Running 'degree.mean.age.R' [9s]
  Running 'dynamic_EGMME.R' [0s]
  Running 'dynamic_MLE_blockdiag.R' [0s]
  Running 'dynamic_MLE_blockdiag.bipartite.R' [0s]
  Running 'sim_gf_sum.R' [8s]
  Running 'simulate_networkDynamic.R' [6s]
  Running 'tergm_offset_tests.R' [0s]
  Running 'tergm_parallel.R' [0s]
  Running 'testthat.R' [108s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > #  File tests/testthat.R in package tergm, 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 2008-2025 Statnet Commons
  > ################################################################################
  > 
  > require(testthat)
  Loading required package: testthat
  > require(tergm)
  Loading required package: tergm
  Loading required package: 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.
  
  Loading required package: networkDynamic
  
  'networkDynamic' 0.11.5 (2024-11-21), part of the Statnet Project
  * 'news(package="networkDynamic")' for changes since last version
  * 'citation("networkDynamic")' for citation information
  * 'https://statnet.org' for help, support, and other information
  
  Registered S3 method overwritten by 'tergm':
    method                   from
    simulate_formula.network ergm
  
  'tergm' 4.2.2 (2025-06-15), part of the Statnet Project
  * 'news(package="tergm")' for changes since last version
  * 'citation("tergm")' for citation information
  * 'https://statnet.org' for help, support, and other information
  
  
  Attaching package: 'tergm'
  
  The following object is masked from 'package:ergm':
  
      snctrl
  
  > 
  > test_check("tergm")
  Starting 2 test processes.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-bip.R: The log-likelihood improved by 0.0014.
  > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-bip.R: Finished MCMLE.
  > test-CMLE-2-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-bip.R: The log-likelihood improved by 0.0018.
  > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-bip.R: Finished MCMLE.
  > test-CMLE-2-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-dir.R: Iteration 1 of at most 60:
  > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-bip.R: The log-likelihood improved by 0.0002.
  > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-bip.R: Finished MCMLE.
  > test-CMLE-2-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: The log-likelihood improved by 0.0018.
  > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-dir.R: Finished MCMLE.
  > test-CMLE-2-bip.R: The log-likelihood improved by 0.0005.
  > test-CMLE-2-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-bip.R: Finished MCMLE.
  > test-CMLE-2-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-dir.R: Iteration 1 of at most 60:
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: The log-likelihood improved by 0.0031.
  > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-dir.R: Finished MCMLE.
  > test-CMLE-2-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-dir.R: Iteration 1 of at most 60:
  > test-CMLE-2-bip.R: The log-likelihood improved by 0.0007.
  > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-bip.R: Finished MCMLE.
  > test-CMLE-2-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: The log-likelihood improved by 0.0042.
  > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-dir.R: Finished MCMLE.
  > test-CMLE-2-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-dir.R: Iteration 1 of at most 60:
  > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-bip.R: The log-likelihood improved by < 0.0001.
  > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-bip.R: Finished MCMLE.
  > test-CMLE-2-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-bip.R: 1 
  > test-CMLE-2-bip.R: Optimizing with step length 1.0000.
  > test-CMLE-2-bip.R: The log-likelihood improved by 0.0003.
  > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-bip.R: Finished MCMLE.
  > test-CMLE-2-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: The log-likelihood improved by 0.0002.
  > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-dir.R: Finished MCMLE.
  > test-CMLE-2-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-bip.R: The log-likelihood improved by < 0.0001.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-bip.R: Finished MCMLE.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-dir.R: Iteration 1 of at most 60:
  > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: The log-likelihood improved by 0.0033.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-dir.R: Convergence test p-value: 0.0002. Converged with 99% confidence.
  > test-CMLE-2-dir.R: Finished MCMLE.
  > test-CMLE-2-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-dir.R: Iteration 1 of at most 60:
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-dir.R: 1 
  > test-CMLE-2-dir.R: Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: The log-likelihood improved by 0.0063.
  > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-dir.R: Finished MCMLE.
  > test-CMLE-2-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-dir.R: Iteration 1 of at most 60:
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-dir.R: 1 
  > test-CMLE-2-dir.R: Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: The log-likelihood improved by 0.0026.
  > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-dir.R: Finished MCMLE.
  > test-CMLE-2-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-dir.R: Iteration 1 of at most 60:
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-dir.R: 1 
  > test-CMLE-2-dir.R: Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: The log-likelihood improved by 0.0006.
  > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-dir.R: Finished MCMLE.
  > test-CMLE-2-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-bip.R: 1 
  > test-CMLE-2-bip.R: Optimizing with step length 1.0000.
  > test-CMLE-2-bip.R: The log-likelihood improved by 0.0003.
  > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-bip.R: Finished MCMLE.
  > test-CMLE-2-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-bip.R: The log-likelihood improved by 0.0001.
  > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-bip.R: Finished MCMLE.
  > test-CMLE-2-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-dir.R: Iteration 1 of at most 60:
  > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-bip.R: The log-likelihood improved by 0.0031.
  > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-bip.R: Finished MCMLE.
  > test-CMLE-2-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-bip.R: The log-likelihood improved by 0.0075.
  > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-bip.R: Finished MCMLE.
  > test-CMLE-2-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-dir.R: 1 
  > test-CMLE-2-dir.R: Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: The log-likelihood improved by 0.0008.
  > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-dir.R: Finished MCMLE.
  > test-CMLE-2-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-dir.R: Iteration 1 of at most 60:
  > test-CMLE-2-bip.R: The log-likelihood improved by 0.0001.
  > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-bip.R: Finished MCMLE.
  > test-CMLE-2-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-2-dir.R: 1 
  > test-CMLE-2-dir.R: Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: The log-likelihood improved by < 0.0001.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-dir.R: Finished MCMLE.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-bip.R: The log-likelihood improved by 0.0018.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-bip.R: Finished MCMLE.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-dir.R: Iteration 1 of at most 60:
  > test-CMLE-2-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-dir.R: 1 
  > test-CMLE-2-dir.R: Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: The log-likelihood improved by 0.0013.
  > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-dir.R: Finished MCMLE.
  > test-CMLE-2-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-bip.R: The log-likelihood improved by 0.0008.
  > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-bip.R: Finished MCMLE.
  > test-CMLE-2-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-dir.R: Iteration 1 of at most 60:
  > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-2-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-2-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-bip.R: Obtaining the responsible dyads.
  > test-CMLE-2-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-bip.R: Finished MPLE.
  > test-CMLE-2-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-dir.R: 1 
  > test-CMLE-2-dir.R: Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: The log-likelihood improved by 0.0028.
  > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-dir.R: Finished MCMLE.
  > test-CMLE-2-bip.R: 1 
  > test-CMLE-2-bip.R: Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-bip.R: The log-likelihood improved by 0.0010.
  > test-CMLE-2-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-bip.R: Finished MCMLE.
  > test-CMLE-2-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-dir.R: Iteration 1 of at most 60:
  > test-CMLE-2-dir.R: 1 
  > test-CMLE-2-dir.R: Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: The log-likelihood improved by 0.0110.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-dir.R: Finished MCMLE.
