• using R Under development (unstable) (2025-11-13 r89017)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     clang version 21.1.5     flang version 21.1.5
• running under: Fedora Linux 42 (Workstation Edition)
• using session charset: UTF-8
• using option ‘--no-stop-on-test-error’
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• this is package ‘ergm’ version ‘4.10.1’
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• checking whether package ‘ergm’ can be installed ... [122s/163s] OK See the install log for details.
• used C compiler: ‘clang version 21.1.5’
• checking installed package size ... INFO   installed size is 5.2Mb   sub-directories of 1Mb or more:     R 1.1Mb     doc 1.3Mb     help 1.1Mb
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• checking tests ... [559s/373s] ERROR   Running ‘requireNamespaceTest.R’   Running ‘testthat.R’ [553s/365s] 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   > 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.   > test-basis.R: 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:   > test-basis.R: 1   > test-basis.R: 2   > test-basis.R: 3   > test-basis.R: 4   > test-basis.R: 5   > test-basis.R: 6   > test-basis.R: 7   > test-basis.R: 8   > test-basis.R: 9   > test-basis.R: 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: .   > 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:   > test-basis.R: 1   > test-basis.R: 2   > test-basis.R: 3   > test-basis.R: 4   > test-basis.R: 5   > test-basis.R: 6   > test-basis.R: 7   > test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > test-basis.R: 8   > test-basis.R: 9   > test-basis.R: 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: .   > 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-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > 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 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:   > test-basis.R: 1   > test-basis.R: 2   > test-basis.R: 3   > test-basis.R: 4   > test-basis.R: 5   > test-basis.R: 6   > test-basis.R: 7   > test-basis.R: 8   > test-basis.R: 9   > test-basis.R: 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: .   > 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   > 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-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   > 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:   > 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:   > 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   > 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: 1   > test-bridge-target.stats.R: 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.   > test-bridge-target.stats.R: 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-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: 1   > test-basis.R: Optimizing with step length 1.0000.   > test-basis.R: The log-likelihood improved by 0.0072.   > test-bridge-target.stats.R: Using 16 bridges:   > test-bridge-target.stats.R: 1   > test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence.   > test-basis.R: Finished MCMLE.   > test-bridge-target.stats.R: 2   > test-basis.R: Evaluating log-likelihood at the estimate. Setting up bridge sampling...   > test-bridge-target.stats.R: 3   > test-bridge-target.stats.R: 4   > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > test-bridge-target.stats.R: 5   > test-bridge-target.stats.R: 6   > test-bridge-target.stats.R: 7   > test-basis.R: Using 16 bridges: 1   > test-bridge-target.stats.R: 8   > test-bridge-target.stats.R: 9   > test-basis.R: 2   > test-bridge-target.stats.R: 10   > test-basis.R: 3   > test-bridge-target.stats.R: 11   > test-basis.R: 4   > test-bridge-target.stats.R: 12   > test-basis.R: 5   > test-bridge-target.stats.R: 13   > test-basis.R: 6   > test-basis.R: 7   > test-bridge-target.stats.R: 14   > test-basis.R: 8   > test-bridge-target.stats.R: 15   > test-basis.R: 9   > test-bridge-target.stats.R: 16   > test-basis.R: 10   > test-bridge-target.stats.R: .   > 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: 11   > test-basis.R: 12   > test-basis.R: 13   > test-basis.R: 14   > test-basis.R: 15   > test-basis.R: 16   > 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: Fitting the dyad-independent submodel...   > 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-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: 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-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-bridge-target.stats.R: Using 16 bridges: 1   > 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   > 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: 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-bridge-target.stats.R: 7   > 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: 8   > 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   > test-bridge-target.stats.R: 9   > test-basis.R: 3   > test-basis.R: 4   > test-basis.R: 5   > test-basis.R: 6   > test-basis.R: 7   > test-bridge-target.stats.R: 10   > test-basis.R: 8   > test-basis.R: 9   > test-basis.R: 10   > test-basis.R: 11   > test-basis.R: 12   > test-basis.R: 13   > test-basis.R: 14   > test-bridge-target.stats.R: 11   > test-basis.R: 15   > test-basis.R: 16   > 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: 12   > 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-bridge-target.stats.R: 13   > 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   > 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-basis.R: 1   > test-basis.R: The log-likelihood improved by 0.8158.   > test-basis.R: Iteration 4 of at most 60:   > test-bridge-target.stats.R: 14   > 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-bridge-target.stats.R: 15   > 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:   > test-bridge-target.stats.R: 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   > 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: 6   > test-bridge-target.stats.R: 7   > test-bridge-target.stats.R: 8   > test-bridge-target.stats.R: 9   > test-basis.R: 1   > test-basis.R: Optimizing with step length 1.0000.   > test-bridge-target.stats.R: 10   > test-basis.R: The log-likelihood improved by 0.0072.   > test-basis.R: Convergence test p-value: 0.0001.   > test-basis.R: 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-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   > test-basis.R: 3   > test-bridge-target.stats.R: 11   > test-basis.R: 4   > test-basis.R: 5   > test-basis.R: 6   > test-basis.R: 7   > test-basis.R: 8   > test-basis.R: 9   > test-bridge-target.stats.R: 12   > test-basis.R: 10   > test-basis.R: 11   > test-basis.R: 12   > test-basis.R: 13   > test-bridge-target.stats.R: 13   > test-basis.R: 14   > test-basis.R: 15   > test-basis.R: 16   > 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   > test-basis.R: 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: 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-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/RtmpddOSCg/working_dir/RtmpQ5Lrjm/file184e0213f9e8a6_001.RData'.   > test-bridge-target.stats.R: Using 16 bridges:   > test-bridge-target.stats.R: 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-bridge-target.stats.R: 6   > test-checkpointing.R: 1   > test-checkpointing.R: 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/RtmpddOSCg/working_dir/RtmpQ5Lrjm/file184e0213f9e8a6_002.RData'.   > 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-checkpointing.R: 1   > test-checkpointing.R: Optimizing with step length 1.0000.   > test-bridge-target.stats.R: 11   > test-bridge-target.stats.R: 12   > test-bridge-target.stats.R: 13   > 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.   > test-bridge-target.stats.R: 14   > test-checkpointing.R: Fitting the dyad-independent submodel...   > test-bridge-target.stats.R: 15   > 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: 16   > test-checkpointing.R: Using 16 bridges:   > test-checkpointing.R: 1   > test-bridge-target.stats.R: .   > test-bridge-target.stats.R: Bridging finished.   > test-checkpointing.R: 2   > test-bridge-target.stats.R: Fitting the dyad-independent submodel...   > test-checkpointing.R: 3   > test-checkpointing.R: 4   > test-checkpointing.R: 5   > test-checkpointing.R: 6   > test-checkpointing.R: 7   > test-checkpointing.R: 8   > 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: 9   > test-checkpointing.R: 10   > test-bridge-target.stats.R: Using 16 bridges:   > test-bridge-target.stats.R: 1   > test-checkpointing.R: 11   > test-checkpointing.R: 12   > test-checkpointing.R: 13   > test-checkpointing.R: 14   > test-checkpointing.R: 15   > test-checkpointing.R: 16   > test-bridge-target.stats.R: 2   > 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/RtmpddOSCg/working_dir/RtmpQ5Lrjm/file184e0213f9e8a6_002.RData'.   > test-bridge-target.stats.R: 3   > test-checkpointing.R: Iteration 1 of at most 60:   > 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-checkpointing.R: 1   > test-checkpointing.R: Optimizing with step length 1.0000.   > 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: 9   > test-checkpointing.R: Fitting the dyad-independent submodel...   > test-bridge-target.stats.R: 10   > test-checkpointing.R: Bridging between the dyad-independent submodel and the full model...   > test-checkpointing.R: Setting up bridge sampling...   > test-checkpointing.R: Using 16 bridges:   > test-checkpointing.R: 1   > test-bridge-target.stats.R: 11   > test-checkpointing.R: 2   > test-checkpointing.R: 3   > test-checkpointing.R: 4   > test-checkpointing.R: 5   > test-checkpointing.R: 6   > test-checkpointing.R: 7   > test-bridge-target.stats.R: 12   > test-checkpointing.R: 8   > test-checkpointing.R: 9   > test-checkpointing.R: 10   > test-checkpointing.R: 11   > test-checkpointing.R: 12   > test-checkpointing.R: 13   > test-checkpointing.R: 14   > test-bridge-target.stats.R: 13   > 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-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   > test-constrain-degrees-edges.R: 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-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-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-degrees-edges.R: Best valid proposal 'ConstantEdges' 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: 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: * 'c   > test-constrain-blockdiag.R: itation("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 'ConstantEdges' 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 '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: 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   > test-constrain-dind.R: 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.   > test-constrain-dind.R: 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-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   > test-constrain-dind.R: 3   > test-constrain-dind.R: 4   > test-constrain-dind.R: 5   > 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   > test-constrain-dind.R: 13   > test-constrain-dind.R: 14 15 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-dind.R:   > 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:   > 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:   > 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:   > 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:   > 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:   > 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-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: .   > 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:   > 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: .   > 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:   > 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:   > 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:   > 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:   > 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-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-drop.R: 1   > test-drop.R: Optimizing with step length 1.0000.   > test-drop.R: The log-likelihood improved by 0.0001.   > test-drop.R: Convergence test p-value: < 0.0001.   > test-drop.R: Converged with 99% confidence.   > test-drop.R: Finished MCMLE.   > test-drop.R: Evaluating log-likelihood at the estimate.   > test-drop.R: 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:   > test-drop.R: 1   > test-drop.R: 2   > test-drop.R: 3   > test-drop.R: 4   > test-drop.R: 5   > test-drop.R: 6   > test-drop.R: 7   > test-drop.R: 8   > test-drop.R: 9   > test-drop.R: 10   > test-drop.R: 11   > 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-drop.R: 12   > test-constraints.R: Finished MPLE.   > test-constraints.R: Evaluating log-likelihood at the estimate.   > test-drop.R: 13   > 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: 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:   > 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-drop.R: 1   > test-drop.R: Optimizing with step length 1.0000.   > test-drop.R: The log-likelihood improved by 0.0068.   > 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.   > test-drop.R: 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:   > test-drop.R: 1   > test-drop.R: 2   > test-drop.R: 3   > 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-drop.R: 4   > test-constraints.R: Maximizing the pseudolikelihood.   > test-constraints.R: Finished MPLE.   > test-drop.R: 5   > test-constraints.R: Evaluating log-likelihood at the estimate.   > test-constraints.R:   > test-drop.R: 6   > test-drop.R: 7   > test-drop.R: 8   > test-drop.R: 9   > test-drop.R: 10   > test-drop.R: 11   > test-drop.R: 12   > test-drop.R: 13   > 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:   > test-drop.R: '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-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-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-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:   > 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   > test-drop.R: 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.   > test-drop.R: Initializing model to obtain the list of dyad-independent terms...   > test-drop.R: Fitting the dyad-independent submodel...   > 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.   > test-constraints.R: Setting up bridge sampling...   > 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-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'.   > test-constraints.R: Using 16 bridges: 1   > 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-constraints.R: 2   > 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-constraints.R: 3   > 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-constraints.R: 4   > 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-constraints.R: 5   > 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-constraints.R: 6   > 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-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-constraints.R: 14   > test-constraints.R: 15   > test-constraints.R: 16   > 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   > test-constraints.R: 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-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-constraints.R:   > 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   > test-constraints.R: 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   > test-constraints.R: 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   > test-constraints.R: 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-constraints.R: 1   > test-constraints.R: 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.   > test-constraints.R: Converged with 99% confidence.   > test-constraints.R: Finished MCMLE.   > test-constraints.R: Evaluating log-likelihood at the estimate.   > test-constraints.R: Setting up bridge sampling...   > test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'.   > test-constraints.R: Using 16 bridges:   > test-constraints.R: 1   > test-constraints.R: 2   > test-constraints.R: 3   > test-constraints.R: 4   > test-constraints.R: 5   > test-constraints.R: 6   > test-ergm-san.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.   > 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-constraints.R: 14   > test-constraints.R: 15   > test-constraints.R: 16   > 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   > test-constraints.R: 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-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: 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-term-doc.R: Found   > test-ergm-term-doc.R: 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.bridge.llr.R: 5   > 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: 6   > 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: 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.bridge.llr.R: 7   > 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)   > test-ergm-term-doc.R: 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   > test-ergm-term-doc.R: 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.bridge.llr.R: 8   > 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-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.bridge.llr.R: 9   > 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.bridge.llr.R: 10   > 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 '   > test-ergm-term-doc.R: mandomtoggle' were found. Try searching with search='mandomtoggle'instead.   > test-ergm.bridge.llr.R: 11   > 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: 12   > test-ergm.bridge.llr.R: 13   > test-ergm.bridge.llr.R: 14   > test-ergm.bridge.llr.R: 15   > 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-ergmMPLE.R:   > test-ergm.bridge.llr.R: 16   > 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-ergm.bridge.llr.R: 6   > 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-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE):   > test-ergm.bridge.llr.R: 11   > 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-ergm.bridge.llr.R: 12   > 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:   > test-ergm.bridge.llr.R: 13   > 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: 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:   > test-ergm.bridge.llr.R: 1   > test-ergm.bridge.llr.R: 2   > test-ergm.bridge.llr.R: 3   > test-ergm.bridge.llr.R: 4   > test-gflomiss.R: 1   > test-gflomiss.R: Optimizing with step length 1.0000.   > test-ergm.bridge.llr.R: 5   > test-gflomiss.R: The log-likelihood improved by 0.0002.   > test-ergm.bridge.llr.R: 6   > 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-gflomiss.R: Fitting the dyad-independent submodel...   > test-ergm.bridge.llr.R: 7   > 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: 8   > test-gflomiss.R: Using 16 bridges:   > test-gflomiss.R: 1   > test-gflomiss.R: 2   > test-ergm.bridge.llr.R: 9   > test-gflomiss.R: 3   > test-gflomiss.R: 4   > test-ergm.bridge.llr.R: 10   > test-gflomiss.R: 5   > test-gflomiss.R: 6   > test-gflomiss.R: 7   > test-ergm.bridge.llr.R: 11   > test-gflomiss.R: 8   > test-gflomiss.R: 9   > test-ergm.bridge.llr.R: 12   > test-gflomiss.R: 10   > test-gflomiss.R: 11   > test-ergm.bridge.llr.R: 13   > test-gflomiss.R: 12   > test-gflomiss.R: 13   > test-ergm.bridge.llr.R: 14   > test-gflomiss.R: 14   > test-ergm.bridge.llr.R: 15   > test-gflomiss.R: 15   > test-gflomiss.R: 16   > test-ergm.bridge.llr.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: .   > test-ergm.bridge.llr.R: Bridging finished.   > test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):   > test-gflomiss.R: Iteration 1 of at most 60:   > test-ergm.bridge.llr.R: Setting up bridge sampling...   > test-ergm.bridge.llr.R: Using 16 bridges:   > test-ergm.bridge.llr.R: 1   > test-ergm.bridge.llr.R: 2   > test-gflomiss.R: 1 Optimizing with step length 1.0000.   > test-gflomiss.R: The log-likelihood improved by 0.0241.   > 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-gflomiss.R: Fitting the dyad-independent submodel...   > 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:   > test-gflomiss.R: 1   > test-gflomiss.R: 2   > test-ergm.bridge.llr.R: 3   > test-gflomiss.R: 3   > test-gflomiss.R: 4   > test-gflomiss.R: 5   > test-gflomiss.R: 6   > test-gflomiss.R: 7   > test-gflomiss.R: 8   > test-ergm.bridge.llr.R: 4   > test-gflomiss.R: 9   > test-gflomiss.R: 10   > test-gflomiss.R: 11   > test-gflomiss.R: 12   > test-ergm.bridge.llr.R: 5   > test-gflomiss.R: 13   > test-gflomiss.R: 14   > test-gflomiss.R: 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: 6   > 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: 7   > 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 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: 8   > test-ergm.bridge.llr.R: 9   > test-ergm.bridge.llr.R: 10   > test-ergm.bridge.llr.R: 11   > test-ergm.bridge.llr.R: 12   > test-gmonkmiss.R: 1   > test-gmonkmiss.R: 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: 13   > test-ergm.bridge.llr.R: 14   > test-gmonkmiss.R: 1   > test-gmonkmiss.R: Optimizing with step length 1.0000.   > test-gmonkmiss.R: The log-likelihood improved by 0.0078.   > test-ergm.bridge.llr.R: 15   > 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-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   > test-ergm.bridge.llr.R: 16   > test-gmonkmiss.R: 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   > test-gmonkmiss.R: 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   > test-gmonkmiss.R: 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: .   > 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   > test-gmonkmiss.R: Optimizing with step length 1.0000.   > test-gmonkmiss.R: The log-likelihood improved by 0.4514.   > test-ergm.bridge.llr.R: 3   > test-gmonkmiss.R: Estimating equations are not within tolerance region.   > test-gmonkmiss.R: Iteration 2 of at most 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-gmonkmiss.R: 1   > test-gmonkmiss.R: Optimizing with step length 1.0000.   > test-ergm.bridge.llr.R: 9   > 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-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE):   > test-gmonkmiss.R: Obtaining the responsible dyads.   > test-gmonkmiss.R: Evaluating the predictor and response matrix.   > test-ergm.bridge.llr.R: 10   > test-gmonkmiss.R: Maximizing the pseudolikelihood.   > test-gmonkmiss.R: Finished MPLE.   > 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: 11   > 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: 12   > test-ergm.bridge.llr.R: 13   > test-ergm.bridge.llr.R: 14   > test-gmonkmiss.R: 1   > 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: 15   > test-ergm.bridge.llr.R: 16   > test-gmonkmiss.R: 1   > test-gmonkmiss.R: 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: .   > 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-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-ergm.bridge.llr.R: Using 16 bridges: 1   > test-gof.R: Finished MPLE.   > test-gof.R: Evaluating log-likelihood at the estimate.   > test-gof.R:   > test-ergm.bridge.llr.R: 2   > test-ergm.bridge.llr.R: 3   > 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: 4   > test-ergm.bridge.llr.R: 5   > 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: 6   > 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-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-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-metrics.R: 1   > test-metrics.R: Optimizing with step length 0.4613.   > 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-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-miss-dep.R: Iteration 1 of at most 60:   > test-miss-dep.R: Convergence test P-value:4.6e-47   > test-miss-dep.R: 1   > test-miss-dep.R: The log-likelihood improved by 1.824.   > test-miss-dep.R: Iteration 2 of at most 60:   > test-miss-dep.R: Convergence test P-value:1.5e-23   > test-miss-dep.R: 1   > test-miss-dep.R: The log-likelihood improved by 0.6054.   > test-miss-dep.R: Iteration 3 of at most 60:   > test-miss-dep.R: Convergence test P-value:1.1e-07   > test-miss-dep.R: 1   > test-miss-dep.R: The log-likelihood improved by 0.1283.   > test-miss-dep.R: Iteration 4 of at most 60:   > test-metrics.R: 1   > test-metrics.R: 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-miss-dep.R: Convergence test P-value:3.3e-04   > test-miss-dep.R: 1   > test-miss-dep.R: The log-likelihood improved by 0.05435.   > test-miss-dep.R: Iteration 5 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 1.1346.   > test-metrics.R: Estimating equations are not within tolerance region.   > test-metrics.R: Iteration 4 of at most 60:   > test-miss-dep.R: Convergence test P-value:2.1e-01   > test-miss-dep.R: 1   > test-metrics.R: 1   > test-metrics.R: 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-miss-dep.R: The log-likelihood improved by 0.006185.   > test-miss-dep.R: Iteration 6 of at most 60:   > test-miss-dep.R: Convergence test P-value:4.1e-01   > test-miss-dep.R: 1   > test-miss-dep.R: The log-likelihood improved by 0.002664.   > test-miss-dep.R: Iteration 7 of at most 60:   > test-miss-dep.R: Convergence test P-value:1.6e-01   > test-miss-dep.R: 1   > test-metrics.R: 1   > test-miss-dep.R: The log-likelihood improved by 0.007694.   > test-miss-dep.R: Iteration 8 of at most 60:   > test-metrics.R: 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-miss-dep.R: Convergence test P-value:1.9e-01   > test-miss-dep.R: 1   > test-miss-dep.R: The log-likelihood improved by 0.006878.   > test-miss-dep.R: Iteration 9 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-dep.R: Convergence test P-value:7.8e-01   > test-miss-dep.R: Convergence detected. Stopping.   > test-miss-dep.R: 1   > test-miss-dep.R: The log-likelihood improved by 0.0003111.   > 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: 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: Post-burnin sample is constant; returning.   > test-miss-dep.R: 1   > test-miss-dep.R: Optimizing with step length 1.0000.   > test-metrics.R: 1   > test-metrics.R: 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-metrics.R: 1   > test-metrics.R: Optimizing with step length 1.0000.   > test-metrics.R: The log-likelihood improved by 2.2132.   > test-miss-dep.R: The log-likelihood improved by 0.0017.   > test-metrics.R: Estimating equations are not within tolerance region.   > test-metrics.R: Iteration 4 of at most 60:   > 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-miss-dep.R: Using 16 bridges:   > test-miss-dep.R: 1   > test-miss-dep.R: 2   > test-miss-dep.R: 3   > test-metrics.R: 1   > test-metrics.R: Optimizing with step length 1.0000.   > test-miss-dep.R: 4   > test-metrics.R: The log-likelihood improved by 0.0377.   > test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.   > test-metrics.R: Finished MCMLE.   > test-miss-dep.R: 5   > 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: 6   > test-miss-dep.R: 7   > test-miss-dep.R: 8   > test-miss-dep.R: 9   > test-miss-dep.R: 10   > test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):   > test-metrics.R: Iteration 1 of at most 60:   > test-miss-dep.R: 11   > test-miss-dep.R: 12   > test-miss-dep.R: 13   > test-miss-dep.R: 14   > test-miss-dep.R: 15   > test-miss-dep.R: 16   > test-miss-dep.R: .   > 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: 1   > test-metrics.R: 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.CD.R: n=20, density=0.1, missing=0.1   > test-metrics.R: 1   > test-metrics.R: 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.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   > test-miss.CD.R: 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   > test-miss.CD.R: 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   > test-metrics.R: 1   > test-metrics.R: Optimizing with step length 1.0000.   > test-miss.CD.R: The log-likelihood improved by 0.341.   > test-miss.CD.R: Iteration 4 of at most 60:   > 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.CD.R: Convergence test P-value:4e-15   > test-miss.CD.R: 1   > test-miss.CD.R: 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   > test-miss.CD.R: 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   > test-miss.CD.R: 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   > test-miss.CD.R: 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-miss.CD.R: The log-likelihood improved by 0.03854.   > test-miss.CD.R: Iteration 11 of at most 60:   > test-miss.CD.R: Convergence test P-value:7.9e-05   > test-miss.CD.R: 1   > test-miss.CD.R: The log-likelihood improved by 0.1259.   > test-miss.CD.R: Iteration 12 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 0.1226.   > test-miss.CD.R: Convergence test P-value:7.9e-01   > test-miss.CD.R: Convergence detected. Stopping.   > test-miss.CD.R: 1   > test-miss.CD.R: The log-likelihood improved by 0.0004877.   > test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.   > test-metrics.R: Finished MCMLE.   > 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: 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-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: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   > test-miss.CD.R: 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   > test-miss.CD.R: 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-miss.CD.R: Convergence test P-value:8.6e-19   > test-miss.CD.R: 1   > test-miss.CD.R: 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   > test-miss.CD.R: 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   > test-miss.CD.R: 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   > 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-metrics.R: 1   > test-metrics.R: 2 3   > test-metrics.R: 4 5   > test-metrics.R: 6 7   > test-metrics.R: 8 9   > test-metrics.R: 10 11 Optimizing with step length 0.3934.   > 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: 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   > test-miss.CD.R: 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   > test-miss.CD.R: The log-likelihood improved by 0.6164.   > test-miss.CD.R: Iteration 3 of at most 60:   > test-miss.CD.R: Convergence test P-value:4.5e-46   > test-miss.CD.R: 1   > test-miss.CD.R: 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   > test-miss.CD.R: 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   > test-miss.CD.R: 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   > test-miss.CD.R: 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-metrics.R: 1 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: Network statistics:   > test-miss.CD.R: edges   > test-miss.CD.R: 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-miss.CD.R: Convergence test P-value:1.8e-283   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-miss.CD.R: The log-likelihood improved by 1.712.   > test-miss.CD.R: Iteration 2 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:1.8e-235   > test-miss.CD.R: 1   > test-miss.CD.R: 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 2   > test-metrics.R: 1 Optimizing with step length 1.0000.   > test-metrics.R: The log-likelihood improved by 0.0827.   > test-miss.CD.R: The log-likelihood improved by 1.167.   > test-metrics.R: Convergence test p-value: 0.0004. Converged with 99% confidence.   > test-metrics.R: Finished MCMLE.   > test-miss.CD.R: Iteration 4 of at most 60:   > 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:8.9e-150   > 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 5 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:1.3e-156   > 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 6 of at most 60:   > test-miss.CD.R: Convergence test P-value:1.5e-147   > 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 7 of at most 60:   > test-miss.CD.R: Convergence test P-value:2.2e-155   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-metrics.R: 1   > test-metrics.R: 2   > test-metrics.R: 3 4 5   > test-metrics.R: 6 7   > test-metrics.R: 8 9 10 11   > test-metrics.R: Optimizing with step length 0.3701.   > test-miss.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.CD.R: Iteration 8 of at most 60:   > 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:2.8e-153   > 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 9 of at most 60:   > test-metrics.R: 1   > test-metrics.R: 2   > test-metrics.R: 3   > test-metrics.R: 4 5   > test-metrics.R: 6   > test-metrics.R: 7   > test-metrics.R: 8 9   > test-metrics.R: 10   > test-metrics.R: 11   > test-metrics.R: 12 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: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-miss.CD.R: Convergence test P-value:2.5e-153   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-miss.CD.R: The log-likelihood improved by < 0.0001.   > test-metrics.R: 1   > test-metrics.R: 2 3 Optimizing with step length 0.8048.   > test-miss.CD.R: Iteration 11 of at most 60:   > 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:8e-150   > 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 12 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-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: 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   > test-miss.CD.R: 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   > test-miss.CD.R: 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   > test-metrics.R: Optimizing with step length 1.0000.   > test-metrics.R: The log-likelihood improved by 0.0012.   > test-miss.CD.R: Convergence test P-value:1.3e-158   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-metrics.R: Convergence test p-value: 0.0099.   > test-metrics.R: Converged with 99% confidence.   > test-metrics.R: Finished MCMLE.   > test-miss.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.CD.R: Iteration 16 of at most 60:   > 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:1.1e-159   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):   > test-metrics.R: Iteration 1 of at most 60:   > 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   > test-miss.CD.R: 2   > test-miss.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.CD.R: Iteration 18 of at most 60:   > test-miss.CD.R: Convergence test P-value:6.6e-157   > 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 19 of at most 60:   > test-miss.CD.R: Convergence test P-value:2.5e-163   > 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 20 of at most 60:   > test-metrics.R: 1   > test-metrics.R: 2 3   > test-metrics.R: 4   > test-metrics.R: 5 6   > test-metrics.R: 7   > test-metrics.R: 8   > test-metrics.R: 9 10   > test-miss.CD.R: Convergence test P-value:3.5e-156   > test-miss.CD.R: 1   > test-metrics.R: 11 12 13   > test-miss.CD.R: 2   > test-metrics.R: 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: The log-likelihood improved by < 0.0001.   > test-miss.CD.R: Iteration 21 of at most 60:   > test-miss.CD.R: Convergence test P-value:2.3e-153   > 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 22 of at most 60:   > test-miss.CD.R: Convergence test P-value:1e-157   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-metrics.R: 1   > test-metrics.R: 2   > test-metrics.R: 3 4 5   > test-miss.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.CD.R: Iteration 23 of at most 60:   > test-metrics.R: 6 7   > test-metrics.R: 8   > test-metrics.R: 9   > test-metrics.R: 10 11 12   > test-metrics.R: 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:1.4e-160   > 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 24 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:2.1e-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 25 of at most 60:   > test-miss.CD.R: Convergence test P-value:1.3e-150   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-metrics.R: 1 Optimizing with step length 1.0000.   > test-metrics.R: The log-likelihood improved by 0.0821.   > test-miss.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.CD.R: Iteration 26 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.CD.R: Convergence test P-value:4.5e-158   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-miss.R: n=20, density=0.1, missing=0.05   > test-miss.CD.R: 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   > test-miss.CD.R: 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 28 of at most 60:   > test-miss.CD.R: Convergence test P-value:8.6e-160   > test-miss.CD.R: 1   > test-miss.CD.R: 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.R: Evaluating log-likelihood at the estimate.   > test-miss.CD.R: Iteration 29 of at most 60:   > test-miss.R: MPLE estimate = -2.118156 with log-likelihood -120.6883 OK.   > test-miss.R:   > 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.R: 'ergm:MH_SPDyad'.   > test-miss.R: Initializing model...   > test-miss.R: Model initialized.   > test-miss.CD.R: Convergence test P-value:2.9e-157   > test-miss.CD.R: 1 2   > 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: 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.R: Starting constrained MCMC...   > test-miss.CD.R: Convergence test P-value:1.9e-155   > 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 32 of at most 60:   > test-miss.CD.R: Convergence test P-value:1.5e-156   > 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.CD.R: 1   > test-miss.R: 1 2 3 4   > test-miss.R: 5   > test-miss.CD.R: 2   > test-miss.R: 6 7 8 9   > test-miss.R: 10 11   > test-miss.R: 12   > test-miss.R: 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: The log-likelihood improved by < 0.0001.   > test-miss.CD.R: Iteration 33 of at most 60:   > test-miss.CD.R: Convergence test P-value:1e-153   > 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 34 of at most 60:   > test-miss.CD.R: Convergence test P-value:2.5e-165   > test-miss.CD.R: 1   > test-miss.CD.R: 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 35 of at most 60:   > test-miss.CD.R: Convergence test P-value:3.7e-164   > test-miss.R: Back from constrained MCMC.   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > 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   > test-miss.R: 2 3 4   > test-miss.R: 5   > test-miss.R: 6   > test-miss.R: 7   > test-miss.R: 8   > test-miss.R: 9   > test-miss.R: 10 11 12   > test-miss.R: 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.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.CD.R: Iteration 36 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:3.5e-165   > 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 37 of at most 60:   > test-miss.CD.R: Convergence test P-value:3.8e-167   > 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 38 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   > test-miss.R: 2   > test-miss.R: 3 4   > test-miss.R: 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.CD.R: Convergence test P-value:4.3e-166   > 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 39 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:1.8e-166   > test-miss.CD.R: 1   > test-miss.CD.R: 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   > 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.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.R: The log-likelihood improved by 0.3258.   > test-miss.CD.R: Iteration 40 of at most 60:   > 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: Convergence test P-value:1.4e-154   > test-miss.CD.R: 1   > test-miss.CD.R: 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 41 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.CD.R: Convergence test P-value:1.3e-139   > test-miss.R: Using log-normal approx with missing (no optim)   > test-miss.CD.R: 1   > test-miss.R: Starting MCMC s.e. computation.   > test-miss.CD.R: 2   > 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.   > test-miss.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.CD.R: Iteration 42 of at most 60:   > test-miss.R: Converged with 99% confidence.   > test-miss.R: Finished MCMLE.   > test-miss.R: Evaluating log-likelihood at the estimate.   > test-miss.R: Initializing model to obtain the list of dyad-independent terms...   > test-miss.R: Fitting the dyad-independent submodel...   > test-miss.CD.R: Convergence test P-value:7.6e-160   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > 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: The log-likelihood improved by < 0.0001.   > test-miss.CD.R: Iteration 43 of at most 60:   > 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=[-2.133081, 0.000000].   > test-miss.R: Running theta=[-2.132118, 0.000000].   > test-miss.CD.R: Convergence test P-value:3e-152   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-miss.R: Running theta=[-2.131155, 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=[-2.130192, 0.000000].   > test-miss.R: Running theta=[-2.129229, 0.000000].   > 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.R: Running theta=[-2.125378, 0.000000].   > test-miss.CD.R: Convergence test P-value:3.1e-154   > test-miss.CD.R: 1   > test-miss.R: Running theta=[-2.124415, 0.000000].   > test-miss.CD.R: 2   > test-miss.R: Running theta=[-2.123452, 0.000000].   > test-miss.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.R: Running theta=[-2.122489, 0.000000].   > test-miss.CD.R: Iteration 45 of at most 60:   > test-miss.R: Running theta=[-2.121526, 0.000000].   > test-miss.R: Running theta=[-2.120563, 0.000000].   > test-miss.CD.R: Convergence test P-value:1.9e-150   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-miss.R: Running theta=[-2.1196, 0.0000].   > test-miss.R: Running theta=[-2.118637, 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: .   > 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: Convergence test P-value:8.8e-148   > test-miss.CD.R: 1 2   > test-miss.R: Running theta=[-2.121894, 0.000000].   > test-miss.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.R: Running theta=[-2.122857, 0.000000].   > test-miss.CD.R: Iteration 47 of at most 60:   > test-miss.R: Running theta=[-2.12382, 0.00000].   > test-miss.R: Running theta=[-2.124783, 0.000000].   > test-miss.CD.R: Convergence test P-value:1.8e-153   > test-miss.R: Running theta=[-2.125746, 0.000000].   > test-miss.CD.R: 1 2   > test-miss.R: Running theta=[-2.126709, 0.000000].   > test-miss.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.CD.R: Iteration 48 of at most 60:   > test-miss.R: Running theta=[-2.127671, 0.000000].   > 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.CD.R: Convergence test P-value:4.8e-158   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-miss.R: Running theta=[-2.131523, 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=[-2.132486, 0.000000].   > test-miss.R: Running theta=[-2.133449, 0.000000].   > 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: Convergence test P-value:6e-154   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-miss.R: Running theta=[-2.131891, 0.000000].   > test-miss.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.CD.R: Iteration 50 of at most 60:   > test-miss.R: Running theta=[-2.130928, 0.000000].   > test-miss.R: Running theta=[-2.129965, 0.000000].   > test-miss.R: Running theta=[-2.129002, 0.000000].   > test-miss.R: Running theta=[-2.128039, 0.000000].   > 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.CD.R: Convergence test P-value:1.4e-158   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-miss.R: Running theta=[-2.123225, 0.000000].   > test-miss.R: Running theta=[-2.122262, 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=[-2.121299, 0.000000].   > test-miss.R: Running theta=[-2.120336, 0.000000].   > test-miss.CD.R: Convergence test P-value:1.2e-150   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-miss.R: Running theta=[-2.119373, 0.000000].   > test-miss.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.R: Running theta=[-2.11841, 0.00000].   > test-miss.CD.R: Iteration 52 of at most 60:   > 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.CD.R: Convergence test P-value:2.2e-161   > test-miss.R: Running theta=[-2.119741, 0.000000].   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-miss.R: Running theta=[-2.120704, 0.000000].   > test-miss.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.R: Running theta=[-2.121667, 0.000000].   > test-miss.CD.R: Iteration 53 of at most 60:   > 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.CD.R: Convergence test P-value:5.8e-145   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-miss.R: Running theta=[-2.126481, 0.000000].   > test-miss.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.R: Running theta=[-2.127444, 0.000000].   > test-miss.CD.R: Iteration 54 of at most 60:   > test-miss.R: Running theta=[-2.128407, 0.000000].   > test-miss.R: Running theta=[-2.12937, 0.00000].   > test-miss.R: Running theta=[-2.130333, 0.000000].   > test-miss.CD.R: Convergence test P-value:5.8e-163   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-miss.R: Running theta=[-2.131296, 0.000000].   > test-miss.R: Running theta=[-2.132259, 0.000000].   > test-miss.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.CD.R: Iteration 55 of at most 60:   > 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:1.3e-156   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > 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.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.CD.R: Iteration 56 of at most 60:   > test-miss.R: Running theta=[-2.127812, 0.000000].   > test-miss.R: Running theta=[-2.126849, 0.000000].   > test-miss.R: Running theta=[-2.125886, 0.000000].   > 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.CD.R: Convergence test P-value:2e-150   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > test-miss.R: Running theta=[-2.121072, 0.000000].   > test-miss.R: Running theta=[-2.120109, 0.000000].   > test-miss.R: Running theta=[-2.119146, 0.000000].   > test-miss.R: Running theta=[-2.118183, 0.000000].   > test-miss.R: .   > test-miss.R: Bridge sampling finished. Collating...   > test-miss.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.CD.R: Iteration 57 of at most 60:   > 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:2.4e-151   > 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 58 of at most 60:   > test-miss.CD.R: Convergence test P-value:1.2e-168   > 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 59 of at most 60:   > test-miss.CD.R: Convergence test P-value:3e-159   > 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 60 of at most 60:   > test-miss.CD.R: Convergence test P-value:6.5e-157   > test-miss.CD.R: 1   > test-miss.CD.R: 2   > 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:   > 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   > test-miss.R: 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.CD.R: The log-likelihood improved by < 0.0001.   > test-miss.R: Chain   > test-miss.CD.R: Finished CD.   > test-miss.R: 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.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: Chain   > test-miss.R: 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.R: Correct estimate = -1.663142 with log-likelihood -79.82064 .   > 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: 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:   > test-miss.R: MPLE estimate = -1.663142 with log-likelihood -79.82064 OK.   > 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.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.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.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   > test-miss.R: 3 4   > test-miss.R: 5 6   > test-miss.R: 7 8   > test-miss.R: 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-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   > test-miss.R: 2   > test-miss.R: 3   > 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: 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   > test-miss.R: Optimizing with step length 1.0000.   > test-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks.   > 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.   > test-miss.R: 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-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:   > 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: 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:   > 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: 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:   > test-miss.R: MPLE estimate = -3.157 with log-likelihood -8.355963 OK.   > test-miss.R: Evaluating network in model.   > test-miss.R: Initializing unconstrained Metropolis-Hastings proposal:   > test-miss.R: '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   > 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: 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   > 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 0.0008.   > 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.   > test-miss.R: 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:   > 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:   > 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   > test-miss.R: 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   > test-miss.R: 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-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 5   > test-miss.R: 6   > test-miss.R: 7   > test-miss.R: 8 9 10   > test-miss.R: 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: Estimating Godambe Matrix using 500 simulated networks.   > test-miss.R: 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   > test-miss.R: 8   > test-miss.R: 9   > 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 3 of at most 5:   > test-miss.R: 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   > test-miss.R: 10   > test-miss.R: 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-miss.R: 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   > test-miss.R: 8   > 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 5 of at most 5:   > test-miss.R: 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   > test-miss.R: 8   > test-miss.R: 9   > 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: 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.   > test-miss.R: 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:   > test-miss.R: 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   > test-miss.R: 8   > test-miss.R: 9   > test-miss.R: 10   > test-miss.R: 11   > test-miss.R: 12   > test-miss.R: 13   > test-miss.R: 14   > test-miss.R: 15   > test-miss.R: 16   > test-miss.R: .   > 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:   > 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:   > 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-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-mple-cov.R:   > 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-offset.R: 1   > test-mple-offset.R: 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-networkLite.R: Loading required package: networkLite   > 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:   > 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-mple-cov.R:   > 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   > test-networkLite.R: 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   > test-networkLite.R: 2   > test-networkLite.R: 3   > test-networkLite.R: 4 5   > test-networkLite.R: 6   > test-networkLite.R: 7   > test-networkLite.R: 8   > test-networkLite.R: 9   > test-networkLite.R: 10 11   > test-networkLite.R: 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   > test-networkLite.R: 2   > test-networkLite.R: 3   > test-networkLite.R: 4   > test-networkLite.R: 5   > test-networkLite.R: 6   > test-networkLite.R: 7   > test-networkLite.R: 8 9   > test-networkLite.R: 10   > test-networkLite.R: 11   > test-networkLite.R: 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   > test-networkLite.R: 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   > test-networkLite.R: 3 4   > test-networkLite.R: 5   > test-networkLite.R: 6 7   > test-networkLite.R: 8 9   > test-networkLite.R: 10 11   > test-networkLite.R: 12   > test-networkLite.R: 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   > test-networkLite.R: 2 3   > test-networkLite.R: 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   > test-networkLite.R: 11   > test-networkLite.R: 12   > test-networkLite.R: 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   > test-networkLite.R: 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   > test-networkLite.R: 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.   > 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 2   > test-networkLite.R: 3 4   > test-networkLite.R: 5   > test-networkLite.R: 6 7 8 9   > test-networkLite.R: 10 11   > test-networkLite.R: Optimizing with step length 1.0000.   > 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-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   > test-networkLite.R: 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.   > 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-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE):   > test-nodrop.R: Obtaining the responsible dyads.   > test-nodrop.R: Evaluating the predictor and response matrix.   > test-networkLite.R: Maximizing the pseudolikelihood.   > test-nodrop.R: Maximizing the pseudolikelihood.   > test-networkLite.R: Finished MPLE.   > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):   > test-nodrop.R: Finished MPLE.   > 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-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):   > test-nodrop.R: Iteration 1 of at most 2:   > test-networkLite.R: 1   > test-networkLite.R: 2   > test-networkLite.R: 3   > test-networkLite.R: 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   > test-networkLite.R: 11   > test-networkLite.R: Optimizing with step length 1.0000.   > test-networkLite.R: The log-likelihood improved by 0.0392.   > test-networkLite.R: Convergence test p-value: 0.0019.   > 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-nodrop.R: 1   > test-nodrop.R: 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-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-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):   > test-nodrop.R: Iteration 1 of at most 2:   > test-networkLite.R: 1   > test-networkLite.R: 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-nodrop.R: 1   > 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: 1   > test-networkLite.R: 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-nodrop.R: 1   > test-nodrop.R: 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-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   > test-networkLite.R: 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-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):   > test-nodrop.R: Iteration 1 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 0.0176.   > test-nodrop.R: 1   > test-nodrop.R: 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: Convergence test p-value: 0.0247.   > test-networkLite.R: Not converged with 99% confidence; increasing sample size.   > test-networkLite.R: Iteration 5 of at most 60:   > test-nodrop.R: 1   > test-nodrop.R: Optimizing with step length 0.3098.   > 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-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-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   > test-nonident-test.R: Finished MPLE.   > test-networkLite.R: 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: 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   > test-networkLite.R: Optimizing with step length 1.0000.   > test-networkLite.R: The log-likelihood improved by 0.0419.   > test-networkLite.R: Convergence test p-value: 0.0085. 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-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-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-networkLite.R: 2   > test-networkLite.R: 3   > test-networkLite.R: 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   > test-networkLite.R: 11   > test-networkLite.R: 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   > test-networkLite.R: 22   > test-networkLite.R: 23   > test-networkLite.R: 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-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   > test-networkLite.R: 2   > test-networkLite.R: 3   > test-networkLite.R: 4   > test-networkLite.R: 5   > test-networkLite.R: 6   > test-networkLite.R: 7 8   > test-networkLite.R: 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.   > 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-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-networkLite.R: 1   > test-networkLite.R: 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-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-networkLite.R: Optimizing with step length 1.0000.   > 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: The log-likelihood improved by 1.8905.   > test-nonident-test.R: Finished MPLE.   > test-networkLite.R: Estimating equations are not within tolerance region.   > test-networkLite.R: Iteration 3 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: 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-networkLite.R: 1   > test-networkLite.R: Optimizing with step length 1.0000.   > test-networkLite.R: The log-likelihood improved by 0.0176.   > test-networkLite.R: Convergence test p-value: 0.0247.   > test-networkLite.R: Not converged with 99% confidence; increasing sample size.   > test-networkLite.R: Iteration 5 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 0.0103.   > 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-networkLite.R: 1   > test-networkLite.R: Optimizing with step length 1.0000.   > test-networkLite.R: The log-likelihood improved by 0.0419.   > test-networkLite.R: Convergence test p-value: 0.0085. 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-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE):   > test-networkLite.R: Obtaining the responsible dyads.   > test-networkLite.R: Evaluating the predictor and response matrix.   > test-nonunique-names.R: Optimizing with step length 1.0000.   > 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: The log-likelihood improved by 0.0084.   > test-nonunique-names.R: Convergence test p-value: 0.0480.   > test-nonunique-names.R: 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   > test-networkLite.R: 2   > test-networkLite.R: 3   > test-networkLite.R: 4 5   > test-networkLite.R: 6   > test-networkLite.R: 7 8   > test-networkLite.R: 9 10   > test-networkLite.R: 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   > test-networkLite.R: 22   > test-networkLite.R: 23   > test-networkLite.R: 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-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   > test-networkLite.R: 2   > test-networkLite.R: 3 4   > test-networkLite.R: 5   > 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-networkLite.R: 6   > test-networkLite.R: 7   > test-networkLite.R: 8   > test-networkLite.R: 9   > test-offsets.R: Maximizing the pseudolikelihood.   > test-networkLite.R: 10   > test-networkLite.R: 11   > test-networkLite.R: Optimizing with step length 1.0000.   > test-offsets.R: Finished MPLE.   > test-offsets.R: Evaluating log-likelihood at the estimate.   > test-offsets.R:   > 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-networkLite.R: The log-likelihood improved by 0.0094.   > 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: Convergence test p-value: 0.0010.   > 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-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-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-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: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.   > 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: Starting contrastive divergence estimation via CD-MCMLE:   > test-networkLite.R: Iteration 1 of at most 60:   > test-offsets.R: Finished MPLE.   > test-networkLite.R: Convergence test P-value:1e-01   > test-networkLite.R: 1   > test-offsets.R: Evaluating log-likelihood at the estimate.   > 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   > test-networkLite.R: 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-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: 1 Optimizing with step length 1.0000.   > 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: 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-offsets.R: 1   > test-offsets.R: Optimizing with step length 1.0000.   > 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-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: 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   > test-networkLite.R: 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   > 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-offsets.R: 1   > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.   > test-offsets.R: Optimizing with step length 1.0000.   > test-offsets.R: The log-likelihood improved by 0.0061.   > 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-offsets.R: Fitting the dyad-independent submodel...   > 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   > test-networkLite.R: 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-offsets.R: Bridging between the dyad-independent submodel and the full model...   > test-offsets.R: Setting up bridge sampling...   > test-networkLite.R: 1   > test-networkLite.R: Optimizing with step length 1.0000.   > test-networkLite.R: The log-likelihood improved by 0.1926.   > test-offsets.R: Using 16 bridges: 1   > test-networkLite.R: Estimating equations are not within tolerance region.   > test-networkLite.R: Iteration 2 of at most 60:   > test-offsets.R: 2   > test-offsets.R: 3   > test-offsets.R: 4   > test-networkLite.R: 1   > test-networkLite.R: Optimizing with step length 1.0000.   > test-offsets.R: 5   > test-networkLite.R: The log-likelihood improved by 0.0160.   > test-networkLite.R: Convergence test p-value: 0.0001.   > test-offsets.R: 6   > 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-offsets.R: 7   > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.   > test-offsets.R: 8   > test-offsets.R: 9   > test-offsets.R: 10   > test-offsets.R: 11   > test-offsets.R: 12   > test-offsets.R: 13   > test-offsets.R: 14   > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'.   > test-offsets.R: 15   > 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-offsets.R: 16   > test-networkLite.R: Convergence detected. Stopping.   > test-networkLite.R: 1   > test-networkLite.R: The log-likelihood improved by < 0.0001.   > test-networkLite.R: Finished CD.   > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):   > 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: 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 0.1926.   > 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   > test-networkLite.R: 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   > 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   > 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   > 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   > 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   > test-networkLite.R: 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.   > test-networkLite.R: Not converged with 99% confidence; increasing sample size.   > test-networkLite.R: Iteration 3 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 0.0803.   > 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   > 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   > 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   > test-networkLite.R: 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   > test-networkLite.R: 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.   > 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: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'.   > 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 0.7959.   > test-offsets.R: Estimating equations are not within tolerance region.   > test-offsets.R: Iteration 2 of at most 2:   > 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-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-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-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-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-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-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-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.   > test-offsets.R: Converged with 99% confidence.   > test-offsets.R: Finished MCMLE.   > test-offsets.R: Evaluating log-likelihood at the estimate.   > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > test-offsets.R: Fitting the dyad-independent submodel...   > test-offsets.R: Bridging between the dyad-independent submodel and the full model...   > test-offsets.R: Setting up bridge sampling...   > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > test-offsets.R: Using 16 bridges:   > test-offsets.R: 1   > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > test-offsets.R: 2   > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > test-offsets.R: 3   > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > test-offsets.R: 4   > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > test-offsets.R: 5   > test-offsets.R: 6   > test-offsets.R: 7   > test-offsets.R: 8   > test-offsets.R: 9   > test-offsets.R: 10   > test-offsets.R: 11   > test-offsets.R: 12   > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > test-offsets.R: 13   > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > test-offsets.R: 14   > 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: 15   > test-offsets.R: 16   > 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-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.   > test-runtime-diags.R: 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.   > test-scoping.R: 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   > test-scoping.R: 2   > test-scoping.R: 3   > test-scoping.R: 4   > test-scoping.R: 5   > test-scoping.R: 6   > test-scoping.R: 7   > test-scoping.R: 8   > test-scoping.R: 9   > test-scoping.R: 10   > test-scoping.R: 11   > test-scoping.R: 12   > test-scoping.R: 13   > test-scoping.R: 14   > test-scoping.R: 15   > test-scoping.R: 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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > 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.   > test-scoping.R: 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:   > test-scoping.R: 1   > test-scoping.R: 2   > test-scoping.R: 3   > test-scoping.R: 4   > test-scoping.R: 5   > test-scoping.R: 6   > test-scoping.R: 7   > test-scoping.R: 8   > test-scoping.R: 9   > test-scoping.R: 10   > test-scoping.R: 11   > test-scoping.R: 12   > test-scoping.R: 13   > test-scoping.R: 14   > test-scoping.R: 15   > test-scoping.R: 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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > test-shrink-into-CH.R: 1   > test-shrink-into-CH.R: 2   > test-shrink-into-CH.R: 3   > test-shrink-into-CH.R: 4   > test-shrink-into-CH.R: 5   > test-shrink-into-CH.R: 6   > test-shrink-into-CH.R: 7   > test-shrink-into-CH.R: 8   > test-shrink-into-CH.R: 9   > test-shrink-into-CH.R: 10   > test-shrink-into-CH.R: 11   > test-shrink-into-CH.R: 12   > test-shrink-into-CH.R: 13   > test-shrink-into-CH.R: 14   > test-shrink-into-CH.R: 15   > test-shrink-into-CH.R: 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   > test-shrink-into-CH.R: 1   > test-shrink-into-CH.R: 2   > test-shrink-into-CH.R: 3   > test-shrink-into-CH.R: 4   > test-shrink-into-CH.R: 5 6   > test-shrink-into-CH.R: 7   > test-shrink-into-CH.R: 8   > test-shrink-into-CH.R: 9   > test-shrink-into-CH.R: 10   > test-shrink-into-CH.R: 11   > test-shrink-into-CH.R: 12   > test-shrink-into-CH.R: 13   > test-shrink-into-CH.R: 14   > test-shrink-into-CH.R: 15   > test-shrink-into-CH.R: 16   > test-shrink-into-CH.R: 17   > test-shrink-into-CH.R: 18   > test-shrink-into-CH.R: 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-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:   > 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:   > 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:   > test-skip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):   > test-skip.R: Iteration 1 of at most 60:   > 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.   > test-skip.R: 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-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-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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > test-snctrl.R: Evaluating log-likelihood at the estimate.   > test-snctrl.R:   > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > 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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > 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-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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > 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: Stochastic approximation algorithm with theta_0 equal to:   > test-stocapprox.R: Starting burnin of 16384 steps   > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > 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-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-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: Starting maximum pseudolikelihood estimation (MPLE):   > test-stocapprox.R: Obtaining the responsible dyads.   > test-stocapprox.R: Evaluating the predictor and response matrix.   > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > 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   > test-stocapprox.R: 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: 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   > test-stocapprox.R: 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   > test-stocapprox.R: 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: nonzero transitiveweights.min.max.min   > test-stocapprox.R: -1.743217 0.112619   > 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: 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-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:1.4e-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   > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > test-stocapprox.R: 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   > test-stocapprox.R: 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   > test-stocapprox.R: 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   > test-stocapprox.R: 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-stocapprox.R: 1   > test-stocapprox.R: Optimizing with step length 1.0000.   > test-stocapprox.R: The log-likelihood improved by 0.0022.   > test-stocapprox.R: Convergence test p-value: < 0.0001.   > test-stocapprox.R: 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: * 'c   > test-stocapprox.R: itation("ergm.count")' for citation information   > test-stocapprox.R: * 'https://statnet.org' for help, support, and other information   > test-stocapprox.R:   > 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-target-offset.R: 1   > test-target-offset.R: Optimizing with step length 1.0000.   > test-target-offset.R: The log-likelihood improved by 0.0210.   > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > test-target-offset.R: Convergence test p-value: 0.0002.   > test-target-offset.R: 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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > test-target-offset.R: Using 16 bridges:   > test-target-offset.R: 1   > test-target-offset.R: 2   > test-target-offset.R: 3   > test-target-offset.R: 4   > test-target-offset.R: 5   > test-target-offset.R: 6   > test-target-offset.R: 7   > test-target-offset.R: 8   > test-target-offset.R: 9   > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > test-target-offset.R: 10   > test-target-offset.R: 11   > test-target-offset.R: 12   > test-target-offset.R: 13   > test-target-offset.R: 14   > test-target-offset.R: 15   > test-target-offset.R: 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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > test-target-offset.R:   > 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-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 0.7297.   > 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-target-offset.R: 1   > test-target-offset.R: 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-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-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'.   > 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:   > test-target-offset.R: 1   > test-target-offset.R: 2   > test-target-offset.R: 3   > test-target-offset.R: 4   > test-target-offset.R: 5   > test-target-offset.R: 6   > test-target-offset.R: 7   > test-target-offset.R: 8   > test-target-offset.R: 9   > test-target-offset.R: 10   > test-target-offset.R: 11   > test-target-offset.R: 12   > test-target-offset.R: 13   > test-target-offset.R: 14   > test-target-offset.R: 15   > test-target-offset.R: 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-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 0.0066.   > 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-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.   > test-term-Offset.R: 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-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-term-b12nodematch.R: In term 'b1nodematch' in package 'ergm': Argument 'keep' has been superseded by 'levels', and it is recommended to use the latte   > test-term-b12nodematch.R: r. 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: 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-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: 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: 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: 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-directed.R: Starting maximum pseudolikelihood estimation (MPLE):   > test-term-bipartite.R: Finished 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: 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-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-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-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: 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-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: Finished MPLE.   > 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-bipartite.R: Observed statistic(s) b2dsp3 are at their smallest attainable values. Their coefficients will be fixed at -Inf.   > 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) 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-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-directed.R: Finished MPLE.   > 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-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-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE):   > test-term-directed.R: Finished 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-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-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-directed.R: Starting maximum pseudolikelihood estimation (MPLE):   > test-term-directed.R: Obtaining the responsible dyads.   > test-term-bipartite.R: Maximizing the pseudolikelihood.   > test-term-directed.R: Evaluating the predictor and response matrix.   > test-term-directed.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-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: 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: 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: In term 'nodeifactor' in package 'ergm': Argument 'base' has been superseded by 'levels', and it is recommended to use the latte   > test-term-directed.R: r. 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-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-directed.R: Maximizing the pseudolikelihood.   > test-term-directed.R: Finished MPLE.   > 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-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-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: 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-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-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-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-directed.R: Maximizing the pseudolikelihood.   > test-term-flexible.R: Maximizing the pseudolikelihood.   > test-term-directed.R: Finished MPLE.   > 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-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-directed.R: Finished MPLE.   > 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-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-flexible.R: Maximizing the pseudolikelihood.   > test-term-directed.R: Finished MPLE.   > 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: 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: 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-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-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-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-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-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-gw-sp.R: Finished MPLE.   > 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-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-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-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-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-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:   > 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:   > 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   > test-term-options.R: 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.   > test-term-options.R: 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:   > test-term-options.R: 1   > test-term-options.R: 2   > test-term-options.R: 3   > test-term-options.R: 4   > test-term-options.R: 5   > test-term-options.R: 6   > test-term-options.R: 7   > test-term-options.R: 8   > test-term-options.R: 9   > test-term-options.R: 10   > test-term-options.R: 11   > test-term-options.R: 12   > test-term-options.R: 13   > test-term-options.R: 14   > test-term-options.R: 15   > 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-options.R: 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: 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   > test-term-options.R: 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.   > test-term-options.R: 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-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-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: Maximizing the pseudolikelihood.   > test-term-gw-sp.R: Maximizing the pseudolikelihood.   > test-term-gw-sp.R: Finished MPLE.   > 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-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: Maximizing the pseudolikelihood.   > test-term-gw-sp.R: Finished MPLE.   > 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-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE):   > test-term-undirected.R: Maximizing the pseudolikelihood.   > test-term-gw-sp.R: Obtaining the responsible dyads.   > test-term-gw-sp.R: Evaluating the predictor and response matrix.   > 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-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-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-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-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-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):   > test-valued-sim.R: 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:   > 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=   > test-valued-sim.R: 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=   > test-valued-sim.R: 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: * 'c   > test-valued-sim.R: itation("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
• checking for unstated dependencies in vignettes ... OK
• checking package vignettes ... OK
• checking re-building of vignette outputs ... [81s/118s] OK
• checking PDF version of manual ... [48s/164s] OK
• checking HTML version of manual ... [72s/273s] OK
• checking for non-standard things in the check directory ... OK
• checking for detritus in the temp directory ... OK
• DONE

Status: 1 ERROR