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:9.2e-302
The log-likelihood improved by 1.631.
Iteration 2 of at most 60:
Convergence test P-value:1.1e-158
The log-likelihood improved by 1.209.
Iteration 3 of at most 60:
Convergence test P-value:5.4e-33
The log-likelihood improved by 0.142.
Iteration 4 of at most 60:
Convergence test P-value:4.4e-03
The log-likelihood improved by 0.01357.
Iteration 5 of at most 60:
Convergence test P-value:2.9e-03
The log-likelihood improved by 0.01438.
Iteration 6 of at most 60:
Convergence test P-value:1.9e-01
The log-likelihood improved by 0.005835.
Iteration 7 of at most 60:
Convergence test P-value:1.3e-01
The log-likelihood improved by 0.006728.
Iteration 8 of at most 60:
Convergence test P-value:5.2e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.003369.
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 0.4927.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0138.
Convergence test p-value: 0.2670. 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 0.0236.
Convergence test p-value: 0.1281. 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 0.0140.
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-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:1.4e-108
The log-likelihood improved by 1.35.
Iteration 2 of at most 60:
Convergence test P-value:1.3e-33
The log-likelihood improved by 0.3504.
Iteration 3 of at most 60:
Convergence test P-value:8.8e-11
The log-likelihood improved by 0.09032.
Iteration 4 of at most 60:
Convergence test P-value:2.6e-16
The log-likelihood improved by 0.1473.
Iteration 5 of at most 60:
Convergence test P-value:1.1e-05
The log-likelihood improved by 0.04445.
Iteration 6 of at most 60:
Convergence test P-value:1.7e-04
The log-likelihood improved by 0.03278.
Iteration 7 of at most 60:
Convergence test P-value:1.2e-05
The log-likelihood improved by 0.04537.
Iteration 8 of at most 60:
Convergence test P-value:3.1e-02
The log-likelihood improved by 0.01362.
Iteration 9 of at most 60:
Convergence test P-value:1.5e-02
The log-likelihood improved by 0.01639.
Iteration 10 of at most 60:
Convergence test P-value:6.5e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.00167.
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 2.3171.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 1.5940.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1179.
Estimating equations are not within tolerance region.
Iteration 4 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0129.
Convergence test p-value: 0.1198. 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 0.0177.
Convergence test p-value: 0.0222. 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 0.0040.
Convergence test p-value: 0.0001. 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 1.53.
Iteration 2 of at most 60:
Convergence test P-value:8.9e-256
The log-likelihood improved by 1.263.
Iteration 3 of at most 60:
Convergence test P-value:3.2e-64
The log-likelihood improved by 0.2523.
Iteration 4 of at most 60:
Convergence test P-value:3.2e-17
The log-likelihood improved by 0.06511.
Iteration 5 of at most 60:
Convergence test P-value:1.7e-03
The log-likelihood improved by 0.01414.
Iteration 6 of at most 60:
Convergence test P-value:8.6e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.001705.
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 1.9207.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1189.
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: 0.7859. 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 0.1041.
Estimating equations are not within tolerance region.
Iteration 5 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1057.
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 6 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0623.
Convergence test p-value: 0.0180. 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 0.0131.
Convergence test p-value: 0.0037. 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 1.633.
Iteration 2 of at most 60:
Convergence test P-value:4.5e-167
The log-likelihood improved by 1.347.
Iteration 3 of at most 60:
Convergence test P-value:1.4e-26
The log-likelihood improved by 0.1199.
Iteration 4 of at most 60:
Convergence test P-value:7.5e-04
The log-likelihood improved by 0.0172.
Iteration 5 of at most 60:
Convergence test P-value:4.5e-04
The log-likelihood improved by 0.01817.
Iteration 6 of at most 60:
Convergence test P-value:2.6e-01
The log-likelihood improved by 0.005172.
Iteration 7 of at most 60:
Convergence test P-value:6.2e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.002808.
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 2.2538.
Estimating equations are not within tolerance region.
Iteration 2 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.2272.
Estimating equations are not within tolerance region.
Iteration 3 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.5925.
Estimating equations are not within tolerance region.
Iteration 4 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.2076.
Estimating equations are not within tolerance region.
Iteration 5 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.1833.
Estimating equations are not within tolerance region.
Iteration 6 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.2024.
Estimating equations are not within tolerance region.
Iteration 7 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0594.
Convergence test p-value: 0.8814. 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 0.0990.
Convergence test p-value: 0.7334. 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 0.0504.
Convergence test p-value: 0.0197. Not converged with 99% confidence; increasing sample size.
Iteration 10 of at most 60:
Optimizing with step length 1.0000.
The log-likelihood improved by 0.0128.
Convergence test p-value: 0.0061. 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 1.361.
Iteration 2 of at most 60:
Convergence test P-value:0e+00
The log-likelihood improved by 1.754.
Iteration 3 of at most 60:
Convergence test P-value:1.6e-159
The log-likelihood improved by 0.6724.
Iteration 4 of at most 60:
Convergence test P-value:6e-17
The log-likelihood improved by 0.04872.
Iteration 5 of at most 60:
Convergence test P-value:1.2e-01
The log-likelihood improved by 0.006313.
Iteration 6 of at most 60:
Convergence test P-value:3.9e-01
The log-likelihood improved by 0.00416.
Iteration 7 of at most 60:
Convergence test P-value:6.6e-02
The log-likelihood improved by 0.007396.
Iteration 8 of at most 60:
Convergence test P-value:6.1e-01
Convergence detected. Stopping.
The log-likelihood improved by 0.003133.
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 
                1.23009301                -4.70503842 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
               -0.12813851                 0.05644091 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.24604026                -1.00100170 
  absdiff.sum.faction.id.4                  nodecovar 
               -1.10604740                 3.23104257 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 4096.
Average estimating function values:
                       sum                    nonzero 
                 775.11588                   51.85837 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 -37.07725                  402.06438 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 129.85837                  158.72103 
  absdiff.sum.faction.id.4                  nodecovar 
                  28.75107                  327.01328 
Starting MCMLE Optimization...
Optimizing with step length 0.0458.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 3.5272.
Estimating equations are not within tolerance region.

