Running ‘requireNamespaceTest.R’ [2s/3s]
  Running ‘testthat.R’ [235s/127s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > #  File tests/testthat.R in package ergm, part of the Statnet suite of packages
  > #  for network analysis, https://statnet.org .
  > #
  > #  This software is distributed under the GPL-3 license.  It is free, open
  > #  source, and has the attribution requirements (GPL Section 7) at
  > #  https://statnet.org/attribution .
  > #
  > #  Copyright 2003-2025 Statnet Commons
  > ################################################################################
  > library(testthat)
  > library(ergm)
  Loading required package: network
  
  'network' 1.19.0 (2024-12-08), 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' 4.10.1 (2025-08-26), 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.
  
  > 
  > test_check("ergm")
  Starting 2 test processes.
  > test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-basis.R: Obtaining the responsible dyads.
  > test-basis.R: Evaluating the predictor and response matrix.
  > test-basis.R: Maximizing the pseudolikelihood.
  > test-basis.R: Finished MPLE.
  > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-basis.R: Iteration 1 of at most 60:
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.6124.
  > test-basis.R: Estimating equations are not within tolerance region.
  > test-basis.R: Iteration 2 of at most 60:
  > test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-basis.R: 1 Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.0128.
  > test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
  > test-basis.R: Finished MCMLE.
  > test-basis.R: Evaluating log-likelihood at the estimate. 
  > test-basis.R: Fitting the dyad-independent submodel...
  > test-basis.R: Bridging between the dyad-independent submodel and the full model...
  > test-basis.R: Setting up bridge sampling...
  > test-basis.R: Using 16 bridges: 1 
  > test-basis.R: 2 3 4 5 
  > test-basis.R: 6 7 
  > test-basis.R: 8 
  > test-basis.R: 9 10 
  > test-basis.R: 11 
  > test-basis.R: 12 
  > test-basis.R: 13 
  > test-basis.R: 14 
  > test-basis.R: 15 
  > test-basis.R: 16 .
  > test-basis.R: Bridging finished.
  > test-basis.R: 
  > test-basis.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-basis.R: Obtaining the responsible dyads.
  > test-basis.R: Evaluating the predictor and response matrix.
  > test-basis.R: Maximizing the pseudolikelihood.
  > test-basis.R: Finished MPLE.
  > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-basis.R: Iteration 1 of at most 60:
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.6124.
  > test-basis.R: Estimating equations are not within tolerance region.
  > test-basis.R: Iteration 2 of at most 60:
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.0128.
  > test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
  > test-basis.R: Finished MCMLE.
  > test-basis.R: Evaluating log-likelihood at the estimate. 
  > test-basis.R: Fitting the dyad-independent submodel...
  > test-basis.R: Bridging between the dyad-independent submodel and the full model...
  > test-basis.R: Setting up bridge sampling...
  > test-basis.R: Using 16 bridges: 1 2 
  > test-basis.R: 3 
  > test-basis.R: 4 
  > test-basis.R: 5 
  > test-basis.R: 6 
  > test-basis.R: 7 8 
  > test-basis.R: 9 10 11 
  > test-basis.R: 12 
  > test-basis.R: 13 14 
  > test-basis.R: 15 16 
  > test-basis.R: .
  > test-basis.R: Bridging finished.
  > test-basis.R: 
  > test-basis.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-basis.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-basis.R: Obtaining the responsible dyads.
  > test-basis.R: Evaluating the predictor and response matrix.
  > test-basis.R: Maximizing the pseudolikelihood.
  > test-basis.R: Finished MPLE.
  > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-basis.R: Iteration 1 of at most 60:
  > test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-basis.R: 1 Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.6124.
  > test-basis.R: Estimating equations are not within tolerance region.
  > test-basis.R: Iteration 2 of at most 60:
  > test-basis.R: 1 Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.0128.
  > test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence.
  > test-basis.R: Finished MCMLE.
  > test-basis.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
  > test-basis.R: Bridging between the dyad-independent submodel and the full model...
  > test-basis.R: Setting up bridge sampling...
  > test-basis.R: Using 16 bridges: 1 2 3 4 5 
  > test-basis.R: 6 
  > test-basis.R: 7 
  > test-basis.R: 8 
  > test-basis.R: 9 10 
  > test-basis.R: 11 
  > test-basis.R: 12 
  > test-basis.R: 13 
  > test-basis.R: 14 15 16 
  > test-basis.R: .
  > test-basis.R: Bridging finished.
  > test-basis.R: 
  > test-basis.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
  > test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-basis.R: Iteration 1 of at most 60:
  > test-basis.R: Convergence test P-value:4.7e-173
  > test-basis.R: 1 
  > test-basis.R: The log-likelihood improved by 1.861.
  > test-basis.R: Iteration 2 of at most 60:
  > test-basis.R: Convergence test P-value:2.5e-135
  > test-basis.R: 1 The log-likelihood improved by 1.577.
  > test-basis.R: Iteration 3 of at most 60:
  > test-basis.R: Convergence test P-value:1.9e-80
  > test-basis.R: 1 
  > test-basis.R: The log-likelihood improved by 0.8158.
  > test-basis.R: Iteration 4 of at most 60:
  > test-basis.R: Convergence test P-value:4.5e-32
  > test-basis.R: 1 
  > test-basis.R: The log-likelihood improved by 0.2411.
  > test-basis.R: Iteration 5 of at most 60:
  > test-basis.R: Convergence test P-value:1.3e-09
  > test-basis.R: 1 
  > test-basis.R: The log-likelihood improved by 0.0608.
  > test-basis.R: Iteration 6 of at most 60:
  > test-basis.R: Convergence test P-value:2.8e-03
  > test-basis.R: 1 
  > test-basis.R: The log-likelihood improved by 0.01871.
  > test-basis.R: Iteration 7 of at most 60:
  > test-basis.R: Convergence test P-value:7.5e-01
  > test-basis.R: Convergence detected. Stopping.
  > test-basis.R: 1 The log-likelihood improved by 0.001578.
  > test-basis.R: Finished CD.
  > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-basis.R: Iteration 1 of at most 60:
  > test-bridge-target.stats.R: 
  > test-bridge-target.stats.R: Attaching package: 'statnet.common'
  > test-bridge-target.stats.R: 
  > test-bridge-target.stats.R: The following objects are masked from 'package:base':
  > test-bridge-target.stats.R: 
  > test-bridge-target.stats.R:     attr, order, replace
  > test-bridge-target.stats.R: 
  > test-bridge-target.stats.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-bridge-target.stats.R: Obtaining the responsible dyads.
  > test-bridge-target.stats.R: Evaluating the predictor and response matrix.
  > test-bridge-target.stats.R: Maximizing the pseudolikelihood.
  > test-bridge-target.stats.R: Finished MPLE.
  > test-bridge-target.stats.R: Evaluating log-likelihood at the estimate. 
  > test-bridge-target.stats.R: Unable to match target stats. Using MCMLE estimation.
  > test-bridge-target.stats.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-bridge-target.stats.R: Obtaining the responsible dyads.
  > test-bridge-target.stats.R: Evaluating the predictor and response matrix.
  > test-bridge-target.stats.R: Maximizing the pseudolikelihood.
  > test-bridge-target.stats.R: Finished MPLE.
  > test-bridge-target.stats.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-bridge-target.stats.R: Iteration 1 of at most 60:
  > test-basis.R: 1 Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.1892.
  > test-basis.R: Estimating equations are not within tolerance region.
  > test-basis.R: Iteration 2 of at most 60:
  > test-bridge-target.stats.R: 1 Optimizing with step length 1.0000.
  > test-bridge-target.stats.R: The log-likelihood improved by 0.0219.
  > test-bridge-target.stats.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-bridge-target.stats.R: Finished MCMLE.
  > test-bridge-target.stats.R: Evaluating log-likelihood at the estimate. 
  > test-bridge-target.stats.R: Fitting the dyad-independent submodel...
  > test-basis.R: 1 Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.0072.
  > test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
  > test-basis.R: Finished MCMLE.
  > test-basis.R: Evaluating log-likelihood at the estimate. 
  > test-basis.R: Setting up bridge sampling...
  > test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
  > test-bridge-target.stats.R: Setting up bridge sampling...
  > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-basis.R: Using 16 bridges: 1 2 3 4 5 6 
  > test-bridge-target.stats.R: Using 16 bridges: 1 2 3 4 
  > test-basis.R: 7 
  > test-bridge-target.stats.R: 5 
  > test-basis.R: 8 
  > test-basis.R: 9 10 11 12 13 14 
  > test-bridge-target.stats.R: 6 7 8 9 10 
  > test-basis.R: 15 16 
  > test-bridge-target.stats.R: 11 12 
  > test-basis.R: .
  > test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
  > test-basis.R: model likelihood is only implemented for dyad-independent constraints
  > test-basis.R: at this time. Number of observations is similarly poorly defined.  This
  > test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
  > test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
  > test-basis.R: distribution and constraints.
  > test-basis.R: 
  > test-basis.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-bridge-target.stats.R: 13 
  > test-bridge-target.stats.R: 14 
  > test-bridge-target.stats.R: 15 
  > test-bridge-target.stats.R: 16 
  > test-bridge-target.stats.R: .
  > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-bridge-target.stats.R: Bridging finished.
  > test-bridge-target.stats.R: 
  > test-bridge-target.stats.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-bridge-target.stats.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
  > test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-basis.R: Iteration 1 of at most 60:
  > test-basis.R: Convergence test P-value:4.7e-173
  > test-basis.R: 1 The log-likelihood improved by 1.861.
  > test-basis.R: Iteration 2 of at most 60:
  > test-basis.R: Convergence test P-value:2.5e-135
  > test-basis.R: 1 
  > test-basis.R: The log-likelihood improved by 1.577.
  > test-basis.R: Iteration 3 of at most 60:
  > test-basis.R: Convergence test P-value:1.9e-80
  > test-bridge-target.stats.R: Fitting the dyad-independent submodel...
  > test-basis.R: 1 
  > test-basis.R: The log-likelihood improved by 0.8158.
  > test-basis.R: Iteration 4 of at most 60:
  > test-basis.R: Convergence test P-value:4.5e-32
  > test-basis.R: 1 The log-likelihood improved by 0.2411.
  > test-basis.R: Iteration 5 of at most 60:
  > test-basis.R: Convergence test P-value:1.3e-09
  > test-basis.R: 1 
  > test-basis.R: The log-likelihood improved by 0.0608.
  > test-basis.R: Iteration 6 of at most 60:
  > test-basis.R: Convergence test P-value:2.8e-03
  > test-basis.R: 1 The log-likelihood improved by 0.01871.
  > test-basis.R: Iteration 7 of at most 60:
  > test-basis.R: Convergence test P-value:7.5e-01
  > test-basis.R: Convergence detected. Stopping.
  > test-basis.R: 1 
  > test-basis.R: The log-likelihood improved by 0.001578.
  > test-basis.R: Finished CD.
  > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
  > test-bridge-target.stats.R: Setting up bridge sampling...
  > test-basis.R: Iteration 1 of at most 60:
  > test-bridge-target.stats.R: Using 16 bridges: 1 
  > test-bridge-target.stats.R: 2 
  > test-bridge-target.stats.R: 3 
  > test-bridge-target.stats.R: 4 
  > test-bridge-target.stats.R: 5 
  > test-basis.R: 1 Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.1892.
  > test-basis.R: Estimating equations are not within tolerance region.
  > test-basis.R: Iteration 2 of at most 60:
  > test-bridge-target.stats.R: 6 
  > test-bridge-target.stats.R: 7 
  > test-basis.R: 1 Optimizing with step length 1.0000.
  > test-bridge-target.stats.R: 8 
  > test-basis.R: The log-likelihood improved by 0.0072.
  > test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
  > test-basis.R: Finished MCMLE.
  > test-basis.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
  > test-bridge-target.stats.R: 9 
  > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-basis.R: Using 16 bridges: 
  > test-basis.R: 1 
  > test-basis.R: 2 3 4 
  > test-basis.R: 5 6 7 8 9 
  > test-bridge-target.stats.R: 10 
  > test-basis.R: 10 
  > test-basis.R: 11 
  > test-bridge-target.stats.R: 11 
  > test-basis.R: 12 13 
  > test-basis.R: 14 15 16 .
  > test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
  > test-basis.R: model likelihood is only implemented for dyad-independent constraints
  > test-basis.R: at this time. Number of observations is similarly poorly defined.  This
  > test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
  > test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
  > test-basis.R: distribution and constraints.
  > test-basis.R: 
  > test-basis.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-bridge-target.stats.R: 12 
  > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-bridge-target.stats.R: 13 
  > test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
  > test-basis.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-basis.R: Iteration 1 of at most 60:
  > test-basis.R: Convergence test P-value:4.7e-173
  > test-basis.R: 1 The log-likelihood improved by 1.861.
  > test-basis.R: Iteration 2 of at most 60:
  > test-basis.R: Convergence test P-value:2.5e-135
  > test-basis.R: 1 The log-likelihood improved by 1.577.
  > test-basis.R: Iteration 3 of at most 60:
  > test-basis.R: Convergence test P-value:1.9e-80
  > test-basis.R: 1 
  > test-bridge-target.stats.R: 14 
  > test-basis.R: The log-likelihood improved by 0.8158.
  > test-basis.R: Iteration 4 of at most 60:
  > test-basis.R: Convergence test P-value:4.5e-32
  > test-basis.R: 1 
  > test-basis.R: The log-likelihood improved by 0.2411.
  > test-basis.R: Iteration 5 of at most 60:
  > test-basis.R: Convergence test P-value:1.3e-09
  > test-basis.R: 1 The log-likelihood improved by 0.0608.
  > test-basis.R: Iteration 6 of at most 60:
  > test-basis.R: Convergence test P-value:2.8e-03
  > test-basis.R: 1 The log-likelihood improved by 0.01871.
  > test-basis.R: Iteration 7 of at most 60:
  > test-bridge-target.stats.R: 15 
  > test-basis.R: Convergence test P-value:7.5e-01
  > test-basis.R: Convergence detected. Stopping.
  > test-basis.R: 1 
  > test-basis.R: The log-likelihood improved by 0.001578.
  > test-basis.R: Finished CD.
  > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-basis.R: Iteration 1 of at most 60:
  > test-bridge-target.stats.R: 16 
  > test-bridge-target.stats.R: .
  > test-bridge-target.stats.R: Bridging finished.
  > test-bridge-target.stats.R: Fitting the dyad-independent submodel...
  > test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
  > test-bridge-target.stats.R: Setting up bridge sampling...
  > test-bridge-target.stats.R: Using 16 bridges: 1 
  > test-bridge-target.stats.R: 2 
  > test-bridge-target.stats.R: 3 
  > test-bridge-target.stats.R: 4 
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.1892.
  > test-basis.R: Estimating equations are not within tolerance region.
  > test-basis.R: Iteration 2 of at most 60:
  > test-bridge-target.stats.R: 5 
  > test-bridge-target.stats.R: 6 
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.0072.
  > test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
  > test-basis.R: Finished MCMLE.
  > test-basis.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
  > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-bridge-target.stats.R: 7 
  > test-basis.R: Using 16 bridges: 1 
  > test-basis.R: 2 
  > test-basis.R: 3 
  > test-basis.R: 4 5 6 7 
  > test-bridge-target.stats.R: 8 
  > test-basis.R: 8 
  > test-basis.R: 9 10 
  > test-basis.R: 11 
  > test-basis.R: 12 
  > test-basis.R: 13 14 
  > test-basis.R: 15 16 .
  > test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null
  > test-basis.R: model likelihood is only implemented for dyad-independent constraints
  > test-basis.R: at this time. Number of observations is similarly poorly defined.  This
  > test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
  > test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference
  > test-basis.R: distribution and constraints.
  > test-basis.R: 
  > test-basis.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-basis.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-bridge-target.stats.R: 9 
  > test-bridge-target.stats.R: 10 
  > test-bridge-target.stats.R: 11 
  > test-bridge-target.stats.R: 12 
  > test-bridge-target.stats.R: 13 
  > test-checkpointing.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-checkpointing.R: Obtaining the responsible dyads.
  > test-checkpointing.R: Evaluating the predictor and response matrix.
  > test-checkpointing.R: Maximizing the pseudolikelihood.
  > test-checkpointing.R: Finished MPLE.
  > test-checkpointing.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-checkpointing.R: Iteration 1 of at most 60:
  > test-checkpointing.R: Saving state in '/tmp/RtmpC6L1F5/file35cb6e72045276_001.RData'.
  > test-bridge-target.stats.R: 14 
  > test-bridge-target.stats.R: 15 
  > test-bridge-target.stats.R: 16 
  > test-bridge-target.stats.R: .
  > test-bridge-target.stats.R: Bridging finished.
  > test-bridge-target.stats.R: Fitting the dyad-independent submodel...
  > test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
  > test-bridge-target.stats.R: Setting up bridge sampling...
  > test-bridge-target.stats.R: Using 16 bridges: 1 
  > test-checkpointing.R: 1 Optimizing with step length 1.0000.
  > test-checkpointing.R: The log-likelihood improved by 0.0213.
  > test-checkpointing.R: Step length converged once. Increasing MCMC sample size.
  > test-checkpointing.R: Iteration 2 of at most 60:
  > test-checkpointing.R: Saving state in '/tmp/RtmpC6L1F5/file35cb6e72045276_002.RData'.
  > test-bridge-target.stats.R: 2 
  > test-bridge-target.stats.R: 3 
  > test-bridge-target.stats.R: 4 
  > test-bridge-target.stats.R: 5 
  > test-checkpointing.R: 1 
  > test-bridge-target.stats.R: 6 
  > test-checkpointing.R: Optimizing with step length 1.0000.
  > test-checkpointing.R: The log-likelihood improved by 0.0238.
  > test-checkpointing.R: Step length converged twice. Stopping.
  > test-checkpointing.R: Finished MCMLE.
  > test-checkpointing.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
  > test-bridge-target.stats.R: 7 
  > test-checkpointing.R: Bridging between the dyad-independent submodel and the full model...
  > test-checkpointing.R: Setting up bridge sampling...
  > test-bridge-target.stats.R: 8 
  > test-checkpointing.R: Using 16 bridges: 1 
  > test-checkpointing.R: 2 
  > test-checkpointing.R: 3 4 
  > test-bridge-target.stats.R: 9 
  > test-checkpointing.R: 5 
  > test-checkpointing.R: 6 7 
  > test-bridge-target.stats.R: 10 
  > test-checkpointing.R: 8 9 
  > test-checkpointing.R: 10 
  > test-checkpointing.R: 11 12 
  > test-checkpointing.R: 13 14 
  > test-checkpointing.R: 15 
  > test-checkpointing.R: 16 
  > test-checkpointing.R: .
  > test-checkpointing.R: Bridging finished.
  > test-checkpointing.R: 
  > test-checkpointing.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-checkpointing.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-checkpointing.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-checkpointing.R: Obtaining the responsible dyads.
  > test-checkpointing.R: Evaluating the predictor and response matrix.
  > test-checkpointing.R: Maximizing the pseudolikelihood.
  > test-checkpointing.R: Finished MPLE.
  > test-checkpointing.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-checkpointing.R: Resuming from state saved in '/tmp/RtmpC6L1F5/file35cb6e72045276_002.RData'.
  > test-checkpointing.R: Iteration 1 of at most 60:
  > test-bridge-target.stats.R: 11 
  > test-bridge-target.stats.R: 12 
  > test-bridge-target.stats.R: 13 
  > test-checkpointing.R: 1 Optimizing with step length 1.0000.
  > test-bridge-target.stats.R: 14 
  > test-checkpointing.R: The log-likelihood improved by 0.0145.
  > test-checkpointing.R: Step length converged twice. Stopping.
  > test-checkpointing.R: Finished MCMLE.
  > test-checkpointing.R: Evaluating log-likelihood at the estimate. 
  > test-bridge-target.stats.R: 15 
  > test-checkpointing.R: Fitting the dyad-independent submodel...
  > test-bridge-target.stats.R: 16 .
  > test-bridge-target.stats.R: Bridging finished.
  > test-checkpointing.R: Bridging between the dyad-independent submodel and the full model...
  > test-checkpointing.R: Setting up bridge sampling...
  > test-bridge-target.stats.R: Fitting the dyad-independent submodel...
  > test-checkpointing.R: Using 16 bridges: 1 
  > test-checkpointing.R: 2 
  > test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model...
  > test-bridge-target.stats.R: Setting up bridge sampling...
  > test-checkpointing.R: 3 4 
  > test-checkpointing.R: 5 
  > test-bridge-target.stats.R: Using 16 bridges: 1 
  > test-checkpointing.R: 6 7 
  > test-checkpointing.R: 8 
  > test-checkpointing.R: 9 10 
  > test-bridge-target.stats.R: 2 
  > test-checkpointing.R: 11 12 13 
  > test-checkpointing.R: 14 15 
  > test-checkpointing.R: 16 
  > test-checkpointing.R: .
  > test-checkpointing.R: Bridging finished.
  > test-checkpointing.R: 
  > test-checkpointing.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-checkpointing.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-bridge-target.stats.R: 3 
  > test-bridge-target.stats.R: 4 
  > test-bridge-target.stats.R: 5 
  > test-bridge-target.stats.R: 6 
  > test-bridge-target.stats.R: 7 
  > test-bridge-target.stats.R: 8 
  > test-bridge-target.stats.R: 9 
  > test-bridge-target.stats.R: 10 
  > test-bridge-target.stats.R: 11 
  > test-bridge-target.stats.R: 12 
  > test-bridge-target.stats.R: 13 
  > test-bridge-target.stats.R: 14 
  > test-bridge-target.stats.R: 15 
  > test-bridge-target.stats.R: 16 
  > test-bridge-target.stats.R: .
  > test-bridge-target.stats.R: Bridging finished.
  > test-constrain-degrees-edges.R: Best valid proposal 'CondOutDegree' cannot take into account hint(s) 'triadic'.
  > test-constrain-degrees-edges.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
  > test-constrain-degrees-edges.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-constrain-degrees-edges.R: Iteration 1 of at most 2:
  > test-constrain-degrees-edges.R: Convergence test P-value:5.2e-06
  > test-constrain-degrees-edges.R: 1 
  > test-constrain-degrees-edges.R: The log-likelihood improved by 0.05768.
  > test-constrain-degrees-edges.R: Iteration 2 of at most 2:
  > test-constrain-degrees-edges.R: Convergence test P-value:3.7e-03
  > test-constrain-degrees-edges.R: 1 
  > test-constrain-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-constrain-degrees-edges.R: The log-likelihood improved by 0.09813.
  > test-constrain-degrees-edges.R: Finished CD.
  > test-constrain-degrees-edges.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-constrain-degrees-edges.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-constrain-degrees-edges.R: Best valid proposal 'CondOutDegree' cannot take into account hint(s) 'triadic'.
  > test-constrain-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-constrain-blockdiag.R: 
  > test-constrain-blockdiag.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
  > test-constrain-blockdiag.R: * 'news(package="ergm.count")' for changes since last version
  > test-constrain-blockdiag.R: * 'citation("ergm.count")' for citation information
  > test-constrain-blockdiag.R: * 'https://statnet.org' for help, support, and other information
  > test-constrain-blockdiag.R: 
  > test-constrain-degrees-edges.R: Best valid proposal 'CondInDegree' cannot take into account hint(s) 'triadic'.
  > test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
  > test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
  > test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-constrain-dind.R: Obtaining the responsible dyads.
  > test-constrain-dind.R: Evaluating the predictor and response matrix.
  > test-constrain-dind.R: Maximizing the pseudolikelihood.
  > test-constrain-dind.R: Finished MPLE.
  > test-constrain-dind.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-constrain-dind.R: Iteration 1 of at most 60:
  > test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
  > test-constrain-dind.R: 1 Optimizing with step length 1.0000.
  > test-constrain-dind.R: The log-likelihood improved by 0.0020.
  > test-constrain-dind.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-constrain-dind.R: Finished MCMLE.
  > test-constrain-dind.R: Evaluating log-likelihood at the estimate. 
  > test-constrain-dind.R: Fitting the dyad-independent submodel...
  > test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
  > test-constrain-dind.R: Bridging between the dyad-independent submodel and the full model...
  > test-constrain-dind.R: Setting up bridge sampling...
  > test-constrain-dind.R: Using 16 bridges: 1 
  > test-constrain-dind.R: 2 3 
  > test-constrain-dind.R: 4 
  > test-constrain-dind.R: 5 
  > test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
  > test-constrain-dind.R: 6 
  > test-constrain-dind.R: 7 
  > test-constrain-dind.R: 8 
  > test-constrain-dind.R: 9 
  > test-constrain-dind.R: 10 
  > test-constrain-dind.R: 11 
  > test-constrain-dind.R: 12 13 
  > test-constrain-dind.R: 14 
  > test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
  > test-constrain-dind.R: 15 
  > test-constrain-dind.R: 16 
  > test-constrain-dind.R: .
  > test-constrain-dind.R: Bridging finished.
  > test-constrain-dind.R: 
  > test-constrain-dind.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-constrain-dind.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-constrain-dind.R: Obtaining the responsible dyads.
  > test-constrain-dind.R: Evaluating the predictor and response matrix.
  > test-constrain-dind.R: Maximizing the pseudolikelihood.
  > test-constrain-dind.R: Finished MPLE.
  > test-constrain-dind.R: Evaluating log-likelihood at the estimate. 
  > test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
  > test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-constrain-dind.R: Obtaining the responsible dyads.
  > test-constrain-dind.R: Evaluating the predictor and response matrix.
  > test-constrain-dind.R: Maximizing the pseudolikelihood.
  > test-constrain-dind.R: Finished MPLE.
  > test-constrain-dind.R: Evaluating log-likelihood at the estimate. 
  > test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-constrain-dind.R: Obtaining the responsible dyads.
  > test-constrain-dind.R: Evaluating the predictor and response matrix.
  > test-constrain-dind.R: Maximizing the pseudolikelihood.
  > test-constrain-dind.R: Finished MPLE.
  > test-constrain-dind.R: Evaluating log-likelihood at the estimate. 
  > test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-constrain-dind.R: Obtaining the responsible dyads.
  > test-constrain-dind.R: Evaluating the predictor and response matrix.
  > test-constrain-dind.R: Maximizing the pseudolikelihood.
  > test-constrain-dind.R: Finished MPLE.
  > test-constrain-dind.R: Evaluating log-likelihood at the estimate. 
  > test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-constrain-dind.R: Obtaining the responsible dyads.
  > test-constrain-dind.R: Evaluating the predictor and response matrix.
  > test-constrain-dind.R: Maximizing the pseudolikelihood.
  > test-constrain-dind.R: Finished MPLE.
  > test-constrain-dind.R: Evaluating log-likelihood at the estimate. 
  > test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-constrain-dind.R: Obtaining the responsible dyads.
  > test-constrain-dind.R: Evaluating the predictor and response matrix.
  > test-constrain-dind.R: Maximizing the pseudolikelihood.
  > test-constrain-dind.R: Finished MPLE.
  > test-constrain-dind.R: Evaluating log-likelihood at the estimate. 
  > test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-constraints.R: Obtaining the responsible dyads.
  > test-constraints.R: Evaluating the predictor and response matrix.
  > test-constraints.R: Maximizing the pseudolikelihood.
  > test-constraints.R: Finished MPLE.
  > test-constraints.R: Evaluating log-likelihood at the estimate. 
  > test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-constraints.R: Obtaining the responsible dyads.
  > test-constraints.R: Evaluating the predictor and response matrix.
  > test-constraints.R: Maximizing the pseudolikelihood.
  > test-constraints.R: Finished MPLE.
  > test-constraints.R: Evaluating log-likelihood at the estimate. 
  > test-drop.R: Observed statistic(s) edgecov.samplike.m - 1/2 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
  > test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
  > test-drop.R: Evaluating log-likelihood at the estimate. 
  > test-drop.R: Observed statistic(s) edgecov.samplike.m - 1/2 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
  > test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
  > test-drop.R: Evaluating log-likelihood at the estimate. 
  > test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
  > test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
  > test-drop.R: Evaluating log-likelihood at the estimate. 
  > test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-constraints.R: Obtaining the responsible dyads.