  > test-CMLE-2-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-dir.R: Iteration 1 of at most 60:
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: The log-likelihood improved by < 0.0001.
  > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-dir.R: Finished MCMLE.
  > test-CMLE-2-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-dir.R: Iteration 1 of at most 60:
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: The log-likelihood improved by 0.0145.
  > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-dir.R: Finished MCMLE.
  > test-CMLE-2-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-dir.R: Obtaining the responsible dyads.
  > test-CMLE-2-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-dir.R: Finished MPLE.
  > test-CMLE-2-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-dir.R: Iteration 1 of at most 60:
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-und.R: Iteration 1 of at most 60:
  > test-CMLE-2-dir.R: 1 
  > test-CMLE-2-dir.R: Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: The log-likelihood improved by 0.0034.
  > test-CMLE-2-und.R: 1 
  > test-CMLE-2-und.R: Optimizing with step length 1.0000.
  > test-CMLE-2-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-dir.R: Finished MCMLE.
  > test-CMLE-2-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-und.R: The log-likelihood improved by 0.0038.
  > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-und.R: Finished MCMLE.
  > test-CMLE-2-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-und.R: Iteration 1 of at most 60:
  > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-und.R: The log-likelihood improved by 0.0003.
  > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-und.R: Finished MCMLE.
  > test-CMLE-2-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-und.R: Iteration 1 of at most 60:
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-und.R: The log-likelihood improved by 0.0024.
  > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-und.R: Finished MCMLE.
  > test-CMLE-2-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-und.R: Iteration 1 of at most 60:
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-2-und.R: 1 
  > test-CMLE-2-und.R: Optimizing with step length 1.0000.
  > test-CMLE-2-und.R: The log-likelihood improved by 0.0010.
  > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-und.R: Finished MCMLE.
  > test-CMLE-2-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-und.R: Iteration 1 of at most 60:
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: The log-likelihood improved by 0.0004.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-2-und.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
  > test-CMLE-2-und.R: Finished MCMLE.
  > test-CMLE-2-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-und.R: Iteration 1 of at most 60:
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-und.R: The log-likelihood improved by 0.0001.
  > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-und.R: Finished MCMLE.
  > test-CMLE-2-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: 1 
  > test-CMLE-bip.R: Optimizing with step length 1.0000.
  > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-bip.R: The log-likelihood improved by 0.0015.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-bip.R: Finished MCMLE.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-und.R: Iteration 1 of at most 60:
  > test-CMLE-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-und.R: The log-likelihood improved by 0.0021.
  > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-und.R: Finished MCMLE.
  > test-CMLE-2-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-bip.R: The log-likelihood improved by 0.0107.
  > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-bip.R: Finished MCMLE.
  > test-CMLE-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-und.R: Iteration 1 of at most 60:
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-bip.R: Iteration 1 of at most 60:
  > test-CMLE-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-bip.R: The log-likelihood improved by 0.0051.
  > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-bip.R: Finished MCMLE.
  > test-CMLE-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-und.R: The log-likelihood improved by 0.0007.
  > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-und.R: Finished MCMLE.
  > test-CMLE-2-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-bip.R: The log-likelihood improved by 0.0020.
  > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-bip.R: Finished MCMLE.
  > test-CMLE-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-bip.R: 1 
  > test-CMLE-bip.R: Optimizing with step length 1.0000.
  > test-CMLE-bip.R: The log-likelihood improved by 0.0029.
  > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-bip.R: Finished MCMLE.
  > test-CMLE-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-bip.R: The log-likelihood improved by 0.0004.
  > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-bip.R: Finished MCMLE.
  > test-CMLE-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-bip.R: The log-likelihood improved by 0.0049.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-bip.R: Finished MCMLE.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-und.R: Iteration 1 of at most 60:
  > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-bip.R: Iteration 1 of at most 60:
  > test-CMLE-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-bip.R: The log-likelihood improved by 0.0018.
  > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-bip.R: Finished MCMLE.
  > test-CMLE-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-und.R: The log-likelihood improved by 0.0025.
  > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-und.R: Finished MCMLE.
  > test-CMLE-2-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-und.R: Iteration 1 of at most 60:
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-und.R: The log-likelihood improved by 0.0005.
  > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-und.R: Finished MCMLE.
  > test-CMLE-2-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-und.R: Iteration 1 of at most 60:
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-und.R: The log-likelihood improved by 0.0005.
  > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-und.R: Finished MCMLE.
  > test-CMLE-2-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-und.R: Iteration 1 of at most 60:
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-und.R: The log-likelihood improved by 0.0005.
  > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-und.R: Finished MCMLE.
  > test-CMLE-2-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-und.R: Iteration 1 of at most 60:
  > test-CMLE-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-bip.R: The log-likelihood improved by 0.0033.
  > test-CMLE-bip.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
  > test-CMLE-bip.R: Finished MCMLE.
  > test-CMLE-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-und.R: 1 
  > test-CMLE-2-und.R: Optimizing with step length 1.0000.
  > test-CMLE-2-und.R: The log-likelihood improved by 0.0016.
  > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-und.R: Finished MCMLE.
  > test-CMLE-2-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-und.R: Iteration 1 of at most 60:
  > test-CMLE-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-bip.R: The log-likelihood improved by 0.0004.
  > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-bip.R: Finished MCMLE.
  > test-CMLE-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-und.R: The log-likelihood improved by 0.0005.
  > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-und.R: Finished MCMLE.
  > test-CMLE-2-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-und.R: Iteration 1 of at most 60:
  > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-und.R: The log-likelihood improved by 0.0008.
  > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-und.R: Finished MCMLE.
  > test-CMLE-2-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-bip.R: The log-likelihood improved by 0.0095.
  > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-bip.R: Finished MCMLE.
  > test-CMLE-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-2-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-2-und.R: Obtaining the responsible dyads.
  > test-CMLE-2-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-2-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-2-und.R: Finished MPLE.
  > test-CMLE-2-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-2-und.R: Iteration 1 of at most 60:
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-bip.R: Iteration 1 of at most 60:
  > test-CMLE-2-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-2-und.R: The log-likelihood improved by 0.0012.
  > test-CMLE-2-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-2-und.R: Finished MCMLE.
  > test-CMLE-2-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-2-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-bip.R: The log-likelihood improved by 0.0002.
  > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-bip.R: Finished MCMLE.
  > test-CMLE-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-bip.R: Iteration 1 of at most 60:
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-bip.R: 1 
  > test-CMLE-bip.R: Optimizing with step length 1.0000.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-bip.R: The log-likelihood improved by 0.0081.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-bip.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
  > test-CMLE-bip.R: Finished MCMLE.
  > test-CMLE-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-bip.R: Iteration 1 of at most 60:
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-bip.R: The log-likelihood improved by 0.0014.
  > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-bip.R: Finished MCMLE.
  > test-CMLE-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-bip.R: Iteration 1 of at most 60:
  > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-dir.R: Iteration 1 of at most 60:
  > test-CMLE-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-bip.R: The log-likelihood improved by 0.0005.
  > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-bip.R: Finished MCMLE.
  > test-CMLE-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-bip.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord' and 'sparse'.
  > test-CMLE-bip.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-bip.R: Obtaining the responsible dyads.
  > test-CMLE-bip.R: Evaluating the predictor and response matrix.
  > test-CMLE-bip.R: Maximizing the pseudolikelihood.
  > test-CMLE-bip.R: Finished MPLE.
  > test-CMLE-bip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-bip.R: Iteration 1 of at most 60:
  > test-CMLE-dir.R: 1 
  > test-CMLE-dir.R: Optimizing with step length 1.0000.
  > test-CMLE-dir.R: The log-likelihood improved by 0.0108.
  > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-dir.R: Finished MCMLE.
  > test-CMLE-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-dir.R: Iteration 1 of at most 60:
  > test-CMLE-bip.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-bip.R: The log-likelihood improved by 0.0004.
  > test-CMLE-bip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-bip.R: Finished MCMLE.
  > test-CMLE-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-bip.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-bip.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-dir.R: The log-likelihood improved by < 0.0001.
  > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-dir.R: Finished MCMLE.
  > test-CMLE-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-dir.R: Iteration 1 of at most 60:
  > test-CMLE-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-dir.R: The log-likelihood improved by 0.0003.
  > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-dir.R: Finished MCMLE.
  > test-CMLE-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-dir.R: Iteration 1 of at most 60:
  > test-CMLE-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-dir.R: The log-likelihood improved by 0.0044.
  > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-dir.R: Finished MCMLE.
  > test-CMLE-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-dir.R: Iteration 1 of at most 60:
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-dir.R: The log-likelihood improved by 0.0001.
  > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-dir.R: Finished MCMLE.
  > test-CMLE-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-dir.R: Iteration 1 of at most 60:
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-dir.R: The log-likelihood improved by 0.0002.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-dir.R: Finished MCMLE.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Iteration 1 of at most 60:
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-dir.R: The log-likelihood improved by 0.0005.
  > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-dir.R: Finished MCMLE.
  > test-CMLE-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-und.R: Iteration 1 of at most 60:
  > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-dir.R: Iteration 1 of at most 60:
  > test-CMLE-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-dir.R: The log-likelihood improved by 0.0021.
  > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-dir.R: Finished MCMLE.
  > test-CMLE-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-und.R: The log-likelihood improved by 0.0062.
  > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-und.R: Finished MCMLE.
  > test-CMLE-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-und.R: Iteration 1 of at most 60:
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-und.R: The log-likelihood improved by 0.0020.
  > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-und.R: Finished MCMLE.
  > test-CMLE-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-und.R: Iteration 1 of at most 60:
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-und.R: The log-likelihood improved by 0.0001.
  > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-und.R: Finished MCMLE.
  > test-CMLE-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-und.R: Iteration 1 of at most 60:
  > test-CMLE-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-und.R: The log-likelihood improved by 0.0006.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-und.R: Finished MCMLE.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-und.R: Iteration 1 of at most 60:
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-und.R: The log-likelihood improved by 0.0016.
  > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-und.R: Finished MCMLE.
  > test-CMLE-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-dir.R: Iteration 1 of at most 60:
  > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-und.R: Iteration 1 of at most 60:
  > test-CMLE-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-und.R: The log-likelihood improved by 0.0003.
  > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-und.R: Finished MCMLE.
  > test-CMLE-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-und.R: Iteration 1 of at most 60:
  > test-CMLE-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-und.R: The log-likelihood improved by 0.0121.
  > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-und.R: Finished MCMLE.
  > test-CMLE-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: 1 
  > test-CMLE-dir.R: Optimizing with step length 1.0000.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-und.R: Iteration 1 of at most 60:
  > test-CMLE-dir.R: The log-likelihood improved by 0.0173.
  > test-CMLE-dir.R: Convergence test p-value: 0.0002. Converged with 99% confidence.
  > test-CMLE-dir.R: Finished MCMLE.
  > test-CMLE-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: 1 
  > test-CMLE-und.R: Optimizing with step length 1.0000.
  > test-CMLE-und.R: The log-likelihood improved by 0.0001.
  > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-und.R: Finished MCMLE.
  > test-CMLE-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-dir.R: Iteration 1 of at most 60:
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-dir.R: The log-likelihood improved by 0.0002.
  > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-dir.R: Finished MCMLE.
  > test-CMLE-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-dir.R: Iteration 1 of at most 60:
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-dir.R: The log-likelihood improved by < 0.0001.
  > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-dir.R: Finished MCMLE.
  > test-CMLE-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-dir.R: Iteration 1 of at most 60:
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-dir.R: The log-likelihood improved by < 0.0001.
  > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-dir.R: Finished MCMLE.
  > test-CMLE-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-dir.R: Iteration 1 of at most 60:
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-dir.R: The log-likelihood improved by 0.0044.
  > test-CMLE-dir.R: Convergence test p-value: 0.0001. Converged with 99% confidence.
  > test-CMLE-dir.R: Finished MCMLE.
  > test-CMLE-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-und.R: Iteration 1 of at most 60:
  > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-und.R: Model statistics 'Persist(1)~edges' are not varying. This may indicate that the observed data occupies an extreme point in the sample space or that the estimation has reached a dead-end configuration.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-dir.R: Iteration 1 of at most 60:
  > test-CMLE-und.R: Post-burnin sample is constant; returning.
  > test-CMLE-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-und.R: The log-likelihood improved by 0.0002.
  > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-und.R: Finished MCMLE.
  > test-CMLE-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-und.R: Iteration 1 of at most 60:
  > test-CMLE-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-dir.R: The log-likelihood improved by 0.0008.
  > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-dir.R: Finished MCMLE.
  > test-CMLE-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-dir.R: Iteration 1 of at most 60:
  > test-CMLE-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-und.R: The log-likelihood improved by 0.0013.
  > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-und.R: Finished MCMLE.
  > test-CMLE-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-dir.R: The log-likelihood improved by 0.0042.
  > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-dir.R: Finished MCMLE.
  > test-CMLE-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-und.R: Iteration 1 of at most 60:
  > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-dir.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-dir.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-dir.R: Obtaining the responsible dyads.
  > test-CMLE-dir.R: Evaluating the predictor and response matrix.
  > test-CMLE-dir.R: Maximizing the pseudolikelihood.
  > test-CMLE-dir.R: Finished MPLE.
  > test-CMLE-dir.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-dir.R: Iteration 1 of at most 60:
  > test-CMLE-dir.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-dir.R: The log-likelihood improved by 0.0002.
  > test-CMLE-dir.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-dir.R: Finished MCMLE.
  > test-CMLE-dir.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-dir.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-und.R: The log-likelihood improved by 0.0041.
  > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-und.R: Finished MCMLE.
