Running ‘testthat.R’ [85s/57s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > #  File tests/testthat.R in package ergm.ego, 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 2015-2025 Statnet Commons
  > ################################################################################
  > library(testthat)
  > library(ergm.ego)
  Loading required package: ergm
  Loading required package: network
  
  'network' 1.20.0 (2026-02-06), 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' 4.12.0 (2026-02-17), 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: egor
  Loading required package: dplyr
  
  Attaching package: 'dplyr'
  
  The following objects are masked from 'package:stats':
  
      filter, lag
  
  The following objects are masked from 'package:base':
  
      intersect, setdiff, setequal, union
  
  Loading required package: tibble
  
  'ergm.ego' 1.1.3 (2025-06-10), part of the Statnet Project
  * 'news(package="ergm.ego")' for changes since last version
  * 'citation("ergm.ego")' for citation information
  * 'https://statnet.org' for help, support, and other information
  
  
  Attaching package: 'ergm.ego'
  
  The following objects are masked from 'package:ergm':
  
      COLLAPSE_SMALLEST, snctrl
  
  The following object is masked from 'package:base':
  
      sample
  
  > 
  > test_check("ergm.ego")
  Starting 2 test processes.
  > test-EgoStat.R: Starting simulated annealing (SAN)
  > test-EgoStat.R: Iteration 1 of at most 4
  > test-EgoStat.R: Iteration 2 of at most 4
  > test-attrmismatch.R: Constructing pseudopopulation network.
  > test-EgoStat.R: Iteration 3 of at most 4
  > test-EgoStat.R: Finished simulated annealing
  > test-attrmismatch.R: Starting simulated annealing (SAN)
  > test-attrmismatch.R: Iteration 1 of at most 4
  > test-attrmismatch.R: Iteration 2 of at most 4
  > test-attrmismatch.R: Iteration 3 of at most 4
  > test-attrmismatch.R: Iteration 4 of at most 4
  > test-attrmismatch.R: Finished simulated annealing
  > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation.
  > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-attrmismatch.R: Obtaining the responsible dyads.
  > test-attrmismatch.R: Evaluating the predictor and response matrix.
  > test-attrmismatch.R: Maximizing the pseudolikelihood.
  > test-attrmismatch.R: Finished MPLE.
  > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-attrmismatch.R: Iteration 1 of at most 60:
  > test-attrmismatch.R: 1 Optimizing with step length 1.0000.
  > test-attrmismatch.R: The log-likelihood improved by 0.0025.
  > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-attrmismatch.R: Finished MCMLE.
  > test-attrmismatch.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-attrmismatch.R: Constructing pseudopopulation network.
  > test-attrmismatch.R: Starting simulated annealing (SAN)
  > test-attrmismatch.R: Iteration 1 of at most 4
  > test-attrmismatch.R: Iteration 2 of at most 4
  > test-attrmismatch.R: Iteration 3 of at most 4
  > test-attrmismatch.R: Iteration 4 of at most 4
  > test-attrmismatch.R: Finished simulated annealing
  > test-attrmismatch.R: Unable to match target stats. Using MCMLE estimation.
  > test-attrmismatch.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-attrmismatch.R: Obtaining the responsible dyads.
  > test-attrmismatch.R: Evaluating the predictor and response matrix.
  > test-attrmismatch.R: Maximizing the pseudolikelihood.
  > test-attrmismatch.R: Finished MPLE.
  > test-attrmismatch.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-attrmismatch.R: Iteration 1 of at most 60:
  > test-attrmismatch.R: 1 Optimizing with step length 1.0000.
  > test-attrmismatch.R: The log-likelihood improved by 0.0286.
  > test-attrmismatch.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-attrmismatch.R: Finished MCMLE.
  > test-attrmismatch.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-attrmismatch.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-boot_jack.R: Constructing pseudopopulation network.
  > test-boot_jack.R: Starting simulated annealing (SAN)
  > test-boot_jack.R: Iteration 1 of at most 4
  > test-boot_jack.R: Finished simulated annealing
  > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
  > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-boot_jack.R: Obtaining the responsible dyads.
  > test-boot_jack.R: Evaluating the predictor and response matrix.
  > test-boot_jack.R: Maximizing the pseudolikelihood.
  > test-boot_jack.R: Finished MPLE.
  > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-boot_jack.R: Iteration 1 of at most 60:
  > test-boot_jack.R: 1 Optimizing with step length 1.0000.
  > test-boot_jack.R: The log-likelihood improved by 0.0223.
  > test-boot_jack.R: Convergence test p-value: 0.0016. Converged with 99% confidence.
  > test-boot_jack.R: Finished MCMLE.
  > test-boot_jack.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-boot_jack.R: Constructing pseudopopulation network.
  > test-boot_jack.R: Starting simulated annealing (SAN)
  > test-boot_jack.R: Iteration 1 of at most 4
  > test-boot_jack.R: Iteration 2 of at most 4
  > test-boot_jack.R: Iteration 3 of at most 4
  > test-boot_jack.R: Iteration 4 of at most 4
  > test-boot_jack.R: Finished simulated annealing
  > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
  > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-boot_jack.R: Obtaining the responsible dyads.
  > test-boot_jack.R: Evaluating the predictor and response matrix.
  > test-boot_jack.R: Maximizing the pseudolikelihood.
  > test-boot_jack.R: Finished MPLE.
  > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-boot_jack.R: Iteration 1 of at most 60:
  > test-boot_jack.R: 1 Optimizing with step length 1.0000.
  > test-boot_jack.R: The log-likelihood improved by 0.0009.
  > test-boot_jack.R: Convergence test p-value: 0.0006. Converged with 99% confidence.
  > test-boot_jack.R: Finished MCMLE.
  > test-boot_jack.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-boot_jack.R: Constructing pseudopopulation network.
  > test-boot_jack.R: Starting simulated annealing (SAN)
  > test-boot_jack.R: Iteration 1 of at most 4
  > test-boot_jack.R: Finished simulated annealing
  > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
  > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-boot_jack.R: Obtaining the responsible dyads.
  > test-boot_jack.R: Evaluating the predictor and response matrix.
  > test-boot_jack.R: Maximizing the pseudolikelihood.
  > test-boot_jack.R: Finished MPLE.
  > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-boot_jack.R: Iteration 1 of at most 60:
  > test-boot_jack.R: 1 
  > test-boot_jack.R: Optimizing with step length 1.0000.
  > test-boot_jack.R: The log-likelihood improved by 0.0488.
  > test-boot_jack.R: Convergence test p-value: 0.0024. Converged with 99% confidence.
  > test-boot_jack.R: Finished MCMLE.
  > test-boot_jack.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-boot_jack.R: Constructing pseudopopulation network.
  > test-boot_jack.R: Starting simulated annealing (SAN)
  > test-boot_jack.R: Iteration 1 of at most 4
  > test-boot_jack.R: Finished simulated annealing
  > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
  > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-boot_jack.R: Obtaining the responsible dyads.
  > test-boot_jack.R: Evaluating the predictor and response matrix.
  > test-boot_jack.R: Maximizing the pseudolikelihood.
  > test-boot_jack.R: Finished MPLE.
  > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-boot_jack.R: Iteration 1 of at most 60:
  > test-boot_jack.R: 1 Optimizing with step length 1.0000.
  > test-boot_jack.R: The log-likelihood improved by 0.0002.
  > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-boot_jack.R: Finished MCMLE.
  > test-boot_jack.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-boot_jack.R: Constructing pseudopopulation network.
  > test-boot_jack.R: Starting simulated annealing (SAN)
  > test-boot_jack.R: Iteration 1 of at most 4
  > test-boot_jack.R: Iteration 2 of at most 4
  > test-boot_jack.R: Iteration 3 of at most 4
  > test-boot_jack.R: Iteration 4 of at most 4
  > test-boot_jack.R: Finished simulated annealing
  > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
  > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-boot_jack.R: Obtaining the responsible dyads.