  > test-constraints.R: Evaluating the predictor and response matrix.
  > test-constraints.R: Maximizing the pseudolikelihood.
  > test-constraints.R: Finished MPLE.
  > test-constraints.R: Evaluating log-likelihood at the estimate. 
  > test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
  > test-drop.R: All terms are either offsets or extreme values. No optimization is performed.
  > test-drop.R: Evaluating log-likelihood at the estimate. 
  > test-drop.R: Observed statistic(s) edgecov.samplike.m are at their greatest attainable values. Their coefficients will be fixed at +Inf.
  > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-drop.R: Obtaining the responsible dyads.
  > test-drop.R: Evaluating the predictor and response matrix.
  > test-drop.R: Maximizing the pseudolikelihood.
  > test-drop.R: Finished MPLE.
  > test-drop.R: Evaluating log-likelihood at the estimate. 
  > test-drop.R: Observed statistic(s) edgecov.samplike.m are at their greatest attainable values. Their coefficients will be fixed at +Inf.
  > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-drop.R: Obtaining the responsible dyads.
  > test-drop.R: Evaluating the predictor and response matrix.
  > test-drop.R: Maximizing the pseudolikelihood.
  > test-drop.R: Finished MPLE.
  > test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-drop.R: Iteration 1 of at most 10:
  > test-drop.R: 1 Optimizing with step length 1.0000.
  > test-drop.R: The log-likelihood improved by 0.0007.
  > test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-constraints.R: Obtaining the responsible dyads.
  > test-constraints.R: Evaluating the predictor and response matrix.
  > test-constraints.R: Maximizing the pseudolikelihood.
  > test-constraints.R: Finished MPLE.
  > test-constraints.R: Evaluating log-likelihood at the estimate. 
  > test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-drop.R: Finished MCMLE.
  > test-drop.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
  > test-drop.R: Bridging between the dyad-independent submodel and the full model...
  > test-drop.R: Setting up bridge sampling...
  > test-drop.R: Using 16 bridges: 1 
  > test-drop.R: 2 3 4 
  > test-drop.R: 5 6 7 
  > test-drop.R: 8 
  > test-drop.R: 9 10 11 12 13 
  > test-drop.R: 14 
  > test-drop.R: 15 
  > test-drop.R: 16 .
  > test-drop.R: Bridging finished.
  > test-drop.R: 
  > test-drop.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-constraints.R: Obtaining the responsible dyads.
  > test-constraints.R: Evaluating the predictor and response matrix.
  > test-constraints.R: Maximizing the pseudolikelihood.
  > test-constraints.R: Finished MPLE.
  > test-constraints.R: Evaluating log-likelihood at the estimate. 
  > test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
  > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-drop.R: Obtaining the responsible dyads.
  > test-drop.R: Evaluating the predictor and response matrix.
  > test-drop.R: Maximizing the pseudolikelihood.
  > test-drop.R: Finished MPLE.
  > test-drop.R: Evaluating log-likelihood at the estimate. 
  > test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf.
  > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-drop.R: Obtaining the responsible dyads.
  > test-drop.R: Evaluating the predictor and response matrix.
  > test-drop.R: Maximizing the pseudolikelihood.
  > test-drop.R: Finished MPLE.
  > test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-drop.R: Iteration 1 of at most 10:
  > test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-constraints.R: Obtaining the responsible dyads.
  > test-constraints.R: Evaluating the predictor and response matrix.
  > test-constraints.R: Maximizing the pseudolikelihood.
  > test-constraints.R: Finished MPLE.
  > test-constraints.R: Evaluating log-likelihood at the estimate. 
  > test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-constraints.R: Obtaining the responsible dyads.
  > test-constraints.R: Evaluating the predictor and response matrix.
  > test-constraints.R: Maximizing the pseudolikelihood.
  > test-constraints.R: Finished MPLE.
  > test-constraints.R: Evaluating log-likelihood at the estimate. 
  > test-constraints.R: 
  > test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-constraints.R: Obtaining the responsible dyads.
  > test-constraints.R: Evaluating the predictor and response matrix.
  > test-constraints.R: Maximizing the pseudolikelihood.
  > test-constraints.R: Finished MPLE.
  > test-constraints.R: Evaluating log-likelihood at the estimate. 
  > test-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
  > test-constraints.R: All terms are either offsets or extreme values. No optimization is performed.
  > test-constraints.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
  > test-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
  > test-constraints.R: Using 16 bridges: 1 
  > test-constraints.R: 2 
  > test-constraints.R: 3 
  > test-constraints.R: 4 
  > test-constraints.R: 5 
  > test-constraints.R: 6 
  > test-constraints.R: 7 
  > test-constraints.R: 8 
  > test-constraints.R: 9 
  > test-constraints.R: 10 
  > test-constraints.R: 11 
  > test-constraints.R: 12 
  > test-constraints.R: 13 
  > test-drop.R: 1 
  > test-constraints.R: 14 
  > test-drop.R: Optimizing with step length 1.0000.
  > test-drop.R: The log-likelihood improved by < 0.0001.
  > test-constraints.R: 15 
  > test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-drop.R: Finished MCMLE.
  > test-drop.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
  > test-constraints.R: 16 
  > test-drop.R: Bridging between the dyad-independent submodel and the full model...
  > test-drop.R: Setting up bridge sampling...
  > test-constraints.R: .
  > test-constraints.R: Note: The constraint on the sample space is not dyad-independent. Null
  > test-constraints.R: model likelihood is only implemented for dyad-independent constraints
  > test-constraints.R: at this time. Number of observations is similarly poorly defined.  This
  > test-constraints.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
  > test-constraints.R: AIC, BIC, etc.) is only valid between models with the same reference
  > test-constraints.R: distribution and constraints.
  > test-constraints.R: 
  > test-drop.R: Using 16 bridges: 1 
  > test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
  > test-constraints.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
  > test-constraints.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-constraints.R: Iteration 1 of at most 60:
  > test-constraints.R: Convergence test P-value:1.1e-05
  > test-constraints.R: 1 The log-likelihood improved by 0.07919.
  > test-constraints.R: Iteration 2 of at most 60:
  > test-constraints.R: Convergence test P-value:3.7e-02
  > test-constraints.R: 1 The log-likelihood improved by 0.01687.
  > test-constraints.R: Iteration 3 of at most 60:
  > test-constraints.R: Convergence test P-value:7e-01
  > test-constraints.R: Convergence detected. Stopping.
  > test-constraints.R: 1 The log-likelihood improved by 0.0006029.
  > test-constraints.R: Finished CD.
  > test-constraints.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-constraints.R: Iteration 1 of at most 60:
  > test-drop.R: 2 
  > test-drop.R: 3 
  > test-drop.R: 4 
  > test-constraints.R: 1 Optimizing with step length 1.0000.
  > test-constraints.R: The log-likelihood improved by 0.0263.
  > test-constraints.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-constraints.R: Finished MCMLE.
  > test-constraints.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...
  > test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
  > test-constraints.R: Using 16 bridges: 1 
  > test-drop.R: 5 
  > test-constraints.R: 2 
  > test-constraints.R: 3 4 
  > test-drop.R: 6 
  > test-constraints.R: 5 
  > test-constraints.R: 6 
  > test-constraints.R: 7 
  > test-constraints.R: 8 
  > test-drop.R: 7 
  > test-constraints.R: 9 
  > test-constraints.R: 10 
  > test-constraints.R: 11 
  > test-constraints.R: 12 
  > test-drop.R: 8 
  > test-constraints.R: 13 
  > test-constraints.R: 14 
  > test-constraints.R: 15 
  > test-constraints.R: 16 
  > test-constraints.R: .
  > test-drop.R: 9 
  > test-constraints.R: Note: The constraint on the sample space is not dyad-independent. Null
  > test-constraints.R: model likelihood is only implemented for dyad-independent constraints
  > test-constraints.R: at this time. Number of observations is similarly poorly defined.  This
  > test-constraints.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
  > test-constraints.R: AIC, BIC, etc.) is only valid between models with the same reference
  > test-constraints.R: distribution and constraints.
  > test-constraints.R: 
  > test-constraints.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-constraints.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-drop.R: 10 
  > test-drop.R: 11 
  > test-drop.R: 12 
  > test-drop.R: 13 
  > test-ergm-proposal-unload.R: 
  > test-ergm-proposal-unload.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
  > test-ergm-proposal-unload.R: * 'news(package="ergm.count")' for changes since last version
  > test-ergm-proposal-unload.R: * 'citation("ergm.count")' for citation information
  > test-ergm-proposal-unload.R: * 'https://statnet.org' for help, support, and other information
  > test-ergm-proposal-unload.R: 
  > test-ergm-proposal-unload.R: 
  > test-ergm-proposal-unload.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
  > test-ergm-proposal-unload.R: * 'news(package="ergm.count")' for changes since last version
  > test-ergm-proposal-unload.R: * 'citation("ergm.count")' for citation information
  > test-ergm-proposal-unload.R: * 'https://statnet.org' for help, support, and other information
  > test-ergm-proposal-unload.R: 
  > test-drop.R: 14 
  > test-drop.R: 15 
  > test-drop.R: 16 
  > test-drop.R: .
  > test-drop.R: Bridging finished.
  > test-drop.R: 
  > test-drop.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-drop.R: Evaluating network in model.
  > test-drop.R: Initializing unconstrained Metropolis-Hastings proposal: 'ergm:MH_SPDyad'.
  > test-drop.R: Initializing model...
  > test-drop.R: Model initialized.
  > test-drop.R: Using initial method 'MPLE'.
  > test-drop.R: Initial parameters provided by caller: None.
  > test-drop.R:   number of free parameters: 7
  > test-drop.R:   number of fixed parameters: 0
  > test-drop.R: Observed statistic(s) triangle and kstar5 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
  > test-drop.R: Fitting initial model.
  > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-drop.R: Obtaining the responsible dyads.
  > test-drop.R: Evaluating the predictor and response matrix.
  > test-drop.R: Maximizing the pseudolikelihood.
  > test-drop.R: Finished MPLE.
  > test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-drop.R: Density guard set to 10000 from an initial count of 3 edges.
  > test-drop.R: 
  > test-drop.R: Iteration 1 of at most 3 with parameter:
  > test-drop.R:          edges       triangle        degree2         kstar2         kstar5 
  > test-drop.R:       1.313175           -Inf       6.009173      -4.163221           -Inf 
  > test-drop.R:       gwdegree gwdegree.decay 
  > test-drop.R:      -2.860022       1.666671 
  > test-drop.R: Starting unconstrained MCMC...
  > test-ergm-san.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-drop.R: Back from unconstrained MCMC.
  > test-drop.R: New interval = 512.
  > test-drop.R: Average estimating function values:
  > test-drop.R:          edges        degree2         kstar2       gwdegree gwdegree.decay 
  > test-drop.R:    -0.29629630    -0.11934156    -0.11934156    -0.57005199     0.06446661 
  > test-drop.R: Starting MCMLE Optimization...
  > test-drop.R: 1 Optimizing with step length 1.0000.
  > test-drop.R: Using lognormal metric (see control.ergm function).
  > test-drop.R: Optimizing loglikelihood
  > test-drop.R: Starting MCMC s.e. computation.
  > test-drop.R: The log-likelihood improved by 0.0385.
  > test-drop.R: Estimated covariance matrix of the statistics has nullity 3. Effective parameter number adjusted to 2.
  > test-drop.R: Test statistic: T^2 = 21.94136, with 2 free parameter(s) and 240.9959 degrees of freedom.
  > test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-drop.R: Finished MCMLE.
  > test-drop.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
  > test-drop.R: Fitting the dyad-independent submodel...
  > test-drop.R: Dyad-independent submodel MLE has likelihood -11.02185 at:
  > test-drop.R: [1] -2.639057  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000
  > test-drop.R: [8]  0.000000
  > test-drop.R: Bridging between the dyad-independent submodel and the full model...
  > test-drop.R: Setting up bridge sampling...
  > test-drop.R: Initializing model and proposals...
  > test-drop.R: Model and proposals initialized.
  > test-drop.R: Using 16 bridges: Running theta=[-1.175967,     -Inf, 6.177257,-4.435272,     -Inf,-1.386658, 1.950625, 0.000000].
  > test-drop.R: Running theta=[-1.270360,     -Inf, 5.778724,-4.149126,     -Inf,-1.297196, 1.824778, 0.000000].
  > test-drop.R: Running theta=[-1.364753,     -Inf, 5.380191,-3.862979,     -Inf,-1.207735, 1.698931, 0.000000].
  > test-drop.R: Running theta=[-1.459146,     -Inf, 4.981659,-3.576833,     -Inf,-1.118273, 1.573085, 0.000000].
  > test-drop.R: Running theta=[-1.553539,     -Inf, 4.583126,-3.290686,     -Inf,-1.028811, 1.447238, 0.000000].
  > test-drop.R: Running theta=[-1.6479316,      -Inf, 4.1845933,-3.0045393,      -Inf,-0.9393491, 1.3213911, 0.0000000].
  > test-drop.R: Running theta=[-1.7423245,      -Inf, 3.7860606,-2.7183927,      -Inf,-0.8498873, 1.1955444, 0.0000000].
  > test-drop.R: Running theta=[-1.8367174,      -Inf, 3.3875279,-2.4322461,      -Inf,-0.7604255, 1.0696976, 0.0000000].
  > test-drop.R: Running theta=[-1.9311104,      -Inf, 2.9889952,-2.1460995,      -Inf,-0.6709636, 0.9438508, 0.0000000].
  > test-drop.R: Running theta=[-2.0255033,      -Inf, 2.5904625,-1.8599529,      -Inf,-0.5815018, 0.8180040, 0.0000000].
  > test-drop.R: Running theta=[-2.1198962,      -Inf, 2.1919298,-1.5738063,      -Inf,-0.4920400, 0.6921573, 0.0000000].
  > test-drop.R: Running theta=[-2.2142891,      -Inf, 1.7933971,-1.2876597,      -Inf,-0.4025782, 0.5663105, 0.0000000].
  > test-drop.R: Running theta=[-2.3086821,      -Inf, 1.3948644,-1.0015131,      -Inf,-0.3131164, 0.4404637, 0.0000000].
  > test-drop.R: Running theta=[-2.4030750,      -Inf, 0.9963317,-0.7153665,      -Inf,-0.2236545, 0.3146169, 0.0000000].
  > test-drop.R: Running theta=[-2.4974679,      -Inf, 0.5977990,-0.4292199,      -Inf,-0.1341927, 0.1887702, 0.0000000].
  > test-drop.R: Running theta=[-2.59186086,       -Inf, 0.19926635,-0.14307330,       -Inf,-0.04473091, 0.06292339, 0.00000000].
  > test-drop.R: .
  > test-drop.R: Bridge sampling finished. Collating...
  > test-drop.R: Bridging finished.
  > test-drop.R: 
  > test-drop.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-ergm.bridge.llr.R: Setting up bridge sampling...
  > test-ergm.bridge.llr.R: Using 16 bridges: 1 
  > test-ergm.bridge.llr.R: 2 
  > test-ergm-term-doc.R: Found  9  matching ergm terms:
  > test-ergm-term-doc.R: Symmetrize(formula, rule="weak") (binary, valued)
  > test-ergm-term-doc.R:     Evaluation on symmetrized (undirected) network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: ctriple(attr=NULL, diff=FALSE, levels=NULL) (binary)
  > test-ergm-term-doc.R: ctriad (binary)
  > test-ergm-term-doc.R:     Cyclic triples
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: localtriangle(x) (binary)
  > test-ergm-term-doc.R:     Triangles within neighborhoods
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1) (binary)
  > test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Nodal attribute mixing
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: opentriad (binary)
  > test-ergm-term-doc.R:     Open triads
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: threetrail(keep=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R: threepath(keep=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Three-trails
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: triangle(attr=NULL, diff=FALSE, levels=NULL) (binary)
  > test-ergm-term-doc.R: triangles(attr=NULL, diff=FALSE, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Triangles
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: tripercent(attr=NULL, diff=FALSE, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Triangle percentage
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: ttriple(attr=NULL, diff=FALSE, levels=NULL) (binary)
  > test-ergm-term-doc.R: ttriad (binary)
  > test-ergm-term-doc.R:     Transitive triples
  > test-ergm-term-doc.R: Found  31  matching ergm terms:
  > test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Concurrent node count for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1cov(attr) (binary)
  > test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
  > test-ergm-term-doc.R:     Main effect of a covariate for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Degree range for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Degree for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1dsp(d) (binary)
  > test-ergm-term-doc.R:     Dyadwise shared partners for dyads in the first bipartition
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
  > test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Factor attribute effect for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1mindegree(d) (binary)
  > test-ergm-term-doc.R:     Minimum degree for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Nodal attribute-based homophily effect for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
  > test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Degree
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     k-stars for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
  > test-ergm-term-doc.R:     Mixing matrix for k-stars centered on the first mode of a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
  > test-ergm-term-doc.R:     Two-star census for central nodes centered on the first mode of a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
  > test-ergm-term-doc.R:     Concurrent node count for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2cov(attr) (binary)
  > test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
  > test-ergm-term-doc.R:     Main effect of a covariate for the second mode in a bipartite  network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Degree range for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
  > test-ergm-term-doc.R:     Degree for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2dsp(d) (binary)
  > test-ergm-term-doc.R:     Dyadwise shared partners for dyads in the second bipartition
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
  > test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Factor attribute effect for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2mindegree(d) (binary)
  > test-ergm-term-doc.R:     Minimum degree for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Nodal attribute-based homophily effect for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
  > test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Degree
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     k-stars for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
  > test-ergm-term-doc.R:     Mixing matrix for k-stars centered on the second mode of a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
  > test-ergm-term-doc.R:     Two-star census for central nodes centered on the second mode of a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
  > test-ergm-term-doc.R:     Coincident node count for the second mode in a bipartite (aka two-mode) network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
  > test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
  > test-ergm-term-doc.R:     Difference
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Geometrically weighted degree distribution for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
  > test-ergm-term-doc.R:     Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Geometrically weighted degree distribution for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
  > test-ergm-term-doc.R:     Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: isolatededges (binary)
  > test-ergm-term-doc.R:     Isolated edges
  > test-ergm-term-doc.R: Found  36  matching ergm terms:
  > test-ergm-term-doc.R: Project(formula, mode) (binary)
  > test-ergm-term-doc.R: Proj1(formula) (binary)
  > test-ergm-term-doc.R: Proj2(formula) (binary)
  > test-ergm-term-doc.R:     Evaluation on a projection of a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Concurrent node count for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1cov(attr) (binary)
  > test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
  > test-ergm-term-doc.R:     Main effect of a covariate for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: nodecovrange(attr) (binary)
  > test-ergm-term-doc.R:     Range of covariate values for neighbors of a mode-1 node
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Degree range for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Degree for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1dsp(d) (binary)
  > test-ergm-term-doc.R:     Dyadwise shared partners for dyads in the first bipartition
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
  > test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Factor attribute effect for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1factordistinct(attr, levels=TRUE) (binary)
  > test-ergm-term-doc.R:     Number of distinct neighbor types for the first node
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1mindegree(d) (binary)
  > test-ergm-term-doc.R:     Minimum degree for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Nodal attribute-based homophily effect for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
  > test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Degree
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     k-stars for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
  > test-ergm-term-doc.R:     Mixing matrix for k-stars centered on the first mode of a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
  > test-ergm-term-doc.R:     Two-star census for central nodes centered on the first mode of a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
  > test-ergm-term-doc.R:     Concurrent node count for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2cov(attr) (binary)
  > test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
  > test-ergm-term-doc.R:     Main effect of a covariate for the second mode in a bipartite  network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: nodecovrange(attr) (binary)
  > test-ergm-term-doc.R:     Range of covariate values for neighbors of a mode-2 node
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Degree range for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
  > test-ergm-term-doc.R:     Degree for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2dsp(d) (binary)
  > test-ergm-term-doc.R:     Dyadwise shared partners for dyads in the second bipartition
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
  > test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Factor attribute effect for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2factordistinct(attr, levels=TRUE) (binary)
  > test-ergm-term-doc.R:     Number of distinct neighbor types for the second mode
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2mindegree(d) (binary)
  > test-ergm-term-doc.R:     Minimum degree for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Nodal attribute-based homophily effect for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
  > test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Degree
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     k-stars for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
  > test-ergm-term-doc.R:     Mixing matrix for k-stars centered on the second mode of a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
  > test-ergm-term-doc.R:     Two-star census for central nodes centered on the second mode of a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
  > test-ergm-term-doc.R:     Coincident node count for the second mode in a bipartite (aka two-mode) network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
  > test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
  > test-ergm-term-doc.R:     Difference
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Geometrically weighted degree distribution for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
  > test-ergm-term-doc.R:     Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Geometrically weighted degree distribution for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
  > test-ergm-term-doc.R:     Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: isolatededges (binary)
  > test-ergm-term-doc.R:     Isolated edges
  > test-ergm-term-doc.R: Definitions for term(s)  b2factor :
  > test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1)
  > test-ergm-term-doc.R:     Factor attribute effect for the second mode in a bipartite network: This term adds multiple network statistics to the model, one for each of (a subset of) the
  > test-ergm-term-doc.R: unique values of the attr attribute. Each of these statistics
  > test-ergm-term-doc.R: gives the number of times a node with that attribute in the second mode of
  > test-ergm-term-doc.R: the network appears in an edge. The second mode of a bipartite network
  > test-ergm-term-doc.R: object is sometimes known as the "event" mode.
  > test-ergm-term-doc.R:     Keywords: bipartite, categorical nodal attribute, dyad-independent, frequently-used, undirected, binary, valued
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: No terms named 'b3factor' were found. Try searching with search='b3factor'instead.
  > test-ergm.bridge.llr.R: 3 
  > test-ergm-term-doc.R: Found  36  matching ergm terms:
  > test-ergm-term-doc.R: Project(formula, mode) (binary)
  > test-ergm-term-doc.R: Proj1(formula) (binary)
  > test-ergm-term-doc.R: Proj2(formula) (binary)
  > test-ergm-term-doc.R:     Evaluation on a projection of a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Concurrent node count for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1cov(attr) (binary)
  > test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
  > test-ergm-term-doc.R:     Main effect of a covariate for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: nodecovrange(attr) (binary)
  > test-ergm-term-doc.R:     Range of covariate values for neighbors of a mode-1 node
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Degree range for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Degree for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1dsp(d) (binary)
  > test-ergm-term-doc.R:     Dyadwise shared partners for dyads in the first bipartition
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary)
  > test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Factor attribute effect for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1factordistinct(attr, levels=TRUE) (binary)
  > test-ergm-term-doc.R:     Number of distinct neighbor types for the first node
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1mindegree(d) (binary)
  > test-ergm-term-doc.R:     Minimum degree for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Nodal attribute-based homophily effect for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1sociality(nodes=-1) (binary)
  > test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Degree
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     k-stars for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary)
  > test-ergm-term-doc.R:     Mixing matrix for k-stars centered on the first mode of a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
  > test-ergm-term-doc.R:     Two-star census for central nodes centered on the first mode of a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2concurrent(by=NULL) (binary)
  > test-ergm-term-doc.R:     Concurrent node count for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2cov(attr) (binary)
  > test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
  > test-ergm-term-doc.R:     Main effect of a covariate for the second mode in a bipartite  network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: nodecovrange(attr) (binary)
  > test-ergm-term-doc.R:     Range of covariate values for neighbors of a mode-2 node
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Degree range for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2degree(d, by=NULL) (binary)
  > test-ergm-term-doc.R:     Degree for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2dsp(d) (binary)
  > test-ergm-term-doc.R:     Dyadwise shared partners for dyads in the second bipartition
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary)
  > test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Factor attribute effect for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2factordistinct(attr, levels=TRUE) (binary)
  > test-ergm-term-doc.R:     Number of distinct neighbor types for the second mode
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2mindegree(d) (binary)
  > test-ergm-term-doc.R:     Minimum degree for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Nodal attribute-based homophily effect for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2sociality(nodes=-1) (binary)
  > test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Degree
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary)
  > test-ergm-term-doc.R:     k-stars for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary)
  > test-ergm-term-doc.R:     Mixing matrix for k-stars centered on the second mode of a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary)
  > test-ergm-term-doc.R:     Two-star census for central nodes centered on the second mode of a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary)
  > test-ergm-term-doc.R:     Coincident node count for the second mode in a bipartite (aka two-mode) network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary)
  > test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
  > test-ergm-term-doc.R:     Difference
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Geometrically weighted degree distribution for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
  > test-ergm-term-doc.R:     Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary)
  > test-ergm-term-doc.R:     Geometrically weighted degree distribution for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary)
  > test-ergm-term-doc.R:     Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: isolatededges (binary)
  > test-ergm-term-doc.R:     Isolated edges
  > test-ergm.bridge.llr.R: 4 
  > test-ergm-term-doc.R: Found  48  matching ergm terms:
  > test-ergm-term-doc.R: B(formula, form) (valued)
  > test-ergm-term-doc.R:     Wrap binary terms for use in valued models
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: Curve(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
  > test-ergm-term-doc.R: Parametrise(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
  > test-ergm-term-doc.R: Parametrize(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued)
  > test-ergm-term-doc.R:     Impose a curved structure on term parameters
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: Exp(formula) (valued)
  > test-ergm-term-doc.R:     Exponentiate a network's statistic
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: For(...) (valued)
  > test-ergm-term-doc.R:     A for operator for terms
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: Label(formula, label, pos) (valued)
  > test-ergm-term-doc.R:     Modify terms' coefficient names
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: Log(formula, log0=-1/sqrt(.Machine$double.eps)) (valued)
  > test-ergm-term-doc.R:     Take a natural logarithm of a network's statistic
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: Prod(formulas, label) (valued)
  > test-ergm-term-doc.R:     A product (or an arbitrary power combination) of one or more formulas
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: S(formula, attrs) (valued)
  > test-ergm-term-doc.R:     Evaluation on an induced subgraph
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: Sum(formulas, label) (valued)
  > test-ergm-term-doc.R:     A sum (or an arbitrary linear combination) of one or more formulas
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: Symmetrize(formula, rule="weak") (valued)
  > test-ergm-term-doc.R:     Evaluation on symmetrized (undirected) network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: absdiff(attr, pow=1, form="sum") (valued)
  > test-ergm-term-doc.R:     Absolute difference in nodal attribute
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: absdiffcat(attr, base=NULL, levels=NULL, form="sum") (valued)
  > test-ergm-term-doc.R:     Categorical absolute difference in nodal attribute
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: atleast(threshold=0) (valued)
  > test-ergm-term-doc.R:     Number of dyads with values greater than or equal to a threshold
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: atmost(threshold=0) (valued)
  > test-ergm-term-doc.R:     Number of dyads with values less than or equal to a threshold
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: attrcov(attr, mat, form="sum") (valued)
  > test-ergm-term-doc.R:     Edge covariate by attribute pairing
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1cov(attr, form="sum") (valued)
  > test-ergm-term-doc.R:     Main effect of a covariate for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Factor attribute effect for the first mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Degree
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2cov(attr, form="sum") (valued)
  > test-ergm-term-doc.R:     Main effect of a covariate for the second mode in a bipartite  network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Factor attribute effect for the second mode in a bipartite network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Degree
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: cyclicalties(threshold=0) (valued)
  > test-ergm-term-doc.R:     Cyclical ties
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: cyclicalweights(twopath="min", combine="max", affect="min") (valued)
  > test-ergm-term-doc.R:     Cyclical weights
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued)
  > test-ergm-term-doc.R:     Difference
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: edgecov(x, attrname=NULL, form="sum") (valued)
  > test-ergm-term-doc.R:     Edge covariate
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: edges (valued)
  > test-ergm-term-doc.R: nonzero (valued)
  > test-ergm-term-doc.R:     Number of edges in the network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: equalto(value=0, tolerance=0) (valued)
  > test-ergm-term-doc.R:     Number of dyads with values equal to a specific value (within tolerance)
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: greaterthan(threshold=0) (valued)
  > test-ergm-term-doc.R:     Number of dyads with values strictly greater than a threshold
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: ininterval(lower=-Inf, upper=+Inf, open=c(TRUE,TRUE)) (valued)
  > test-ergm-term-doc.R:     Number of dyads whose values are in an interval
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: mm(attrs, levels=NULL, levels2=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Mixing matrix cells and margins
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: mutual(form="min",threshold=0) (valued)
  > test-ergm-term-doc.R:     Mutuality
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: nodecov(attr, form="sum") (valued)
  > test-ergm-term-doc.R: nodemain(attr, form="sum") (valued)
  > test-ergm-term-doc.R:     Main effect of a covariate
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: nodecovar(center, transform) (valued)
  > test-ergm-term-doc.R:     Covariance of undirected dyad values incident on each actor
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: nodefactor(attr, base=1, levels=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Factor attribute effect
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: nodeicov(attr, form="sum") (valued)
  > test-ergm-term-doc.R:     Main effect of a covariate for in-edges
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: nodeicovar(center, transform) (valued)
  > test-ergm-term-doc.R:     Covariance of in-dyad values incident on each actor
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: nodeifactor(attr, base=1, levels=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Factor attribute effect for in-edges
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: nodematch(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum") (valued)
  > test-ergm-term-doc.R: match(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum") (valued)
  > test-ergm-term-doc.R:     Uniform homophily and differential homophily
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Nodal attribute mixing
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: nodeocov(attr, form="sum") (valued)
  > test-ergm-term-doc.R:     Main effect of a covariate for out-edges
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: nodeocovar(center, transform) (valued)
  > test-ergm-term-doc.R:     Covariance of out-dyad values incident on each actor
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: nodeofactor(attr, base=1, levels=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Factor attribute effect for out-edges
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: receiver(base=1, nodes=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Receiver effect
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: sender(base=1, nodes=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Sender effect
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: smallerthan(threshold=0) (valued)
  > test-ergm-term-doc.R:     Number of dyads with values strictly smaller than a threshold
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: sociality(attr=NULL, base=1, levels=NULL, nodes=-1, form="sum") (valued)
  > test-ergm-term-doc.R:     Undirected degree
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: sum(pow=1) (valued)
  > test-ergm-term-doc.R:     Sum of dyad values (optionally taken to a power)
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: transitiveweights(twopath="min", combine="max", affect="min") (valued)
  > test-ergm-term-doc.R:     Transitive weights
  > test-ergm-term-doc.R: Found  4  matching ergm terms:
  > test-ergm-term-doc.R: Bernoulli
  > test-ergm-term-doc.R:     Bernoulli reference
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: DiscUnif(a,b)
  > test-ergm-term-doc.R:     Discrete Uniform reference
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: StdNormal
  > test-ergm-term-doc.R:     Standard Normal reference
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: Unif(a,b)
  > test-ergm-term-doc.R:     Continuous Uniform reference
  > test-ergm-term-doc.R: Found  0  matching ergm terms:
  > test-ergm-term-doc.R: Found  1  matching ergm terms:
  > test-ergm-term-doc.R: Bernoulli
  > test-ergm-term-doc.R:     Bernoulli reference
  > test-ergm-term-doc.R: Definitions for term(s)  Bernoulli :
  > test-ergm-term-doc.R: Bernoulli
  > test-ergm-term-doc.R:     Bernoulli reference: Specifies each
  > test-ergm-term-doc.R: dyad's baseline distribution to be Bernoulli with probability of
  > test-ergm-term-doc.R: the tie being 0.5 . This is the only reference measure used
  > test-ergm-term-doc.R: in binary mode.