  > test-CMLE-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-und.R: Best valid proposal 'staticDiscordTNT' cannot take into account hint(s) 'triadic'.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-und.R: Iteration 1 of at most 60:
  > test-EGMME-errors.R: Targets contains offset statistics; they will only be used during the SAN run, and removal of the offset statistics will be attempted for the EGMME targets.
  > test-CMLE-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-und.R: The log-likelihood improved by 0.0124.
  > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-und.R: Finished MCMLE.
  > test-CMLE-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-EGMME-errors.R: Targets contains offset statistics; they will only be used during the SAN run, and removal of the offset statistics will be attempted for the EGMME targets.
  > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-und.R: Iteration 1 of at most 60:
  > test-CMLE-und.R: Model statistics 'Persist(1)~edges' are not varying. This may indicate that the observed data occupies an extreme point in the sample space or that the estimation has reached a dead-end configuration.
  > test-CMLE-und.R: Post-burnin sample is constant; returning.
  > test-CMLE-und.R: 1 
  > test-CMLE-und.R: Optimizing with step length 1.0000.
  > test-CMLE-und.R: The log-likelihood improved by 0.0007.
  > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-und.R: Finished MCMLE.
  > test-CMLE-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-und.R: Iteration 1 of at most 60:
  > test-CMLE-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-und.R: The log-likelihood improved by 0.0124.
  > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-und.R: Finished MCMLE.
  > test-CMLE-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-EGMME-initialfit.R: Obtaining the responsible dyads.
  > test-EGMME-initialfit.R: Evaluating the predictor and response matrix.
  > test-EGMME-initialfit.R: Maximizing the pseudolikelihood.
  > test-EGMME-initialfit.R: Finished MPLE.
  > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-EGMME-initialfit.R: Obtaining the responsible dyads.
  > test-EGMME-initialfit.R: Evaluating the predictor and response matrix.
  > test-EGMME-initialfit.R: Maximizing the pseudolikelihood.
  > test-EGMME-initialfit.R: Finished MPLE.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-und.R: Iteration 1 of at most 60:
  > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-EGMME-initialfit.R: Obtaining the responsible dyads.
  > test-EGMME-initialfit.R: Evaluating the predictor and response matrix.
  > test-EGMME-initialfit.R: Maximizing the pseudolikelihood.
  > test-EGMME-initialfit.R: Finished MPLE.
  > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-EGMME-initialfit.R: Obtaining the responsible dyads.
  > test-EGMME-initialfit.R: Evaluating the predictor and response matrix.
  > test-EGMME-initialfit.R: Maximizing the pseudolikelihood.
  > test-EGMME-initialfit.R: Finished MPLE.
  > test-CMLE-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-und.R: The log-likelihood improved by 0.0040.
  > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-und.R: Finished MCMLE.
  > test-CMLE-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-und.R: Best valid proposal 'randomtoggle' cannot take into account hint(s) 'discord', 'sparse', and 'triadic'.
  > test-CMLE-und.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-CMLE-und.R: Obtaining the responsible dyads.
  > test-CMLE-und.R: Evaluating the predictor and response matrix.
  > test-CMLE-und.R: Maximizing the pseudolikelihood.
  > test-CMLE-und.R: Finished MPLE.
  > test-CMLE-und.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-CMLE-und.R: Iteration 1 of at most 60:
  > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation.
  > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-EGMME-initialfit.R: Obtaining the responsible dyads.
  > test-EGMME-initialfit.R: Evaluating the predictor and response matrix.
  > test-EGMME-initialfit.R: Maximizing the pseudolikelihood.
  > test-EGMME-initialfit.R: Finished MPLE.
  > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-EGMME-initialfit.R: Iteration 1 of at most 60:
  > test-CMLE-und.R: 1 Optimizing with step length 1.0000.
  > test-CMLE-und.R: The log-likelihood improved by 0.0039.
  > test-CMLE-und.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-CMLE-und.R: Finished MCMLE.
  > test-CMLE-und.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-CMLE-und.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-EGMME-simple.R: Initializing unconstrained Metropolis-Hastings proposal: 
  > test-EGMME-simple.R: 'ergm:MH_SPDyad'.
  > test-EGMME-simple.R: Initializing model...
  > test-EGMME-simple.R: Model initialized.
  > test-EGMME-simple.R: Starting 4 SAN iterations of 524288 steps each.
  > test-EGMME-simple.R: #1 of 4: 
  > test-EGMME-simple.R: SAN Metropolis-Hastings accepted  75.742% of 32768 proposed steps.
  > test-EGMME-simple.R: SAN summary statistics:
  > test-EGMME-simple.R: meandeg 
  > test-EGMME-simple.R:     6.9 
  > test-EGMME-simple.R: Meanstats Goal:
  > test-EGMME-simple.R: meandeg 
  > test-EGMME-simple.R:      10 
  > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats =
  > test-EGMME-simple.R: meandeg 
  > test-EGMME-simple.R:    -3.1 
  > test-EGMME-simple.R: New statistics scaling =
  > test-EGMME-simple.R: [1] 1
  > test-EGMME-simple.R: Scaled Mahalanobis distance = 9.61000000000013
  > test-EGMME-simple.R: #2 of 4: 
  > test-EGMME-initialfit.R: 1 Optimizing with step length 1.0000.
  > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157.
  > test-EGMME-simple.R: SAN Metropolis-Hastings accepted  66.619% of 69632 proposed steps.
  > test-EGMME-simple.R: SAN summary statistics:
  > test-EGMME-simple.R: meandeg 
  > test-EGMME-simple.R:     8.4 
  > test-EGMME-simple.R: Meanstats Goal:
  > test-EGMME-simple.R: meandeg 
  > test-EGMME-simple.R:      10 
  > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats =
  > test-EGMME-simple.R: meandeg 
  > test-EGMME-simple.R:    -1.6 
  > test-EGMME-simple.R: New statistics scaling =
  > test-EGMME-simple.R: [1] 1
  > test-EGMME-simple.R: Scaled Mahalanobis distance = 2.56000000000006
  > test-EGMME-simple.R: #3 of 4: 
  > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-EGMME-initialfit.R: Finished MCMLE.
  > test-EGMME-initialfit.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-EGMME-simple.R: SAN Metropolis-Hastings accepted  61.490% of 139264 proposed steps.
  > test-EGMME-simple.R: SAN summary statistics:
  > test-EGMME-simple.R: meandeg 
  > test-EGMME-simple.R:     9.7 
  > test-EGMME-simple.R: Meanstats Goal:
  > test-EGMME-simple.R: meandeg 
  > test-EGMME-simple.R:      10 
  > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats =
  > test-EGMME-simple.R: meandeg 
  > test-EGMME-simple.R:    -0.3 
  > test-EGMME-simple.R: New statistics scaling =
  > test-EGMME-simple.R: [1] 1
  > test-EGMME-simple.R: Scaled Mahalanobis distance = 0.0900000000000113
  > test-EGMME-simple.R: #4 of 4: 
  > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation.
  > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-EGMME-initialfit.R: Obtaining the responsible dyads.
  > test-EGMME-initialfit.R: Evaluating the predictor and response matrix.