  > test-boot_jack.R: Evaluating the predictor and response matrix.
  > test-boot_jack.R: Maximizing the pseudolikelihood.
  > test-boot_jack.R: Finished MPLE.
  > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-boot_jack.R: Iteration 1 of at most 60:
  > test-EgoStat.R: Starting simulated annealing (SAN)
  > test-EgoStat.R: Iteration 1 of at most 4
  > test-EgoStat.R: Iteration 2 of at most 4
  > test-EgoStat.R: Iteration 3 of at most 4
  > test-EgoStat.R: Finished simulated annealing
  > test-boot_jack.R: 1 
  > test-boot_jack.R: Optimizing with step length 1.0000.
  > test-boot_jack.R: The log-likelihood improved by 0.0011.
  > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-boot_jack.R: Finished MCMLE.
  > test-boot_jack.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-boot_jack.R: Constructing pseudopopulation network.
  > test-boot_jack.R: Starting simulated annealing (SAN)
  > test-boot_jack.R: Iteration 1 of at most 4
  > test-boot_jack.R: Finished simulated annealing
  > test-boot_jack.R: Unable to match target stats. Using MCMLE estimation.
  > test-boot_jack.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-boot_jack.R: Obtaining the responsible dyads.
  > test-boot_jack.R: Evaluating the predictor and response matrix.
  > test-boot_jack.R: Maximizing the pseudolikelihood.
  > test-boot_jack.R: Finished MPLE.
  > test-boot_jack.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-boot_jack.R: Iteration 1 of at most 60:
  > test-boot_jack.R: 1 Optimizing with step length 1.0000.
  > test-boot_jack.R: The log-likelihood improved by 0.0001.
  > test-boot_jack.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-boot_jack.R: Finished MCMLE.
  > test-boot_jack.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-boot_jack.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-coef_recovery.R: Constructing pseudopopulation network.
  > test-coef_recovery.R: Starting simulated annealing (SAN)
  > test-coef_recovery.R: Iteration 1 of at most 4
  > test-coef_recovery.R: Iteration 2 of at most 4
  > test-coef_recovery.R: Iteration 3 of at most 4
  > test-coef_recovery.R: Iteration 4 of at most 4
  > test-coef_recovery.R: Finished simulated annealing
  > test-coef_recovery.R: Unable to match target stats. Using MCMLE estimation.
  > test-coef_recovery.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-coef_recovery.R: Obtaining the responsible dyads.
  > test-coef_recovery.R: Evaluating the predictor and response matrix.
  > test-coef_recovery.R: Maximizing the pseudolikelihood.
  > test-coef_recovery.R: Finished MPLE.
  > test-coef_recovery.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-coef_recovery.R: Iteration 1 of at most 60:
  > test-coef_recovery.R: 1 
  > test-coef_recovery.R: Optimizing with step length 0.3646.
  > test-coef_recovery.R: The log-likelihood improved by 2.8321.
  > test-coef_recovery.R: Iteration 2 of at most 60:
  > test-coef_recovery.R: 1 
  > test-coef_recovery.R: Optimizing with step length 0.8183.
  > test-coef_recovery.R: The log-likelihood improved by 3.0954.
  > test-coef_recovery.R: Iteration 3 of at most 60:
  > test-coef_recovery.R: 1 
  > test-coef_recovery.R: Optimizing with step length 1.0000.
  > test-coef_recovery.R: The log-likelihood improved by 1.4921.
  > test-coef_recovery.R: Step length converged once. Increasing MCMC sample size.
  > test-coef_recovery.R: Iteration 4 of at most 60:
  > test-coef_recovery.R: 1 
  > test-coef_recovery.R: Optimizing with step length 1.0000.
  > test-coef_recovery.R: The log-likelihood improved by 0.7405.
  > test-coef_recovery.R: Step length converged twice. Stopping.
  > test-coef_recovery.R: Finished MCMLE.
  > test-coef_recovery.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-coef_recovery.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-drop.R: Constructing pseudopopulation network.
  > test-drop.R: Starting simulated annealing (SAN)
  > test-drop.R: Iteration 1 of at most 4
  > test-drop.R: Iteration 2 of at most 4
  > test-drop.R: Iteration 3 of at most 4
  > test-drop.R: Finished simulated annealing
  > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf.