  > test-ergm-term-doc.R:     Keywords: binary, discrete, finite, nonnegative
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: No terms named 'Cernoulli' were found. Try searching with search='Cernoulli'instead.
  > test-ergm-term-doc.R: Found  9  matching ergm terms:
  > test-ergm-term-doc.R: b1degrees
  > test-ergm-term-doc.R:     Preserve the actor degree for bipartite networks
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: b2degrees
  > test-ergm-term-doc.R:     Preserve the receiver degree for bipartite networks
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: bd(attribs, maxout, maxin, minout, minin)
  > test-ergm-term-doc.R:     Constrain maximum and minimum vertex degree
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: degreedist
  > test-ergm-term-doc.R:     Preserve the degree distribution of the given network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: degrees
  > test-ergm-term-doc.R: nodedegrees
  > test-ergm-term-doc.R:     Preserve the degree of each vertex of the given network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: idegreedist
  > test-ergm-term-doc.R:     Preserve the indegree distribution
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: idegrees
  > test-ergm-term-doc.R:     Preserve indegree for directed networks
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: odegreedist
  > test-ergm-term-doc.R:     Preserve the outdegree distribution
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: odegrees
  > test-ergm-term-doc.R:     Preserve outdegree for directed networks
  > test-ergm-term-doc.R: Found  0  matching ergm terms:
  > test-ergm-term-doc.R: Found  16  matching ergm terms:
  > test-ergm-term-doc.R: Dyads(fix=NULL, vary=NULL)
  > test-ergm-term-doc.R:     Constrain fixed or varying dyad-independent terms
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: bd(attribs, maxout, maxin, minout, minin)
  > test-ergm-term-doc.R:     Constrain maximum and minimum vertex degree
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: blockdiag(attr)
  > test-ergm-term-doc.R:     Block-diagonal structure constraint
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: blocks(attr=NULL, levels=NULL, levels2=FALSE, b1levels=NULL, b2levels=NULL)
  > test-ergm-term-doc.R:     Constrain blocks of dyads defined by mixing type on a vertex attribute.
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: degreedist
  > test-ergm-term-doc.R:     Preserve the degree distribution of the given network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: degrees
  > test-ergm-term-doc.R: nodedegrees
  > test-ergm-term-doc.R:     Preserve the degree of each vertex of the given network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: dyadnoise(p01, p10)
  > test-ergm-term-doc.R:     A soft constraint to adjust the sampled distribution for
  > test-ergm-term-doc.R: dyad-level noise with known perturbation probabilities
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: egocentric(attr=NULL, direction="both")
  > test-ergm-term-doc.R:     Preserve values of dyads incident on vertices with given attribute
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: fixallbut(free.dyads)
  > test-ergm-term-doc.R:     Preserve the dyad status in all but the given edges
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: fixedas(fixed.dyads, present, absent)
  > test-ergm-term-doc.R:     Fix specific dyads
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: hamming
  > test-ergm-term-doc.R:     Preserve the hamming distance to the given network (BROKEN: Do NOT Use)
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: idegreedist
  > test-ergm-term-doc.R:     Preserve the indegree distribution
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: idegrees
  > test-ergm-term-doc.R:     Preserve indegree for directed networks
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: observed
  > test-ergm-term-doc.R:     Preserve the observed dyads of the given network
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: odegreedist
  > test-ergm-term-doc.R:     Preserve the outdegree distribution
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: odegrees
  > test-ergm-term-doc.R:     Preserve outdegree for directed networks
  > test-ergm-term-doc.R: Definitions for term(s)  b1degrees :
  > test-ergm-term-doc.R: b1degrees
  > test-ergm-term-doc.R:     Preserve the actor degree for bipartite networks: For bipartite networks, preserve the degree for the first mode of each vertex of the given
  > test-ergm-term-doc.R: network, while allowing the degree for the second mode to vary.
  > test-ergm-term-doc.R:     Keywords: bipartite
  > test-ergm-term-doc.R: 
  > test-ergm.bridge.llr.R: 5 
  > test-ergm-term-doc.R: No terms named 'b3degrees' were found. Try searching with search='b3degrees'instead.
  > test-ergm-term-doc.R: Found  2  matching ergm proposals:
  > test-ergm-term-doc.R: CondB1Degree
  > test-ergm-term-doc.R:     MHp for b1degree constraints
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: CondB2Degree
  > test-ergm-term-doc.R:     MHp for b2degree constraints
  > test-ergm-term-doc.R: Found  5  matching ergm proposals:
  > test-ergm-term-doc.R: ConstantEdges
  > test-ergm-term-doc.R:     MHp for edges constraints
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: DistRLE
  > test-ergm-term-doc.R:     TODO
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: SPDyad
  > test-ergm-term-doc.R:     A proposal alternating between TNT and a triad-focused
  > test-ergm-term-doc.R: proposal
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: TNT
  > test-ergm-term-doc.R:     Default MH algorithm
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: randomtoggle
  > test-ergm-term-doc.R:     Propose a randomly selected dyad to toggle
  > test-ergm-term-doc.R: Found  0  matching ergm proposals:
  > test-ergm-term-doc.R: Found  18  matching ergm proposals:
  > test-ergm-term-doc.R: BDStratTNT
  > test-ergm-term-doc.R:     TNT proposal with degree bounds, stratification, and a blocks constraint
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: CondB1Degree
  > test-ergm-term-doc.R:     MHp for b1degree constraints
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: CondB2Degree
  > test-ergm-term-doc.R:     MHp for b2degree constraints
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: CondDegree
  > test-ergm-term-doc.R:     MHp for degree constraints
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: CondDegreeDist
  > test-ergm-term-doc.R:     MHp for degreedist constraints
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: CondDegreeMix
  > test-ergm-term-doc.R:     MHp for degree mix constraints
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: CondInDegree
  > test-ergm-term-doc.R:     MHp for idegree constraints
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: CondInDegreeDist
  > test-ergm-term-doc.R:     MHp for idegreedist constraints
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: CondOutDegree
  > test-ergm-term-doc.R:     MHp for odegree constraints
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: CondOutDegreeDist
  > test-ergm-term-doc.R:     MHp for odegreedist constraints
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: ConstantEdges
  > test-ergm-term-doc.R:     MHp for edges constraints
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: HammingConstantEdges
  > test-ergm-term-doc.R:     TODO
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: HammingTNT
  > test-ergm-term-doc.R:     TODO
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: SPDyad
  > test-ergm-term-doc.R:     A proposal alternating between TNT and a triad-focused
  > test-ergm-term-doc.R: proposal
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: TNT
  > test-ergm-term-doc.R:     Default MH algorithm
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: dyadnoise
  > test-ergm-term-doc.R:     TODO
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: dyadnoiseTNT
  > test-ergm-term-doc.R:     TODO
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: randomtoggle
  > test-ergm-term-doc.R:     Propose a randomly selected dyad to toggle
  > test-ergm-term-doc.R: Definitions for proposal(s)  randomtoggle :
  > test-ergm-term-doc.R: randomtoggle
  > test-ergm-term-doc.R:     Propose a randomly selected dyad to toggle: Propose a randomly selected dyad to toggle
  > test-ergm-term-doc.R:     Reference: Bernoulli Class: cross-sectional
  > test-ergm-term-doc.R:     May Enforce: .dyads bd 
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: No proposals named 'mandomtoggle' were found. Try searching with search='mandomtoggle'instead.
  > test-ergm.bridge.llr.R: 6 
  > test-ergm-term-doc.R: 
  > test-ergm-term-doc.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
  > test-ergm-term-doc.R: * 'news(package="ergm.count")' for changes since last version
  > test-ergm-term-doc.R: * 'citation("ergm.count")' for citation information
  > test-ergm-term-doc.R: * 'https://statnet.org' for help, support, and other information
  > test-ergm-term-doc.R: 
  > test-ergm.bridge.llr.R: 7 
  > test-ergm.bridge.llr.R: 8 
  > test-ergm.bridge.llr.R: 9 
  > test-ergm.bridge.llr.R: 10 
  > test-ergmMPLE.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-ergmMPLE.R: Obtaining the responsible dyads.
  > test-ergmMPLE.R: Evaluating the predictor and response matrix.
  > test-ergmMPLE.R: Maximizing the pseudolikelihood.
  > test-ergmMPLE.R: Finished MPLE.
  > test-ergmMPLE.R: Evaluating log-likelihood at the estimate. 
  > test-ergm.bridge.llr.R: 11 
  > test-ergm.bridge.llr.R: 12 
  > test-ergm.bridge.llr.R: 13 
  > test-ergm.bridge.llr.R: 14 
  > test-ergm.bridge.llr.R: 15 
  > test-ergm.bridge.llr.R: 16 
  > test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-gflomiss.R: Obtaining the responsible dyads.
  > test-gflomiss.R: Evaluating the predictor and response matrix.
  > test-gflomiss.R: Maximizing the pseudolikelihood.
  > test-gflomiss.R: Finished MPLE.
  > test-gflomiss.R: Evaluating log-likelihood at the estimate. 
  > test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-gflomiss.R: Obtaining the responsible dyads.
  > test-gflomiss.R: Evaluating the predictor and response matrix.
  > test-gflomiss.R: Maximizing the pseudolikelihood.
  > test-gflomiss.R: Finished MPLE.
  > test-gflomiss.R: Evaluating log-likelihood at the estimate. 
  > test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-gflomiss.R: Obtaining the responsible dyads.
  > test-gflomiss.R: Evaluating the predictor and response matrix.
  > test-gflomiss.R: Maximizing the pseudolikelihood.
  > test-gflomiss.R: Finished MPLE.
  > test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-gflomiss.R: Iteration 1 of at most 60:
  > test-ergm.bridge.llr.R: .
  > test-ergm.bridge.llr.R: Setting up bridge sampling...
  > test-ergm.bridge.llr.R: Using 16 bridges: 1 
  > test-ergm.bridge.llr.R: 2 
  > test-ergm.bridge.llr.R: 3 
  > test-ergm.bridge.llr.R: 4 
  > test-ergm.bridge.llr.R: 5 
  > test-gflomiss.R: 1 
  > test-ergm.bridge.llr.R: 6 
  > test-gflomiss.R: Optimizing with step length 1.0000.
  > test-gflomiss.R: The log-likelihood improved by 0.0001.
  > test-gflomiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-gflomiss.R: Finished MCMLE.
  > test-gflomiss.R: Evaluating log-likelihood at the estimate. 
  > test-ergm.bridge.llr.R: 7 
  > test-gflomiss.R: Fitting the dyad-independent submodel...
  > test-ergm.bridge.llr.R: 8 
  > test-gflomiss.R: Bridging between the dyad-independent submodel and the full model...
  > test-gflomiss.R: Setting up bridge sampling...
  > test-gflomiss.R: Using 16 bridges: 1 
  > test-ergm.bridge.llr.R: 9 
  > test-gflomiss.R: 2 
  > test-gflomiss.R: 3 
  > test-ergm.bridge.llr.R: 10 
  > test-gflomiss.R: 4 
  > test-gflomiss.R: 5 
  > test-gflomiss.R: 6 
  > test-ergm.bridge.llr.R: 11 
  > test-gflomiss.R: 7 8 
  > test-gflomiss.R: 9 
  > test-ergm.bridge.llr.R: 12 
  > test-gflomiss.R: 10 
  > test-gflomiss.R: 11 
  > test-gflomiss.R: 12 
  > test-ergm.bridge.llr.R: 13 
  > test-gflomiss.R: 13 
  > test-gflomiss.R: 14 
  > test-ergm.bridge.llr.R: 14 
  > test-gflomiss.R: 15 16 .
  > test-gflomiss.R: Bridging finished.
  > test-gflomiss.R: 
  > test-gflomiss.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-gflomiss.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-ergm.bridge.llr.R: 15 
  > test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-gflomiss.R: Iteration 1 of at most 60:
  > test-ergm.bridge.llr.R: 16 
  > test-ergm.bridge.llr.R: .
  > test-ergm.bridge.llr.R: Fitting the dyad-independent submodel...
  > test-ergm.bridge.llr.R: Bridging between the dyad-independent submodel and the full model...
  > test-ergm.bridge.llr.R: Setting up bridge sampling...
  > test-ergm.bridge.llr.R: Using 16 bridges: 1 
  > test-gflomiss.R: 1 
  > test-ergm.bridge.llr.R: 2 
  > test-gflomiss.R: Optimizing with step length 1.0000.
  > test-gflomiss.R: The log-likelihood improved by 0.0172.
  > test-gflomiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-gflomiss.R: Finished MCMLE.
  > test-gflomiss.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
  > test-ergm.bridge.llr.R: 3 
  > test-gflomiss.R: Bridging between the dyad-independent submodel and the full model...
  > test-gflomiss.R: Setting up bridge sampling...
  > test-ergm.bridge.llr.R: 4 
  > test-gflomiss.R: Using 16 bridges: 
  > test-gflomiss.R: 1 
  > test-ergm.bridge.llr.R: 5 
  > test-gflomiss.R: 2 
  > test-gflomiss.R: 3 
  > test-gflomiss.R: 4 
  > test-ergm.bridge.llr.R: 6 
  > test-gflomiss.R: 5 6 
  > test-gflomiss.R: 7 
  > test-ergm.bridge.llr.R: 7 8 
  > test-gflomiss.R: 8 9 10 11 12 
  > test-gflomiss.R: 13 
  > test-ergm.bridge.llr.R: 9 
  > test-gflomiss.R: 14 15 
  > test-gflomiss.R: 16 
  > test-gflomiss.R: .
  > test-gflomiss.R: Bridging finished.
  > test-gflomiss.R: 
  > test-gflomiss.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-gflomiss.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-ergm.bridge.llr.R: 10 
  > test-ergm.bridge.llr.R: 11 
  > test-gmonkmiss.R: odegree3 odegree4 odegree5 odegree6 
  > test-gmonkmiss.R:        1        5        7        5 
  > test-gmonkmiss.R:  idegree2  idegree3  idegree4  idegree5  idegree6  idegree7  idegree8 idegree10 
  > test-gmonkmiss.R:         3         5         1         3         2         1         1         1 
  > test-gmonkmiss.R: idegree11 
  > test-gmonkmiss.R:         1 
  > test-ergm.bridge.llr.R: 12 
  > test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-gmonkmiss.R: Obtaining the responsible dyads.
  > test-gmonkmiss.R: Evaluating the predictor and response matrix.
  > test-gmonkmiss.R: Maximizing the pseudolikelihood.
  > test-ergm.bridge.llr.R: 13 
  > test-gmonkmiss.R: Finished MPLE.
  > test-ergm.bridge.llr.R: 14 
  > test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-gmonkmiss.R: Obtaining the responsible dyads.
  > test-gmonkmiss.R: Evaluating the predictor and response matrix.
  > test-gmonkmiss.R: Maximizing the pseudolikelihood.
  > test-gmonkmiss.R: Finished MPLE.
  > test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-gmonkmiss.R: Iteration 1 of at most 3:
  > test-ergm.bridge.llr.R: 15 
  > test-ergm.bridge.llr.R: 16 
  > test-ergm.bridge.llr.R: .
  > test-ergm.bridge.llr.R: Bridging finished.
  > test-ergm.bridge.llr.R: Setting up bridge sampling...
  > test-ergm.bridge.llr.R: Using 16 bridges: 1 
  > test-ergm.bridge.llr.R: 2 
  > test-gmonkmiss.R: 1 Optimizing with step length 1.0000.
  > test-gmonkmiss.R: The log-likelihood improved by 0.6245.
  > test-gmonkmiss.R: Estimating equations are not within tolerance region.
  > test-gmonkmiss.R: Iteration 2 of at most 3:
  > test-ergm.bridge.llr.R: 3 
  > test-ergm.bridge.llr.R: 4 
  > test-gmonkmiss.R: 1 Optimizing with step length 1.0000.
  > test-gmonkmiss.R: The log-likelihood improved by 0.0078.
  > test-ergm.bridge.llr.R: 5 
  > test-gmonkmiss.R: Convergence test p-value: 0.0005. Converged with 99% confidence.
  > test-gmonkmiss.R: Finished MCMLE.
  > test-gmonkmiss.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-gmonkmiss.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-gmonkmiss.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
  > test-gmonkmiss.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-gmonkmiss.R: Iteration 1 of at most 60:
  > test-gmonkmiss.R: Convergence test P-value:3.3e-34
  > test-gmonkmiss.R: 1 
  > test-gmonkmiss.R: The log-likelihood improved by 0.4205.
  > test-gmonkmiss.R: Iteration 2 of at most 60:
  > test-gmonkmiss.R: Convergence test P-value:1.8e-14
  > test-gmonkmiss.R: 1 
  > test-gmonkmiss.R: The log-likelihood improved by 0.1501.
  > test-gmonkmiss.R: Iteration 3 of at most 60:
  > test-ergm.bridge.llr.R: 6 
  > test-gmonkmiss.R: Convergence test P-value:2e-04
  > test-gmonkmiss.R: 1 
  > test-gmonkmiss.R: The log-likelihood improved by 0.03536.
  > test-gmonkmiss.R: Iteration 4 of at most 60:
  > test-gmonkmiss.R: Convergence test P-value:1.6e-01
  > test-gmonkmiss.R: 1 The log-likelihood improved by 0.007343.
  > test-gmonkmiss.R: Iteration 5 of at most 60:
  > test-gmonkmiss.R: Convergence test P-value:2.4e-01
  > test-gmonkmiss.R: 1 
  > test-gmonkmiss.R: The log-likelihood improved by 0.00569.
  > test-gmonkmiss.R: Iteration 6 of at most 60:
  > test-gmonkmiss.R: Convergence test P-value:9.9e-02
  > test-gmonkmiss.R: 1 The log-likelihood improved by 0.00904.
  > test-gmonkmiss.R: Iteration 7 of at most 60:
  > test-gmonkmiss.R: Convergence test P-value:7.6e-01
  > test-gmonkmiss.R: Convergence detected. Stopping.
  > test-gmonkmiss.R: 1 The log-likelihood improved by 0.001102.
  > test-gmonkmiss.R: Finished CD.
  > test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-gmonkmiss.R: Iteration 1 of at most 3:
  > test-ergm.bridge.llr.R: 7 
  > test-ergm.bridge.llr.R: 8 
  > test-ergm.bridge.llr.R: 9 
  > test-ergm.bridge.llr.R: 10 
  > test-gmonkmiss.R: 1 
  > test-gmonkmiss.R: Optimizing with step length 1.0000.
  > test-gmonkmiss.R: The log-likelihood improved by 0.4514.
  > test-gmonkmiss.R: Estimating equations are not within tolerance region.
  > test-gmonkmiss.R: Iteration 2 of at most 3:
  > test-ergm.bridge.llr.R: 11 
  > test-ergm.bridge.llr.R: 12 
  > test-ergm.bridge.llr.R: 13 
  > test-ergm.bridge.llr.R: 14 
  > test-ergm.bridge.llr.R: 15 
  > test-gmonkmiss.R: 1 
  > test-ergm.bridge.llr.R: 16 
  > test-gmonkmiss.R: Optimizing with step length 1.0000.
  > test-gmonkmiss.R: The log-likelihood improved by 0.0105.
  > test-gmonkmiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-gmonkmiss.R: Finished MCMLE.
  > test-gmonkmiss.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-ergm.bridge.llr.R: .
  > test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-gmonkmiss.R: Obtaining the responsible dyads.
  > test-gmonkmiss.R: Evaluating the predictor and response matrix.
  > test-gmonkmiss.R: Maximizing the pseudolikelihood.
  > test-ergm.bridge.llr.R: Setting up bridge sampling...
  > test-gmonkmiss.R: Finished MPLE.
  > test-ergm.bridge.llr.R: Using 16 bridges: 1 
  > test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-gmonkmiss.R: Obtaining the responsible dyads.
  > test-gmonkmiss.R: Evaluating the predictor and response matrix.
  > test-gmonkmiss.R: Maximizing the pseudolikelihood.
  > test-gmonkmiss.R: Finished MPLE.
  > test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-gmonkmiss.R: Iteration 1 of at most 3:
  > test-ergm.bridge.llr.R: 2 
  > test-ergm.bridge.llr.R: 3 
  > test-ergm.bridge.llr.R: 4 
  > test-gmonkmiss.R: 1 
  > test-ergm.bridge.llr.R: 5 
  > test-gmonkmiss.R: Optimizing with step length 1.0000.
  > test-gmonkmiss.R: The log-likelihood improved by 0.7035.
  > test-gmonkmiss.R: Estimating equations are not within tolerance region.
  > test-gmonkmiss.R: Iteration 2 of at most 3:
  > test-ergm.bridge.llr.R: 6 
  > test-ergm.bridge.llr.R: 7 
  > test-gmonkmiss.R: 1 Optimizing with step length 1.0000.
  > test-gmonkmiss.R: The log-likelihood improved by 0.0078.
  > test-gmonkmiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-gmonkmiss.R: Finished MCMLE.
  > test-gmonkmiss.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-ergm.bridge.llr.R: 8 
  > test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-gof.R: Obtaining the responsible dyads.
  > test-gof.R: Evaluating the predictor and response matrix.
  > test-gof.R: Maximizing the pseudolikelihood.
  > test-gof.R: Finished MPLE.
  > test-gof.R: Evaluating log-likelihood at the estimate. 
  > test-ergm.bridge.llr.R: 9 
  > test-ergm.bridge.llr.R: 10 
  > test-ergm.bridge.llr.R: 11 
  > test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-gof.R: Obtaining the responsible dyads.
  > test-gof.R: Evaluating the predictor and response matrix.
  > test-gof.R: Maximizing the pseudolikelihood.
  > test-gof.R: Finished MPLE.
  > test-gof.R: Evaluating log-likelihood at the estimate. 
  > test-gof.R: 
  > test-ergm.bridge.llr.R: 12 
  > test-ergm.bridge.llr.R: 13 
  > test-gof.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-gof.R: Obtaining the responsible dyads.
  > test-gof.R: Evaluating the predictor and response matrix.
  > test-gof.R: Maximizing the pseudolikelihood.
  > test-gof.R: Finished MPLE.
  > test-gof.R: Evaluating log-likelihood at the estimate. 
  > test-ergm.bridge.llr.R: 14 
  > test-ergm.bridge.llr.R: 15 
  > test-ergm.bridge.llr.R: 16 
  > test-ergm.bridge.llr.R: .
  > test-ergm.bridge.llr.R: Fitting the dyad-independent submodel...
  > test-ergm.bridge.llr.R: Bridging between the dyad-independent submodel and the full model...
  > test-ergm.bridge.llr.R: Setting up bridge sampling...
  > test-ergm.bridge.llr.R: Using 16 bridges: 1 
  > test-ergm.bridge.llr.R: 2 
  > test-ergm.bridge.llr.R: 3 
  > test-ergm.bridge.llr.R: 4 
  > test-ergm.bridge.llr.R: 5 
  > test-ergm.bridge.llr.R: 6 
  > test-ergm.bridge.llr.R: 7 
  > test-ergm.bridge.llr.R: 8 
  > test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-metrics.R: Iteration 1 of at most 60:
  > test-ergm.bridge.llr.R: 9 
  > test-ergm.bridge.llr.R: 10 
  > test-ergm.bridge.llr.R: 11 
  > test-metrics.R: 1 
  > test-metrics.R: Optimizing with step length 0.4613.
  > test-metrics.R: The log-likelihood improved by 4.1429.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 2 of at most 60:
  > test-ergm.bridge.llr.R: 12 
  > test-ergm.bridge.llr.R: 13 
  > test-metrics.R: 1 Optimizing with step length 0.8364.
  > test-metrics.R: The log-likelihood improved by 4.7215.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 3 of at most 60:
  > test-ergm.bridge.llr.R: 14 
  > test-metrics.R: 1 Optimizing with step length 1.0000.
  > test-metrics.R: The log-likelihood improved by 1.1346.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 4 of at most 60:
  > test-ergm.bridge.llr.R: 15 
  > test-metrics.R: 1 Optimizing with step length 1.0000.
  > test-metrics.R: The log-likelihood improved by 0.1129.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 5 of at most 60:
  > test-ergm.bridge.llr.R: 16 
  > test-metrics.R: 1 Optimizing with step length 1.0000.
  > test-metrics.R: The log-likelihood improved by 0.0037.
  > test-metrics.R: Convergence test p-value: 0.0004. Converged with 99% confidence.
  > test-metrics.R: Finished MCMLE.
  > test-metrics.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-ergm.bridge.llr.R: .
  > test-ergm.bridge.llr.R: Bridging finished.
  > test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-metrics.R: Iteration 1 of at most 60:
  > test-miss-dep.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.
  > test-miss-dep.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints.
  > test-miss-dep.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-miss-dep.R: Iteration 1 of at most 60:
  > test-miss-dep.R: Convergence test P-value:2.2e-47
  > test-miss-dep.R: 1 
  > test-miss-dep.R: The log-likelihood improved by 1.504.