  > test-EGMME-initialfit.R: Maximizing the pseudolikelihood.
  > test-EGMME-initialfit.R: Finished MPLE.
  > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-EGMME-initialfit.R: Iteration 1 of at most 60:
  > test-EGMME-simple.R: SAN Metropolis-Hastings accepted   0.000% of 278528 proposed steps.
  > test-EGMME-simple.R: SAN summary statistics:
  > test-EGMME-simple.R: meandeg 
  > test-EGMME-simple.R:      10 
  > test-EGMME-simple.R: Meanstats Goal:
  > test-EGMME-simple.R: meandeg 
  > test-EGMME-simple.R:      10 
  > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats =
  > test-EGMME-simple.R:       meandeg 
  > test-EGMME-simple.R: -1.879052e-14 
  > test-EGMME-simple.R: New statistics scaling =
  > test-EGMME-simple.R: [1] 1
  > test-EGMME-simple.R: Scaled Mahalanobis distance = 3.53083818192423e-28
  > test-EGMME-simple.R: Initializing Metropolis-Hastings proposal.
  > test-EGMME-simple.R: Constructing an approximate response network.
  > test-EGMME-simple.R: Starting 4 SAN iterations of 80000 steps each.
  > test-EGMME-simple.R: #1 of 4: 
  > test-EGMME-simple.R: SAN Metropolis-Hastings accepted  59.619% of 4096 proposed steps.
  > test-EGMME-simple.R: SAN summary statistics:
  > test-EGMME-simple.R:      edges   mean.age 
  > test-EGMME-simple.R:         11 1073741824 
  > test-EGMME-simple.R: Meanstats Goal:
  > test-EGMME-simple.R:    edges mean.age 
  > test-EGMME-simple.R:       10       10 
  > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats =
  > test-EGMME-simple.R:      edges   mean.age 
  > test-EGMME-simple.R:          1 1073741814 
  > test-EGMME-simple.R: New statistics scaling =
  > test-EGMME-simple.R: [1] 0.5 0.5
  > test-EGMME-simple.R: Scaled Mahalanobis distance = 576460741566005312
  > test-EGMME-simple.R: #2 of 4: 
  > test-EGMME-simple.R: SAN Metropolis-Hastings accepted  31.152% of 8192 proposed steps.
  > test-EGMME-simple.R: SAN summary statistics:
  > test-EGMME-simple.R:      edges   mean.age 
  > test-EGMME-simple.R:         10 1073741824 
  > test-EGMME-simple.R: Meanstats Goal:
  > test-EGMME-simple.R:    edges mean.age 
  > test-EGMME-simple.R:       10       10 
  > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats =
  > test-EGMME-simple.R:      edges   mean.age 
  > test-EGMME-simple.R:          0 1073741814 
  > test-EGMME-simple.R: New statistics scaling =
  > test-EGMME-simple.R: [1] 0.5 0.5
  > test-EGMME-simple.R: Scaled Mahalanobis distance = 576460741566005312
  > test-EGMME-simple.R: #3 of 4: 
  > test-EGMME-simple.R: SAN Metropolis-Hastings accepted   2.061% of 20480 proposed steps.
  > test-EGMME-simple.R: SAN summary statistics:
  > test-EGMME-simple.R:      edges   mean.age 
  > test-EGMME-simple.R:         10 1073741824 
  > test-EGMME-simple.R: Meanstats Goal:
  > test-EGMME-simple.R:    edges mean.age 
  > test-EGMME-simple.R:       10       10 
  > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats =
  > test-EGMME-simple.R:      edges   mean.age 
  > test-EGMME-simple.R:          0 1073741814 
  > test-EGMME-simple.R: New statistics scaling =
  > test-EGMME-simple.R: [1] 0.5 0.5
  > test-EGMME-simple.R: Scaled Mahalanobis distance = 576460741566005312
  > test-EGMME-simple.R: #4 of 4: 
  > test-EGMME-simple.R: SAN Metropolis-Hastings accepted   0.000% of 40960 proposed steps.
  > test-EGMME-simple.R: SAN summary statistics:
  > test-EGMME-simple.R:      edges   mean.age 
  > test-EGMME-simple.R:         10 1073741824 
  > test-EGMME-simple.R: Meanstats Goal:
  > test-EGMME-simple.R:    edges mean.age 
  > test-EGMME-simple.R:       10       10 
  > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats =
  > test-EGMME-simple.R:      edges   mean.age 
  > test-EGMME-simple.R:          0 1073741814 
  > test-EGMME-simple.R: New statistics scaling =
  > test-EGMME-simple.R: [1] 0.5 0.5
  > test-EGMME-simple.R: Scaled Mahalanobis distance = 576460741566005312
  > test-EGMME-simple.R: SAN summary statistics:
  > test-EGMME-simple.R:      edges   mean.age 
  > test-EGMME-simple.R:         10 1073741824 
  > test-EGMME-simple.R: Meanstats Goal:
  > test-EGMME-simple.R: [1] 10 10
  > test-EGMME-simple.R: Difference: SAN target.stats - Goal target.stats =
  > test-EGMME-simple.R:      edges   mean.age 
  > test-EGMME-simple.R:          0 1073741814 
  > test-EGMME-simple.R: Fitting TERGM Equilibrium GMME.
  > test-EGMME-simple.R: Starting optimization with with coef_0 = ( -2.94443897916644 1 ).
  > test-EGMME-simple.R: ========  Phase 1: Burn in, get initial gradient values, and find a configuration under which all targets vary. ========
  > test-EGMME-simple.R: Burning in... 
  > test-EGMME-initialfit.R: 1 
  > test-EGMME-initialfit.R: Optimizing with step length 1.0000.
  > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157.
  > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-EGMME-initialfit.R: Finished MCMLE.
  > test-EGMME-initialfit.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation.
  > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-EGMME-initialfit.R: Obtaining the responsible dyads.
  > test-EGMME-initialfit.R: Evaluating the predictor and response matrix.
  > test-EGMME-initialfit.R: Maximizing the pseudolikelihood.
  > test-EGMME-initialfit.R: Finished MPLE.
  > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-EGMME-initialfit.R: Iteration 1 of at most 60:
  > test-EGMME-initialfit.R: 1 Optimizing with step length 1.0000.
  > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157.
  > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-EGMME-initialfit.R: Finished MCMLE.
  > test-EGMME-initialfit.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation.
  > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-EGMME-initialfit.R: Obtaining the responsible dyads.
  > test-EGMME-initialfit.R: Evaluating the predictor and response matrix.
  > test-EGMME-initialfit.R: Maximizing the pseudolikelihood.
  > test-EGMME-initialfit.R: Finished MPLE.
  > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-EGMME-initialfit.R: Iteration 1 of at most 60:
  > test-EGMME-simple.R: Returned from STERGM burnin
  > test-EGMME-simple.R: Done.
  > test-EGMME-simple.R: ======== Attempt 1 ========
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-initialfit.R: 1 
  > test-EGMME-initialfit.R: Optimizing with step length 1.0000.
  > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157.
  > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-EGMME-initialfit.R: Finished MCMLE.
  > test-EGMME-initialfit.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation.
  > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-EGMME-initialfit.R: Obtaining the responsible dyads.
  > test-EGMME-initialfit.R: Evaluating the predictor and response matrix.
  > test-EGMME-initialfit.R: Maximizing the pseudolikelihood.
  > test-EGMME-initialfit.R: Finished MPLE.
  > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-EGMME-initialfit.R: Iteration 1 of at most 60:
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-initialfit.R: 1 
  > test-EGMME-initialfit.R: Optimizing with step length 1.0000.
  > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-EGMME-initialfit.R: Finished MCMLE.
  > test-EGMME-initialfit.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-EGMME-initialfit.R: Unable to match target stats. Using MCMLE estimation.
  > test-EGMME-initialfit.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-EGMME-initialfit.R: Obtaining the responsible dyads.
  > test-EGMME-initialfit.R: Evaluating the predictor and response matrix.
  > test-EGMME-initialfit.R: Maximizing the pseudolikelihood.
  > test-EGMME-initialfit.R: Finished MPLE.
  > test-EGMME-initialfit.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-EGMME-initialfit.R: Iteration 1 of at most 60:
  > test-EGMME-initialfit.R: 1 
  > test-EGMME-initialfit.R: Optimizing with step length 1.0000.
  > test-EGMME-initialfit.R: The log-likelihood improved by 0.0157.
  > test-EGMME-initialfit.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-EGMME-initialfit.R: Finished MCMLE.
  > test-EGMME-initialfit.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-EGMME-initialfit.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > 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-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 1.2158.
  > test-basis.R: Estimating equations are not within tolerance region.
  > test-basis.R: Iteration 2 of at most 60:
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: All parameters have some effect and all statistics are moving. Proceeding to Phase 2.
  > test-EGMME-simple.R: ========  Phase 2: Find and refine the estimate. ========
  > test-EGMME-simple.R: ======== Subphase 1 ========
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.0687.
  > test-basis.R: Convergence test p-value: 0.0280. Not converged with 99% confidence; increasing sample size.
  > test-basis.R: Iteration 3 of at most 60:
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -3.535745      1.475307 
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 1.3914799890544e-110 , trending: 3.27131906336549e-08 .
  > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-basis.R: The log-likelihood improved by 0.0837.
  > test-basis.R: Convergence test p-value: 0.6094. Not converged with 99% confidence; increasing sample size.
  > test-basis.R: Iteration 4 of at most 60:
  > test-EGMME-simple.R: Finished. Extracting.
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.0656.
  > test-basis.R: Convergence test p-value: 0.5885. Not converged with 99% confidence; increasing sample size.
  > test-basis.R: Iteration 5 of at most 60:
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -4.118841      1.802270 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 3.01245591281223e-42 , trending: 3.81048849286777e-19 .
  > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -4.930926      2.325456 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 5.01298005644045e-24 , trending: 5.10804229077213e-13 .
  > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.0914.
  > test-basis.R: Convergence test p-value: 0.2077. Not converged with 99% confidence; increasing sample size.
  > test-basis.R: Iteration 6 of at most 60:
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.280278      2.102755 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 3.60431227021322e-07 , trending: 4.19606113727738e-13 .
  > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.068490      2.059625 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.00114190507638694 , trending: 5.57325010969205e-19 .
  > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.226398      2.400553 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 7.02401736927543e-05 , trending: 7.5737719187006e-11 .
  > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.0096.
  > test-basis.R: Convergence test p-value: 0.0031. Converged with 99% confidence.
  > test-basis.R: Finished MCMLE.
  > test-basis.R: Evaluating log-likelihood at the estimate. 
  > test-EGMME-simple.R: Finished. Extracting.
  > 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-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.106173      2.441485 
  > test-basis.R: Using 16 bridges: 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-EGMME-simple.R: Estimating equations = 0 p-value: 0.00583560731513221 , trending: 3.11134253124785e-11 .
  > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > 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-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.633203      2.130450 
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 6.22153092853623e-05 , trending: 6.06750743355267e-07 .
  > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-basis.R: The log-likelihood improved by 1.2158.
  > 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 0.0687.
  > test-basis.R: Convergence test p-value: 0.0280. Not converged with 99% confidence; increasing sample size.
  > test-basis.R: Iteration 3 of at most 60:
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.330221      2.182673 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.00496616862578934 , trending: 2.60795493515843e-16 .
  > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.0837.
  > test-basis.R: Convergence test p-value: 0.6094. Not converged with 99% confidence; increasing sample size.
  > test-basis.R: Iteration 4 of at most 60:
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.268428      2.343567 
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.0656.
  > test-basis.R: Convergence test p-value: 0.5885. Not converged with 99% confidence; increasing sample size.
  > test-basis.R: Iteration 5 of at most 60:
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.963218136606521 , trending: 0.0671395053447981 .
  > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend.  4 / 5  to go.
  > test-EGMME-simple.R: ======== Subphase 2 ========
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.224301      2.272558 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.878641316766649 , trending: 0.0564044565523399 .
  > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend.  4 / 5  to go.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.0914.
  > test-basis.R: Convergence test p-value: 0.2077. Not converged with 99% confidence; increasing sample size.
  > test-basis.R: Iteration 6 of at most 60:
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.157947      2.304233 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.828767985605079 , trending: 0.038176878635704 .
  > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend.  3 / 5  to go.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.097439      2.306586 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.726377956354663 , trending: 0.00550122868318379 .
  > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:      -5.18524       2.07143 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.688236715199918 , trending: 0.000244622114012557 .
  > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.165517      2.377492 
  > test-basis.R: 1 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.887135556274084 , trending: 0.00435733865560731 .
  > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.0096.
  > test-basis.R: Convergence test p-value: 0.0031. 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-EGMME-simple.R: Finished. Extracting.
  > test-basis.R: Using 16 bridges: 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-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.082794      2.227639 
  > 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-EGMME-simple.R: Estimating equations = 0 p-value: 0.746906892213617 , trending: 0.00984507486438503 .
  > test-EGMME-simple.R: Estimating equations significantly differ from 0 or exhibit a significant trend. Resetting counter.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.332418      2.184492 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.72903979754815 , trending: 0.143800594218699 .
  > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend.  4 / 5  to go.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 1.2158.
  > test-basis.R: Estimating equations are not within tolerance region.
  > test-basis.R: Iteration 2 of at most 60:
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.204873      2.165226 
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.0687.
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.915517851686919 , trending: 0.0308422566828079 .
  > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend.  3 / 5  to go.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-basis.R: Convergence test p-value: 0.0280. Not converged with 99% confidence; increasing sample size.
  > test-basis.R: Iteration 3 of at most 60:
  > test-EGMME-simple.R: Finished. Extracting.
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.251966      1.944937 
  > test-basis.R: The log-likelihood improved by 0.0837.
  > test-basis.R: Convergence test p-value: 0.6094. Not converged with 99% confidence; increasing sample size.