  > test-drop.R: Unable to match target stats. Using MCMLE estimation.
  > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-drop.R: Obtaining the responsible dyads.
  > test-drop.R: Evaluating the predictor and response matrix.
  > test-drop.R: Maximizing the pseudolikelihood.
  > test-drop.R: Finished MPLE.
  > test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-drop.R: Iteration 1 of at most 60:
  > test-drop.R: 1 Optimizing with step length 1.0000.
  > test-drop.R: The log-likelihood improved by 0.0044.
  > test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence.
  > test-drop.R: Finished MCMLE.
  > test-drop.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-drop.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-drop.R: Observed statistic(s) nodematch.a are at their smallest attainable values. Their coefficients will be fixed at -Inf.
  > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-drop.R: Obtaining the responsible dyads.
  > test-drop.R: Evaluating the predictor and response matrix.
  > test-drop.R: Maximizing the pseudolikelihood.
  > test-drop.R: Finished MPLE.
  > test-drop.R: Evaluating log-likelihood at the estimate. 
  > test-drop.R: 
  > test-predict.ergm.ego.R: Constructing pseudopopulation network.
  > test-predict.ergm.ego.R: Starting simulated annealing (SAN)
  > test-predict.ergm.ego.R: Iteration 1 of at most 4
  > test-predict.ergm.ego.R: Iteration 2 of at most 4
  > test-predict.ergm.ego.R: Iteration 3 of at most 4
  > test-predict.ergm.ego.R: Iteration 4 of at most 4
  > test-predict.ergm.ego.R: Finished simulated annealing
  > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
  > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-predict.ergm.ego.R: Obtaining the responsible dyads.
  > test-predict.ergm.ego.R: Evaluating the predictor and response matrix.
  > test-predict.ergm.ego.R: Maximizing the pseudolikelihood.
  > test-predict.ergm.ego.R: Finished MPLE.
  > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-predict.ergm.ego.R: Iteration 1 of at most 2:
  > test-predict.ergm.ego.R: 1 Optimizing with step length 0.5473.
  > test-predict.ergm.ego.R: The log-likelihood improved by 1.8671.
  > test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
  > test-predict.ergm.ego.R: Iteration 2 of at most 2:
  > test-predict.ergm.ego.R: 1 
  > test-predict.ergm.ego.R: Optimizing with step length 1.0000.
  > test-predict.ergm.ego.R: The log-likelihood improved by 1.0036.
  > test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
  > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
  > test-predict.ergm.ego.R: Finished MCMLE.
  > test-predict.ergm.ego.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-predict.ergm.ego.R: Constructing pseudopopulation network.
  > test-predict.ergm.ego.R: Starting simulated annealing (SAN)
  > test-predict.ergm.ego.R: Iteration 1 of at most 4
  > test-predict.ergm.ego.R: Iteration 2 of at most 4
  > test-predict.ergm.ego.R: Iteration 3 of at most 4
  > test-predict.ergm.ego.R: Iteration 4 of at most 4
  > test-predict.ergm.ego.R: Finished simulated annealing
  > test-predict.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
  > test-predict.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-predict.ergm.ego.R: Obtaining the responsible dyads.
  > test-predict.ergm.ego.R: Evaluating the predictor and response matrix.
  > test-predict.ergm.ego.R: Maximizing the pseudolikelihood.
  > test-predict.ergm.ego.R: Finished MPLE.
  > test-predict.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-predict.ergm.ego.R: Iteration 1 of at most 2:
  > test-predict.ergm.ego.R: 1 
  > test-predict.ergm.ego.R: Optimizing with step length 0.7865.
  > test-predict.ergm.ego.R: The log-likelihood improved by 1.8825.
  > test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
  > test-predict.ergm.ego.R: Iteration 2 of at most 2:
  > test-predict.ergm.ego.R: 1 
  > test-predict.ergm.ego.R: Optimizing with step length 1.0000.
  > test-predict.ergm.ego.R: The log-likelihood improved by 0.3775.
  > test-predict.ergm.ego.R: Estimating equations are not within tolerance region.
  > test-predict.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