  > test-miss-dep.R: Iteration 2 of at most 60:
  > test-metrics.R: 1 
  > test-metrics.R: Optimizing with step length 0.4018.
  > test-metrics.R: The log-likelihood improved by 3.2780.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 2 of at most 60:
  > test-miss-dep.R: Convergence test P-value:2.1e-22
  > test-miss-dep.R: 1 
  > test-miss-dep.R: The log-likelihood improved by 0.5752.
  > test-miss-dep.R: Iteration 3 of at most 60:
  > test-miss-dep.R: Convergence test P-value:4.7e-04
  > test-miss-dep.R: 1 
  > test-miss-dep.R: The log-likelihood improved by 0.0516.
  > test-miss-dep.R: Iteration 4 of at most 60:
  > test-metrics.R: 1 Optimizing with step length 0.6020.
  > test-metrics.R: The log-likelihood improved by 3.4584.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 3 of at most 60:
  > test-miss-dep.R: Convergence test P-value:2.1e-03
  > test-miss-dep.R: 1 The log-likelihood improved by 0.03965.
  > test-miss-dep.R: Iteration 5 of at most 60:
  > test-miss-dep.R: Convergence test P-value:2.9e-01
  > test-metrics.R: 1 Optimizing with step length 1.0000.
  > test-metrics.R: The log-likelihood improved by 2.2132.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 4 of at most 60:
  > test-miss-dep.R: 1 The log-likelihood improved by 0.004546.
  > test-miss-dep.R: Iteration 6 of at most 60:
  > test-miss-dep.R: Convergence test P-value:7.4e-04
  > test-miss-dep.R: 1 
  > test-miss-dep.R: The log-likelihood improved by 0.0461.
  > test-miss-dep.R: Iteration 7 of at most 60:
  > test-miss-dep.R: Convergence test P-value:2e-02
  > test-miss-dep.R: 1 
  > test-miss-dep.R: The log-likelihood improved by 0.02163.
  > test-miss-dep.R: Iteration 8 of at most 60:
  > test-metrics.R: 1 
  > test-metrics.R: Optimizing with step length 1.0000.
  > test-metrics.R: The log-likelihood improved by 0.0377.
  > test-miss-dep.R: Convergence test P-value:1.4e-01
  > test-miss-dep.R: 1 The log-likelihood improved by 0.008556.
  > test-miss-dep.R: Iteration 9 of at most 60:
  > test-metrics.R: Convergence test p-value: < 0.0001. 
  > test-metrics.R: Converged with 99% confidence.
  > test-metrics.R: Finished MCMLE.
  > test-metrics.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-miss-dep.R: Convergence test P-value:9.1e-01
  > test-miss-dep.R: Convergence detected. Stopping.
  > test-miss-dep.R: 1 The log-likelihood improved by < 0.0001.
  > test-miss-dep.R: Finished CD.
  > test-miss-dep.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-miss-dep.R: Iteration 1 of at most 60:
  > test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-metrics.R: Iteration 1 of at most 60:
  > test-metrics.R: 1 Optimizing with step length 0.4158.
  > test-metrics.R: The log-likelihood improved by 1.9115.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 2 of at most 60:
  > test-miss-dep.R: Post-burnin sample is constant; returning.
  > test-miss-dep.R: 1 Optimizing with step length 1.0000.
  > test-miss-dep.R: The log-likelihood improved by 0.0082.
  > test-miss-dep.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-miss-dep.R: Finished MCMLE.
  > test-miss-dep.R: Evaluating log-likelihood at the estimate. 
  > test-miss-dep.R: Setting up bridge sampling...
  > test-metrics.R: 1 Optimizing with step length 0.4804.
  > test-metrics.R: The log-likelihood improved by 2.5519.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 3 of at most 60:
  > test-miss-dep.R: Using 16 bridges: 1 2 
  > test-miss-dep.R: 3 
  > test-metrics.R: 1 Optimizing with step length 1.0000.
  > test-miss-dep.R: 4 5 
  > test-metrics.R: The log-likelihood improved by 2.9415.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 4 of at most 60:
  > test-miss-dep.R: 6 
  > test-miss-dep.R: 7 8 9 10 
  > test-miss-dep.R: 11 
  > test-miss-dep.R: 12 13 14 
  > test-miss-dep.R: 15 
  > test-metrics.R: 1 Optimizing with step length 1.0000.
  > test-metrics.R: The log-likelihood improved by 0.1226.
  > test-miss-dep.R: 16 .
  > test-miss-dep.R: Note: The constraint on the sample space is not dyad-independent. Null
  > test-miss-dep.R: model likelihood is only implemented for dyad-independent constraints
  > test-miss-dep.R: at this time. Number of observations is similarly poorly defined.  This
  > test-miss-dep.R: means that all likelihood-based inference (LRT, Analysis of Deviance,
  > test-miss-dep.R: AIC, BIC, etc.) is only valid between models with the same reference
  > test-miss-dep.R: distribution and constraints.
  > test-miss-dep.R: 
  > test-miss-dep.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-miss-dep.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-metrics.R: Finished MCMLE.
  > test-metrics.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-miss.CD.R: n=20, density=0.1, missing=0.1
  > test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-metrics.R: Iteration 1 of at most 60:
  > test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-miss.CD.R: Iteration 1 of at most 60:
  > test-miss.CD.R: Convergence test P-value:3e-13
  > test-miss.CD.R: 1 The log-likelihood improved by 0.2096.
  > test-miss.CD.R: Iteration 2 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1.4e-12
  > test-miss.CD.R: 1 The log-likelihood improved by 0.1974.
  > test-miss.CD.R: Iteration 3 of at most 60:
  > test-miss.CD.R: Convergence test P-value:5.8e-11
  > test-miss.CD.R: 1 The log-likelihood improved by 0.341.
  > test-miss.CD.R: Iteration 4 of at most 60:
  > test-miss.CD.R: Convergence test P-value:4e-15
  > test-miss.CD.R: 1 The log-likelihood improved by 0.1744.
  > test-miss.CD.R: Iteration 5 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1.3e-16
  > test-miss.CD.R: 1 
  > test-miss.CD.R: The log-likelihood improved by 0.6862.
  > test-miss.CD.R: Iteration 6 of at most 60:
  > test-miss.CD.R: Convergence test P-value:8.3e-15
  > test-miss.CD.R: 1 
  > test-miss.CD.R: The log-likelihood improved by 0.3025.
  > test-miss.CD.R: Iteration 7 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1.5e-19
  > test-miss.CD.R: 1 The log-likelihood improved by 0.1888.
  > test-miss.CD.R: Iteration 8 of at most 60:
  > test-miss.CD.R: Convergence test P-value:2.3e-17
  > test-miss.CD.R: 1 The log-likelihood improved by 0.2862.
  > test-miss.CD.R: Iteration 9 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1.6e-07
  > test-miss.CD.R: 1 The log-likelihood improved by 0.2007.
  > test-miss.CD.R: Iteration 10 of at most 60:
  > test-miss.CD.R: Convergence test P-value:2.1e-02
  > test-miss.CD.R: 1 
  > test-metrics.R: 1 2 3 4 5 6 7 8 9 10 11 Optimizing with step length 0.3934.
  > test-miss.CD.R: The log-likelihood improved by 0.03854.
  > test-miss.CD.R: Iteration 11 of at most 60:
  > test-metrics.R: The log-likelihood improved by 4.1457.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 2 of at most 60:
  > test-miss.CD.R: Convergence test P-value:7.9e-05
  > test-miss.CD.R: 1 The log-likelihood improved by 0.1259.
  > test-miss.CD.R: Iteration 12 of at most 60:
  > test-miss.CD.R: Convergence test P-value:7.9e-01
  > test-miss.CD.R: Convergence detected. Stopping.
  > test-miss.CD.R: 1 The log-likelihood improved by 0.0004877.
  > test-miss.CD.R: Finished CD.
  > test-miss.CD.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-metrics.R: 1 
  > test-metrics.R: Optimizing with step length 1.0000.
  > test-metrics.R: The log-likelihood improved by 2.8651.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 3 of at most 60:
  > test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-miss.CD.R: Iteration 1 of at most 60:
  > test-miss.CD.R: Convergence test P-value:9.5e-68
  > test-miss.CD.R: 1 
  > test-miss.CD.R: The log-likelihood improved by 0.7099.
  > test-miss.CD.R: Iteration 2 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1.6e-64
  > test-miss.CD.R: 1 The log-likelihood improved by 0.5925.
  > test-miss.CD.R: Iteration 3 of at most 60:
  > test-miss.CD.R: Convergence test P-value:9.6e-53
  > test-miss.CD.R: 1 The log-likelihood improved by 0.6327.
  > test-miss.CD.R: Iteration 4 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1.1e-37
  > test-miss.CD.R: 1 
  > test-miss.CD.R: The log-likelihood improved by 0.8025.
  > test-miss.CD.R: Iteration 5 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1.4e-30
  > test-miss.CD.R: 1 
  > test-miss.CD.R: The log-likelihood improved by 0.583.
  > test-miss.CD.R: Iteration 6 of at most 60:
  > test-metrics.R: 1 
  > test-metrics.R: Optimizing with step length 1.0000.
  > test-metrics.R: The log-likelihood improved by 0.3360.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 4 of at most 60:
  > test-miss.CD.R: Convergence test P-value:8.6e-19
  > test-miss.CD.R: 1 The log-likelihood improved by 0.6591.
  > test-miss.CD.R: Iteration 7 of at most 60:
  > test-miss.CD.R: Convergence test P-value:6.2e-04
  > test-miss.CD.R: 1 The log-likelihood improved by 0.05663.
  > test-miss.CD.R: Iteration 8 of at most 60:
  > test-miss.CD.R: Convergence test P-value:2.4e-01
  > test-miss.CD.R: 1 The log-likelihood improved by 0.007268.
  > test-miss.CD.R: Iteration 9 of at most 60:
  > test-miss.CD.R: Convergence test P-value:9.1e-01
  > test-miss.CD.R: Convergence detected. Stopping.
  > test-miss.CD.R: 1 The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Finished CD.
  > test-miss.CD.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-metrics.R: 1 Optimizing with step length 1.0000.
  > test-metrics.R: The log-likelihood improved by 0.0827.
  > test-metrics.R: Convergence test p-value: 0.0004. Converged with 99% confidence.
  > test-metrics.R: Finished MCMLE.
  > test-metrics.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-miss.CD.R: Iteration 1 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1.3e-54
  > test-miss.CD.R: 1 The log-likelihood improved by 0.5854.
  > test-miss.CD.R: Iteration 2 of at most 60:
  > test-miss.CD.R: Convergence test P-value:4.4e-52
  > test-miss.CD.R: 1 The log-likelihood improved by 0.6164.
  > test-miss.CD.R: Iteration 3 of at most 60:
  > test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-metrics.R: Iteration 1 of at most 60:
  > test-miss.CD.R: Convergence test P-value:4.5e-46
  > test-miss.CD.R: 1 The log-likelihood improved by 0.6486.
  > test-miss.CD.R: Iteration 4 of at most 60:
  > test-miss.CD.R: Convergence test P-value:4.6e-32
  > test-miss.CD.R: 1 The log-likelihood improved by 1.361.
  > test-miss.CD.R: Iteration 5 of at most 60:
  > test-miss.CD.R: Convergence test P-value:6.8e-14
  > test-miss.CD.R: 1 
  > test-miss.CD.R: The log-likelihood improved by 0.379.
  > test-miss.CD.R: Iteration 6 of at most 60:
  > test-miss.CD.R: Convergence test P-value:4.6e-03
  > test-miss.CD.R: 1 
  > test-miss.CD.R: The log-likelihood improved by 0.05118.
  > test-miss.CD.R: Iteration 7 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1.9e-02
  > test-miss.CD.R: 1 The log-likelihood improved by 0.03478.
  > test-miss.CD.R: Iteration 8 of at most 60:
  > test-miss.CD.R: Convergence test P-value:6.5e-01
  > test-miss.CD.R: Convergence detected. Stopping.
  > test-miss.CD.R: 1 The log-likelihood improved by 0.001186.
  > test-miss.CD.R: Finished CD.
  > test-miss.CD.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-miss.CD.R: Network statistics:
  > test-miss.CD.R:  edges  esp#1  esp#2  esp#3  esp#4  esp#5  esp#6  esp#7  esp#8  esp#9 esp#10 
  > test-miss.CD.R:     50     24      3      0      0      0      0      0      0      0      0 
  > test-miss.CD.R: esp#11 esp#12 esp#13 esp#14 esp#15 esp#16 esp#17 esp#18 esp#19 esp#20 esp#21 
  > test-miss.CD.R:      0      0      0      0      0      0      0      0      0      0      0 
  > test-miss.CD.R: esp#22 esp#23 esp#24 esp#25 esp#26 esp#27 esp#28 
  > test-miss.CD.R:      0      0      0      0      0      0      0 
  > test-miss.CD.R: Correct estimate = -2.028148 
  > test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-miss.CD.R: Iteration 1 of at most 60:
  > test-metrics.R: 1 2 3 
  > test-metrics.R: 4 5 6 7 8 9 10 11 Optimizing with step length 0.3701.
  > test-metrics.R: The log-likelihood improved by 2.3554.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 2 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1.8e-283
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by 1.712.
  > test-miss.CD.R: Iteration 2 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1.8e-235
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by 1.493.
  > test-miss.CD.R: Iteration 3 of at most 60:
  > test-miss.CD.R: Convergence test P-value:9.3e-184
  > test-miss.CD.R: 1 
  > test-miss.CD.R: 2 
  > test-metrics.R: 1 2 3 4 5 6 7 8 
  > test-miss.CD.R: The log-likelihood improved by 1.167.
  > test-miss.CD.R: Iteration 4 of at most 60:
  > test-metrics.R: 9 10 
  > test-metrics.R: 11 
  > test-metrics.R: 12 
  > test-metrics.R: Optimizing with step length 0.5218.
  > test-metrics.R: The log-likelihood improved by 2.9696.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 3 of at most 60:
  > test-miss.CD.R: Convergence test P-value:8.9e-150
  > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 5 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1.3e-156
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-metrics.R: 1 
  > test-miss.CD.R: Iteration 6 of at most 60:
  > test-metrics.R: 2 3 Optimizing with step length 0.8048.
  > test-metrics.R: The log-likelihood improved by 1.8226.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 4 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1.5e-147
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 7 of at most 60:
  > test-miss.CD.R: Convergence test P-value:2.2e-155
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 8 of at most 60:
  > test-metrics.R: 1 
  > test-metrics.R: Optimizing with step length 1.0000.
  > test-metrics.R: The log-likelihood improved by 0.2705.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 5 of at most 60:
  > test-miss.CD.R: Convergence test P-value:2.8e-153
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 9 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1e-154
  > test-miss.CD.R: 1 
  > test-miss.CD.R: 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 10 of at most 60:
  > test-metrics.R: 1 Optimizing with step length 1.0000.
  > test-miss.CD.R: Convergence test P-value:2.5e-153
  > test-miss.CD.R: 1 
  > test-miss.CD.R: 2 
  > test-metrics.R: The log-likelihood improved by 0.0012.
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 11 of at most 60:
  > test-metrics.R: Convergence test p-value: 0.0099. Converged with 99% confidence.
  > test-metrics.R: Finished MCMLE.
  > test-metrics.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-miss.CD.R: Convergence test P-value:8e-150
  > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 12 of at most 60:
  > test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-metrics.R: Iteration 1 of at most 60:
  > test-miss.CD.R: Convergence test P-value:6.7e-158
  > test-miss.CD.R: 1 
  > test-miss.CD.R: 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 13 of at most 60:
  > test-miss.CD.R: Convergence test P-value:8.6e-156
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 14 of at most 60:
  > test-miss.CD.R: Convergence test P-value:7.9e-162
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 15 of at most 60:
  > test-metrics.R: 1 2 3 4 5 6 7 8 9 10 11 12 13 Optimizing with step length 0.4397.
  > test-metrics.R: The log-likelihood improved by 3.0119.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 2 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1.3e-158
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 16 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1.1e-159
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 17 of at most 60:
  > test-miss.CD.R: Convergence test P-value:3.3e-159
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 18 of at most 60:
  > test-metrics.R: 1 2 3 
  > test-metrics.R: 4 5 6 7 8 9 10 11 12 Optimizing with step length 0.6225.
  > test-metrics.R: The log-likelihood improved by 3.6934.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 3 of at most 60:
  > test-miss.CD.R: Convergence test P-value:6.6e-157
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 19 of at most 60:
  > test-miss.CD.R: Convergence test P-value:2.5e-163
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 20 of at most 60:
  > test-metrics.R: 1 
  > test-metrics.R: Optimizing with step length 1.0000.
  > test-metrics.R: The log-likelihood improved by 1.0488.
  > test-metrics.R: Estimating equations are not within tolerance region.
  > test-metrics.R: Iteration 4 of at most 60:
  > test-miss.CD.R: Convergence test P-value:3.5e-156
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 21 of at most 60:
  > test-metrics.R: 1 Optimizing with step length 1.0000.
  > test-metrics.R: The log-likelihood improved by 0.0821.
  > test-miss.CD.R: Convergence test P-value:2.3e-153
  > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 22 of at most 60:
  > test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-metrics.R: Finished MCMLE.
  > test-metrics.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-metrics.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-miss.CD.R: Convergence test P-value:1e-157
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 23 of at most 60:
  > test-miss.R: n=20, density=0.1, missing=0.05
  > test-miss.CD.R: Convergence test P-value:1.4e-160
  > test-miss.CD.R: 1 2 
  > test-miss.R: Correct estimate = -2.118156 with log-likelihood -120.6883 .
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 24 of at most 60:
  > test-miss.CD.R: Convergence test P-value:2.1e-158
  > test-miss.CD.R: 1 2 
  > test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-miss.R: Obtaining the responsible dyads.
  > test-miss.R: Evaluating the predictor and response matrix.
  > test-miss.R: Maximizing the pseudolikelihood.
  > test-miss.R: Finished MPLE.
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 25 of at most 60:
  > test-miss.R: MPLE estimate = -2.118156 with log-likelihood -120.6883 OK. 
  > test-miss.R: Evaluating log-likelihood at the estimate. 
  > test-miss.R: Evaluating network in model.
  > test-miss.R: Initializing unconstrained Metropolis-Hastings proposal: 
  > test-miss.R: 'ergm:MH_SPDyad'.
  > test-miss.R: Initializing constrained Metropolis-Hastings proposal: 
  > test-miss.CD.R: Convergence test P-value:1.3e-150
  > test-miss.CD.R: 1 
  > test-miss.CD.R: 2 
  > test-miss.R: 'ergm:MH_SPDyad'.
  > test-miss.R: Initializing model...
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 26 of at most 60:
  > test-miss.R: Model initialized.
  > test-miss.R: Using initial method 'MPLE'.
  > test-miss.R: Initial parameters provided by caller:
  > test-miss.R:     edges 
  > test-miss.R: -1.118156 
  > test-miss.R:   number of free parameters: 1
  > test-miss.R:   number of fixed parameters: 0
  > test-miss.R: Fitting initial model.
  > test-miss.R: Imputing 26 dyads is required.
  > test-miss.R: Imputing 3 edges at random.
  > test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-miss.R: Density guard set to 10000 from an initial count of 41 edges.
  > test-miss.R: 
  > test-miss.R: Iteration 1 of at most 60 with parameter:
  > test-miss.R:     edges 
  > test-miss.R: -1.118156 
  > test-miss.R: Starting unconstrained MCMC...
  > test-miss.CD.R: Convergence test P-value:4.5e-158
  > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 27 of at most 60:
  > test-miss.CD.R: Convergence test P-value:2.8e-154
  > test-miss.CD.R: 1 2 
  > test-miss.R: Back from unconstrained MCMC.
  > test-miss.R: Starting constrained MCMC...
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 28 of at most 60:
  > test-miss.CD.R: Convergence test P-value:8.6e-160
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 29 of at most 60:
  > test-miss.R: Back from constrained MCMC.
  > test-miss.R: New interval = 512.
  > test-miss.R: New constrained interval = 256.
  > test-miss.R: Average estimating function values:
  > test-miss.R:    edges 
  > test-miss.R: 49.45267 
  > test-miss.R: Starting MCMLE Optimization...
  > test-miss.R: 1 2 3 4 5 6 7 8 9 10 11 12 Optimizing with step length 0.4099.
  > test-miss.R: Using lognormal metric (see control.ergm function).
  > test-miss.R: Using log-normal approx with missing (no optim)
  > test-miss.R: The log-likelihood improved by 2.5936.
  > test-miss.R: Estimating equations are not within tolerance region.
  > test-miss.R: 
  > test-miss.R: Iteration 2 of at most 60 with parameter:
  > test-miss.R:     edges 
  > test-miss.R: -1.374066 
  > test-miss.R: Starting unconstrained MCMC...
  > test-miss.CD.R: Convergence test P-value:2.9e-157
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 30 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1.3e-156
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 31 of at most 60:
  > test-miss.R: Back from unconstrained MCMC.
  > test-miss.CD.R: Convergence test P-value:1.9e-155
  > test-miss.CD.R: 1 2 
  > test-miss.R: Starting constrained MCMC...
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 32 of at most 60:
  > test-miss.CD.R: Convergence test P-value:1.5e-156
  > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 33 of at most 60:
  > test-miss.R: Back from constrained MCMC.
  > test-miss.R: New interval = 256.
  > test-miss.R: New constrained interval = 128.
  > test-miss.R: Average estimating function values:
  > test-miss.R:    edges 
  > test-miss.R: 33.36008 
  > test-miss.R: Starting MCMLE Optimization...
  > test-miss.R: 1 2 3 4 5 6 7 8 9 10 11 12 Optimizing with step length 0.4981.
  > test-miss.R: Using lognormal metric (see control.ergm function).
  > test-miss.R: Using log-normal approx with missing (no optim)
  > test-miss.R: The log-likelihood improved by 2.2702.
  > test-miss.R: Distance from origin on tolerance region scale: 192.073 (previously 422.077).
  > test-miss.R: Estimating equations are not within tolerance region.
  > test-miss.R: 
  > test-miss.R: Iteration 3 of at most 60 with parameter:
  > test-miss.R:     edges 
  > test-miss.R: -1.647302 
  > test-miss.R: Starting unconstrained MCMC...
  > test-miss.R: Back from unconstrained MCMC.
  > test-miss.R: Starting constrained MCMC...
  > test-miss.CD.R: Convergence test P-value:1e-153
  > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 34 of at most 60:
  > test-miss.CD.R: Convergence test P-value:2.5e-165
  > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 35 of at most 60:
  > test-miss.R: Back from constrained MCMC.
  > test-miss.R: New interval = 128.
  > test-miss.R: New constrained interval = 128.
  > test-miss.R: Average estimating function values:
  > test-miss.R:    edges 
  > test-miss.R: 17.50766 
  > test-miss.R: Starting MCMLE Optimization...
  > test-miss.R: 1 2 3 4 5 Optimizing with step length 0.7736.
  > test-miss.R: Using lognormal metric (see control.ergm function).
  > test-miss.R: Using log-normal approx with missing (no optim)
  > test-miss.R: The log-likelihood improved by 2.0776.
  > test-miss.R: Distance from origin on tolerance region scale: 71.38353 (previously 259.1767).
  > test-miss.R: Estimating equations are not within tolerance region.
  > test-miss.R: 
  > test-miss.R: Iteration 4 of at most 60 with parameter:
  > test-miss.R:    edges 
  > test-miss.R: -1.95412 
  > test-miss.R: Starting unconstrained MCMC...
  > test-miss.R: Back from unconstrained MCMC.
  > test-miss.R: Starting constrained MCMC...
  > test-miss.CD.R: Convergence test P-value:3.7e-164
  > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 36 of at most 60:
  > test-miss.CD.R: Convergence test P-value:3.5e-165
  > test-miss.CD.R: 1 2 
  > test-miss.R: Back from constrained MCMC.
  > test-miss.R: New interval = 64.
  > test-miss.R: New constrained interval = 64.
  > test-miss.R: Average estimating function values:
  > test-miss.R:    edges 
  > test-miss.R: 5.621399 
  > test-miss.R: Starting MCMLE Optimization...
  > test-miss.R: 1 Optimizing with step length 1.0000.
  > test-miss.R: Using lognormal metric (see control.ergm function).
  > test-miss.R: Using log-normal approx with missing (no optim)
  > test-miss.R: The log-likelihood improved by 0.3258.
  > test-miss.R: Distance from origin on tolerance region scale: 6.488425 (previously 62.9371).
  > test-miss.R: Estimating equations are not within tolerance region.
  > test-miss.R: 
  > test-miss.R: Iteration 5 of at most 60 with parameter:
  > test-miss.R:     edges 
  > test-miss.R: -2.070021 
  > test-miss.R: Starting unconstrained MCMC...
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 37 of at most 60:
  > test-miss.R: Back from unconstrained MCMC.
  > test-miss.R: Starting constrained MCMC...
  > test-miss.CD.R: Convergence test P-value:3.8e-167
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 38 of at most 60:
  > test-miss.R: Back from constrained MCMC.
  > test-miss.R: New interval = 32.
  > test-miss.R: New constrained interval = 32.
  > test-miss.R: Average estimating function values:
  > test-miss.R:    edges 
  > test-miss.R: 1.853909 
  > test-miss.R: Starting MCMLE Optimization...
  > test-miss.R: 1 Optimizing with step length 1.0000.
  > test-miss.R: Using lognormal metric (see control.ergm function).
  > test-miss.R: Using log-normal approx with missing (no optim)
  > test-miss.R: Starting MCMC s.e. computation.
  > test-miss.R: The log-likelihood improved by 0.0589.
  > test-miss.R: Distance from origin on tolerance region scale: 1.17581 (previously 10.81058).
  > test-miss.R: Test statistic: T^2 = 11.54078, with 1 free parameter(s) and 179.1884 degrees of freedom.
  > test-miss.R: Convergence test p-value: 0.0008. Converged with 99% confidence.
  > test-miss.R: Finished MCMLE.
  > test-miss.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
  > test-miss.CD.R: Convergence test P-value:4.3e-166
  > test-miss.CD.R: 1 
  > test-miss.R: Fitting the dyad-independent submodel...
  > test-miss.CD.R: 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 39 of at most 60:
  > test-miss.R: Dyad-independent submodel MLE has likelihood -120.6883 at:
  > test-miss.R: [1] -2.118156  0.000000
  > test-miss.R: Bridging between the dyad-independent submodel and the full model...
  > test-miss.R: Setting up bridge sampling...
  > test-miss.R: Initializing model and proposals...
  > test-miss.CD.R: Convergence test P-value:1.8e-166
  > test-miss.CD.R: 1 2 
  > test-miss.R: Model and proposals initialized.
  > test-miss.R: Initializing constrained model and proposals...
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 40 of at most 60:
  > test-miss.R: Constrained model and proposals initialized.
  > test-miss.R: Using 16 bridges: 
  > test-miss.R: Running theta=[-2.133081, 0.000000].
  > test-miss.CD.R: Convergence test P-value:1.4e-154
  > test-miss.CD.R: 1 2 
  > test-miss.R: Running theta=[-2.132118, 0.000000].
  > test-miss.R: Running theta=[-2.131155, 0.000000].
  > test-miss.R: Running theta=[-2.130192, 0.000000].
  > test-miss.R: Running theta=[-2.129229, 0.000000].
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 41 of at most 60:
  > test-miss.R: Running theta=[-2.128267, 0.000000].
  > test-miss.R: Running theta=[-2.127304, 0.000000].
  > test-miss.R: Running theta=[-2.126341, 0.000000].
  > test-miss.CD.R: Convergence test P-value:1.3e-139
  > test-miss.CD.R: 1 2 
  > test-miss.R: Running theta=[-2.125378, 0.000000].
  > test-miss.R: Running theta=[-2.124415, 0.000000].
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 42 of at most 60:
  > test-miss.R: Running theta=[-2.123452, 0.000000].
  > test-miss.R: Running theta=[-2.122489, 0.000000].
  > test-miss.R: Running theta=[-2.121526, 0.000000].
  > test-miss.R: Running theta=[-2.120563, 0.000000].