Iteration 2 of at most 50 with parameter:
                       sum                    nonzero 
               1.466398185               -6.268418404 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
              -0.003486577               -0.016853345 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
              -0.294445334               -1.000534455 
  absdiff.sum.faction.id.4                  nodecovar 
              -1.045852023                2.565558694 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 4096.
Average estimating function values:
                       sum                    nonzero 
                 594.74400                   21.87200 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                 -42.75467                  380.44533 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                 113.48000                  143.06667 
  absdiff.sum.faction.id.4                  nodecovar 
                  -9.75200                  281.67849 
Starting MCMLE Optimization...
Optimizing with step length 0.0529.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 2.8807.
Distance from origin on tolerance region scale: 20537.3591433308 (previously 36807.6628434285).
Estimating equations are not within tolerance region.

Iteration 3 of at most 50 with parameter:
                       sum                    nonzero 
                1.72116316                -7.50323773 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.08295984                -0.16042563 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
               -0.42343168                -1.01109770 
  absdiff.sum.faction.id.4                  nodecovar 
                0.29124149                 2.04749202 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 2048.
Average estimating function values:
                       sum                    nonzero 
                  2140.615                    153.440 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                   379.755                    -70.790 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                   -11.440                    -10.795 
  absdiff.sum.faction.id.4                  nodecovar 
                  1361.645                    474.403 
Starting MCMLE Optimization...
Optimizing with step length 0.0086.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 1.8729.
Distance from origin on tolerance region scale: 507672.250055529 (previously 2697303.72373541).
Estimating equations are not within tolerance region.

Iteration 4 of at most 50 with parameter:
                       sum                    nonzero 
                1.51529180                -9.80094597 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.06513757                 0.80205883 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                0.64848113                 0.67263104 
  absdiff.sum.faction.id.4                  nodecovar 
                0.31217956                 2.96002761 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                 2840.8460                   105.0631 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  612.7904                   529.8434 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  322.8687                   461.8586 
  absdiff.sum.faction.id.4                  nodecovar 
                  991.7121                   895.8771 
Starting MCMLE Optimization...
Optimizing with step length 0.0006.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 0.0316.
Distance from origin on tolerance region scale: 1948225.97008542 (previously 3855300.92309683).
Estimating equations are not within tolerance region.

Iteration 5 of at most 50 with parameter:
                       sum                    nonzero 
                1.52349794               -10.82964469 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.06561538                 0.79933637 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                0.64658587                 0.66993460 
  absdiff.sum.faction.id.4                  nodecovar 
                0.31021674                 2.93611168 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 512.
Average estimating function values:
                       sum                    nonzero 
                 2851.7842                   105.0041 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  619.9876                   534.2241 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  325.1992                   460.3651 
  absdiff.sum.faction.id.4                  nodecovar 
                  997.3361                   899.1244 
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: 29210.9421937555 (previously 29078.9988688007).
Estimating equations are not within tolerance region.

Iteration 6 of at most 50 with parameter:
                       sum                    nonzero 
                1.52349794               -10.82964469 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.06561538                 0.79933637 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                0.64658587                 0.66993460 
  absdiff.sum.faction.id.4                  nodecovar 
                0.31021674                 2.93611168 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                 2829.5551                   105.0456 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  611.1255                   527.6692 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  322.1027                   459.0038 
  absdiff.sum.faction.id.4                  nodecovar 
                  989.3726                   891.9260 
Starting MCMLE Optimization...
Optimizing with step length 0.0004.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 0.0206.
Distance from origin on tolerance region scale: 2913011.31272096 (previously 2911797.53443267).
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 7 of at most 50 with parameter:
                       sum                    nonzero 
                1.52775329               -11.85596845 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.06470089                 0.79998647 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                0.64515581                 0.66959959 
  absdiff.sum.faction.id.4                  nodecovar 
                0.30969917                 2.92202421 
Starting unconstrained MCMC...
Back from unconstrained MCMC.
New interval = 1024.
Average estimating function values:
                       sum                    nonzero 
                 2828.0020                   105.0059 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                  612.7652                   527.4599 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                  319.2270                   461.0215 
  absdiff.sum.faction.id.4                  nodecovar 
                  988.4207                   892.2006 
Starting MCMLE Optimization...
Optimizing with step length 0.0045.
Using lognormal metric (see control.ergm function).
Using log-normal approx (no optim)
The log-likelihood improved by 8.3647.
Distance from origin on tolerance region scale: 8224246.44241698 (previously 8230117.93715541).
Estimating equations are not within tolerance region.

Iteration 8 of at most 50 with parameter:
                       sum                    nonzero 
                1.56872088               -47.09413208 
nodefactor.sum.leader.TRUE   absdiff.sum.faction.id.1 
                0.05266585                 0.78022477 
  absdiff.sum.faction.id.2   absdiff.sum.faction.id.3 
                0.62369789                 0.65517474 
  absdiff.sum.faction.id.4                  nodecovar 
                0.30929740                 2.81347983 
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