  > test-predict.ergm.ego.R: Finished MCMLE.
  > test-predict.ergm.ego.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-predict.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
  > test-table_ppop.R: Constructing pseudopopulation network.
  > test-table_ppop.R: Starting simulated annealing (SAN)
  > test-table_ppop.R: Iteration 1 of at most 4
  > test-table_ppop.R: Iteration 2 of at most 4
  > test-table_ppop.R: Iteration 3 of at most 4
  > test-table_ppop.R: Iteration 4 of at most 4
  > test-table_ppop.R: Finished simulated annealing
  > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
  > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-table_ppop.R: Obtaining the responsible dyads.
  > test-table_ppop.R: Evaluating the predictor and response matrix.
  > test-table_ppop.R: Maximizing the pseudolikelihood.
  > test-table_ppop.R: Finished MPLE.
  > test-table_ppop.R: Constructing pseudopopulation network.
  > test-table_ppop.R: Starting simulated annealing (SAN)
  > test-table_ppop.R: Iteration 1 of at most 4
  > test-table_ppop.R: Iteration 2 of at most 4
  > test-table_ppop.R: Iteration 3 of at most 4
  > test-table_ppop.R: Iteration 4 of at most 4
  > test-table_ppop.R: Finished simulated annealing
  > test-table_ppop.R: Unable to match target stats. Using MCMLE estimation.
  > test-table_ppop.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-table_ppop.R: Obtaining the responsible dyads.
  > test-table_ppop.R: Evaluating the predictor and response matrix.
  > test-table_ppop.R: Maximizing the pseudolikelihood.
  > test-table_ppop.R: Finished MPLE.
  Saving _problems/test-table_ppop-39.R
  > test-gof.ergm.ego.R: Constructing pseudopopulation network.
  > test-gof.ergm.ego.R: Starting simulated annealing (SAN)
  > test-gof.ergm.ego.R: Iteration 1 of at most 4
  > test-gof.ergm.ego.R: Finished simulated annealing
  > test-gof.ergm.ego.R: Unable to match target stats. Using MCMLE estimation.
  > test-gof.ergm.ego.R: Starting maximum pseudolikelihood estimation (MPLE):
  > test-gof.ergm.ego.R: Obtaining the responsible dyads.
  > test-gof.ergm.ego.R: Evaluating the predictor and response matrix.
  > test-gof.ergm.ego.R: Maximizing the pseudolikelihood.
  > test-gof.ergm.ego.R: Finished MPLE.
  > test-gof.ergm.ego.R: Starting Monte Carlo maximum likelihood estimation (MCMLE):
  > test-gof.ergm.ego.R: Iteration 1 of at most 2:
  > test-gof.ergm.ego.R: 1 Optimizing with step length 1.0000.
  > test-gof.ergm.ego.R: The log-likelihood improved by 1.6103.
  > test-gof.ergm.ego.R: Estimating equations are not within tolerance region.
  > test-gof.ergm.ego.R: Iteration 2 of at most 2:
  > test-gof.ergm.ego.R: 1 Optimizing with step length 1.0000.
  > test-gof.ergm.ego.R: The log-likelihood improved by 0.0094.
  > test-gof.ergm.ego.R: Convergence test p-value: 0.3069. Not converged with 99% confidence; increasing sample size.
  > test-gof.ergm.ego.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details.
  > test-gof.ergm.ego.R: Finished MCMLE.
  > test-gof.ergm.ego.R: This model was fit using MCMC.  To examine model diagnostics and check
  > test-gof.ergm.ego.R: for degeneracy, use the mcmc.diagnostics() function.
  Saving _problems/test-gof.ergm.ego-17.R
  Saving _problems/test-gof.ergm.ego-32.R
  Saving _problems/test-gof.ergm.ego-48.R
  [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Failure ('test-table_ppop.R:39:3'): estimation and simulation work ──────────
  Expected `(egosim <- simulate(egofit_scl, popsize = ppop))` to run silently.
  Actual noise: messages.
  ── Failure ('test-gof.ergm.ego.R:15:3'): GOF='model' works ─────────────────────
  Expected `z <- gof(fmhfit, GOF = "model")` to run silently.
  Actual noise: messages.
  ── Failure ('test-gof.ergm.ego.R:30:3'): GOF='degree' works ────────────────────
  Expected `z <- gof(fmhfit, GOF = "degree")` to run silently.
  Actual noise: messages.
  ── Failure ('test-gof.ergm.ego.R:46:3'): GOF='espartners' works ────────────────
  Expected `z <- gof(fmhfit, GOF = "espartners")` to run silently.
  Actual noise: messages.
  
  [ FAIL 4 | WARN 2 | SKIP 0 | PASS 104 ]
  Error:
  ! Test failures.
  Execution halted