  > test-miss.R: Running theta=[-2.1196, 0.0000].
  > test-miss.R: Running theta=[-2.118637, 0.000000].
  > test-miss.CD.R: Convergence test P-value:7.6e-160
  > test-miss.CD.R: 1 2 
  > test-miss.R: .
  > test-miss.R: Bridge sampling finished. Collating...
  > test-miss.R: Estimated standard error (0.009575496) above target (0.005). Drawing additional samples.
  > test-miss.R: Running theta=[-2.119005, 0.000000].
  > test-miss.R: Running theta=[-2.119968, 0.000000].
  > test-miss.R: Running theta=[-2.120931, 0.000000].
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 43 of at most 60:
  > test-miss.R: Running theta=[-2.121894, 0.000000].
  > test-miss.R: Running theta=[-2.122857, 0.000000].
  > test-miss.R: Running theta=[-2.12382, 0.00000].
  > test-miss.R: Running theta=[-2.124783, 0.000000].
  > test-miss.R: Running theta=[-2.125746, 0.000000].
  > test-miss.CD.R: Convergence test P-value:3e-152
  > test-miss.CD.R: 1 2 
  > test-miss.R: Running theta=[-2.126709, 0.000000].
  > test-miss.R: Running theta=[-2.127671, 0.000000].
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 44 of at most 60:
  > test-miss.R: Running theta=[-2.128634, 0.000000].
  > test-miss.R: Running theta=[-2.129597, 0.000000].
  > test-miss.R: Running theta=[-2.13056, 0.00000].
  > test-miss.R: Running theta=[-2.131523, 0.000000].
  > test-miss.R: Running theta=[-2.132486, 0.000000].
  > test-miss.CD.R: Convergence test P-value:3.1e-154
  > test-miss.CD.R: 1 
  > test-miss.R: Running theta=[-2.133449, 0.000000].
  > test-miss.CD.R: 2 
  > test-miss.R: .
  > test-miss.R: Bridge sampling finished. Collating...
  > test-miss.R: Estimated standard error (0.007542247) above target (0.005). Drawing additional samples.
  > test-miss.R: Running theta=[-2.132854, 0.000000].
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 45 of at most 60:
  > test-miss.R: Running theta=[-2.131891, 0.000000].
  > test-miss.R: Running theta=[-2.130928, 0.000000].
  > test-miss.R: Running theta=[-2.129965, 0.000000].
  > test-miss.CD.R: Convergence test P-value:1.9e-150
  > test-miss.CD.R: 1 2 
  > test-miss.R: Running theta=[-2.129002, 0.000000].
  > test-miss.R: Running theta=[-2.128039, 0.000000].
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 46 of at most 60:
  > test-miss.R: Running theta=[-2.127076, 0.000000].
  > test-miss.R: Running theta=[-2.126113, 0.000000].
  > test-miss.R: Running theta=[-2.125151, 0.000000].
  > test-miss.R: Running theta=[-2.124188, 0.000000].
  > test-miss.R: Running theta=[-2.123225, 0.000000].
  > test-miss.CD.R: Convergence test P-value:8.8e-148
  > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 47 of at most 60:
  > test-miss.R: Running theta=[-2.122262, 0.000000].
  > test-miss.R: Running theta=[-2.121299, 0.000000].
  > test-miss.R: Running theta=[-2.120336, 0.000000].
  > test-miss.R: Running theta=[-2.119373, 0.000000].
  > test-miss.R: Running theta=[-2.11841, 0.00000].
  > test-miss.R: .
  > test-miss.R: Bridge sampling finished. Collating...
  > test-miss.R: Estimated standard error (0.006188692) above target (0.005). Drawing additional samples.
  > test-miss.R: Running theta=[-2.118778, 0.000000].
  > test-miss.R: Running theta=[-2.119741, 0.000000].
  > test-miss.R: Running theta=[-2.120704, 0.000000].
  > test-miss.CD.R: Convergence test P-value:1.8e-153
  > test-miss.CD.R: 1 
  > test-miss.CD.R: 2 The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 48 of at most 60:
  > test-miss.R: Running theta=[-2.121667, 0.000000].
  > test-miss.R: Running theta=[-2.12263, 0.00000].
  > test-miss.R: Running theta=[-2.123593, 0.000000].
  > test-miss.R: Running theta=[-2.124555, 0.000000].
  > test-miss.R: Running theta=[-2.125518, 0.000000].
  > test-miss.R: Running theta=[-2.126481, 0.000000].
  > test-miss.CD.R: Convergence test P-value:4.8e-158
  > test-miss.CD.R: 1 2 
  > test-miss.R: Running theta=[-2.127444, 0.000000].
  > test-miss.R: Running theta=[-2.128407, 0.000000].
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 49 of at most 60:
  > test-miss.R: Running theta=[-2.12937, 0.00000].
  > test-miss.R: Running theta=[-2.130333, 0.000000].
  > test-miss.R: Running theta=[-2.131296, 0.000000].
  > test-miss.R: Running theta=[-2.132259, 0.000000].
  > test-miss.R: Running theta=[-2.133222, 0.000000].
  > test-miss.R: .
  > test-miss.R: Bridge sampling finished. Collating...
  > test-miss.R: Estimated standard error (0.005396227) above target (0.005). Drawing additional samples.
  > test-miss.R: Running theta=[-2.132626, 0.000000].
  > test-miss.R: Running theta=[-2.131664, 0.000000].
  > test-miss.CD.R: Convergence test P-value:6e-154
  > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 50 of at most 60:
  > test-miss.R: Running theta=[-2.130701, 0.000000].
  > test-miss.R: Running theta=[-2.129738, 0.000000].
  > test-miss.R: Running theta=[-2.128775, 0.000000].
  > test-miss.R: Running theta=[-2.127812, 0.000000].
  > test-miss.R: Running theta=[-2.126849, 0.000000].
  > test-miss.CD.R: Convergence test P-value:1.4e-158
  > test-miss.CD.R: 1 2 
  > test-miss.R: Running theta=[-2.125886, 0.000000].
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 51 of at most 60:
  > test-miss.R: Running theta=[-2.124923, 0.000000].
  > test-miss.R: Running theta=[-2.12396, 0.00000].
  > test-miss.R: Running theta=[-2.122997, 0.000000].
  > test-miss.R: Running theta=[-2.122035, 0.000000].
  > test-miss.R: Running theta=[-2.121072, 0.000000].
  > test-miss.R: Running theta=[-2.120109, 0.000000].
  > test-miss.CD.R: Convergence test P-value:1.2e-150
  > test-miss.CD.R: 1 2 
  > test-miss.R: Running theta=[-2.119146, 0.000000].
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 52 of at most 60:
  > test-miss.R: Running theta=[-2.118183, 0.000000].
  > test-miss.R: .
  > test-miss.R: Bridge sampling finished. Collating...
  > test-miss.R: Bridging finished.
  > test-miss.R: 
  > test-miss.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-miss.CD.R: Convergence test P-value:2.2e-161
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 53 of at most 60:
  > test-miss.CD.R: Convergence test P-value:5.8e-145
  > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 54 of at most 60:
  > test-miss.CD.R: Convergence test P-value:5.8e-163
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 55 of at most 60:
  > test-miss.R: Sample statistics summary:
  > test-miss.R: 
  > test-miss.R: Iterations = 1728:32768
  > test-miss.R: Thinning interval = 64 
  > test-miss.R: Number of chains = 1 
  > test-miss.R: Sample size per chain = 486 
  > test-miss.R: 
  > test-miss.R: 1. Empirical mean and standard deviation for each variable,
  > test-miss.R:    plus standard error of the mean:
  > test-miss.R: 
  > test-miss.R:           Mean             SD       Naive SE Time-series SE 
  > test-miss.R:         1.8539         5.6566         0.2566         0.4463 
  > test-miss.R: 
  > test-miss.R: 2. Quantiles for each variable:
  > test-miss.R: 
  > test-miss.R:   2.5%    25%    50%    75%  97.5% 
  > test-miss.R: -8.881 -1.881  2.119  5.119 14.119 
  > test-miss.R: 
  > test-miss.R: Constrained sample statistics summary:
  > test-miss.R: 
  > test-miss.R: Iterations = 896:16384
  > test-miss.R: Thinning interval = 64 
  > test-miss.R: Number of chains = 1 
  > test-miss.R: Sample size per chain = 243 
  > test-miss.R: 
  > test-miss.R: 1. Empirical mean and standard deviation for each variable,
  > test-miss.R:    plus standard error of the mean:
  > test-miss.R: 
  > test-miss.R:           Mean             SD       Naive SE Time-series SE 
  > test-miss.R:     -2.129e-17      1.663e+00      1.067e-01      1.067e-01 
  > test-miss.R: 
  > test-miss.R: 2. Quantiles for each variable:
  > test-miss.R: 
  > test-miss.R:    2.5%     25%     50%     75%   97.5% 
  > test-miss.R: -2.8807 -1.3807  0.1193  1.1193  4.0693 
  > test-miss.R: 
  > test-miss.R: 
  > test-miss.R: Are unconstrained sample statistics significantly different from constrained?
  > test-miss.R:                   edges       (Omni)
  > test-miss.R: diff.      1.853909e+00           NA
  > test-miss.R: test stat. 4.040502e+00 1.632566e+01
  > test-miss.R: P-val.     5.333689e-05 7.904382e-05
  > test-miss.R: 
  > test-miss.R: Sample statistics cross-correlations:
  > test-miss.R:       edges
  > test-miss.R: edges     1
  > test-miss.R: Constrained sample statistics cross-correlations:
  > test-miss.R:       edges
  > test-miss.R: edges     1
  > test-miss.R: 
  > test-miss.R: Sample statistics auto-correlation:
  > test-miss.R: Chain 1 
  > test-miss.R:              edges
  > test-miss.R: Lag 0   1.00000000
  > test-miss.R: Lag 64  0.50229634
  > test-miss.R: Lag 128 0.27968825
  > test-miss.R: Lag 192 0.14989734
  > test-miss.R: Lag 256 0.08553620
  > test-miss.R: Lag 320 0.01488518
  > test-miss.R: Constrained sample statistics auto-correlation:
  > test-miss.R: Chain 1 
  > test-miss.R:               edges
  > test-miss.R: Lag 0    1.00000000
  > test-miss.R: Lag 64  -0.04999730
  > test-miss.R: Lag 128  0.05488745
  > test-miss.R: Lag 192 -0.03080175
  > test-miss.R: Lag 256  0.03734623
  > test-miss.R: Lag 320  0.01289319
  > test-miss.R: 
  > test-miss.R: Sample statistics burn-in diagnostic (Geweke):
  > test-miss.R: Chain 1 
  > test-miss.R: 
  > test-miss.R: Fraction in 1st window = 0.1
  > test-miss.R: Fraction in 2nd window = 0.5 
  > test-miss.R: 
  > test-miss.R:      edges 
  > test-miss.R: -0.4658741 
  > test-miss.R: 
  > test-miss.R: Individual P-values (lower = worse):
  > test-miss.R:     edges 
  > test-miss.R: 0.6413056 
  > test-miss.R: Joint P-value (lower = worse):  0.5503774 
  > test-miss.R: Sample statistics burn-in diagnostic (Geweke):
  > test-miss.R: Chain 1 
  > test-miss.R: 
  > test-miss.R: Fraction in 1st window = 0.1
  > test-miss.R: Fraction in 2nd window = 0.5 
  > test-miss.R: 
  > test-miss.R:      edges 
  > test-miss.R: -0.2553269 
  > test-miss.R: 
  > test-miss.R: P-values (lower = worse):
  > test-miss.R:     edges 
  > test-miss.R: 0.7984706 
  > test-miss.R: Joint P-value (lower = worse):  0.5503774 .
  > test-miss.R: 
  > test-miss.R: Note: MCMC diagnostics shown here are from the last round of
  > test-miss.R:   simulation, prior to computation of final parameter estimates.
  > test-miss.R:   Because the final estimates are refinements of those used for this
  > test-miss.R:   simulation run, these diagnostics may understate model performance.
  > test-miss.R:   To directly assess the performance of the final model on in-model
  > test-miss.R:   statistics, please use the GOF command: gof(ergmFitObject,
  > test-miss.R:   GOF=~model).
  > test-miss.R: 
  > test-miss.R: MCMCMLE estimate = -2.133563 with log-likelihood -120.7039 OK. 
  > test-miss.CD.R: Convergence test P-value:1.3e-156
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 56 of at most 60:
  > test-miss.CD.R: Convergence test P-value:2e-150
  > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 57 of at most 60:
  > test-miss.R: Correct estimate = -1.663142 with log-likelihood -79.82064 .
  > test-miss.CD.R: Convergence test P-value:2.4e-151
  > test-miss.CD.R: 1 2 The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 58 of at most 60:
  > test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-miss.R: Obtaining the responsible dyads.
  > test-miss.R: Evaluating the predictor and response matrix.
  > test-miss.R: MPLE estimate = -1.663142 with log-likelihood -79.82064 OK. 
  > test-miss.R: Maximizing the pseudolikelihood.
  > test-miss.R: Finished MPLE.
  > test-miss.R: Evaluating log-likelihood at the estimate. 
  > test-miss.CD.R: Convergence test P-value:1.2e-168
  > test-miss.CD.R: 1 
  > test-miss.CD.R: 2 
  > test-miss.R: Evaluating network in model.
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.R: Initializing unconstrained Metropolis-Hastings proposal: 
  > test-miss.CD.R: Iteration 59 of at most 60:
  > test-miss.R: 'ergm:MH_SPDyad'.
  > test-miss.R: Initializing constrained Metropolis-Hastings proposal: 
  > test-miss.R: 'ergm:MH_SPDyad'.
  > test-miss.R: Initializing model...
  > test-miss.R: Model initialized.
  > test-miss.R: Using initial method 'MPLE'.
  > test-miss.R: Initial parameters provided by caller:
  > test-miss.R:      edges 
  > test-miss.R: -0.6631421 
  > test-miss.R:   number of free parameters: 1
  > test-miss.R:   number of fixed parameters: 0
  > test-miss.R: Fitting initial model.
  > test-miss.R: Imputing 8 dyads is required.
  > test-miss.R: Imputing 1 edges at random.
  > test-miss.CD.R: Convergence test P-value:3e-159
  > test-miss.CD.R: 1 2 
  > test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-miss.R: Density guard set to 10000 from an initial count of 30 edges.
  > test-miss.R: 
  > test-miss.R: Iteration 1 of at most 60 with parameter:
  > test-miss.R:      edges 
  > test-miss.R: -0.6631421 
  > test-miss.R: Starting unconstrained MCMC...
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Iteration 60 of at most 60:
  > test-miss.CD.R: Convergence test P-value:6.5e-157
  > test-miss.CD.R: 1 2 
  > test-miss.CD.R: The log-likelihood improved by < 0.0001.
  > test-miss.CD.R: Finished CD.
  > test-miss.CD.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-miss.R: Back from unconstrained MCMC.
  > test-miss.R: Starting constrained MCMC...
  > test-miss.R: Back from constrained MCMC.
  > test-miss.R: New interval = 512.
  > test-miss.R: New constrained interval = 256.
  > test-miss.R: Average estimating function values:
  > test-miss.R:    edges 
  > test-miss.R: 33.15638 
  > test-miss.R: Starting MCMLE Optimization...
  > test-miss.R: 1 2 3 4 5 6 7 8 Optimizing with step length 0.5368.
  > test-miss.R: Using lognormal metric (see control.ergm function).
  > test-miss.R: Using log-normal approx with missing (no optim)
  > test-miss.R: The log-likelihood improved by 4.3000.
  > test-miss.R: Estimating equations are not within tolerance region.
  > test-miss.R: 
  > test-miss.R: Iteration 2 of at most 60 with parameter:
  > test-miss.R:     edges 
  > test-miss.R: -1.146333 
  > test-miss.R: Starting unconstrained MCMC...
  > test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-mple-cov.R: Obtaining the responsible dyads.
  > test-mple-cov.R: Evaluating the predictor and response matrix.
  > test-mple-cov.R: Maximizing the pseudolikelihood.
  > test-miss.R: Back from unconstrained MCMC.
  > test-miss.R: Starting constrained MCMC...
  > test-miss.R: Back from constrained MCMC.
  > test-miss.R: New interval = 256.
  > test-miss.R: New constrained interval = 128.
  > test-miss.R: Average estimating function values:
  > test-miss.R:    edges 
  > test-miss.R: 14.85185 
  > test-miss.R: Starting MCMLE Optimization...
  > test-miss.R: 1 2 3 Optimizing with step length 1.0000.
  > test-miss.R: Using lognormal metric (see control.ergm function).
  > test-miss.R: Using log-normal approx with missing (no optim)
  > test-miss.R: The log-likelihood improved by 3.2552.
  > test-miss.R: Distance from origin on tolerance region scale: 64.83604 (previously 323.1391).
  > test-miss.R: Estimating equations are not within tolerance region.
  > test-miss.R: 
  > test-miss.R: Iteration 3 of at most 60 with parameter:
  > test-miss.R:     edges 
  > test-miss.R: -1.584689 
  > test-miss.R: Starting unconstrained MCMC...
  > test-miss.R: Back from unconstrained MCMC.
  > test-miss.R: Starting constrained MCMC...
  > test-miss.R: Back from constrained MCMC.
  > test-miss.R: New interval = 128.
  > test-miss.R: New constrained interval = 64.
  > test-miss.R: Average estimating function values:
  > test-miss.R:    edges 
  > test-miss.R: 2.600823 
  > test-miss.R: Starting MCMLE Optimization...
  > test-miss.R: 1 Optimizing with step length 1.0000.
  > test-miss.R: Using lognormal metric (see control.ergm function).
  > test-miss.R: Using log-normal approx with missing (no optim)
  > test-miss.R: The log-likelihood improved by 0.1297.
  > test-miss.R: Distance from origin on tolerance region scale: 2.582218 (previously 84.20395).
  > test-miss.R: Estimating equations are not within tolerance region.
  > test-miss.R: 
  > test-miss.R: Iteration 4 of at most 60 with parameter:
  > test-miss.R:     edges 
  > test-miss.R: -1.684393 
  > test-miss.R: Starting unconstrained MCMC...
  > test-miss.R: Back from unconstrained MCMC.
  > test-miss.R: Starting constrained MCMC...
  > test-miss.R: Back from constrained MCMC.
  > test-miss.R: New interval = 64.
  > test-miss.R: New constrained interval = 32.
  > test-miss.R: Average estimating function values:
  > test-miss.R:      edges 
  > test-miss.R: -0.2386831 
  > test-miss.R: Starting MCMLE Optimization...
  > test-miss.R: 1 
  > test-miss.R: Optimizing with step length 1.0000.
  > test-miss.R: Using lognormal metric (see control.ergm function).
  > test-miss.R: Using log-normal approx with missing (no optim)
  > test-miss.R: Starting MCMC s.e. computation.
  > test-miss.R: The log-likelihood improved by 0.0011.
  > test-miss.R: Distance from origin on tolerance region scale: 0.02175454 (previously 2.583022).
  > test-miss.R: Test statistic: T^2 = 16.95471, with 1 free parameter(s) and 192.1073 degrees of freedom.
  > test-miss.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
  > test-miss.R: Finished MCMLE.
  > test-miss.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
  > test-miss.R: Fitting the dyad-independent submodel...
  > test-miss.R: Dyad-independent submodel MLE has likelihood -79.82064 at:
  > test-miss.R: [1] -1.663142  0.000000
  > test-miss.R: Bridging between the dyad-independent submodel and the full model...
  > test-miss.R: Setting up bridge sampling...
  > test-miss.R: Initializing model and proposals...
  > test-miss.R: Model and proposals initialized.
  > test-miss.R: Initializing constrained model and proposals...
  > test-miss.R: Constrained model and proposals initialized.
  > test-miss.R: Using 16 bridges: Running theta=[-1.674863, 0.000000].
  > test-miss.R: Running theta=[-1.674107, 0.000000].
  > test-miss.R: Running theta=[-1.673351, 0.000000].
  > test-miss.R: Running theta=[-1.672594, 0.000000].
  > test-miss.R: Running theta=[-1.671838, 0.000000].
  > test-miss.R: Running theta=[-1.671082, 0.000000].
  > test-miss.R: Running theta=[-1.670326, 0.000000].
  > test-miss.R: Running theta=[-1.66957, 0.00000].
  > test-miss.R: Running theta=[-1.668813, 0.000000].
  > test-miss.R: Running theta=[-1.668057, 0.000000].
  > test-miss.R: Running theta=[-1.667301, 0.000000].
  > test-miss.R: Running theta=[-1.666545, 0.000000].
  > test-miss.R: Running theta=[-1.665789, 0.000000].
  > test-miss.R: Running theta=[-1.665033, 0.000000].
  > test-miss.R: Running theta=[-1.664276, 0.000000].
  > test-miss.R: Running theta=[-1.66352, 0.00000].
  > test-miss.R: .
  > test-miss.R: Bridge sampling finished. Collating...
  > test-miss.R: Estimated standard error (0.005483681) above target (0.005). Drawing additional samples.
  > test-miss.R: Running theta=[-1.663809, 0.000000].
  > test-miss.R: Running theta=[-1.664565, 0.000000].
  > test-miss.R: Running theta=[-1.665321, 0.000000].
  > test-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks.
  > test-miss.R: Running theta=[-1.666078, 0.000000].
  > test-miss.R: Running theta=[-1.666834, 0.000000].
  > test-miss.R: Running theta=[-1.66759, 0.00000].
  > test-miss.R: Running theta=[-1.668346, 0.000000].
  > test-miss.R: Running theta=[-1.669102, 0.000000].
  > test-miss.R: Running theta=[-1.669858, 0.000000].
  > test-miss.R: Running theta=[-1.670615, 0.000000].
  > test-miss.R: Running theta=[-1.671371, 0.000000].
  > test-miss.R: Running theta=[-1.672127, 0.000000].
  > test-miss.R: Running theta=[-1.672883, 0.000000].
  > test-miss.R: Running theta=[-1.673639, 0.000000].
  > test-miss.R: Running theta=[-1.674396, 0.000000].
  > test-miss.R: Running theta=[-1.675152, 0.000000].
  > test-miss.R: .
  > test-miss.R: Bridge sampling finished. Collating...
  > test-miss.R: Bridging finished.
  > test-miss.R: 
  > test-miss.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-miss.R: Sample statistics summary:
  > test-miss.R: 
  > test-miss.R: Iterations = 1792:32768
  > test-miss.R: Thinning interval = 128 
  > test-miss.R: Number of chains = 1 
  > test-miss.R: Sample size per chain = 243 
  > test-miss.R: 
  > test-miss.R: 1. Empirical mean and standard deviation for each variable,
  > test-miss.R:    plus standard error of the mean:
  > test-miss.R: 
  > test-miss.R:           Mean             SD       Naive SE Time-series SE 
  > test-miss.R:        -0.2387         5.2156         0.3346         0.3867 
  > test-miss.R: 
  > test-miss.R: 2. Quantiles for each variable:
  > test-miss.R: 
  > test-miss.R:    2.5%     25%     50%     75%   97.5% 
  > test-miss.R: -9.2346 -4.2346 -0.2346  2.7654 11.7154 
  > test-miss.R: 
  > test-miss.R: Constrained sample statistics summary:
  > test-miss.R: 
  > test-miss.R: Iterations = 896:16384
  > test-miss.R: Thinning interval = 64 
  > test-miss.R: Number of chains = 1 
  > test-miss.R: Sample size per chain = 243 
  > test-miss.R: 
  > test-miss.R: 1. Empirical mean and standard deviation for each variable,
  > test-miss.R:    plus standard error of the mean:
  > test-miss.R: 
  > test-miss.R:           Mean             SD       Naive SE Time-series SE 
  > test-miss.R:      4.040e-17      1.007e+00      6.463e-02      6.463e-02 
  > test-miss.R: 
  > test-miss.R: 2. Quantiles for each variable:
  > test-miss.R: 
  > test-miss.R:    2.5%     25%     50%     75%   97.5% 
  > test-miss.R: -1.2346 -0.7346 -0.2346  0.7654  2.7154 
  > test-miss.R: 
  > test-miss.R: 
  > test-miss.R: Are unconstrained sample statistics significantly different from constrained?
  > test-miss.R:                 edges    (Omni)
  > test-miss.R: diff.      -0.2386831        NA
  > test-miss.R: test stat. -0.6087923 0.3706280
  > test-miss.R: P-val.      0.5426621 0.5433813
  > test-miss.R: 
  > test-miss.R: Sample statistics cross-correlations:
  > test-miss.R:       edges
  > test-miss.R: edges     1
  > test-miss.R: Constrained sample statistics cross-correlations:
  > test-miss.R:       edges
  > test-miss.R: edges     1
  > test-miss.R: 
  > test-miss.R: Sample statistics auto-correlation:
  > test-miss.R: Chain 1 
  > test-miss.R:                edges
  > test-miss.R: Lag 0    1.000000000
  > test-miss.R: Lag 128  0.141730657
  > test-miss.R: Lag 256 -0.008044799
  > test-miss.R: Lag 384  0.039503814
  > test-miss.R: Lag 512  0.016265240
  > test-miss.R: Lag 640  0.025525909
  > test-miss.R: Constrained sample statistics auto-correlation:
  > test-miss.R: Chain 1 
  > test-miss.R:               edges
  > test-miss.R: Lag 0    1.00000000
  > test-miss.R: Lag 64  -0.03837238
  > test-miss.R: Lag 128  0.06102163
  > test-miss.R: Lag 192  0.01817600
  > test-miss.R: Lag 256 -0.07663989
  > test-miss.R: Lag 320 -0.02107378
  > test-miss.R: 
  > test-miss.R: Sample statistics burn-in diagnostic (Geweke):
  > test-miss.R: Chain 1 
  > test-miss.R: 
  > test-miss.R: Fraction in 1st window = 0.1
  > test-miss.R: Fraction in 2nd window = 0.5 
  > test-miss.R: 
  > test-miss.R:     edges 
  > test-miss.R: -1.387683 
  > test-miss.R: 
  > test-miss.R: Individual P-values (lower = worse):
  > test-miss.R:     edges 
  > test-miss.R: 0.1652336 
  > test-miss.R: Joint P-value (lower = worse):  0.2706448 
  > test-miss.R: 
  > test-miss.R: Sample statistics burn-in diagnostic (Geweke):
  > test-miss.R: Chain 1 
  > test-miss.R: 
  > test-miss.R: Fraction in 1st window = 0.1
  > test-miss.R: Fraction in 2nd window = 0.5 
  > test-miss.R: 
  > test-miss.R:      edges 
  > test-miss.R: -0.5702328 
  > test-miss.R: 
  > test-miss.R: P-values (lower = worse):
  > test-miss.R:     edges 
  > test-miss.R: 0.5685198 
  > test-miss.R: Joint P-value (lower = worse):  0.2706448 .
  > test-miss.R: 
  > test-miss.R: Note: MCMC diagnostics shown here are from the last round of
  > test-miss.R:   simulation, prior to computation of final parameter estimates.
  > test-miss.R:   Because the final estimates are refinements of those used for this
  > test-miss.R:   simulation run, these diagnostics may understate model performance.
  > test-miss.R:   To directly assess the performance of the final model on in-model
  > test-miss.R:   statistics, please use the GOF command: gof(ergmFitObject,
  > test-miss.R:   GOF=~model).
  > test-miss.R: 
  > test-miss.R: MCMCMLE estimate = -1.675241 with log-likelihood -79.81794 OK. 
  > test-miss.R: Correct estimate = -3.157 with log-likelihood -8.355963 .
  > test-miss.R: MPLE estimate = -3.157 with log-likelihood -8.355963 OK. 
  > test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-miss.R: Obtaining the responsible dyads.
  > test-miss.R: Evaluating the predictor and response matrix.
  > test-miss.R: Maximizing the pseudolikelihood.
  > test-miss.R: Finished MPLE.
  > test-miss.R: Evaluating log-likelihood at the estimate. 
  > test-miss.R: Evaluating network in model.
  > test-miss.R: Initializing unconstrained Metropolis-Hastings proposal: 'ergm:MH_TNT'.