  > test-basis.R: Iteration 4 of at most 60:
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.849842619342716 , trending: 0.156756643900556 .
  > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend.  2 / 5  to go.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.0656.
  > test-basis.R: Convergence test p-value: 0.5885. Not converged with 99% confidence; increasing sample size.
  > test-basis.R: Iteration 5 of at most 60:
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.108031      2.223334 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.898419792326419 , trending: 0.0787005201225524 .
  > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend.  1 / 5  to go.
  > test-EGMME-simple.R: Approximate standard error of the estimate:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     0.4922024     0.5499035 
  > test-EGMME-simple.R: Approximate standard error of window means:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:    0.01975644    0.01576257 
  > test-EGMME-simple.R: par. var. / (std. var. + par. var.):
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:  0.0016085363  0.0008209645 
  > test-EGMME-simple.R: Local nonlinearity p-value: 0.0948548594075957
  > test-EGMME-simple.R: There is evidence of local nonlinearity. Continuing.
  > test-EGMME-simple.R: ======== Subphase 3 ========
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.104021      2.255011 
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.0914.
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.852201823894968 , trending: 0.0481180630546847 .
  > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend.  4 / 5  to go.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-basis.R: Convergence test p-value: 0.2077. Not converged with 99% confidence; increasing sample size.
  > test-basis.R: Iteration 6 of at most 60:
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.143689      2.200477 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.865672941020689 , trending: 0.219231451535762 .
  > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend.  3 / 5  to go.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.151976      2.173453 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.811071100224124 , trending: 0.528424223198149 .
  > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend.  2 / 5  to go.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.139175      2.102897 
  > test-basis.R: 1 
  > test-basis.R: Optimizing with step length 1.0000.
  > test-basis.R: The log-likelihood improved by 0.0096.
  > test-basis.R: Convergence test p-value: 0.0031. Converged with 99% confidence.
  > test-basis.R: Finished MCMLE.
  > test-basis.R: Evaluating log-likelihood at the estimate. 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.947368135956696 , trending: 0.535566962489831 .
  > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend.  1 / 5  to go.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-basis.R: Fitting the dyad-independent submodel...
  > test-basis.R: Bridging between the dyad-independent submodel and the full model...
  > test-basis.R: Setting up bridge sampling...
  > test-basis.R: Using 16 bridges: 1 
  > test-basis.R: 2 
  > 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-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.175996      2.148141 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.974056708436821 , trending: 0.744214575272452 .
  > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and neither they nor the parameters exhibit a significant trend. Reducing gain.
  > test-EGMME-simple.R: Approximate standard error of the estimate:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     0.3104041     0.5135670 
  > test-EGMME-simple.R: Approximate standard error of window means:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:    0.01014973    0.01687378 
  > test-EGMME-simple.R: par. var. / (std. var. + par. var.):
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:   0.001068046   0.001078356 
  > test-EGMME-simple.R: Local nonlinearity p-value: 0.720167289336991
  > test-EGMME-simple.R: EGMME does not appear to be estimated with sufficient prescision. Continuing.
  > test-EGMME-simple.R: ======== Subphase 4 ========
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.231970      2.259602 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.982532758325356 , trending: 0.605756452659413 .
  > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend.  4 / 5  to go.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.185316      2.237948 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.974448108052989 , trending: 0.545326240721661 .
  > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend.  3 / 5  to go.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.244654      2.173530 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.936352885655037 , trending: 0.968030111947793 .
  > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend.  2 / 5  to go.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.138225      2.248962 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.995431953517572 , trending: 0.433599476477221 .
  > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and do not exhibit a significant trend.  1 / 5  to go.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: New parameters:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.153525      2.245627 
  > test-EGMME-simple.R: Estimating equations = 0 p-value: 0.986594407698035 , trending: 0.478569355659096 .
  > test-EGMME-simple.R: Estimating equations do not significantly differ from 0 and neither they nor the parameters exhibit a significant trend. Reducing gain.
  > test-EGMME-simple.R: Approximate standard error of the estimate:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     0.3338714     0.4219504 
  > test-EGMME-simple.R: Approximate standard error of window means:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:    0.01006127    0.01301498 
  > test-EGMME-simple.R: par. var. / (std. var. + par. var.):
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:  0.0009073040  0.0009504983 
  > test-EGMME-simple.R: Local nonlinearity p-value: 0.778429922249166
  > test-EGMME-simple.R: Maximum number of gain levels exceeded. Stopping.Refining the estimate using the mean method. New estimate:
  > test-EGMME-simple.R:    Form~edges Persist~edges 
  > test-EGMME-simple.R:     -5.171135      2.188753 
  > test-EGMME-simple.R: ========  Phase 3: Simulate from the fit and estimate standard errors. ========
  > test-EGMME-simple.R: Evaluating target statistics at the estimate.
  > test-EGMME-simple.R: Running stochastic optimization... 
  > test-EGMME-simple.R: Finished. Extracting.
  > test-EGMME-simple.R: Finished.
  > test-EGMME-simple.R: Estimating equation = 0 p-value: 0.798393159978643
  > test-EGMME-simple.R: Maximum number of gain levels exceeded. Stopping.
  > test-EGMME-simple.R: Call:
  > test-EGMME-simple.R: tergm(formula = g1 ~ Form(~edges) + Persist(~edges), constraints = ~., 
  > test-EGMME-simple.R:     target.stats = target.stats[-3], estimate = "EGMME", control = control.tergm(SA.plot.progress = do.plot, 
  > test-EGMME-simple.R:         SA.phase2.levels.min = 2, SA.phase2.levels.max = 4, SA.phase2.repeats = 10, 
  > test-EGMME-simple.R:         SA.restart.on.err = FALSE, init = c(-log(0.95/0.05), 
  > test-EGMME-simple.R:             1)), verbose = TRUE, targets = ~edges + mean.age)
  > test-EGMME-simple.R: 
  > test-EGMME-simple.R: Gradient Descent Equilibrium Generalized Method of Moments Results Results:
  > test-EGMME-simple.R: 
  > test-EGMME-simple.R:               Estimate Std. Error MCMC % z value Pr(>|z|)    
  > test-EGMME-simple.R: Form~edges     -5.1711     0.3348      0 -15.447   <1e-04 ***
  > test-EGMME-simple.R: Persist~edges   2.1888     0.4104      0   5.333   <1e-04 ***
  > test-EGMME-simple.R: ---
  > test-EGMME-simple.R: Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
  > test-EGMME-simple.R: 
  > test-EGMME-simple.R: Sample statistics summary:
  > test-EGMME-simple.R: 
  > test-EGMME-simple.R: Iterations = 21:5270
  > test-EGMME-simple.R: Thinning interval = 1 
  > test-EGMME-simple.R: Number of chains = 1 
  > test-EGMME-simple.R: Sample size per chain = 5250 
  > test-EGMME-simple.R: 
  > test-EGMME-simple.R: 1. Empirical mean and standard deviation for each variable,
  > test-EGMME-simple.R:    plus standard error of the mean:
  > test-EGMME-simple.R: 
  > test-EGMME-simple.R:              Mean    SD Naive SE Time-series SE
  > test-EGMME-simple.R: edges     0.04171 3.150  0.04348         0.1888
  > test-EGMME-simple.R: mean.age -0.08878 3.178  0.04385         0.1920
  > test-EGMME-simple.R: 
  > test-EGMME-simple.R: 2. Quantiles for each variable:
  > test-EGMME-simple.R: 
  > test-EGMME-simple.R:           2.5%  25%     50%   75% 97.5%
  > test-EGMME-simple.R: edges    -6.00 -2.0  0.0000 2.000     7
  > test-EGMME-simple.R: mean.age -5.49
  > test-EGMME-simple.R:  -2.2 -0.4286 1.778     7
  > test-EGMME-simple.R: 
  > test-EGMME-simple.R: 
  > test-EGMME-simple.R: Are sample statistics significantly different from observed?
