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 461.93033                   59.90164 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  54.30738                   96.74590 
  absdiff.sum.faction.id.4                  nodecovar 
                 238.19262                  303.58225 
Starting MCMLE Optimization...
Optimizing with step length 0.1123.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.4765.
Distance from origin on tolerance region scale: 3909.77515836082 (previously 4041.45532155877).
Estimating equations are not within tolerance region.

Iteration 8 of at most 50 with parameter:
                       sum                    nonzero 
                1.94597973                -8.44672474 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.34688998                -0.09942091 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.50057600                -0.46228726 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.41298116                 1.90298524 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 4096.
Average estimating function values:
                       sum                    nonzero 
                578.361364                  -4.404545 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -51.893182                  44.206818 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 47.443182                  90.861364 
  absdiff.sum.faction.id.4                  nodecovar 
                193.211364                 283.852671 
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 2.8436.
Distance from origin on tolerance region scale: 9256.91094856406 (previously 82959.8275605601).
Estimating equations are not within tolerance region.

Iteration 9 of at most 50 with parameter:
                       sum                    nonzero 
                2.02984486                -9.37143498 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.10790354                -0.06464003 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.45413463                -0.45722169 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.41161590                 1.48981332 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                532.896465                  -4.570707 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -45.623737                  45.441919 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 43.565657                  80.388889 
  absdiff.sum.faction.id.4                  nodecovar 
                179.851010                 260.566997 
Starting MCMLE Optimization...
Optimizing with step length 0.0858.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.9527.
Distance from origin on tolerance region scale: 5288.9039798074 (previously 6297.53606566301).
Estimating equations are not within tolerance region.

Iteration 10 of at most 50 with parameter:
                       sum                    nonzero 
               2.010839104               -9.328396746 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               0.005974454               -0.079156239 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
              -0.446625437               -0.456440387 
  absdiff.sum.faction.id.4                  nodecovar 
              -0.430446847                1.388767883 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                497.980562                  -3.568035 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -36.987041                  40.816415 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 40.205184                  74.578834 
  absdiff.sum.faction.id.4                  nodecovar 
                165.280778                 240.721201 
Starting MCMLE Optimization...
Optimizing with step length 0.1018.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.2970.
Distance from origin on tolerance region scale: 4421.7938234484 (previously 5238.42538973431).
Estimating equations are not within tolerance region.

Iteration 11 of at most 50 with parameter:
                       sum                    nonzero 
                1.96548650                -9.04643178 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.06178597                -0.08530915 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.43084997                -0.44936942 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.44534533                 1.31435615 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 262144.
Average estimating function values:
                       sum                    nonzero 
                479.371230                  -3.647332 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                573.032483                  47.540603 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 40.257541                  65.990719 
  absdiff.sum.faction.id.4                  nodecovar 
                173.890951                 232.584821 
Starting MCMLE Optimization...
Optimizing with step length 0.1059.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.6676.
Distance from origin on tolerance region scale: 4748.99606856047 (previously 147286.53116431).
Estimating equations are not within tolerance region.

Iteration 12 of at most 50 with parameter:
                       sum                    nonzero 
                2.01371442                -9.18179858 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.14442713                -0.08085037 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.43780285                -0.49151072 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.48595056                 1.54682699 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 262144.
Average estimating function values:
                       sum                    nonzero 
                529.366154                  -4.692308 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -48.086154                 170.418462 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 63.689231                 124.590769 
  absdiff.sum.faction.id.4                  nodecovar 
                 80.778462                 260.224531 
Starting MCMLE Optimization...
Optimizing with step length 0.0920.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.6312.
Distance from origin on tolerance region scale: 6202.13015363556 (previously 61171.1691720424).
Estimating equations are not within tolerance region.

Iteration 13 of at most 50 with parameter:
                       sum                    nonzero 
               2.040538009               -9.497371417 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
              -0.006245654               -0.093705357 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
              -0.418006314               -0.473739060 
  absdiff.sum.faction.id.4                  nodecovar 
              -0.491191985                1.288919125 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 262144.
Average estimating function values:
                       sum                    nonzero 
                286.384615                 -22.615385 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                -50.661538                 138.458462 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 39.381538                  83.446154 
  absdiff.sum.faction.id.4                  nodecovar 
                  5.369231                 141.582307 
Starting MCMLE Optimization...
Optimizing with step length 0.1034.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.2431.
Distance from origin on tolerance region scale: 4187.05283545219 (previously 5083.14822217223).
Estimating equations are not within tolerance region.

Iteration 14 of at most 50 with parameter:
                       sum                    nonzero 
                1.93204522                -8.64222938 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.03638490                -0.09901922 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.42289705                -0.49707839 
  absdiff.sum.faction.id.4                  nodecovar 
               -0.51798123                 1.37496327 
Starting unconstrained MCMC...
Killed