  > test-miss.R: Initializing constrained Metropolis-Hastings proposal: 
  > test-miss.R: 'ergm:MH_TNT'.
  > test-miss.R: Initializing model...
  > test-miss.R: Model initialized.
  > test-miss.R: Using initial method 'MPLE'.
  > test-miss.R: Initial parameters provided by caller:
  > test-miss.R:  edges 
  > test-miss.R: -2.157 
  > test-miss.R:   number of free parameters: 1
  > test-miss.R:   number of fixed parameters: 0
  > test-miss.R: Fitting initial model.
  > test-miss.R: Imputing 2 dyads is required.
  > test-miss.R: Imputing 0 edges at random.
  > test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-miss.R: Density guard set to 10000 from an initial count of 2 edges.
  > test-miss.R: 
  > test-miss.R: Iteration 1 of at most 60 with parameter:
  > test-miss.R:  edges 
  > test-miss.R: -2.157 
  > test-miss.R: Starting unconstrained MCMC...
  > test-miss.R: Back from unconstrained MCMC.
  > test-miss.R: Starting constrained MCMC...
  > test-miss.R: Back from constrained MCMC.
  > test-miss.R: New interval = 512.
  > test-miss.R: New constrained interval = 256.
  > test-miss.R: Average estimating function values:
  > test-miss.R:    edges 
  > test-miss.R: 2.942387 
  > test-miss.R: Starting MCMLE Optimization...
  > test-miss.R: 1 Optimizing with step length 1.0000.
  > test-miss.R: Using lognormal metric (see control.ergm function).
  > test-miss.R: Using log-normal approx with missing (no optim)
  > test-miss.R: The log-likelihood improved by 0.8416.
  > test-miss.R: Estimating equations are not within tolerance region.
  > test-miss.R: 
  > test-miss.R: Iteration 2 of at most 60 with parameter:
  > test-miss.R:     edges 
  > test-miss.R: -2.729057 
  > test-miss.R: Starting unconstrained MCMC...
  > test-miss.R: Back from unconstrained MCMC.
  > test-miss.R: Starting constrained MCMC...
  > test-miss.R: Back from constrained MCMC.
  > test-miss.R: New interval = 256.
  > test-miss.R: New constrained interval = 128.
  > test-miss.R: Average estimating function values:
  > test-miss.R:     edges 
  > test-miss.R: 0.9012346 
  > test-miss.R: Starting MCMLE Optimization...
  > test-miss.R: 1 Optimizing with step length 1.0000.
  > test-miss.R: Using lognormal metric (see control.ergm function).
  > test-miss.R: Using log-normal approx with missing (no optim)
  > test-miss.R: The log-likelihood improved by 0.1847.
  > test-miss.R: Distance from origin on tolerance region scale: 3.678483 (previously 39.20962).
  > test-miss.R: Estimating equations are not within tolerance region.
  > test-miss.R: 
  > test-miss.R: Iteration 3 of at most 60 with parameter:
  > test-miss.R:     edges 
  > test-miss.R: -3.138904 
  > test-miss.R: Starting unconstrained MCMC...
  > test-miss.R: Back from unconstrained MCMC.
  > test-miss.R: Starting constrained MCMC...
  > test-miss.R: Back from constrained MCMC.
  > test-miss.R: New interval = 128.
  > test-miss.R: New constrained interval = 64.
  > test-miss.R: Average estimating function values:
  > test-miss.R:       edges 
  > test-miss.R: -0.05349794 
  > test-miss.R: Starting MCMLE Optimization...
  > test-miss.R: 1 Optimizing with step length 1.0000.
  > test-miss.R: Using lognormal metric (see control.ergm function).
  > test-miss.R: Using log-normal approx with missing (no optim)
  > test-miss.R: Starting MCMC s.e. computation.
  > test-miss.R: The log-likelihood improved by 0.0008.
  > test-miss.R: Distance from origin on tolerance region scale: 0.01512904 (previously 4.293514).
  > test-miss.R: Test statistic: T^2 = 22.84874, with 1 free parameter(s) and 256.603 degrees of freedom.
  > test-miss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-miss.R: Finished MCMLE.
  > test-miss.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms...
  > test-miss.R: Fitting the dyad-independent submodel...
  > test-miss.R: Dyad-independent submodel MLE has likelihood -8.355963 at:
  > test-miss.R: [1] -3.157  0.000
  > test-miss.R: Bridging between the dyad-independent submodel and the full model...
  > test-miss.R: Setting up bridge sampling...
  > test-miss.R: Initializing model and proposals...
  > test-miss.R: Model and proposals initialized.
  > test-miss.R: Initializing constrained model and proposals...
  > test-miss.R: Constrained model and proposals initialized.
  > test-miss.R: Using 16 bridges: Running theta=[-3.11196, 0.00000].
  > test-miss.R: Running theta=[-3.114866, 0.000000].
  > test-miss.R: Running theta=[-3.117772, 0.000000].
  > test-miss.R: Running theta=[-3.120678, 0.000000].
  > test-miss.R: Running theta=[-3.123584, 0.000000].
  > test-miss.R: Running theta=[-3.126489, 0.000000].
  > test-miss.R: Running theta=[-3.129395, 0.000000].
  > test-miss.R: Running theta=[-3.132301, 0.000000].
  > test-miss.R: Running theta=[-3.135207, 0.000000].
  > test-miss.R: Running theta=[-3.138113, 0.000000].
  > test-miss.R: Running theta=[-3.141018, 0.000000].
  > test-miss.R: Running theta=[-3.143924, 0.000000].
  > test-miss.R: Running theta=[-3.14683, 0.00000].
  > test-miss.R: Running theta=[-3.149736, 0.000000].
  > test-miss.R: Running theta=[-3.152642, 0.000000].
  > test-miss.R: Running theta=[-3.155548, 0.000000].
  > test-miss.R: .
  > test-miss.R: Bridge sampling finished. Collating...
  > test-miss.R: Bridging finished.
  > test-miss.R: 
  > test-miss.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-miss.R: Sample statistics summary:
  > test-miss.R: 
  > test-miss.R: Iterations = 3584:65536
  > test-miss.R: Thinning interval = 256 
  > test-miss.R: Number of chains = 1 
  > test-miss.R: Sample size per chain = 243 
  > test-miss.R: 
  > test-miss.R: 1. Empirical mean and standard deviation for each variable,
  > test-miss.R:    plus standard error of the mean:
  > test-miss.R: 
  > test-miss.R:           Mean             SD       Naive SE Time-series SE 
  > test-miss.R:       -0.05350        1.39509        0.08949        0.08949 
  > test-miss.R: 
  > test-miss.R: 2. Quantiles for each variable:
  > test-miss.R: 
  > test-miss.R:     2.5%      25%      50%      75%    97.5% 
  > test-miss.R: -2.05761 -1.05761 -0.05761  0.94239  2.94239 
  > test-miss.R: 
  > test-miss.R: Constrained sample statistics summary:
  > test-miss.R: 
  > test-miss.R: Iterations = 1792:32768
  > test-miss.R: Thinning interval = 128 
  > test-miss.R: Number of chains = 1 
  > test-miss.R: Sample size per chain = 243 
  > test-miss.R: 
  > test-miss.R: 1. Empirical mean and standard deviation for each variable,
  > test-miss.R:    plus standard error of the mean:
  > test-miss.R: 
  > test-miss.R:           Mean             SD       Naive SE Time-series SE 
  > test-miss.R:      9.838e-19      2.335e-01      1.498e-02      1.763e-02 
  > test-miss.R: 
  > test-miss.R: 2. Quantiles for each variable:
  > test-miss.R: 
  > test-miss.R:     2.5%      25%      50%      75%    97.5% 
  > test-miss.R: -0.05761 -0.05761 -0.05761 -0.05761  0.94239 
  > test-miss.R: 
  > test-miss.R: 
  > test-miss.R: Are unconstrained sample statistics significantly different from constrained?
  > test-miss.R:                  edges    (Omni)
  > test-miss.R: diff.      -0.05349794        NA
  > test-miss.R: test stat. -0.58650248 0.3476007
  > test-miss.R: P-val.      0.55753790 0.5559932
  > test-miss.R: 
  > test-miss.R: Sample statistics cross-correlations:
  > test-miss.R:       edges
  > test-miss.R: edges     1
  > test-miss.R: Constrained sample statistics cross-correlations:
  > test-miss.R:       edges
  > test-miss.R: edges     1
  > test-miss.R: 
  > test-miss.R: Sample statistics auto-correlation:
  > test-miss.R: Chain 1 
  > test-miss.R:                edges
  > test-miss.R: Lag 0     1.00000000
  > test-miss.R: Lag 256  -0.07640761
  > test-miss.R: Lag 512   0.04674441
  > test-miss.R: Lag 768  -0.08703223
  > test-miss.R: Lag 1024 -0.01273911
  > test-miss.R: Lag 1280  0.08918131
  > test-miss.R: Constrained sample statistics auto-correlation:
  > test-miss.R: Chain 1 
  > test-miss.R:               edges
  > test-miss.R: Lag 0    1.00000000
  > test-miss.R: Lag 128  0.01440843
  > test-miss.R: Lag 256 -0.06163854
  > test-miss.R: Lag 384 -0.05752332
  > test-miss.R: Lag 512  0.09381586
  > test-miss.R: Lag 640  0.16935966
  > test-miss.R: 
  > test-miss.R: Sample statistics burn-in diagnostic (Geweke):
  > test-miss.R: Chain 1 
  > test-miss.R: 
  > test-miss.R: Fraction in 1st window = 0.1
  > test-miss.R: Fraction in 2nd window = 0.5 
  > test-miss.R: 
  > test-miss.R:    edges 
  > test-miss.R: 1.222791 
  > test-miss.R: 
  > test-miss.R: Individual P-values (lower = worse):
  > test-miss.R:     edges 
  > test-miss.R: 0.2214088 
  > test-miss.R: Joint P-value (lower = worse):  0.530107 
  > test-miss.R: Sample statistics burn-in diagnostic (Geweke):
  > test-miss.R: Chain 1 
  > test-miss.R: 
  > test-miss.R: Fraction in 1st window = 0.1
  > test-miss.R: Fraction in 2nd window = 0.5 
  > test-miss.R: 
  > test-miss.R:    edges 
  > test-miss.R: 0.899493 
  > test-miss.R: 
  > test-miss.R: P-values (lower = worse):
  > test-miss.R:     edges 
  > test-miss.R: 0.3683901 
  > test-miss.R: Joint P-value (lower = worse):  0.530107 .
  > test-miss.R: 
  > test-miss.R: Note: MCMC diagnostics shown here are from the last round of
  > test-miss.R:   simulation, prior to computation of final parameter estimates.
  > test-miss.R:   Because the final estimates are refinements of those used for this
  > test-miss.R:   simulation run, these diagnostics may understate model performance.
  > test-miss.R:   To directly assess the performance of the final model on in-model
  > test-miss.R:   statistics, please use the GOF command: gof(ergmFitObject,
  > test-miss.R:   GOF=~model).
  > test-miss.R: 
  > test-miss.R: MCMCMLE estimate = -3.110507 with log-likelihood -8.357166 OK. 
  > test-miss.R: Network statistics:
  > test-miss.R:  edges  esp#1  esp#2  esp#3  esp#4  esp#5  esp#6  esp#7  esp#8  esp#9 esp#10 
  > test-miss.R:     50     24      3      0      0      0      0      0      0      0      0 
  > test-miss.R: esp#11 esp#12 esp#13 esp#14 esp#15 esp#16 esp#17 esp#18 esp#19 esp#20 esp#21 
  > test-miss.R:      0      0      0      0      0      0      0      0      0      0      0 
  > test-miss.R: esp#22 esp#23 esp#24 esp#25 esp#26 esp#27 esp#28 
  > test-miss.R:      0      0      0      0      0      0      0 
  > test-miss.R: Correct estimate = -2.028148 
  > test-miss.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-miss.R: Obtaining the responsible dyads.
  > test-miss.R: Evaluating the predictor and response matrix.
  > test-miss.R: Maximizing the pseudolikelihood.
  > test-miss.R: Finished MPLE.
  > test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-miss.R: Iteration 1 of at most 5:
  > test-mple-cov.R: Finished MPLE.
  > test-mple-cov.R: Evaluating log-likelihood at the estimate. 
  > test-mple-cov.R: 
  > test-miss.R: 1 
  > test-miss.R: 2 
  > test-miss.R: 3 
  > test-miss.R: 4 
  > test-miss.R: 5 6 7 
  > test-miss.R: 8 
  > test-miss.R: 9 10 11 
  > test-miss.R: Optimizing with step length 1.0000.
  > test-miss.R: The log-likelihood improved by < 0.0001.
  > test-miss.R: Estimating equations are not within tolerance region.
  > test-miss.R: Iteration 2 of at most 5:
  > test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-mple-cov.R: Obtaining the responsible dyads.
  > test-mple-cov.R: Evaluating the predictor and response matrix.
  > test-mple-cov.R: Maximizing the pseudolikelihood.
  > test-miss.R: 1 
  > test-miss.R: 2 
  > test-miss.R: 3 
  > test-miss.R: 4 
  > test-miss.R: 5 6 7 8 9 Optimizing with step length 1.0000.
  > test-miss.R: The log-likelihood improved by < 0.0001.
  > test-miss.R: Estimating equations are not within tolerance region.
  > test-miss.R: Iteration 3 of at most 5:
  > test-miss.R: 1 2 3 4 
  > test-miss.R: 5 6 7 8 
  > test-miss.R: 9 
  > test-miss.R: 10 11 
  > test-miss.R: Optimizing with step length 1.0000.
  > test-miss.R: The log-likelihood improved by < 0.0001.
  > test-miss.R: Estimating equations are not within tolerance region.
  > test-miss.R: Iteration 4 of at most 5:
  > test-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks.
  > test-miss.R: 1 2 
  > test-miss.R: 3 4 5 6 7 8 Optimizing with step length 1.0000.
  > test-miss.R: The log-likelihood improved by < 0.0001.
  > test-miss.R: Estimating equations are not within tolerance region.
  > test-miss.R: Iteration 5 of at most 5:
  > test-miss.R: 1 2 3 4 5 6 
  > test-miss.R: 7 8 9 Optimizing with step length 1.0000.
  > test-miss.R: The log-likelihood improved by < 0.0001.
  > test-miss.R: Estimating equations are not within tolerance region.
  > test-miss.R: Estimating equations did not move closer to tolerance region more than 1 time(s) in 4 steps; increasing sample size.
  > test-miss.R: MCMLE estimation did not converge after 5 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
  > test-miss.R: Finished MCMLE.
  > test-miss.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
  > test-miss.R: Bridging between the dyad-independent submodel and the full model...
  > test-miss.R: Setting up bridge sampling...
  > test-miss.R: Using 16 bridges: 1 
  > test-miss.R: 2 
  > test-miss.R: 3 
  > test-miss.R: 4 
  > test-miss.R: 5 
  > test-miss.R: 6 
  > test-miss.R: 7 8 
  > test-miss.R: 9 10 
  > test-miss.R: 11 
  > test-miss.R: 12 13 14 
  > test-miss.R: 15 
  > test-miss.R: 16 .
  > test-miss.R: Bridging finished.
  > test-miss.R: 
  > test-miss.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-miss.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-mple-largenetwork.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-mple-largenetwork.R: Obtaining the responsible dyads.
  > test-mple-largenetwork.R: Evaluating the predictor and response matrix.
  > test-mple-largenetwork.R: Maximizing the pseudolikelihood.
  > test-mple-largenetwork.R: Finished MPLE.
  > test-mple-largenetwork.R: Evaluating log-likelihood at the estimate. 
  > test-mple-largenetwork.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-mple-largenetwork.R: Obtaining the responsible dyads.
  > test-mple-largenetwork.R: Evaluating the predictor and response matrix.
  > test-mple-largenetwork.R: Maximizing the pseudolikelihood.
  > test-mple-largenetwork.R: Finished MPLE.
  > test-mple-largenetwork.R: Evaluating log-likelihood at the estimate. 
  > test-mple-offset.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-mple-offset.R: Obtaining the responsible dyads.
  > test-mple-offset.R: Evaluating the predictor and response matrix.
  > test-mple-offset.R: Maximizing the pseudolikelihood.
  > test-mple-offset.R: Finished MPLE.
  > test-mple-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-mple-offset.R: Iteration 1 of at most 60:
  > test-mple-offset.R: 1 Optimizing with step length 1.0000.
  > test-mple-offset.R: The log-likelihood improved by 0.0040.
  > test-mple-offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-mple-offset.R: Finished MCMLE.
  > test-mple-offset.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-mple-offset.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-mple-target.R: [1] 350  50 250
  > test-mple-target.R: Structural check:
  > test-mple-target.R: Mean degree: 1.4 .
  > test-mple-target.R: Average degree among nodes with degree 2 or higher: 2.25 .
  > test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-mple-target.R: Obtaining the responsible dyads.
  > test-mple-target.R: Evaluating the predictor and response matrix.
  > test-mple-target.R: Maximizing the pseudolikelihood.
  > test-mple-target.R: Finished MPLE.
  > test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-mple-target.R: Obtaining the responsible dyads.
  > test-mple-target.R: Evaluating the predictor and response matrix.
  > test-mple-target.R: Maximizing the pseudolikelihood.
  > test-mple-target.R: Finished MPLE.
  > test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-mple-target.R: Obtaining the responsible dyads.
  > test-mple-target.R: Evaluating the predictor and response matrix.
  > test-mple-target.R: Maximizing the pseudolikelihood.
  > test-mple-target.R: Finished MPLE.
  > test-mple-target.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-mple-target.R: Iteration 1 of at most 60:
  > test-mple-cov.R: Finished MPLE.
  > test-mple-cov.R: Evaluating log-likelihood at the estimate. 
  > test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-mple-cov.R: Obtaining the responsible dyads.
  > test-mple-cov.R: Evaluating the predictor and response matrix.
  > test-mple-cov.R: Maximizing the pseudolikelihood.
  > test-mple-cov.R: Finished MPLE.
  > test-mple-cov.R: Evaluating log-likelihood at the estimate. 
  > test-networkLite.R: Loading required package: networkLite
  > test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-mple-cov.R: Obtaining the responsible dyads.
  > test-mple-cov.R: Evaluating the predictor and response matrix.
  > test-mple-cov.R: Maximizing the pseudolikelihood.
  > test-mple-cov.R: Estimating Bootstrap Standard Errors using 500 simulated networks.
  > test-mple-cov.R: Finished MPLE.
  > test-mple-cov.R: Evaluating log-likelihood at the estimate. 
  > test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-mple-cov.R: Obtaining the responsible dyads.
  > test-mple-cov.R: Evaluating the predictor and response matrix.
  > test-mple-cov.R: Maximizing the pseudolikelihood.
  > test-mple-cov.R: Finished MPLE.
  > test-mple-cov.R: Evaluating log-likelihood at the estimate. 
  > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-networkLite.R: Obtaining the responsible dyads.
  > test-networkLite.R: Evaluating the predictor and response matrix.
  > test-networkLite.R: Maximizing the pseudolikelihood.
  > test-networkLite.R: Finished MPLE.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0297.
  > test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-networkLite.R: Obtaining the responsible dyads.
  > test-networkLite.R: Evaluating the predictor and response matrix.
  > test-networkLite.R: Maximizing the pseudolikelihood.
  > test-networkLite.R: Finished MPLE.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: 1 2 3 4 5 
  > test-networkLite.R: 6 7 8 9 10 11 12 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.1793.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-networkLite.R: 1 2 
  > test-networkLite.R: 3 4 5 6 7 8 9 10 11 12 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0302.
  > test-networkLite.R: Convergence test p-value: 0.0036. Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-networkLite.R: Obtaining the responsible dyads.
  > test-networkLite.R: Evaluating the predictor and response matrix.
  > test-networkLite.R: Maximizing the pseudolikelihood.
  > test-networkLite.R: Finished MPLE.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0297.
  > test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-networkLite.R: Obtaining the responsible dyads.
  > test-networkLite.R: Evaluating the predictor and response matrix.
  > test-networkLite.R: Maximizing the pseudolikelihood.
  > test-networkLite.R: Finished MPLE.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: 1 2 3 
  > test-networkLite.R: 4 5 6 7 
  > test-networkLite.R: 8 
  > test-networkLite.R: 9 
  > test-networkLite.R: 10 11 12 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.1793.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-networkLite.R: 1 2 
  > test-networkLite.R: 3 4 
  > test-networkLite.R: 5 6 
  > test-networkLite.R: 7 
  > test-networkLite.R: 8 
  > test-networkLite.R: 9 10 11 12 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0302.
  > test-networkLite.R: Convergence test p-value: 0.0036. 
  > test-networkLite.R: Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-networkLite.R: Obtaining the responsible dyads.
  > test-networkLite.R: Evaluating the predictor and response matrix.
  > test-networkLite.R: Maximizing the pseudolikelihood.
  > test-networkLite.R: Finished MPLE.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.1592.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0101.
  > test-networkLite.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-nodrop.R: Obtaining the responsible dyads.
  > test-nodrop.R: Evaluating the predictor and response matrix.
  > test-nodrop.R: Maximizing the pseudolikelihood.
  > test-nodrop.R: Finished MPLE.
  > test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-nodrop.R: Obtaining the responsible dyads.
  > test-nodrop.R: Evaluating the predictor and response matrix.
  > test-nodrop.R: Maximizing the pseudolikelihood.
  > test-nodrop.R: Finished MPLE.
  > test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-nodrop.R: Iteration 1 of at most 2:
  > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-networkLite.R: Obtaining the responsible dyads.
  > test-networkLite.R: Evaluating the predictor and response matrix.
  > test-networkLite.R: Maximizing the pseudolikelihood.
  > test-networkLite.R: Finished MPLE.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: 1 2 3 4 5 6 7 8 9 10 11 Optimizing with step length 1.0000.
  > test-nodrop.R: 1 Optimizing with step length 1.0000.
  > test-nodrop.R: The log-likelihood improved by 0.0005.
  > test-nodrop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-nodrop.R: Finished MCMLE.
  > test-networkLite.R: The log-likelihood improved by 0.0392.
  > test-networkLite.R: Convergence test p-value: 0.0019. Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-nodrop.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-nodrop.R: Obtaining the responsible dyads.
  > test-nodrop.R: Evaluating the predictor and response matrix.
  > test-nodrop.R: Maximizing the pseudolikelihood.
  > test-nodrop.R: Finished MPLE.
  > test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-nodrop.R: Iteration 1 of at most 2:
  > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-networkLite.R: Obtaining the responsible dyads.
  > test-networkLite.R: Evaluating the predictor and response matrix.
  > test-networkLite.R: Maximizing the pseudolikelihood.
  > test-networkLite.R: Finished MPLE.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.1592.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-nodrop.R: 1 
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-nodrop.R: Optimizing with step length 0.2247.
  > test-nodrop.R: The log-likelihood improved by 2.2822.
  > test-nodrop.R: Estimating equations are not within tolerance region.
  > test-nodrop.R: Iteration 2 of at most 2:
  > test-networkLite.R: The log-likelihood improved by 0.0101.
  > test-networkLite.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-nodrop.R: 1 Optimizing with step length 0.2817.
  > test-nodrop.R: The log-likelihood improved by 2.4734.
  > test-nodrop.R: Estimating equations are not within tolerance region.
  > test-nodrop.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
  > test-nodrop.R: Finished MCMLE.
  > test-nodrop.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-networkLite.R: Obtaining the responsible dyads.
  > test-networkLite.R: Evaluating the predictor and response matrix.
  > test-networkLite.R: Maximizing the pseudolikelihood.
  > test-networkLite.R: Finished MPLE.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-nodrop.R: Obtaining the responsible dyads.
  > test-nodrop.R: Evaluating the predictor and response matrix.
  > test-nodrop.R: Maximizing the pseudolikelihood.
  > test-nodrop.R: Finished MPLE.
  > test-networkLite.R: 1 2 
  > test-networkLite.R: 3 
  > test-networkLite.R: 4 5 
  > test-networkLite.R: 6 7 
  > test-networkLite.R: 8 9 
  > test-networkLite.R: 10 11 Optimizing with step length 1.0000.
  > test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-nodrop.R: Iteration 1 of at most 2:
  > test-networkLite.R: The log-likelihood improved by 0.0392.
  > test-networkLite.R: Convergence test p-value: 0.0019. Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-nodrop.R: 1 Optimizing with step length 0.2675.
  > test-nodrop.R: The log-likelihood improved by 3.3752.
  > test-nodrop.R: Estimating equations are not within tolerance region.
  > test-nodrop.R: Iteration 2 of at most 2:
  > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-networkLite.R: Obtaining the responsible dyads.
  > test-networkLite.R: Evaluating the predictor and response matrix.
  > test-nodrop.R: 1 Optimizing with step length 0.3098.
  > test-networkLite.R: Maximizing the pseudolikelihood.
  > test-nodrop.R: The log-likelihood improved by 2.4783.
  > test-nodrop.R: Estimating equations are not within tolerance region.
  > test-nodrop.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
  > test-nodrop.R: Finished MCMLE.
  > test-networkLite.R: Finished MPLE.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-nodrop.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-nonident-test.R: Obtaining the responsible dyads.
  > test-nonident-test.R: Evaluating the predictor and response matrix.
  > test-nonident-test.R: Maximizing the pseudolikelihood.
  > test-nonident-test.R: Finished MPLE.
  > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-nonident-test.R: Obtaining the responsible dyads.
  > test-nonident-test.R: Evaluating the predictor and response matrix.
  > test-networkLite.R: 1 Optimizing with step length 0.9410.
  > test-nonident-test.R: Maximizing the pseudolikelihood.
  > test-nonident-test.R: Finished MPLE.
  > test-networkLite.R: The log-likelihood improved by 5.9387.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-nonident-test.R: Obtaining the responsible dyads.
  > test-nonident-test.R: Evaluating the predictor and response matrix.
  > test-nonident-test.R: Maximizing the pseudolikelihood.
  > test-nonident-test.R: Finished MPLE.
  > test-nonident-test.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-nonident-test.R: Iteration 1 of at most 1:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 1.8905.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 3 of at most 60:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.1368.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 4 of at most 60:
  > test-nonident-test.R: 1 
  > test-nonident-test.R: Optimizing with step length 1.0000.
  > test-nonident-test.R: The log-likelihood improved by < 0.0001.
  > test-nonident-test.R: Estimating equations are not within tolerance region.
  > test-nonident-test.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
  > test-nonident-test.R: Finished MCMLE.
  > test-nonident-test.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-nonident-test.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-nonident-test.R: Obtaining the responsible dyads.
  > test-nonident-test.R: Evaluating the predictor and response matrix.
  > test-nonident-test.R: Maximizing the pseudolikelihood.
  > test-nonident-test.R: Finished MPLE.
  > test-networkLite.R: 1 
  > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-nonident-test.R: Obtaining the responsible dyads.
  > test-nonident-test.R: Evaluating the predictor and response matrix.
  > test-nonident-test.R: Maximizing the pseudolikelihood.
  > test-networkLite.R: Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0176.
  > test-nonident-test.R: Finished MPLE.
  > test-networkLite.R: Convergence test p-value: 0.0247. Not converged with 99% confidence; increasing sample size.
  > test-networkLite.R: Iteration 5 of at most 60:
  > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-nonident-test.R: Obtaining the responsible dyads.
  > test-nonident-test.R: Evaluating the predictor and response matrix.
  > test-nonident-test.R: Maximizing the pseudolikelihood.
  > test-nonident-test.R: Finished MPLE.
  > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-nonident-test.R: Obtaining the responsible dyads.
  > test-nonident-test.R: Evaluating the predictor and response matrix.
  > test-nonident-test.R: Maximizing the pseudolikelihood.
  > test-nonident-test.R: Finished MPLE.
  > test-nonident-test.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-nonident-test.R: Iteration 1 of at most 1:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0103.
  > test-networkLite.R: Convergence test p-value: 0.0227. Not converged with 99% confidence; increasing sample size.
  > test-networkLite.R: Iteration 6 of at most 60:
  > test-nonident-test.R: 1 
  > test-nonident-test.R: Optimizing with step length 0.8147.
  > test-nonident-test.R: The log-likelihood improved by < 0.0001.