  > test-EGMME-simple.R:                 edges    mean.age    (Omni)
  > test-EGMME-simple.R: diff.      0.04171429 -0.08877985        NA
  > test-EGMME-simple.R: test stat. 0.22090947 -0.46228617 0.4520099
  > test-EGMME-simple.R: P-val.     0.82516293  0.64387611 0.7983932
  > test-EGMME-simple.R: 
  > test-EGMME-simple.R: Sample statistics cross-correlations:
  > test-EGMME-simple.R:                edges    mean.age
  > test-EGMME-simple.R: edges     1.00000000 -0.03095688
  > test-EGMME-simple.R: mean.age -0.03095688  1.00000000
  > test-EGMME-simple.R: 
  > test-EGMME-simple.R: Sample statistics auto-correlation:
  > test-EGMME-simple.R: Chain 1 
  > test-EGMME-simple.R:           edges  mean.age
  > test-EGMME-simple.R: Lag 0 1.0000000 1.0000000
  > test-EGMME-simple.R: Lag 1 0.8992875 0.8840140
  > test-EGMME-simple.R: Lag 2 0.8095137 0.7907080
  > test-EGMME-simple.R: Lag 3 0.7277822 0.7070889
  > test-EGMME-simple.R: Lag 4 0.6590848 0.6408322
  > test-EGMME-simple.R: Lag 5 0.5975276 0.5821078
  > test-EGMME-simple.R: 
  > test-EGMME-simple.R: Sample statistics burn-in diagnostic (Geweke):
  > test-EGMME-simple.R: Chain 1 
  > test-EGMME-simple.R: 
  > test-EGMME-simple.R: Fraction in 1st window = 0.1
  > test-EGMME-simple.R: Fraction in 2nd window = 0.5 
  > test-EGMME-simple.R: 
  > test-EGMME-simple.R:      edges   mean.age 
  > test-EGMME-simple.R: -0.3064371 -0.3217347 
  > test-EGMME-simple.R: 
  > test-EGMME-simple.R: Individual P-values (lower = worse):
  > test-EGMME-simple.R:     edges  mean.age 
  > test-EGMME-simple.R: 0.7592719 0.7476537 
  > test-EGMME-simple.R: Joint P-value (lower = worse):  0.943036 
  > test-EGMME-simple.R: 
  > test-EGMME-simple.R: Note: MCMC diagnostics shown here are from the last round of
  > test-EGMME-simple.R:   simulation, prior to computation of final parameter estimates.
  > test-EGMME-simple.R:   Because the final estimates are refinements of those used for this
  > test-EGMME-simple.R:   simulation run, these diagnostics may understate model performance.
  > test-EGMME-simple.R:   To directly assess the performance of the final model on in-model
  > test-EGMME-simple.R:   statistics, please use the GOF command: gof(ergmFitObject,
  > test-EGMME-simple.R:   GOF=~model).
  > test-EGMME-simple.R: 
  > test-networkLite.R: Loading required package: networkLite
  > test-nwelt.R: Edge activity in base.net was ignored
  > test-nwelt.R: Created net.obs.period to describe network
  > test-nwelt.R:  Network observation period info:
  > test-nwelt.R:   Number of observation spells: 1 
  > test-nwelt.R:   Maximal time range observed: -Inf until Inf 
  > test-nwelt.R:   Temporal mode: discrete 
  > test-nwelt.R:   Time unit: step 
  > test-nwelt.R:   Suggested time increment: 1 
  > test-nwelt.R: Edge activity in base.net was ignored
  > test-nwelt.R: Created net.obs.period to describe network
  > test-nwelt.R:  Network observation period info:
  > test-nwelt.R:   Number of observation spells: 1 
  > test-nwelt.R:   Maximal time range observed: -Inf until Inf 
  > test-nwelt.R:   Temporal mode: discrete 
  > test-nwelt.R:   Time unit: step 
  > test-nwelt.R:   Suggested time increment: 1 
  > test-nwelt.R: Edge activity in base.net was ignored
  > test-nwelt.R: Created net.obs.period to describe network
  > test-nwelt.R:  Network observation period info:
  > test-nwelt.R:   Number of observation spells: 1 
  > test-nwelt.R:   Maximal time range observed: -Inf until Inf 
  > test-nwelt.R:   Temporal mode: discrete 
  > test-nwelt.R:   Time unit: step 
  > test-nwelt.R:   Suggested time increment: 1 
  > test-nwelt.R: Created net.obs.period to describe network
  > test-nwelt.R:  Network observation period info:
  > test-nwelt.R:   Number of observation spells: 1 
  > test-nwelt.R:   Maximal time range observed: 1 until Inf 
  > test-nwelt.R:   Temporal mode: discrete 
  > test-nwelt.R:   Time unit: step 
  > test-nwelt.R:   Suggested time increment: 1 
  > test-simulate.R: simulate.tergm test(s) skipped. Set ENABLE_statnet_TESTS environment variable to run.
  > test-term-EdgeAges.R: 
  
  Error:
  ! testthat subprocess exited in file 'test-term-EdgeAges.R'.
  Caused by error:
  ! R session crashed with exit code -1073741819
  Backtrace:
       ▆
    1. └─testthat::test_check("tergm")
    2.   └─testthat::test_dir(...)
    3.     └─testthat:::test_files(...)
    4.       └─testthat:::test_files_parallel(...)
    5.         ├─withr::with_dir(...)
    6.         │ └─base::force(code)
    7.         ├─testthat::with_reporter(...)
    8.         │ └─base::tryCatch(...)
    9.         │   └─base (local) tryCatchList(expr, classes, parentenv, handlers)
   10.         │     └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
   11.         │       └─base (local) doTryCatch(return(expr), name, parentenv, handler)
   12.         └─testthat:::parallel_event_loop_chunky(queue, reporters, ".")
   13.           └─queue$poll(Inf)
   14.             └─base::lapply(...)
   15.               └─testthat (local) FUN(X[[i]], ...)
   16.                 └─private$handle_error(msg, i)
   17.                   └─cli::cli_abort(...)
   18.                     └─rlang::abort(...)
  Execution halted