  > test-nonident-test.R: Estimating equations are not within tolerance region.
  > test-nonident-test.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
  > test-nonident-test.R: Finished MCMLE.
  > test-nonident-test.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-nonident-test.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-nonident-test.R: Obtaining the responsible dyads.
  > test-nonident-test.R: Evaluating the predictor and response matrix.
  > test-nonident-test.R: Maximizing the pseudolikelihood.
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-nonident-test.R: Finished MPLE.
  > test-networkLite.R: The log-likelihood improved by 0.0419.
  > test-networkLite.R: Convergence test p-value: 0.0085. 
  > test-networkLite.R: Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-nonident-test.R: Obtaining the responsible dyads.
  > test-nonident-test.R: Evaluating the predictor and response matrix.
  > test-nonident-test.R: Maximizing the pseudolikelihood.
  > test-nonident-test.R: Finished MPLE.
  > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-networkLite.R: Obtaining the responsible dyads.
  > test-networkLite.R: Evaluating the predictor and response matrix.
  > test-networkLite.R: Maximizing the pseudolikelihood.
  > test-networkLite.R: Finished MPLE.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-nonunique-names.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-nonunique-names.R: Obtaining the responsible dyads.
  > test-nonunique-names.R: Evaluating the predictor and response matrix.
  > test-nonunique-names.R: Maximizing the pseudolikelihood.
  > test-nonunique-names.R: Finished MPLE.
  > test-nonunique-names.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-nonunique-names.R: Iteration 1 of at most 1:
  > test-networkLite.R: 1 
  > test-networkLite.R: 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.1680.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-networkLite.R: 1 2 3 4 
  > test-networkLite.R: 5 
  > test-networkLite.R: 6 
  > test-networkLite.R: 7 
  > test-networkLite.R: 8 
  > test-networkLite.R: 9 
  > test-networkLite.R: 10 11 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0094.
  > test-networkLite.R: Convergence test p-value: 0.0010. Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-nonunique-names.R: 1 
  > test-nonunique-names.R: Optimizing with step length 1.0000.
  > test-nonunique-names.R: The log-likelihood improved by 0.0084.
  > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-networkLite.R: Obtaining the responsible dyads.
  > test-networkLite.R: Evaluating the predictor and response matrix.
  > test-networkLite.R: Maximizing the pseudolikelihood.
  > test-networkLite.R: Finished MPLE.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-nonunique-names.R: Convergence test p-value: 0.0480. Not converged with 99% confidence; increasing sample size.
  > test-nonunique-names.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
  > test-nonunique-names.R: Finished MCMLE.
  > test-nonunique-names.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-nonunique-names.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-networkLite.R: 1 Optimizing with step length 0.9410.
  > test-networkLite.R: The log-likelihood improved by 5.9387.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-nonunique-names.R: Sample statistics summary:
  > test-nonunique-names.R: 
  > test-nonunique-names.R: Iterations = 2304:44032
  > test-nonunique-names.R: Thinning interval = 128 
  > test-nonunique-names.R: Number of chains = 1 
  > test-nonunique-names.R: Sample size per chain = 327 
  > test-nonunique-names.R: 
  > test-nonunique-names.R: 1. Empirical mean and standard deviation for each variable,
  > test-nonunique-names.R:    plus standard error of the mean:
  > test-nonunique-names.R: 
  > test-nonunique-names.R:              Mean    SD Naive SE Time-series SE
  > test-nonunique-names.R: edgecov.a -0.2171 3.380   0.1869         0.3114
  > test-nonunique-names.R: edgecov.a  0.1346 3.565   0.1971         0.4311
  > test-nonunique-names.R: 
  > test-nonunique-names.R: 2. Quantiles for each variable:
  > test-nonunique-names.R: 
  > test-nonunique-names.R:           2.5% 25% 50% 75% 97.5%
  > test-nonunique-names.R: edgecov.a   -7  -2   0   2  6.85
  > test-nonunique-names.R: edgecov.a   -7  -2   0   3  7.00
  > test-nonunique-names.R: 
  > test-nonunique-names.R: 
  > test-nonunique-names.R: Are sample statistics significantly different from observed?
  > test-nonunique-names.R:             edgecov.a edgecov.a    (Omni)
  > test-nonunique-names.R: diff.      -0.2171254 0.1345566        NA
  > test-nonunique-names.R: test stat. -0.6971750 0.3120903 1.2954084
  > test-nonunique-names.R: P-val.      0.4856933 0.7549719 0.5307488
  > test-nonunique-names.R: 
  > test-nonunique-names.R: Sample statistics cross-correlations:
  > test-nonunique-names.R:           edgecov.a edgecov.a
  > test-nonunique-names.R: edgecov.a 1.0000000 0.6853513
  > test-nonunique-names.R: edgecov.a 0.6853513 1.0000000
  > test-nonunique-names.R: 
  > test-nonunique-names.R: Sample statistics auto-correlation:
  > test-nonunique-names.R: Chain 1 
  > test-nonunique-names.R:            edgecov.a  edgecov.a
  > test-nonunique-names.R: Lag 0    1.000000000 1.00000000
  > test-nonunique-names.R: Lag 128  0.592467538 0.65334235
  > test-nonunique-names.R: Lag 256  0.298337375 0.41906307
  > test-nonunique-names.R: Lag 384  0.082532656 0.22830518
  > test-nonunique-names.R: Lag 512 -0.003477022 0.08811653
  > test-nonunique-names.R: Lag 640 -0.090771181 0.03701357
  > test-nonunique-names.R: 
  > test-nonunique-names.R: Sample statistics burn-in diagnostic (Geweke):
  > test-nonunique-names.R: Chain 1 
  > test-nonunique-names.R: 
  > test-nonunique-names.R: Fraction in 1st window = 0.1
  > test-nonunique-names.R: Fraction in 2nd window = 0.5 
  > test-nonunique-names.R: 
  > test-nonunique-names.R:   edgecov.a   edgecov.a 
  > test-nonunique-names.R: -0.41748721  0.04619135 
  > test-nonunique-names.R: 
  > test-nonunique-names.R: Individual P-values (lower = worse):
  > test-nonunique-names.R: edgecov.a edgecov.a 
  > test-nonunique-names.R: 0.6763221 0.9631577 
  > test-nonunique-names.R: Joint P-value (lower = worse):  0.8447246 
  > test-nonunique-names.R: 
  > test-nonunique-names.R: Note: MCMC diagnostics shown here are from the last round of
  > test-nonunique-names.R:   simulation, prior to computation of final parameter estimates.
  > test-nonunique-names.R:   Because the final estimates are refinements of those used for this
  > test-nonunique-names.R:   simulation run, these diagnostics may understate model performance.
  > test-nonunique-names.R:   To directly assess the performance of the final model on in-model
  > test-nonunique-names.R:   statistics, please use the GOF command: gof(ergmFitObject,
  > test-nonunique-names.R:   GOF=~model).
  > test-nonunique-names.R: 
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 1.8905.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 3 of at most 60:
  > test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-offsets.R: Obtaining the responsible dyads.
  > test-offsets.R: Evaluating the predictor and response matrix.
  > test-offsets.R: Maximizing the pseudolikelihood.
  > test-offsets.R: Finished MPLE.
  > test-offsets.R: Evaluating log-likelihood at the estimate. 
  > test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-offsets.R: Obtaining the responsible dyads.
  > test-offsets.R: Evaluating the predictor and response matrix.
  > test-offsets.R: Maximizing the pseudolikelihood.
  > test-offsets.R: Finished MPLE.
  > test-offsets.R: Evaluating log-likelihood at the estimate. 
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-offsets.R: Obtaining the responsible dyads.
  > test-offsets.R: Evaluating the predictor and response matrix.
  > test-offsets.R: Maximizing the pseudolikelihood.
  > test-networkLite.R: The log-likelihood improved by 0.1368.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 4 of at most 60:
  > test-offsets.R: Finished MPLE.
  > test-offsets.R: Evaluating log-likelihood at the estimate. 
  > test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-offsets.R: Obtaining the responsible dyads.
  > test-offsets.R: Evaluating the predictor and response matrix.
  > test-offsets.R: Maximizing the pseudolikelihood.
  > test-offsets.R: Finished MPLE.
  > test-offsets.R: Evaluating log-likelihood at the estimate. 
  > test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-offsets.R: Obtaining the responsible dyads.
  > test-offsets.R: Evaluating the predictor and response matrix.
  > test-offsets.R: Maximizing the pseudolikelihood.
  > test-offsets.R: Finished MPLE.
  > test-offsets.R: Evaluating log-likelihood at the estimate. 
  > test-offsets.R: 
  > test-networkLite.R: 1 
  > test-networkLite.R: Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0176.
  > test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-offsets.R: Obtaining the responsible dyads.
  > test-offsets.R: Evaluating the predictor and response matrix.
  > test-offsets.R: Maximizing the pseudolikelihood.
  > test-offsets.R: Finished MPLE.
  > test-offsets.R: Evaluating log-likelihood at the estimate. 
  > test-networkLite.R: Convergence test p-value: 0.0247. Not converged with 99% confidence; increasing sample size.
  > test-networkLite.R: Iteration 5 of at most 60:
  > test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-offsets.R: Obtaining the responsible dyads.
  > test-offsets.R: Evaluating the predictor and response matrix.
  > test-offsets.R: Maximizing the pseudolikelihood.
  > test-offsets.R: Finished MPLE.
  > test-offsets.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-offsets.R: Iteration 1 of at most 2:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0103.
  > test-networkLite.R: Convergence test p-value: 0.0227. Not converged with 99% confidence; increasing sample size.
  > test-networkLite.R: Iteration 6 of at most 60:
  > test-offsets.R: 1 Optimizing with step length 1.0000.
  > test-offsets.R: The log-likelihood improved by 0.6004.
  > test-offsets.R: Estimating equations are not within tolerance region.
  > test-offsets.R: Iteration 2 of at most 2:
  > test-networkLite.R: 1 
  > test-networkLite.R: Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0419.
  > test-offsets.R: 1 Optimizing with step length 1.0000.
  > test-offsets.R: The log-likelihood improved by 0.0061.
  > test-networkLite.R: Convergence test p-value: 0.0085. Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-offsets.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-offsets.R: Finished MCMLE.
  > test-offsets.R: Evaluating log-likelihood at the estimate. 
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-offsets.R: Fitting the dyad-independent submodel...
  > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-networkLite.R: Obtaining the responsible dyads.
  > test-networkLite.R: Evaluating the predictor and response matrix.
  > test-offsets.R: Bridging between the dyad-independent submodel and the full model...
  > test-offsets.R: Setting up bridge sampling...
  > test-offsets.R: Using 16 bridges: 1 
  > test-networkLite.R: Maximizing the pseudolikelihood.
  > test-networkLite.R: Finished MPLE.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-offsets.R: 2 3 
  > test-offsets.R: 4 
  > test-offsets.R: 5 6 7 
  > test-offsets.R: 8 9 
  > test-offsets.R: 10 
  > test-offsets.R: 11 
  > test-offsets.R: 12 
  > test-offsets.R: 13 
  > test-offsets.R: 14 
  > test-networkLite.R: 1 
  > test-networkLite.R: 2 3 4 
  > test-networkLite.R: 5 
  > test-offsets.R: 15 
  > test-networkLite.R: 6 7 
  > test-networkLite.R: 8 9 10 11 12 
  > test-networkLite.R: 13 
  > test-networkLite.R: 14 
  > test-networkLite.R: 15 
  > test-networkLite.R: 16 
  > test-networkLite.R: 17 
  > test-networkLite.R: 18 
  > test-networkLite.R: 19 
  > test-networkLite.R: 20 
  > test-networkLite.R: 21 22 23 Optimizing with step length 1.0000.
  > test-offsets.R: 16 
  > test-offsets.R: .
  > test-offsets.R: Bridging finished.
  > test-offsets.R: 
  > test-offsets.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-offsets.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-networkLite.R: The log-likelihood improved by 0.1680.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-offsets.R: Obtaining the responsible dyads.
  > test-offsets.R: Evaluating the predictor and response matrix.
  > test-offsets.R: Maximizing the pseudolikelihood.
  > test-offsets.R: Finished MPLE.
  > test-offsets.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-offsets.R: Iteration 1 of at most 2:
  > test-networkLite.R: 1 2 
  > test-networkLite.R: 3 4 5 6 7 8 9 10 
  > test-networkLite.R: 11 
  > test-networkLite.R: Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0094.
  > test-networkLite.R: Convergence test p-value: 0.0010. Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: Convergence test P-value:1e-01
  > test-networkLite.R: 1 
  > test-networkLite.R: The log-likelihood improved by 0.01081.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-networkLite.R: Convergence test P-value:8.1e-01
  > test-networkLite.R: Convergence detected. Stopping.
  > test-networkLite.R: 1 The log-likelihood improved by 0.000224.
  > test-networkLite.R: Finished CD.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 1.0636.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-networkLite.R: 1 
  > test-networkLite.R: Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0078.
  > test-networkLite.R: Convergence test p-value: 0.0012. Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: Convergence test P-value:1e-01
  > test-networkLite.R: 1 The log-likelihood improved by 0.01081.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-networkLite.R: Convergence test P-value:8.1e-01
  > test-networkLite.R: Convergence detected. Stopping.
  > test-networkLite.R: 1 The log-likelihood improved by 0.000224.
  > test-networkLite.R: Finished CD.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: 1 
  > test-networkLite.R: Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 1.0636.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0078.
  > test-networkLite.R: Convergence test p-value: 0.0012. Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
  > test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: Convergence test P-value:9.2e-01
  > test-networkLite.R: Convergence detected. Stopping.
  > test-networkLite.R: 1 The log-likelihood improved by < 0.0001.
  > test-networkLite.R: Finished CD.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.1926.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0160.
  > test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
  > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
  > test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: Convergence test P-value:9.2e-01
  > test-networkLite.R: Convergence detected. Stopping.
  > test-networkLite.R: 1 The log-likelihood improved by < 0.0001.
  > test-networkLite.R: Finished CD.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.1926.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0160.
  > test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.
  > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: Convergence test P-value:3e-01
  > test-networkLite.R: 1 The log-likelihood improved by 0.004165.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-networkLite.R: Convergence test P-value:1.2e-01
  > test-networkLite.R: 1 
  > test-networkLite.R: The log-likelihood improved by 0.009777.
  > test-networkLite.R: Iteration 3 of at most 60:
  > test-networkLite.R: Convergence test P-value:6.5e-01
  > test-networkLite.R: Convergence detected. Stopping.
  > test-networkLite.R: 1 The log-likelihood improved by 0.0008154.
  > test-networkLite.R: Finished CD.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: 1 
  > test-networkLite.R: Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.7841.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0006.
  > test-networkLite.R: Convergence test p-value: 0.0458. Not converged with 99% confidence; increasing sample size.
  > test-networkLite.R: Iteration 3 of at most 60:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0803.
  > test-networkLite.R: Convergence test p-value: 0.0031. Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: Convergence test P-value:3e-01
  > test-networkLite.R: 1 
  > test-networkLite.R: The log-likelihood improved by 0.004165.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-networkLite.R: Convergence test P-value:1.2e-01
  > test-networkLite.R: 1 The log-likelihood improved by 0.009777.
  > test-networkLite.R: Iteration 3 of at most 60:
  > test-networkLite.R: Convergence test P-value:6.5e-01
  > test-networkLite.R: Convergence detected. Stopping.
  > test-networkLite.R: 1 
  > test-networkLite.R: The log-likelihood improved by 0.0008154.
  > test-networkLite.R: Finished CD.
  > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-networkLite.R: Iteration 1 of at most 60:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.7841.
  > test-networkLite.R: Estimating equations are not within tolerance region.
  > test-networkLite.R: Iteration 2 of at most 60:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0006.
  > test-networkLite.R: Convergence test p-value: 0.0458. Not converged with 99% confidence; increasing sample size.
  > test-networkLite.R: Iteration 3 of at most 60:
  > test-networkLite.R: 1 Optimizing with step length 1.0000.
  > test-networkLite.R: The log-likelihood improved by 0.0803.
  > test-networkLite.R: Convergence test p-value: 0.0031. Converged with 99% confidence.
  > test-networkLite.R: Finished MCMLE.
  > test-networkLite.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-offsets.R: 1 Optimizing with step length 1.0000.
  > test-offsets.R: The log-likelihood improved by 0.7959.
  > test-offsets.R: Estimating equations are not within tolerance region.
  > test-offsets.R: Iteration 2 of at most 2:
  > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-operators.R: Obtaining the responsible dyads.
  > test-operators.R: Evaluating the predictor and response matrix.
  > test-operators.R: Maximizing the pseudolikelihood.
  > test-operators.R: Finished MPLE.
  > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-operators.R: Obtaining the responsible dyads.
  > test-operators.R: Evaluating the predictor and response matrix.
  > test-operators.R: Maximizing the pseudolikelihood.
  > test-operators.R: Finished MPLE.
  > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-operators.R: Obtaining the responsible dyads.
  > test-operators.R: Evaluating the predictor and response matrix.
  > test-operators.R: Maximizing the pseudolikelihood.
  > test-operators.R: Finished MPLE.
  > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-operators.R: Obtaining the responsible dyads.
  > test-operators.R: Evaluating the predictor and response matrix.
  > test-operators.R: Maximizing the pseudolikelihood.
  > test-operators.R: Finished MPLE.
  > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-operators.R: Obtaining the responsible dyads.
  > test-operators.R: Evaluating the predictor and response matrix.
  > test-operators.R: Maximizing the pseudolikelihood.
  > test-operators.R: Finished MPLE.
  > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-operators.R: Obtaining the responsible dyads.
  > test-operators.R: Evaluating the predictor and response matrix.
  > test-operators.R: Maximizing the pseudolikelihood.
  > test-operators.R: Finished MPLE.
  > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-operators.R: Obtaining the responsible dyads.
  > test-operators.R: Evaluating the predictor and response matrix.
  > test-operators.R: Maximizing the pseudolikelihood.
  > test-operators.R: Finished MPLE.
  > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-operators.R: Obtaining the responsible dyads.
  > test-operators.R: Evaluating the predictor and response matrix.
  > test-operators.R: Maximizing the pseudolikelihood.
  > test-operators.R: Finished MPLE.
  > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-operators.R: Obtaining the responsible dyads.
  > test-operators.R: Evaluating the predictor and response matrix.
  > test-operators.R: Maximizing the pseudolikelihood.
  > test-operators.R: Finished MPLE.
  > test-offsets.R: 1 
  > test-offsets.R: Optimizing with step length 1.0000.
  > test-offsets.R: The log-likelihood improved by 0.0207.
  > test-offsets.R: Convergence test p-value: 0.0005. Converged with 99% confidence.
  > test-offsets.R: Finished MCMLE.
  > test-offsets.R: Evaluating log-likelihood at the estimate. 
  > test-offsets.R: Fitting the dyad-independent submodel...
  > test-parallel.R: 
  > test-parallel.R: Attaching package: 'statnet.common'
  > test-parallel.R: 
  > test-parallel.R: The following objects are masked from 'package:base':
  > test-parallel.R: 
  > test-parallel.R:     attr, order, replace
  > test-parallel.R: 
  > test-parallel.R: parallel test(s) skipped. Set ENABLE_statnet_TESTS environment variable to run.
  > test-parallel.R: Skipping OpenMP test. This package installation was built without OpenMP support.
  > test-offsets.R: Bridging between the dyad-independent submodel and the full model...
  > test-offsets.R: Setting up bridge sampling...
  > test-offsets.R: Using 16 bridges: 1 
  > test-offsets.R: 2 
  > test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-predict.ergm.R: Obtaining the responsible dyads.
  > test-predict.ergm.R: Evaluating the predictor and response matrix.
  > test-predict.ergm.R: Maximizing the pseudolikelihood.
  > test-predict.ergm.R: Finished MPLE.
  > test-offsets.R: 3 
  > test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-predict.ergm.R: Obtaining the responsible dyads.
  > test-predict.ergm.R: Evaluating the predictor and response matrix.
  > test-predict.ergm.R: Maximizing the pseudolikelihood.
  > test-predict.ergm.R: Finished MPLE.
  > test-offsets.R: 4 
  > test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-predict.ergm.R: Obtaining the responsible dyads.
  > test-predict.ergm.R: Evaluating the predictor and response matrix.
  > test-predict.ergm.R: Maximizing the pseudolikelihood.
  > test-predict.ergm.R: Finished MPLE.
  > test-offsets.R: 5 
  > test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-predict.ergm.R: Obtaining the responsible dyads.
  > test-predict.ergm.R: Evaluating the predictor and response matrix.
  > test-offsets.R: 6 
  > test-predict.ergm.R: Maximizing the pseudolikelihood.
  > test-predict.ergm.R: Finished MPLE.
  > test-offsets.R: 7 
  > test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-predict.ergm.R: Obtaining the responsible dyads.
  > test-predict.ergm.R: Evaluating the predictor and response matrix.
  > test-predict.ergm.R: Maximizing the pseudolikelihood.
  > test-predict.ergm.R: Finished MPLE.
  > test-offsets.R: 8 
  > test-offsets.R: 9 
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-offsets.R: 10 
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-offsets.R: 11 
  > test-offsets.R: 12 
  > test-offsets.R: 13 
  > test-offsets.R: 14 
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-offsets.R: 15 
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-offsets.R: 16 
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-offsets.R: .
  > test-offsets.R: Bridging finished.
  > test-offsets.R: 
  > test-offsets.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-offsets.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-runtime-diags.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-runtime-diags.R: Obtaining the responsible dyads.
  > test-runtime-diags.R: Evaluating the predictor and response matrix.
  > test-runtime-diags.R: Maximizing the pseudolikelihood.
  > test-runtime-diags.R: Finished MPLE.
  > test-runtime-diags.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-runtime-diags.R: Iteration 1 of at most 60:
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-runtime-diags.R: 1 
  > test-runtime-diags.R: Optimizing with step length 1.0000.
  > test-runtime-diags.R: The log-likelihood improved by 0.0414.
  > test-runtime-diags.R: Convergence test p-value: 0.0016. Converged with 99% confidence.
  > test-runtime-diags.R: Finished MCMLE.
  > test-runtime-diags.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-runtime-diags.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-scoping.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-scoping.R: Obtaining the responsible dyads.
  > test-scoping.R: Evaluating the predictor and response matrix.
  > test-scoping.R: Maximizing the pseudolikelihood.
  > test-scoping.R: Finished MPLE.
  > test-scoping.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-scoping.R: Iteration 1 of at most 1:
  > test-scoping.R: 1 
  > test-scoping.R: Optimizing with step length 1.0000.
  > test-scoping.R: The log-likelihood improved by 0.2011.
  > test-scoping.R: Estimating equations are not within tolerance region.
  > test-scoping.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
  > test-scoping.R: Finished MCMLE.
  > test-scoping.R: Evaluating log-likelihood at the estimate. Fitting the dyad-independent submodel...
  > test-scoping.R: Bridging between the dyad-independent submodel and the full model...
  > test-scoping.R: Setting up bridge sampling...
  > test-scoping.R: Using 16 bridges: 1 2 3 
  > test-scoping.R: 4 
  > test-scoping.R: 5 
  > test-scoping.R: 6 
  > test-scoping.R: 7 8 
  > test-scoping.R: 9 10 11 12 
  > test-scoping.R: 13 14 15 16 
  > test-scoping.R: .
  > test-scoping.R: Bridging finished.
  > test-scoping.R: 
  > test-scoping.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-scoping.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-scoping.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-scoping.R: Obtaining the responsible dyads.
  > test-scoping.R: Evaluating the predictor and response matrix.
  > test-scoping.R: Maximizing the pseudolikelihood.
  > test-scoping.R: Finished MPLE.
  > test-scoping.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-scoping.R: Iteration 1 of at most 1:
  > test-scoping.R: 1 Optimizing with step length 1.0000.
  > test-scoping.R: The log-likelihood improved by 0.2011.
  > test-scoping.R: Estimating equations are not within tolerance region.
  > test-scoping.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
  > test-scoping.R: Finished MCMLE.
  > test-scoping.R: Evaluating log-likelihood at the estimate. 
  > test-scoping.R: Fitting the dyad-independent submodel...
  > test-scoping.R: Bridging between the dyad-independent submodel and the full model...
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-scoping.R: Setting up bridge sampling...
  > test-scoping.R: Using 16 bridges: 1 2 3 
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-scoping.R: 4 5 6 
  > test-scoping.R: 7 8 9 10 
  > test-scoping.R: 11 
  > test-scoping.R: 12 13 14 
  > test-scoping.R: 15 16 .
  > test-scoping.R: Bridging finished.
  > test-scoping.R: 
  > test-scoping.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-scoping.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-shrink-into-CH.R: 1 2 3 4 5 6 7 8 9 10 
  > test-shrink-into-CH.R: 11 12 13 14 15 16 
  > test-shrink-into-CH.R: 17 
  > test-shrink-into-CH.R: 18 
  > test-shrink-into-CH.R: 19 
  > test-shrink-into-CH.R: 20 1 2 
  > test-shrink-into-CH.R: 3 
  > test-shrink-into-CH.R: 4 5 6 7 8 
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-shrink-into-CH.R: 9 10 11 12 
  > test-shrink-into-CH.R: 13 14 
  > test-shrink-into-CH.R: 15 16 17 18 19 20 
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-skip.R: Obtaining the responsible dyads.
  > test-skip.R: Evaluating the predictor and response matrix.
  > test-skip.R: Maximizing the pseudolikelihood.
  > test-skip.R: Finished MPLE.
  > test-skip.R: Evaluating log-likelihood at the estimate. 
  > test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-skip.R: Obtaining the responsible dyads.
  > test-skip.R: Evaluating the predictor and response matrix.
  > test-skip.R: Maximizing the pseudolikelihood.
  > test-skip.R: Finished MPLE.
  > test-skip.R: Evaluating log-likelihood at the estimate. 
  > test-skip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-skip.R: Obtaining the responsible dyads.
  > test-skip.R: Evaluating the predictor and response matrix.
  > test-skip.R: Maximizing the pseudolikelihood.
  > test-skip.R: Finished MPLE.
  > test-skip.R: Evaluating log-likelihood at the estimate. 
  > test-skip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-skip.R: Iteration 1 of at most 60:
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-skip.R: 1 
  > test-skip.R: Optimizing with step length 1.0000.
  > test-skip.R: The log-likelihood improved by 0.0360.
  > test-skip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-skip.R: Finished MCMLE.
  > test-skip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-skip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-snctrl.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-snctrl.R: Obtaining the responsible dyads.
  > test-snctrl.R: Evaluating the predictor and response matrix.
  > test-snctrl.R: Maximizing the pseudolikelihood.
  > test-snctrl.R: Finished MPLE.
  > test-snctrl.R: Evaluating log-likelihood at the estimate. 
  > test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-stocapprox.R: Obtaining the responsible dyads.
  > test-stocapprox.R: Evaluating the predictor and response matrix.
  > test-stocapprox.R: Maximizing the pseudolikelihood.
  > test-stocapprox.R: Finished MPLE.
  > test-stocapprox.R:      edges   triangle 
  > test-stocapprox.R: -1.7009355  0.2208488 
  > test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
  > test-stocapprox.R: Starting burnin of 16384 steps
  > test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
  > test-stocapprox.R: Stochastic Approximation estimate:
  > test-stocapprox.R:      edges   triangle 
  > test-stocapprox.R: -1.6617183  0.1405334 
  > test-stocapprox.R: Phase 3:  1000 iterations (interval=1024)
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-stocapprox.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-stocapprox.R: Obtaining the responsible dyads.
  > test-stocapprox.R: Evaluating the predictor and response matrix.
  > test-stocapprox.R: Maximizing the pseudolikelihood.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-stocapprox.R: Finished MPLE.
  > test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-stocapprox.R: Iteration 1 of at most 60:
  > test-stocapprox.R: 1 
  > test-stocapprox.R: Optimizing with step length 1.0000.
  > test-stocapprox.R: The log-likelihood improved by 0.0034.
  > test-stocapprox.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-stocapprox.R: Finished MCMLE.
  > test-stocapprox.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-stocapprox.R: Obtaining the responsible dyads.
  > test-stocapprox.R: Evaluating the predictor and response matrix.
  > test-stocapprox.R: Maximizing the pseudolikelihood.
  > test-stocapprox.R: Finished MPLE.
  > test-stocapprox.R:          edges       gwdegree gwdegree.decay 
  > test-stocapprox.R:     -1.5333754     -0.1317716      0.6729982 
  > test-stocapprox.R: Starting burnin of 16384 steps
  > test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
  > test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
  > test-stocapprox.R: Stochastic Approximation estimate:
  > test-stocapprox.R:          edges       gwdegree gwdegree.decay 
  > test-stocapprox.R:    -1.57231795    -0.05712682     0.44962020 
  > test-stocapprox.R: Phase 3:  1000 iterations (interval=1024)
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-stocapprox.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-stocapprox.R: Obtaining the responsible dyads.
  > test-stocapprox.R: Evaluating the predictor and response matrix.
  > test-stocapprox.R: Maximizing the pseudolikelihood.
  > test-stocapprox.R: Finished MPLE.
  > test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-stocapprox.R: Iteration 1 of at most 60:
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-stocapprox.R: 1 Optimizing with step length 1.0000.
  > test-stocapprox.R: The log-likelihood improved by 0.0007.
  > test-stocapprox.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
  > test-stocapprox.R: Finished MCMLE.
  > test-stocapprox.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-stocapprox.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-stocapprox.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-stocapprox.R: Iteration 1 of at most 60:
  > test-stocapprox.R: Convergence test P-value:1.1e-111
  > test-stocapprox.R: 1 
  > test-stocapprox.R: The log-likelihood improved by 1.945.
  > test-stocapprox.R: Iteration 2 of at most 60:
  > test-stocapprox.R: Convergence test P-value:1.4e-44
  > test-stocapprox.R: 1 
  > test-stocapprox.R: The log-likelihood improved by 0.5962.
  > test-stocapprox.R: Iteration 3 of at most 60:
  > test-stocapprox.R: Convergence test P-value:4e-07
  > test-stocapprox.R: 1 The log-likelihood improved by 0.06364.
  > test-stocapprox.R: Iteration 4 of at most 60:
  > test-stocapprox.R: Convergence test P-value:5.6e-05
  > test-stocapprox.R: 1 
  > test-stocapprox.R:                       nonzero transitiveweights.min.max.min 
  > test-stocapprox.R:                     -1.743217                      0.112619 
  > test-stocapprox.R: Starting burnin of 16384 steps
  > test-stocapprox.R: The log-likelihood improved by 0.04097.
  > test-stocapprox.R: Iteration 5 of at most 60:
  > test-stocapprox.R: Convergence test P-value:9.3e-03
  > test-stocapprox.R: 1 The log-likelihood improved by 0.01842.
  > test-stocapprox.R: Iteration 6 of at most 60:
  > test-stocapprox.R: Convergence test P-value:5.9e-01
  > test-stocapprox.R: Convergence detected. Stopping.
  > test-stocapprox.R: 1 The log-likelihood improved by 0.002083.
  > test-stocapprox.R: Finished CD.
  > test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to:
  > test-stocapprox.R: Phase 1: 200 steps (interval = 1024)
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-stocapprox.R: Stochastic Approximation estimate:
  > test-stocapprox.R:                       nonzero transitiveweights.min.max.min 
  > test-stocapprox.R:                    -1.7631980                     0.1383531 
  > test-stocapprox.R: Phase 3:  1000 iterations (interval=1024)
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-stocapprox.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-stocapprox.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-stocapprox.R: Starting contrastive divergence estimation via CD-MCMLE:
  > test-stocapprox.R: Iteration 1 of at most 60:
  > test-stocapprox.R: Convergence test P-value:1.4e-98
  > test-stocapprox.R: 1 
  > test-stocapprox.R: The log-likelihood improved by 1.862.
  > test-stocapprox.R: Iteration 2 of at most 60:
  > test-stocapprox.R: Convergence test P-value:3.5e-30
  > test-stocapprox.R: 1 The log-likelihood improved by 0.3427.
  > test-stocapprox.R: Iteration 3 of at most 60:
  > test-stocapprox.R: Convergence test P-value:3e-09
  > test-stocapprox.R: 1 The log-likelihood improved by 0.08204.
  > test-stocapprox.R: Iteration 4 of at most 60:
  > test-stocapprox.R: Convergence test P-value:3.9e-02
  > test-stocapprox.R: 1 
  > test-stocapprox.R: The log-likelihood improved by 0.01313.
  > test-stocapprox.R: Iteration 5 of at most 60:
  > test-stocapprox.R: Convergence test P-value:9.2e-02
  > test-stocapprox.R: 1 
  > test-stocapprox.R: The log-likelihood improved by 0.009411.
  > test-stocapprox.R: Iteration 6 of at most 60:
  > test-stocapprox.R: Convergence test P-value:2.6e-01
  > test-stocapprox.R: 1 The log-likelihood improved by 0.005336.
  > test-stocapprox.R: Iteration 7 of at most 60:
  > test-stocapprox.R: Convergence test P-value:7.9e-01
  > test-stocapprox.R: Convergence detected. Stopping.
  > test-stocapprox.R: 1 The log-likelihood improved by 0.0009177.
  > test-stocapprox.R: Finished CD.
  > test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-stocapprox.R: Iteration 1 of at most 60:
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-stocapprox.R: 1 Optimizing with step length 1.0000.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-stocapprox.R: The log-likelihood improved by 0.0022.
  > test-stocapprox.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-stocapprox.R: Finished MCMLE.
  > test-stocapprox.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-stocapprox.R: 
  > test-stocapprox.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
  > test-stocapprox.R: * 'news(package="ergm.count")' for changes since last version
  > test-stocapprox.R: * 'citation("ergm.count")' for citation information
  > test-stocapprox.R: * 'https://statnet.org' for help, support, and other information
  > test-stocapprox.R: 
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-target-offset.R: Unable to match target stats. Using MCMLE estimation.
  > test-target-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-target-offset.R: Iteration 1 of at most 60:
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-target-offset.R: 1 Optimizing with step length 1.0000.
  > test-target-offset.R: The log-likelihood improved by 0.0210.
  > test-target-offset.R: Convergence test p-value: 0.0002. Converged with 99% confidence.
  > test-target-offset.R: Finished MCMLE.
  > test-target-offset.R: Evaluating log-likelihood at the estimate. 
  > test-target-offset.R: Fitting the dyad-independent submodel...
  > test-target-offset.R: Bridging between the dyad-independent submodel and the full model...
  > test-target-offset.R: Setting up bridge sampling...
  > test-target-offset.R: Using 16 bridges: 1 
  > test-target-offset.R: 2 3 4 5 
  > test-target-offset.R: 6 7 
  > test-target-offset.R: 8 
  > test-target-offset.R: 9 10 11 12 13 14 
  > test-target-offset.R: 15 16 
  > test-target-offset.R: .
  > test-target-offset.R: Bridging finished.
  > test-target-offset.R: 
  > test-target-offset.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-target-offset.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-target-offset.R: Sample statistics summary:
  > test-target-offset.R: 
  > test-target-offset.R: Iterations = 14336:262144
  > test-target-offset.R: Thinning interval = 1024 
  > test-target-offset.R: Number of chains = 1 
  > test-target-offset.R: Sample size per chain = 243 
  > test-target-offset.R: 
  > test-target-offset.R: 1. Empirical mean and standard deviation for each variable,
  > test-target-offset.R:    plus standard error of the mean:
  > test-target-offset.R: 
  > test-target-offset.R:            Mean    SD Naive SE Time-series SE
  > test-target-offset.R: edges   0.55556 4.490   0.2880         0.2880
  > test-target-offset.R: degree1 0.04527 2.005   0.1286         0.1286
  > test-target-offset.R: 
  > test-target-offset.R: 2. Quantiles for each variable:
  > test-target-offset.R: 
  > test-target-offset.R:         2.5%  25% 50% 75% 97.5%
  > test-target-offset.R: edges     -7 -2.5   0   3  9.00
  > test-target-offset.R: degree1   -3 -1.0   0   1  4.95
  > test-target-offset.R: 
  > test-target-offset.R: 
  > test-target-offset.R: Are sample statistics significantly different from observed?
  > test-target-offset.R:                 edges    degree1      (Omni)
  > test-target-offset.R: diff.      0.55555556 0.04526749          NA
  > test-target-offset.R: test stat. 1.92893422 0.35200754 9.455405747
  > test-target-offset.R: P-val.     0.05373903 0.72483261 0.009922641
  > test-target-offset.R: 
  > test-target-offset.R: Sample statistics cross-correlations:
  > test-target-offset.R:              edges    degree1
  > test-target-offset.R: edges    1.0000000 -0.7250142
  > test-target-offset.R: degree1 -0.7250142  1.0000000
  > test-target-offset.R: 
  > test-target-offset.R: Sample statistics auto-correlation:
  > test-target-offset.R: Chain 1 
  > test-target-offset.R:                 edges      degree1
  > test-target-offset.R: Lag 0     1.000000000  1.000000000
  > test-target-offset.R: Lag 1024 -0.061427219  0.030194492
  > test-target-offset.R: Lag 2048  0.007868535  0.092075103
  > test-target-offset.R: Lag 3072  0.018624816  0.047810603
  > test-target-offset.R: Lag 4096 -0.047471894  0.007659206
  > test-target-offset.R: Lag 5120 -0.073593205 -0.009870131
  > test-target-offset.R: 
  > test-target-offset.R: Sample statistics burn-in diagnostic (Geweke):
  > test-target-offset.R: Chain 1 
  > test-target-offset.R: 
  > test-target-offset.R: Fraction in 1st window = 0.1
  > test-target-offset.R: Fraction in 2nd window = 0.5 
  > test-target-offset.R: 
  > test-target-offset.R:      edges    degree1 
  > test-target-offset.R:  0.4498910 -0.1601093 
  > test-target-offset.R: 
  > test-target-offset.R: Individual P-values (lower = worse):
  > test-target-offset.R:    edges  degree1 
  > test-target-offset.R: 0.652789 0.872795 
  > test-target-offset.R: Joint P-value (lower = worse):  0.8380775 
  > test-target-offset.R: 
  > test-target-offset.R: Note: MCMC diagnostics shown here are from the last round of
  > test-target-offset.R:   simulation, prior to computation of final parameter estimates.
  > test-target-offset.R:   Because the final estimates are refinements of those used for this
  > test-target-offset.R:   simulation run, these diagnostics may understate model performance.
  > test-target-offset.R:   To directly assess the performance of the final model on in-model
  > test-target-offset.R:   statistics, please use the GOF command: gof(ergmFitObject,
  > test-target-offset.R:   GOF=~model).
  > test-target-offset.R: 
  > test-target-offset.R: Unable to match target stats. Using MCMLE estimation.
  > test-target-offset.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-target-offset.R: Obtaining the responsible dyads.
  > test-target-offset.R: Evaluating the predictor and response matrix.
  > test-target-offset.R: Maximizing the pseudolikelihood.
  > test-target-offset.R: Finished MPLE.
  > test-target-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-target-offset.R: Iteration 1 of at most 3:
  > test-target-offset.R: 1 
  > test-target-offset.R: Optimizing with step length 0.7297.
  > test-target-offset.R: The log-likelihood improved by < 0.0001.
  > test-target-offset.R: Iteration 2 of at most 3:
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-target-offset.R: 1 Optimizing with step length 0.6389.
  > test-target-offset.R: The log-likelihood improved by < 0.0001.
  > test-target-offset.R: Iteration 3 of at most 3:
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-target-offset.R: 1 
  > test-target-offset.R: Optimizing with step length 0.8386.
  > test-target-offset.R: The log-likelihood improved by < 0.0001.
  > test-target-offset.R: MCMLE estimation did not converge after 3 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
  > test-target-offset.R: Finished MCMLE.
  > test-target-offset.R: Evaluating log-likelihood at the estimate. 
  > test-target-offset.R: Fitting the dyad-independent submodel...
  > test-target-offset.R: Bridging between the dyad-independent submodel and the full model...
  > test-target-offset.R: Setting up bridge sampling...
  > test-target-offset.R: Using 16 bridges: 1 
  > test-target-offset.R: 2 3 
  > test-target-offset.R: 4 5 
  > test-target-offset.R: 6 
  > test-target-offset.R: 7 8 9 10 
  > test-target-offset.R: 11 12 
  > test-target-offset.R: 13 
  > test-target-offset.R: 14 15 16 
  > test-target-offset.R: .
  > test-target-offset.R: Bridging finished.
  > test-target-offset.R: 
  > test-target-offset.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-target-offset.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-target-offset.R: Sample statistics summary:
  > test-target-offset.R: 
  > test-target-offset.R: Iterations = 3584:65536
  > test-target-offset.R: Thinning interval = 256 
  > test-target-offset.R: Number of chains = 1 
  > test-target-offset.R: Sample size per chain = 243 
  > test-target-offset.R: 
  > test-target-offset.R: 1. Empirical mean and standard deviation for each variable,
  > test-target-offset.R:    plus standard error of the mean:
  > test-target-offset.R: 
  > test-target-offset.R:                    Mean       SD Naive SE Time-series SE
  > test-target-offset.R: edges          12.77778 5.118432  0.32835      0.3283476
  > test-target-offset.R: gwdegree        0.84362 0.386003  0.02476      0.0247621
  > test-target-offset.R: gwdegree.decay  0.01236 0.002961  0.00019      0.0001293
  > test-target-offset.R: degree0        -0.84362 0.386003  0.02476      0.0247621
  > test-target-offset.R: 
  > test-target-offset.R: 2. Quantiles for each variable:
  > test-target-offset.R: 
  > test-target-offset.R:                      2.5%      25%      50%      75%    97.5%
  > test-target-offset.R: edges           3.000e+00  9.00000 13.00000 16.00000 23.00000
  > test-target-offset.R: gwdegree        1.063e-12  1.00000  1.00000  1.00000  1.00000
  > test-target-offset.R: gwdegree.decay  5.955e-03  0.01191  0.01191  0.01489  0.01489
  > test-target-offset.R: degree0        -1.000e+00 -1.00000 -1.00000 -1.00000  0.00000
  > test-target-offset.R: 
  > test-target-offset.R: 
  > test-target-offset.R: Sample statistics cross-correlations:
  > test-target-offset.R:                     edges   gwdegree gwdegree.decay    degree0
  > test-target-offset.R: edges           1.0000000  0.2709648      0.6049364 -0.2709648
  > test-target-offset.R: gwdegree        0.2709648  1.0000000      0.5143643 -1.0000000
  > test-target-offset.R: gwdegree.decay  0.6049364  0.5143643      1.0000000 -0.5143643
  > test-target-offset.R: degree0        -0.2709648 -1.0000000     -0.5143643  1.0000000
  > test-target-offset.R: 
  > test-target-offset.R: Sample statistics auto-correlation:
  > test-target-offset.R: Chain 1 
  > test-target-offset.R:                 edges    gwdegree gwdegree.decay     degree0
  > test-target-offset.R: Lag 0     1.000000000  1.00000000    1.000000000  1.00000000
  > test-target-offset.R: Lag 256   0.006056003  0.02865300   -0.034893817  0.02865300
  > test-target-offset.R: Lag 512   0.062813023 -0.07862186    0.002608612 -0.07862186
  > test-target-offset.R: Lag 768  -0.089332866 -0.07930006   -0.150790861 -0.07930006
  > test-target-offset.R: Lag 1024 -0.091496281 -0.07564135   -0.189123113 -0.07564135
  > test-target-offset.R: Lag 1280  0.030192390  0.03461402    0.073975025  0.03461402
  > test-target-offset.R: 
  > test-target-offset.R: Sample statistics burn-in diagnostic (Geweke):
  > test-target-offset.R: Chain 1 
  > test-target-offset.R: 
  > test-target-offset.R: Fraction in 1st window = 0.1
  > test-target-offset.R: Fraction in 2nd window = 0.5 
  > test-target-offset.R: 
  > test-target-offset.R:          edges       gwdegree gwdegree.decay        degree0 
  > test-target-offset.R:     0.05663036    -1.34419649     0.38772965     1.34419649 
  > test-target-offset.R: 
  > test-target-offset.R: Individual P-values (lower = worse):
  > test-target-offset.R:          edges       gwdegree gwdegree.decay        degree0 
  > test-target-offset.R:      0.9548397      0.1788849      0.6982161      0.1788849 
  > test-target-offset.R: Joint P-value (lower = worse):  0.3850888 
  > test-target-offset.R: 
  > test-target-offset.R: Note: MCMC diagnostics shown here are from the last round of
  > test-target-offset.R:   simulation, prior to computation of final parameter estimates.
  > test-target-offset.R:   Because the final estimates are refinements of those used for this
  > test-target-offset.R:   simulation run, these diagnostics may understate model performance.
  > test-target-offset.R:   To directly assess the performance of the final model on in-model
  > test-target-offset.R:   statistics, please use the GOF command: gof(ergmFitObject,
  > test-target-offset.R:   GOF=~model).
  > test-target-offset.R: 
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-term-Offset.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-Offset.R: Obtaining the responsible dyads.
  > test-term-Offset.R: Evaluating the predictor and response matrix.
  > test-term-Offset.R: Maximizing the pseudolikelihood.
  > test-term-Offset.R: Finished MPLE.
  > test-term-Offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-term-Offset.R: Iteration 1 of at most 60:
  > test-term-Offset.R: 1 Optimizing with step length 1.0000.
  > test-term-Offset.R: The log-likelihood improved by 0.0066.
  > test-term-Offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-term-Offset.R: Finished MCMLE.
  > test-term-Offset.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-term-Offset.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-term-Offset.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-Offset.R: Obtaining the responsible dyads.
  > test-term-Offset.R: Evaluating the predictor and response matrix.
  > test-term-Offset.R: Maximizing the pseudolikelihood.
  > test-term-Offset.R: Finished MPLE.
  > test-term-Offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-term-Offset.R: Iteration 1 of at most 60:
  > test-term-Offset.R: 1 
  > test-term-Offset.R: Optimizing with step length 1.0000.
  > test-term-Offset.R: The log-likelihood improved by 0.0024.
  > test-term-Offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-term-Offset.R: Finished MCMLE.
  > test-term-Offset.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-term-Offset.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  Saving _problems/test-term-Offset-21.R
  Saving _problems/test-term-Offset-25.R
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-term-b12nodematch.R: In term 'b1nodematch' in package 'ergm': Argument 'keep' has been superseded by 'levels', and it is recommended to use the latter.  Note that its interpretation may be different.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Observed statistic(s) b1dsp3 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: In term 'b1factor' in package 'ergm': Argument 'base' has been superseded by 'levels', and it is recommended to use the latter.  Note that its interpretation may be different.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: In term 'b1twostar' in package 'ergm': Argument 'base' has been superseded by 'levels2', and it is recommended to use the latter.  Note that its interpretation may be different.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: In term 'asymmetric' in package 'ergm': Argument 'keep' has been superseded by 'levels', and it is recommended to use the latter.  Note that its interpretation may be different.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Observed statistic(s) b2dsp3 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Observed statistic(s) ideg7+.homophily.group and ideg8+.homophily.group are at their smallest attainable values. Their coefficients will be fixed at -Inf.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Observed statistic(s) gwodeg.fixed.0 are at their greatest attainable values. Their coefficients will be fixed at +Inf.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-bipartite.R: Obtaining the responsible dyads.
  > test-term-bipartite.R: Evaluating the predictor and response matrix.
  > test-term-bipartite.R: Maximizing the pseudolikelihood.
  > test-term-bipartite.R: Finished MPLE.
  > test-term-directed.R: In term 'nodeifactor' in package 'ergm': Argument 'base' has been superseded by 'levels', and it is recommended to use the latter.  Note that its interpretation may be different.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-directed.R: Observed statistic(s) odeg7+ and odeg8+ are at their smallest attainable values. Their coefficients will be fixed at -Inf.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-directed.R: Observed statistic(s) odeg6+.homophily.group, odeg7+.homophily.group, and odeg8+.homophily.group are at their smallest attainable values. Their coefficients will be fixed at -Inf.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Finished MPLE.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-directed.R: Obtaining the responsible dyads.
  > test-term-directed.R: Evaluating the predictor and response matrix.
  > test-term-directed.R: Maximizing the pseudolikelihood.
  > test-term-directed.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Observed statistic(s) edgecov.YearsTrusted are at their greatest attainable values. Their coefficients will be fixed at +Inf.
  > test-term-flexible.R: All terms are either offsets or extreme values. No optimization is performed.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-flexible.R: Obtaining the responsible dyads.
  > test-term-flexible.R: Evaluating the predictor and response matrix.
  > test-term-flexible.R: Maximizing the pseudolikelihood.
  > test-term-flexible.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-mm.R: Note: Term 'mm(~Grade >= 10, levels = -1)' skipped because it contributes no statistics.
  > test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-mm.R: Obtaining the responsible dyads.
  > test-term-mm.R: Evaluating the predictor and response matrix.
  > test-term-mm.R: Maximizing the pseudolikelihood.
  > test-term-mm.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-mm.R: Obtaining the responsible dyads.
  > test-term-mm.R: Evaluating the predictor and response matrix.
  > test-term-mm.R: Maximizing the pseudolikelihood.
  > test-term-mm.R: Finished MPLE.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-mm.R: Obtaining the responsible dyads.
  > test-term-mm.R: Evaluating the predictor and response matrix.
  > test-term-mm.R: Maximizing the pseudolikelihood.
  > test-term-mm.R: Finished MPLE.
  > test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-mm.R: Obtaining the responsible dyads.
  > test-term-mm.R: Evaluating the predictor and response matrix.
  > test-term-mm.R: Maximizing the pseudolikelihood.
  > test-term-mm.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-options.R: Obtaining the responsible dyads.
  > test-term-options.R: Evaluating the predictor and response matrix.
  > test-term-options.R: Maximizing the pseudolikelihood.
  > test-term-options.R: Finished MPLE.
  > test-term-options.R: Evaluating log-likelihood at the estimate. 
  > test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-options.R: Obtaining the responsible dyads.
  > test-term-options.R: Evaluating the predictor and response matrix.
  > test-term-options.R: Maximizing the pseudolikelihood.
  > test-term-options.R: Finished MPLE.
  > test-term-options.R: Evaluating log-likelihood at the estimate. 
  > test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-options.R: Obtaining the responsible dyads.
  > test-term-options.R: Evaluating the predictor and response matrix.
  > test-term-options.R: Maximizing the pseudolikelihood.
  > test-term-options.R: Finished MPLE.
  > test-term-options.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-term-options.R: Iteration 1 of at most 60:
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-options.R: 1 Optimizing with step length 1.0000.
  > test-term-options.R: The log-likelihood improved by 0.0013.
  > test-term-options.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-term-options.R: Finished MCMLE.
  > test-term-options.R: Evaluating log-likelihood at the estimate. 
  > test-term-options.R: Fitting the dyad-independent submodel...
  > test-term-options.R: Bridging between the dyad-independent submodel and the full model...
  > test-term-options.R: Setting up bridge sampling...
  > test-term-options.R: Using 16 bridges: 1 
  > test-term-options.R: 2 
  > test-term-options.R: 3 
  > test-term-options.R: 4 
  > test-term-options.R: 5 6 
  > test-term-options.R: 7 
  > test-term-options.R: 8 
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-options.R: 9 10 
  > test-term-options.R: 11 
  > test-term-options.R: 12 
  > test-term-options.R: 13 
  > test-term-options.R: 14 15 16 
  > test-term-options.R: .
  > test-term-options.R: Bridging finished.
  > test-term-options.R: 
  > test-term-options.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-term-options.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-options.R: Obtaining the responsible dyads.
  > test-term-options.R: Evaluating the predictor and response matrix.
  > test-term-options.R: Maximizing the pseudolikelihood.
  > test-term-options.R: Finished MPLE.
  > test-term-options.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-term-options.R: Iteration 1 of at most 60:
  > test-term-options.R: 1 Optimizing with step length 1.0000.
  > test-term-options.R: The log-likelihood improved by 0.0003.
  > test-term-options.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-term-options.R: Finished MCMLE.
  > test-term-options.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-term-options.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-undirected.R: Obtaining the responsible dyads.
  > test-term-undirected.R: Evaluating the predictor and response matrix.
  > test-term-undirected.R: Maximizing the pseudolikelihood.
  > test-term-undirected.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-term-gw-sp.R: Obtaining the responsible dyads.
  > test-term-gw-sp.R: Evaluating the predictor and response matrix.
  > test-term-gw-sp.R: Maximizing the pseudolikelihood.
  > test-term-gw-sp.R: Finished MPLE.
  > test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
  > test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
  > test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.
  > test-u-function.R: Best valid proposal 'DiscUnif2' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-u-function.R: Best valid proposal 'DiscTNT' cannot take into account hint(s) 'triadic'.
  > test-valued-sim.R: mean=1, var=4, corr=0.3
  > test-valued-sim.R: eta=(0.192307692307692,0.0824175824175824,0.362637362637363)
  > test-valued-sim.R: Best valid proposal 'StdNormal' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-valued-sim.R: Simulated mean (stats only):0.9964445
  > test-valued-sim.R: Best valid proposal 'StdNormal' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-valued-sim.R: Simulated means (target=1):
  > test-valued-sim.R:           [,1]      [,2]      [,3]
  > test-valued-sim.R: [1,]        NA 1.0034478 0.9683903
  > test-valued-sim.R: [2,] 1.0302079        NA 0.8923730
  > test-valued-sim.R: [3,] 0.6870641 0.7628159        NA
  > test-valued-sim.R: Simulated vars (target=4):
  > test-valued-sim.R:          [,1]     [,2]     [,3]
  > test-valued-sim.R: [1,]       NA 3.863045 4.034701
  > test-valued-sim.R: [2,] 3.944388       NA 3.888997
  > test-valued-sim.R: [3,] 3.740890 4.202553       NA
  > test-valued-sim.R: Simulated correlations (1,2) (1,3) (2,3) (target=0.3):
  > test-valued-sim.R: [1] 0.2781206 0.2324470 0.3420405
  > test-valued-sim.R: ==== output='stats', coef=2.380183
  > test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-valued-sim.R: ==== output='network', coef=2.380183
  > test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-valued-sim.R: ==== output='stats', coef=0
  > test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-valued-sim.R: ==== output='network', coef=0
  > test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-valued-sim.R: ==== output='stats', coef=2.8858
  > test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-valued-sim.R: ==== output='network', coef=2.8858
  > test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-valued-sim.R: ==== output='stats', coef=0
  > test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-valued-sim.R: ==== output='network', coef=0
  > test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-valued-sim.R: 
  > test-valued-sim.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
  > test-valued-sim.R: * 'news(package="ergm.count")' for changes since last version
  > test-valued-sim.R: * 'citation("ergm.count")' for citation information
  > test-valued-sim.R: * 'https://statnet.org' for help, support, and other information
  > test-valued-sim.R: 
  > test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'.
  > test-valued-terms.R: 
  > test-valued-terms.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project
  > test-valued-terms.R: * 'news(package="ergm.count")' for changes since last version
  > test-valued-terms.R: * 'citation("ergm.count")' for citation information
  > test-valued-terms.R: * 'https://statnet.org' for help, support, and other information
  > test-valued-terms.R: 
  [ FAIL 2 | WARN 0 | SKIP 1 | PASS 4275 ]
  
  ══ Skipped tests (1) ═══════════════════════════════════════════════════════════
  • empty test (1):
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Failure ('test-term-Offset.R:19:3'): Estimation with Offset() operator works ──
  Expected failure message to match regexp ".* did not throw the expected message.*".
  Actual message:
  x | Expected `off <- ergm(nw ~ edges + offset(triangle), offset.coef = 0.1)` to throw a message.
  ── Failure ('test-term-Offset.R:23:3'): Estimation with Offset() operator works ──
  Expected failure message to match regexp ".* did not throw the expected message.*".
  Actual message:
  x | Expected `Off <- ergm(nw ~ edges + Offset(~triangle, which = 1, coef = 0.1))` to throw a message.
  
  [ FAIL 2 | WARN 0 | SKIP 1 | PASS 4275 ]
  Error:
  ! Test failures.
  Execution halted